after_merge
stringlengths 28
79.6k
| before_merge
stringlengths 20
79.6k
| url
stringlengths 38
71
| full_traceback
stringlengths 43
922k
| traceback_type
stringclasses 555
values |
|---|---|---|---|---|
def read_and_validate_csv(path, delimiter=","):
"""Generator for reading a CSV file.
Args:
path: Path to the CSV file
delimiter: character used as a field separator, default: ','
"""
# Columns that must be present in the CSV file.
mandatory_fields = ["message", "datetime", "timestamp_desc"]
# Ensures delimiter is a string.
if not isinstance(delimiter, six.text_type):
delimiter = codecs.decode(delimiter, "utf8")
# Due to issues with python2.
if six.PY2:
delimiter = str(delimiter)
open_function = open(path, "r")
else:
open_function = open(path, mode="r", encoding="utf-8")
with open_function as fh:
reader = csv.DictReader(fh, delimiter=delimiter)
csv_header = reader.fieldnames
missing_fields = []
# Validate the CSV header
for field in mandatory_fields:
if field not in csv_header:
missing_fields.append(field)
if missing_fields:
raise RuntimeError(
"Missing fields in CSV header: {0:s}".format(",".join(missing_fields))
)
for row in reader:
try:
# normalize datetime to ISO 8601 format if it's not the case.
parsed_datetime = parser.parse(row["datetime"])
row["datetime"] = parsed_datetime.isoformat()
normalized_timestamp = int(
time.mktime(parsed_datetime.utctimetuple()) * 1000000
)
normalized_timestamp += parsed_datetime.microsecond
row["timestamp"] = str(normalized_timestamp)
except ValueError:
continue
yield row
|
def read_and_validate_csv(path, delimiter=","):
"""Generator for reading a CSV file.
Args:
path: Path to the CSV file
delimiter: character used as a field separator, default: ','
"""
# Columns that must be present in the CSV file
mandatory_fields = ["message", "datetime", "timestamp_desc"]
# Ensures delimiter is a string
if not isinstance(delimiter, six.text_type):
delimiter = codecs.decode(delimiter, "utf8")
with open(path, "r", encoding="utf-8") as fh:
reader = csv.DictReader(fh, delimiter=delimiter)
csv_header = reader.fieldnames
missing_fields = []
# Validate the CSV header
for field in mandatory_fields:
if field not in csv_header:
missing_fields.append(field)
if missing_fields:
raise RuntimeError(
"Missing fields in CSV header: {0:s}".format(",".join(missing_fields))
)
for row in reader:
try:
# normalize datetime to ISO 8601 format if it's not the case.
parsed_datetime = parser.parse(row["datetime"])
row["datetime"] = parsed_datetime.isoformat()
normalized_timestamp = int(
time.mktime(parsed_datetime.utctimetuple()) * 1000000
)
normalized_timestamp += parsed_datetime.microsecond
row["timestamp"] = str(normalized_timestamp)
except ValueError:
continue
yield row
|
https://github.com/google/timesketch/issues/1017
|
Traceback (most recent call last):
File "...lib/python2.7/site-packages/timesketch/lib/tasks.py", line 467, in run_csv_jsonl
for event in read_and_validate(source_file_path):
File ".../lib/python2.7/site-packages/timesketch/lib/utils.py", line 81, in read_and_validate_csv
for row in reader:
File "/usr/lib/python2.7/csv.py", line 108, in next
row = self.reader.next()
UnicodeEncodeError: 'ascii' codec can't encode character u'\u2019' in position 92: ordinal not in range(128)
|
UnicodeEncodeError
|
def run(self):
"""Entry point for the analyzer.
Returns:
String with summary of the analyzer result
"""
query = (
'{"query": { "bool": { "should": [ '
'{ "exists" : { "field" : "url" }}, '
'{ "exists" : { "field" : "domain" }} ] } } }'
)
return_fields = ["domain", "url"]
events = self.event_stream("", query_dsl=query, return_fields=return_fields)
domains = {}
domain_counter = collections.Counter()
tld_counter = collections.Counter()
cdn_counter = collections.Counter()
for event in events:
domain = event.source.get("domain")
if not domain:
url = event.source.get("url")
if not url:
continue
domain = utils.get_domain_from_url(url)
if not domain:
continue
domain_counter[domain] += 1
domains.setdefault(domain, [])
domains[domain].append(event)
tld = ".".join(domain.split(".")[-2:])
tld_counter[tld] += 1
# Exit early if there are no domains in the data set to analyze.
if not domain_counter:
return "No domains to analyze."
domain_count_array = numpy.array(list(domain_counter.values()))
domain_20th_percentile = int(numpy.percentile(domain_count_array, 20))
domain_85th_percentile = int(numpy.percentile(domain_count_array, 85))
common_domains = [
x for x, y in domain_counter.most_common() if y >= domain_85th_percentile
]
rare_domains = [
x for x, y in domain_counter.most_common() if y <= domain_20th_percentile
]
satellite_emoji = emojis.get_emoji("SATELLITE")
for domain, count in iter(domain_counter.items()):
emojis_to_add = [satellite_emoji]
tags_to_add = []
cdn_provider = utils.get_cdn_provider(domain)
if cdn_provider:
tags_to_add.append("known-cdn")
cdn_counter[cdn_provider] += 1
if domain in common_domains:
tags_to_add.append("common_domain")
if domain in rare_domains:
tags_to_add.append("rare_domain")
for event in domains.get(domain, []):
event.add_tags(tags_to_add)
event.add_emojis(emojis_to_add)
new_attributes = {"domain": domain, "domain_count": count}
if cdn_provider:
new_attributes["cdn_provider"] = cdn_provider
event.add_attributes(new_attributes)
# Commit the event to the datastore.
event.commit()
return (
"{0:d} domains discovered ({1:d} TLDs) and {2:d} known CDN networks found."
).format(len(domains), len(tld_counter), len(cdn_counter))
|
def run(self):
"""Entry point for the analyzer.
Returns:
String with summary of the analyzer result
"""
query = (
'{"query": { "bool": { "should": [ '
'{ "exists" : { "field" : "url" }}, '
'{ "exists" : { "field" : "domain" }} ] } } }'
)
return_fields = ["domain", "url"]
events = self.event_stream("", query_dsl=query, return_fields=return_fields)
domains = {}
domain_counter = collections.Counter()
tld_counter = collections.Counter()
cdn_counter = collections.Counter()
for event in events:
domain = event.source.get("domain")
if not domain:
url = event.source.get("url")
if not url:
continue
domain = utils.get_domain_from_url(url)
if not domain:
continue
domain_counter[domain] += 1
domains.setdefault(domain, [])
domains[domain].append(event)
tld = ".".join(domain.split(".")[-2:])
tld_counter[tld] += 1
domain_count_array = numpy.array(list(domain_counter.values()))
domain_20th_percentile = int(numpy.percentile(domain_count_array, 20))
domain_85th_percentile = int(numpy.percentile(domain_count_array, 85))
common_domains = [
x for x, y in domain_counter.most_common() if y >= domain_85th_percentile
]
rare_domains = [
x for x, y in domain_counter.most_common() if y <= domain_20th_percentile
]
satellite_emoji = emojis.get_emoji("SATELLITE")
for domain, count in iter(domain_counter.items()):
emojis_to_add = [satellite_emoji]
tags_to_add = []
cdn_provider = utils.get_cdn_provider(domain)
if cdn_provider:
tags_to_add.append("known-cdn")
cdn_counter[cdn_provider] += 1
if domain in common_domains:
tags_to_add.append("common_domain")
if domain in rare_domains:
tags_to_add.append("rare_domain")
for event in domains.get(domain, []):
event.add_tags(tags_to_add)
event.add_emojis(emojis_to_add)
new_attributes = {"domain": domain, "domain_count": count}
if cdn_provider:
new_attributes["cdn_provider"] = cdn_provider
event.add_attributes(new_attributes)
# Commit the event to the datastore.
event.commit()
return (
"{0:d} domains discovered ({1:d} TLDs) and {2:d} known CDN networks found."
).format(len(domains), len(tld_counter), len(cdn_counter))
|
https://github.com/google/timesketch/issues/892
|
[2019-05-15 15:57:25,067: ERROR/ForkPoolWorker-1] Task timesketch.lib.tasks.run_sketch_analyzer[87c2fee5-d10c-4a92-8d28-a6acc970a7fe] raised unexpected: IndexError('cannot do a non-empty take from an empty axes.',)
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/celery/app/trace.py", line 385, in trace_task
R = retval = fun(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/timesketch-20190207-py3.6.egg/timesketch/__init__.py", line 181, in __call__
return TaskBase.__call__(self, *args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/celery/app/trace.py", line 648, in __protected_call__
return self.run(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/timesketch-20190207-py3.6.egg/timesketch/lib/tasks.py", line 334, in run_sketch_analyzer
result = analyzer.run_wrapper()
File "/usr/local/lib/python3.6/dist-packages/timesketch-20190207-py3.6.egg/timesketch/lib/analyzers/interface.py", line 37, in wrapper
func_return = func(self, *args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/timesketch-20190207-py3.6.egg/timesketch/lib/analyzers/interface.py", line 403, in run_wrapper
result = self.run()
File "/usr/local/lib/python3.6/dist-packages/timesketch-20190207-py3.6.egg/timesketch/lib/analyzers/domain.py", line 71, in run
domain_20th_percentile = int(numpy.percentile(domain_count_array, 20))
File "/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py", line 3707, in percentile
a, q, axis, out, overwrite_input, interpolation, keepdims)
File "/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py", line 3826, in _quantile_unchecked
interpolation=interpolation)
File "/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py", line 3405, in _ureduce
r = func(a, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py", line 3941, in _quantile_ureduce_func
x1 = take(ap, indices_below, axis=axis) * weights_below
File "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py", line 189, in take
return _wrapfunc(a, 'take', indices, axis=axis, out=out, mode=mode)
File "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py", line 56, in _wrapfunc
return getattr(obj, method)(*args, **kwds)
IndexError: cannot do a non-empty take from an empty axes.```
Should I add something here https://github.com/google/timesketch/blob/7244f821b9c257d42402115f6a39cab266f0a84c/timesketch/lib/analyzers/domain.py#L70
in order to set at 0 for example in case domain_count_array returns empty?
|
IndexError
|
def get_hypothesis_conversions(self, location: str) -> Optional[Callable]:
definitions = [
item
for item in self.definition.resolved.get("parameters", [])
if item["in"] == location
]
if definitions:
return self.schema.get_hypothesis_conversion(definitions)
return None
|
def get_hypothesis_conversions(self, location: str) -> Optional[Callable]:
definitions = [
item
for item in self.definition.raw.get("parameters", [])
if item["in"] == location
]
if definitions:
return self.schema.get_hypothesis_conversion(definitions)
return None
|
https://github.com/schemathesis/schemathesis/issues/612
|
An internal error happened during a test run
Error: Traceback (most recent call last):
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/__init__.py", line 245, in execute_from_schema
yield from runner.execute()
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/core.py", line 100, in execute
for event in self._execute(results):
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/solo.py", line 19, in _execute
yield from self._run_tests(
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/core.py", line 126, in _run_tests
for endpoint, test in maker(template, settings, seed):
File "/usr/local/lib/python3.8/site-packages/schemathesis/schemas.py", line 79, in get_all_tests
test = make_test_or_exception(endpoint, func, settings, seed)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 45, in make_test_or_exception
return create_test(endpoint, func, settings, seed=seed)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 30, in create_test
strategy = endpoint.as_strategy(hooks=hook_dispatcher)
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 285, in as_strategy
return get_case_strategy(self, hooks)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 153, in get_case_strategy
parameter, value, endpoint.get_hypothesis_conversions(location)
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 291, in get_hypothesis_conversions
definitions = [item for item in self.definition.raw.get("parameters", []) if item["in"] == location]
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 291, in <listcomp>
definitions = [item for item in self.definition.raw.get("parameters", []) if item["in"] == location]
KeyError: 'in'
|
KeyError
|
def get_all_endpoints(self) -> Generator[Endpoint, None, None]:
try:
paths = self.raw_schema["paths"] # pylint: disable=unsubscriptable-object
context = HookContext()
for path, methods in paths.items():
full_path = self.get_full_path(path)
if should_skip_endpoint(full_path, self.endpoint):
continue
self.dispatch_hook("before_process_path", context, path, methods)
scope, raw_methods = self._resolve_methods(methods)
methods = self.resolver.resolve_all(methods)
common_parameters = get_common_parameters(methods)
for method, resolved_definition in methods.items():
# Only method definitions are parsed
if (
method not in self.operations
or should_skip_method(method, self.method)
or should_skip_by_tag(resolved_definition.get("tags"), self.tag)
or should_skip_by_operation_id(
resolved_definition.get("operationId"), self.operation_id
)
):
continue
parameters = itertools.chain(
resolved_definition.get("parameters", ()), common_parameters
)
# To prevent recursion errors we need to pass not resolved schema as well
# It could be used for response validation
raw_definition = EndpointDefinition(
raw_methods[method], resolved_definition, scope
)
yield self.make_endpoint(
full_path, method, parameters, resolved_definition, raw_definition
)
except (KeyError, AttributeError, jsonschema.exceptions.RefResolutionError):
raise InvalidSchema("Schema parsing failed. Please check your schema.")
|
def get_all_endpoints(self) -> Generator[Endpoint, None, None]:
try:
paths = self.raw_schema["paths"] # pylint: disable=unsubscriptable-object
context = HookContext()
for path, methods in paths.items():
full_path = self.get_full_path(path)
if should_skip_endpoint(full_path, self.endpoint):
continue
self.dispatch_hook("before_process_path", context, path, methods)
scope, raw_methods = self._resolve_methods(methods)
methods = self.resolver.resolve_all(methods)
common_parameters = get_common_parameters(methods)
for method, resolved_definition in methods.items():
# Only method definitions are parsed
if (
method not in self.operations
or should_skip_method(method, self.method)
or should_skip_by_tag(resolved_definition.get("tags"), self.tag)
or should_skip_by_operation_id(
resolved_definition.get("operationId"), self.operation_id
)
):
continue
parameters = itertools.chain(
resolved_definition.get("parameters", ()), common_parameters
)
# To prevent recursion errors we need to pass not resolved schema as well
# It could be used for response validation
raw_definition = EndpointDefinition(raw_methods[method], scope)
yield self.make_endpoint(
full_path, method, parameters, resolved_definition, raw_definition
)
except (KeyError, AttributeError, jsonschema.exceptions.RefResolutionError):
raise InvalidSchema("Schema parsing failed. Please check your schema.")
|
https://github.com/schemathesis/schemathesis/issues/612
|
An internal error happened during a test run
Error: Traceback (most recent call last):
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/__init__.py", line 245, in execute_from_schema
yield from runner.execute()
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/core.py", line 100, in execute
for event in self._execute(results):
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/solo.py", line 19, in _execute
yield from self._run_tests(
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/core.py", line 126, in _run_tests
for endpoint, test in maker(template, settings, seed):
File "/usr/local/lib/python3.8/site-packages/schemathesis/schemas.py", line 79, in get_all_tests
test = make_test_or_exception(endpoint, func, settings, seed)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 45, in make_test_or_exception
return create_test(endpoint, func, settings, seed=seed)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 30, in create_test
strategy = endpoint.as_strategy(hooks=hook_dispatcher)
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 285, in as_strategy
return get_case_strategy(self, hooks)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 153, in get_case_strategy
parameter, value, endpoint.get_hypothesis_conversions(location)
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 291, in get_hypothesis_conversions
definitions = [item for item in self.definition.raw.get("parameters", []) if item["in"] == location]
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 291, in <listcomp>
definitions = [item for item in self.definition.raw.get("parameters", []) if item["in"] == location]
KeyError: 'in'
|
KeyError
|
def _group_endpoints_by_operation_id(
self,
) -> Generator[Tuple[str, Endpoint], None, None]:
for path, methods in self.raw_schema["paths"].items():
full_path = self.get_full_path(path)
scope, raw_methods = self._resolve_methods(methods)
methods = self.resolver.resolve_all(methods)
common_parameters = get_common_parameters(methods)
for method, resolved_definition in methods.items():
if (
method not in self.operations
or "operationId" not in resolved_definition
):
continue
parameters = itertools.chain(
resolved_definition.get("parameters", ()), common_parameters
)
raw_definition = EndpointDefinition(
raw_methods[method], resolved_definition, scope
)
yield (
resolved_definition["operationId"],
self.make_endpoint(
full_path, method, parameters, resolved_definition, raw_definition
),
)
|
def _group_endpoints_by_operation_id(
self,
) -> Generator[Tuple[str, Endpoint], None, None]:
for path, methods in self.raw_schema["paths"].items():
full_path = self.get_full_path(path)
scope, raw_methods = self._resolve_methods(methods)
methods = self.resolver.resolve_all(methods)
common_parameters = get_common_parameters(methods)
for method, resolved_definition in methods.items():
if (
method not in self.operations
or "operationId" not in resolved_definition
):
continue
parameters = itertools.chain(
resolved_definition.get("parameters", ()), common_parameters
)
raw_definition = EndpointDefinition(raw_methods[method], scope)
yield (
resolved_definition["operationId"],
self.make_endpoint(
full_path, method, parameters, resolved_definition, raw_definition
),
)
|
https://github.com/schemathesis/schemathesis/issues/612
|
An internal error happened during a test run
Error: Traceback (most recent call last):
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/__init__.py", line 245, in execute_from_schema
yield from runner.execute()
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/core.py", line 100, in execute
for event in self._execute(results):
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/solo.py", line 19, in _execute
yield from self._run_tests(
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/core.py", line 126, in _run_tests
for endpoint, test in maker(template, settings, seed):
File "/usr/local/lib/python3.8/site-packages/schemathesis/schemas.py", line 79, in get_all_tests
test = make_test_or_exception(endpoint, func, settings, seed)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 45, in make_test_or_exception
return create_test(endpoint, func, settings, seed=seed)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 30, in create_test
strategy = endpoint.as_strategy(hooks=hook_dispatcher)
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 285, in as_strategy
return get_case_strategy(self, hooks)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 153, in get_case_strategy
parameter, value, endpoint.get_hypothesis_conversions(location)
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 291, in get_hypothesis_conversions
definitions = [item for item in self.definition.raw.get("parameters", []) if item["in"] == location]
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 291, in <listcomp>
definitions = [item for item in self.definition.raw.get("parameters", []) if item["in"] == location]
KeyError: 'in'
|
KeyError
|
def get_endpoint_by_reference(self, reference: str) -> Endpoint:
"""Get local or external `Endpoint` instance by reference.
Reference example: #/paths/~1users~1{user_id}/patch
"""
scope, data = self.resolver.resolve(reference)
path, method = scope.rsplit("/", maxsplit=2)[-2:]
path = path.replace("~1", "/").replace("~0", "~")
full_path = self.get_full_path(path)
resolved_definition = self.resolver.resolve_all(data)
parent_ref, _ = reference.rsplit("/", maxsplit=1)
_, methods = self.resolver.resolve(parent_ref)
common_parameters = get_common_parameters(methods)
parameters = itertools.chain(
resolved_definition.get("parameters", ()), common_parameters
)
raw_definition = EndpointDefinition(data, resolved_definition, scope)
return self.make_endpoint(
full_path, method, parameters, resolved_definition, raw_definition
)
|
def get_endpoint_by_reference(self, reference: str) -> Endpoint:
"""Get local or external `Endpoint` instance by reference.
Reference example: #/paths/~1users~1{user_id}/patch
"""
scope, data = self.resolver.resolve(reference)
path, method = scope.rsplit("/", maxsplit=2)[-2:]
path = path.replace("~1", "/").replace("~0", "~")
full_path = self.get_full_path(path)
resolved_definition = self.resolver.resolve_all(data)
parent_ref, _ = reference.rsplit("/", maxsplit=1)
_, methods = self.resolver.resolve(parent_ref)
common_parameters = get_common_parameters(methods)
parameters = itertools.chain(
resolved_definition.get("parameters", ()), common_parameters
)
raw_definition = EndpointDefinition(data, scope)
return self.make_endpoint(
full_path, method, parameters, resolved_definition, raw_definition
)
|
https://github.com/schemathesis/schemathesis/issues/612
|
An internal error happened during a test run
Error: Traceback (most recent call last):
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/__init__.py", line 245, in execute_from_schema
yield from runner.execute()
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/core.py", line 100, in execute
for event in self._execute(results):
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/solo.py", line 19, in _execute
yield from self._run_tests(
File "/usr/local/lib/python3.8/site-packages/schemathesis/runner/impl/core.py", line 126, in _run_tests
for endpoint, test in maker(template, settings, seed):
File "/usr/local/lib/python3.8/site-packages/schemathesis/schemas.py", line 79, in get_all_tests
test = make_test_or_exception(endpoint, func, settings, seed)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 45, in make_test_or_exception
return create_test(endpoint, func, settings, seed=seed)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 30, in create_test
strategy = endpoint.as_strategy(hooks=hook_dispatcher)
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 285, in as_strategy
return get_case_strategy(self, hooks)
File "/usr/local/lib/python3.8/site-packages/schemathesis/_hypothesis.py", line 153, in get_case_strategy
parameter, value, endpoint.get_hypothesis_conversions(location)
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 291, in get_hypothesis_conversions
definitions = [item for item in self.definition.raw.get("parameters", []) if item["in"] == location]
File "/usr/local/lib/python3.8/site-packages/schemathesis/models.py", line 291, in <listcomp>
definitions = [item for item in self.definition.raw.get("parameters", []) if item["in"] == location]
KeyError: 'in'
|
KeyError
|
def as_requests_kwargs(self, base_url: Optional[str] = None) -> Dict[str, Any]:
"""Convert the case into a dictionary acceptable by requests."""
base_url = self._get_base_url(base_url)
formatted_path = self.formatted_path.lstrip("/") # pragma: no mutate
url = urljoin(base_url + "/", formatted_path)
# Form data and body are mutually exclusive
extra: Dict[str, Optional[Union[Dict, bytes]]]
if self.form_data:
extra = {"files": self.form_data}
elif is_multipart(self.body):
extra = {"data": self.body}
else:
extra = {"json": self.body}
return {
"method": self.method,
"url": url,
"cookies": self.cookies,
"headers": self.headers,
"params": self.query,
**extra,
}
|
def as_requests_kwargs(self, base_url: Optional[str] = None) -> Dict[str, Any]:
"""Convert the case into a dictionary acceptable by requests."""
base_url = self._get_base_url(base_url)
formatted_path = self.formatted_path.lstrip("/") # pragma: no mutate
url = urljoin(base_url + "/", formatted_path)
# Form data and body are mutually exclusive
extra: Dict[str, Optional[Union[Dict, bytes]]]
if self.form_data:
extra = {"files": self.form_data}
elif isinstance(self.body, bytes):
extra = {"data": self.body}
else:
extra = {"json": self.body}
return {
"method": self.method,
"url": url,
"cookies": self.cookies,
"headers": self.headers,
"params": self.query,
**extra,
}
|
https://github.com/schemathesis/schemathesis/issues/473
|
_____________________________ POST: /api/workspace/meshtransform ____________________________
Traceback (most recent call last):
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/schemathesis/runner/impl/core.py", line 85, in run_test
test(checks, result, **kwargs)
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/schemathesis/runner/impl/core.py", line 141, in network_test
case: Case,
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/hypothesis/core.py", line 1090, in wrapped_test
raise the_error_hypothesis_found
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/schemathesis/runner/impl/core.py", line 150, in network_test
response = case.call(session=session, timeout=timeout)
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/schemathesis/models.py", line 124, in call
response = session.request(**data, **kwargs) # type: ignore
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/requests/sessions.py", line 516, in request
prep = self.prepare_request(req)
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/requests/sessions.py", line 459, in prepare_request
hooks=merge_hooks(request.hooks, self.hooks),
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/requests/models.py", line 317, in prepare
self.prepare_body(data, files, json)
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/requests/models.py", line 467, in prepare_body
body = complexjson.dumps(json)
File "/usr/lib64/python3.7/json/__init__.py", line 231, in dumps
return _default_encoder.encode(obj)
File "/usr/lib64/python3.7/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/usr/lib64/python3.7/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/usr/lib64/python3.7/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type bytes is not JSON serializable
|
TypeError
|
def as_werkzeug_kwargs(self) -> Dict[str, Any]:
"""Convert the case into a dictionary acceptable by werkzeug.Client."""
headers = self.headers
extra: Dict[str, Optional[Union[Dict, bytes]]]
if self.form_data:
extra = {"data": self.form_data}
headers = headers or {}
headers.setdefault("Content-Type", "multipart/form-data")
elif is_multipart(self.body):
extra = {"data": self.body}
else:
extra = {"json": self.body}
return {
"method": self.method,
"path": self.formatted_path,
"headers": headers,
"query_string": self.query,
**extra,
}
|
def as_werkzeug_kwargs(self) -> Dict[str, Any]:
"""Convert the case into a dictionary acceptable by werkzeug.Client."""
headers = self.headers
extra: Dict[str, Optional[Union[Dict, bytes]]]
if self.form_data:
extra = {"data": self.form_data}
headers = headers or {}
headers.setdefault("Content-Type", "multipart/form-data")
elif isinstance(self.body, bytes):
extra = {"data": self.body}
else:
extra = {"json": self.body}
return {
"method": self.method,
"path": self.formatted_path,
"headers": headers,
"query_string": self.query,
**extra,
}
|
https://github.com/schemathesis/schemathesis/issues/473
|
_____________________________ POST: /api/workspace/meshtransform ____________________________
Traceback (most recent call last):
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/schemathesis/runner/impl/core.py", line 85, in run_test
test(checks, result, **kwargs)
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/schemathesis/runner/impl/core.py", line 141, in network_test
case: Case,
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/hypothesis/core.py", line 1090, in wrapped_test
raise the_error_hypothesis_found
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/schemathesis/runner/impl/core.py", line 150, in network_test
response = case.call(session=session, timeout=timeout)
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/schemathesis/models.py", line 124, in call
response = session.request(**data, **kwargs) # type: ignore
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/requests/sessions.py", line 516, in request
prep = self.prepare_request(req)
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/requests/sessions.py", line 459, in prepare_request
hooks=merge_hooks(request.hooks, self.hooks),
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/requests/models.py", line 317, in prepare
self.prepare_body(data, files, json)
File "/home/nobody/.local/share/virtualenvs/backend-TE7gHulD/lib/python3.7/site-packages/requests/models.py", line 467, in prepare_body
body = complexjson.dumps(json)
File "/usr/lib64/python3.7/json/__init__.py", line 231, in dumps
return _default_encoder.encode(obj)
File "/usr/lib64/python3.7/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/usr/lib64/python3.7/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/usr/lib64/python3.7/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type bytes is not JSON serializable
|
TypeError
|
def get_all_endpoints(self) -> Generator[Endpoint, None, None]:
try:
paths = self.raw_schema["paths"] # pylint: disable=unsubscriptable-object
for path, methods in paths.items():
full_path = self.get_full_path(path)
if should_skip_endpoint(full_path, self.endpoint):
continue
methods = self.resolve(methods)
common_parameters = get_common_parameters(methods)
for method, definition in methods.items():
# Only method definitions are parsed
if (
method == "parameters"
or method.startswith("x-")
or should_skip_method(method, self.method)
or should_skip_by_tag(definition.get("tags"), self.tag)
):
continue
parameters = itertools.chain(
definition.get("parameters", ()), common_parameters
)
yield self.make_endpoint(full_path, method, parameters, definition)
except (KeyError, AttributeError, jsonschema.exceptions.RefResolutionError):
raise InvalidSchema("Schema parsing failed. Please check your schema.")
|
def get_all_endpoints(self) -> Generator[Endpoint, None, None]:
try:
paths = self.raw_schema["paths"] # pylint: disable=unsubscriptable-object
for path, methods in paths.items():
full_path = self.get_full_path(path)
if should_skip_endpoint(full_path, self.endpoint):
continue
methods = self.resolve(methods)
common_parameters = get_common_parameters(methods)
for method, definition in methods.items():
if (
method == "parameters"
or should_skip_method(method, self.method)
or should_skip_by_tag(definition.get("tags"), self.tag)
):
continue
parameters = itertools.chain(
definition.get("parameters", ()), common_parameters
)
yield self.make_endpoint(full_path, method, parameters, definition)
except (KeyError, AttributeError, jsonschema.exceptions.RefResolutionError):
raise InvalidSchema("Schema parsing failed. Please check your schema.")
|
https://github.com/schemathesis/schemathesis/issues/448
|
Traceback (most recent call last):
File "/var/lang/lib/python3.7/site-packages/schemathesis/schemas.py", line 210, in get_all_endpoints
or should_skip_by_tag(definition.get("tags"), self.tag)
AttributeError: 'str' object has no attribute 'get'
|
AttributeError
|
def display_errors(results: TestResultSet) -> None:
"""Display all errors in the test run."""
if not results.has_errors:
return
display_section_name("ERRORS")
for result in results:
if not result.has_errors:
continue
display_single_error(result)
|
def display_errors(results: TestResultSet) -> None:
"""Display all errors in the test run."""
if not results.has_errors:
return
display_section_name("ERRORS")
for result in results.results:
if not result.has_errors:
continue
display_single_error(result)
|
https://github.com/schemathesis/schemathesis/issues/215
|
Failed to reproduce exception. Expected:
Traceback (most recent call last):
File "/home/stranger6667/programming/PycharmProjects/schemathesis/src/schemathesis/runner/__init__.py", line 148, in single_test
raise AssertionError
AssertionError
|
AssertionError
|
def display_single_error(result: TestResult) -> None:
display_subsection(result)
for error, example in result.errors:
message = "".join(traceback.format_exception_only(type(error), error))
click.secho(message, fg="red")
if example is not None:
display_example(example)
|
def display_single_error(result: TestResult) -> None:
display_subsection(result)
for error in result.errors:
message = "".join(traceback.format_exception_only(type(error), error))
click.secho(message, fg="red")
|
https://github.com/schemathesis/schemathesis/issues/215
|
Failed to reproduce exception. Expected:
Traceback (most recent call last):
File "/home/stranger6667/programming/PycharmProjects/schemathesis/src/schemathesis/runner/__init__.py", line 148, in single_test
raise AssertionError
AssertionError
|
AssertionError
|
def display_failures(results: TestResultSet) -> None:
"""Display all failures in the test run."""
if not results.has_failures:
return
relevant_results = [result for result in results if not result.is_errored]
if not relevant_results:
return
display_section_name("FAILURES")
for result in relevant_results:
if not result.has_failures:
continue
display_single_failure(result)
|
def display_failures(results: TestResultSet) -> None:
"""Display all failures in the test run."""
if not results.has_failures:
return
display_section_name("FAILURES")
for result in results.results:
if not result.has_failures:
continue
display_single_failure(result)
|
https://github.com/schemathesis/schemathesis/issues/215
|
Failed to reproduce exception. Expected:
Traceback (most recent call last):
File "/home/stranger6667/programming/PycharmProjects/schemathesis/src/schemathesis/runner/__init__.py", line 148, in single_test
raise AssertionError
AssertionError
|
AssertionError
|
def display_single_failure(result: TestResult) -> None:
"""Display a failure for a single method / endpoint."""
display_subsection(result)
for check in reversed(result.checks):
if check.example is not None:
display_example(check.example, check.name)
# Display only the latest case
# (dd): It is possible to find multiple errors, but the simplest option for now is to display
# the latest and avoid deduplication, which will be done in the future.
break
|
def display_single_failure(result: TestResult) -> None:
"""Display a failure for a single method / endpoint."""
display_subsection(result)
for check in reversed(result.checks):
if check.example is not None:
output = {
make_verbose_name(attribute): getattr(check.example, attribute.name)
for attribute in Case.__attrs_attrs__ # type: ignore
if attribute.name not in ("path", "method", "base_url")
}
max_length = max(map(len, output))
template = f"{{:<{max_length}}} : {{}}"
click.secho(template.format("Check", check.name), fg="red")
for key, value in output.items():
if (key == "Body" and value is not None) or value not in (None, {}):
click.secho(template.format(key, value), fg="red")
# Display only the latest case
# (dd): It is possible to find multiple errors, but the simplest option for now is to display
# the latest and avoid deduplication, which will be done in the future.
break
|
https://github.com/schemathesis/schemathesis/issues/215
|
Failed to reproduce exception. Expected:
Traceback (most recent call last):
File "/home/stranger6667/programming/PycharmProjects/schemathesis/src/schemathesis/runner/__init__.py", line 148, in single_test
raise AssertionError
AssertionError
|
AssertionError
|
def add_success(self, name: str, example: Case) -> None:
self.checks.append(Check(name, Status.success, example))
|
def add_success(self, name: str) -> None:
self.checks.append(Check(name, Status.success))
|
https://github.com/schemathesis/schemathesis/issues/215
|
Failed to reproduce exception. Expected:
Traceback (most recent call last):
File "/home/stranger6667/programming/PycharmProjects/schemathesis/src/schemathesis/runner/__init__.py", line 148, in single_test
raise AssertionError
AssertionError
|
AssertionError
|
def add_error(self, exception: Exception, example: Optional[Case] = None) -> None:
self.errors.append((exception, example))
|
def add_error(self, exception: Exception) -> None:
self.errors.append(exception)
|
https://github.com/schemathesis/schemathesis/issues/215
|
Failed to reproduce exception. Expected:
Traceback (most recent call last):
File "/home/stranger6667/programming/PycharmProjects/schemathesis/src/schemathesis/runner/__init__.py", line 148, in single_test
raise AssertionError
AssertionError
|
AssertionError
|
def execute_from_schema(
schema: BaseSchema,
base_url: str,
checks: Iterable[Callable],
*,
hypothesis_options: Optional[Dict[str, Any]] = None,
auth: Optional[Auth] = None,
headers: Optional[Dict[str, Any]] = None,
) -> Generator[events.ExecutionEvent, None, None]:
"""Execute tests for the given schema.
Provides the main testing loop and preparation step.
"""
results = TestResultSet()
with get_session(auth, headers) as session:
settings = get_hypothesis_settings(hypothesis_options)
yield events.Initialized(
results=results, schema=schema, checks=checks, hypothesis_settings=settings
)
for endpoint, test in schema.get_all_tests(single_test, settings):
result = TestResult(path=endpoint.path, method=endpoint.method)
yield events.BeforeExecution(
results=results, schema=schema, endpoint=endpoint
)
try:
if isinstance(test, InvalidSchema):
status = Status.error
result.add_error(test)
else:
test(session, base_url, checks, result)
status = Status.success
except AssertionError:
status = Status.failure
except hypothesis.errors.Flaky:
status = Status.error
result.mark_errored()
flaky_example = result.checks[-1].example
result.add_error(
hypothesis.errors.Flaky(
"Tests on this endpoint produce unreliable results: \n"
"Falsified on the first call but did not on a subsequent one"
),
flaky_example,
)
except hypothesis.errors.Unsatisfiable:
# We need more clear error message here
status = Status.error
result.add_error(
hypothesis.errors.Unsatisfiable(
"Unable to satisfy schema parameters for this endpoint"
)
)
except Exception as error:
status = Status.error
result.add_error(error)
results.append(result)
yield events.AfterExecution(
results=results, schema=schema, endpoint=endpoint, status=status
)
yield events.Finished(results=results, schema=schema)
|
def execute_from_schema(
schema: BaseSchema,
base_url: str,
checks: Iterable[Callable],
*,
hypothesis_options: Optional[Dict[str, Any]] = None,
auth: Optional[Auth] = None,
headers: Optional[Dict[str, Any]] = None,
) -> Generator[events.ExecutionEvent, None, None]:
"""Execute tests for the given schema.
Provides the main testing loop and preparation step.
"""
results = TestResultSet()
with get_session(auth, headers) as session:
settings = get_hypothesis_settings(hypothesis_options)
yield events.Initialized(
results=results, schema=schema, checks=checks, hypothesis_settings=settings
)
for endpoint, test in schema.get_all_tests(single_test, settings):
result = TestResult(path=endpoint.path, method=endpoint.method)
yield events.BeforeExecution(
results=results, schema=schema, endpoint=endpoint
)
try:
if isinstance(test, InvalidSchema):
status = Status.error
result.add_error(test)
else:
test(session, base_url, checks, result)
status = Status.success
except AssertionError:
status = Status.failure
except hypothesis.errors.Unsatisfiable:
# We need more clear error message here
status = Status.error
result.add_error(
hypothesis.errors.Unsatisfiable(
"Unable to satisfy schema parameters for this endpoint"
)
)
except Exception as error:
status = Status.error
result.add_error(error)
results.append(result)
yield events.AfterExecution(
results=results, schema=schema, endpoint=endpoint, status=status
)
yield events.Finished(results=results, schema=schema)
|
https://github.com/schemathesis/schemathesis/issues/215
|
Failed to reproduce exception. Expected:
Traceback (most recent call last):
File "/home/stranger6667/programming/PycharmProjects/schemathesis/src/schemathesis/runner/__init__.py", line 148, in single_test
raise AssertionError
AssertionError
|
AssertionError
|
def capture_hypothesis_output() -> Generator[List[str], None, None]:
"""Capture all output of Hypothesis into a list of strings.
It allows us to have more granular control over Schemathesis output.
Usage::
@given(i=st.integers())
def test(i):
assert 0
with capture_hypothesis_output() as output:
test() # hypothesis test
# output == ["Falsifying example: test(i=0)"]
"""
output = []
def get_output(value: str) -> None:
# Drop messages that could be confusing in the Schemathesis context
if value.startswith(
(
"Falsifying example: ",
"You can add @seed",
"Failed to reproduce exception. Expected:",
)
):
return
output.append(value)
# the following context manager is untyped
with with_reporter(get_output): # type: ignore
yield output
|
def capture_hypothesis_output() -> Generator[List[str], None, None]:
"""Capture all output of Hypothesis into a list of strings.
It allows us to have more granular control over Schemathesis output.
Usage::
@given(i=st.integers())
def test(i):
assert 0
with capture_hypothesis_output() as output:
test() # hypothesis test
# output == ["Falsifying example: test(i=0)"]
"""
output = []
def get_output(value: str) -> None:
# Drop messages that could be confusing in the Schemathesis context
if value.startswith(("Falsifying example: ", "You can add @seed")):
return
output.append(value)
# the following context manager is untyped
with with_reporter(get_output): # type: ignore
yield output
|
https://github.com/schemathesis/schemathesis/issues/215
|
Failed to reproduce exception. Expected:
Traceback (most recent call last):
File "/home/stranger6667/programming/PycharmProjects/schemathesis/src/schemathesis/runner/__init__.py", line 148, in single_test
raise AssertionError
AssertionError
|
AssertionError
|
def get_output(value: str) -> None:
# Drop messages that could be confusing in the Schemathesis context
if value.startswith(
(
"Falsifying example: ",
"You can add @seed",
"Failed to reproduce exception. Expected:",
)
):
return
output.append(value)
|
def get_output(value: str) -> None:
# Drop messages that could be confusing in the Schemathesis context
if value.startswith(("Falsifying example: ", "You can add @seed")):
return
output.append(value)
|
https://github.com/schemathesis/schemathesis/issues/215
|
Failed to reproduce exception. Expected:
Traceback (most recent call last):
File "/home/stranger6667/programming/PycharmProjects/schemathesis/src/schemathesis/runner/__init__.py", line 148, in single_test
raise AssertionError
AssertionError
|
AssertionError
|
def save(self, fname, protocol=utils.PICKLE_PROTOCOL):
"""Save AnnoyIndexer instance to disk.
Parameters
----------
fname : str
Path to output. Save will produce 2 files:
`fname`: Annoy index itself.
`fname.dict`: Index metadata.
protocol : int, optional
Protocol for pickle.
Notes
-----
This method saves **only the index**. The trained model isn't preserved.
"""
self.index.save(fname)
d = {
"f": self.model.vector_size,
"num_trees": self.num_trees,
"labels": self.labels,
}
with utils.open(fname + ".dict", "wb") as fout:
_pickle.dump(d, fout, protocol=protocol)
|
def save(self, fname, protocol=2):
"""Save AnnoyIndexer instance to disk.
Parameters
----------
fname : str
Path to output. Save will produce 2 files:
`fname`: Annoy index itself.
`fname.dict`: Index metadata.
protocol : int, optional
Protocol for pickle.
Notes
-----
This method saves **only the index**. The trained model isn't preserved.
"""
self.index.save(fname)
d = {
"f": self.model.vector_size,
"num_trees": self.num_trees,
"labels": self.labels,
}
with utils.open(fname + ".dict", "wb") as fout:
_pickle.dump(d, fout, protocol=protocol)
|
https://github.com/RaRe-Technologies/gensim/issues/1851
|
[INFO] 2018-01-21T20:44:59.613Z f2689816-feeb-11e7-b397-b7ff2947dcec testing keys in event dict
[INFO] 2018-01-21T20:44:59.614Z f2689816-feeb-11e7-b397-b7ff2947dcec loading model from s3://data-d2v/trained_models/model_law
[INFO] 2018-01-21T20:44:59.614Z f2689816-feeb-11e7-b397-b7ff2947dcec loading Doc2Vec object from s3://data-d2v/trained_models/model_law
[INFO] 2018-01-21T20:44:59.650Z f2689816-feeb-11e7-b397-b7ff2947dcec Found credentials in environment variables.
[INFO] 2018-01-21T20:44:59.707Z f2689816-feeb-11e7-b397-b7ff2947dcec Starting new HTTPS connection (1): s3.eu-west-1.amazonaws.com
[INFO] 2018-01-21T20:44:59.801Z f2689816-feeb-11e7-b397-b7ff2947dcec Starting new HTTPS connection (2): s3.eu-west-1.amazonaws.com
[INFO] 2018-01-21T20:45:35.830Z f2689816-feeb-11e7-b397-b7ff2947dcec loading wv recursively from s3://data-d2v/trained_models/model_law.wv.* with mmap=None
[INFO] 2018-01-21T20:45:35.830Z f2689816-feeb-11e7-b397-b7ff2947dcec loading syn0 from s3://data-d2v/trained_models/model_law.wv.syn0.npy with mmap=None
[Errno 2] No such file or directory: 's3://data-d2v/trained_models/model_law.wv.syn0.npy': FileNotFoundError
Traceback (most recent call last):
File "/var/task/handler.py", line 20, in infer_handler
event['input_text'], event['model_file'], inferred_docs=10)
File "/var/task/infer_doc.py", line 26, in infer_docs
model = load_model(model_file)
File "/var/task/infer_doc.py", line 21, in load_model
return Doc2Vec.load(model_file)
File "/var/task/gensim/models/word2vec.py", line 1569, in load
model = super(Word2Vec, cls).load(*args, **kwargs)
File "/var/task/gensim/utils.py", line 282, in load
obj._load_specials(fname, mmap, compress, subname)
File "/var/task/gensim/models/word2vec.py", line 1593, in _load_specials
super(Word2Vec, self)._load_specials(*args, **kwargs)
File "/var/task/gensim/utils.py", line 301, in _load_specials
getattr(self, attrib)._load_specials(cfname, mmap, compress, subname)
File "/var/task/gensim/utils.py", line 312, in _load_specials
val = np.load(subname(fname, attrib), mmap_mode=mmap)
File "/var/task/numpy/lib/npyio.py", line 372, in load
fid = open(file, "rb")
FileNotFoundError: [Errno 2] No such file or directory: 's3://data-d2v/trained_models/model_law.wv.syn0.npy'
|
FileNotFoundError
|
def save(self, fname, protocol=utils.PICKLE_PROTOCOL):
"""Save this NmslibIndexer instance to a file.
Parameters
----------
fname : str
Path to the output file,
will produce 2 files: `fname` - parameters and `fname`.d - :class:`~nmslib.NmslibIndex`.
protocol : int, optional
Protocol for pickle.
Notes
-----
This method saves **only** the index (**the model isn't preserved**).
"""
fname_dict = fname + ".d"
self.index.saveIndex(fname)
d = {
"index_params": self.index_params,
"query_time_params": self.query_time_params,
"labels": self.labels,
}
with open(fname_dict, "wb") as fout:
_pickle.dump(d, fout, protocol=protocol)
|
def save(self, fname, protocol=2):
"""Save this NmslibIndexer instance to a file.
Parameters
----------
fname : str
Path to the output file,
will produce 2 files: `fname` - parameters and `fname`.d - :class:`~nmslib.NmslibIndex`.
protocol : int, optional
Protocol for pickle.
Notes
-----
This method saves **only** the index (**the model isn't preserved**).
"""
fname_dict = fname + ".d"
self.index.saveIndex(fname)
d = {
"index_params": self.index_params,
"query_time_params": self.query_time_params,
"labels": self.labels,
}
with open(fname_dict, "wb") as fout:
_pickle.dump(d, fout, protocol=protocol)
|
https://github.com/RaRe-Technologies/gensim/issues/1851
|
[INFO] 2018-01-21T20:44:59.613Z f2689816-feeb-11e7-b397-b7ff2947dcec testing keys in event dict
[INFO] 2018-01-21T20:44:59.614Z f2689816-feeb-11e7-b397-b7ff2947dcec loading model from s3://data-d2v/trained_models/model_law
[INFO] 2018-01-21T20:44:59.614Z f2689816-feeb-11e7-b397-b7ff2947dcec loading Doc2Vec object from s3://data-d2v/trained_models/model_law
[INFO] 2018-01-21T20:44:59.650Z f2689816-feeb-11e7-b397-b7ff2947dcec Found credentials in environment variables.
[INFO] 2018-01-21T20:44:59.707Z f2689816-feeb-11e7-b397-b7ff2947dcec Starting new HTTPS connection (1): s3.eu-west-1.amazonaws.com
[INFO] 2018-01-21T20:44:59.801Z f2689816-feeb-11e7-b397-b7ff2947dcec Starting new HTTPS connection (2): s3.eu-west-1.amazonaws.com
[INFO] 2018-01-21T20:45:35.830Z f2689816-feeb-11e7-b397-b7ff2947dcec loading wv recursively from s3://data-d2v/trained_models/model_law.wv.* with mmap=None
[INFO] 2018-01-21T20:45:35.830Z f2689816-feeb-11e7-b397-b7ff2947dcec loading syn0 from s3://data-d2v/trained_models/model_law.wv.syn0.npy with mmap=None
[Errno 2] No such file or directory: 's3://data-d2v/trained_models/model_law.wv.syn0.npy': FileNotFoundError
Traceback (most recent call last):
File "/var/task/handler.py", line 20, in infer_handler
event['input_text'], event['model_file'], inferred_docs=10)
File "/var/task/infer_doc.py", line 26, in infer_docs
model = load_model(model_file)
File "/var/task/infer_doc.py", line 21, in load_model
return Doc2Vec.load(model_file)
File "/var/task/gensim/models/word2vec.py", line 1569, in load
model = super(Word2Vec, cls).load(*args, **kwargs)
File "/var/task/gensim/utils.py", line 282, in load
obj._load_specials(fname, mmap, compress, subname)
File "/var/task/gensim/models/word2vec.py", line 1593, in _load_specials
super(Word2Vec, self)._load_specials(*args, **kwargs)
File "/var/task/gensim/utils.py", line 301, in _load_specials
getattr(self, attrib)._load_specials(cfname, mmap, compress, subname)
File "/var/task/gensim/utils.py", line 312, in _load_specials
val = np.load(subname(fname, attrib), mmap_mode=mmap)
File "/var/task/numpy/lib/npyio.py", line 372, in load
fid = open(file, "rb")
FileNotFoundError: [Errno 2] No such file or directory: 's3://data-d2v/trained_models/model_law.wv.syn0.npy'
|
FileNotFoundError
|
def _smart_save(
self,
fname,
separately=None,
sep_limit=10 * 1024**2,
ignore=frozenset(),
pickle_protocol=PICKLE_PROTOCOL,
):
"""Save the object to a file. Used internally by :meth:`gensim.utils.SaveLoad.save()`.
Parameters
----------
fname : str
Path to file.
separately : list, optional
Iterable of attributes than need to store distinctly.
sep_limit : int, optional
Limit for separation.
ignore : frozenset, optional
Attributes that shouldn't be store.
pickle_protocol : int, optional
Protocol number for pickle.
Notes
-----
If `separately` is None, automatically detect large numpy/scipy.sparse arrays in the object being stored,
and store them into separate files. This avoids pickle memory errors and allows mmap'ing large arrays back
on load efficiently.
You can also set `separately` manually, in which case it must be a list of attribute names to be stored
in separate files. The automatic check is not performed in this case.
"""
compress, subname = SaveLoad._adapt_by_suffix(fname)
restores = self._save_specials(
fname,
separately,
sep_limit,
ignore,
pickle_protocol,
compress,
subname,
)
try:
pickle(self, fname, protocol=pickle_protocol)
finally:
# restore attribs handled specially
for obj, asides in restores:
for attrib, val in asides.items():
with ignore_deprecation_warning():
setattr(obj, attrib, val)
logger.info("saved %s", fname)
|
def _smart_save(
self,
fname,
separately=None,
sep_limit=10 * 1024**2,
ignore=frozenset(),
pickle_protocol=2,
):
"""Save the object to a file. Used internally by :meth:`gensim.utils.SaveLoad.save()`.
Parameters
----------
fname : str
Path to file.
separately : list, optional
Iterable of attributes than need to store distinctly.
sep_limit : int, optional
Limit for separation.
ignore : frozenset, optional
Attributes that shouldn't be store.
pickle_protocol : int, optional
Protocol number for pickle.
Notes
-----
If `separately` is None, automatically detect large numpy/scipy.sparse arrays in the object being stored,
and store them into separate files. This avoids pickle memory errors and allows mmap'ing large arrays back
on load efficiently.
You can also set `separately` manually, in which case it must be a list of attribute names to be stored
in separate files. The automatic check is not performed in this case.
"""
compress, subname = SaveLoad._adapt_by_suffix(fname)
restores = self._save_specials(
fname, separately, sep_limit, ignore, pickle_protocol, compress, subname
)
try:
pickle(self, fname, protocol=pickle_protocol)
finally:
# restore attribs handled specially
for obj, asides in restores:
for attrib, val in asides.items():
with ignore_deprecation_warning():
setattr(obj, attrib, val)
logger.info("saved %s", fname)
|
https://github.com/RaRe-Technologies/gensim/issues/1851
|
[INFO] 2018-01-21T20:44:59.613Z f2689816-feeb-11e7-b397-b7ff2947dcec testing keys in event dict
[INFO] 2018-01-21T20:44:59.614Z f2689816-feeb-11e7-b397-b7ff2947dcec loading model from s3://data-d2v/trained_models/model_law
[INFO] 2018-01-21T20:44:59.614Z f2689816-feeb-11e7-b397-b7ff2947dcec loading Doc2Vec object from s3://data-d2v/trained_models/model_law
[INFO] 2018-01-21T20:44:59.650Z f2689816-feeb-11e7-b397-b7ff2947dcec Found credentials in environment variables.
[INFO] 2018-01-21T20:44:59.707Z f2689816-feeb-11e7-b397-b7ff2947dcec Starting new HTTPS connection (1): s3.eu-west-1.amazonaws.com
[INFO] 2018-01-21T20:44:59.801Z f2689816-feeb-11e7-b397-b7ff2947dcec Starting new HTTPS connection (2): s3.eu-west-1.amazonaws.com
[INFO] 2018-01-21T20:45:35.830Z f2689816-feeb-11e7-b397-b7ff2947dcec loading wv recursively from s3://data-d2v/trained_models/model_law.wv.* with mmap=None
[INFO] 2018-01-21T20:45:35.830Z f2689816-feeb-11e7-b397-b7ff2947dcec loading syn0 from s3://data-d2v/trained_models/model_law.wv.syn0.npy with mmap=None
[Errno 2] No such file or directory: 's3://data-d2v/trained_models/model_law.wv.syn0.npy': FileNotFoundError
Traceback (most recent call last):
File "/var/task/handler.py", line 20, in infer_handler
event['input_text'], event['model_file'], inferred_docs=10)
File "/var/task/infer_doc.py", line 26, in infer_docs
model = load_model(model_file)
File "/var/task/infer_doc.py", line 21, in load_model
return Doc2Vec.load(model_file)
File "/var/task/gensim/models/word2vec.py", line 1569, in load
model = super(Word2Vec, cls).load(*args, **kwargs)
File "/var/task/gensim/utils.py", line 282, in load
obj._load_specials(fname, mmap, compress, subname)
File "/var/task/gensim/models/word2vec.py", line 1593, in _load_specials
super(Word2Vec, self)._load_specials(*args, **kwargs)
File "/var/task/gensim/utils.py", line 301, in _load_specials
getattr(self, attrib)._load_specials(cfname, mmap, compress, subname)
File "/var/task/gensim/utils.py", line 312, in _load_specials
val = np.load(subname(fname, attrib), mmap_mode=mmap)
File "/var/task/numpy/lib/npyio.py", line 372, in load
fid = open(file, "rb")
FileNotFoundError: [Errno 2] No such file or directory: 's3://data-d2v/trained_models/model_law.wv.syn0.npy'
|
FileNotFoundError
|
def save(
self,
fname_or_handle,
separately=None,
sep_limit=10 * 1024**2,
ignore=frozenset(),
pickle_protocol=PICKLE_PROTOCOL,
):
"""Save the object to a file.
Parameters
----------
fname_or_handle : str or file-like
Path to output file or already opened file-like object. If the object is a file handle,
no special array handling will be performed, all attributes will be saved to the same file.
separately : list of str or None, optional
If None, automatically detect large numpy/scipy.sparse arrays in the object being stored, and store
them into separate files. This prevent memory errors for large objects, and also allows
`memory-mapping <https://en.wikipedia.org/wiki/Mmap>`_ the large arrays for efficient
loading and sharing the large arrays in RAM between multiple processes.
If list of str: store these attributes into separate files. The automated size check
is not performed in this case.
sep_limit : int, optional
Don't store arrays smaller than this separately. In bytes.
ignore : frozenset of str, optional
Attributes that shouldn't be stored at all.
pickle_protocol : int, optional
Protocol number for pickle.
See Also
--------
:meth:`~gensim.utils.SaveLoad.load`
Load object from file.
"""
self.add_lifecycle_event(
"saving",
fname_or_handle=str(fname_or_handle),
separately=str(separately),
sep_limit=sep_limit,
ignore=ignore,
)
try:
_pickle.dump(self, fname_or_handle, protocol=pickle_protocol)
logger.info("saved %s object", self.__class__.__name__)
except TypeError: # `fname_or_handle` does not have write attribute
self._smart_save(
fname_or_handle,
separately,
sep_limit,
ignore,
pickle_protocol=pickle_protocol,
)
|
def save(
self,
fname_or_handle,
separately=None,
sep_limit=10 * 1024**2,
ignore=frozenset(),
pickle_protocol=2,
):
"""Save the object to a file.
Parameters
----------
fname_or_handle : str or file-like
Path to output file or already opened file-like object. If the object is a file handle,
no special array handling will be performed, all attributes will be saved to the same file.
separately : list of str or None, optional
If None, automatically detect large numpy/scipy.sparse arrays in the object being stored, and store
them into separate files. This prevent memory errors for large objects, and also allows
`memory-mapping <https://en.wikipedia.org/wiki/Mmap>`_ the large arrays for efficient
loading and sharing the large arrays in RAM between multiple processes.
If list of str: store these attributes into separate files. The automated size check
is not performed in this case.
sep_limit : int, optional
Don't store arrays smaller than this separately. In bytes.
ignore : frozenset of str, optional
Attributes that shouldn't be stored at all.
pickle_protocol : int, optional
Protocol number for pickle.
See Also
--------
:meth:`~gensim.utils.SaveLoad.load`
Load object from file.
"""
self.add_lifecycle_event(
"saving",
fname_or_handle=str(fname_or_handle),
separately=str(separately),
sep_limit=sep_limit,
ignore=ignore,
)
try:
_pickle.dump(self, fname_or_handle, protocol=pickle_protocol)
logger.info("saved %s object", self.__class__.__name__)
except TypeError: # `fname_or_handle` does not have write attribute
self._smart_save(
fname_or_handle,
separately,
sep_limit,
ignore,
pickle_protocol=pickle_protocol,
)
|
https://github.com/RaRe-Technologies/gensim/issues/1851
|
[INFO] 2018-01-21T20:44:59.613Z f2689816-feeb-11e7-b397-b7ff2947dcec testing keys in event dict
[INFO] 2018-01-21T20:44:59.614Z f2689816-feeb-11e7-b397-b7ff2947dcec loading model from s3://data-d2v/trained_models/model_law
[INFO] 2018-01-21T20:44:59.614Z f2689816-feeb-11e7-b397-b7ff2947dcec loading Doc2Vec object from s3://data-d2v/trained_models/model_law
[INFO] 2018-01-21T20:44:59.650Z f2689816-feeb-11e7-b397-b7ff2947dcec Found credentials in environment variables.
[INFO] 2018-01-21T20:44:59.707Z f2689816-feeb-11e7-b397-b7ff2947dcec Starting new HTTPS connection (1): s3.eu-west-1.amazonaws.com
[INFO] 2018-01-21T20:44:59.801Z f2689816-feeb-11e7-b397-b7ff2947dcec Starting new HTTPS connection (2): s3.eu-west-1.amazonaws.com
[INFO] 2018-01-21T20:45:35.830Z f2689816-feeb-11e7-b397-b7ff2947dcec loading wv recursively from s3://data-d2v/trained_models/model_law.wv.* with mmap=None
[INFO] 2018-01-21T20:45:35.830Z f2689816-feeb-11e7-b397-b7ff2947dcec loading syn0 from s3://data-d2v/trained_models/model_law.wv.syn0.npy with mmap=None
[Errno 2] No such file or directory: 's3://data-d2v/trained_models/model_law.wv.syn0.npy': FileNotFoundError
Traceback (most recent call last):
File "/var/task/handler.py", line 20, in infer_handler
event['input_text'], event['model_file'], inferred_docs=10)
File "/var/task/infer_doc.py", line 26, in infer_docs
model = load_model(model_file)
File "/var/task/infer_doc.py", line 21, in load_model
return Doc2Vec.load(model_file)
File "/var/task/gensim/models/word2vec.py", line 1569, in load
model = super(Word2Vec, cls).load(*args, **kwargs)
File "/var/task/gensim/utils.py", line 282, in load
obj._load_specials(fname, mmap, compress, subname)
File "/var/task/gensim/models/word2vec.py", line 1593, in _load_specials
super(Word2Vec, self)._load_specials(*args, **kwargs)
File "/var/task/gensim/utils.py", line 301, in _load_specials
getattr(self, attrib)._load_specials(cfname, mmap, compress, subname)
File "/var/task/gensim/utils.py", line 312, in _load_specials
val = np.load(subname(fname, attrib), mmap_mode=mmap)
File "/var/task/numpy/lib/npyio.py", line 372, in load
fid = open(file, "rb")
FileNotFoundError: [Errno 2] No such file or directory: 's3://data-d2v/trained_models/model_law.wv.syn0.npy'
|
FileNotFoundError
|
def pickle(obj, fname, protocol=PICKLE_PROTOCOL):
"""Pickle object `obj` to file `fname`, using smart_open so that `fname` can be on S3, HDFS, compressed etc.
Parameters
----------
obj : object
Any python object.
fname : str
Path to pickle file.
protocol : int, optional
Pickle protocol number.
"""
with open(fname, "wb") as fout: # 'b' for binary, needed on Windows
_pickle.dump(obj, fout, protocol=protocol)
|
def pickle(obj, fname, protocol=2):
"""Pickle object `obj` to file `fname`, using smart_open so that `fname` can be on S3, HDFS, compressed etc.
Parameters
----------
obj : object
Any python object.
fname : str
Path to pickle file.
protocol : int, optional
Pickle protocol number. Default is 2 in order to support compatibility across python 2.x and 3.x.
"""
with open(fname, "wb") as fout: # 'b' for binary, needed on Windows
_pickle.dump(obj, fout, protocol=protocol)
|
https://github.com/RaRe-Technologies/gensim/issues/1851
|
[INFO] 2018-01-21T20:44:59.613Z f2689816-feeb-11e7-b397-b7ff2947dcec testing keys in event dict
[INFO] 2018-01-21T20:44:59.614Z f2689816-feeb-11e7-b397-b7ff2947dcec loading model from s3://data-d2v/trained_models/model_law
[INFO] 2018-01-21T20:44:59.614Z f2689816-feeb-11e7-b397-b7ff2947dcec loading Doc2Vec object from s3://data-d2v/trained_models/model_law
[INFO] 2018-01-21T20:44:59.650Z f2689816-feeb-11e7-b397-b7ff2947dcec Found credentials in environment variables.
[INFO] 2018-01-21T20:44:59.707Z f2689816-feeb-11e7-b397-b7ff2947dcec Starting new HTTPS connection (1): s3.eu-west-1.amazonaws.com
[INFO] 2018-01-21T20:44:59.801Z f2689816-feeb-11e7-b397-b7ff2947dcec Starting new HTTPS connection (2): s3.eu-west-1.amazonaws.com
[INFO] 2018-01-21T20:45:35.830Z f2689816-feeb-11e7-b397-b7ff2947dcec loading wv recursively from s3://data-d2v/trained_models/model_law.wv.* with mmap=None
[INFO] 2018-01-21T20:45:35.830Z f2689816-feeb-11e7-b397-b7ff2947dcec loading syn0 from s3://data-d2v/trained_models/model_law.wv.syn0.npy with mmap=None
[Errno 2] No such file or directory: 's3://data-d2v/trained_models/model_law.wv.syn0.npy': FileNotFoundError
Traceback (most recent call last):
File "/var/task/handler.py", line 20, in infer_handler
event['input_text'], event['model_file'], inferred_docs=10)
File "/var/task/infer_doc.py", line 26, in infer_docs
model = load_model(model_file)
File "/var/task/infer_doc.py", line 21, in load_model
return Doc2Vec.load(model_file)
File "/var/task/gensim/models/word2vec.py", line 1569, in load
model = super(Word2Vec, cls).load(*args, **kwargs)
File "/var/task/gensim/utils.py", line 282, in load
obj._load_specials(fname, mmap, compress, subname)
File "/var/task/gensim/models/word2vec.py", line 1593, in _load_specials
super(Word2Vec, self)._load_specials(*args, **kwargs)
File "/var/task/gensim/utils.py", line 301, in _load_specials
getattr(self, attrib)._load_specials(cfname, mmap, compress, subname)
File "/var/task/gensim/utils.py", line 312, in _load_specials
val = np.load(subname(fname, attrib), mmap_mode=mmap)
File "/var/task/numpy/lib/npyio.py", line 372, in load
fid = open(file, "rb")
FileNotFoundError: [Errno 2] No such file or directory: 's3://data-d2v/trained_models/model_law.wv.syn0.npy'
|
FileNotFoundError
|
def load(cls, *args, **kwargs):
"""Load a previously saved :class:`~gensim.models.phrases.Phrases` /
:class:`~gensim.models.phrases.FrozenPhrases` model.
Handles backwards compatibility from older versions which did not support pluggable scoring functions.
Parameters
----------
args : object
See :class:`~gensim.utils.SaveLoad.load`.
kwargs : object
See :class:`~gensim.utils.SaveLoad.load`.
"""
model = super(_PhrasesTransformation, cls).load(*args, **kwargs)
# Upgrade FrozenPhrases
try:
phrasegrams = getattr(model, "phrasegrams", {})
component, score = next(iter(phrasegrams.items()))
if isinstance(score, tuple):
# Value in phrasegrams used to be a tuple; keep only the 2nd tuple component = score.
model.phrasegrams = {
str(model.delimiter.join(key), encoding="utf8"): val[1]
for key, val in phrasegrams.items()
}
elif isinstance(
component, tuple
): # 3.8 => 4.0: phrasegram keys are strings, not tuples with bytestrings
model.phrasegrams = {
str(model.delimiter.join(component), encoding="utf8"): score
for key, val in phrasegrams.items()
}
except StopIteration:
# no phrasegrams, nothing to upgrade
pass
# If no scoring parameter, use default scoring.
if not hasattr(model, "scoring"):
logger.warning(
"older version of %s loaded without scoring function", cls.__name__
)
logger.warning(
"setting pluggable scoring method to original_scorer for compatibility"
)
model.scoring = original_scorer
# If there is a scoring parameter, and it's a text value, load the proper scoring function.
if hasattr(model, "scoring"):
if isinstance(model.scoring, str):
if model.scoring == "default":
logger.warning(
'older version of %s loaded with "default" scoring parameter',
cls.__name__,
)
logger.warning(
"setting scoring method to original_scorer for compatibility"
)
model.scoring = original_scorer
elif model.scoring == "npmi":
logger.warning(
'older version of %s loaded with "npmi" scoring parameter',
cls.__name__,
)
logger.warning(
"setting scoring method to npmi_scorer for compatibility"
)
model.scoring = npmi_scorer
else:
raise ValueError(
f'failed to load {cls.__name__} model, unknown scoring "{model.scoring}"'
)
# common_terms didn't exist pre-3.?, and was renamed to connector in 4.0.0.
if hasattr(model, "common_terms"):
model.connector_words = model.common_terms
del model.common_terms
else:
logger.warning(
"older version of %s loaded without common_terms attribute, setting connector_words to an empty set",
cls.__name__,
)
model.connector_words = frozenset()
if not hasattr(model, "corpus_word_count"):
logger.warning(
"older version of %s loaded without corpus_word_count", cls.__name__
)
logger.warning(
"setting corpus_word_count to 0, do not use it in your scoring function"
)
model.corpus_word_count = 0
# Before 4.0.0, we stored strings as UTF8 bytes internally, to save RAM. Since 4.0.0, we use strings.
if getattr(model, "vocab", None):
word = next(iter(model.vocab)) # get a random key – any key will do
if not isinstance(word, str):
logger.info(
"old version of %s loaded, upgrading %i words in memory",
cls.__name__,
len(model.vocab),
)
logger.info("re-save the loaded model to avoid this upgrade in the future")
vocab = {}
for (
key,
value,
) in model.vocab.items(): # needs lots of extra RAM temporarily!
vocab[str(key, encoding="utf8")] = value
model.vocab = vocab
if not isinstance(model.delimiter, str):
model.delimiter = str(model.delimiter, encoding="utf8")
return model
|
def load(cls, *args, **kwargs):
"""Load a previously saved :class:`~gensim.models.phrases.Phrases` /
:class:`~gensim.models.phrases.FrozenPhrases` model.
Handles backwards compatibility from older versions which did not support pluggable scoring functions.
Parameters
----------
args : object
See :class:`~gensim.utils.SaveLoad.load`.
kwargs : object
See :class:`~gensim.utils.SaveLoad.load`.
"""
model = super(_PhrasesTransformation, cls).load(*args, **kwargs)
# Upgrade FrozenPhrases
try:
phrasegrams = getattr(model, "phrasegrams", {})
component, score = next(iter(phrasegrams.items()))
if isinstance(score, tuple):
# Value in phrasegrams used to be a tuple; keep only the 2nd tuple component = score.
model.phrasegrams = {
str(model.delimiter.join(key), encoding="utf8"): val[1]
for key, val in phrasegrams.items()
}
elif isinstance(
component, tuple
): # 3.8 => 4.0: phrasegram keys are strings, not tuples with bytestrings
model.phrasegrams = {
str(model.delimiter.join(component), encoding="utf8"): score
for key, val in phrasegrams.items()
}
except StopIteration:
# no phrasegrams, nothing to upgrade
pass
# If no scoring parameter, use default scoring.
if not hasattr(model, "scoring"):
logger.warning(
"older version of %s loaded without scoring function", cls.__name__
)
logger.warning(
"setting pluggable scoring method to original_scorer for compatibility"
)
model.scoring = original_scorer
# If there is a scoring parameter, and it's a text value, load the proper scoring function.
if hasattr(model, "scoring"):
if isinstance(model.scoring, str):
if model.scoring == "default":
logger.warning(
'older version of %s loaded with "default" scoring parameter',
cls.__name__,
)
logger.warning(
"setting scoring method to original_scorer for compatibility"
)
model.scoring = original_scorer
elif model.scoring == "npmi":
logger.warning(
'older version of %s loaded with "npmi" scoring parameter',
cls.__name__,
)
logger.warning(
"setting scoring method to npmi_scorer for compatibility"
)
model.scoring = npmi_scorer
else:
raise ValueError(
f'failed to load {cls.__name__} model, unknown scoring "{model.scoring}"'
)
# common_terms didn't exist pre-3.?, and was renamed to connector in 4.0.0.
if hasattr(model, "common_terms"):
model.connector_words = model.common_terms
del model.common_terms
else:
logger.warning(
"older version of %s loaded without common_terms attribute, setting connector_words to an empty set",
cls.__name__,
)
model.connector_words = frozenset()
if not hasattr(model, "corpus_word_count"):
logger.warning(
"older version of %s loaded without corpus_word_count", cls.__name__
)
logger.warning(
"setting corpus_word_count to 0, do not use it in your scoring function"
)
model.corpus_word_count = 0
# Before 4.0.0, we stored strings as UTF8 bytes internally, to save RAM. Since 4.0.0, we use strings.
if getattr(model, "vocab", None):
word = next(iter(model.vocab)) # get a random key – any key will do
if not isinstance(word, str):
logger.info(
"old version of %s loaded, upgrading %i words in memory",
cls.__name__,
len(model.vocab),
)
logger.info("re-save the loaded model to avoid this upgrade in the future")
vocab = defaultdict(int)
for (
key,
value,
) in model.vocab.items(): # needs lots of extra RAM temporarily!
vocab[str(key, encoding="utf8")] = value
model.vocab = vocab
if not isinstance(model.delimiter, str):
model.delimiter = str(model.delimiter, encoding="utf8")
return model
|
https://github.com/RaRe-Technologies/gensim/issues/3031
|
RuntimeError Traceback (most recent call last)
<ipython-input-190-0f7e41471301> in <module>
----> 1 trigrams.export_phrases()
~\Anaconda3\lib\site-packages\gensim\models\phrases.py in export_phrases(self)
716 """
717 result, source_vocab = {}, self.vocab
--> 718 for token in source_vocab:
719 unigrams = token.split(self.delimiter)
720 if len(unigrams) < 2:
RuntimeError: dictionary changed size during iteration
|
RuntimeError
|
def __init__(
self,
sentences=None,
min_count=5,
threshold=10.0,
max_vocab_size=40000000,
delimiter="_",
progress_per=10000,
scoring="default",
connector_words=frozenset(),
):
"""
Parameters
----------
sentences : iterable of list of str, optional
The `sentences` iterable can be simply a list, but for larger corpora, consider a generator that streams
the sentences directly from disk/network, See :class:`~gensim.models.word2vec.BrownCorpus`,
:class:`~gensim.models.word2vec.Text8Corpus` or :class:`~gensim.models.word2vec.LineSentence`
for such examples.
min_count : float, optional
Ignore all words and bigrams with total collected count lower than this value.
threshold : float, optional
Represent a score threshold for forming the phrases (higher means fewer phrases).
A phrase of words `a` followed by `b` is accepted if the score of the phrase is greater than threshold.
Heavily depends on concrete scoring-function, see the `scoring` parameter.
max_vocab_size : int, optional
Maximum size (number of tokens) of the vocabulary. Used to control pruning of less common words,
to keep memory under control. The default of 40M needs about 3.6GB of RAM. Increase/decrease
`max_vocab_size` depending on how much available memory you have.
delimiter : str, optional
Glue character used to join collocation tokens.
scoring : {'default', 'npmi', function}, optional
Specify how potential phrases are scored. `scoring` can be set with either a string that refers to a
built-in scoring function, or with a function with the expected parameter names.
Two built-in scoring functions are available by setting `scoring` to a string:
#. "default" - :func:`~gensim.models.phrases.original_scorer`.
#. "npmi" - :func:`~gensim.models.phrases.npmi_scorer`.
connector_words : set of str, optional
Set of words that may be included within a phrase, without affecting its scoring.
No phrase can start nor end with a connector word; a phrase may contain any number of
connector words in the middle.
**If your texts are in English, set** ``connector_words=phrases.ENGLISH_CONNECTOR_WORDS``.
This will cause phrases to include common English articles, prepositions and
conjuctions, such as `bank_of_america` or `eye_of_the_beholder`.
For other languages or specific applications domains, use custom ``connector_words``
that make sense there: ``connector_words=frozenset("der die das".split())`` etc.
Examples
--------
.. sourcecode:: pycon
>>> from gensim.test.utils import datapath
>>> from gensim.models.word2vec import Text8Corpus
>>> from gensim.models.phrases import Phrases, ENGLISH_CONNECTOR_WORDS
>>>
>>> # Load corpus and train a model.
>>> sentences = Text8Corpus(datapath('testcorpus.txt'))
>>> phrases = Phrases(sentences, min_count=1, threshold=1, connector_words=ENGLISH_CONNECTOR_WORDS)
>>>
>>> # Use the model to detect phrases in a new sentence.
>>> sent = [u'trees', u'graph', u'minors']
>>> print(phrases[sent])
[u'trees_graph', u'minors']
>>>
>>> # Or transform multiple sentences at once.
>>> sents = [[u'trees', u'graph', u'minors'], [u'graph', u'minors']]
>>> for phrase in phrases[sents]:
... print(phrase)
[u'trees_graph', u'minors']
[u'graph_minors']
>>>
>>> # Export a FrozenPhrases object that is more efficient but doesn't allow any more training.
>>> frozen_phrases = phrases.freeze()
>>> print(frozen_phrases[sent])
[u'trees_graph', u'minors']
Notes
-----
The ``scoring="npmi"`` is more robust when dealing with common words that form part of common bigrams, and
ranges from -1 to 1, but is slower to calculate than the default ``scoring="default"``.
The default is the PMI-like scoring as described in `Mikolov, et. al: "Distributed
Representations of Words and Phrases and their Compositionality" <https://arxiv.org/abs/1310.4546>`_.
To use your own custom ``scoring`` function, pass in a function with the following signature:
* ``worda_count`` - number of corpus occurrences in `sentences` of the first token in the bigram being scored
* ``wordb_count`` - number of corpus occurrences in `sentences` of the second token in the bigram being scored
* ``bigram_count`` - number of occurrences in `sentences` of the whole bigram
* ``len_vocab`` - the number of unique tokens in `sentences`
* ``min_count`` - the `min_count` setting of the Phrases class
* ``corpus_word_count`` - the total number of tokens (non-unique) in `sentences`
The scoring function must accept all these parameters, even if it doesn't use them in its scoring.
The scoring function **must be pickleable**.
"""
super().__init__(connector_words=connector_words)
if min_count <= 0:
raise ValueError("min_count should be at least 1")
if threshold <= 0 and scoring == "default":
raise ValueError("threshold should be positive for default scoring")
if scoring == "npmi" and (threshold < -1 or threshold > 1):
raise ValueError("threshold should be between -1 and 1 for npmi scoring")
# Set scoring based on string.
# Intentially override the value of the scoring parameter rather than set self.scoring here,
# to still run the check of scoring function parameters in the next code block.
if isinstance(scoring, str):
if scoring == "default":
scoring = original_scorer
elif scoring == "npmi":
scoring = npmi_scorer
else:
raise ValueError(f"unknown scoring method string {scoring} specified")
scoring_params = [
"worda_count",
"wordb_count",
"bigram_count",
"len_vocab",
"min_count",
"corpus_word_count",
]
if callable(scoring):
missing = [
param for param in scoring_params if param not in getargspec(scoring)[0]
]
if not missing:
self.scoring = scoring
else:
raise ValueError(f"scoring function missing expected parameters {missing}")
self.min_count = min_count
self.threshold = threshold
self.max_vocab_size = max_vocab_size
self.vocab = {} # mapping between token => its count
self.min_reduce = 1 # ignore any tokens with count smaller than this
self.delimiter = delimiter
self.progress_per = progress_per
self.corpus_word_count = 0
# Ensure picklability of the scorer.
try:
pickle.loads(pickle.dumps(self.scoring))
except pickle.PickleError:
raise pickle.PickleError(
f"Custom scoring function in {self.__class__.__name__} must be pickle-able"
)
if sentences is not None:
self.add_vocab(sentences)
|
def __init__(
self,
sentences=None,
min_count=5,
threshold=10.0,
max_vocab_size=40000000,
delimiter="_",
progress_per=10000,
scoring="default",
connector_words=frozenset(),
):
"""
Parameters
----------
sentences : iterable of list of str, optional
The `sentences` iterable can be simply a list, but for larger corpora, consider a generator that streams
the sentences directly from disk/network, See :class:`~gensim.models.word2vec.BrownCorpus`,
:class:`~gensim.models.word2vec.Text8Corpus` or :class:`~gensim.models.word2vec.LineSentence`
for such examples.
min_count : float, optional
Ignore all words and bigrams with total collected count lower than this value.
threshold : float, optional
Represent a score threshold for forming the phrases (higher means fewer phrases).
A phrase of words `a` followed by `b` is accepted if the score of the phrase is greater than threshold.
Heavily depends on concrete scoring-function, see the `scoring` parameter.
max_vocab_size : int, optional
Maximum size (number of tokens) of the vocabulary. Used to control pruning of less common words,
to keep memory under control. The default of 40M needs about 3.6GB of RAM. Increase/decrease
`max_vocab_size` depending on how much available memory you have.
delimiter : str, optional
Glue character used to join collocation tokens.
scoring : {'default', 'npmi', function}, optional
Specify how potential phrases are scored. `scoring` can be set with either a string that refers to a
built-in scoring function, or with a function with the expected parameter names.
Two built-in scoring functions are available by setting `scoring` to a string:
#. "default" - :func:`~gensim.models.phrases.original_scorer`.
#. "npmi" - :func:`~gensim.models.phrases.npmi_scorer`.
connector_words : set of str, optional
Set of words that may be included within a phrase, without affecting its scoring.
No phrase can start nor end with a connector word; a phrase may contain any number of
connector words in the middle.
**If your texts are in English, set** ``connector_words=phrases.ENGLISH_CONNECTOR_WORDS``.
This will cause phrases to include common English articles, prepositions and
conjuctions, such as `bank_of_america` or `eye_of_the_beholder`.
For other languages or specific applications domains, use custom ``connector_words``
that make sense there: ``connector_words=frozenset("der die das".split())`` etc.
Examples
--------
.. sourcecode:: pycon
>>> from gensim.test.utils import datapath
>>> from gensim.models.word2vec import Text8Corpus
>>> from gensim.models.phrases import Phrases, ENGLISH_CONNECTOR_WORDS
>>>
>>> # Load corpus and train a model.
>>> sentences = Text8Corpus(datapath('testcorpus.txt'))
>>> phrases = Phrases(sentences, min_count=1, threshold=1, connector_words=ENGLISH_CONNECTOR_WORDS)
>>>
>>> # Use the model to detect phrases in a new sentence.
>>> sent = [u'trees', u'graph', u'minors']
>>> print(phrases[sent])
[u'trees_graph', u'minors']
>>>
>>> # Or transform multiple sentences at once.
>>> sents = [[u'trees', u'graph', u'minors'], [u'graph', u'minors']]
>>> for phrase in phrases[sents]:
... print(phrase)
[u'trees_graph', u'minors']
[u'graph_minors']
>>>
>>> # Export a FrozenPhrases object that is more efficient but doesn't allow any more training.
>>> frozen_phrases = phrases.freeze()
>>> print(frozen_phrases[sent])
[u'trees_graph', u'minors']
Notes
-----
The ``scoring="npmi"`` is more robust when dealing with common words that form part of common bigrams, and
ranges from -1 to 1, but is slower to calculate than the default ``scoring="default"``.
The default is the PMI-like scoring as described in `Mikolov, et. al: "Distributed
Representations of Words and Phrases and their Compositionality" <https://arxiv.org/abs/1310.4546>`_.
To use your own custom ``scoring`` function, pass in a function with the following signature:
* ``worda_count`` - number of corpus occurrences in `sentences` of the first token in the bigram being scored
* ``wordb_count`` - number of corpus occurrences in `sentences` of the second token in the bigram being scored
* ``bigram_count`` - number of occurrences in `sentences` of the whole bigram
* ``len_vocab`` - the number of unique tokens in `sentences`
* ``min_count`` - the `min_count` setting of the Phrases class
* ``corpus_word_count`` - the total number of tokens (non-unique) in `sentences`
The scoring function must accept all these parameters, even if it doesn't use them in its scoring.
The scoring function **must be pickleable**.
"""
super().__init__(connector_words=connector_words)
if min_count <= 0:
raise ValueError("min_count should be at least 1")
if threshold <= 0 and scoring == "default":
raise ValueError("threshold should be positive for default scoring")
if scoring == "npmi" and (threshold < -1 or threshold > 1):
raise ValueError("threshold should be between -1 and 1 for npmi scoring")
# Set scoring based on string.
# Intentially override the value of the scoring parameter rather than set self.scoring here,
# to still run the check of scoring function parameters in the next code block.
if isinstance(scoring, str):
if scoring == "default":
scoring = original_scorer
elif scoring == "npmi":
scoring = npmi_scorer
else:
raise ValueError(f"unknown scoring method string {scoring} specified")
scoring_params = [
"worda_count",
"wordb_count",
"bigram_count",
"len_vocab",
"min_count",
"corpus_word_count",
]
if callable(scoring):
missing = [
param for param in scoring_params if param not in getargspec(scoring)[0]
]
if not missing:
self.scoring = scoring
else:
raise ValueError(f"scoring function missing expected parameters {missing}")
self.min_count = min_count
self.threshold = threshold
self.max_vocab_size = max_vocab_size
self.vocab = defaultdict(int) # mapping between token => its count
self.min_reduce = 1 # ignore any tokens with count smaller than this
self.delimiter = delimiter
self.progress_per = progress_per
self.corpus_word_count = 0
# Ensure picklability of the scorer.
try:
pickle.loads(pickle.dumps(self.scoring))
except pickle.PickleError:
raise pickle.PickleError(
f"Custom scoring function in {self.__class__.__name__} must be pickle-able"
)
if sentences is not None:
self.add_vocab(sentences)
|
https://github.com/RaRe-Technologies/gensim/issues/3031
|
RuntimeError Traceback (most recent call last)
<ipython-input-190-0f7e41471301> in <module>
----> 1 trigrams.export_phrases()
~\Anaconda3\lib\site-packages\gensim\models\phrases.py in export_phrases(self)
716 """
717 result, source_vocab = {}, self.vocab
--> 718 for token in source_vocab:
719 unigrams = token.split(self.delimiter)
720 if len(unigrams) < 2:
RuntimeError: dictionary changed size during iteration
|
RuntimeError
|
def _learn_vocab(sentences, max_vocab_size, delimiter, connector_words, progress_per):
"""Collect unigram and bigram counts from the `sentences` iterable."""
sentence_no, total_words, min_reduce = -1, 0, 1
vocab = {}
logger.info("collecting all words and their counts")
for sentence_no, sentence in enumerate(sentences):
if sentence_no % progress_per == 0:
logger.info(
"PROGRESS: at sentence #%i, processed %i words and %i word types",
sentence_no,
total_words,
len(vocab),
)
start_token, in_between = None, []
for word in sentence:
if word not in connector_words:
vocab[word] = vocab.get(word, 0) + 1
if start_token is not None:
phrase_tokens = itertools.chain([start_token], in_between, [word])
joined_phrase_token = delimiter.join(phrase_tokens)
vocab[joined_phrase_token] = vocab.get(joined_phrase_token, 0) + 1
start_token, in_between = (
word,
[],
) # treat word as both end of a phrase AND beginning of another
elif start_token is not None:
in_between.append(word)
total_words += 1
if len(vocab) > max_vocab_size:
utils.prune_vocab(vocab, min_reduce)
min_reduce += 1
logger.info(
"collected %i token types (unigram + bigrams) from a corpus of %i words and %i sentences",
len(vocab),
total_words,
sentence_no + 1,
)
return min_reduce, vocab, total_words
|
def _learn_vocab(sentences, max_vocab_size, delimiter, connector_words, progress_per):
"""Collect unigram and bigram counts from the `sentences` iterable."""
sentence_no, total_words, min_reduce = -1, 0, 1
vocab = defaultdict(int)
logger.info("collecting all words and their counts")
for sentence_no, sentence in enumerate(sentences):
if sentence_no % progress_per == 0:
logger.info(
"PROGRESS: at sentence #%i, processed %i words and %i word types",
sentence_no,
total_words,
len(vocab),
)
start_token, in_between = None, []
for word in sentence:
if word not in connector_words:
vocab[word] += 1
if start_token is not None:
phrase_tokens = itertools.chain([start_token], in_between, [word])
vocab[delimiter.join(phrase_tokens)] += 1
start_token, in_between = (
word,
[],
) # treat word as both end of a phrase AND beginning of another
elif start_token is not None:
in_between.append(word)
total_words += 1
if len(vocab) > max_vocab_size:
utils.prune_vocab(vocab, min_reduce)
min_reduce += 1
logger.info(
"collected %i token types (unigram + bigrams) from a corpus of %i words and %i sentences",
len(vocab),
total_words,
sentence_no + 1,
)
return min_reduce, vocab, total_words
|
https://github.com/RaRe-Technologies/gensim/issues/3031
|
RuntimeError Traceback (most recent call last)
<ipython-input-190-0f7e41471301> in <module>
----> 1 trigrams.export_phrases()
~\Anaconda3\lib\site-packages\gensim\models\phrases.py in export_phrases(self)
716 """
717 result, source_vocab = {}, self.vocab
--> 718 for token in source_vocab:
719 unigrams = token.split(self.delimiter)
720 if len(unigrams) < 2:
RuntimeError: dictionary changed size during iteration
|
RuntimeError
|
def add_vocab(self, sentences):
"""Update model parameters with new `sentences`.
Parameters
----------
sentences : iterable of list of str
Text corpus to update this model's parameters from.
Example
-------
.. sourcecode:: pycon
>>> from gensim.test.utils import datapath
>>> from gensim.models.word2vec import Text8Corpus
>>> from gensim.models.phrases import Phrases, ENGLISH_CONNECTOR_WORDS
>>>
>>> # Train a phrase detector from a text corpus.
>>> sentences = Text8Corpus(datapath('testcorpus.txt'))
>>> phrases = Phrases(sentences, connector_words=ENGLISH_CONNECTOR_WORDS) # train model
>>> assert len(phrases.vocab) == 37
>>>
>>> more_sentences = [
... [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there'],
... [u'machine', u'learning', u'can', u'be', u'new', u'york', u'sometimes'],
... ]
>>>
>>> phrases.add_vocab(more_sentences) # add new sentences to model
>>> assert len(phrases.vocab) == 60
"""
# Uses a separate vocab to collect the token counts from `sentences`.
# This consumes more RAM than merging new sentences into `self.vocab`
# directly, but gives the new sentences a fighting chance to collect
# sufficient counts, before being pruned out by the (large) accumulated
# counts collected in previous learn_vocab runs.
min_reduce, vocab, total_words = self._learn_vocab(
sentences,
max_vocab_size=self.max_vocab_size,
delimiter=self.delimiter,
progress_per=self.progress_per,
connector_words=self.connector_words,
)
self.corpus_word_count += total_words
if self.vocab:
logger.info("merging %i counts into %s", len(vocab), self)
self.min_reduce = max(self.min_reduce, min_reduce)
for word, count in vocab.items():
self.vocab[word] = self.vocab.get(word, 0) + count
if len(self.vocab) > self.max_vocab_size:
utils.prune_vocab(self.vocab, self.min_reduce)
self.min_reduce += 1
else:
# Optimization for a common case: the current vocab is empty, so apply
# the new vocab directly, no need to double it in memory.
self.vocab = vocab
logger.info("merged %s", self)
|
def add_vocab(self, sentences):
"""Update model parameters with new `sentences`.
Parameters
----------
sentences : iterable of list of str
Text corpus to update this model's parameters from.
Example
-------
.. sourcecode:: pycon
>>> from gensim.test.utils import datapath
>>> from gensim.models.word2vec import Text8Corpus
>>> from gensim.models.phrases import Phrases, ENGLISH_CONNECTOR_WORDS
>>>
>>> # Train a phrase detector from a text corpus.
>>> sentences = Text8Corpus(datapath('testcorpus.txt'))
>>> phrases = Phrases(sentences, connector_words=ENGLISH_CONNECTOR_WORDS) # train model
>>> assert len(phrases.vocab) == 37
>>>
>>> more_sentences = [
... [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there'],
... [u'machine', u'learning', u'can', u'be', u'new', u'york', u'sometimes'],
... ]
>>>
>>> phrases.add_vocab(more_sentences) # add new sentences to model
>>> assert len(phrases.vocab) == 60
"""
# Uses a separate vocab to collect the token counts from `sentences`.
# This consumes more RAM than merging new sentences into `self.vocab`
# directly, but gives the new sentences a fighting chance to collect
# sufficient counts, before being pruned out by the (large) accumulated
# counts collected in previous learn_vocab runs.
min_reduce, vocab, total_words = self._learn_vocab(
sentences,
max_vocab_size=self.max_vocab_size,
delimiter=self.delimiter,
progress_per=self.progress_per,
connector_words=self.connector_words,
)
self.corpus_word_count += total_words
if self.vocab:
logger.info("merging %i counts into %s", len(vocab), self)
self.min_reduce = max(self.min_reduce, min_reduce)
for word, count in vocab.items():
self.vocab[word] += count
if len(self.vocab) > self.max_vocab_size:
utils.prune_vocab(self.vocab, self.min_reduce)
self.min_reduce += 1
else:
# Optimization for a common case: the current vocab is empty, so apply
# the new vocab directly, no need to double it in memory.
self.vocab = vocab
logger.info("merged %s", self)
|
https://github.com/RaRe-Technologies/gensim/issues/3031
|
RuntimeError Traceback (most recent call last)
<ipython-input-190-0f7e41471301> in <module>
----> 1 trigrams.export_phrases()
~\Anaconda3\lib\site-packages\gensim\models\phrases.py in export_phrases(self)
716 """
717 result, source_vocab = {}, self.vocab
--> 718 for token in source_vocab:
719 unigrams = token.split(self.delimiter)
720 if len(unigrams) < 2:
RuntimeError: dictionary changed size during iteration
|
RuntimeError
|
def score_candidate(self, word_a, word_b, in_between):
# Micro optimization: check for quick early-out conditions, before the actual scoring.
word_a_cnt = self.vocab.get(word_a, 0)
if word_a_cnt <= 0:
return None, None
word_b_cnt = self.vocab.get(word_b, 0)
if word_b_cnt <= 0:
return None, None
phrase = self.delimiter.join([word_a] + in_between + [word_b])
# XXX: Why do we care about *all* phrase tokens? Why not just score the start+end bigram?
phrase_cnt = self.vocab.get(phrase, 0)
if phrase_cnt <= 0:
return None, None
score = self.scoring(
worda_count=word_a_cnt,
wordb_count=word_b_cnt,
bigram_count=phrase_cnt,
len_vocab=len(self.vocab),
min_count=self.min_count,
corpus_word_count=self.corpus_word_count,
)
if score <= self.threshold:
return None, None
return phrase, score
|
def score_candidate(self, word_a, word_b, in_between):
# Micro optimization: check for quick early-out conditions, before the actual scoring.
word_a_cnt = self.vocab[word_a]
if word_a_cnt <= 0:
return None, None
word_b_cnt = self.vocab[word_b]
if word_b_cnt <= 0:
return None, None
phrase = self.delimiter.join([word_a] + in_between + [word_b])
# XXX: Why do we care about *all* phrase tokens? Why not just score the start+end bigram?
phrase_cnt = self.vocab[phrase]
if phrase_cnt <= 0:
return None, None
score = self.scoring(
worda_count=word_a_cnt,
wordb_count=word_b_cnt,
bigram_count=phrase_cnt,
len_vocab=len(self.vocab),
min_count=self.min_count,
corpus_word_count=self.corpus_word_count,
)
if score <= self.threshold:
return None, None
return phrase, score
|
https://github.com/RaRe-Technologies/gensim/issues/3031
|
RuntimeError Traceback (most recent call last)
<ipython-input-190-0f7e41471301> in <module>
----> 1 trigrams.export_phrases()
~\Anaconda3\lib\site-packages\gensim\models\phrases.py in export_phrases(self)
716 """
717 result, source_vocab = {}, self.vocab
--> 718 for token in source_vocab:
719 unigrams = token.split(self.delimiter)
720 if len(unigrams) < 2:
RuntimeError: dictionary changed size during iteration
|
RuntimeError
|
def fit(self, X, y=None):
"""Fit the model according to the given training data.
Parameters
----------
X : {iterable of :class:`~gensim.models.doc2vec.TaggedDocument`, iterable of list of str}
A collection of tagged documents used for training the model.
Returns
-------
:class:`~gensim.sklearn_api.d2vmodel.D2VTransformer`
The trained model.
"""
if isinstance([i for i in X[:1]][0], doc2vec.TaggedDocument):
d2v_sentences = X
else:
d2v_sentences = [
doc2vec.TaggedDocument(words, [i]) for i, words in enumerate(X)
]
self.gensim_model = models.Doc2Vec(
documents=d2v_sentences,
dm_mean=self.dm_mean,
dm=self.dm,
dbow_words=self.dbow_words,
dm_concat=self.dm_concat,
dm_tag_count=self.dm_tag_count,
docvecs=self.docvecs,
docvecs_mapfile=self.docvecs_mapfile,
comment=self.comment,
trim_rule=self.trim_rule,
vector_size=self.size,
alpha=self.alpha,
window=self.window,
min_count=self.min_count,
max_vocab_size=self.max_vocab_size,
sample=self.sample,
seed=self.seed,
workers=self.workers,
min_alpha=self.min_alpha,
hs=self.hs,
negative=self.negative,
cbow_mean=self.cbow_mean,
hashfxn=self.hashfxn,
epochs=self.iter,
sorted_vocab=self.sorted_vocab,
batch_words=self.batch_words,
)
return self
|
def fit(self, X, y=None):
"""Fit the model according to the given training data.
Parameters
----------
X : {iterable of :class:`~gensim.models.doc2vec.TaggedDocument`, iterable of list of str}
A collection of tagged documents used for training the model.
Returns
-------
:class:`~gensim.sklearn_api.d2vmodel.D2VTransformer`
The trained model.
"""
if isinstance(X[0], doc2vec.TaggedDocument):
d2v_sentences = X
else:
d2v_sentences = [
doc2vec.TaggedDocument(words, [i]) for i, words in enumerate(X)
]
self.gensim_model = models.Doc2Vec(
documents=d2v_sentences,
dm_mean=self.dm_mean,
dm=self.dm,
dbow_words=self.dbow_words,
dm_concat=self.dm_concat,
dm_tag_count=self.dm_tag_count,
docvecs=self.docvecs,
docvecs_mapfile=self.docvecs_mapfile,
comment=self.comment,
trim_rule=self.trim_rule,
vector_size=self.size,
alpha=self.alpha,
window=self.window,
min_count=self.min_count,
max_vocab_size=self.max_vocab_size,
sample=self.sample,
seed=self.seed,
workers=self.workers,
min_alpha=self.min_alpha,
hs=self.hs,
negative=self.negative,
cbow_mean=self.cbow_mean,
hashfxn=self.hashfxn,
epochs=self.iter,
sorted_vocab=self.sorted_vocab,
batch_words=self.batch_words,
)
return self
|
https://github.com/RaRe-Technologies/gensim/issues/2556
|
Traceback (most recent call last):
File "main.py", line 9, in <module>
transformer.fit(series)
File "venv/lib/python3.7/site-packages/gensim/sklearn_api/d2vmodel.py", line 162, in fit
if isinstance(X[0], doc2vec.TaggedDocument):
File "venv/lib/python3.7/site-packages/pandas/core/series.py", line 868, in __getitem__
result = self.index.get_value(self, key)
File "venv/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 4375, in get_value
tz=getattr(series.dtype, 'tz', None))
File "pandas/_libs/index.pyx", line 81, in pandas._libs.index.IndexEngine.get_value
File "pandas/_libs/index.pyx", line 89, in pandas._libs.index.IndexEngine.get_value
File "pandas/_libs/index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 987, in pandas._libs.hashtable.Int64HashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 993, in pandas._libs.hashtable.Int64HashTable.get_item
KeyError: 0
|
KeyError
|
def _load_vocab(fin, new_format, encoding="utf-8"):
"""Load a vocabulary from a FB binary.
Before the vocab is ready for use, call the prepare_vocab function and pass
in the relevant parameters from the model.
Parameters
----------
fin : file
An open file pointer to the binary.
new_format: boolean
True if the binary is of the newer format.
encoding : str
The encoding to use when decoding binary data into words.
Returns
-------
tuple
The loaded vocabulary. Keys are words, values are counts.
The vocabulary size.
The number of words.
"""
vocab_size, nwords, nlabels = _struct_unpack(fin, "@3i")
# Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc)
if nlabels > 0:
raise NotImplementedError("Supervised fastText models are not supported")
logger.info("loading %s words for fastText model from %s", vocab_size, fin.name)
_struct_unpack(fin, "@1q") # number of tokens
if new_format:
(pruneidx_size,) = _struct_unpack(fin, "@q")
raw_vocab = collections.OrderedDict()
for i in range(vocab_size):
word_bytes = io.BytesIO()
char_byte = fin.read(1)
while char_byte != _END_OF_WORD_MARKER:
word_bytes.write(char_byte)
char_byte = fin.read(1)
word_bytes = word_bytes.getvalue()
try:
word = word_bytes.decode(encoding)
except UnicodeDecodeError:
word = word_bytes.decode(encoding, errors="ignore")
logger.error(
"failed to decode invalid unicode bytes %r; ignoring invalid characters, using %r",
word_bytes,
word,
)
count, _ = _struct_unpack(fin, "@qb")
raw_vocab[word] = count
if new_format:
for j in range(pruneidx_size):
_struct_unpack(fin, "@2i")
return raw_vocab, vocab_size, nwords
|
def _load_vocab(fin, new_format, encoding="utf-8"):
"""Load a vocabulary from a FB binary.
Before the vocab is ready for use, call the prepare_vocab function and pass
in the relevant parameters from the model.
Parameters
----------
fin : file
An open file pointer to the binary.
new_format: boolean
True if the binary is of the newer format.
encoding : str
The encoding to use when decoding binary data into words.
Returns
-------
tuple
The loaded vocabulary. Keys are words, values are counts.
The vocabulary size.
The number of words.
"""
vocab_size, nwords, nlabels = _struct_unpack(fin, "@3i")
# Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc)
if nlabels > 0:
raise NotImplementedError("Supervised fastText models are not supported")
logger.info("loading %s words for fastText model from %s", vocab_size, fin.name)
_struct_unpack(fin, "@1q") # number of tokens
if new_format:
(pruneidx_size,) = _struct_unpack(fin, "@q")
raw_vocab = collections.OrderedDict()
for i in range(vocab_size):
word_bytes = b""
char_byte = fin.read(1)
# Read vocab word
while char_byte != b"\x00":
word_bytes += char_byte
char_byte = fin.read(1)
word = word_bytes.decode(encoding)
count, _ = _struct_unpack(fin, "@qb")
raw_vocab[word] = count
if new_format:
for j in range(pruneidx_size):
_struct_unpack(fin, "@2i")
return raw_vocab, vocab_size, nwords
|
https://github.com/RaRe-Technologies/gensim/issues/2378
|
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-29-a054256d6f88> in <module>
1 #load model
2 from gensim.models.fasttext import FastText
----> 3 mod = FastText.load_fasttext_format('wiki-news-300d-1M-subword.bin')
4 # from gensim.models import KeyedVectors
5 # wv = KeyedVectors.load_word2vec_format('wiki-news-300d-1M-subword.vec')
/anaconda3/envs/tensor_env/lib/python3.7/site-packages/gensim/models/fasttext.py in load_fasttext_format(cls, model_file, encoding, full_model)
1012
1013 """
-> 1014 return _load_fasttext_format(model_file, encoding=encoding, full_model=full_model)
1015
1016 def load_binary_data(self, encoding='utf8'):
/anaconda3/envs/tensor_env/lib/python3.7/site-packages/gensim/models/fasttext.py in _load_fasttext_format(model_file, encoding, full_model)
1270 #
1271 # We explicitly set min_count=1 regardless of the model's parameters to
-> 1272 # ignore the trim rule when building the vocabulary. We do this in order
1273 # to support loading native models that were trained with pretrained vectors.
1274 # Such models will contain vectors for _all_ encountered words, not only
/anaconda3/envs/tensor_env/lib/python3.7/site-packages/gensim/models/keyedvectors.py in init_post_load(self, vectors, match_gensim)
2205 """
2206 vocab_words = len(self.vocab)
-> 2207 assert vectors.shape[0] == vocab_words + self.bucket, 'unexpected number of vectors'
2208 assert vectors.shape[1] == self.vector_size, 'unexpected vector dimensionality'
2209
AssertionError: unexpected number of vectors
|
AssertionError
|
def _load_matrix(fin, new_format=True):
"""Load a matrix from fastText native format.
Interprets the matrix dimensions and type from the file stream.
Parameters
----------
fin : file
A file handle opened for reading.
new_format : bool, optional
True if the quant_input variable precedes
the matrix declaration. Should be True for newer versions of fastText.
Returns
-------
:class:`numpy.array`
The vectors as an array.
Each vector will be a row in the array.
The number of columns of the array will correspond to the vector size.
"""
if _FLOAT_DTYPE is None:
raise ValueError("bad _FLOAT_SIZE: %r" % _FLOAT_SIZE)
if new_format:
_struct_unpack(fin, "@?") # bool quant_input in fasttext.cc
num_vectors, dim = _struct_unpack(fin, "@2q")
count = num_vectors * dim
#
# numpy.fromfile doesn't play well with gzip.GzipFile as input:
#
# - https://github.com/RaRe-Technologies/gensim/pull/2476
# - https://github.com/numpy/numpy/issues/13470
#
# Until they fix it, we have to apply a workaround. We only apply the
# workaround when it's necessary, because np.fromfile is heavily optimized
# and very efficient (when it works).
#
if isinstance(fin, gzip.GzipFile):
logger.warning(
"Loading model from a compressed .gz file. This can be slow. "
"This is a work-around for a bug in NumPy: https://github.com/numpy/numpy/issues/13470. "
"Consider decompressing your model file for a faster load. "
)
matrix = _fromfile(fin, _FLOAT_DTYPE, count)
else:
matrix = np.fromfile(fin, _FLOAT_DTYPE, count)
assert matrix.shape == (count,), "expected (%r,), got %r" % (count, matrix.shape)
matrix = matrix.reshape((num_vectors, dim))
return matrix
|
def _load_matrix(fin, new_format=True):
"""Load a matrix from fastText native format.
Interprets the matrix dimensions and type from the file stream.
Parameters
----------
fin : file
A file handle opened for reading.
new_format : bool, optional
True if the quant_input variable precedes
the matrix declaration. Should be True for newer versions of fastText.
Returns
-------
:class:`numpy.array`
The vectors as an array.
Each vector will be a row in the array.
The number of columns of the array will correspond to the vector size.
"""
if new_format:
_struct_unpack(fin, "@?") # bool quant_input in fasttext.cc
num_vectors, dim = _struct_unpack(fin, "@2q")
float_size = struct.calcsize("@f")
if float_size == 4:
dtype = np.dtype(np.float32)
elif float_size == 8:
dtype = np.dtype(np.float64)
else:
raise ValueError("Incompatible float size: %r" % float_size)
matrix = np.fromfile(fin, dtype=dtype, count=num_vectors * dim)
matrix = matrix.reshape((num_vectors, dim))
return matrix
|
https://github.com/RaRe-Technologies/gensim/issues/2473
|
ValueError Traceback (most recent call last)
<ipython-input-7-615bb517a8f5> in <module>
----> 1 model_fast = gensim.models.fasttext.load_facebook_model('cc.en.300.bin.gz')
c:\users\david\desktop\bennet~1\bennet~1\bert_t~1\env\lib\site-packages\gensim\models\fasttext.py in load_facebook_model(path, encoding)
1241
1242 """
-> 1243 return _load_fasttext_format(path, encoding=encoding, full_model=True)
1244
1245
c:\users\david\desktop\bennet~1\bennet~1\bert_t~1\env\lib\site-packages\gensim\models\fasttext.py in _load_fasttext_format(model_file, encoding, full_model)
1321 """
1322 with smart_open(model_file, 'rb') as fin:
-> 1323 m = gensim.models._fasttext_bin.load(fin, encoding=encoding, full_model=full_model)
1324
1325 model = FastText(
c:\users\david\desktop\bennet~1\bennet~1\bert_t~1\env\lib\site-packages\gensim\models\_fasttext_bin.py in load(fin, encoding, full_model)
272 model.update(raw_vocab=raw_vocab, vocab_size=vocab_size, nwords=nwords)
273
--> 274 vectors_ngrams = _load_matrix(fin, new_format=new_format)
275
276 if not full_model:
c:\users\david\desktop\bennet~1\bennet~1\bert_t~1\env\lib\site-packages\gensim\models\_fasttext_bin.py in _load_matrix(fin, new_format)
235
236 matrix = np.fromfile(fin, dtype=dtype, count=num_vectors * dim)
--> 237 matrix = matrix.reshape((num_vectors, dim))
238 return matrix
239
ValueError: cannot reshape array of size 1116604308 into shape (4000000,300)
|
ValueError
|
def load(cls, *args, **kwargs):
"""Load a previously saved `FastText` model.
Parameters
----------
fname : str
Path to the saved file.
Returns
-------
:class:`~gensim.models.fasttext.FastText`
Loaded model.
See Also
--------
:meth:`~gensim.models.fasttext.FastText.save`
Save :class:`~gensim.models.fasttext.FastText` model.
"""
try:
model = super(FastText, cls).load(*args, **kwargs)
if not hasattr(model.trainables, "vectors_vocab_lockf") and hasattr(
model.wv, "vectors_vocab"
):
model.trainables.vectors_vocab_lockf = ones(
model.wv.vectors_vocab.shape, dtype=REAL
)
if not hasattr(model.trainables, "vectors_ngrams_lockf") and hasattr(
model.wv, "vectors_ngrams"
):
model.trainables.vectors_ngrams_lockf = ones(
model.wv.vectors_ngrams.shape, dtype=REAL
)
if not hasattr(model.wv, "bucket"):
model.wv.bucket = model.trainables.bucket
except AttributeError:
logger.info(
"Model saved using code from earlier Gensim Version. Re-loading old model in a compatible way."
)
from gensim.models.deprecated.fasttext import load_old_fasttext
model = load_old_fasttext(*args, **kwargs)
gensim.models.keyedvectors._try_upgrade(model.wv)
return model
|
def load(cls, *args, **kwargs):
"""Load a previously saved `FastText` model.
Parameters
----------
fname : str
Path to the saved file.
Returns
-------
:class:`~gensim.models.fasttext.FastText`
Loaded model.
See Also
--------
:meth:`~gensim.models.fasttext.FastText.save`
Save :class:`~gensim.models.fasttext.FastText` model.
"""
try:
model = super(FastText, cls).load(*args, **kwargs)
if hasattr(model.wv, "hash2index"):
gensim.models.keyedvectors._rollback_optimization(model.wv)
if not hasattr(model.trainables, "vectors_vocab_lockf") and hasattr(
model.wv, "vectors_vocab"
):
model.trainables.vectors_vocab_lockf = ones(
model.wv.vectors_vocab.shape, dtype=REAL
)
if not hasattr(model.trainables, "vectors_ngrams_lockf") and hasattr(
model.wv, "vectors_ngrams"
):
model.trainables.vectors_ngrams_lockf = ones(
model.wv.vectors_ngrams.shape, dtype=REAL
)
if not hasattr(model.wv, "compatible_hash"):
logger.warning(
"This older model was trained with a buggy hash function. "
"The model will continue to work, but consider training it "
"from scratch."
)
model.wv.compatible_hash = False
if not hasattr(model.wv, "bucket"):
model.wv.bucket = model.trainables.bucket
return model
except AttributeError:
logger.info(
"Model saved using code from earlier Gensim Version. Re-loading old model in a compatible way."
)
from gensim.models.deprecated.fasttext import load_old_fasttext
return load_old_fasttext(*args, **kwargs)
|
https://github.com/RaRe-Technologies/gensim/issues/2453
|
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-14-5fac1e42e24a> in <module>()
----> 1 ft2.wv.word_vec('слово123')
~/anaconda3/lib/python3.6/site-packages/gensim/models/keyedvectors.py in word_vec(self, word, use_norm)
2111 return word_vec
2112 for nh in ngram_hashes:
-> 2113 word_vec += ngram_weights[nh]
2114 return word_vec / len(ngram_hashes)
2115
IndexError: index 1369364 is out of bounds for axis 0 with size 27355
|
IndexError
|
def load(cls, fname_or_handle, **kwargs):
model = super(WordEmbeddingsKeyedVectors, cls).load(fname_or_handle, **kwargs)
_try_upgrade(model)
return model
|
def load(cls, fname_or_handle, **kwargs):
model = super(WordEmbeddingsKeyedVectors, cls).load(fname_or_handle, **kwargs)
if not hasattr(model, "compatible_hash"):
model.compatible_hash = False
if hasattr(model, "hash2index"):
_rollback_optimization(model)
return model
|
https://github.com/RaRe-Technologies/gensim/issues/2453
|
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-14-5fac1e42e24a> in <module>()
----> 1 ft2.wv.word_vec('слово123')
~/anaconda3/lib/python3.6/site-packages/gensim/models/keyedvectors.py in word_vec(self, word, use_norm)
2111 return word_vec
2112 for nh in ngram_hashes:
-> 2113 word_vec += ngram_weights[nh]
2114 return word_vec / len(ngram_hashes)
2115
IndexError: index 1369364 is out of bounds for axis 0 with size 27355
|
IndexError
|
def _load_fasttext_format(model_file, encoding="utf-8", full_model=True):
"""Load the input-hidden weight matrix from Facebook's native fasttext `.bin` and `.vec` output files.
Parameters
----------
model_file : str
Path to the FastText output files.
FastText outputs two model files - `/path/to/model.vec` and `/path/to/model.bin`
Expected value for this example: `/path/to/model` or `/path/to/model.bin`,
as Gensim requires only `.bin` file to the load entire fastText model.
encoding : str, optional
Specifies the file encoding.
full_model : boolean, optional
If False, skips loading the hidden output matrix. This saves a fair bit
of CPU time and RAM, but prevents training continuation.
Returns
-------
:class: `~gensim.models.fasttext.FastText`
The loaded model.
"""
if not model_file.endswith(".bin"):
model_file += ".bin"
with smart_open(model_file, "rb") as fin:
m = gensim.models._fasttext_bin.load(
fin, encoding=encoding, full_model=full_model
)
model = FastText(
size=m.dim,
window=m.ws,
iter=m.epoch,
negative=m.neg,
hs=(m.loss == 1),
sg=(m.model == 2),
bucket=m.bucket,
min_count=m.min_count,
sample=m.t,
min_n=m.minn,
max_n=m.maxn,
)
model.vocabulary.raw_vocab = m.raw_vocab
model.vocabulary.nwords = m.nwords
model.vocabulary.vocab_size = m.vocab_size
#
# This is here to fix https://github.com/RaRe-Technologies/gensim/pull/2373.
#
# We explicitly set min_count=1 regardless of the model's parameters to
# ignore the trim rule when building the vocabulary. We do this in order
# to support loading native models that were trained with pretrained vectors.
# Such models will contain vectors for _all_ encountered words, not only
# those occurring more frequently than min_count.
#
# Native models trained _without_ pretrained vectors already contain the
# trimmed raw_vocab, so this change does not affect them.
#
model.vocabulary.prepare_vocab(
model.hs,
model.negative,
model.wv,
update=True,
min_count=1,
)
model.num_original_vectors = m.vectors_ngrams.shape[0]
model.wv.init_post_load(m.vectors_ngrams)
model.trainables.init_post_load(model, m.hidden_output)
_check_model(model)
logger.info(
"loaded %s weight matrix for fastText model from %s",
m.vectors_ngrams.shape,
fin.name,
)
return model
|
def _load_fasttext_format(model_file, encoding="utf-8", full_model=True):
"""Load the input-hidden weight matrix from Facebook's native fasttext `.bin` and `.vec` output files.
Parameters
----------
model_file : str
Path to the FastText output files.
FastText outputs two model files - `/path/to/model.vec` and `/path/to/model.bin`
Expected value for this example: `/path/to/model` or `/path/to/model.bin`,
as Gensim requires only `.bin` file to the load entire fastText model.
encoding : str, optional
Specifies the file encoding.
full_model : boolean, optional
If False, skips loading the hidden output matrix. This saves a fair bit
of CPU time and RAM, but prevents training continuation.
Returns
-------
:class: `~gensim.models.fasttext.FastText`
The loaded model.
"""
if not model_file.endswith(".bin"):
model_file += ".bin"
with smart_open(model_file, "rb") as fin:
m = gensim.models._fasttext_bin.load(
fin, encoding=encoding, full_model=full_model
)
model = FastText(
size=m.dim,
window=m.ws,
iter=m.epoch,
negative=m.neg,
hs=(m.loss == 1),
sg=(m.model == 2),
bucket=m.bucket,
min_count=m.min_count,
sample=m.t,
min_n=m.minn,
max_n=m.maxn,
)
model.vocabulary.raw_vocab = m.raw_vocab
model.vocabulary.nwords = m.nwords
model.vocabulary.vocab_size = m.vocab_size
model.vocabulary.prepare_vocab(
model.hs, model.negative, model.wv, update=True, min_count=model.min_count
)
model.num_original_vectors = m.vectors_ngrams.shape[0]
model.wv.init_post_load(m.vectors_ngrams)
model.trainables.init_post_load(model, m.hidden_output)
_check_model(model)
logger.info(
"loaded %s weight matrix for fastText model from %s",
m.vectors_ngrams.shape,
fin.name,
)
return model
|
https://github.com/RaRe-Technologies/gensim/issues/2350
|
AssertionError: unexpected number of vectors
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<command-3269280551404242> in <module>()
1 #gensim FastText (having some different features)
----> 2 ge_model = ge_ft.load_fasttext_format(FASTTEXT_MODEL_BIN)
/databricks/python/lib/python3.5/site-packages/gensim/models/fasttext.py in load_fasttext_format(cls, model_file, encoding)
778
779 """
--> 780 return _load_fasttext_format(model_file, encoding=encoding)
781
782 def load_binary_data(self, encoding='utf8'):
/databricks/python/lib/python3.5/site-packages/gensim/models/fasttext.py in _load_fasttext_format(model_file, encoding)
1005 model.num_original_vectors = m.vectors_ngrams.shape[0]
1006
-> 1007 model.wv.init_post_load(m.vectors_ngrams)
1008 model.trainables.init_post_load(model, m.hidden_output)
1009
/databricks/python/lib/python3.5/site-packages/gensim/models/keyedvectors.py in init_post_load(self, vectors, match_gensim)
2189 """
2190 vocab_words = len(self.vocab)
-> 2191 assert vectors.shape[0] == vocab_words + self.bucket, 'unexpected number of vectors'
2192 assert vectors.shape[1] == self.vector_size, 'unexpected vector dimensionality'
2193
AssertionError: unexpected number of vectors
|
AssertionError
|
def load_old_doc2vec(*args, **kwargs):
old_model = Doc2Vec.load(*args, **kwargs)
params = {
"dm_mean": old_model.__dict__.get("dm_mean", None),
"dm": old_model.dm,
"dbow_words": old_model.dbow_words,
"dm_concat": old_model.dm_concat,
"dm_tag_count": old_model.dm_tag_count,
"docvecs_mapfile": old_model.__dict__.get("docvecs_mapfile", None),
"comment": old_model.__dict__.get("comment", None),
"size": old_model.vector_size,
"alpha": old_model.alpha,
"window": old_model.window,
"min_count": old_model.min_count,
"max_vocab_size": old_model.__dict__.get("max_vocab_size", None),
"sample": old_model.sample,
"seed": old_model.seed,
"workers": old_model.workers,
"min_alpha": old_model.min_alpha,
"hs": old_model.hs,
"negative": old_model.negative,
"cbow_mean": old_model.cbow_mean,
"hashfxn": old_model.hashfxn,
"iter": old_model.iter,
"sorted_vocab": old_model.__dict__.get("sorted_vocab", 1),
"batch_words": old_model.__dict__.get("batch_words", MAX_WORDS_IN_BATCH),
"compute_loss": old_model.__dict__.get("compute_loss", None),
}
new_model = NewDoc2Vec(**params)
# set word2vec trainables attributes
new_model.wv.vectors = old_model.wv.syn0
if hasattr(old_model.wv, "syn0norm"):
new_model.docvecs.vectors_norm = old_model.wv.syn0norm
if hasattr(old_model, "syn1"):
new_model.trainables.syn1 = old_model.syn1
if hasattr(old_model, "syn1neg"):
new_model.trainables.syn1neg = old_model.syn1neg
if hasattr(old_model, "syn0_lockf"):
new_model.trainables.vectors_lockf = old_model.syn0_lockf
# set doc2vec trainables attributes
new_model.docvecs.vectors_docs = old_model.docvecs.doctag_syn0
if hasattr(old_model.docvecs, "doctag_syn0norm"):
new_model.docvecs.vectors_docs_norm = old_model.docvecs.doctag_syn0norm
if hasattr(old_model.docvecs, "doctag_syn0_lockf"):
new_model.trainables.vectors_docs_lockf = old_model.docvecs.doctag_syn0_lockf
if hasattr(old_model.docvecs, "mapfile_path"):
new_model.docvecs.mapfile_path = old_model.docvecs.mapfile_path
# set word2vec vocabulary attributes
new_model.wv.vocab = old_model.wv.vocab
new_model.wv.index2word = old_model.wv.index2word
new_model.vocabulary.cum_table = old_model.cum_table
# set doc2vec vocabulary attributes
new_model.docvecs.doctags = old_model.docvecs.doctags
new_model.docvecs.count = old_model.docvecs.count
if hasattr(
old_model.docvecs, "max_rawint"
): # `doc2vec` models before `0.12.3` do not have these 2 attributes
new_model.docvecs.max_rawint = old_model.docvecs.__dict__.get("max_rawint")
new_model.docvecs.offset2doctag = old_model.docvecs.__dict__.get(
"offset2doctag"
)
else:
# Doc2Vec models before Gensim version 0.12.3 did not have `max_rawint` and `offset2doctag` as they did not
# mixing of string and int tags. This implies the new attribute `offset2doctag` equals the old `index2doctag`
# (which was only filled if the documents had string tags).
# This also implies that the new attribute, `max_rawint`(highest rawint-indexed doctag) would either be equal
# to the initial value -1, in case only string tags are used or would be equal to `count` if only int indexing
# was used.
new_model.docvecs.max_rawint = (
-1 if old_model.docvecs.index2doctag else old_model.docvecs.count - 1
)
new_model.docvecs.offset2doctag = old_model.docvecs.index2doctag
new_model.train_count = old_model.__dict__.get("train_count", None)
new_model.corpus_count = old_model.__dict__.get("corpus_count", None)
new_model.running_training_loss = old_model.__dict__.get("running_training_loss", 0)
new_model.total_train_time = old_model.__dict__.get("total_train_time", None)
new_model.min_alpha_yet_reached = old_model.__dict__.get(
"min_alpha_yet_reached", old_model.alpha
)
new_model.model_trimmed_post_training = old_model.__dict__.get(
"model_trimmed_post_training", None
)
return new_model
|
def load_old_doc2vec(*args, **kwargs):
old_model = Doc2Vec.load(*args, **kwargs)
params = {
"dm_mean": old_model.__dict__.get("dm_mean", None),
"dm": old_model.dm,
"dbow_words": old_model.dbow_words,
"dm_concat": old_model.dm_concat,
"dm_tag_count": old_model.dm_tag_count,
"docvecs_mapfile": old_model.__dict__.get("docvecs_mapfile", None),
"comment": old_model.__dict__.get("comment", None),
"size": old_model.vector_size,
"alpha": old_model.alpha,
"window": old_model.window,
"min_count": old_model.min_count,
"max_vocab_size": old_model.__dict__.get("max_vocab_size", None),
"sample": old_model.sample,
"seed": old_model.seed,
"workers": old_model.workers,
"min_alpha": old_model.min_alpha,
"hs": old_model.hs,
"negative": old_model.negative,
"cbow_mean": old_model.cbow_mean,
"hashfxn": old_model.hashfxn,
"iter": old_model.iter,
"sorted_vocab": old_model.sorted_vocab,
"batch_words": old_model.batch_words,
"compute_loss": old_model.__dict__.get("compute_loss", None),
}
new_model = NewDoc2Vec(**params)
# set word2vec trainables attributes
new_model.wv.vectors = old_model.wv.syn0
if hasattr(old_model.wv, "syn0norm"):
new_model.docvecs.vectors_norm = old_model.wv.syn0norm
if hasattr(old_model, "syn1"):
new_model.trainables.syn1 = old_model.syn1
if hasattr(old_model, "syn1neg"):
new_model.trainables.syn1neg = old_model.syn1neg
if hasattr(old_model, "syn0_lockf"):
new_model.trainables.vectors_lockf = old_model.syn0_lockf
# set doc2vec trainables attributes
new_model.docvecs.vectors_docs = old_model.docvecs.doctag_syn0
if hasattr(old_model.docvecs, "doctag_syn0norm"):
new_model.docvecs.vectors_docs_norm = old_model.docvecs.doctag_syn0norm
if hasattr(old_model.docvecs, "doctag_syn0_lockf"):
new_model.trainables.vectors_docs_lockf = old_model.docvecs.doctag_syn0_lockf
if hasattr(old_model.docvecs, "mapfile_path"):
new_model.docvecs.mapfile_path = old_model.docvecs.mapfile_path
# set word2vec vocabulary attributes
new_model.wv.vocab = old_model.wv.vocab
new_model.wv.index2word = old_model.wv.index2word
new_model.vocabulary.cum_table = old_model.cum_table
# set doc2vec vocabulary attributes
new_model.docvecs.doctags = old_model.docvecs.doctags
new_model.docvecs.max_rawint = old_model.docvecs.max_rawint
new_model.docvecs.offset2doctag = old_model.docvecs.offset2doctag
new_model.docvecs.count = old_model.docvecs.count
new_model.train_count = old_model.train_count
new_model.corpus_count = old_model.corpus_count
new_model.running_training_loss = old_model.running_training_loss
new_model.total_train_time = old_model.total_train_time
new_model.min_alpha_yet_reached = old_model.min_alpha_yet_reached
new_model.model_trimmed_post_training = old_model.model_trimmed_post_training
return new_model
|
https://github.com/RaRe-Technologies/gensim/issues/2000
|
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/gensim/models/word2vec.py", line 975, in load
return super(Word2Vec, cls).load(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/gensim/models/base_any2vec.py", line 629, in load
model = super(BaseWordEmbeddingsModel, cls).load(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/gensim/models/base_any2vec.py", line 278, in load
return super(BaseAny2VecModel, cls).load(fname_or_handle, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/gensim/utils.py", line 426, in load
obj._load_specials(fname, mmap, compress, subname)
File "/usr/local/lib/python3.5/dist-packages/gensim/utils.py", line 469, in _load_specials
setattr(self, attrib, val)
File "/usr/local/lib/python3.5/dist-packages/gensim/utils.py", line 1398, in new_func1
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/gensim/models/base_any2vec.py", line 380, in syn1neg
self.trainables.syn1neg = value
AttributeError: 'Word2Vec' object has no attribute 'trainables'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "word.py", line 164, in <module>
X_vector, Y_vector = XY_vector(X, Y)
File "word.py", line 116, in XY_vector
model = gensim.models.word2vec.Word2Vec.load('./word_vector/Word60.model')
File "/usr/local/lib/python3.5/dist-packages/gensim/models/word2vec.py", line 979, in load
return load_old_word2vec(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/gensim/models/deprecated/word2vec.py", line 195, in load_old_word2vec
new_model.min_alpha_yet_reached = old_model.min_alpha_yet_reached
AttributeError: 'Word2Vec' object has no attribute 'min_alpha_yet_reached'
|
AttributeError
|
def load_old_word2vec(*args, **kwargs):
old_model = Word2Vec.load(*args, **kwargs)
vector_size = getattr(old_model, "vector_size", old_model.layer1_size)
params = {
"size": vector_size,
"alpha": old_model.alpha,
"window": old_model.window,
"min_count": old_model.min_count,
"max_vocab_size": old_model.__dict__.get("max_vocab_size", None),
"sample": old_model.__dict__.get("sample", 1e-3),
"seed": old_model.seed,
"workers": old_model.workers,
"min_alpha": old_model.min_alpha,
"sg": old_model.sg,
"hs": old_model.hs,
"negative": old_model.negative,
"cbow_mean": old_model.cbow_mean,
"hashfxn": old_model.__dict__.get("hashfxn", hash),
"iter": old_model.__dict__.get("iter", 5),
"null_word": old_model.__dict__.get("null_word", 0),
"sorted_vocab": old_model.__dict__.get("sorted_vocab", 1),
"batch_words": old_model.__dict__.get("batch_words", MAX_WORDS_IN_BATCH),
"compute_loss": old_model.__dict__.get("compute_loss", None),
}
new_model = NewWord2Vec(**params)
# set trainables attributes
new_model.wv.vectors = old_model.wv.syn0
if hasattr(old_model.wv, "syn0norm"):
new_model.wv.vectors_norm = old_model.wv.syn0norm
if hasattr(old_model, "syn1"):
new_model.trainables.syn1 = old_model.syn1
if hasattr(old_model, "syn1neg"):
new_model.trainables.syn1neg = old_model.syn1neg
if hasattr(old_model, "syn0_lockf"):
new_model.trainables.vectors_lockf = old_model.syn0_lockf
# set vocabulary attributes
new_model.wv.vocab = old_model.wv.vocab
new_model.wv.index2word = old_model.wv.index2word
new_model.vocabulary.cum_table = old_model.__dict__.get("cum_table", None)
new_model.train_count = old_model.__dict__.get("train_count", None)
new_model.corpus_count = old_model.__dict__.get("corpus_count", None)
new_model.running_training_loss = old_model.__dict__.get("running_training_loss", 0)
new_model.total_train_time = old_model.__dict__.get("total_train_time", None)
new_model.min_alpha_yet_reached = old_model.__dict__.get(
"min_alpha_yet_reached", old_model.alpha
)
new_model.model_trimmed_post_training = old_model.__dict__.get(
"model_trimmed_post_training", None
)
return new_model
|
def load_old_word2vec(*args, **kwargs):
old_model = Word2Vec.load(*args, **kwargs)
params = {
"size": old_model.vector_size,
"alpha": old_model.alpha,
"window": old_model.window,
"min_count": old_model.min_count,
"max_vocab_size": old_model.__dict__.get("max_vocab_size", None),
"sample": old_model.sample,
"seed": old_model.seed,
"workers": old_model.workers,
"min_alpha": old_model.min_alpha,
"sg": old_model.sg,
"hs": old_model.hs,
"negative": old_model.negative,
"cbow_mean": old_model.cbow_mean,
"hashfxn": old_model.hashfxn,
"iter": old_model.iter,
"null_word": old_model.null_word,
"sorted_vocab": old_model.sorted_vocab,
"batch_words": old_model.batch_words,
"compute_loss": old_model.__dict__.get("compute_loss", None),
}
new_model = NewWord2Vec(**params)
# set trainables attributes
new_model.wv.vectors = old_model.wv.syn0
if hasattr(old_model.wv, "syn0norm"):
new_model.wv.vectors_norm = old_model.wv.syn0norm
if hasattr(old_model, "syn1"):
new_model.trainables.syn1 = old_model.syn1
if hasattr(old_model, "syn1neg"):
new_model.trainables.syn1neg = old_model.syn1neg
if hasattr(old_model, "syn0_lockf"):
new_model.trainables.vectors_lockf = old_model.syn0_lockf
# set vocabulary attributes
new_model.wv.vocab = old_model.wv.vocab
new_model.wv.index2word = old_model.wv.index2word
new_model.vocabulary.cum_table = old_model.cum_table
new_model.train_count = old_model.train_count
new_model.corpus_count = old_model.corpus_count
new_model.running_training_loss = old_model.__dict__.get(
"running_training_loss", None
)
new_model.total_train_time = old_model.total_train_time
new_model.min_alpha_yet_reached = old_model.min_alpha_yet_reached
new_model.model_trimmed_post_training = old_model.__dict__.get(
"model_trimmed_post_training", None
)
return new_model
|
https://github.com/RaRe-Technologies/gensim/issues/2000
|
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/gensim/models/word2vec.py", line 975, in load
return super(Word2Vec, cls).load(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/gensim/models/base_any2vec.py", line 629, in load
model = super(BaseWordEmbeddingsModel, cls).load(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/gensim/models/base_any2vec.py", line 278, in load
return super(BaseAny2VecModel, cls).load(fname_or_handle, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/gensim/utils.py", line 426, in load
obj._load_specials(fname, mmap, compress, subname)
File "/usr/local/lib/python3.5/dist-packages/gensim/utils.py", line 469, in _load_specials
setattr(self, attrib, val)
File "/usr/local/lib/python3.5/dist-packages/gensim/utils.py", line 1398, in new_func1
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/gensim/models/base_any2vec.py", line 380, in syn1neg
self.trainables.syn1neg = value
AttributeError: 'Word2Vec' object has no attribute 'trainables'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "word.py", line 164, in <module>
X_vector, Y_vector = XY_vector(X, Y)
File "word.py", line 116, in XY_vector
model = gensim.models.word2vec.Word2Vec.load('./word_vector/Word60.model')
File "/usr/local/lib/python3.5/dist-packages/gensim/models/word2vec.py", line 979, in load
return load_old_word2vec(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/gensim/models/deprecated/word2vec.py", line 195, in load_old_word2vec
new_model.min_alpha_yet_reached = old_model.min_alpha_yet_reached
AttributeError: 'Word2Vec' object has no attribute 'min_alpha_yet_reached'
|
AttributeError
|
def inference(
self, chunk, author2doc, doc2author, rhot, collect_sstats=False, chunk_doc_idx=None
):
"""Give a `chunk` of sparse document vectors, update gamma for each author corresponding to the `chuck`.
Warnings
--------
The whole input chunk of document is assumed to fit in RAM, chunking of a large corpus must be done earlier
in the pipeline.
Avoids computing the `phi` variational parameter directly using the
optimization presented in `Lee, Seung: "Algorithms for non-negative matrix factorization", NIPS 2001
<https://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf>`_.
Parameters
----------
chunk : iterable of list of (int, float)
Corpus in BoW format.
author2doc : dict of (str, list of int), optional
A dictionary where keys are the names of authors and values are lists of document IDs that the author
contributes to.
doc2author : dict of (int, list of str), optional
A dictionary where the keys are document IDs and the values are lists of author names.
rhot : float
Value of rho for conducting inference on documents.
collect_sstats : boolean, optional
If True - collect sufficient statistics needed to update the model's topic-word distributions, and return
`(gamma_chunk, sstats)`. Otherwise, return `(gamma_chunk, None)`. `gamma_chunk` is of shape
`len(chunk_authors) x self.num_topics`,where `chunk_authors` is the number of authors in the documents in
the current chunk.
chunk_doc_idx : numpy.ndarray, optional
Assigns the value for document index.
Returns
-------
(numpy.ndarray, numpy.ndarray)
gamma_chunk and sstats (if `collect_sstats == True`, otherwise - None)
"""
try:
len(chunk)
except TypeError:
# convert iterators/generators to plain list, so we have len() etc.
chunk = list(chunk)
if len(chunk) > 1:
logger.debug("performing inference on a chunk of %i documents", len(chunk))
# Initialize the variational distribution q(theta|gamma) for the chunk
if collect_sstats:
sstats = np.zeros_like(self.expElogbeta)
else:
sstats = None
converged = 0
# Stack all the computed gammas into this output array.
gamma_chunk = np.zeros((0, self.num_topics))
# Now, for each document d update gamma and phi w.r.t. all authors in those documents.
for d, doc in enumerate(chunk):
if chunk_doc_idx is not None:
doc_no = chunk_doc_idx[d]
else:
doc_no = d
# Get the IDs and counts of all the words in the current document.
# TODO: this is duplication of code in LdaModel. Refactor.
if doc and not isinstance(doc[0][0], six.integer_types + (np.integer,)):
# make sure the term IDs are ints, otherwise np will get upset
ids = [int(idx) for idx, _ in doc]
else:
ids = [idx for idx, _ in doc]
ids = np.array(ids, dtype=np.integer)
cts = np.array([cnt for _, cnt in doc], dtype=np.integer)
# Get all authors in current document, and convert the author names to integer IDs.
authors_d = np.array(
[self.author2id[a] for a in self.doc2author[doc_no]], dtype=np.integer
)
gammad = self.state.gamma[authors_d, :] # gamma of document d before update.
tilde_gamma = gammad.copy() # gamma that will be updated.
# Compute the expectation of the log of the Dirichlet parameters theta and beta.
Elogthetad = dirichlet_expectation(tilde_gamma)
expElogthetad = np.exp(Elogthetad)
expElogbetad = self.expElogbeta[:, ids]
# Compute the normalizing constant of phi for the current document.
phinorm = self.compute_phinorm(expElogthetad, expElogbetad)
# Iterate between gamma and phi until convergence
for _ in xrange(self.iterations):
lastgamma = tilde_gamma.copy()
# Update gamma.
# phi is computed implicitly below,
for ai, a in enumerate(authors_d):
tilde_gamma[ai, :] = self.alpha + len(
self.author2doc[self.id2author[a]]
) * expElogthetad[ai, :] * np.dot(cts / phinorm, expElogbetad.T)
# Update gamma.
# Interpolation between document d's "local" gamma (tilde_gamma),
# and "global" gamma (gammad).
tilde_gamma = (1 - rhot) * gammad + rhot * tilde_gamma
# Update Elogtheta and Elogbeta, since gamma and lambda have been updated.
Elogthetad = dirichlet_expectation(tilde_gamma)
expElogthetad = np.exp(Elogthetad)
# Update the normalizing constant in phi.
phinorm = self.compute_phinorm(expElogthetad, expElogbetad)
# Check for convergence.
# Criterion is mean change in "local" gamma.
meanchange_gamma = np.mean(abs(tilde_gamma - lastgamma))
gamma_condition = meanchange_gamma < self.gamma_threshold
if gamma_condition:
converged += 1
break
# End of iterations loop.
# Store the updated gammas in the model state.
self.state.gamma[authors_d, :] = tilde_gamma
# Stack the new gammas into the output array.
gamma_chunk = np.vstack([gamma_chunk, tilde_gamma])
if collect_sstats:
# Contribution of document d to the expected sufficient
# statistics for the M step.
expElogtheta_sum_a = expElogthetad.sum(axis=0)
sstats[:, ids] += np.outer(expElogtheta_sum_a.T, cts / phinorm)
if len(chunk) > 1:
logger.debug(
"%i/%i documents converged within %i iterations",
converged,
len(chunk),
self.iterations,
)
if collect_sstats:
# This step finishes computing the sufficient statistics for the
# M step, so that
# sstats[k, w] = \sum_d n_{dw} * \sum_a phi_{dwak}
# = \sum_d n_{dw} * exp{Elogtheta_{ak} + Elogbeta_{kw}} / phinorm_{dw}.
sstats *= self.expElogbeta
return gamma_chunk, sstats
|
def inference(
self, chunk, author2doc, doc2author, rhot, collect_sstats=False, chunk_doc_idx=None
):
"""Give a `chunk` of sparse document vectors, update gamma for each author corresponding to the `chuck`.
Warnings
--------
The whole input chunk of document is assumed to fit in RAM, chunking of a large corpus must be done earlier
in the pipeline.
Avoids computing the `phi` variational parameter directly using the
optimization presented in `Lee, Seung: "Algorithms for non-negative matrix factorization", NIPS 2001
<https://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf>`_.
Parameters
----------
chunk : iterable of list of (int, float)
Corpus in BoW format.
author2doc : dict of (str, list of int), optional
A dictionary where keys are the names of authors and values are lists of document IDs that the author
contributes to.
doc2author : dict of (int, list of str), optional
A dictionary where the keys are document IDs and the values are lists of author names.
rhot : float
Value of rho for conducting inference on documents.
collect_sstats : boolean, optional
If True - collect sufficient statistics needed to update the model's topic-word distributions, and return
`(gamma_chunk, sstats)`. Otherwise, return `(gamma_chunk, None)`. `gamma_chunk` is of shape
`len(chunk_authors) x self.num_topics`,where `chunk_authors` is the number of authors in the documents in
the current chunk.
chunk_doc_idx : numpy.ndarray, optional
Assigns the value for document index.
Returns
-------
(numpy.ndarray, numpy.ndarray)
gamma_chunk and sstats (if `collect_sstats == True`, otherwise - None)
"""
try:
len(chunk)
except TypeError:
# convert iterators/generators to plain list, so we have len() etc.
chunk = list(chunk)
if len(chunk) > 1:
logger.debug("performing inference on a chunk of %i documents", len(chunk))
# Initialize the variational distribution q(theta|gamma) for the chunk
if collect_sstats:
sstats = np.zeros_like(self.expElogbeta)
else:
sstats = None
converged = 0
# Stack all the computed gammas into this output array.
gamma_chunk = np.zeros((0, self.num_topics))
# Now, for each document d update gamma and phi w.r.t. all authors in those documents.
for d, doc in enumerate(chunk):
if chunk_doc_idx is not None:
doc_no = chunk_doc_idx[d]
else:
doc_no = d
# Get the IDs and counts of all the words in the current document.
# TODO: this is duplication of code in LdaModel. Refactor.
if doc and not isinstance(doc[0][0], six.integer_types + (np.integer,)):
# make sure the term IDs are ints, otherwise np will get upset
ids = [int(idx) for idx, _ in doc]
else:
ids = [idx for idx, _ in doc]
cts = np.array([cnt for _, cnt in doc])
# Get all authors in current document, and convert the author names to integer IDs.
authors_d = [self.author2id[a] for a in self.doc2author[doc_no]]
gammad = self.state.gamma[authors_d, :] # gamma of document d before update.
tilde_gamma = gammad.copy() # gamma that will be updated.
# Compute the expectation of the log of the Dirichlet parameters theta and beta.
Elogthetad = dirichlet_expectation(tilde_gamma)
expElogthetad = np.exp(Elogthetad)
expElogbetad = self.expElogbeta[:, ids]
# Compute the normalizing constant of phi for the current document.
phinorm = self.compute_phinorm(expElogthetad, expElogbetad)
# Iterate between gamma and phi until convergence
for _ in xrange(self.iterations):
lastgamma = tilde_gamma.copy()
# Update gamma.
# phi is computed implicitly below,
for ai, a in enumerate(authors_d):
tilde_gamma[ai, :] = self.alpha + len(
self.author2doc[self.id2author[a]]
) * expElogthetad[ai, :] * np.dot(cts / phinorm, expElogbetad.T)
# Update gamma.
# Interpolation between document d's "local" gamma (tilde_gamma),
# and "global" gamma (gammad).
tilde_gamma = (1 - rhot) * gammad + rhot * tilde_gamma
# Update Elogtheta and Elogbeta, since gamma and lambda have been updated.
Elogthetad = dirichlet_expectation(tilde_gamma)
expElogthetad = np.exp(Elogthetad)
# Update the normalizing constant in phi.
phinorm = self.compute_phinorm(expElogthetad, expElogbetad)
# Check for convergence.
# Criterion is mean change in "local" gamma.
meanchange_gamma = np.mean(abs(tilde_gamma - lastgamma))
gamma_condition = meanchange_gamma < self.gamma_threshold
if gamma_condition:
converged += 1
break
# End of iterations loop.
# Store the updated gammas in the model state.
self.state.gamma[authors_d, :] = tilde_gamma
# Stack the new gammas into the output array.
gamma_chunk = np.vstack([gamma_chunk, tilde_gamma])
if collect_sstats:
# Contribution of document d to the expected sufficient
# statistics for the M step.
expElogtheta_sum_a = expElogthetad.sum(axis=0)
sstats[:, ids] += np.outer(expElogtheta_sum_a.T, cts / phinorm)
if len(chunk) > 1:
logger.debug(
"%i/%i documents converged within %i iterations",
converged,
len(chunk),
self.iterations,
)
if collect_sstats:
# This step finishes computing the sufficient statistics for the
# M step, so that
# sstats[k, w] = \sum_d n_{dw} * \sum_a phi_{dwak}
# = \sum_d n_{dw} * exp{Elogtheta_{ak} + Elogbeta_{kw}} / phinorm_{dw}.
sstats *= self.expElogbeta
return gamma_chunk, sstats
|
https://github.com/RaRe-Technologies/gensim/issues/1589
|
Traceback (most recent call last):
File "C:\Users\Sara\Desktop\E-COM\Final Project\Working on data\at_clustering.py", line 161, in <module>
test_corpus = test_corpus, test_d2a = test_doc2author, test_a2d = test_author2doc, limit = 25)
File "C:\Users\Sara\Desktop\E-COM\Final Project\Working on data\at_clustering.py", line 70, in evaluate_k
pr = np.exp2(-model.bound(test_corpus, doc2author=test_d2a, author2doc=test_a2d)/number_of_words)
File "C:\Python27\lib\site-packages\gensim\models\atmodel.py", line 835, in bound
phinorm = self.compute_phinorm(ids, authors_d, expElogtheta[authors_d, :], expElogbeta[:, ids])
IndexError: arrays used as indices must be of integer (or boolean) type
|
IndexError
|
def bound(
self,
chunk,
chunk_doc_idx=None,
subsample_ratio=1.0,
author2doc=None,
doc2author=None,
):
"""Estimate the variational bound of documents from `corpus`.
:math:`\mathbb{E_{q}}[\log p(corpus)] - \mathbb{E_{q}}[\log q(corpus)]`
Notes
-----
There are basically two use cases of this method:
#. `chunk` is a subset of the training corpus, and `chunk_doc_idx` is provided,
indicating the indexes of the documents in the training corpus.
#. `chunk` is a test set (held-out data), and `author2doc` and `doc2author` corresponding to this test set
are provided. There must not be any new authors passed to this method, `chunk_doc_idx` is not needed
in this case.
Parameters
----------
chunk : iterable of list of (int, float)
Corpus in BoW format.
chunk_doc_idx : numpy.ndarray, optional
Assigns the value for document index.
subsample_ratio : float, optional
Used for calculation of word score for estimation of variational bound.
author2doc : dict of (str, list of int), optinal
A dictionary where keys are the names of authors and values are lists of documents that the author
contributes to.
doc2author : dict of (int, list of str), optional
A dictionary where the keys are document IDs and the values are lists of author names.
Returns
-------
float
Value of variational bound score.
"""
# TODO: enable evaluation of documents with new authors. One could, for example, make it
# possible to pass a list of documents to self.inference with no author dictionaries,
# assuming all the documents correspond to one (unseen) author, learn the author's
# gamma, and return gamma (without adding it to self.state.gamma). Of course,
# collect_sstats should be set to false, so that the model is not updated w.r.t. these
# new documents.
_lambda = self.state.get_lambda()
Elogbeta = dirichlet_expectation(_lambda)
expElogbeta = np.exp(Elogbeta)
gamma = self.state.gamma
if author2doc is None and doc2author is None:
# Evaluating on training documents (chunk of self.corpus).
author2doc = self.author2doc
doc2author = self.doc2author
if not chunk_doc_idx:
# If author2doc and doc2author are not provided, chunk is assumed to be a subset of
# self.corpus, and chunk_doc_idx is thus required.
raise ValueError(
"Either author dictionaries or chunk_doc_idx must be provided. "
"Consult documentation of bound method."
)
elif author2doc is not None and doc2author is not None:
# Training on held-out documents (documents not seen during training).
# All authors in dictionaries must still be seen during training.
for a in author2doc.keys():
if not self.author2doc.get(a):
raise ValueError(
"bound cannot be called with authors not seen during training."
)
if chunk_doc_idx:
raise ValueError(
"Either author dictionaries or chunk_doc_idx must be provided, not both. "
"Consult documentation of bound method."
)
else:
raise ValueError(
"Either both author2doc and doc2author should be provided, or neither. "
"Consult documentation of bound method."
)
Elogtheta = dirichlet_expectation(gamma)
expElogtheta = np.exp(Elogtheta)
word_score = 0.0
theta_score = 0.0
for d, doc in enumerate(chunk):
if chunk_doc_idx:
doc_no = chunk_doc_idx[d]
else:
doc_no = d
# Get all authors in current document, and convert the author names to integer IDs.
authors_d = np.array(
[self.author2id[a] for a in self.doc2author[doc_no]], dtype=np.integer
)
ids = np.array([id for id, _ in doc], dtype=np.integer) # Word IDs in doc.
cts = np.array([cnt for _, cnt in doc], dtype=np.integer) # Word counts.
if d % self.chunksize == 0:
logger.debug("bound: at document #%i in chunk", d)
# Computing the bound requires summing over expElogtheta[a, k] * expElogbeta[k, v], which
# is the same computation as in normalizing phi.
phinorm = self.compute_phinorm(expElogtheta[authors_d, :], expElogbeta[:, ids])
word_score += np.log(1.0 / len(authors_d)) * sum(cts) + cts.dot(np.log(phinorm))
# Compensate likelihood for when `chunk` above is only a sample of the whole corpus. This ensures
# that the likelihood is always roughly on the same scale.
word_score *= subsample_ratio
# E[log p(theta | alpha) - log q(theta | gamma)]
for a in author2doc.keys():
a = self.author2id[a]
theta_score += np.sum((self.alpha - gamma[a, :]) * Elogtheta[a, :])
theta_score += np.sum(gammaln(gamma[a, :]) - gammaln(self.alpha))
theta_score += gammaln(np.sum(self.alpha)) - gammaln(np.sum(gamma[a, :]))
# theta_score is rescaled in a similar fashion.
# TODO: treat this in a more general way, similar to how it is done with word_score.
theta_score *= self.num_authors / len(author2doc)
# E[log p(beta | eta) - log q (beta | lambda)]
beta_score = 0.0
beta_score += np.sum((self.eta - _lambda) * Elogbeta)
beta_score += np.sum(gammaln(_lambda) - gammaln(self.eta))
sum_eta = np.sum(self.eta)
beta_score += np.sum(gammaln(sum_eta) - gammaln(np.sum(_lambda, 1)))
total_score = word_score + theta_score + beta_score
return total_score
|
def bound(
self,
chunk,
chunk_doc_idx=None,
subsample_ratio=1.0,
author2doc=None,
doc2author=None,
):
"""Estimate the variational bound of documents from `corpus`.
:math:`\mathbb{E_{q}}[\log p(corpus)] - \mathbb{E_{q}}[\log q(corpus)]`
Notes
-----
There are basically two use cases of this method:
#. `chunk` is a subset of the training corpus, and `chunk_doc_idx` is provided,
indicating the indexes of the documents in the training corpus.
#. `chunk` is a test set (held-out data), and `author2doc` and `doc2author` corresponding to this test set
are provided. There must not be any new authors passed to this method, `chunk_doc_idx` is not needed
in this case.
Parameters
----------
chunk : iterable of list of (int, float)
Corpus in BoW format.
chunk_doc_idx : numpy.ndarray, optional
Assigns the value for document index.
subsample_ratio : float, optional
Used for calculation of word score for estimation of variational bound.
author2doc : dict of (str, list of int), optinal
A dictionary where keys are the names of authors and values are lists of documents that the author
contributes to.
doc2author : dict of (int, list of str), optional
A dictionary where the keys are document IDs and the values are lists of author names.
Returns
-------
float
Value of variational bound score.
"""
# TODO: enable evaluation of documents with new authors. One could, for example, make it
# possible to pass a list of documents to self.inference with no author dictionaries,
# assuming all the documents correspond to one (unseen) author, learn the author's
# gamma, and return gamma (without adding it to self.state.gamma). Of course,
# collect_sstats should be set to false, so that the model is not updated w.r.t. these
# new documents.
_lambda = self.state.get_lambda()
Elogbeta = dirichlet_expectation(_lambda)
expElogbeta = np.exp(Elogbeta)
gamma = self.state.gamma
if author2doc is None and doc2author is None:
# Evaluating on training documents (chunk of self.corpus).
author2doc = self.author2doc
doc2author = self.doc2author
if not chunk_doc_idx:
# If author2doc and doc2author are not provided, chunk is assumed to be a subset of
# self.corpus, and chunk_doc_idx is thus required.
raise ValueError(
"Either author dictionaries or chunk_doc_idx must be provided. "
"Consult documentation of bound method."
)
elif author2doc is not None and doc2author is not None:
# Training on held-out documents (documents not seen during training).
# All authors in dictionaries must still be seen during training.
for a in author2doc.keys():
if not self.author2doc.get(a):
raise ValueError(
"bound cannot be called with authors not seen during training."
)
if chunk_doc_idx:
raise ValueError(
"Either author dictionaries or chunk_doc_idx must be provided, not both. "
"Consult documentation of bound method."
)
else:
raise ValueError(
"Either both author2doc and doc2author should be provided, or neither. "
"Consult documentation of bound method."
)
Elogtheta = dirichlet_expectation(gamma)
expElogtheta = np.exp(Elogtheta)
word_score = 0.0
theta_score = 0.0
for d, doc in enumerate(chunk):
if chunk_doc_idx:
doc_no = chunk_doc_idx[d]
else:
doc_no = d
# Get all authors in current document, and convert the author names to integer IDs.
authors_d = [self.author2id[a] for a in self.doc2author[doc_no]]
ids = np.array([id for id, _ in doc]) # Word IDs in doc.
cts = np.array([cnt for _, cnt in doc]) # Word counts.
if d % self.chunksize == 0:
logger.debug("bound: at document #%i in chunk", d)
# Computing the bound requires summing over expElogtheta[a, k] * expElogbeta[k, v], which
# is the same computation as in normalizing phi.
phinorm = self.compute_phinorm(expElogtheta[authors_d, :], expElogbeta[:, ids])
word_score += np.log(1.0 / len(authors_d)) * sum(cts) + cts.dot(np.log(phinorm))
# Compensate likelihood for when `chunk` above is only a sample of the whole corpus. This ensures
# that the likelihood is always roughly on the same scale.
word_score *= subsample_ratio
# E[log p(theta | alpha) - log q(theta | gamma)]
for a in author2doc.keys():
a = self.author2id[a]
theta_score += np.sum((self.alpha - gamma[a, :]) * Elogtheta[a, :])
theta_score += np.sum(gammaln(gamma[a, :]) - gammaln(self.alpha))
theta_score += gammaln(np.sum(self.alpha)) - gammaln(np.sum(gamma[a, :]))
# theta_score is rescaled in a similar fashion.
# TODO: treat this in a more general way, similar to how it is done with word_score.
theta_score *= self.num_authors / len(author2doc)
# E[log p(beta | eta) - log q (beta | lambda)]
beta_score = 0.0
beta_score += np.sum((self.eta - _lambda) * Elogbeta)
beta_score += np.sum(gammaln(_lambda) - gammaln(self.eta))
sum_eta = np.sum(self.eta)
beta_score += np.sum(gammaln(sum_eta) - gammaln(np.sum(_lambda, 1)))
total_score = word_score + theta_score + beta_score
return total_score
|
https://github.com/RaRe-Technologies/gensim/issues/1589
|
Traceback (most recent call last):
File "C:\Users\Sara\Desktop\E-COM\Final Project\Working on data\at_clustering.py", line 161, in <module>
test_corpus = test_corpus, test_d2a = test_doc2author, test_a2d = test_author2doc, limit = 25)
File "C:\Users\Sara\Desktop\E-COM\Final Project\Working on data\at_clustering.py", line 70, in evaluate_k
pr = np.exp2(-model.bound(test_corpus, doc2author=test_d2a, author2doc=test_a2d)/number_of_words)
File "C:\Python27\lib\site-packages\gensim\models\atmodel.py", line 835, in bound
phinorm = self.compute_phinorm(ids, authors_d, expElogtheta[authors_d, :], expElogbeta[:, ids])
IndexError: arrays used as indices must be of integer (or boolean) type
|
IndexError
|
def _load_relations(self):
"""Load relations from the train data and build vocab."""
vocab = {}
index2word = []
all_relations = [] # List of all relation pairs
node_relations = defaultdict(
set
) # Mapping from node index to its related node indices
logger.info("Loading relations from train data..")
for relation in self.train_data:
if len(relation) != 2:
raise ValueError(
'Relation pair "%s" should have exactly two items' % repr(relation)
)
for item in relation:
if item in vocab:
vocab[item].count += 1
else:
vocab[item] = Vocab(count=1, index=len(index2word))
index2word.append(item)
node_1, node_2 = relation
node_1_index, node_2_index = vocab[node_1].index, vocab[node_2].index
node_relations[node_1_index].add(node_2_index)
relation = (node_1_index, node_2_index)
all_relations.append(relation)
logger.info(
"Loaded %d relations from train data, %d nodes", len(all_relations), len(vocab)
)
self.kv.vocab = vocab
self.kv.index2word = index2word
self.indices_set = set((range(len(index2word)))) # Set of all node indices
self.indices_array = np.array(
range(len(index2word))
) # Numpy array of all node indices
self.all_relations = all_relations
self.node_relations = node_relations
self._init_node_probabilities()
self._negatives_buffer = NegativesBuffer(
[]
) # Buffer for negative samples, to reduce calls to sampling method
self._negatives_buffer_size = 2000
|
def _load_relations(self):
"""Load relations from the train data and build vocab."""
vocab = {}
index2word = []
all_relations = [] # List of all relation pairs
node_relations = defaultdict(
set
) # Mapping from node index to its related node indices
logger.info("Loading relations from train data..")
for relation in self.train_data:
if len(relation) != 2:
raise ValueError(
'Relation pair "%s" should have exactly two items' % repr(relation)
)
for item in relation:
if item in vocab:
vocab[item].count += 1
else:
vocab[item] = Vocab(count=1, index=len(index2word))
index2word.append(item)
node_1, node_2 = relation
node_1_index, node_2_index = vocab[node_1].index, vocab[node_2].index
node_relations[node_1_index].add(node_2_index)
relation = (node_1_index, node_2_index)
all_relations.append(relation)
logger.info(
"Loaded %d relations from train data, %d nodes", len(all_relations), len(vocab)
)
self.kv.vocab = vocab
self.kv.index2word = index2word
self.indices_set = set((range(len(index2word)))) # Set of all node indices
self.indices_array = np.array(
range(len(index2word))
) # Numpy array of all node indices
counts = np.array(
[self.kv.vocab[index2word[i]].count for i in range(len(index2word))],
dtype=np.float64,
)
self._node_probabilities = counts / counts.sum()
self._node_probabilities_cumsum = np.cumsum(self._node_probabilities)
self.all_relations = all_relations
self.node_relations = node_relations
self._negatives_buffer = NegativesBuffer(
[]
) # Buffer for negative samples, to reduce calls to sampling method
self._negatives_buffer_size = 2000
|
https://github.com/RaRe-Technologies/gensim/issues/1917
|
2018-02-05 10:49:58,008 - training model of size 50 with 1 workers on 128138847 relations for 1 epochs and 10 burn-in epochs, using lr=0.01000 burn-in lr=0.01000 negative=10
2018-02-05 10:49:58,010 - Starting burn-in (10 epochs)----------------------------------------
2018-02-05 10:56:51,400 - Training on epoch 1, examples #999000-#1000000, loss: 2188.85
2018-02-05 10:56:51,404 - Time taken for 1000000 examples: 329.60 s, 3033.98 examples / s
2018-02-05 11:01:44,625 - Training on epoch 1, examples #1999000-#2000000, loss: 2187.71
2018-02-05 11:01:44,627 - Time taken for 1000000 examples: 293.22 s, 3410.41 examples / s
2018-02-05 11:06:38,729 - Training on epoch 1, examples #2999000-#3000000, loss: 2186.41
2018-02-05 11:06:38,731 - Time taken for 1000000 examples: 294.10 s, 3400.18 examples / s
2018-02-05 11:11:28,291 - Training on epoch 1, examples #3999000-#4000000, loss: 2185.42
2018-02-05 11:11:28,293 - Time taken for 1000000 examples: 289.56 s, 3453.52 examples / s
2018-02-05 11:16:16,831 - Training on epoch 1, examples #4999000-#5000000, loss: 2184.04
2018-02-05 11:16:16,833 - Time taken for 1000000 examples: 288.54 s, 3465.75 examples / s
2018-02-05 11:21:06,625 - Training on epoch 1, examples #5999000-#6000000, loss: 2182.88
2018-02-05 11:21:06,630 - Time taken for 1000000 examples: 289.79 s, 3450.75 examples / s
2018-02-05 11:26:55,483 - Training on epoch 1, examples #6999000-#7000000, loss: 2181.47
2018-02-05 11:26:55,484 - Time taken for 1000000 examples: 348.85 s, 2866.54 examples / s
2018-02-05 11:31:45,830 - Training on epoch 1, examples #7999000-#8000000, loss: 2180.34
2018-02-05 11:31:45,839 - Time taken for 1000000 examples: 290.34 s, 3444.18 examples / s
2018-02-05 11:36:30,690 - Training on epoch 1, examples #8999000-#9000000, loss: 2179.56
2018-02-05 11:36:30,692 - Time taken for 1000000 examples: 284.85 s, 3510.62 examples / s
2018-02-05 11:41:15,313 - Training on epoch 1, examples #9999000-#10000000, loss: 2178.03
2018-02-05 11:41:15,315 - Time taken for 1000000 examples: 284.62 s, 3513.45 examples / s
2018-02-05 11:46:00,357 - Training on epoch 1, examples #10999000-#11000000, loss: 2177.52
2018-02-05 11:46:00,358 - Time taken for 1000000 examples: 285.04 s, 3508.26 examples / s
2018-02-05 11:50:48,905 - Training on epoch 1, examples #11999000-#12000000, loss: 2175.87
2018-02-05 11:50:48,910 - Time taken for 1000000 examples: 288.55 s, 3465.64 examples / s
2018-02-05 11:55:35,918 - Training on epoch 1, examples #12999000-#13000000, loss: 2174.76
2018-02-05 11:55:35,919 - Time taken for 1000000 examples: 287.01 s, 3484.23 examples / s
2018-02-05 12:00:24,240 - Training on epoch 1, examples #13999000-#14000000, loss: 2173.49
2018-02-05 12:00:24,242 - Time taken for 1000000 examples: 288.32 s, 3468.36 examples / s
2018-02-05 12:05:07,573 - Training on epoch 1, examples #14999000-#15000000, loss: 2172.35
2018-02-05 12:05:07,574 - Time taken for 1000000 examples: 283.33 s, 3529.45 examples / s
2018-02-05 12:09:52,164 - Training on epoch 1, examples #15999000-#16000000, loss: 2171.20
2018-02-05 12:09:52,165 - Time taken for 1000000 examples: 284.59 s, 3513.83 examples / s
2018-02-05 12:14:41,436 - Training on epoch 1, examples #16999000-#17000000, loss: 2170.33
2018-02-05 12:14:41,438 - Time taken for 1000000 examples: 289.27 s, 3456.97 examples / s
2018-02-05 12:19:34,138 - Training on epoch 1, examples #17999000-#18000000, loss: 2169.56
2018-02-05 12:19:34,142 - Time taken for 1000000 examples: 292.70 s, 3416.47 examples / s
2018-02-05 12:24:27,812 - Training on epoch 1, examples #18999000-#19000000, loss: 2168.17
2018-02-05 12:24:27,814 - Time taken for 1000000 examples: 293.67 s, 3405.19 examples / s
2018-02-05 12:29:15,083 - Training on epoch 1, examples #19999000-#20000000, loss: 2167.16
2018-02-05 12:29:15,085 - Time taken for 1000000 examples: 287.27 s, 3481.06 examples / s
2018-02-05 12:34:03,589 - Training on epoch 1, examples #20999000-#21000000, loss: 2165.85
2018-02-05 12:34:03,590 - Time taken for 1000000 examples: 288.50 s, 3466.17 examples / s
2018-02-05 12:38:50,770 - Training on epoch 1, examples #21999000-#22000000, loss: 2164.89
2018-02-05 12:38:50,772 - Time taken for 1000000 examples: 287.18 s, 3482.14 examples / s
2018-02-05 12:43:41,125 - Training on epoch 1, examples #22999000-#23000000, loss: 2163.63
2018-02-05 12:43:41,129 - Time taken for 1000000 examples: 290.35 s, 3444.09 examples / s
2018-02-05 12:48:27,127 - Training on epoch 1, examples #23999000-#24000000, loss: 2162.46
2018-02-05 12:48:27,129 - Time taken for 1000000 examples: 286.00 s, 3496.53 examples / s
2018-02-05 12:53:17,683 - Training on epoch 1, examples #24999000-#25000000, loss: 2161.23
2018-02-05 12:53:17,684 - Time taken for 1000000 examples: 290.55 s, 3441.71 examples / s
2018-02-05 12:58:02,880 - Training on epoch 1, examples #25999000-#26000000, loss: 2160.17
2018-02-05 12:58:02,881 - Time taken for 1000000 examples: 285.20 s, 3506.37 examples / s
2018-02-05 13:02:47,177 - Training on epoch 1, examples #26999000-#27000000, loss: 2158.66
2018-02-05 13:02:47,179 - Time taken for 1000000 examples: 284.30 s, 3517.46 examples / s
2018-02-05 13:07:31,441 - Training on epoch 1, examples #27999000-#28000000, loss: 2157.93
2018-02-05 13:07:31,442 - Time taken for 1000000 examples: 284.26 s, 3517.89 examples / s
2018-02-05 13:12:20,000 - Training on epoch 1, examples #28999000-#29000000, loss: 2156.97
2018-02-05 13:12:20,004 - Time taken for 1000000 examples: 288.56 s, 3465.52 examples / s
2018-02-05 13:17:06,050 - Training on epoch 1, examples #29999000-#30000000, loss: 2155.66
2018-02-05 13:17:06,051 - Time taken for 1000000 examples: 286.04 s, 3495.96 examples / s
2018-02-05 13:21:56,627 - Training on epoch 1, examples #30999000-#31000000, loss: 2154.42
2018-02-05 13:21:56,628 - Time taken for 1000000 examples: 290.58 s, 3441.45 examples / s
2018-02-05 13:26:41,004 - Training on epoch 1, examples #31999000-#32000000, loss: 2153.39
2018-02-05 13:26:41,005 - Time taken for 1000000 examples: 284.37 s, 3516.49 examples / s
2018-02-05 13:31:26,601 - Training on epoch 1, examples #32999000-#33000000, loss: 2152.29
2018-02-05 13:31:26,603 - Time taken for 1000000 examples: 285.59 s, 3501.49 examples / s
2018-02-05 13:36:11,844 - Training on epoch 1, examples #33999000-#34000000, loss: 2151.36
2018-02-05 13:36:11,845 - Time taken for 1000000 examples: 285.24 s, 3505.82 examples / s
2018-02-05 13:41:08,003 - Training on epoch 1, examples #34999000-#35000000, loss: 2150.06
2018-02-05 13:41:08,008 - Time taken for 1000000 examples: 296.16 s, 3376.58 examples / s
2018-02-05 13:45:59,593 - Training on epoch 1, examples #35999000-#36000000, loss: 2149.02
2018-02-05 13:45:59,594 - Time taken for 1000000 examples: 291.58 s, 3429.54 examples / s
2018-02-05 13:50:52,455 - Training on epoch 1, examples #36999000-#37000000, loss: 2148.05
2018-02-05 13:50:52,457 - Time taken for 1000000 examples: 292.86 s, 3414.59 examples / s
2018-02-05 13:55:42,711 - Training on epoch 1, examples #37999000-#38000000, loss: 2146.37
2018-02-05 13:55:42,712 - Time taken for 1000000 examples: 290.25 s, 3445.26 examples / s
2018-02-05 14:00:31,112 - Training on epoch 1, examples #38999000-#39000000, loss: 2145.71
2018-02-05 14:00:31,113 - Time taken for 1000000 examples: 288.40 s, 3467.42 examples / s
2018-02-05 14:05:18,087 - Training on epoch 1, examples #39999000-#40000000, loss: 2144.32
2018-02-05 14:05:18,088 - Time taken for 1000000 examples: 286.97 s, 3484.65 examples / s
2018-02-05 14:10:08,383 - Training on epoch 1, examples #40999000-#41000000, loss: 2143.63
2018-02-05 14:10:08,388 - Time taken for 1000000 examples: 290.29 s, 3444.78 examples / s
2018-02-05 14:15:01,954 - Training on epoch 1, examples #41999000-#42000000, loss: 2142.36
2018-02-05 14:15:01,955 - Time taken for 1000000 examples: 293.57 s, 3406.40 examples / s
2018-02-05 14:19:58,021 - Training on epoch 1, examples #42999000-#43000000, loss: 2141.21
2018-02-05 14:19:58,023 - Time taken for 1000000 examples: 296.07 s, 3377.63 examples / s
2018-02-05 14:24:43,944 - Training on epoch 1, examples #43999000-#44000000, loss: 2140.30
2018-02-05 14:24:43,945 - Time taken for 1000000 examples: 285.92 s, 3497.48 examples / s
2018-02-05 14:29:36,938 - Training on epoch 1, examples #44999000-#45000000, loss: 2138.98
2018-02-05 14:29:36,939 - Time taken for 1000000 examples: 292.99 s, 3413.06 examples / s
2018-02-05 14:34:31,522 - Training on epoch 1, examples #45999000-#46000000, loss: 2137.78
2018-02-05 14:34:31,523 - Time taken for 1000000 examples: 294.58 s, 3394.64 examples / s
2018-02-05 14:39:24,775 - Training on epoch 1, examples #46999000-#47000000, loss: 2136.79
2018-02-05 14:39:24,780 - Time taken for 1000000 examples: 293.25 s, 3410.04 examples / s
2018-02-05 14:44:15,172 - Training on epoch 1, examples #47999000-#48000000, loss: 2135.49
2018-02-05 14:44:15,174 - Time taken for 1000000 examples: 290.39 s, 3443.62 examples / s
2018-02-05 14:49:07,628 - Training on epoch 1, examples #48999000-#49000000, loss: 2135.08
2018-02-05 14:49:07,630 - Time taken for 1000000 examples: 292.45 s, 3419.34 examples / s
2018-02-05 14:53:51,284 - Training on epoch 1, examples #49999000-#50000000, loss: 2133.45
2018-02-05 14:53:51,285 - Time taken for 1000000 examples: 283.65 s, 3525.43 examples / s
2018-02-05 14:58:39,403 - Training on epoch 1, examples #50999000-#51000000, loss: 2132.59
2018-02-05 14:58:39,404 - Time taken for 1000000 examples: 288.12 s, 3470.81 examples / s
2018-02-05 15:03:27,455 - Training on epoch 1, examples #51999000-#52000000, loss: 2131.60
2018-02-05 15:03:27,456 - Time taken for 1000000 examples: 288.05 s, 3471.65 examples / s
2018-02-05 15:08:19,622 - Training on epoch 1, examples #52999000-#53000000, loss: 2130.17
2018-02-05 15:08:19,627 - Time taken for 1000000 examples: 292.17 s, 3422.71 examples / s
2018-02-05 15:13:12,975 - Training on epoch 1, examples #53999000-#54000000, loss: 2129.34
2018-02-05 15:13:12,976 - Time taken for 1000000 examples: 293.35 s, 3408.92 examples / s
2018-02-05 15:18:01,815 - Training on epoch 1, examples #54999000-#55000000, loss: 2128.32
2018-02-05 15:18:01,816 - Time taken for 1000000 examples: 288.84 s, 3462.15 examples / s
2018-02-05 15:22:45,226 - Training on epoch 1, examples #55999000-#56000000, loss: 2126.67
2018-02-05 15:22:45,227 - Time taken for 1000000 examples: 283.41 s, 3528.47 examples / s
2018-02-05 15:27:31,026 - Training on epoch 1, examples #56999000-#57000000, loss: 2126.11
2018-02-05 15:27:31,027 - Time taken for 1000000 examples: 285.79 s, 3499.01 examples / s
2018-02-05 15:32:19,805 - Training on epoch 1, examples #57999000-#58000000, loss: 2125.11
2018-02-05 15:32:19,807 - Time taken for 1000000 examples: 288.77 s, 3462.90 examples / s
2018-02-05 15:37:11,024 - Training on epoch 1, examples #58999000-#59000000, loss: 2123.99
2018-02-05 15:37:11,028 - Time taken for 1000000 examples: 291.22 s, 3433.87 examples / s
2018-02-05 15:42:06,631 - Training on epoch 1, examples #59999000-#60000000, loss: 2123.01
2018-02-05 15:42:06,632 - Time taken for 1000000 examples: 295.60 s, 3382.92 examples / s
2018-02-05 15:46:54,707 - Training on epoch 1, examples #60999000-#61000000, loss: 2121.46
2018-02-05 15:46:54,709 - Time taken for 1000000 examples: 288.07 s, 3471.33 examples / s
2018-02-05 15:51:42,019 - Training on epoch 1, examples #61999000-#62000000, loss: 2120.72
2018-02-05 15:51:42,021 - Time taken for 1000000 examples: 287.31 s, 3480.57 examples / s
2018-02-05 15:56:29,973 - Training on epoch 1, examples #62999000-#63000000, loss: 2119.82
2018-02-05 15:56:29,974 - Time taken for 1000000 examples: 287.95 s, 3472.81 examples / s
2018-02-05 16:01:22,243 - Training on epoch 1, examples #63999000-#64000000, loss: 2118.50
2018-02-05 16:01:22,247 - Time taken for 1000000 examples: 292.27 s, 3421.52 examples / s
2018-02-05 16:06:09,893 - Training on epoch 1, examples #64999000-#65000000, loss: 2117.51
2018-02-05 16:06:09,894 - Time taken for 1000000 examples: 287.64 s, 3476.51 examples / s
2018-02-05 16:11:00,706 - Training on epoch 1, examples #65999000-#66000000, loss: 2116.77
2018-02-05 16:11:00,707 - Time taken for 1000000 examples: 290.81 s, 3438.66 examples / s
2018-02-05 16:15:46,906 - Training on epoch 1, examples #66999000-#67000000, loss: 2115.44
2018-02-05 16:15:46,908 - Time taken for 1000000 examples: 286.20 s, 3494.08 examples / s
2018-02-05 16:20:32,582 - Training on epoch 1, examples #67999000-#68000000, loss: 2114.07
2018-02-05 16:20:32,584 - Time taken for 1000000 examples: 285.67 s, 3500.49 examples / s
2018-02-05 16:25:18,195 - Training on epoch 1, examples #68999000-#69000000, loss: 2113.42
2018-02-05 16:25:18,197 - Time taken for 1000000 examples: 285.61 s, 3501.27 examples / s
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-15-50d51e10cfa2> in <module>()
----> 1 model.train(epochs=1, batch_size=1000)
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in train(self, epochs, batch_size, print_every, check_gradients_every)
542 self._train_batchwise(
543 epochs=self.burn_in, batch_size=batch_size, print_every=print_every,
--> 544 check_gradients_every=check_gradients_every)
545 self._burn_in_done = True
546 logger.info("Burn-in finished")
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _train_batchwise(self, epochs, batch_size, print_every, check_gradients_every)
583 batch_indices = indices[i:i + batch_size]
584 relations = [self.all_relations[idx] for idx in batch_indices]
--> 585 result = self._train_on_batch(relations, check_gradients=check_gradients)
586 avg_loss += result.loss
587 if should_print:
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _train_on_batch(self, relations, check_gradients)
442 """
443 all_negatives = self._sample_negatives_batch([relation[0] for relation in relations])
--> 444 batch = self._prepare_training_batch(relations, all_negatives, check_gradients)
445 self._update_vectors_batch(batch)
446 return batch
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _prepare_training_batch(self, relations, all_negatives, check_gradients)
363
364 vectors_u = self.kv.syn0[indices_u]
--> 365 vectors_v = self.kv.syn0[indices_v].reshape((batch_size, 1 + self.negative, self.size))
366 vectors_v = vectors_v.swapaxes(0, 1).swapaxes(1, 2)
367 batch = PoincareBatch(vectors_u, vectors_v, indices_u, indices_v, self.regularization_coeff)
IndexError: index 13971421 is out of bounds for axis 0 with size 13971421
|
IndexError
|
def _get_candidate_negatives(self):
"""Returns candidate negatives of size `self.negative` from the negative examples buffer.
Returns
-------
numpy.array
Array of shape (`self.negative`,) containing indices of negative nodes.
"""
if self._negatives_buffer.num_items() < self.negative:
# cumsum table of counts used instead of the standard approach of a probability cumsum table
# this is to avoid floating point errors that result when the number of nodes is very high
# for reference: https://github.com/RaRe-Technologies/gensim/issues/1917
max_cumsum_value = self._node_counts_cumsum[-1]
uniform_numbers = self._np_random.randint(
1, max_cumsum_value + 1, self._negatives_buffer_size
)
cumsum_table_indices = np.searchsorted(
self._node_counts_cumsum, uniform_numbers
)
self._negatives_buffer = NegativesBuffer(cumsum_table_indices)
return self._negatives_buffer.get_items(self.negative)
|
def _get_candidate_negatives(self):
"""Returns candidate negatives of size `self.negative` from the negative examples buffer.
Returns
-------
numpy.array
Array of shape (`self.negative`,) containing indices of negative nodes.
"""
if self._negatives_buffer.num_items() < self.negative:
# Note: np.random.choice much slower than random.sample for large populations, possible bottleneck
uniform_numbers = self._np_random.random_sample(self._negatives_buffer_size)
cumsum_table_indices = np.searchsorted(
self._node_probabilities_cumsum, uniform_numbers
)
self._negatives_buffer = NegativesBuffer(cumsum_table_indices)
return self._negatives_buffer.get_items(self.negative)
|
https://github.com/RaRe-Technologies/gensim/issues/1917
|
2018-02-05 10:49:58,008 - training model of size 50 with 1 workers on 128138847 relations for 1 epochs and 10 burn-in epochs, using lr=0.01000 burn-in lr=0.01000 negative=10
2018-02-05 10:49:58,010 - Starting burn-in (10 epochs)----------------------------------------
2018-02-05 10:56:51,400 - Training on epoch 1, examples #999000-#1000000, loss: 2188.85
2018-02-05 10:56:51,404 - Time taken for 1000000 examples: 329.60 s, 3033.98 examples / s
2018-02-05 11:01:44,625 - Training on epoch 1, examples #1999000-#2000000, loss: 2187.71
2018-02-05 11:01:44,627 - Time taken for 1000000 examples: 293.22 s, 3410.41 examples / s
2018-02-05 11:06:38,729 - Training on epoch 1, examples #2999000-#3000000, loss: 2186.41
2018-02-05 11:06:38,731 - Time taken for 1000000 examples: 294.10 s, 3400.18 examples / s
2018-02-05 11:11:28,291 - Training on epoch 1, examples #3999000-#4000000, loss: 2185.42
2018-02-05 11:11:28,293 - Time taken for 1000000 examples: 289.56 s, 3453.52 examples / s
2018-02-05 11:16:16,831 - Training on epoch 1, examples #4999000-#5000000, loss: 2184.04
2018-02-05 11:16:16,833 - Time taken for 1000000 examples: 288.54 s, 3465.75 examples / s
2018-02-05 11:21:06,625 - Training on epoch 1, examples #5999000-#6000000, loss: 2182.88
2018-02-05 11:21:06,630 - Time taken for 1000000 examples: 289.79 s, 3450.75 examples / s
2018-02-05 11:26:55,483 - Training on epoch 1, examples #6999000-#7000000, loss: 2181.47
2018-02-05 11:26:55,484 - Time taken for 1000000 examples: 348.85 s, 2866.54 examples / s
2018-02-05 11:31:45,830 - Training on epoch 1, examples #7999000-#8000000, loss: 2180.34
2018-02-05 11:31:45,839 - Time taken for 1000000 examples: 290.34 s, 3444.18 examples / s
2018-02-05 11:36:30,690 - Training on epoch 1, examples #8999000-#9000000, loss: 2179.56
2018-02-05 11:36:30,692 - Time taken for 1000000 examples: 284.85 s, 3510.62 examples / s
2018-02-05 11:41:15,313 - Training on epoch 1, examples #9999000-#10000000, loss: 2178.03
2018-02-05 11:41:15,315 - Time taken for 1000000 examples: 284.62 s, 3513.45 examples / s
2018-02-05 11:46:00,357 - Training on epoch 1, examples #10999000-#11000000, loss: 2177.52
2018-02-05 11:46:00,358 - Time taken for 1000000 examples: 285.04 s, 3508.26 examples / s
2018-02-05 11:50:48,905 - Training on epoch 1, examples #11999000-#12000000, loss: 2175.87
2018-02-05 11:50:48,910 - Time taken for 1000000 examples: 288.55 s, 3465.64 examples / s
2018-02-05 11:55:35,918 - Training on epoch 1, examples #12999000-#13000000, loss: 2174.76
2018-02-05 11:55:35,919 - Time taken for 1000000 examples: 287.01 s, 3484.23 examples / s
2018-02-05 12:00:24,240 - Training on epoch 1, examples #13999000-#14000000, loss: 2173.49
2018-02-05 12:00:24,242 - Time taken for 1000000 examples: 288.32 s, 3468.36 examples / s
2018-02-05 12:05:07,573 - Training on epoch 1, examples #14999000-#15000000, loss: 2172.35
2018-02-05 12:05:07,574 - Time taken for 1000000 examples: 283.33 s, 3529.45 examples / s
2018-02-05 12:09:52,164 - Training on epoch 1, examples #15999000-#16000000, loss: 2171.20
2018-02-05 12:09:52,165 - Time taken for 1000000 examples: 284.59 s, 3513.83 examples / s
2018-02-05 12:14:41,436 - Training on epoch 1, examples #16999000-#17000000, loss: 2170.33
2018-02-05 12:14:41,438 - Time taken for 1000000 examples: 289.27 s, 3456.97 examples / s
2018-02-05 12:19:34,138 - Training on epoch 1, examples #17999000-#18000000, loss: 2169.56
2018-02-05 12:19:34,142 - Time taken for 1000000 examples: 292.70 s, 3416.47 examples / s
2018-02-05 12:24:27,812 - Training on epoch 1, examples #18999000-#19000000, loss: 2168.17
2018-02-05 12:24:27,814 - Time taken for 1000000 examples: 293.67 s, 3405.19 examples / s
2018-02-05 12:29:15,083 - Training on epoch 1, examples #19999000-#20000000, loss: 2167.16
2018-02-05 12:29:15,085 - Time taken for 1000000 examples: 287.27 s, 3481.06 examples / s
2018-02-05 12:34:03,589 - Training on epoch 1, examples #20999000-#21000000, loss: 2165.85
2018-02-05 12:34:03,590 - Time taken for 1000000 examples: 288.50 s, 3466.17 examples / s
2018-02-05 12:38:50,770 - Training on epoch 1, examples #21999000-#22000000, loss: 2164.89
2018-02-05 12:38:50,772 - Time taken for 1000000 examples: 287.18 s, 3482.14 examples / s
2018-02-05 12:43:41,125 - Training on epoch 1, examples #22999000-#23000000, loss: 2163.63
2018-02-05 12:43:41,129 - Time taken for 1000000 examples: 290.35 s, 3444.09 examples / s
2018-02-05 12:48:27,127 - Training on epoch 1, examples #23999000-#24000000, loss: 2162.46
2018-02-05 12:48:27,129 - Time taken for 1000000 examples: 286.00 s, 3496.53 examples / s
2018-02-05 12:53:17,683 - Training on epoch 1, examples #24999000-#25000000, loss: 2161.23
2018-02-05 12:53:17,684 - Time taken for 1000000 examples: 290.55 s, 3441.71 examples / s
2018-02-05 12:58:02,880 - Training on epoch 1, examples #25999000-#26000000, loss: 2160.17
2018-02-05 12:58:02,881 - Time taken for 1000000 examples: 285.20 s, 3506.37 examples / s
2018-02-05 13:02:47,177 - Training on epoch 1, examples #26999000-#27000000, loss: 2158.66
2018-02-05 13:02:47,179 - Time taken for 1000000 examples: 284.30 s, 3517.46 examples / s
2018-02-05 13:07:31,441 - Training on epoch 1, examples #27999000-#28000000, loss: 2157.93
2018-02-05 13:07:31,442 - Time taken for 1000000 examples: 284.26 s, 3517.89 examples / s
2018-02-05 13:12:20,000 - Training on epoch 1, examples #28999000-#29000000, loss: 2156.97
2018-02-05 13:12:20,004 - Time taken for 1000000 examples: 288.56 s, 3465.52 examples / s
2018-02-05 13:17:06,050 - Training on epoch 1, examples #29999000-#30000000, loss: 2155.66
2018-02-05 13:17:06,051 - Time taken for 1000000 examples: 286.04 s, 3495.96 examples / s
2018-02-05 13:21:56,627 - Training on epoch 1, examples #30999000-#31000000, loss: 2154.42
2018-02-05 13:21:56,628 - Time taken for 1000000 examples: 290.58 s, 3441.45 examples / s
2018-02-05 13:26:41,004 - Training on epoch 1, examples #31999000-#32000000, loss: 2153.39
2018-02-05 13:26:41,005 - Time taken for 1000000 examples: 284.37 s, 3516.49 examples / s
2018-02-05 13:31:26,601 - Training on epoch 1, examples #32999000-#33000000, loss: 2152.29
2018-02-05 13:31:26,603 - Time taken for 1000000 examples: 285.59 s, 3501.49 examples / s
2018-02-05 13:36:11,844 - Training on epoch 1, examples #33999000-#34000000, loss: 2151.36
2018-02-05 13:36:11,845 - Time taken for 1000000 examples: 285.24 s, 3505.82 examples / s
2018-02-05 13:41:08,003 - Training on epoch 1, examples #34999000-#35000000, loss: 2150.06
2018-02-05 13:41:08,008 - Time taken for 1000000 examples: 296.16 s, 3376.58 examples / s
2018-02-05 13:45:59,593 - Training on epoch 1, examples #35999000-#36000000, loss: 2149.02
2018-02-05 13:45:59,594 - Time taken for 1000000 examples: 291.58 s, 3429.54 examples / s
2018-02-05 13:50:52,455 - Training on epoch 1, examples #36999000-#37000000, loss: 2148.05
2018-02-05 13:50:52,457 - Time taken for 1000000 examples: 292.86 s, 3414.59 examples / s
2018-02-05 13:55:42,711 - Training on epoch 1, examples #37999000-#38000000, loss: 2146.37
2018-02-05 13:55:42,712 - Time taken for 1000000 examples: 290.25 s, 3445.26 examples / s
2018-02-05 14:00:31,112 - Training on epoch 1, examples #38999000-#39000000, loss: 2145.71
2018-02-05 14:00:31,113 - Time taken for 1000000 examples: 288.40 s, 3467.42 examples / s
2018-02-05 14:05:18,087 - Training on epoch 1, examples #39999000-#40000000, loss: 2144.32
2018-02-05 14:05:18,088 - Time taken for 1000000 examples: 286.97 s, 3484.65 examples / s
2018-02-05 14:10:08,383 - Training on epoch 1, examples #40999000-#41000000, loss: 2143.63
2018-02-05 14:10:08,388 - Time taken for 1000000 examples: 290.29 s, 3444.78 examples / s
2018-02-05 14:15:01,954 - Training on epoch 1, examples #41999000-#42000000, loss: 2142.36
2018-02-05 14:15:01,955 - Time taken for 1000000 examples: 293.57 s, 3406.40 examples / s
2018-02-05 14:19:58,021 - Training on epoch 1, examples #42999000-#43000000, loss: 2141.21
2018-02-05 14:19:58,023 - Time taken for 1000000 examples: 296.07 s, 3377.63 examples / s
2018-02-05 14:24:43,944 - Training on epoch 1, examples #43999000-#44000000, loss: 2140.30
2018-02-05 14:24:43,945 - Time taken for 1000000 examples: 285.92 s, 3497.48 examples / s
2018-02-05 14:29:36,938 - Training on epoch 1, examples #44999000-#45000000, loss: 2138.98
2018-02-05 14:29:36,939 - Time taken for 1000000 examples: 292.99 s, 3413.06 examples / s
2018-02-05 14:34:31,522 - Training on epoch 1, examples #45999000-#46000000, loss: 2137.78
2018-02-05 14:34:31,523 - Time taken for 1000000 examples: 294.58 s, 3394.64 examples / s
2018-02-05 14:39:24,775 - Training on epoch 1, examples #46999000-#47000000, loss: 2136.79
2018-02-05 14:39:24,780 - Time taken for 1000000 examples: 293.25 s, 3410.04 examples / s
2018-02-05 14:44:15,172 - Training on epoch 1, examples #47999000-#48000000, loss: 2135.49
2018-02-05 14:44:15,174 - Time taken for 1000000 examples: 290.39 s, 3443.62 examples / s
2018-02-05 14:49:07,628 - Training on epoch 1, examples #48999000-#49000000, loss: 2135.08
2018-02-05 14:49:07,630 - Time taken for 1000000 examples: 292.45 s, 3419.34 examples / s
2018-02-05 14:53:51,284 - Training on epoch 1, examples #49999000-#50000000, loss: 2133.45
2018-02-05 14:53:51,285 - Time taken for 1000000 examples: 283.65 s, 3525.43 examples / s
2018-02-05 14:58:39,403 - Training on epoch 1, examples #50999000-#51000000, loss: 2132.59
2018-02-05 14:58:39,404 - Time taken for 1000000 examples: 288.12 s, 3470.81 examples / s
2018-02-05 15:03:27,455 - Training on epoch 1, examples #51999000-#52000000, loss: 2131.60
2018-02-05 15:03:27,456 - Time taken for 1000000 examples: 288.05 s, 3471.65 examples / s
2018-02-05 15:08:19,622 - Training on epoch 1, examples #52999000-#53000000, loss: 2130.17
2018-02-05 15:08:19,627 - Time taken for 1000000 examples: 292.17 s, 3422.71 examples / s
2018-02-05 15:13:12,975 - Training on epoch 1, examples #53999000-#54000000, loss: 2129.34
2018-02-05 15:13:12,976 - Time taken for 1000000 examples: 293.35 s, 3408.92 examples / s
2018-02-05 15:18:01,815 - Training on epoch 1, examples #54999000-#55000000, loss: 2128.32
2018-02-05 15:18:01,816 - Time taken for 1000000 examples: 288.84 s, 3462.15 examples / s
2018-02-05 15:22:45,226 - Training on epoch 1, examples #55999000-#56000000, loss: 2126.67
2018-02-05 15:22:45,227 - Time taken for 1000000 examples: 283.41 s, 3528.47 examples / s
2018-02-05 15:27:31,026 - Training on epoch 1, examples #56999000-#57000000, loss: 2126.11
2018-02-05 15:27:31,027 - Time taken for 1000000 examples: 285.79 s, 3499.01 examples / s
2018-02-05 15:32:19,805 - Training on epoch 1, examples #57999000-#58000000, loss: 2125.11
2018-02-05 15:32:19,807 - Time taken for 1000000 examples: 288.77 s, 3462.90 examples / s
2018-02-05 15:37:11,024 - Training on epoch 1, examples #58999000-#59000000, loss: 2123.99
2018-02-05 15:37:11,028 - Time taken for 1000000 examples: 291.22 s, 3433.87 examples / s
2018-02-05 15:42:06,631 - Training on epoch 1, examples #59999000-#60000000, loss: 2123.01
2018-02-05 15:42:06,632 - Time taken for 1000000 examples: 295.60 s, 3382.92 examples / s
2018-02-05 15:46:54,707 - Training on epoch 1, examples #60999000-#61000000, loss: 2121.46
2018-02-05 15:46:54,709 - Time taken for 1000000 examples: 288.07 s, 3471.33 examples / s
2018-02-05 15:51:42,019 - Training on epoch 1, examples #61999000-#62000000, loss: 2120.72
2018-02-05 15:51:42,021 - Time taken for 1000000 examples: 287.31 s, 3480.57 examples / s
2018-02-05 15:56:29,973 - Training on epoch 1, examples #62999000-#63000000, loss: 2119.82
2018-02-05 15:56:29,974 - Time taken for 1000000 examples: 287.95 s, 3472.81 examples / s
2018-02-05 16:01:22,243 - Training on epoch 1, examples #63999000-#64000000, loss: 2118.50
2018-02-05 16:01:22,247 - Time taken for 1000000 examples: 292.27 s, 3421.52 examples / s
2018-02-05 16:06:09,893 - Training on epoch 1, examples #64999000-#65000000, loss: 2117.51
2018-02-05 16:06:09,894 - Time taken for 1000000 examples: 287.64 s, 3476.51 examples / s
2018-02-05 16:11:00,706 - Training on epoch 1, examples #65999000-#66000000, loss: 2116.77
2018-02-05 16:11:00,707 - Time taken for 1000000 examples: 290.81 s, 3438.66 examples / s
2018-02-05 16:15:46,906 - Training on epoch 1, examples #66999000-#67000000, loss: 2115.44
2018-02-05 16:15:46,908 - Time taken for 1000000 examples: 286.20 s, 3494.08 examples / s
2018-02-05 16:20:32,582 - Training on epoch 1, examples #67999000-#68000000, loss: 2114.07
2018-02-05 16:20:32,584 - Time taken for 1000000 examples: 285.67 s, 3500.49 examples / s
2018-02-05 16:25:18,195 - Training on epoch 1, examples #68999000-#69000000, loss: 2113.42
2018-02-05 16:25:18,197 - Time taken for 1000000 examples: 285.61 s, 3501.27 examples / s
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-15-50d51e10cfa2> in <module>()
----> 1 model.train(epochs=1, batch_size=1000)
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in train(self, epochs, batch_size, print_every, check_gradients_every)
542 self._train_batchwise(
543 epochs=self.burn_in, batch_size=batch_size, print_every=print_every,
--> 544 check_gradients_every=check_gradients_every)
545 self._burn_in_done = True
546 logger.info("Burn-in finished")
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _train_batchwise(self, epochs, batch_size, print_every, check_gradients_every)
583 batch_indices = indices[i:i + batch_size]
584 relations = [self.all_relations[idx] for idx in batch_indices]
--> 585 result = self._train_on_batch(relations, check_gradients=check_gradients)
586 avg_loss += result.loss
587 if should_print:
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _train_on_batch(self, relations, check_gradients)
442 """
443 all_negatives = self._sample_negatives_batch([relation[0] for relation in relations])
--> 444 batch = self._prepare_training_batch(relations, all_negatives, check_gradients)
445 self._update_vectors_batch(batch)
446 return batch
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _prepare_training_batch(self, relations, all_negatives, check_gradients)
363
364 vectors_u = self.kv.syn0[indices_u]
--> 365 vectors_v = self.kv.syn0[indices_v].reshape((batch_size, 1 + self.negative, self.size))
366 vectors_v = vectors_v.swapaxes(0, 1).swapaxes(1, 2)
367 batch = PoincareBatch(vectors_u, vectors_v, indices_u, indices_v, self.regularization_coeff)
IndexError: index 13971421 is out of bounds for axis 0 with size 13971421
|
IndexError
|
def save(self, *args, **kwargs):
"""Save complete model to disk, inherited from :class:`gensim.utils.SaveLoad`."""
self._loss_grad = None # Can't pickle autograd fn to disk
attrs_to_ignore = ["_node_probabilities", "_node_counts_cumsum"]
kwargs["ignore"] = set(list(kwargs.get("ignore", [])) + attrs_to_ignore)
super(PoincareModel, self).save(*args, **kwargs)
|
def save(self, *args, **kwargs):
"""Save complete model to disk, inherited from :class:`gensim.utils.SaveLoad`."""
self._loss_grad = None # Can't pickle autograd fn to disk
super(PoincareModel, self).save(*args, **kwargs)
|
https://github.com/RaRe-Technologies/gensim/issues/1917
|
2018-02-05 10:49:58,008 - training model of size 50 with 1 workers on 128138847 relations for 1 epochs and 10 burn-in epochs, using lr=0.01000 burn-in lr=0.01000 negative=10
2018-02-05 10:49:58,010 - Starting burn-in (10 epochs)----------------------------------------
2018-02-05 10:56:51,400 - Training on epoch 1, examples #999000-#1000000, loss: 2188.85
2018-02-05 10:56:51,404 - Time taken for 1000000 examples: 329.60 s, 3033.98 examples / s
2018-02-05 11:01:44,625 - Training on epoch 1, examples #1999000-#2000000, loss: 2187.71
2018-02-05 11:01:44,627 - Time taken for 1000000 examples: 293.22 s, 3410.41 examples / s
2018-02-05 11:06:38,729 - Training on epoch 1, examples #2999000-#3000000, loss: 2186.41
2018-02-05 11:06:38,731 - Time taken for 1000000 examples: 294.10 s, 3400.18 examples / s
2018-02-05 11:11:28,291 - Training on epoch 1, examples #3999000-#4000000, loss: 2185.42
2018-02-05 11:11:28,293 - Time taken for 1000000 examples: 289.56 s, 3453.52 examples / s
2018-02-05 11:16:16,831 - Training on epoch 1, examples #4999000-#5000000, loss: 2184.04
2018-02-05 11:16:16,833 - Time taken for 1000000 examples: 288.54 s, 3465.75 examples / s
2018-02-05 11:21:06,625 - Training on epoch 1, examples #5999000-#6000000, loss: 2182.88
2018-02-05 11:21:06,630 - Time taken for 1000000 examples: 289.79 s, 3450.75 examples / s
2018-02-05 11:26:55,483 - Training on epoch 1, examples #6999000-#7000000, loss: 2181.47
2018-02-05 11:26:55,484 - Time taken for 1000000 examples: 348.85 s, 2866.54 examples / s
2018-02-05 11:31:45,830 - Training on epoch 1, examples #7999000-#8000000, loss: 2180.34
2018-02-05 11:31:45,839 - Time taken for 1000000 examples: 290.34 s, 3444.18 examples / s
2018-02-05 11:36:30,690 - Training on epoch 1, examples #8999000-#9000000, loss: 2179.56
2018-02-05 11:36:30,692 - Time taken for 1000000 examples: 284.85 s, 3510.62 examples / s
2018-02-05 11:41:15,313 - Training on epoch 1, examples #9999000-#10000000, loss: 2178.03
2018-02-05 11:41:15,315 - Time taken for 1000000 examples: 284.62 s, 3513.45 examples / s
2018-02-05 11:46:00,357 - Training on epoch 1, examples #10999000-#11000000, loss: 2177.52
2018-02-05 11:46:00,358 - Time taken for 1000000 examples: 285.04 s, 3508.26 examples / s
2018-02-05 11:50:48,905 - Training on epoch 1, examples #11999000-#12000000, loss: 2175.87
2018-02-05 11:50:48,910 - Time taken for 1000000 examples: 288.55 s, 3465.64 examples / s
2018-02-05 11:55:35,918 - Training on epoch 1, examples #12999000-#13000000, loss: 2174.76
2018-02-05 11:55:35,919 - Time taken for 1000000 examples: 287.01 s, 3484.23 examples / s
2018-02-05 12:00:24,240 - Training on epoch 1, examples #13999000-#14000000, loss: 2173.49
2018-02-05 12:00:24,242 - Time taken for 1000000 examples: 288.32 s, 3468.36 examples / s
2018-02-05 12:05:07,573 - Training on epoch 1, examples #14999000-#15000000, loss: 2172.35
2018-02-05 12:05:07,574 - Time taken for 1000000 examples: 283.33 s, 3529.45 examples / s
2018-02-05 12:09:52,164 - Training on epoch 1, examples #15999000-#16000000, loss: 2171.20
2018-02-05 12:09:52,165 - Time taken for 1000000 examples: 284.59 s, 3513.83 examples / s
2018-02-05 12:14:41,436 - Training on epoch 1, examples #16999000-#17000000, loss: 2170.33
2018-02-05 12:14:41,438 - Time taken for 1000000 examples: 289.27 s, 3456.97 examples / s
2018-02-05 12:19:34,138 - Training on epoch 1, examples #17999000-#18000000, loss: 2169.56
2018-02-05 12:19:34,142 - Time taken for 1000000 examples: 292.70 s, 3416.47 examples / s
2018-02-05 12:24:27,812 - Training on epoch 1, examples #18999000-#19000000, loss: 2168.17
2018-02-05 12:24:27,814 - Time taken for 1000000 examples: 293.67 s, 3405.19 examples / s
2018-02-05 12:29:15,083 - Training on epoch 1, examples #19999000-#20000000, loss: 2167.16
2018-02-05 12:29:15,085 - Time taken for 1000000 examples: 287.27 s, 3481.06 examples / s
2018-02-05 12:34:03,589 - Training on epoch 1, examples #20999000-#21000000, loss: 2165.85
2018-02-05 12:34:03,590 - Time taken for 1000000 examples: 288.50 s, 3466.17 examples / s
2018-02-05 12:38:50,770 - Training on epoch 1, examples #21999000-#22000000, loss: 2164.89
2018-02-05 12:38:50,772 - Time taken for 1000000 examples: 287.18 s, 3482.14 examples / s
2018-02-05 12:43:41,125 - Training on epoch 1, examples #22999000-#23000000, loss: 2163.63
2018-02-05 12:43:41,129 - Time taken for 1000000 examples: 290.35 s, 3444.09 examples / s
2018-02-05 12:48:27,127 - Training on epoch 1, examples #23999000-#24000000, loss: 2162.46
2018-02-05 12:48:27,129 - Time taken for 1000000 examples: 286.00 s, 3496.53 examples / s
2018-02-05 12:53:17,683 - Training on epoch 1, examples #24999000-#25000000, loss: 2161.23
2018-02-05 12:53:17,684 - Time taken for 1000000 examples: 290.55 s, 3441.71 examples / s
2018-02-05 12:58:02,880 - Training on epoch 1, examples #25999000-#26000000, loss: 2160.17
2018-02-05 12:58:02,881 - Time taken for 1000000 examples: 285.20 s, 3506.37 examples / s
2018-02-05 13:02:47,177 - Training on epoch 1, examples #26999000-#27000000, loss: 2158.66
2018-02-05 13:02:47,179 - Time taken for 1000000 examples: 284.30 s, 3517.46 examples / s
2018-02-05 13:07:31,441 - Training on epoch 1, examples #27999000-#28000000, loss: 2157.93
2018-02-05 13:07:31,442 - Time taken for 1000000 examples: 284.26 s, 3517.89 examples / s
2018-02-05 13:12:20,000 - Training on epoch 1, examples #28999000-#29000000, loss: 2156.97
2018-02-05 13:12:20,004 - Time taken for 1000000 examples: 288.56 s, 3465.52 examples / s
2018-02-05 13:17:06,050 - Training on epoch 1, examples #29999000-#30000000, loss: 2155.66
2018-02-05 13:17:06,051 - Time taken for 1000000 examples: 286.04 s, 3495.96 examples / s
2018-02-05 13:21:56,627 - Training on epoch 1, examples #30999000-#31000000, loss: 2154.42
2018-02-05 13:21:56,628 - Time taken for 1000000 examples: 290.58 s, 3441.45 examples / s
2018-02-05 13:26:41,004 - Training on epoch 1, examples #31999000-#32000000, loss: 2153.39
2018-02-05 13:26:41,005 - Time taken for 1000000 examples: 284.37 s, 3516.49 examples / s
2018-02-05 13:31:26,601 - Training on epoch 1, examples #32999000-#33000000, loss: 2152.29
2018-02-05 13:31:26,603 - Time taken for 1000000 examples: 285.59 s, 3501.49 examples / s
2018-02-05 13:36:11,844 - Training on epoch 1, examples #33999000-#34000000, loss: 2151.36
2018-02-05 13:36:11,845 - Time taken for 1000000 examples: 285.24 s, 3505.82 examples / s
2018-02-05 13:41:08,003 - Training on epoch 1, examples #34999000-#35000000, loss: 2150.06
2018-02-05 13:41:08,008 - Time taken for 1000000 examples: 296.16 s, 3376.58 examples / s
2018-02-05 13:45:59,593 - Training on epoch 1, examples #35999000-#36000000, loss: 2149.02
2018-02-05 13:45:59,594 - Time taken for 1000000 examples: 291.58 s, 3429.54 examples / s
2018-02-05 13:50:52,455 - Training on epoch 1, examples #36999000-#37000000, loss: 2148.05
2018-02-05 13:50:52,457 - Time taken for 1000000 examples: 292.86 s, 3414.59 examples / s
2018-02-05 13:55:42,711 - Training on epoch 1, examples #37999000-#38000000, loss: 2146.37
2018-02-05 13:55:42,712 - Time taken for 1000000 examples: 290.25 s, 3445.26 examples / s
2018-02-05 14:00:31,112 - Training on epoch 1, examples #38999000-#39000000, loss: 2145.71
2018-02-05 14:00:31,113 - Time taken for 1000000 examples: 288.40 s, 3467.42 examples / s
2018-02-05 14:05:18,087 - Training on epoch 1, examples #39999000-#40000000, loss: 2144.32
2018-02-05 14:05:18,088 - Time taken for 1000000 examples: 286.97 s, 3484.65 examples / s
2018-02-05 14:10:08,383 - Training on epoch 1, examples #40999000-#41000000, loss: 2143.63
2018-02-05 14:10:08,388 - Time taken for 1000000 examples: 290.29 s, 3444.78 examples / s
2018-02-05 14:15:01,954 - Training on epoch 1, examples #41999000-#42000000, loss: 2142.36
2018-02-05 14:15:01,955 - Time taken for 1000000 examples: 293.57 s, 3406.40 examples / s
2018-02-05 14:19:58,021 - Training on epoch 1, examples #42999000-#43000000, loss: 2141.21
2018-02-05 14:19:58,023 - Time taken for 1000000 examples: 296.07 s, 3377.63 examples / s
2018-02-05 14:24:43,944 - Training on epoch 1, examples #43999000-#44000000, loss: 2140.30
2018-02-05 14:24:43,945 - Time taken for 1000000 examples: 285.92 s, 3497.48 examples / s
2018-02-05 14:29:36,938 - Training on epoch 1, examples #44999000-#45000000, loss: 2138.98
2018-02-05 14:29:36,939 - Time taken for 1000000 examples: 292.99 s, 3413.06 examples / s
2018-02-05 14:34:31,522 - Training on epoch 1, examples #45999000-#46000000, loss: 2137.78
2018-02-05 14:34:31,523 - Time taken for 1000000 examples: 294.58 s, 3394.64 examples / s
2018-02-05 14:39:24,775 - Training on epoch 1, examples #46999000-#47000000, loss: 2136.79
2018-02-05 14:39:24,780 - Time taken for 1000000 examples: 293.25 s, 3410.04 examples / s
2018-02-05 14:44:15,172 - Training on epoch 1, examples #47999000-#48000000, loss: 2135.49
2018-02-05 14:44:15,174 - Time taken for 1000000 examples: 290.39 s, 3443.62 examples / s
2018-02-05 14:49:07,628 - Training on epoch 1, examples #48999000-#49000000, loss: 2135.08
2018-02-05 14:49:07,630 - Time taken for 1000000 examples: 292.45 s, 3419.34 examples / s
2018-02-05 14:53:51,284 - Training on epoch 1, examples #49999000-#50000000, loss: 2133.45
2018-02-05 14:53:51,285 - Time taken for 1000000 examples: 283.65 s, 3525.43 examples / s
2018-02-05 14:58:39,403 - Training on epoch 1, examples #50999000-#51000000, loss: 2132.59
2018-02-05 14:58:39,404 - Time taken for 1000000 examples: 288.12 s, 3470.81 examples / s
2018-02-05 15:03:27,455 - Training on epoch 1, examples #51999000-#52000000, loss: 2131.60
2018-02-05 15:03:27,456 - Time taken for 1000000 examples: 288.05 s, 3471.65 examples / s
2018-02-05 15:08:19,622 - Training on epoch 1, examples #52999000-#53000000, loss: 2130.17
2018-02-05 15:08:19,627 - Time taken for 1000000 examples: 292.17 s, 3422.71 examples / s
2018-02-05 15:13:12,975 - Training on epoch 1, examples #53999000-#54000000, loss: 2129.34
2018-02-05 15:13:12,976 - Time taken for 1000000 examples: 293.35 s, 3408.92 examples / s
2018-02-05 15:18:01,815 - Training on epoch 1, examples #54999000-#55000000, loss: 2128.32
2018-02-05 15:18:01,816 - Time taken for 1000000 examples: 288.84 s, 3462.15 examples / s
2018-02-05 15:22:45,226 - Training on epoch 1, examples #55999000-#56000000, loss: 2126.67
2018-02-05 15:22:45,227 - Time taken for 1000000 examples: 283.41 s, 3528.47 examples / s
2018-02-05 15:27:31,026 - Training on epoch 1, examples #56999000-#57000000, loss: 2126.11
2018-02-05 15:27:31,027 - Time taken for 1000000 examples: 285.79 s, 3499.01 examples / s
2018-02-05 15:32:19,805 - Training on epoch 1, examples #57999000-#58000000, loss: 2125.11
2018-02-05 15:32:19,807 - Time taken for 1000000 examples: 288.77 s, 3462.90 examples / s
2018-02-05 15:37:11,024 - Training on epoch 1, examples #58999000-#59000000, loss: 2123.99
2018-02-05 15:37:11,028 - Time taken for 1000000 examples: 291.22 s, 3433.87 examples / s
2018-02-05 15:42:06,631 - Training on epoch 1, examples #59999000-#60000000, loss: 2123.01
2018-02-05 15:42:06,632 - Time taken for 1000000 examples: 295.60 s, 3382.92 examples / s
2018-02-05 15:46:54,707 - Training on epoch 1, examples #60999000-#61000000, loss: 2121.46
2018-02-05 15:46:54,709 - Time taken for 1000000 examples: 288.07 s, 3471.33 examples / s
2018-02-05 15:51:42,019 - Training on epoch 1, examples #61999000-#62000000, loss: 2120.72
2018-02-05 15:51:42,021 - Time taken for 1000000 examples: 287.31 s, 3480.57 examples / s
2018-02-05 15:56:29,973 - Training on epoch 1, examples #62999000-#63000000, loss: 2119.82
2018-02-05 15:56:29,974 - Time taken for 1000000 examples: 287.95 s, 3472.81 examples / s
2018-02-05 16:01:22,243 - Training on epoch 1, examples #63999000-#64000000, loss: 2118.50
2018-02-05 16:01:22,247 - Time taken for 1000000 examples: 292.27 s, 3421.52 examples / s
2018-02-05 16:06:09,893 - Training on epoch 1, examples #64999000-#65000000, loss: 2117.51
2018-02-05 16:06:09,894 - Time taken for 1000000 examples: 287.64 s, 3476.51 examples / s
2018-02-05 16:11:00,706 - Training on epoch 1, examples #65999000-#66000000, loss: 2116.77
2018-02-05 16:11:00,707 - Time taken for 1000000 examples: 290.81 s, 3438.66 examples / s
2018-02-05 16:15:46,906 - Training on epoch 1, examples #66999000-#67000000, loss: 2115.44
2018-02-05 16:15:46,908 - Time taken for 1000000 examples: 286.20 s, 3494.08 examples / s
2018-02-05 16:20:32,582 - Training on epoch 1, examples #67999000-#68000000, loss: 2114.07
2018-02-05 16:20:32,584 - Time taken for 1000000 examples: 285.67 s, 3500.49 examples / s
2018-02-05 16:25:18,195 - Training on epoch 1, examples #68999000-#69000000, loss: 2113.42
2018-02-05 16:25:18,197 - Time taken for 1000000 examples: 285.61 s, 3501.27 examples / s
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-15-50d51e10cfa2> in <module>()
----> 1 model.train(epochs=1, batch_size=1000)
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in train(self, epochs, batch_size, print_every, check_gradients_every)
542 self._train_batchwise(
543 epochs=self.burn_in, batch_size=batch_size, print_every=print_every,
--> 544 check_gradients_every=check_gradients_every)
545 self._burn_in_done = True
546 logger.info("Burn-in finished")
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _train_batchwise(self, epochs, batch_size, print_every, check_gradients_every)
583 batch_indices = indices[i:i + batch_size]
584 relations = [self.all_relations[idx] for idx in batch_indices]
--> 585 result = self._train_on_batch(relations, check_gradients=check_gradients)
586 avg_loss += result.loss
587 if should_print:
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _train_on_batch(self, relations, check_gradients)
442 """
443 all_negatives = self._sample_negatives_batch([relation[0] for relation in relations])
--> 444 batch = self._prepare_training_batch(relations, all_negatives, check_gradients)
445 self._update_vectors_batch(batch)
446 return batch
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _prepare_training_batch(self, relations, all_negatives, check_gradients)
363
364 vectors_u = self.kv.syn0[indices_u]
--> 365 vectors_v = self.kv.syn0[indices_v].reshape((batch_size, 1 + self.negative, self.size))
366 vectors_v = vectors_v.swapaxes(0, 1).swapaxes(1, 2)
367 batch = PoincareBatch(vectors_u, vectors_v, indices_u, indices_v, self.regularization_coeff)
IndexError: index 13971421 is out of bounds for axis 0 with size 13971421
|
IndexError
|
def load(cls, *args, **kwargs):
"""Load model from disk, inherited from :class:`~gensim.utils.SaveLoad`."""
model = super(PoincareModel, cls).load(*args, **kwargs)
model._init_node_probabilities()
return model
|
def load(cls, *args, **kwargs):
"""Load model from disk, inherited from :class:`~gensim.utils.SaveLoad`."""
model = super(PoincareModel, cls).load(*args, **kwargs)
return model
|
https://github.com/RaRe-Technologies/gensim/issues/1917
|
2018-02-05 10:49:58,008 - training model of size 50 with 1 workers on 128138847 relations for 1 epochs and 10 burn-in epochs, using lr=0.01000 burn-in lr=0.01000 negative=10
2018-02-05 10:49:58,010 - Starting burn-in (10 epochs)----------------------------------------
2018-02-05 10:56:51,400 - Training on epoch 1, examples #999000-#1000000, loss: 2188.85
2018-02-05 10:56:51,404 - Time taken for 1000000 examples: 329.60 s, 3033.98 examples / s
2018-02-05 11:01:44,625 - Training on epoch 1, examples #1999000-#2000000, loss: 2187.71
2018-02-05 11:01:44,627 - Time taken for 1000000 examples: 293.22 s, 3410.41 examples / s
2018-02-05 11:06:38,729 - Training on epoch 1, examples #2999000-#3000000, loss: 2186.41
2018-02-05 11:06:38,731 - Time taken for 1000000 examples: 294.10 s, 3400.18 examples / s
2018-02-05 11:11:28,291 - Training on epoch 1, examples #3999000-#4000000, loss: 2185.42
2018-02-05 11:11:28,293 - Time taken for 1000000 examples: 289.56 s, 3453.52 examples / s
2018-02-05 11:16:16,831 - Training on epoch 1, examples #4999000-#5000000, loss: 2184.04
2018-02-05 11:16:16,833 - Time taken for 1000000 examples: 288.54 s, 3465.75 examples / s
2018-02-05 11:21:06,625 - Training on epoch 1, examples #5999000-#6000000, loss: 2182.88
2018-02-05 11:21:06,630 - Time taken for 1000000 examples: 289.79 s, 3450.75 examples / s
2018-02-05 11:26:55,483 - Training on epoch 1, examples #6999000-#7000000, loss: 2181.47
2018-02-05 11:26:55,484 - Time taken for 1000000 examples: 348.85 s, 2866.54 examples / s
2018-02-05 11:31:45,830 - Training on epoch 1, examples #7999000-#8000000, loss: 2180.34
2018-02-05 11:31:45,839 - Time taken for 1000000 examples: 290.34 s, 3444.18 examples / s
2018-02-05 11:36:30,690 - Training on epoch 1, examples #8999000-#9000000, loss: 2179.56
2018-02-05 11:36:30,692 - Time taken for 1000000 examples: 284.85 s, 3510.62 examples / s
2018-02-05 11:41:15,313 - Training on epoch 1, examples #9999000-#10000000, loss: 2178.03
2018-02-05 11:41:15,315 - Time taken for 1000000 examples: 284.62 s, 3513.45 examples / s
2018-02-05 11:46:00,357 - Training on epoch 1, examples #10999000-#11000000, loss: 2177.52
2018-02-05 11:46:00,358 - Time taken for 1000000 examples: 285.04 s, 3508.26 examples / s
2018-02-05 11:50:48,905 - Training on epoch 1, examples #11999000-#12000000, loss: 2175.87
2018-02-05 11:50:48,910 - Time taken for 1000000 examples: 288.55 s, 3465.64 examples / s
2018-02-05 11:55:35,918 - Training on epoch 1, examples #12999000-#13000000, loss: 2174.76
2018-02-05 11:55:35,919 - Time taken for 1000000 examples: 287.01 s, 3484.23 examples / s
2018-02-05 12:00:24,240 - Training on epoch 1, examples #13999000-#14000000, loss: 2173.49
2018-02-05 12:00:24,242 - Time taken for 1000000 examples: 288.32 s, 3468.36 examples / s
2018-02-05 12:05:07,573 - Training on epoch 1, examples #14999000-#15000000, loss: 2172.35
2018-02-05 12:05:07,574 - Time taken for 1000000 examples: 283.33 s, 3529.45 examples / s
2018-02-05 12:09:52,164 - Training on epoch 1, examples #15999000-#16000000, loss: 2171.20
2018-02-05 12:09:52,165 - Time taken for 1000000 examples: 284.59 s, 3513.83 examples / s
2018-02-05 12:14:41,436 - Training on epoch 1, examples #16999000-#17000000, loss: 2170.33
2018-02-05 12:14:41,438 - Time taken for 1000000 examples: 289.27 s, 3456.97 examples / s
2018-02-05 12:19:34,138 - Training on epoch 1, examples #17999000-#18000000, loss: 2169.56
2018-02-05 12:19:34,142 - Time taken for 1000000 examples: 292.70 s, 3416.47 examples / s
2018-02-05 12:24:27,812 - Training on epoch 1, examples #18999000-#19000000, loss: 2168.17
2018-02-05 12:24:27,814 - Time taken for 1000000 examples: 293.67 s, 3405.19 examples / s
2018-02-05 12:29:15,083 - Training on epoch 1, examples #19999000-#20000000, loss: 2167.16
2018-02-05 12:29:15,085 - Time taken for 1000000 examples: 287.27 s, 3481.06 examples / s
2018-02-05 12:34:03,589 - Training on epoch 1, examples #20999000-#21000000, loss: 2165.85
2018-02-05 12:34:03,590 - Time taken for 1000000 examples: 288.50 s, 3466.17 examples / s
2018-02-05 12:38:50,770 - Training on epoch 1, examples #21999000-#22000000, loss: 2164.89
2018-02-05 12:38:50,772 - Time taken for 1000000 examples: 287.18 s, 3482.14 examples / s
2018-02-05 12:43:41,125 - Training on epoch 1, examples #22999000-#23000000, loss: 2163.63
2018-02-05 12:43:41,129 - Time taken for 1000000 examples: 290.35 s, 3444.09 examples / s
2018-02-05 12:48:27,127 - Training on epoch 1, examples #23999000-#24000000, loss: 2162.46
2018-02-05 12:48:27,129 - Time taken for 1000000 examples: 286.00 s, 3496.53 examples / s
2018-02-05 12:53:17,683 - Training on epoch 1, examples #24999000-#25000000, loss: 2161.23
2018-02-05 12:53:17,684 - Time taken for 1000000 examples: 290.55 s, 3441.71 examples / s
2018-02-05 12:58:02,880 - Training on epoch 1, examples #25999000-#26000000, loss: 2160.17
2018-02-05 12:58:02,881 - Time taken for 1000000 examples: 285.20 s, 3506.37 examples / s
2018-02-05 13:02:47,177 - Training on epoch 1, examples #26999000-#27000000, loss: 2158.66
2018-02-05 13:02:47,179 - Time taken for 1000000 examples: 284.30 s, 3517.46 examples / s
2018-02-05 13:07:31,441 - Training on epoch 1, examples #27999000-#28000000, loss: 2157.93
2018-02-05 13:07:31,442 - Time taken for 1000000 examples: 284.26 s, 3517.89 examples / s
2018-02-05 13:12:20,000 - Training on epoch 1, examples #28999000-#29000000, loss: 2156.97
2018-02-05 13:12:20,004 - Time taken for 1000000 examples: 288.56 s, 3465.52 examples / s
2018-02-05 13:17:06,050 - Training on epoch 1, examples #29999000-#30000000, loss: 2155.66
2018-02-05 13:17:06,051 - Time taken for 1000000 examples: 286.04 s, 3495.96 examples / s
2018-02-05 13:21:56,627 - Training on epoch 1, examples #30999000-#31000000, loss: 2154.42
2018-02-05 13:21:56,628 - Time taken for 1000000 examples: 290.58 s, 3441.45 examples / s
2018-02-05 13:26:41,004 - Training on epoch 1, examples #31999000-#32000000, loss: 2153.39
2018-02-05 13:26:41,005 - Time taken for 1000000 examples: 284.37 s, 3516.49 examples / s
2018-02-05 13:31:26,601 - Training on epoch 1, examples #32999000-#33000000, loss: 2152.29
2018-02-05 13:31:26,603 - Time taken for 1000000 examples: 285.59 s, 3501.49 examples / s
2018-02-05 13:36:11,844 - Training on epoch 1, examples #33999000-#34000000, loss: 2151.36
2018-02-05 13:36:11,845 - Time taken for 1000000 examples: 285.24 s, 3505.82 examples / s
2018-02-05 13:41:08,003 - Training on epoch 1, examples #34999000-#35000000, loss: 2150.06
2018-02-05 13:41:08,008 - Time taken for 1000000 examples: 296.16 s, 3376.58 examples / s
2018-02-05 13:45:59,593 - Training on epoch 1, examples #35999000-#36000000, loss: 2149.02
2018-02-05 13:45:59,594 - Time taken for 1000000 examples: 291.58 s, 3429.54 examples / s
2018-02-05 13:50:52,455 - Training on epoch 1, examples #36999000-#37000000, loss: 2148.05
2018-02-05 13:50:52,457 - Time taken for 1000000 examples: 292.86 s, 3414.59 examples / s
2018-02-05 13:55:42,711 - Training on epoch 1, examples #37999000-#38000000, loss: 2146.37
2018-02-05 13:55:42,712 - Time taken for 1000000 examples: 290.25 s, 3445.26 examples / s
2018-02-05 14:00:31,112 - Training on epoch 1, examples #38999000-#39000000, loss: 2145.71
2018-02-05 14:00:31,113 - Time taken for 1000000 examples: 288.40 s, 3467.42 examples / s
2018-02-05 14:05:18,087 - Training on epoch 1, examples #39999000-#40000000, loss: 2144.32
2018-02-05 14:05:18,088 - Time taken for 1000000 examples: 286.97 s, 3484.65 examples / s
2018-02-05 14:10:08,383 - Training on epoch 1, examples #40999000-#41000000, loss: 2143.63
2018-02-05 14:10:08,388 - Time taken for 1000000 examples: 290.29 s, 3444.78 examples / s
2018-02-05 14:15:01,954 - Training on epoch 1, examples #41999000-#42000000, loss: 2142.36
2018-02-05 14:15:01,955 - Time taken for 1000000 examples: 293.57 s, 3406.40 examples / s
2018-02-05 14:19:58,021 - Training on epoch 1, examples #42999000-#43000000, loss: 2141.21
2018-02-05 14:19:58,023 - Time taken for 1000000 examples: 296.07 s, 3377.63 examples / s
2018-02-05 14:24:43,944 - Training on epoch 1, examples #43999000-#44000000, loss: 2140.30
2018-02-05 14:24:43,945 - Time taken for 1000000 examples: 285.92 s, 3497.48 examples / s
2018-02-05 14:29:36,938 - Training on epoch 1, examples #44999000-#45000000, loss: 2138.98
2018-02-05 14:29:36,939 - Time taken for 1000000 examples: 292.99 s, 3413.06 examples / s
2018-02-05 14:34:31,522 - Training on epoch 1, examples #45999000-#46000000, loss: 2137.78
2018-02-05 14:34:31,523 - Time taken for 1000000 examples: 294.58 s, 3394.64 examples / s
2018-02-05 14:39:24,775 - Training on epoch 1, examples #46999000-#47000000, loss: 2136.79
2018-02-05 14:39:24,780 - Time taken for 1000000 examples: 293.25 s, 3410.04 examples / s
2018-02-05 14:44:15,172 - Training on epoch 1, examples #47999000-#48000000, loss: 2135.49
2018-02-05 14:44:15,174 - Time taken for 1000000 examples: 290.39 s, 3443.62 examples / s
2018-02-05 14:49:07,628 - Training on epoch 1, examples #48999000-#49000000, loss: 2135.08
2018-02-05 14:49:07,630 - Time taken for 1000000 examples: 292.45 s, 3419.34 examples / s
2018-02-05 14:53:51,284 - Training on epoch 1, examples #49999000-#50000000, loss: 2133.45
2018-02-05 14:53:51,285 - Time taken for 1000000 examples: 283.65 s, 3525.43 examples / s
2018-02-05 14:58:39,403 - Training on epoch 1, examples #50999000-#51000000, loss: 2132.59
2018-02-05 14:58:39,404 - Time taken for 1000000 examples: 288.12 s, 3470.81 examples / s
2018-02-05 15:03:27,455 - Training on epoch 1, examples #51999000-#52000000, loss: 2131.60
2018-02-05 15:03:27,456 - Time taken for 1000000 examples: 288.05 s, 3471.65 examples / s
2018-02-05 15:08:19,622 - Training on epoch 1, examples #52999000-#53000000, loss: 2130.17
2018-02-05 15:08:19,627 - Time taken for 1000000 examples: 292.17 s, 3422.71 examples / s
2018-02-05 15:13:12,975 - Training on epoch 1, examples #53999000-#54000000, loss: 2129.34
2018-02-05 15:13:12,976 - Time taken for 1000000 examples: 293.35 s, 3408.92 examples / s
2018-02-05 15:18:01,815 - Training on epoch 1, examples #54999000-#55000000, loss: 2128.32
2018-02-05 15:18:01,816 - Time taken for 1000000 examples: 288.84 s, 3462.15 examples / s
2018-02-05 15:22:45,226 - Training on epoch 1, examples #55999000-#56000000, loss: 2126.67
2018-02-05 15:22:45,227 - Time taken for 1000000 examples: 283.41 s, 3528.47 examples / s
2018-02-05 15:27:31,026 - Training on epoch 1, examples #56999000-#57000000, loss: 2126.11
2018-02-05 15:27:31,027 - Time taken for 1000000 examples: 285.79 s, 3499.01 examples / s
2018-02-05 15:32:19,805 - Training on epoch 1, examples #57999000-#58000000, loss: 2125.11
2018-02-05 15:32:19,807 - Time taken for 1000000 examples: 288.77 s, 3462.90 examples / s
2018-02-05 15:37:11,024 - Training on epoch 1, examples #58999000-#59000000, loss: 2123.99
2018-02-05 15:37:11,028 - Time taken for 1000000 examples: 291.22 s, 3433.87 examples / s
2018-02-05 15:42:06,631 - Training on epoch 1, examples #59999000-#60000000, loss: 2123.01
2018-02-05 15:42:06,632 - Time taken for 1000000 examples: 295.60 s, 3382.92 examples / s
2018-02-05 15:46:54,707 - Training on epoch 1, examples #60999000-#61000000, loss: 2121.46
2018-02-05 15:46:54,709 - Time taken for 1000000 examples: 288.07 s, 3471.33 examples / s
2018-02-05 15:51:42,019 - Training on epoch 1, examples #61999000-#62000000, loss: 2120.72
2018-02-05 15:51:42,021 - Time taken for 1000000 examples: 287.31 s, 3480.57 examples / s
2018-02-05 15:56:29,973 - Training on epoch 1, examples #62999000-#63000000, loss: 2119.82
2018-02-05 15:56:29,974 - Time taken for 1000000 examples: 287.95 s, 3472.81 examples / s
2018-02-05 16:01:22,243 - Training on epoch 1, examples #63999000-#64000000, loss: 2118.50
2018-02-05 16:01:22,247 - Time taken for 1000000 examples: 292.27 s, 3421.52 examples / s
2018-02-05 16:06:09,893 - Training on epoch 1, examples #64999000-#65000000, loss: 2117.51
2018-02-05 16:06:09,894 - Time taken for 1000000 examples: 287.64 s, 3476.51 examples / s
2018-02-05 16:11:00,706 - Training on epoch 1, examples #65999000-#66000000, loss: 2116.77
2018-02-05 16:11:00,707 - Time taken for 1000000 examples: 290.81 s, 3438.66 examples / s
2018-02-05 16:15:46,906 - Training on epoch 1, examples #66999000-#67000000, loss: 2115.44
2018-02-05 16:15:46,908 - Time taken for 1000000 examples: 286.20 s, 3494.08 examples / s
2018-02-05 16:20:32,582 - Training on epoch 1, examples #67999000-#68000000, loss: 2114.07
2018-02-05 16:20:32,584 - Time taken for 1000000 examples: 285.67 s, 3500.49 examples / s
2018-02-05 16:25:18,195 - Training on epoch 1, examples #68999000-#69000000, loss: 2113.42
2018-02-05 16:25:18,197 - Time taken for 1000000 examples: 285.61 s, 3501.27 examples / s
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-15-50d51e10cfa2> in <module>()
----> 1 model.train(epochs=1, batch_size=1000)
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in train(self, epochs, batch_size, print_every, check_gradients_every)
542 self._train_batchwise(
543 epochs=self.burn_in, batch_size=batch_size, print_every=print_every,
--> 544 check_gradients_every=check_gradients_every)
545 self._burn_in_done = True
546 logger.info("Burn-in finished")
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _train_batchwise(self, epochs, batch_size, print_every, check_gradients_every)
583 batch_indices = indices[i:i + batch_size]
584 relations = [self.all_relations[idx] for idx in batch_indices]
--> 585 result = self._train_on_batch(relations, check_gradients=check_gradients)
586 avg_loss += result.loss
587 if should_print:
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _train_on_batch(self, relations, check_gradients)
442 """
443 all_negatives = self._sample_negatives_batch([relation[0] for relation in relations])
--> 444 batch = self._prepare_training_batch(relations, all_negatives, check_gradients)
445 self._update_vectors_batch(batch)
446 return batch
~/.virtualenvs/wiki-graph/lib/python3.5/site-packages/gensim/models/poincare.py in _prepare_training_batch(self, relations, all_negatives, check_gradients)
363
364 vectors_u = self.kv.syn0[indices_u]
--> 365 vectors_v = self.kv.syn0[indices_v].reshape((batch_size, 1 + self.negative, self.size))
366 vectors_v = vectors_v.swapaxes(0, 1).swapaxes(1, 2)
367 batch = PoincareBatch(vectors_u, vectors_v, indices_u, indices_v, self.regularization_coeff)
IndexError: index 13971421 is out of bounds for axis 0 with size 13971421
|
IndexError
|
def fit(self, X, y=None):
"""
Fit the model according to the given training data.
Calls gensim.models.Doc2Vec
"""
if isinstance(X[0], doc2vec.TaggedDocument):
d2v_sentences = X
else:
d2v_sentences = [
doc2vec.TaggedDocument(words, [i]) for i, words in enumerate(X)
]
self.gensim_model = models.Doc2Vec(
documents=d2v_sentences,
dm_mean=self.dm_mean,
dm=self.dm,
dbow_words=self.dbow_words,
dm_concat=self.dm_concat,
dm_tag_count=self.dm_tag_count,
docvecs=self.docvecs,
docvecs_mapfile=self.docvecs_mapfile,
comment=self.comment,
trim_rule=self.trim_rule,
vector_size=self.size,
alpha=self.alpha,
window=self.window,
min_count=self.min_count,
max_vocab_size=self.max_vocab_size,
sample=self.sample,
seed=self.seed,
workers=self.workers,
min_alpha=self.min_alpha,
hs=self.hs,
negative=self.negative,
cbow_mean=self.cbow_mean,
hashfxn=self.hashfxn,
epochs=self.iter,
sorted_vocab=self.sorted_vocab,
batch_words=self.batch_words,
)
return self
|
def fit(self, X, y=None):
"""
Fit the model according to the given training data.
Calls gensim.models.Doc2Vec
"""
if isinstance(X[0], doc2vec.TaggedDocument):
d2v_sentences = X
else:
d2v_sentences = [
doc2vec.TaggedDocument(words, [i]) for i, words in enumerate(X)
]
self.gensim_model = models.Doc2Vec(
documents=d2v_sentences,
dm_mean=self.dm_mean,
dm=self.dm,
dbow_words=self.dbow_words,
dm_concat=self.dm_concat,
dm_tag_count=self.dm_tag_count,
docvecs=self.docvecs,
docvecs_mapfile=self.docvecs_mapfile,
comment=self.comment,
trim_rule=self.trim_rule,
size=self.size,
alpha=self.alpha,
window=self.window,
min_count=self.min_count,
max_vocab_size=self.max_vocab_size,
sample=self.sample,
seed=self.seed,
workers=self.workers,
min_alpha=self.min_alpha,
hs=self.hs,
negative=self.negative,
cbow_mean=self.cbow_mean,
hashfxn=self.hashfxn,
iter=self.iter,
sorted_vocab=self.sorted_vocab,
batch_words=self.batch_words,
)
return self
|
https://github.com/RaRe-Technologies/gensim/issues/1937
|
/lib/python3.6/site-packages/gensim/models/doc2vec.py:355: UserWarning: The parameter `iter` is deprecated, will be removed in 4.0.0, use `epochs` instead.
warnings.warn("The parameter `iter` is deprecated, will be removed in 4.0.0, use `epochs` instead.")
/lib/python3.6/site-packages/gensim/models/doc2vec.py:359: UserWarning: The parameter `size` is deprecated, will be removed in 4.0.0, use `vector_size` instead.
warnings.warn("The parameter `size` is deprecated, will be removed in 4.0.0, use `vector_size` instead.")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-108-561949d569bd> in <module>()
2 from gensim.models.doc2vec import TaggedDocument
3 d2v = D2VTransformer(size=1, iter=1).fit([TaggedDocument(['a','a','a','a','a'], [0])])
----> 4 d2v = D2VTransformer(vector_size=1, epochs=1).fit([TaggedDocument(['a','a','a','a','a'], [0])])
TypeError: __init__() got an unexpected keyword argument 'vector_size'
|
TypeError
|
def load_old_doc2vec(*args, **kwargs):
old_model = Doc2Vec.load(*args, **kwargs)
params = {
"dm_mean": old_model.__dict__.get("dm_mean", None),
"dm": old_model.dm,
"dbow_words": old_model.dbow_words,
"dm_concat": old_model.dm_concat,
"dm_tag_count": old_model.dm_tag_count,
"docvecs_mapfile": old_model.__dict__.get("docvecs_mapfile", None),
"comment": old_model.__dict__.get("comment", None),
"size": old_model.vector_size,
"alpha": old_model.alpha,
"window": old_model.window,
"min_count": old_model.min_count,
"max_vocab_size": old_model.__dict__.get("max_vocab_size", None),
"sample": old_model.sample,
"seed": old_model.seed,
"workers": old_model.workers,
"min_alpha": old_model.min_alpha,
"hs": old_model.hs,
"negative": old_model.negative,
"cbow_mean": old_model.cbow_mean,
"hashfxn": old_model.hashfxn,
"iter": old_model.iter,
"sorted_vocab": old_model.sorted_vocab,
"batch_words": old_model.batch_words,
"compute_loss": old_model.__dict__.get("compute_loss", None),
}
new_model = NewDoc2Vec(**params)
# set word2vec trainables attributes
new_model.wv.vectors = old_model.wv.syn0
if hasattr(old_model.wv, "syn0norm"):
new_model.docvecs.vectors_norm = old_model.wv.syn0norm
if hasattr(old_model, "syn1"):
new_model.trainables.syn1 = old_model.syn1
if hasattr(old_model, "syn1neg"):
new_model.trainables.syn1neg = old_model.syn1neg
if hasattr(old_model, "syn0_lockf"):
new_model.trainables.vectors_lockf = old_model.syn0_lockf
# set doc2vec trainables attributes
new_model.docvecs.vectors_docs = old_model.docvecs.doctag_syn0
if hasattr(old_model.docvecs, "doctag_syn0norm"):
new_model.docvecs.vectors_docs_norm = old_model.docvecs.doctag_syn0norm
if hasattr(old_model.docvecs, "doctag_syn0_lockf"):
new_model.trainables.vectors_docs_lockf = old_model.docvecs.doctag_syn0_lockf
if hasattr(old_model.docvecs, "mapfile_path"):
new_model.docvecs.mapfile_path = old_model.docvecs.mapfile_path
# set word2vec vocabulary attributes
new_model.wv.vocab = old_model.wv.vocab
new_model.wv.index2word = old_model.wv.index2word
new_model.vocabulary.cum_table = old_model.cum_table
# set doc2vec vocabulary attributes
new_model.docvecs.doctags = old_model.docvecs.doctags
new_model.docvecs.max_rawint = old_model.docvecs.max_rawint
new_model.docvecs.offset2doctag = old_model.docvecs.offset2doctag
new_model.docvecs.count = old_model.docvecs.count
new_model.train_count = old_model.train_count
new_model.corpus_count = old_model.corpus_count
new_model.running_training_loss = old_model.running_training_loss
new_model.total_train_time = old_model.total_train_time
new_model.min_alpha_yet_reached = old_model.min_alpha_yet_reached
new_model.model_trimmed_post_training = old_model.model_trimmed_post_training
return new_model
|
def load_old_doc2vec(*args, **kwargs):
old_model = Doc2Vec.load(*args, **kwargs)
params = {
"dm_mean": old_model.__dict__.get("dm_mean", None),
"dm": old_model.dm,
"dbow_words": old_model.dbow_words,
"dm_concat": old_model.dm_concat,
"dm_tag_count": old_model.dm_tag_count,
"docvecs": old_model.__dict__.get("docvecs", None),
"docvecs_mapfile": old_model.__dict__.get("docvecs_mapfile", None),
"comment": old_model.__dict__.get("comment", None),
"size": old_model.vector_size,
"alpha": old_model.alpha,
"window": old_model.window,
"min_count": old_model.min_count,
"max_vocab_size": old_model.__dict__.get("max_vocab_size", None),
"sample": old_model.sample,
"seed": old_model.seed,
"workers": old_model.workers,
"min_alpha": old_model.min_alpha,
"hs": old_model.hs,
"negative": old_model.negative,
"cbow_mean": old_model.cbow_mean,
"hashfxn": old_model.hashfxn,
"iter": old_model.iter,
"sorted_vocab": old_model.sorted_vocab,
"batch_words": old_model.batch_words,
"compute_loss": old_model.__dict__.get("compute_loss", None),
}
new_model = NewDoc2Vec(**params)
# set word2vec trainables attributes
new_model.wv.vectors = old_model.wv.syn0
if hasattr(old_model.wv, "syn0norm"):
new_model.docvecs.vectors_norm = old_model.wv.syn0norm
if hasattr(old_model, "syn1"):
new_model.trainables.syn1 = old_model.syn1
if hasattr(old_model, "syn1neg"):
new_model.trainables.syn1neg = old_model.syn1neg
if hasattr(old_model, "syn0_lockf"):
new_model.trainables.vectors_lockf = old_model.syn0_lockf
# set doc2vec trainables attributes
new_model.docvecs.vectors_docs = old_model.docvecs.doctag_syn0
if hasattr(old_model.docvecs, "doctag_syn0norm"):
new_model.docvecs.vectors_docs_norm = old_model.docvecs.doctag_syn0norm
if hasattr(old_model.docvecs, "doctag_syn0_lockf"):
new_model.trainables.vectors_docs_lockf = old_model.docvecs.doctag_syn0_lockf
if hasattr(old_model.docvecs, "mapfile_path"):
new_model.docvecs.mapfile_path = old_model.docvecs.mapfile_path
# set word2vec vocabulary attributes
new_model.wv.vocab = old_model.wv.vocab
new_model.wv.index2word = old_model.wv.index2word
new_model.vocabulary.cum_table = old_model.cum_table
# set doc2vec vocabulary attributes
new_model.docvecs.doctags = old_model.docvecs.doctags
new_model.docvecs.max_rawint = old_model.docvecs.max_rawint
new_model.docvecs.offset2doctag = old_model.docvecs.offset2doctag
new_model.docvecs.count = old_model.docvecs.count
new_model.train_count = old_model.train_count
new_model.corpus_count = old_model.corpus_count
new_model.running_training_loss = old_model.running_training_loss
new_model.total_train_time = old_model.total_train_time
new_model.min_alpha_yet_reached = old_model.min_alpha_yet_reached
new_model.model_trimmed_post_training = old_model.model_trimmed_post_training
return new_model
|
https://github.com/RaRe-Technologies/gensim/issues/1952
|
from gensim.models import Doc2Vec
model = Doc2Vec.load('some-3.2-model/model')
model.infer_vector(['foo'])
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-13-851ff3602fcf> in <module>()
----> 1 model.infer_vector(['foo'])
~/anaconda2/envs/faker/lib/python3.6/site-packages/gensim/models/doc2vec.py in infer_vector(self, doc_words, alpha, min_alpha, steps)
541
542 """
--> 543 doctag_vectors, doctag_locks = self.trainables.get_doctag_trainables(doc_words, self.docvecs.vector_size)
544 doctag_indexes = [0]
545 work = zeros(self.trainables.layer1_size, dtype=REAL)
AttributeError: 'DocvecsArray' object has no attribute 'vector_size'
|
AttributeError
|
def index_to_doctag(self, i_index):
"""Return string key for given i_index, if available. Otherwise return raw int doctag (same int)."""
candidate_offset = i_index - self.max_rawint - 1
if 0 <= candidate_offset < len(self.offset2doctag):
return self.offset2doctag[candidate_offset]
else:
return i_index
|
def index_to_doctag(self, i_index):
"""Return string key for given i_index, if available. Otherwise return raw int doctag (same int)."""
candidate_offset = i_index - self.max_rawint - 1
if 0 <= candidate_offset < len(self.offset2doctag):
return self.ffset2doctag[candidate_offset]
else:
return i_index
|
https://github.com/RaRe-Technologies/gensim/issues/1952
|
from gensim.models import Doc2Vec
model = Doc2Vec.load('some-3.2-model/model')
model.infer_vector(['foo'])
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-13-851ff3602fcf> in <module>()
----> 1 model.infer_vector(['foo'])
~/anaconda2/envs/faker/lib/python3.6/site-packages/gensim/models/doc2vec.py in infer_vector(self, doc_words, alpha, min_alpha, steps)
541
542 """
--> 543 doctag_vectors, doctag_locks = self.trainables.get_doctag_trainables(doc_words, self.docvecs.vector_size)
544 doctag_indexes = [0]
545 work = zeros(self.trainables.layer1_size, dtype=REAL)
AttributeError: 'DocvecsArray' object has no attribute 'vector_size'
|
AttributeError
|
def get_similarities(self, query):
"""Get similarity between `query` and current index instance.
Warnings
--------
Do not use this function directly; use the self[query] syntax instead.
Parameters
----------
query : {list of (int, number), iterable of list of (int, number)
Document or collection of documents.
Return
------
:class:`numpy.ndarray`
Similarity matrix.
"""
is_corpus, query = utils.is_corpus(query)
if not is_corpus:
if isinstance(query, numpy.ndarray):
# Convert document indexes to actual documents.
query = [self.corpus[i] for i in query]
else:
query = [query]
result = []
for query_document in query:
# Compute similarity for each query.
qresult = [
matutils.softcossim(query_document, corpus_document, self.similarity_matrix)
for corpus_document in self.corpus
]
qresult = numpy.array(qresult)
# Append single query result to list of all results.
result.append(qresult)
if is_corpus:
result = numpy.array(result)
else:
result = result[0]
return result
|
def get_similarities(self, query):
"""Get similarity between `query` and current index instance.
Warnings
--------
Do not use this function directly; use the self[query] syntax instead.
Parameters
----------
query : {list of (int, number), iterable of list of (int, number), :class:`scipy.sparse.csr_matrix`
Document or collection of documents.
Return
------
:class:`numpy.ndarray`
Similarity matrix.
"""
if isinstance(query, numpy.ndarray):
# Convert document indexes to actual documents.
query = [self.corpus[i] for i in query]
if not query or not isinstance(query[0], list):
query = [query]
n_queries = len(query)
result = []
for qidx in range(n_queries):
# Compute similarity for each query.
qresult = [
matutils.softcossim(document, query[qidx], self.similarity_matrix)
for document in self.corpus
]
qresult = numpy.array(qresult)
# Append single query result to list of all results.
result.append(qresult)
if len(result) == 1:
# Only one query.
result = result[0]
else:
result = numpy.array(result)
return result
|
https://github.com/RaRe-Technologies/gensim/issues/1955
|
In [249]: softcos_index[tfidf[corpus]]
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-249-2d8c4d759cc9> in <module>()
----> 1 softcos_index[tfidf[corpus]]
/Users/natalied/Projects/canonical_answers/.venv/lib/python2.7/site-packages/gensim/interfaces.pyc in __getitem__(self, query)
365 else:
366 query = matutils.unitvec(query)
--> 367 result = self.get_similarities(query)
368
369 if self.num_best is None:
/Users/natalied/Projects/canonical_answers/.venv/lib/python2.7/site-packages/gensim/similarities/docsim.pyc in get_similarities(self, query)
936 query = [self.corpus[i] for i in query]
937
--> 938 if not query or not isinstance(query[0], list):
939 query = [query]
940
/Users/natalied/Projects/canonical_answers/.venv/lib/python2.7/site-packages/gensim/interfaces.pyc in __getitem__(self, docno)
215 return self.obj[self.corpus[docno]]
216 else:
--> 217 raise RuntimeError('Type {} does not support slicing.'.format(type(self.corpus)))
218
219
RuntimeError: Type <class 'gensim.corpora.textcorpus.TextCorpus'> does not support slicing.
|
RuntimeError
|
def get_similarities(self, query):
"""Get similarity between `query` and current index instance.
Warnings
--------
Do not use this function directly; use the self[query] syntax instead.
Parameters
----------
query : {list of (int, number), iterable of list of (int, number)
Document or collection of documents.
Return
------
:class:`numpy.ndarray`
Similarity matrix.
"""
if isinstance(query, numpy.ndarray):
# Convert document indexes to actual documents.
query = [self.corpus[i] for i in query]
if not query or not isinstance(query[0], list):
query = [query]
n_queries = len(query)
result = []
for qidx in range(n_queries):
# Compute similarity for each query.
qresult = [
self.w2v_model.wmdistance(document, query[qidx]) for document in self.corpus
]
qresult = numpy.array(qresult)
qresult = 1.0 / (1.0 + qresult) # Similarity is the negative of the distance.
# Append single query result to list of all results.
result.append(qresult)
if len(result) == 1:
# Only one query.
result = result[0]
else:
result = numpy.array(result)
return result
|
def get_similarities(self, query):
"""Get similarity between `query` and current index instance.
Warnings
--------
Do not use this function directly; use the self[query] syntax instead.
Parameters
----------
query : {list of (int, number), iterable of list of (int, number), :class:`scipy.sparse.csr_matrix`
Document or collection of documents.
Return
------
:class:`numpy.ndarray`
Similarity matrix.
"""
if isinstance(query, numpy.ndarray):
# Convert document indexes to actual documents.
query = [self.corpus[i] for i in query]
if not query or not isinstance(query[0], list):
query = [query]
n_queries = len(query)
result = []
for qidx in range(n_queries):
# Compute similarity for each query.
qresult = [
self.w2v_model.wmdistance(document, query[qidx]) for document in self.corpus
]
qresult = numpy.array(qresult)
qresult = 1.0 / (1.0 + qresult) # Similarity is the negative of the distance.
# Append single query result to list of all results.
result.append(qresult)
if len(result) == 1:
# Only one query.
result = result[0]
else:
result = numpy.array(result)
return result
|
https://github.com/RaRe-Technologies/gensim/issues/1955
|
In [249]: softcos_index[tfidf[corpus]]
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-249-2d8c4d759cc9> in <module>()
----> 1 softcos_index[tfidf[corpus]]
/Users/natalied/Projects/canonical_answers/.venv/lib/python2.7/site-packages/gensim/interfaces.pyc in __getitem__(self, query)
365 else:
366 query = matutils.unitvec(query)
--> 367 result = self.get_similarities(query)
368
369 if self.num_best is None:
/Users/natalied/Projects/canonical_answers/.venv/lib/python2.7/site-packages/gensim/similarities/docsim.pyc in get_similarities(self, query)
936 query = [self.corpus[i] for i in query]
937
--> 938 if not query or not isinstance(query[0], list):
939 query = [query]
940
/Users/natalied/Projects/canonical_answers/.venv/lib/python2.7/site-packages/gensim/interfaces.pyc in __getitem__(self, docno)
215 return self.obj[self.corpus[docno]]
216 else:
--> 217 raise RuntimeError('Type {} does not support slicing.'.format(type(self.corpus)))
218
219
RuntimeError: Type <class 'gensim.corpora.textcorpus.TextCorpus'> does not support slicing.
|
RuntimeError
|
def summarize_corpus(corpus, ratio=0.2):
"""
Returns a list of the most important documents of a corpus using a
variation of the TextRank algorithm.
The input must have at least INPUT_MIN_LENGTH (%d) documents for the
summary to make sense.
The length of the output can be specified using the ratio parameter,
which determines how many documents will be chosen for the summary
(defaults at 20%% of the number of documents of the corpus).
The most important documents are returned as a list sorted by the
document score, highest first.
""" % INPUT_MIN_LENGTH
hashable_corpus = _build_hasheable_corpus(corpus)
# If the corpus is empty, the function ends.
if len(corpus) == 0:
logger.warning("Input corpus is empty.")
return []
# Warns the user if there are too few documents.
if len(corpus) < INPUT_MIN_LENGTH:
logger.warning(
"Input corpus is expected to have at least %d documents.", INPUT_MIN_LENGTH
)
graph = _build_graph(hashable_corpus)
_set_graph_edge_weights(graph)
_remove_unreachable_nodes(graph)
# Cannot calculate eigenvectors if number of unique documents in corpus < 3.
# Warns user to add more text. The function ends.
if len(graph.nodes()) < 3:
logger.warning(
"Please add more sentences to the text. The number of reachable nodes is below 3"
)
return []
pagerank_scores = _pagerank(graph)
hashable_corpus.sort(key=lambda doc: pagerank_scores.get(doc, 0), reverse=True)
return [list(doc) for doc in hashable_corpus[: int(len(corpus) * ratio)]]
|
def summarize_corpus(corpus, ratio=0.2):
"""
Returns a list of the most important documents of a corpus using a
variation of the TextRank algorithm.
The input must have at least INPUT_MIN_LENGTH (%d) documents for the
summary to make sense.
The length of the output can be specified using the ratio parameter,
which determines how many documents will be chosen for the summary
(defaults at 20%% of the number of documents of the corpus).
The most important documents are returned as a list sorted by the
document score, highest first.
""" % INPUT_MIN_LENGTH
hashable_corpus = _build_hasheable_corpus(corpus)
# If the corpus is empty, the function ends.
if len(corpus) == 0:
logger.warning("Input corpus is empty.")
return
# Warns the user if there are too few documents.
if len(corpus) < INPUT_MIN_LENGTH:
logger.warning(
"Input corpus is expected to have at least %d documents.", INPUT_MIN_LENGTH
)
graph = _build_graph(hashable_corpus)
_set_graph_edge_weights(graph)
_remove_unreachable_nodes(graph)
# Cannot calculate eigenvectors if number of unique words in text < 3. Warns user to add more text. The function ends.
if len(graph.nodes()) < 3:
logger.warning(
"Please add more sentences to the text. The number of reachable nodes is below 3"
)
return
pagerank_scores = _pagerank(graph)
hashable_corpus.sort(key=lambda doc: pagerank_scores.get(doc, 0), reverse=True)
return [list(doc) for doc in hashable_corpus[: int(len(corpus) * ratio)]]
|
https://github.com/RaRe-Technologies/gensim/issues/1531
|
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-15020c19bb47> in <module>()
1 from gensim.summarization import summarize
2 text = "To whom it may concern, Only after weeks of using my new Wal-Mart master " "card was I given the option to opt out of personal information sharing including :" " Social Security #, income, account balance, payment history, credit history and credit" " scores. According to the supervising operator at the wal-mart credit card call center my" " personal information had already been shared. Furthermore, the letter informing me of my " " rights was apart from my billing statement. Seemingly hidden between some flyers in the same" " envelope. I almost threw it in the recycle bin without seeing it. If this is not illegal it " "certainly appears to be an attempt to deceive me and disseminate my private information without " "my permission. Thank you for your time and effort. Sincerely, XXXX XXXX"
----> 3 summary = summarize(text)
/usr/local/lib/python3.6/site-packages/gensim/summarization/summarizer.py in summarize(text, ratio, word_count, split)
213
214 # Extracts the most important sentences with the selected criterion.
--> 215 extracted_sentences = _extract_important_sentences(sentences, corpus, most_important_docs, word_count)
216
217 # Sorts the extracted sentences by apparition order in the original text.
/usr/local/lib/python3.6/site-packages/gensim/summarization/summarizer.py in _extract_important_sentences(sentences, corpus, important_docs, word_count)
112
113 def _extract_important_sentences(sentences, corpus, important_docs, word_count):
--> 114 important_sentences = _get_important_sentences(sentences, corpus, important_docs)
115
116 # If no "word_count" option is provided, the number of sentences is
/usr/local/lib/python3.6/site-packages/gensim/summarization/summarizer.py in _get_important_sentences(sentences, corpus, important_docs)
87 hashable_corpus = _build_hasheable_corpus(corpus)
88 sentences_by_corpus = dict(zip(hashable_corpus, sentences))
---> 89 return [sentences_by_corpus[tuple(important_doc)] for important_doc in important_docs]
90
91
TypeError: 'NoneType' object is not iterable
|
TypeError
|
def summarize(text, ratio=0.2, word_count=None, split=False):
"""
Returns a summarized version of the given text using a variation of
the TextRank algorithm.
The input must be longer than INPUT_MIN_LENGTH sentences for the
summary to make sense and must be given as a string.
The output summary will consist of the most representative sentences
and will also be returned as a string, divided by newlines. If the
split parameter is set to True, a list of sentences will be
returned.
The length of the output can be specified using the ratio and
word_count parameters:
ratio should be a number between 0 and 1 that determines the
percentage of the number of sentences of the original text to be
chosen for the summary (defaults at 0.2).
word_count determines how many words will the output contain.
If both parameters are provided, the ratio will be ignored.
"""
# Gets a list of processed sentences.
sentences = _clean_text_by_sentences(text)
# If no sentence could be identified, the function ends.
if len(sentences) == 0:
logger.warning("Input text is empty.")
return [] if split else ""
# If only one sentence is present, the function raises an error (Avoids ZeroDivisionError).
if len(sentences) == 1:
raise ValueError("input must have more than one sentence")
# Warns if the text is too short.
if len(sentences) < INPUT_MIN_LENGTH:
logger.warning(
"Input text is expected to have at least %d sentences.", INPUT_MIN_LENGTH
)
corpus = _build_corpus(sentences)
most_important_docs = summarize_corpus(
corpus, ratio=ratio if word_count is None else 1
)
# If couldn't get important docs, the algorithm ends.
if not most_important_docs:
logger.warning("Couldn't get relevant sentences.")
return [] if split else ""
# Extracts the most important sentences with the selected criterion.
extracted_sentences = _extract_important_sentences(
sentences, corpus, most_important_docs, word_count
)
# Sorts the extracted sentences by apparition order in the original text.
extracted_sentences.sort(key=lambda s: s.index)
return _format_results(extracted_sentences, split)
|
def summarize(text, ratio=0.2, word_count=None, split=False):
"""
Returns a summarized version of the given text using a variation of
the TextRank algorithm.
The input must be longer than INPUT_MIN_LENGTH sentences for the
summary to make sense and must be given as a string.
The output summary will consist of the most representative sentences
and will also be returned as a string, divided by newlines. If the
split parameter is set to True, a list of sentences will be
returned.
The length of the output can be specified using the ratio and
word_count parameters:
ratio should be a number between 0 and 1 that determines the
percentage of the number of sentences of the original text to be
chosen for the summary (defaults at 0.2).
word_count determines how many words will the output contain.
If both parameters are provided, the ratio will be ignored.
"""
# Gets a list of processed sentences.
sentences = _clean_text_by_sentences(text)
# If no sentence could be identified, the function ends.
if len(sentences) == 0:
logger.warning("Input text is empty.")
return
# If only one sentence is present, the function raises an error (Avoids ZeroDivisionError).
if len(sentences) == 1:
raise ValueError("input must have more than one sentence")
# Warns if the text is too short.
if len(sentences) < INPUT_MIN_LENGTH:
logger.warning(
"Input text is expected to have at least %d sentences.", INPUT_MIN_LENGTH
)
corpus = _build_corpus(sentences)
most_important_docs = summarize_corpus(
corpus, ratio=ratio if word_count is None else 1
)
# Extracts the most important sentences with the selected criterion.
extracted_sentences = _extract_important_sentences(
sentences, corpus, most_important_docs, word_count
)
# Sorts the extracted sentences by apparition order in the original text.
extracted_sentences.sort(key=lambda s: s.index)
return _format_results(extracted_sentences, split)
|
https://github.com/RaRe-Technologies/gensim/issues/1531
|
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-15020c19bb47> in <module>()
1 from gensim.summarization import summarize
2 text = "To whom it may concern, Only after weeks of using my new Wal-Mart master " "card was I given the option to opt out of personal information sharing including :" " Social Security #, income, account balance, payment history, credit history and credit" " scores. According to the supervising operator at the wal-mart credit card call center my" " personal information had already been shared. Furthermore, the letter informing me of my " " rights was apart from my billing statement. Seemingly hidden between some flyers in the same" " envelope. I almost threw it in the recycle bin without seeing it. If this is not illegal it " "certainly appears to be an attempt to deceive me and disseminate my private information without " "my permission. Thank you for your time and effort. Sincerely, XXXX XXXX"
----> 3 summary = summarize(text)
/usr/local/lib/python3.6/site-packages/gensim/summarization/summarizer.py in summarize(text, ratio, word_count, split)
213
214 # Extracts the most important sentences with the selected criterion.
--> 215 extracted_sentences = _extract_important_sentences(sentences, corpus, most_important_docs, word_count)
216
217 # Sorts the extracted sentences by apparition order in the original text.
/usr/local/lib/python3.6/site-packages/gensim/summarization/summarizer.py in _extract_important_sentences(sentences, corpus, important_docs, word_count)
112
113 def _extract_important_sentences(sentences, corpus, important_docs, word_count):
--> 114 important_sentences = _get_important_sentences(sentences, corpus, important_docs)
115
116 # If no "word_count" option is provided, the number of sentences is
/usr/local/lib/python3.6/site-packages/gensim/summarization/summarizer.py in _get_important_sentences(sentences, corpus, important_docs)
87 hashable_corpus = _build_hasheable_corpus(corpus)
88 sentences_by_corpus = dict(zip(hashable_corpus, sentences))
---> 89 return [sentences_by_corpus[tuple(important_doc)] for important_doc in important_docs]
90
91
TypeError: 'NoneType' object is not iterable
|
TypeError
|
def __init__(self, *args):
super(WordOccurrenceAccumulator, self).__init__(*args)
self._occurrences = np.zeros(self._vocab_size, dtype="uint32")
self._co_occurrences = sps.lil_matrix(
(self._vocab_size, self._vocab_size), dtype="uint32"
)
self._uniq_words = np.zeros(
(self._vocab_size + 1,), dtype=bool
) # add 1 for none token
self._counter = Counter()
|
def __init__(self, *args):
super(WordOccurrenceAccumulator, self).__init__(*args)
self._occurrences = np.zeros(self._vocab_size, dtype="uint32")
self._co_occurrences = sps.lil_matrix(
(self._vocab_size, self._vocab_size), dtype="uint32"
)
self._uniq_words = np.zeros(
(self._vocab_size + 1,), dtype=bool
) # add 1 for none token
self._mask = self._uniq_words[:-1] # to exclude none token
self._counter = Counter()
|
https://github.com/RaRe-Technologies/gensim/issues/1441
|
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Python35-x64\lib\site-packages\gensim\test\test_text_analysis.py", line 57, in test_occurrence_counting
self.assertEqual(3, accumulator.get_occurrences("this"))
AssertionError: 3 != 0
-------------------- >> begin captured logging << --------------------
gensim.topic_coherence.text_analysis: INFO: 1 batches submitted to accumulate stats from 64 documents (3 virtual)
gensim.topic_coherence.text_analysis: INFO: 2 accumulators retrieved from output queue
gensim.topic_coherence.text_analysis: INFO: accumulated word occurrence stats for 4 virtual documents
--------------------- >> end captured logging << ---------------------
======================================================================
FAIL: test_occurrence_counting2 (gensim.test.test_text_analysis.TestParallelWordOccurrenceAccumulator)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Python35-x64\lib\site-packages\gensim\test\test_text_analysis.py", line 67, in test_occurrence_counting2
self.assertEqual(2, accumulator.get_occurrences("human"))
AssertionError: 2 != 0
-------------------- >> begin captured logging << --------------------
gensim.topic_coherence.text_analysis: INFO: 2 accumulators retrieved from output queue
gensim.topic_coherence.text_analysis: INFO: accumulated word occurrence stats for 10 virtual documents
--------------------- >> end captured logging << ---------------------
|
AssertionError
|
def analyze_text(self, window, doc_num=None):
self._slide_window(window, doc_num)
mask = self._uniq_words[:-1] # to exclude none token
if mask.any():
self._occurrences[mask] += 1
self._counter.update(itertools.combinations(np.nonzero(mask)[0], 2))
|
def analyze_text(self, window, doc_num=None):
self._slide_window(window, doc_num)
if self._mask.any():
self._occurrences[self._mask] += 1
self._counter.update(itertools.combinations(np.nonzero(self._mask)[0], 2))
|
https://github.com/RaRe-Technologies/gensim/issues/1441
|
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Python35-x64\lib\site-packages\gensim\test\test_text_analysis.py", line 57, in test_occurrence_counting
self.assertEqual(3, accumulator.get_occurrences("this"))
AssertionError: 3 != 0
-------------------- >> begin captured logging << --------------------
gensim.topic_coherence.text_analysis: INFO: 1 batches submitted to accumulate stats from 64 documents (3 virtual)
gensim.topic_coherence.text_analysis: INFO: 2 accumulators retrieved from output queue
gensim.topic_coherence.text_analysis: INFO: accumulated word occurrence stats for 4 virtual documents
--------------------- >> end captured logging << ---------------------
======================================================================
FAIL: test_occurrence_counting2 (gensim.test.test_text_analysis.TestParallelWordOccurrenceAccumulator)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Python35-x64\lib\site-packages\gensim\test\test_text_analysis.py", line 67, in test_occurrence_counting2
self.assertEqual(2, accumulator.get_occurrences("human"))
AssertionError: 2 != 0
-------------------- >> begin captured logging << --------------------
gensim.topic_coherence.text_analysis: INFO: 2 accumulators retrieved from output queue
gensim.topic_coherence.text_analysis: INFO: accumulated word occurrence stats for 10 virtual documents
--------------------- >> end captured logging << ---------------------
|
AssertionError
|
def _symmetrize(self):
"""Word pairs may have been encountered in (i, j) and (j, i) order.
Rather than enforcing a particular ordering during the update process,
we choose to symmetrize the co-occurrence matrix after accumulation has completed.
"""
co_occ = self._co_occurrences
co_occ.setdiag(self._occurrences) # diagonal should be equal to occurrence counts
self._co_occurrences = (
co_occ + co_occ.T - sps.diags(co_occ.diagonal(), offsets=0, dtype="uint32")
)
|
def _symmetrize(self):
"""Word pairs may have been encountered in (i, j) and (j, i) order.
Rather than enforcing a particular ordering during the update process,
we choose to symmetrize the co-occurrence matrix after accumulation has completed.
"""
co_occ = self._co_occurrences
co_occ.setdiag(self._occurrences) # diagonal should be equal to occurrence counts
self._co_occurrences = (
co_occ + co_occ.T - sps.diags(co_occ.diagonal(), dtype="uint32")
)
|
https://github.com/RaRe-Technologies/gensim/issues/1441
|
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Python35-x64\lib\site-packages\gensim\test\test_text_analysis.py", line 57, in test_occurrence_counting
self.assertEqual(3, accumulator.get_occurrences("this"))
AssertionError: 3 != 0
-------------------- >> begin captured logging << --------------------
gensim.topic_coherence.text_analysis: INFO: 1 batches submitted to accumulate stats from 64 documents (3 virtual)
gensim.topic_coherence.text_analysis: INFO: 2 accumulators retrieved from output queue
gensim.topic_coherence.text_analysis: INFO: accumulated word occurrence stats for 4 virtual documents
--------------------- >> end captured logging << ---------------------
======================================================================
FAIL: test_occurrence_counting2 (gensim.test.test_text_analysis.TestParallelWordOccurrenceAccumulator)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Python35-x64\lib\site-packages\gensim\test\test_text_analysis.py", line 67, in test_occurrence_counting2
self.assertEqual(2, accumulator.get_occurrences("human"))
AssertionError: 2 != 0
-------------------- >> begin captured logging << --------------------
gensim.topic_coherence.text_analysis: INFO: 2 accumulators retrieved from output queue
gensim.topic_coherence.text_analysis: INFO: accumulated word occurrence stats for 10 virtual documents
--------------------- >> end captured logging << ---------------------
|
AssertionError
|
def __init__(
self,
model=None,
topics=None,
texts=None,
corpus=None,
dictionary=None,
window_size=None,
coherence="c_v",
topn=10,
processes=-1,
):
"""
Args:
model : Pre-trained topic model. Should be provided if topics is not provided.
Currently supports LdaModel, LdaMallet wrapper and LdaVowpalWabbit wrapper. Use 'topics'
parameter to plug in an as yet unsupported model.
topics : List of tokenized topics. If this is preferred over model, dictionary should be provided.
eg::
topics = [['human', 'machine', 'computer', 'interface'],
['graph', 'trees', 'binary', 'widths']]
texts : Tokenized texts. Needed for coherence models that use sliding window based probability estimator,
eg::
texts = [['system', 'human', 'system', 'eps'],
['user', 'response', 'time'],
['trees'],
['graph', 'trees'],
['graph', 'minors', 'trees'],
['graph', 'minors', 'survey']]
corpus : Gensim document corpus.
dictionary : Gensim dictionary mapping of id word to create corpus. If model.id2word is present,
this is not needed. If both are provided, dictionary will be used.
window_size : Is the size of the window to be used for coherence measures using boolean sliding window as their
probability estimator. For 'u_mass' this doesn't matter.
If left 'None' the default window sizes are used which are:
'c_v' : 110
'c_uci' : 10
'c_npmi' : 10
coherence : Coherence measure to be used. Supported values are:
'u_mass'
'c_v'
'c_uci' also popularly known as c_pmi
'c_npmi'
For 'u_mass' corpus should be provided. If texts is provided, it will be converted
to corpus using the dictionary. For 'c_v', 'c_uci' and 'c_npmi' texts should be provided.
Corpus is not needed.
topn : Integer corresponding to the number of top words to be extracted from each topic.
processes : number of processes to use for probability estimation phase; any value less than 1 will be
interpreted to mean num_cpus - 1; default is -1.
"""
if model is None and topics is None:
raise ValueError("One of model or topics has to be provided.")
elif topics is not None and dictionary is None:
raise ValueError("dictionary has to be provided if topics are to be used.")
if texts is None and corpus is None:
raise ValueError("One of texts or corpus has to be provided.")
# Check if associated dictionary is provided.
if dictionary is None:
if isinstance(model.id2word, FakeDict):
raise ValueError(
"The associated dictionary should be provided with the corpus or 'id2word'"
" for topic model should be set as the associated dictionary."
)
else:
self.dictionary = model.id2word
else:
self.dictionary = dictionary
# Check for correct inputs for u_mass coherence measure.
self.coherence = coherence
if coherence in boolean_document_based:
if is_corpus(corpus)[0]:
self.corpus = corpus
elif texts is not None:
self.texts = texts
self.corpus = [self.dictionary.doc2bow(text) for text in self.texts]
else:
raise ValueError(
"Either 'corpus' with 'dictionary' or 'texts' should "
"be provided for %s coherence.",
coherence,
)
# Check for correct inputs for c_v coherence measure.
elif coherence in sliding_window_based:
self.window_size = window_size
if self.window_size is None:
self.window_size = SLIDING_WINDOW_SIZES[self.coherence]
if texts is None:
raise ValueError("'texts' should be provided for %s coherence.", coherence)
else:
self.texts = texts
else:
raise ValueError("%s coherence is not currently supported.", coherence)
self.topn = topn
self._model = model
self._accumulator = None
self._topics = None
self.topics = topics
self.processes = processes if processes > 1 else max(1, mp.cpu_count() - 1)
|
def __init__(
self,
model=None,
topics=None,
texts=None,
corpus=None,
dictionary=None,
window_size=None,
coherence="c_v",
topn=10,
processes=-1,
):
"""
Args:
----
model : Pre-trained topic model. Should be provided if topics is not provided.
Currently supports LdaModel, LdaMallet wrapper and LdaVowpalWabbit wrapper. Use 'topics'
parameter to plug in an as yet unsupported model.
topics : List of tokenized topics. If this is preferred over model, dictionary should be provided. eg::
topics = [['human', 'machine', 'computer', 'interface'],
['graph', 'trees', 'binary', 'widths']]
texts : Tokenized texts. Needed for coherence models that use sliding window based probability estimator, eg::
texts = [['system', 'human', 'system', 'eps'],
['user', 'response', 'time'],
['trees'],
['graph', 'trees'],
['graph', 'minors', 'trees'],
['graph', 'minors', 'survey']]
corpus : Gensim document corpus.
dictionary : Gensim dictionary mapping of id word to create corpus. If model.id2word is present,
this is not needed. If both are provided, dictionary will be used.
window_size : Is the size of the window to be used for coherence measures using boolean sliding window as their
probability estimator. For 'u_mass' this doesn't matter.
If left 'None' the default window sizes are used which are:
'c_v' : 110
'c_uci' : 10
'c_npmi' : 10
coherence : Coherence measure to be used. Supported values are:
'u_mass'
'c_v'
'c_uci' also popularly known as c_pmi
'c_npmi'
For 'u_mass' corpus should be provided. If texts is provided, it will be converted
to corpus using the dictionary. For 'c_v', 'c_uci' and 'c_npmi' texts should be provided.
Corpus is not needed.
topn : Integer corresponding to the number of top words to be extracted from each topic.
processes : number of processes to use for probability estimation phase; any value less than 1 will be
interpreted to mean num_cpus - 1; default is -1.
"""
if model is None and topics is None:
raise ValueError("One of model or topics has to be provided.")
elif topics is not None and dictionary is None:
raise ValueError("dictionary has to be provided if topics are to be used.")
if texts is None and corpus is None:
raise ValueError("One of texts or corpus has to be provided.")
# Check if associated dictionary is provided.
if dictionary is None:
if isinstance(model.id2word, FakeDict):
raise ValueError(
"The associated dictionary should be provided with the corpus or 'id2word'"
" for topic model should be set as the associated dictionary."
)
else:
self.dictionary = model.id2word
else:
self.dictionary = dictionary
# Check for correct inputs for u_mass coherence measure.
self.coherence = coherence
if coherence in boolean_document_based:
if is_corpus(corpus)[0]:
self.corpus = corpus
elif texts is not None:
self.texts = texts
self.corpus = [self.dictionary.doc2bow(text) for text in self.texts]
else:
raise ValueError(
"Either 'corpus' with 'dictionary' or 'texts' should "
"be provided for %s coherence.",
coherence,
)
# Check for correct inputs for c_v coherence measure.
elif coherence in sliding_window_based:
self.window_size = window_size
if self.window_size is None:
self.window_size = SLIDING_WINDOW_SIZES[self.coherence]
if texts is None:
raise ValueError("'texts' should be provided for %s coherence.", coherence)
else:
self.texts = texts
else:
raise ValueError("%s coherence is not currently supported.", coherence)
self.topn = topn
self._model = model
self._accumulator = None
self._topics = None
self.topics = topics
self.processes = processes if processes > 1 else max(1, mp.cpu_count() - 1)
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 82 source files that are out of date
updating environment: 82 added, 0 changed, 0 removed
reading sources... [ 1%] about
reading sources... [ 2%] apiref
reading sources... [ 3%] changes_080
reading sources... [ 4%] corpora/bleicorpus
reading sources... [ 6%] corpora/corpora
reading sources... [ 7%] corpora/csvcorpus
reading sources... [ 8%] corpora/dictionary
reading sources... [ 9%] corpora/hashdictionary
reading sources... [ 10%] corpora/indexedcorpus
reading sources... [ 12%] corpora/lowcorpus
reading sources... [ 13%] corpora/malletcorpus
reading sources... [ 14%] corpora/mmcorpus
reading sources... [ 15%] corpora/sharded_corpus
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
reading sources... [ 17%] corpora/svmlightcorpus
reading sources... [ 18%] corpora/textcorpus
reading sources... [ 19%] corpora/ucicorpus
reading sources... [ 20%] corpora/wikicorpus
reading sources... [ 21%] dist_lda
reading sources... [ 23%] dist_lsi
reading sources... [ 24%] distributed
reading sources... [ 25%] indextoc
reading sources... [ 26%] install
reading sources... [ 28%] interfaces
reading sources... [ 29%] intro
reading sources... [ 30%] matutils
reading sources... [ 31%] models/atmodel
reading sources... [ 32%] models/coherencemodel
reading sources... [ 34%] models/doc2vec
reading sources... [ 35%] models/hdpmodel
reading sources... [ 36%] models/keyedvectors
reading sources... [ 37%] models/lda_dispatcher
reading sources... [ 39%] models/lda_worker
reading sources... [ 40%] models/ldamodel
reading sources... [ 41%] models/ldamulticore
reading sources... [ 42%] models/ldaseqmodel
reading sources... [ 43%] models/logentropy_model
reading sources... [ 45%] models/lsi_dispatcher
reading sources... [ 46%] models/lsi_worker
reading sources... [ 47%] models/lsimodel
reading sources... [ 48%] models/models
reading sources... [ 50%] models/normmodel
reading sources... [ 51%] models/phrases
reading sources... [ 52%] models/rpmodel
reading sources... [ 53%] models/tfidfmodel
reading sources... [ 54%] models/word2vec
reading sources... [ 56%] models/wrappers/dtmmodel
reading sources... [ 57%] models/wrappers/fasttext
reading sources... [ 58%] models/wrappers/ldamallet
reading sources... [ 59%] models/wrappers/ldavowpalwabbit
reading sources... [ 60%] models/wrappers/varembed
reading sources... [ 62%] models/wrappers/wordrank
reading sources... [ 63%] models/wrappers/wrappers
reading sources... [ 64%] parsing/porter
reading sources... [ 65%] parsing/preprocessing
reading sources... [ 67%] scripts/glove2word2vec
reading sources... [ 68%] scripts/make_wikicorpus
reading sources... [ 69%] scripts/word2vec_standalone
reading sources... [ 70%] similarities/docsim
reading sources... [ 71%] similarities/index
reading sources... [ 73%] similarities/simserver
reading sources... [ 74%] simserver
reading sources... [ 75%] sklearn_integration/sklearn_wrapper_gensim_ldamodel
reading sources... [ 76%] summarization/bm25
reading sources... [ 78%] summarization/commons
reading sources... [ 79%] summarization/graph
reading sources... [ 80%] summarization/keywords
reading sources... [ 81%] summarization/pagerank_weighted
reading sources... [ 82%] summarization/summariser
reading sources... [ 84%] summarization/syntactic_unit
reading sources... [ 85%] summarization/textcleaner
reading sources... [ 86%] support
reading sources... [ 87%] topic_coherence/aggregation
reading sources... [ 89%] topic_coherence/direct_confirmation_measure
reading sources... [ 90%] topic_coherence/indirect_confirmation_measure
reading sources... [ 91%] topic_coherence/probability_estimation
reading sources... [ 92%] topic_coherence/segmentation
reading sources... [ 93%] tut1
reading sources... [ 95%] tut2
reading sources... [ 96%] tut3
reading sources... [ 97%] tutorial
reading sources... [ 98%] utils
reading sources... [100%] wiki
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
done
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|
ImportError
|
def evaluate_word_pairs(
self,
pairs,
delimiter="\t",
restrict_vocab=300000,
case_insensitive=True,
dummy4unknown=False,
):
"""
Compute correlation of the model with human similarity judgments. `pairs` is a filename of a dataset where
lines are 3-tuples, each consisting of a word pair and a similarity value, separated by `delimiter`.
An example dataset is included in Gensim (test/test_data/wordsim353.tsv). More datasets can be found at
http://technion.ac.il/~ira.leviant/MultilingualVSMdata.html or https://www.cl.cam.ac.uk/~fh295/simlex.html.
The model is evaluated using Pearson correlation coefficient and Spearman rank-order correlation coefficient
between the similarities from the dataset and the similarities produced by the model itself.
The results are printed to log and returned as a triple (pearson, spearman, ratio of pairs with unknown words).
Use `restrict_vocab` to ignore all word pairs containing a word not in the first `restrict_vocab`
words (default 300,000). This may be meaningful if you've sorted the vocabulary by descending frequency.
If `case_insensitive` is True, the first `restrict_vocab` words are taken, and then case normalization
is performed.
Use `case_insensitive` to convert all words in the pairs and vocab to their uppercase form before
evaluating the model (default True). Useful when you expect case-mismatch between training tokens
and words pairs in the dataset. If there are multiple case variants of a single word, the vector for the first
occurrence (also the most frequent if vocabulary is sorted) is taken.
Use `dummy4unknown=True` to produce zero-valued similarities for pairs with out-of-vocabulary words.
Otherwise (default False), these pairs are skipped entirely.
"""
ok_vocab = [(w, self.vocab[w]) for w in self.index2word[:restrict_vocab]]
ok_vocab = (
dict((w.upper(), v) for w, v in reversed(ok_vocab))
if case_insensitive
else dict(ok_vocab)
)
similarity_gold = []
similarity_model = []
oov = 0
original_vocab = self.vocab
self.vocab = ok_vocab
for line_no, line in enumerate(utils.smart_open(pairs)):
line = utils.to_unicode(line)
if line.startswith("#"):
# May be a comment
continue
else:
try:
if case_insensitive:
a, b, sim = [word.upper() for word in line.split(delimiter)]
else:
a, b, sim = [word for word in line.split(delimiter)]
sim = float(sim)
except:
logger.info("skipping invalid line #%d in %s", line_no, pairs)
continue
if a not in ok_vocab or b not in ok_vocab:
oov += 1
if dummy4unknown:
similarity_model.append(0.0)
similarity_gold.append(sim)
continue
else:
logger.debug(
"skipping line #%d with OOV words: %s", line_no, line.strip()
)
continue
similarity_gold.append(sim) # Similarity from the dataset
similarity_model.append(self.similarity(a, b)) # Similarity from the model
self.vocab = original_vocab
spearman = stats.spearmanr(similarity_gold, similarity_model)
pearson = stats.pearsonr(similarity_gold, similarity_model)
oov_ratio = float(oov) / (len(similarity_gold) + oov) * 100
logger.debug(
"Pearson correlation coefficient against %s: %f with p-value %f",
pairs,
pearson[0],
pearson[1],
)
logger.debug(
"Spearman rank-order correlation coefficient against %s: %f with p-value %f",
pairs,
spearman[0],
spearman[1],
)
logger.debug("Pairs with unknown words: %d" % oov)
self.log_evaluate_word_pairs(pearson, spearman, oov_ratio, pairs)
return pearson, spearman, oov_ratio
|
def evaluate_word_pairs(
self,
pairs,
delimiter="\t",
restrict_vocab=300000,
case_insensitive=True,
dummy4unknown=False,
):
"""
Compute correlation of the model with human similarity judgments. `pairs` is a filename of a dataset where
lines are 3-tuples, each consisting of a word pair and a similarity value, separated by `delimiter'.
An example dataset is included in Gensim (test/test_data/wordsim353.tsv). More datasets can be found at
http://technion.ac.il/~ira.leviant/MultilingualVSMdata.html or https://www.cl.cam.ac.uk/~fh295/simlex.html.
The model is evaluated using Pearson correlation coefficient and Spearman rank-order correlation coefficient
between the similarities from the dataset and the similarities produced by the model itself.
The results are printed to log and returned as a triple (pearson, spearman, ratio of pairs with unknown words).
Use `restrict_vocab` to ignore all word pairs containing a word not in the first `restrict_vocab`
words (default 300,000). This may be meaningful if you've sorted the vocabulary by descending frequency.
If `case_insensitive` is True, the first `restrict_vocab` words are taken, and then case normalization
is performed.
Use `case_insensitive` to convert all words in the pairs and vocab to their uppercase form before
evaluating the model (default True). Useful when you expect case-mismatch between training tokens
and words pairs in the dataset. If there are multiple case variants of a single word, the vector for the first
occurrence (also the most frequent if vocabulary is sorted) is taken.
Use `dummy4unknown=True' to produce zero-valued similarities for pairs with out-of-vocabulary words.
Otherwise (default False), these pairs are skipped entirely.
"""
ok_vocab = [(w, self.vocab[w]) for w in self.index2word[:restrict_vocab]]
ok_vocab = (
dict((w.upper(), v) for w, v in reversed(ok_vocab))
if case_insensitive
else dict(ok_vocab)
)
similarity_gold = []
similarity_model = []
oov = 0
original_vocab = self.vocab
self.vocab = ok_vocab
for line_no, line in enumerate(utils.smart_open(pairs)):
line = utils.to_unicode(line)
if line.startswith("#"):
# May be a comment
continue
else:
try:
if case_insensitive:
a, b, sim = [word.upper() for word in line.split(delimiter)]
else:
a, b, sim = [word for word in line.split(delimiter)]
sim = float(sim)
except:
logger.info("skipping invalid line #%d in %s", line_no, pairs)
continue
if a not in ok_vocab or b not in ok_vocab:
oov += 1
if dummy4unknown:
similarity_model.append(0.0)
similarity_gold.append(sim)
continue
else:
logger.debug(
"skipping line #%d with OOV words: %s", line_no, line.strip()
)
continue
similarity_gold.append(sim) # Similarity from the dataset
similarity_model.append(self.similarity(a, b)) # Similarity from the model
self.vocab = original_vocab
spearman = stats.spearmanr(similarity_gold, similarity_model)
pearson = stats.pearsonr(similarity_gold, similarity_model)
oov_ratio = float(oov) / (len(similarity_gold) + oov) * 100
logger.debug(
"Pearson correlation coefficient against %s: %f with p-value %f",
pairs,
pearson[0],
pearson[1],
)
logger.debug(
"Spearman rank-order correlation coefficient against %s: %f with p-value %f",
pairs,
spearman[0],
spearman[1],
)
logger.debug("Pairs with unknown words: %d" % oov)
self.log_evaluate_word_pairs(pearson, spearman, oov_ratio, pairs)
return pearson, spearman, oov_ratio
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
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/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
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Build finished. The HTML pages are in ../
|
ImportError
|
def diff(
self, other, distance="kullback_leibler", num_words=100, n_ann_terms=10, normed=True
):
"""
Calculate difference topic2topic between two Lda models
`other` instances of `LdaMulticore` or `LdaModel`
`distance` is function that will be applied to calculate difference between any topic pair.
Available values: `kullback_leibler`, `hellinger` and `jaccard`
`num_words` is quantity of most relevant words that used if distance == `jaccard` (also used for annotation)
`n_ann_terms` is max quantity of words in intersection/symmetric difference between topics (used for annotation)
Returns a matrix Z with shape (m1.num_topics, m2.num_topics), where Z[i][j] - difference between topic_i and topic_j
and matrix annotation with shape (m1.num_topics, m2.num_topics, 2, None),
where:
annotation[i][j] = [[`int_1`, `int_2`, ...], [`diff_1`, `diff_2`, ...]] and
`int_k` is word from intersection of `topic_i` and `topic_j` and
`diff_l` is word from symmetric difference of `topic_i` and `topic_j`
`normed` is a flag. If `true`, matrix Z will be normalized
Example:
>>> m1, m2 = LdaMulticore.load(path_1), LdaMulticore.load(path_2)
>>> mdiff, annotation = m1.diff(m2)
>>> print(mdiff) # get matrix with difference for each topic pair from `m1` and `m2`
>>> print(annotation) # get array with positive/negative words for each topic pair from `m1` and `m2`
"""
distances = {
"kullback_leibler": kullback_leibler,
"hellinger": hellinger,
"jaccard": jaccard_distance,
}
if distance not in distances:
valid_keys = ", ".join("`{}`".format(x) for x in distances.keys())
raise ValueError("Incorrect distance, valid only {}".format(valid_keys))
if not isinstance(other, self.__class__):
raise ValueError(
"The parameter `other` must be of type `{}`".format(self.__name__)
)
distance_func = distances[distance]
d1, d2 = self.state.get_lambda(), other.state.get_lambda()
t1_size, t2_size = d1.shape[0], d2.shape[0]
fst_topics = [
{w for (w, _) in self.show_topic(topic, topn=num_words)}
for topic in xrange(t1_size)
]
snd_topics = [
{w for (w, _) in other.show_topic(topic, topn=num_words)}
for topic in xrange(t2_size)
]
if distance == "jaccard":
d1, d2 = fst_topics, snd_topics
z = np.zeros((t1_size, t2_size))
for topic1 in range(t1_size):
for topic2 in range(t2_size):
z[topic1][topic2] = distance_func(d1[topic1], d2[topic2])
if normed:
if np.abs(np.max(z)) > 1e-8:
z /= np.max(z)
annotation = [[None] * t1_size for _ in range(t2_size)]
for topic1 in range(t1_size):
for topic2 in range(t2_size):
pos_tokens = fst_topics[topic1] & snd_topics[topic2]
neg_tokens = fst_topics[topic1].symmetric_difference(snd_topics[topic2])
pos_tokens = sample(pos_tokens, min(len(pos_tokens), n_ann_terms))
neg_tokens = sample(neg_tokens, min(len(neg_tokens), n_ann_terms))
annotation[topic1][topic2] = [pos_tokens, neg_tokens]
return z, annotation
|
def diff(
self, other, distance="kullback_leibler", num_words=100, n_ann_terms=10, normed=True
):
"""
Calculate difference topic2topic between two Lda models
`other` instances of `LdaMulticore` or `LdaModel`
`distance` is function that will be applied to calculate difference between any topic pair.
Available values: `kullback_leibler`, `hellinger` and `jaccard`
`num_words` is quantity of most relevant words that used if distance == `jaccard` (also used for annotation)
`n_ann_terms` is max quantity of words in intersection/symmetric difference between topics (used for annotation)
Returns a matrix Z with shape (m1.num_topics, m2.num_topics), where Z[i][j] - difference between topic_i and topic_j
and matrix annotation with shape (m1.num_topics, m2.num_topics, 2, None),
where
annotation[i][j] = [[`int_1`, `int_2`, ...], [`diff_1`, `diff_2`, ...]] and
`int_k` is word from intersection of `topic_i` and `topic_j` and
`diff_l` is word from symmetric difference of `topic_i` and `topic_j`
`normed` is a flag. If `true`, matrix Z will be normalized
Example:
>>> m1, m2 = LdaMulticore.load(path_1), LdaMulticore.load(path_2)
>>> mdiff, annotation = m1.diff(m2)
>>> print(mdiff) # get matrix with difference for each topic pair from `m1` and `m2`
>>> print(annotation) # get array with positive/negative words for each topic pair from `m1` and `m2`
"""
distances = {
"kullback_leibler": kullback_leibler,
"hellinger": hellinger,
"jaccard": jaccard_distance,
}
if distance not in distances:
valid_keys = ", ".join("`{}`".format(x) for x in distances.keys())
raise ValueError("Incorrect distance, valid only {}".format(valid_keys))
if not isinstance(other, self.__class__):
raise ValueError(
"The parameter `other` must be of type `{}`".format(self.__name__)
)
distance_func = distances[distance]
d1, d2 = self.state.get_lambda(), other.state.get_lambda()
t1_size, t2_size = d1.shape[0], d2.shape[0]
fst_topics = [
{w for (w, _) in self.show_topic(topic, topn=num_words)}
for topic in xrange(t1_size)
]
snd_topics = [
{w for (w, _) in other.show_topic(topic, topn=num_words)}
for topic in xrange(t2_size)
]
if distance == "jaccard":
d1, d2 = fst_topics, snd_topics
z = np.zeros((t1_size, t2_size))
for topic1 in range(t1_size):
for topic2 in range(t2_size):
z[topic1][topic2] = distance_func(d1[topic1], d2[topic2])
if normed:
if np.abs(np.max(z)) > 1e-8:
z /= np.max(z)
annotation = [[None] * t1_size for _ in range(t2_size)]
for topic1 in range(t1_size):
for topic2 in range(t2_size):
pos_tokens = fst_topics[topic1] & snd_topics[topic2]
neg_tokens = fst_topics[topic1].symmetric_difference(snd_topics[topic2])
pos_tokens = sample(pos_tokens, min(len(pos_tokens), n_ann_terms))
neg_tokens = sample(neg_tokens, min(len(neg_tokens), n_ann_terms))
annotation[topic1][topic2] = [pos_tokens, neg_tokens]
return z, annotation
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
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/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
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done
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dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
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Build finished. The HTML pages are in ../
|
ImportError
|
def fit_lda_seq(
self, corpus, lda_inference_max_iter, em_min_iter, em_max_iter, chunksize
):
"""
fit an lda sequence model:
for each time period:
set up lda model with E[log p(w|z)] and \alpha
for each document:
perform posterior inference
update sufficient statistics/likelihood
maximize topics
"""
LDASQE_EM_THRESHOLD = 1e-4
# if bound is low, then we increase iterations.
LOWER_ITER = 10
ITER_MULT_LOW = 2
MAX_ITER = 500
num_topics = self.num_topics
vocab_len = self.vocab_len
data_len = self.num_time_slices
corpus_len = self.corpus_len
bound = 0
convergence = LDASQE_EM_THRESHOLD + 1
iter_ = 0
while iter_ < em_min_iter or (
(convergence > LDASQE_EM_THRESHOLD) and iter_ <= em_max_iter
):
logger.info(" EM iter %i", iter_)
logger.info("E Step")
# TODO: bound is initialized to 0
old_bound = bound
# initiate sufficient statistics
topic_suffstats = []
for topic in range(0, num_topics):
topic_suffstats.append(
np.resize(np.zeros(vocab_len * data_len), (vocab_len, data_len))
)
# set up variables
gammas = np.resize(np.zeros(corpus_len * num_topics), (corpus_len, num_topics))
lhoods = np.resize(
np.zeros(corpus_len * num_topics + 1), (corpus_len, num_topics + 1)
)
# compute the likelihood of a sequential corpus under an LDA
# seq model and find the evidence lower bound. This is the E - Step
bound, gammas = self.lda_seq_infer(
corpus,
topic_suffstats,
gammas,
lhoods,
iter_,
lda_inference_max_iter,
chunksize,
)
self.gammas = gammas
logger.info("M Step")
# fit the variational distribution. This is the M - Step
topic_bound = self.fit_lda_seq_topics(topic_suffstats)
bound += topic_bound
if (bound - old_bound) < 0:
# if max_iter is too low, increase iterations.
if lda_inference_max_iter < LOWER_ITER:
lda_inference_max_iter *= ITER_MULT_LOW
logger.info(
"Bound went down, increasing iterations to %i", lda_inference_max_iter
)
# check for convergence
convergence = np.fabs((bound - old_bound) / old_bound)
if convergence < LDASQE_EM_THRESHOLD:
lda_inference_max_iter = MAX_ITER
logger.info(
"Starting final iterations, max iter is %i", lda_inference_max_iter
)
convergence = 1.0
logger.info(
"iteration %i iteration lda seq bound is %f convergence is %f",
iter_,
bound,
convergence,
)
iter_ += 1
return bound
|
def fit_lda_seq(
self, corpus, lda_inference_max_iter, em_min_iter, em_max_iter, chunksize
):
"""
fit an lda sequence model:
for each time period
set up lda model with E[log p(w|z)] and \alpha
for each document
perform posterior inference
update sufficient statistics/likelihood
maximize topics
"""
LDASQE_EM_THRESHOLD = 1e-4
# if bound is low, then we increase iterations.
LOWER_ITER = 10
ITER_MULT_LOW = 2
MAX_ITER = 500
num_topics = self.num_topics
vocab_len = self.vocab_len
data_len = self.num_time_slices
corpus_len = self.corpus_len
bound = 0
convergence = LDASQE_EM_THRESHOLD + 1
iter_ = 0
while iter_ < em_min_iter or (
(convergence > LDASQE_EM_THRESHOLD) and iter_ <= em_max_iter
):
logger.info(" EM iter %i", iter_)
logger.info("E Step")
# TODO: bound is initialized to 0
old_bound = bound
# initiate sufficient statistics
topic_suffstats = []
for topic in range(0, num_topics):
topic_suffstats.append(
np.resize(np.zeros(vocab_len * data_len), (vocab_len, data_len))
)
# set up variables
gammas = np.resize(np.zeros(corpus_len * num_topics), (corpus_len, num_topics))
lhoods = np.resize(
np.zeros(corpus_len * num_topics + 1), (corpus_len, num_topics + 1)
)
# compute the likelihood of a sequential corpus under an LDA
# seq model and find the evidence lower bound. This is the E - Step
bound, gammas = self.lda_seq_infer(
corpus,
topic_suffstats,
gammas,
lhoods,
iter_,
lda_inference_max_iter,
chunksize,
)
self.gammas = gammas
logger.info("M Step")
# fit the variational distribution. This is the M - Step
topic_bound = self.fit_lda_seq_topics(topic_suffstats)
bound += topic_bound
if (bound - old_bound) < 0:
# if max_iter is too low, increase iterations.
if lda_inference_max_iter < LOWER_ITER:
lda_inference_max_iter *= ITER_MULT_LOW
logger.info(
"Bound went down, increasing iterations to %i", lda_inference_max_iter
)
# check for convergence
convergence = np.fabs((bound - old_bound) / old_bound)
if convergence < LDASQE_EM_THRESHOLD:
lda_inference_max_iter = MAX_ITER
logger.info(
"Starting final iterations, max iter is %i", lda_inference_max_iter
)
convergence = 1.0
logger.info(
"iteration %i iteration lda seq bound is %f convergence is %f",
iter_,
bound,
convergence,
)
iter_ += 1
return bound
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
pickling environment... done
checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
done
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done
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dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def compute_post_variance(self, word, chain_variance):
"""
Based on the Variational Kalman Filtering approach for Approximate Inference [https://www.cs.princeton.edu/~blei/papers/BleiLafferty2006a.pdf]
This function accepts the word to compute variance for, along with the associated sslm class object, and returns variance and fwd_variance
Computes Var[\beta_{t,w}] for t = 1:T
:math::
fwd\_variance[t] \equiv E((beta_{t,w}-mean_{t,w})^2 |beta_{t}\ for\ 1:t) = (obs\_variance / fwd\_variance[t - 1] + chain\_variance + obs\_variance ) * (fwd\_variance[t - 1] + obs\_variance)
:math::
variance[t] \equiv E((beta_{t,w}-mean\_cap_{t,w})^2 |beta\_cap_{t}\ for\ 1:t) = fwd\_variance[t - 1] + (fwd\_variance[t - 1] / fwd\_variance[t - 1] + obs\_variance)^2 * (variance[t - 1] - (fwd\_variance[t-1] + obs\_variance))
"""
INIT_VARIANCE_CONST = 1000
T = self.num_time_slices
variance = self.variance[word]
fwd_variance = self.fwd_variance[word]
# forward pass. Set initial variance very high
fwd_variance[0] = chain_variance * INIT_VARIANCE_CONST
for t in range(1, T + 1):
if self.obs_variance:
c = self.obs_variance / (
fwd_variance[t - 1] + chain_variance + self.obs_variance
)
else:
c = 0
fwd_variance[t] = c * (fwd_variance[t - 1] + chain_variance)
# backward pass
variance[T] = fwd_variance[T]
for t in range(T - 1, -1, -1):
if fwd_variance[t] > 0.0:
c = np.power((fwd_variance[t] / (fwd_variance[t] + chain_variance)), 2)
else:
c = 0
variance[t] = (c * (variance[t + 1] - chain_variance)) + (
(1 - c) * fwd_variance[t]
)
return variance, fwd_variance
|
def compute_post_variance(self, word, chain_variance):
"""
Based on the Variational Kalman Filtering approach for Approximate Inference [https://www.cs.princeton.edu/~blei/papers/BleiLafferty2006a.pdf]
This function accepts the word to compute variance for, along with the associated sslm class object, and returns variance and fwd_variance
Computes Var[\beta_{t,w}] for t = 1:T
Fwd_Variance(t) ≡ E((beta_{t,w} − mean_{t,w})^2 |beta_{t} for 1:t)
= (obs_variance / fwd_variance[t - 1] + chain_variance + obs_variance ) * (fwd_variance[t - 1] + obs_variance)
Variance(t) ≡ E((beta_{t,w} − mean_cap{t,w})^2 |beta_cap{t} for 1:t)
= fwd_variance[t - 1] + (fwd_variance[t - 1] / fwd_variance[t - 1] + obs_variance)^2 * (variance[t - 1] - (fwd_variance[t-1] + obs_variance))
"""
INIT_VARIANCE_CONST = 1000
T = self.num_time_slices
variance = self.variance[word]
fwd_variance = self.fwd_variance[word]
# forward pass. Set initial variance very high
fwd_variance[0] = chain_variance * INIT_VARIANCE_CONST
for t in range(1, T + 1):
if self.obs_variance:
c = self.obs_variance / (
fwd_variance[t - 1] + chain_variance + self.obs_variance
)
else:
c = 0
fwd_variance[t] = c * (fwd_variance[t - 1] + chain_variance)
# backward pass
variance[T] = fwd_variance[T]
for t in range(T - 1, -1, -1):
if fwd_variance[t] > 0.0:
c = np.power((fwd_variance[t] / (fwd_variance[t] + chain_variance)), 2)
else:
c = 0
variance[t] = (c * (variance[t + 1] - chain_variance)) + (
(1 - c) * fwd_variance[t]
)
return variance, fwd_variance
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
pickling environment... done
checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
done
preparing documents... done
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done
copying extra files... done
dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def malletmodel2ldamodel(mallet_model, gamma_threshold=0.001, iterations=50):
"""
Function to convert mallet model to gensim LdaModel. This works by copying the
training model weights (alpha, beta...) from a trained mallet model into the
gensim model.
Args:
mallet_model : Trained mallet model
gamma_threshold : To be used for inference in the new LdaModel.
iterations : number of iterations to be used for inference in the new LdaModel.
Returns:
model_gensim : LdaModel instance; copied gensim LdaModel
"""
model_gensim = LdaModel(
id2word=mallet_model.id2word,
num_topics=mallet_model.num_topics,
alpha=mallet_model.alpha,
iterations=iterations,
gamma_threshold=gamma_threshold,
)
model_gensim.expElogbeta[:] = mallet_model.wordtopics
return model_gensim
|
def malletmodel2ldamodel(mallet_model, gamma_threshold=0.001, iterations=50):
"""
Function to convert mallet model to gensim LdaModel. This works by copying the
training model weights (alpha, beta...) from a trained mallet model into the
gensim model.
Args:
----
mallet_model : Trained mallet model
gamma_threshold : To be used for inference in the new LdaModel.
iterations : number of iterations to be used for inference in the new LdaModel.
Returns:
-------
model_gensim : LdaModel instance; copied gensim LdaModel
"""
model_gensim = LdaModel(
id2word=mallet_model.id2word,
num_topics=mallet_model.num_topics,
alpha=mallet_model.alpha,
iterations=iterations,
gamma_threshold=gamma_threshold,
)
model_gensim.expElogbeta[:] = mallet_model.wordtopics
return model_gensim
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
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generating indices... genindex
writing additional pages... index search
copying static files... WARNING: favicon file 'favicon.ico' does not exist
done
copying extra files... done
dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def vwmodel2ldamodel(vw_model, iterations=50):
"""
Function to convert vowpal wabbit model to gensim LdaModel. This works by
simply copying the training model weights (alpha, beta...) from a trained
vwmodel into the gensim model.
Args:
vw_model : Trained vowpal wabbit model.
iterations : Number of iterations to be used for inference of the new LdaModel.
Returns:
model_gensim : LdaModel instance; copied gensim LdaModel.
"""
model_gensim = LdaModel(
num_topics=vw_model.num_topics,
id2word=vw_model.id2word,
chunksize=vw_model.chunksize,
passes=vw_model.passes,
alpha=vw_model.alpha,
eta=vw_model.eta,
decay=vw_model.decay,
offset=vw_model.offset,
iterations=iterations,
gamma_threshold=vw_model.gamma_threshold,
)
model_gensim.expElogbeta[:] = vw_model._get_topics()
return model_gensim
|
def vwmodel2ldamodel(vw_model, iterations=50):
"""
Function to convert vowpal wabbit model to gensim LdaModel. This works by
simply copying the training model weights (alpha, beta...) from a trained
vwmodel into the gensim model.
Args:
----
vw_model : Trained vowpal wabbit model.
iterations : Number of iterations to be used for inference of the new LdaModel.
Returns:
-------
model_gensim : LdaModel instance; copied gensim LdaModel.
"""
model_gensim = LdaModel(
num_topics=vw_model.num_topics,
id2word=vw_model.id2word,
chunksize=vw_model.chunksize,
passes=vw_model.passes,
alpha=vw_model.alpha,
eta=vw_model.eta,
decay=vw_model.decay,
offset=vw_model.offset,
iterations=iterations,
gamma_threshold=vw_model.gamma_threshold,
)
model_gensim.expElogbeta[:] = vw_model._get_topics()
return model_gensim
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 82 source files that are out of date
updating environment: 82 added, 0 changed, 0 removed
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
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/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
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/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
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/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
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/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
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/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
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|
ImportError
|
def train(
cls,
wr_path,
corpus_file,
out_name,
size=100,
window=15,
symmetric=1,
min_count=5,
max_vocab_size=0,
sgd_num=100,
lrate=0.001,
period=10,
iter=90,
epsilon=0.75,
dump_period=10,
reg=0,
alpha=100,
beta=99,
loss="hinge",
memory=4.0,
cleanup_files=False,
sorted_vocab=1,
ensemble=0,
):
"""
The word and context embedding files are generated by wordrank binary and are saved in "out_name" directory
which is created inside wordrank directory. The vocab and cooccurence files are generated using glove code
available inside the wordrank directory. These files are used by the wordrank binary for training.
`wr_path` is the path to the Wordrank directory.
`corpus_file` is the filename of the text file to be used for training the Wordrank model.
Expects file to contain space-separated tokens in a single line
`out_name` is name of the directory which will be created (in wordrank folder) to save embeddings and training data.
It will contain following contents:
Word Embeddings saved after every dump_period and stored in a file model_word_current\ iter.txt
Context Embeddings saved after every dump_period and stored in a file model_context_current\ iter.txt
A meta directory which contain: 'vocab.txt' - vocab words, 'wiki.toy' - word-word coccurence values, 'meta' - vocab and coccurence lengths
`size` is the dimensionality of the feature vectors.
`window` is the number of context words to the left (and to the right, if symmetric = 1).
`symmetric` if 0, only use left context words, else use left and right both.
`min_count` = ignore all words with total frequency lower than this.
`max_vocab_size` upper bound on vocabulary size, i.e. keep the <int> most frequent words. Default is 0 for no limit.
`sgd_num` number of SGD taken for each data point.
`lrate` is the learning rate (too high diverges, give Nan).
`period` is the period of xi variable updates
`iter` = number of iterations (epochs) over the corpus.
`epsilon` is the power scaling value for weighting function.
`dump_period` is the period after which embeddings should be dumped.
`reg` is the value of regularization parameter.
`alpha` is the alpha parameter of gamma distribution.
`beta` is the beta parameter of gamma distribution.
`loss` = name of the loss (logistic, hinge).
`memory` = soft limit for memory consumption, in GB.
`cleanup_files` if True, delete directory and files used by this wrapper, setting to False can be useful for debugging
`sorted_vocab` = if 1 (default), sort the vocabulary by descending frequency before assigning word indexes.
`ensemble` = 0 (default), use ensemble of word and context vectors
"""
meta_data_path = "matrix.meta"
vocab_file = "vocab.txt"
temp_vocab_file = "tempvocab.txt"
cooccurrence_file = "cooccurrence"
cooccurrence_shuf_file = "wiki.toy"
meta_file = "meta"
# prepare training data (cooccurrence matrix and vocab)
model_dir = os.path.join(wr_path, out_name)
meta_dir = os.path.join(model_dir, "meta")
os.makedirs(meta_dir)
logger.info("Dumped data will be stored in '%s'", model_dir)
copyfile(corpus_file, os.path.join(meta_dir, corpus_file.split("/")[-1]))
os.chdir(meta_dir)
cmd_vocab_count = [
"../../glove/vocab_count",
"-min-count",
str(min_count),
"-max-vocab",
str(max_vocab_size),
]
cmd_cooccurence_count = [
"../../glove/cooccur",
"-memory",
str(memory),
"-vocab-file",
temp_vocab_file,
"-window-size",
str(window),
"-symmetric",
str(symmetric),
]
cmd_shuffle_cooccurences = ["../../glove/shuffle", "-memory", str(memory)]
cmd_del_vocab_freq = ["cut", "-d", " ", "-f", "1", temp_vocab_file]
commands = [cmd_vocab_count, cmd_cooccurence_count, cmd_shuffle_cooccurences]
input_fnames = [
corpus_file.split("/")[-1],
corpus_file.split("/")[-1],
cooccurrence_file,
]
output_fnames = [temp_vocab_file, cooccurrence_file, cooccurrence_shuf_file]
logger.info("Prepare training data (%s) using glove code", ", ".join(input_fnames))
for command, input_fname, output_fname in zip(
commands, input_fnames, output_fnames
):
with smart_open(input_fname, "rb") as r:
with smart_open(output_fname, "wb") as w:
utils.check_output(w, args=command, stdin=r)
logger.info("Deleting frequencies from vocab file")
with smart_open(vocab_file, "wb") as w:
utils.check_output(w, args=cmd_del_vocab_freq)
with smart_open(vocab_file, "rb") as f:
numwords = sum(1 for line in f)
with smart_open(cooccurrence_shuf_file, "rb") as f:
numlines = sum(1 for line in f)
with smart_open(meta_file, "wb") as f:
meta_info = "{0} {1}\n{2} {3}\n{4} {5}".format(
numwords, numwords, numlines, cooccurrence_shuf_file, numwords, vocab_file
)
f.write(meta_info.encode("utf-8"))
if iter % dump_period == 0:
iter += 1
else:
logger.warning(
"Resultant embedding will be from %d iterations rather than the input %d iterations, "
"as wordrank dumps the embedding only at dump_period intervals. "
"Input an appropriate combination of parameters (iter, dump_period) such that "
'"iter mod dump_period" is zero.',
iter - (iter % dump_period),
iter,
)
wr_args = {
"path": "meta",
"nthread": multiprocessing.cpu_count(),
"sgd_num": sgd_num,
"lrate": lrate,
"period": period,
"iter": iter,
"epsilon": epsilon,
"dump_prefix": "model",
"dump_period": dump_period,
"dim": size,
"reg": reg,
"alpha": alpha,
"beta": beta,
"loss": loss,
}
os.chdir("..")
# run wordrank executable with wr_args
cmd = ["mpirun", "-np", "1", "../wordrank"]
for option, value in wr_args.items():
cmd.append("--%s" % option)
cmd.append(str(value))
logger.info("Running wordrank binary")
output = utils.check_output(args=cmd)
# use embeddings from max. iteration's dump
max_iter_dump = iter - (iter % dump_period)
copyfile("model_word_%d.txt" % max_iter_dump, "wordrank.words")
copyfile("model_context_%d.txt" % max_iter_dump, "wordrank.contexts")
model = cls.load_wordrank_model(
"wordrank.words",
os.path.join("meta", vocab_file),
"wordrank.contexts",
sorted_vocab,
ensemble,
)
os.chdir("../..")
if cleanup_files:
rmtree(model_dir)
return model
|
def train(
cls,
wr_path,
corpus_file,
out_name,
size=100,
window=15,
symmetric=1,
min_count=5,
max_vocab_size=0,
sgd_num=100,
lrate=0.001,
period=10,
iter=90,
epsilon=0.75,
dump_period=10,
reg=0,
alpha=100,
beta=99,
loss="hinge",
memory=4.0,
cleanup_files=False,
sorted_vocab=1,
ensemble=0,
):
"""
The word and context embedding files are generated by wordrank binary and are saved in "out_name" directory
which is created inside wordrank directory. The vocab and cooccurence files are generated using glove code
available inside the wordrank directory. These files are used by the wordrank binary for training.
`wr_path` is the path to the Wordrank directory.
`corpus_file` is the filename of the text file to be used for training the Wordrank model.
Expects file to contain space-separated tokens in a single line
`out_name` is name of the directory which will be created (in wordrank folder) to save embeddings and training data.
It will contain following contents:
Word Embeddings saved after every dump_period and stored in a file model_word_"current iter".txt
Context Embeddings saved after every dump_period and stored in a file model_context_"current iter".txt
A meta directory which contain: 'vocab.txt' - vocab words, 'wiki.toy' - word-word coccurence values, 'meta' - vocab and coccurence lengths
`size` is the dimensionality of the feature vectors.
`window` is the number of context words to the left (and to the right, if symmetric = 1).
`symmetric` if 0, only use left context words, else use left and right both.
`min_count` = ignore all words with total frequency lower than this.
`max_vocab_size` upper bound on vocabulary size, i.e. keep the <int> most frequent words. Default is 0 for no limit.
`sgd_num` number of SGD taken for each data point.
`lrate` is the learning rate (too high diverges, give Nan).
`period` is the period of xi variable updates
`iter` = number of iterations (epochs) over the corpus.
`epsilon` is the power scaling value for weighting function.
`dump_period` is the period after which embeddings should be dumped.
`reg` is the value of regularization parameter.
`alpha` is the alpha parameter of gamma distribution.
`beta` is the beta parameter of gamma distribution.
`loss` = name of the loss (logistic, hinge).
`memory` = soft limit for memory consumption, in GB.
`cleanup_files` if True, delete directory and files used by this wrapper, setting to False can be useful for debugging
`sorted_vocab` = if 1 (default), sort the vocabulary by descending frequency before assigning word indexes.
`ensemble` = 0 (default), use ensemble of word and context vectors
"""
meta_data_path = "matrix.meta"
vocab_file = "vocab.txt"
temp_vocab_file = "tempvocab.txt"
cooccurrence_file = "cooccurrence"
cooccurrence_shuf_file = "wiki.toy"
meta_file = "meta"
# prepare training data (cooccurrence matrix and vocab)
model_dir = os.path.join(wr_path, out_name)
meta_dir = os.path.join(model_dir, "meta")
os.makedirs(meta_dir)
logger.info("Dumped data will be stored in '%s'", model_dir)
copyfile(corpus_file, os.path.join(meta_dir, corpus_file.split("/")[-1]))
os.chdir(meta_dir)
cmd_vocab_count = [
"../../glove/vocab_count",
"-min-count",
str(min_count),
"-max-vocab",
str(max_vocab_size),
]
cmd_cooccurence_count = [
"../../glove/cooccur",
"-memory",
str(memory),
"-vocab-file",
temp_vocab_file,
"-window-size",
str(window),
"-symmetric",
str(symmetric),
]
cmd_shuffle_cooccurences = ["../../glove/shuffle", "-memory", str(memory)]
cmd_del_vocab_freq = ["cut", "-d", " ", "-f", "1", temp_vocab_file]
commands = [cmd_vocab_count, cmd_cooccurence_count, cmd_shuffle_cooccurences]
input_fnames = [
corpus_file.split("/")[-1],
corpus_file.split("/")[-1],
cooccurrence_file,
]
output_fnames = [temp_vocab_file, cooccurrence_file, cooccurrence_shuf_file]
logger.info("Prepare training data (%s) using glove code", ", ".join(input_fnames))
for command, input_fname, output_fname in zip(
commands, input_fnames, output_fnames
):
with smart_open(input_fname, "rb") as r:
with smart_open(output_fname, "wb") as w:
utils.check_output(w, args=command, stdin=r)
logger.info("Deleting frequencies from vocab file")
with smart_open(vocab_file, "wb") as w:
utils.check_output(w, args=cmd_del_vocab_freq)
with smart_open(vocab_file, "rb") as f:
numwords = sum(1 for line in f)
with smart_open(cooccurrence_shuf_file, "rb") as f:
numlines = sum(1 for line in f)
with smart_open(meta_file, "wb") as f:
meta_info = "{0} {1}\n{2} {3}\n{4} {5}".format(
numwords, numwords, numlines, cooccurrence_shuf_file, numwords, vocab_file
)
f.write(meta_info.encode("utf-8"))
if iter % dump_period == 0:
iter += 1
else:
logger.warning(
"Resultant embedding will be from %d iterations rather than the input %d iterations, "
"as wordrank dumps the embedding only at dump_period intervals. "
"Input an appropriate combination of parameters (iter, dump_period) such that "
'"iter mod dump_period" is zero.',
iter - (iter % dump_period),
iter,
)
wr_args = {
"path": "meta",
"nthread": multiprocessing.cpu_count(),
"sgd_num": sgd_num,
"lrate": lrate,
"period": period,
"iter": iter,
"epsilon": epsilon,
"dump_prefix": "model",
"dump_period": dump_period,
"dim": size,
"reg": reg,
"alpha": alpha,
"beta": beta,
"loss": loss,
}
os.chdir("..")
# run wordrank executable with wr_args
cmd = ["mpirun", "-np", "1", "../wordrank"]
for option, value in wr_args.items():
cmd.append("--%s" % option)
cmd.append(str(value))
logger.info("Running wordrank binary")
output = utils.check_output(args=cmd)
# use embeddings from max. iteration's dump
max_iter_dump = iter - (iter % dump_period)
copyfile("model_word_%d.txt" % max_iter_dump, "wordrank.words")
copyfile("model_context_%d.txt" % max_iter_dump, "wordrank.contexts")
model = cls.load_wordrank_model(
"wordrank.words",
os.path.join("meta", vocab_file),
"wordrank.contexts",
sorted_vocab,
ensemble,
)
os.chdir("../..")
if cleanup_files:
rmtree(model_dir)
return model
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 82 source files that are out of date
updating environment: 82 added, 0 changed, 0 removed
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
reading sources... [ 17%] corpora/svmlightcorpus
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
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checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
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generating indices... genindex
writing additional pages... index search
copying static files... WARNING: favicon file 'favicon.ico' does not exist
done
copying extra files... done
dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def glove2word2vec(glove_input_file, word2vec_output_file):
"""Convert `glove_input_file` in GloVe format into `word2vec_output_file` in word2vec format."""
num_lines, num_dims = get_glove_info(glove_input_file)
logger.info(
"converting %i vectors from %s to %s",
num_lines,
glove_input_file,
word2vec_output_file,
)
with smart_open(word2vec_output_file, "wb") as fout:
fout.write("{0} {1}\n".format(num_lines, num_dims).encode("utf-8"))
with smart_open(glove_input_file, "rb") as fin:
for line in fin:
fout.write(line)
return num_lines, num_dims
|
def glove2word2vec(glove_input_file, word2vec_output_file):
"""Convert `glove_input_file` in GloVe format into `word2vec_output_file in word2vec format."""
num_lines, num_dims = get_glove_info(glove_input_file)
logger.info(
"converting %i vectors from %s to %s",
num_lines,
glove_input_file,
word2vec_output_file,
)
with smart_open(word2vec_output_file, "wb") as fout:
fout.write("{0} {1}\n".format(num_lines, num_dims).encode("utf-8"))
with smart_open(glove_input_file, "rb") as fin:
for line in fin:
fout.write(line)
return num_lines, num_dims
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 82 source files that are out of date
updating environment: 82 added, 0 changed, 0 removed
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
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|
ImportError
|
def arithmetic_mean(confirmed_measures):
"""
This functoin performs the arithmetic mean aggregation on the output obtained from
the confirmation measure module.
Args:
confirmed_measures : list of calculated confirmation measure on each set in the segmented topics.
Returns:
mean : Arithmetic mean of all the values contained in confirmation measures.
"""
return np.mean(confirmed_measures)
|
def arithmetic_mean(confirmed_measures):
"""
This functoin performs the arithmetic mean aggregation on the output obtained from
the confirmation measure module.
Args:
----
confirmed_measures : list of calculated confirmation measure on each set in the segmented topics.
Returns:
-------
mean : Arithmetic mean of all the values contained in confirmation measures.
"""
return np.mean(confirmed_measures)
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
reading sources... [ 17%] corpora/svmlightcorpus
reading sources... [ 18%] corpora/textcorpus
reading sources... [ 19%] corpora/ucicorpus
reading sources... [ 20%] corpora/wikicorpus
reading sources... [ 21%] dist_lda
reading sources... [ 23%] dist_lsi
reading sources... [ 24%] distributed
reading sources... [ 25%] indextoc
reading sources... [ 26%] install
reading sources... [ 28%] interfaces
reading sources... [ 29%] intro
reading sources... [ 30%] matutils
reading sources... [ 31%] models/atmodel
reading sources... [ 32%] models/coherencemodel
reading sources... [ 34%] models/doc2vec
reading sources... [ 35%] models/hdpmodel
reading sources... [ 36%] models/keyedvectors
reading sources... [ 37%] models/lda_dispatcher
reading sources... [ 39%] models/lda_worker
reading sources... [ 40%] models/ldamodel
reading sources... [ 41%] models/ldamulticore
reading sources... [ 42%] models/ldaseqmodel
reading sources... [ 43%] models/logentropy_model
reading sources... [ 45%] models/lsi_dispatcher
reading sources... [ 46%] models/lsi_worker
reading sources... [ 47%] models/lsimodel
reading sources... [ 48%] models/models
reading sources... [ 50%] models/normmodel
reading sources... [ 51%] models/phrases
reading sources... [ 52%] models/rpmodel
reading sources... [ 53%] models/tfidfmodel
reading sources... [ 54%] models/word2vec
reading sources... [ 56%] models/wrappers/dtmmodel
reading sources... [ 57%] models/wrappers/fasttext
reading sources... [ 58%] models/wrappers/ldamallet
reading sources... [ 59%] models/wrappers/ldavowpalwabbit
reading sources... [ 60%] models/wrappers/varembed
reading sources... [ 62%] models/wrappers/wordrank
reading sources... [ 63%] models/wrappers/wrappers
reading sources... [ 64%] parsing/porter
reading sources... [ 65%] parsing/preprocessing
reading sources... [ 67%] scripts/glove2word2vec
reading sources... [ 68%] scripts/make_wikicorpus
reading sources... [ 69%] scripts/word2vec_standalone
reading sources... [ 70%] similarities/docsim
reading sources... [ 71%] similarities/index
reading sources... [ 73%] similarities/simserver
reading sources... [ 74%] simserver
reading sources... [ 75%] sklearn_integration/sklearn_wrapper_gensim_ldamodel
reading sources... [ 76%] summarization/bm25
reading sources... [ 78%] summarization/commons
reading sources... [ 79%] summarization/graph
reading sources... [ 80%] summarization/keywords
reading sources... [ 81%] summarization/pagerank_weighted
reading sources... [ 82%] summarization/summariser
reading sources... [ 84%] summarization/syntactic_unit
reading sources... [ 85%] summarization/textcleaner
reading sources... [ 86%] support
reading sources... [ 87%] topic_coherence/aggregation
reading sources... [ 89%] topic_coherence/direct_confirmation_measure
reading sources... [ 90%] topic_coherence/indirect_confirmation_measure
reading sources... [ 91%] topic_coherence/probability_estimation
reading sources... [ 92%] topic_coherence/segmentation
reading sources... [ 93%] tut1
reading sources... [ 95%] tut2
reading sources... [ 96%] tut3
reading sources... [ 97%] tutorial
reading sources... [ 98%] utils
reading sources... [100%] wiki
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
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done
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dumping object inventory... done
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Build finished. The HTML pages are in ../
|
ImportError
|
def log_conditional_probability(segmented_topics, accumulator):
"""
This function calculates the log-conditional-probability measure
which is used by coherence measures such as U_mass.
This is defined as: m_lc(S_i) = log[(P(W', W*) + e) / P(W*)]
Args:
segmented_topics : Output from the segmentation module of the segmented topics.
Is a list of list of tuples.
accumulator: word occurrence accumulator from probability_estimation.
Returns:
m_lc : List of log conditional probability measure for each topic.
"""
m_lc = []
num_docs = float(accumulator.num_docs)
for s_i in segmented_topics:
segment_sims = []
for w_prime, w_star in s_i:
try:
w_star_count = accumulator[w_star]
co_occur_count = accumulator[w_prime, w_star]
m_lc_i = np.log(
((co_occur_count / num_docs) + EPSILON) / (w_star_count / num_docs)
)
except KeyError:
m_lc_i = 0.0
segment_sims.append(m_lc_i)
m_lc.append(np.mean(segment_sims))
return m_lc
|
def log_conditional_probability(segmented_topics, accumulator):
"""
This function calculates the log-conditional-probability measure
which is used by coherence measures such as U_mass.
This is defined as: m_lc(S_i) = log[(P(W', W*) + e) / P(W*)]
Args:
----
segmented_topics : Output from the segmentation module of the segmented topics.
Is a list of list of tuples.
accumulator: word occurrence accumulator from probability_estimation.
Returns:
-------
m_lc : List of log conditional probability measure for each topic.
"""
m_lc = []
num_docs = float(accumulator.num_docs)
for s_i in segmented_topics:
segment_sims = []
for w_prime, w_star in s_i:
try:
w_star_count = accumulator[w_star]
co_occur_count = accumulator[w_prime, w_star]
m_lc_i = np.log(
((co_occur_count / num_docs) + EPSILON) / (w_star_count / num_docs)
)
except KeyError:
m_lc_i = 0.0
segment_sims.append(m_lc_i)
m_lc.append(np.mean(segment_sims))
return m_lc
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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reading sources... [ 97%] tutorial
reading sources... [ 98%] utils
reading sources... [100%] wiki
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
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checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
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Build finished. The HTML pages are in ../
|
ImportError
|
def log_ratio_measure(segmented_topics, accumulator, normalize=False):
"""
If normalize=False:
Popularly known as PMI.
This function calculates the log-ratio-measure which is used by
coherence measures such as c_v.
This is defined as: m_lr(S_i) = log[(P(W', W*) + e) / (P(W') * P(W*))]
If normalize=True:
This function calculates the normalized-log-ratio-measure, popularly knowns as
NPMI which is used by coherence measures such as c_v.
This is defined as: m_nlr(S_i) = m_lr(S_i) / -log[P(W', W*) + e]
Args:
segmented topics : Output from the segmentation module of the segmented topics.
Is a list of list of tuples.
accumulator: word occurrence accumulator from probability_estimation.
Returns:
m_lr : List of log ratio measures for each topic.
"""
m_lr = []
num_docs = float(accumulator.num_docs)
for s_i in segmented_topics:
segment_sims = []
for w_prime, w_star in s_i:
w_prime_count = accumulator[w_prime]
w_star_count = accumulator[w_star]
co_occur_count = accumulator[w_prime, w_star]
if normalize:
# For normalized log ratio measure
numerator = log_ratio_measure([[(w_prime, w_star)]], accumulator)[0]
co_doc_prob = co_occur_count / num_docs
m_lr_i = numerator / (-np.log(co_doc_prob + EPSILON))
else:
# For log ratio measure without normalization
numerator = (co_occur_count / num_docs) + EPSILON
denominator = (w_prime_count / num_docs) * (w_star_count / num_docs)
m_lr_i = np.log(numerator / denominator)
segment_sims.append(m_lr_i)
m_lr.append(np.mean(segment_sims))
return m_lr
|
def log_ratio_measure(segmented_topics, accumulator, normalize=False):
"""
If normalize=False:
Popularly known as PMI.
This function calculates the log-ratio-measure which is used by
coherence measures such as c_v.
This is defined as: m_lr(S_i) = log[(P(W', W*) + e) / (P(W') * P(W*))]
If normalize=True:
This function calculates the normalized-log-ratio-measure, popularly knowns as
NPMI which is used by coherence measures such as c_v.
This is defined as: m_nlr(S_i) = m_lr(S_i) / -log[P(W', W*) + e]
Args:
----
segmented topics : Output from the segmentation module of the segmented topics.
Is a list of list of tuples.
accumulator: word occurrence accumulator from probability_estimation.
Returns:
-------
m_lr : List of log ratio measures for each topic.
"""
m_lr = []
num_docs = float(accumulator.num_docs)
for s_i in segmented_topics:
segment_sims = []
for w_prime, w_star in s_i:
w_prime_count = accumulator[w_prime]
w_star_count = accumulator[w_star]
co_occur_count = accumulator[w_prime, w_star]
if normalize:
# For normalized log ratio measure
numerator = log_ratio_measure([[(w_prime, w_star)]], accumulator)[0]
co_doc_prob = co_occur_count / num_docs
m_lr_i = numerator / (-np.log(co_doc_prob + EPSILON))
else:
# For log ratio measure without normalization
numerator = (co_occur_count / num_docs) + EPSILON
denominator = (w_prime_count / num_docs) * (w_star_count / num_docs)
m_lr_i = np.log(numerator / denominator)
segment_sims.append(m_lr_i)
m_lr.append(np.mean(segment_sims))
return m_lr
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
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|
ImportError
|
def cosine_similarity(segmented_topics, accumulator, topics, measure="nlr", gamma=1):
"""
This function calculates the indirect cosine measure. Given context vectors
u = V(W') and w = V(W*) for the word sets of a pair S_i = (W', W*) indirect
cosine measure is computed as the cosine similarity between u and w.
The formula used is:
m_{sim}_{(m, \gamma)}(W', W*) = s_{sim}(\vec{V}^{\,}_{m,\gamma}(W'), \vec{V}^{\,}_{m,\gamma}(W*))
where each vector:
\vec{V}^{\,}_{m,\gamma}(W') = \Bigg \{{\sum_{w_{i} \in W'}^{ } m(w_{i}, w_{j})^{\gamma}}\Bigg \}_{j = 1,...,|W|}
Args:
segmented_topics : Output from the segmentation module of the segmented topics. Is a list of list of tuples.
accumulator : Output from the probability_estimation module. Is an accumulator of word occurrences (see text_analysis module).
topics : Topics obtained from the trained topic model.
measure : String. Direct confirmation measure to be used. Supported values are "nlr" (normalized log ratio).
gamma : Gamma value for computing W', W* vectors; default is 1.
Returns:
s_cos_sim : list of indirect cosine similarity measure for each topic.
"""
context_vectors = ContextVectorComputer(measure, topics, accumulator, gamma)
s_cos_sim = []
for topic_words, topic_segments in zip(topics, segmented_topics):
topic_words = tuple(topic_words) # because tuples are hashable
segment_sims = np.zeros(len(topic_segments))
for i, (w_prime, w_star) in enumerate(topic_segments):
w_prime_cv = context_vectors[w_prime, topic_words]
w_star_cv = context_vectors[w_star, topic_words]
segment_sims[i] = _cossim(w_prime_cv, w_star_cv)
s_cos_sim.append(np.mean(segment_sims))
return s_cos_sim
|
def cosine_similarity(segmented_topics, accumulator, topics, measure="nlr", gamma=1):
"""
This function calculates the indirect cosine measure. Given context vectors
_ _ _ _
u = V(W') and w = V(W*) for the word sets of a pair S_i = (W', W*) indirect
_ _
cosine measure is computed as the cosine similarity between u and w. The formula used is:
m_{sim}_{(m, \gamma)}(W', W*) = s_{sim}(\vec{V}^{\,}_{m,\gamma}(W'), \vec{V}^{\,}_{m,\gamma}(W*))
where each vector \vec{V}^{\,}_{m,\gamma}(W') = \Bigg \{{\sum_{w_{i} \in W'}^{ } m(w_{i}, w_{j})^{\gamma}}\Bigg \}_{j = 1,...,|W|}
Args:
----
segmented_topics : Output from the segmentation module of the segmented topics.
Is a list of list of tuples.
accumulator : Output from the probability_estimation module.
Is an accumulator of word occurrences (see text_analysis module).
topics : Topics obtained from the trained topic model.
measure : String. Direct confirmation measure to be used.
Supported values are "nlr" (normalized log ratio).
gamma : Gamma value for computing W', W* vectors; default is 1.
Returns:
-------
s_cos_sim : list of indirect cosine similarity measure for each topic.
"""
context_vectors = ContextVectorComputer(measure, topics, accumulator, gamma)
s_cos_sim = []
for topic_words, topic_segments in zip(topics, segmented_topics):
topic_words = tuple(topic_words) # because tuples are hashable
segment_sims = np.zeros(len(topic_segments))
for i, (w_prime, w_star) in enumerate(topic_segments):
w_prime_cv = context_vectors[w_prime, topic_words]
w_star_cv = context_vectors[w_star, topic_words]
segment_sims[i] = _cossim(w_prime_cv, w_star_cv)
s_cos_sim.append(np.mean(segment_sims))
return s_cos_sim
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
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from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
pickling environment... done
checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
done
preparing documents... done
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done
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dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def p_boolean_document(corpus, segmented_topics):
"""This function performs the boolean document probability estimation.
Boolean document estimates the probability of a single word as the number
of documents in which the word occurs divided by the total number of documents.
Args:
corpus : The corpus of documents.
segmented_topics : Output from the segmentation of topics. Could be simply topics too.
Returns:
accumulator : word occurrence accumulator instance that can be used to lookup token
frequencies and co-occurrence frequencies.
"""
top_ids = unique_ids_from_segments(segmented_topics)
return CorpusAccumulator(top_ids).accumulate(corpus)
|
def p_boolean_document(corpus, segmented_topics):
"""This function performs the boolean document probability estimation.
Boolean document estimates the probability of a single word as the number
of documents in which the word occurs divided by the total number of documents.
Args:
----
corpus : The corpus of documents.
segmented_topics : Output from the segmentation of topics. Could be simply topics too.
Returns:
-------
accumulator : word occurrence accumulator instance that can be used to lookup token
frequencies and co-occurrence frequencies.
"""
top_ids = unique_ids_from_segments(segmented_topics)
return CorpusAccumulator(top_ids).accumulate(corpus)
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
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checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
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generating indices... genindex
writing additional pages... index search
copying static files... WARNING: favicon file 'favicon.ico' does not exist
done
copying extra files... done
dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def p_boolean_sliding_window(
texts, segmented_topics, dictionary, window_size, processes=1
):
"""This function performs the boolean sliding window probability estimation.
Boolean sliding window determines word counts using a sliding window. The window
moves over the documents one word token per step. Each step defines a new virtual
document by copying the window content. Boolean document is applied to these virtual
documents to compute word probabilities.
Args:
texts : List of string sentences.
segmented_topics : Output from the segmentation of topics. Could be simply topics too.
dictionary : Gensim dictionary mapping of the tokens and ids.
window_size : Size of the sliding window. 110 found out to be the ideal size for large corpora.
Returns:
accumulator : word occurrence accumulator instance that can be used to lookup token
frequencies and co-occurrence frequencies.
"""
top_ids = unique_ids_from_segments(segmented_topics)
if processes <= 1:
accumulator = WordOccurrenceAccumulator(top_ids, dictionary)
else:
accumulator = ParallelWordOccurrenceAccumulator(processes, top_ids, dictionary)
logger.info("using %s to estimate probabilities from sliding windows", accumulator)
return accumulator.accumulate(texts, window_size)
|
def p_boolean_sliding_window(
texts, segmented_topics, dictionary, window_size, processes=1
):
"""This function performs the boolean sliding window probability estimation.
Boolean sliding window determines word counts using a sliding window. The window
moves over the documents one word token per step. Each step defines a new virtual
document by copying the window content. Boolean document is applied to these virtual
documents to compute word probabilities.
Args:
----
texts : List of string sentences.
segmented_topics : Output from the segmentation of topics. Could be simply topics too.
dictionary : Gensim dictionary mapping of the tokens and ids.
window_size : Size of the sliding window. 110 found out to be the ideal size for large corpora.
Returns:
-------
accumulator : word occurrence accumulator instance that can be used to lookup token
frequencies and co-occurrence frequencies.
"""
top_ids = unique_ids_from_segments(segmented_topics)
if processes <= 1:
accumulator = WordOccurrenceAccumulator(top_ids, dictionary)
else:
accumulator = ParallelWordOccurrenceAccumulator(processes, top_ids, dictionary)
logger.info("using %s to estimate probabilities from sliding windows", accumulator)
return accumulator.accumulate(texts, window_size)
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 82 source files that are out of date
updating environment: 82 added, 0 changed, 0 removed
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
pickling environment... done
checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
done
preparing documents... done
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rm -r _build/html/_sources
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Build finished. The HTML pages are in ../
|
ImportError
|
def unique_ids_from_segments(segmented_topics):
"""Return the set of all unique ids in a list of segmented topics.
Args:
segmented_topics: list of tuples of (word_id_set1, word_id_set2). Each word_id_set
is either a single integer, or a `numpy.ndarray` of integers.
Returns:
unique_ids : set of unique ids across all topic segments.
"""
unique_ids = set() # is a set of all the unique ids contained in topics.
for s_i in segmented_topics:
for word_id in itertools.chain.from_iterable(s_i):
if hasattr(word_id, "__iter__"):
unique_ids.update(word_id)
else:
unique_ids.add(word_id)
return unique_ids
|
def unique_ids_from_segments(segmented_topics):
"""Return the set of all unique ids in a list of segmented topics.
Args:
----
segmented_topics: list of tuples of (word_id_set1, word_id_set2). Each word_id_set
is either a single integer, or a `numpy.ndarray` of integers.
Returns:
unique_ids : set of unique ids across all topic segments.
"""
unique_ids = set() # is a set of all the unique ids contained in topics.
for s_i in segmented_topics:
for word_id in itertools.chain.from_iterable(s_i):
if hasattr(word_id, "__iter__"):
unique_ids.update(word_id)
else:
unique_ids.add(word_id)
return unique_ids
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
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building [html]: targets for 82 source files that are out of date
updating environment: 82 added, 0 changed, 0 removed
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
reading sources... [ 17%] corpora/svmlightcorpus
reading sources... [ 18%] corpora/textcorpus
reading sources... [ 19%] corpora/ucicorpus
reading sources... [ 20%] corpora/wikicorpus
reading sources... [ 21%] dist_lda
reading sources... [ 23%] dist_lsi
reading sources... [ 24%] distributed
reading sources... [ 25%] indextoc
reading sources... [ 26%] install
reading sources... [ 28%] interfaces
reading sources... [ 29%] intro
reading sources... [ 30%] matutils
reading sources... [ 31%] models/atmodel
reading sources... [ 32%] models/coherencemodel
reading sources... [ 34%] models/doc2vec
reading sources... [ 35%] models/hdpmodel
reading sources... [ 36%] models/keyedvectors
reading sources... [ 37%] models/lda_dispatcher
reading sources... [ 39%] models/lda_worker
reading sources... [ 40%] models/ldamodel
reading sources... [ 41%] models/ldamulticore
reading sources... [ 42%] models/ldaseqmodel
reading sources... [ 43%] models/logentropy_model
reading sources... [ 45%] models/lsi_dispatcher
reading sources... [ 46%] models/lsi_worker
reading sources... [ 47%] models/lsimodel
reading sources... [ 48%] models/models
reading sources... [ 50%] models/normmodel
reading sources... [ 51%] models/phrases
reading sources... [ 52%] models/rpmodel
reading sources... [ 53%] models/tfidfmodel
reading sources... [ 54%] models/word2vec
reading sources... [ 56%] models/wrappers/dtmmodel
reading sources... [ 57%] models/wrappers/fasttext
reading sources... [ 58%] models/wrappers/ldamallet
reading sources... [ 59%] models/wrappers/ldavowpalwabbit
reading sources... [ 60%] models/wrappers/varembed
reading sources... [ 62%] models/wrappers/wordrank
reading sources... [ 63%] models/wrappers/wrappers
reading sources... [ 64%] parsing/porter
reading sources... [ 65%] parsing/preprocessing
reading sources... [ 67%] scripts/glove2word2vec
reading sources... [ 68%] scripts/make_wikicorpus
reading sources... [ 69%] scripts/word2vec_standalone
reading sources... [ 70%] similarities/docsim
reading sources... [ 71%] similarities/index
reading sources... [ 73%] similarities/simserver
reading sources... [ 74%] simserver
reading sources... [ 75%] sklearn_integration/sklearn_wrapper_gensim_ldamodel
reading sources... [ 76%] summarization/bm25
reading sources... [ 78%] summarization/commons
reading sources... [ 79%] summarization/graph
reading sources... [ 80%] summarization/keywords
reading sources... [ 81%] summarization/pagerank_weighted
reading sources... [ 82%] summarization/summariser
reading sources... [ 84%] summarization/syntactic_unit
reading sources... [ 85%] summarization/textcleaner
reading sources... [ 86%] support
reading sources... [ 87%] topic_coherence/aggregation
reading sources... [ 89%] topic_coherence/direct_confirmation_measure
reading sources... [ 90%] topic_coherence/indirect_confirmation_measure
reading sources... [ 91%] topic_coherence/probability_estimation
reading sources... [ 92%] topic_coherence/segmentation
reading sources... [ 93%] tut1
reading sources... [ 95%] tut2
reading sources... [ 96%] tut3
reading sources... [ 97%] tutorial
reading sources... [ 98%] utils
reading sources... [100%] wiki
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
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|
ImportError
|
def s_one_pre(topics):
"""
This function performs s_one_pre segmentation on a list of topics.
s_one_pre segmentation is defined as: s_one_pre = {(W', W*) | W' = {w_i}; W* = {w_j}; w_i, w_j belongs to W; i > j}
Example:
>>> topics = [np.array([1, 2, 3]), np.array([4, 5, 6])]
>>> s_one_pre(topics)
[[(2, 1), (3, 1), (3, 2)], [(5, 4), (6, 4), (6, 5)]]
Args:
topics : list of topics obtained from an algorithm such as LDA. Is a list such as [array([ 9, 10, 11]), array([ 9, 10, 7]), ...]
Returns:
s_one_pre : list of list of (W', W*) tuples for all unique topic ids
"""
s_one_pre = []
for top_words in topics:
s_one_pre_t = []
for w_prime_index, w_prime in enumerate(top_words[1:]):
for w_star in top_words[: w_prime_index + 1]:
s_one_pre_t.append((w_prime, w_star))
s_one_pre.append(s_one_pre_t)
return s_one_pre
|
def s_one_pre(topics):
"""
This function performs s_one_pre segmentation on a list of topics.
s_one_pre segmentation is defined as: s_one_pre = {(W', W*) | W' = {w_i};
W* = {w_j}; w_i, w_j belongs to W; i > j}
Example:
>>> topics = [np.array([1, 2, 3]), np.array([4, 5, 6])]
>>> s_one_pre(topics)
[[(2, 1), (3, 1), (3, 2)], [(5, 4), (6, 4), (6, 5)]]
Args:
----
topics : list of topics obtained from an algorithm such as LDA. Is a list such as [array([ 9, 10, 11]), array([ 9, 10, 7]), ...]
Returns:
-------
s_one_pre : list of list of (W', W*) tuples for all unique topic ids
"""
s_one_pre = []
for top_words in topics:
s_one_pre_t = []
for w_prime_index, w_prime in enumerate(top_words[1:]):
for w_star in top_words[: w_prime_index + 1]:
s_one_pre_t.append((w_prime, w_star))
s_one_pre.append(s_one_pre_t)
return s_one_pre
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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reading sources... [ 93%] tut1
reading sources... [ 95%] tut2
reading sources... [ 96%] tut3
reading sources... [ 97%] tutorial
reading sources... [ 98%] utils
reading sources... [100%] wiki
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
pickling environment... done
checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
done
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done
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dumping object inventory... done
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Build finished. The HTML pages are in ../
|
ImportError
|
def s_one_one(topics):
"""
This function performs s_one_one segmentation on a list of topics.
s_one_one segmentation is defined as: s_one_one = {(W', W*) | W' = {w_i}; W* = {w_j}; w_i, w_j belongs to W; i != j}
Example:
>>> topics = [np.array([1, 2, 3]), np.array([4, 5, 6])]
>>> s_one_pre(topics)
[[(1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2)], [(4, 5), (4, 6), (5, 4), (5, 6), (6, 4), (6, 5)]]
Args:
topics : list of topics obtained from an algorithm such as LDA. Is a list such as [array([ 9, 10, 11]), array([ 9, 10, 7]), ...]
Returns:
s_one_one : list of list of (W', W*) tuples for all unique topic ids
"""
s_one_one = []
for top_words in topics:
s_one_one_t = []
for w_prime_index, w_prime in enumerate(top_words):
for w_star_index, w_star in enumerate(top_words):
if w_prime_index == w_star_index:
continue
else:
s_one_one_t.append((w_prime, w_star))
s_one_one.append(s_one_one_t)
return s_one_one
|
def s_one_one(topics):
"""
This function performs s_one_one segmentation on a list of topics.
s_one_one segmentation is defined as: s_one_one = {(W', W*) | W' = {w_i};
W* = {w_j}; w_i, w_j belongs to W; i != j}
Example:
>>> topics = [np.array([1, 2, 3]), np.array([4, 5, 6])]
>>> s_one_pre(topics)
[[(1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2)], [(4, 5), (4, 6), (5, 4), (5, 6), (6, 4), (6, 5)]]
Args:
----
topics : list of topics obtained from an algorithm such as LDA. Is a list such as [array([ 9, 10, 11]), array([ 9, 10, 7]), ...]
Returns:
-------
s_one_one : list of list of (W', W*) tuples for all unique topic ids
"""
s_one_one = []
for top_words in topics:
s_one_one_t = []
for w_prime_index, w_prime in enumerate(top_words):
for w_star_index, w_star in enumerate(top_words):
if w_prime_index == w_star_index:
continue
else:
s_one_one_t.append((w_prime, w_star))
s_one_one.append(s_one_one_t)
return s_one_one
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
done
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done
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dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def s_one_set(topics):
"""
This function performs s_one_set segmentation on a list of topics.
s_one_set segmentation is defined as: s_one_set = {(W', W*) | W' = {w_i}; w_i belongs to W; W* = W}
Example:
>>> topics = [np.array([9, 10, 7])
>>> s_one_set(topics)
[[(9, array([ 9, 10, 7])),
(10, array([ 9, 10, 7])),
(7, array([ 9, 10, 7]))]]
Args:
topics : list of topics obtained from an algorithm such as LDA. Is a list such as [array([ 9, 10, 11]), array([ 9, 10, 7]), ...]
Returns:
s_one_set : list of list of (W', W*) tuples for all unique topic ids.
"""
s_one_set = []
for top_words in topics:
s_one_set_t = []
for w_prime in top_words:
s_one_set_t.append((w_prime, top_words))
s_one_set.append(s_one_set_t)
return s_one_set
|
def s_one_set(topics):
"""
This function performs s_one_set segmentation on a list of topics.
s_one_set segmentation is defined as: s_one_set = {(W', W*) | W' = {w_i}; w_i belongs to W;
W* = W}
Example:
>>> topics = [np.array([9, 10, 7])
>>> s_one_set(topics)
[[(9, array([ 9, 10, 7])),
(10, array([ 9, 10, 7])),
(7, array([ 9, 10, 7]))]]
Args:
----
topics : list of topics obtained from an algorithm such as LDA. Is a list such as [array([ 9, 10, 11]), array([ 9, 10, 7]), ...]
Returns:
-------
s_one_set : list of list of (W', W*) tuples for all unique topic ids.
"""
s_one_set = []
for top_words in topics:
s_one_set_t = []
for w_prime in top_words:
s_one_set_t.append((w_prime, top_words))
s_one_set.append(s_one_set_t)
return s_one_set
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
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checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
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generating indices... genindex
writing additional pages... index search
copying static files... WARNING: favicon file 'favicon.ico' does not exist
done
copying extra files... done
dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def _ids_to_words(ids, dictionary):
"""Convert an iterable of ids to their corresponding words using a dictionary.
This function abstracts away the differences between the HashDictionary and the standard one.
Args:
ids: list of list of tuples, where each tuple contains (token_id, iterable of token_ids).
This is the format returned by the topic_coherence.segmentation functions.
"""
if (
not dictionary.id2token
): # may not be initialized in the standard gensim.corpora.Dictionary
setattr(dictionary, "id2token", {v: k for k, v in dictionary.token2id.items()})
top_words = set()
for word_id in ids:
word = dictionary.id2token[word_id]
if isinstance(word, set):
top_words = top_words.union(word)
else:
top_words.add(word)
return top_words
|
def _ids_to_words(ids, dictionary):
"""Convert an iterable of ids to their corresponding words using a dictionary.
This function abstracts away the differences between the HashDictionary and the standard one.
Args:
----
ids: list of list of tuples, where each tuple contains (token_id, iterable of token_ids).
This is the format returned by the topic_coherence.segmentation functions.
"""
if (
not dictionary.id2token
): # may not be initialized in the standard gensim.corpora.Dictionary
setattr(dictionary, "id2token", {v: k for k, v in dictionary.token2id.items()})
top_words = set()
for word_id in ids:
word = dictionary.id2token[word_id]
if isinstance(word, set):
top_words = top_words.union(word)
else:
top_words.add(word)
return top_words
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 82 source files that are out of date
updating environment: 82 added, 0 changed, 0 removed
reading sources... [ 1%] about
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
pickling environment... done
checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
done
preparing documents... done
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done
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rm -r _build/html/_sources
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Build finished. The HTML pages are in ../
|
ImportError
|
def __init__(self, relevant_ids, dictionary):
"""
Args:
relevant_ids: the set of words that occurrences should be accumulated for.
dictionary: Dictionary instance with mappings for the relevant_ids.
"""
super(WindowedTextsAnalyzer, self).__init__(relevant_ids, dictionary)
self._none_token = self._vocab_size # see _iter_texts for use of none token
|
def __init__(self, relevant_ids, dictionary):
"""
Args:
----
relevant_ids: the set of words that occurrences should be accumulated for.
dictionary: Dictionary instance with mappings for the relevant_ids.
"""
super(WindowedTextsAnalyzer, self).__init__(relevant_ids, dictionary)
self._none_token = self._vocab_size # see _iter_texts for use of none token
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
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building [html]: targets for 82 source files that are out of date
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
reading sources... [ 17%] corpora/svmlightcorpus
reading sources... [ 18%] corpora/textcorpus
reading sources... [ 19%] corpora/ucicorpus
reading sources... [ 20%] corpora/wikicorpus
reading sources... [ 21%] dist_lda
reading sources... [ 23%] dist_lsi
reading sources... [ 24%] distributed
reading sources... [ 25%] indextoc
reading sources... [ 26%] install
reading sources... [ 28%] interfaces
reading sources... [ 29%] intro
reading sources... [ 30%] matutils
reading sources... [ 31%] models/atmodel
reading sources... [ 32%] models/coherencemodel
reading sources... [ 34%] models/doc2vec
reading sources... [ 35%] models/hdpmodel
reading sources... [ 36%] models/keyedvectors
reading sources... [ 37%] models/lda_dispatcher
reading sources... [ 39%] models/lda_worker
reading sources... [ 40%] models/ldamodel
reading sources... [ 41%] models/ldamulticore
reading sources... [ 42%] models/ldaseqmodel
reading sources... [ 43%] models/logentropy_model
reading sources... [ 45%] models/lsi_dispatcher
reading sources... [ 46%] models/lsi_worker
reading sources... [ 47%] models/lsimodel
reading sources... [ 48%] models/models
reading sources... [ 50%] models/normmodel
reading sources... [ 51%] models/phrases
reading sources... [ 52%] models/rpmodel
reading sources... [ 53%] models/tfidfmodel
reading sources... [ 54%] models/word2vec
reading sources... [ 56%] models/wrappers/dtmmodel
reading sources... [ 57%] models/wrappers/fasttext
reading sources... [ 58%] models/wrappers/ldamallet
reading sources... [ 59%] models/wrappers/ldavowpalwabbit
reading sources... [ 60%] models/wrappers/varembed
reading sources... [ 62%] models/wrappers/wordrank
reading sources... [ 63%] models/wrappers/wrappers
reading sources... [ 64%] parsing/porter
reading sources... [ 65%] parsing/preprocessing
reading sources... [ 67%] scripts/glove2word2vec
reading sources... [ 68%] scripts/make_wikicorpus
reading sources... [ 69%] scripts/word2vec_standalone
reading sources... [ 70%] similarities/docsim
reading sources... [ 71%] similarities/index
reading sources... [ 73%] similarities/simserver
reading sources... [ 74%] simserver
reading sources... [ 75%] sklearn_integration/sklearn_wrapper_gensim_ldamodel
reading sources... [ 76%] summarization/bm25
reading sources... [ 78%] summarization/commons
reading sources... [ 79%] summarization/graph
reading sources... [ 80%] summarization/keywords
reading sources... [ 81%] summarization/pagerank_weighted
reading sources... [ 82%] summarization/summariser
reading sources... [ 84%] summarization/syntactic_unit
reading sources... [ 85%] summarization/textcleaner
reading sources... [ 86%] support
reading sources... [ 87%] topic_coherence/aggregation
reading sources... [ 89%] topic_coherence/direct_confirmation_measure
reading sources... [ 90%] topic_coherence/indirect_confirmation_measure
reading sources... [ 91%] topic_coherence/probability_estimation
reading sources... [ 92%] topic_coherence/segmentation
reading sources... [ 93%] tut1
reading sources... [ 95%] tut2
reading sources... [ 96%] tut3
reading sources... [ 97%] tutorial
reading sources... [ 98%] utils
reading sources... [100%] wiki
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
pickling environment... done
checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
done
preparing documents... done
writing output... [ 1%] about
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generating indices... genindex
writing additional pages... index search
copying static files... WARNING: favicon file 'favicon.ico' does not exist
done
copying extra files... done
dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def __init__(self, processes, *args, **kwargs):
"""
Args:
processes : number of processes to use; must be at least two.
args : should include `relevant_ids` and `dictionary` (see `UsesDictionary.__init__`).
kwargs : can include `batch_size`, which is the number of docs to send to a worker at a
time. If not included, it defaults to 64.
"""
super(ParallelWordOccurrenceAccumulator, self).__init__(*args)
if processes < 2:
raise ValueError(
"Must have at least 2 processes to run in parallel; got %d" % processes
)
self.processes = processes
self.batch_size = kwargs.get("batch_size", 64)
|
def __init__(self, processes, *args, **kwargs):
"""
Args:
----
processes : number of processes to use; must be at least two.
args : should include `relevant_ids` and `dictionary` (see `UsesDictionary.__init__`).
kwargs : can include `batch_size`, which is the number of docs to send to a worker at a
time. If not included, it defaults to 64.
"""
super(ParallelWordOccurrenceAccumulator, self).__init__(*args)
if processes < 2:
raise ValueError(
"Must have at least 2 processes to run in parallel; got %d" % processes
)
self.processes = processes
self.batch_size = kwargs.get("batch_size", 64)
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 82 source files that are out of date
updating environment: 82 added, 0 changed, 0 removed
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
pickling environment... done
checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
done
preparing documents... done
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done
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dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def strided_windows(ndarray, window_size):
"""
Produce a numpy.ndarray of windows, as from a sliding window.
>>> strided_windows(np.arange(5), 2)
array([[0, 1],
[1, 2],
[2, 3],
[3, 4]])
>>> strided_windows(np.arange(10), 5)
array([[0, 1, 2, 3, 4],
[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8],
[5, 6, 7, 8, 9]])
Args:
ndarray: either a numpy.ndarray or something that can be converted into one.
window_size: sliding window size.
Returns:
numpy.ndarray of the subsequences produced by sliding a window of the given size over
the `ndarray`. Since this uses striding, the individual arrays are views rather than
copies of `ndarray`. Changes to one view modifies the others and the original.
"""
ndarray = np.asarray(ndarray)
if window_size == ndarray.shape[0]:
return np.array([ndarray])
elif window_size > ndarray.shape[0]:
return np.ndarray((0, 0))
stride = ndarray.strides[0]
return np.lib.stride_tricks.as_strided(
ndarray,
shape=(ndarray.shape[0] - window_size + 1, window_size),
strides=(stride, stride),
)
|
def strided_windows(ndarray, window_size):
"""
Produce a numpy.ndarray of windows, as from a sliding window.
>>> strided_windows(np.arange(5), 2)
array([[0, 1],
[1, 2],
[2, 3],
[3, 4]])
>>> strided_windows(np.arange(10), 5)
array([[0, 1, 2, 3, 4],
[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8],
[5, 6, 7, 8, 9]])
Args:
----
ndarray: either a numpy.ndarray or something that can be converted into one.
window_size: sliding window size.
:param window_size:
:return: numpy.ndarray of the subsequences produced by sliding a window of the given size over
the `ndarray`. Since this uses striding, the individual arrays are views rather than
copies of `ndarray`. Changes to one view modifies the others and the original.
"""
ndarray = np.asarray(ndarray)
if window_size == ndarray.shape[0]:
return np.array([ndarray])
elif window_size > ndarray.shape[0]:
return np.ndarray((0, 0))
stride = ndarray.strides[0]
return np.lib.stride_tricks.as_strided(
ndarray,
shape=(ndarray.shape[0] - window_size + 1, window_size),
strides=(stride, stride),
)
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
pickling environment... done
checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/corpora.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/models.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/wrappers/wrappers.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/simserver.rst:: WARNING: document isn't included in any toctree
done
preparing documents... done
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generating indices... genindex
writing additional pages... index search
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done
copying extra files... done
dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def iter_windows(
texts, window_size, copy=False, ignore_below_size=True, include_doc_num=False
):
"""Produce a generator over the given texts using a sliding window of `window_size`.
The windows produced are views of some subsequence of a text. To use deep copies
instead, pass `copy=True`.
Args:
texts: List of string sentences.
window_size: Size of sliding window.
copy: False to use views of the texts (default) or True to produce deep copies.
ignore_below_size: ignore documents that are not at least `window_size` in length (default behavior).
If False, the documents below `window_size` will be yielded as the full document.
"""
for doc_num, document in enumerate(texts):
for window in _iter_windows(document, window_size, copy, ignore_below_size):
if include_doc_num:
yield (doc_num, window)
else:
yield window
|
def iter_windows(
texts, window_size, copy=False, ignore_below_size=True, include_doc_num=False
):
"""Produce a generator over the given texts using a sliding window of `window_size`.
The windows produced are views of some subsequence of a text. To use deep copies
instead, pass `copy=True`.
Args:
----
texts: List of string sentences.
window_size: Size of sliding window.
copy: False to use views of the texts (default) or True to produce deep copies.
ignore_below_size: ignore documents that are not at least `window_size` in length (default behavior).
If False, the documents below `window_size` will be yielded as the full document.
"""
for doc_num, document in enumerate(texts):
for window in _iter_windows(document, window_size, copy, ignore_below_size):
if include_doc_num:
yield (doc_num, window)
else:
yield window
|
https://github.com/RaRe-Technologies/gensim/issues/1192
|
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.5.3
making output directory...
loading pickled environment... not yet created
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 82 source files that are out of date
updating environment: 82 added, 0 changed, 0 removed
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/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_max_len_seq.py:8: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._max_len_seq_inner import _max_len_seq_inner
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/_upfirdn.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._upfirdn_apply import _output_len, _apply
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .ckdtree import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/spatial/__init__.py:94: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from .qhull import *
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/ndimage/measurements.py:36: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from . import _ni_label
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
from ._spectral import lombscargle
/home/lev/miniconda2/lib/python2.7/site-packages/scipy/signal/spectral.py:10: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192, got 176
from ._spectral import lombscargle
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/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/malletcorpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.malletcorpus` -- Corpus in Mallet format of List-Of-Words.
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/corpora/sharded_corpus.rst:2: WARNING: Title underline too short.
:mod:`corpora.sharded_corpus` -- Corpus stored in separate files
==========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/install.rst:118: WARNING: nonlocal image URI found: https://api.travis-ci.org/piskvorky/gensim.png?branch=develop
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:10: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel:11: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:25: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:39: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:41: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:42: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/coherencemodel.py:docstring of gensim.models.coherencemodel.CoherenceModel:55: WARNING: Definition list ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/keyedvectors.py:docstring of gensim.models.keyedvectors.KeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/ldaseqmodel.rst:2: WARNING: Title underline too short.
:mod:`models.ldaseqmodel` -- Dynamic Topic Modeling in Python
================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.LdaSeqModel.fit_lda_seq:6: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:5: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/ldaseqmodel.py:docstring of gensim.models.ldaseqmodel.sslm.compute_post_variance:8: WARNING: Inline substitution_reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec:31: WARNING: Literal block expected; none found.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/word2vec.py:docstring of gensim.models.word2vec.Word2Vec.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/doc2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/dtmmodel.py:docstring of gensim.models.wrappers.dtmmodel.DtmModel.dtm_coherence:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastTextKeyedVectors.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:13: WARNING: duplicate citation taddy, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/fasttext.py:docstring of gensim.models.wrappers.fasttext.FastText.score:14: WARNING: duplicate citation deepir, other instance in /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/models/word2vec.rst
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldamallet.py:docstring of gensim.models.wrappers.ldamallet.malletmodel2ldamodel:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/ldavowpalwabbit.py:docstring of gensim.models.wrappers.ldavowpalwabbit.vwmodel2ldamodel:11: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/varembed.py:docstring of gensim.models.wrappers.varembed.VarEmbed.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/models/wrappers/wordrank.py:docstring of gensim.models.wrappers.wordrank.Wordrank.evaluate_word_pairs:20: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/parsing/preprocessing.rst:2: WARNING: Title underline too short.
:mod:`parsing.preprocessing` -- Functions to preprocess raw text
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/glove2word2vec.rst:2: WARNING: Title underline too short.
:mod:`scripts.glove2word2vec` -- Convert glove format to word2vec
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/scripts/glove2word2vec.py:docstring of gensim.scripts.glove2word2vec.glove2word2vec:1: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/make_wikicorpus.rst:2: WARNING: Title underline too short.
:mod:`scripts.make_wikicorpus` -- Convert articles from a Wikipedia dump to vectors.
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/scripts/word2vec_standalone.rst:2: WARNING: Title underline too short.
:mod:`scripts.word2vec_standalone` -- Train word2vec on text file CORPUS
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/similarities/docsim.py:docstring of gensim.similarities.docsim.WmdSimilarity:28: ERROR: Unexpected indentation.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/index.rst:2: WARNING: Title underline too short.
:mod:`similarities.index` -- Fast Approximate Nearest Neighbor Similarity with Annoy package
========================================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/similarities/simserver.rst:4: WARNING: autodoc: failed to import module u'simserver.simserver'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named simserver.simserver
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:2: WARNING: Title underline too short.
:mod:`sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel` -- Scikit learn wrapper for Latent Dirichlet Allocation
======================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/sklearn_integration/sklearn_wrapper_gensim_ldamodel.rst:4: WARNING: autodoc: failed to import module u'gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel.SklearnWrapperLdaModel'; the following exception was raised:
Traceback (most recent call last):
File "/home/lev/miniconda2/lib/python2.7/site-packages/sphinx/ext/autodoc.py", line 551, in import_object
__import__(self.modname)
ImportError: No module named SklearnWrapperLdaModel
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/keywords.rst:2: WARNING: Title underline too short.
:mod:`summarization.keywords` -- Keywords for TextRank summarization algorithm
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/pagerank_weighted.rst:2: WARNING: Title underline too short.
:mod:`summarization.pagerank_weighted` -- Weighted PageRank algorithm
=========================================================
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:13: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:14: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:16: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/summarization/summarizer.py:docstring of gensim.summarization.summarizer.summarize:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/syntactic_unit.rst:2: WARNING: Title underline too short.
:mod:`summarization.syntactic_unit` -- Syntactic Unit class
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:2: WARNING: Title underline too short.
:mod:`summarization.textcleaner` -- Summarization pre-processing
=========================================================
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/summarization/textcleaner.rst:10: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/aggregation.py:docstring of gensim.topic_coherence.aggregation.arithmetic_mean:9: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:6: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_conditional_probability:12: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:13: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/direct_confirmation_measure.py:docstring of gensim.topic_coherence.direct_confirmation_measure.log_ratio_measure:19: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:4: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:5: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:8: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:12: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:15: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/indirect_confirmation_measure.py:docstring of gensim.topic_coherence.indirect_confirmation_measure.cosine_similarity:24: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:5: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_document:10: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:7: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/probability_estimation.py:docstring of gensim.topic_coherence.probability_estimation.p_boolean_sliding_window:14: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_one:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:11: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_pre:15: SEVERE: Unexpected section title.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:3: ERROR: Unexpected indentation.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:4: WARNING: Block quote ends without a blank line; unexpected unindent.
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: WARNING: Title underline too short.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:12: SEVERE: Unexpected section title.
Args:
----
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: WARNING: Title underline too short.
Returns:
-------
/home/lev/miniconda2/lib/python2.7/site-packages/gensim-1.0.1-py2.7-linux-x86_64.egg/gensim/topic_coherence/segmentation.py:docstring of gensim.topic_coherence.segmentation.s_one_set:16: SEVERE: Unexpected section title.
Returns:
-------
looking for now-outdated files... none found
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checking consistency... /home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/about.rst:: WARNING: document isn't included in any toctree
/home/lev/Dropbox/raretech/os/release/fix/gensim/docs/src/changes_080.rst:: WARNING: document isn't included in any toctree
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writing output... [ 56%] models/wrappers/dtmmodel
writing output... [ 57%] models/wrappers/fasttext
writing output... [ 58%] models/wrappers/ldamallet
writing output... [ 59%] models/wrappers/ldavowpalwabbit
writing output... [ 60%] models/wrappers/varembed
writing output... [ 62%] models/wrappers/wordrank
writing output... [ 63%] models/wrappers/wrappers
writing output... [ 64%] parsing/porter
writing output... [ 65%] parsing/preprocessing
writing output... [ 67%] scripts/glove2word2vec
writing output... [ 68%] scripts/make_wikicorpus
writing output... [ 69%] scripts/word2vec_standalone
writing output... [ 70%] similarities/docsim
writing output... [ 71%] similarities/index
writing output... [ 73%] similarities/simserver
writing output... [ 74%] simserver
writing output... [ 75%] sklearn_integration/sklearn_wrapper_gensim_ldamodel
writing output... [ 76%] summarization/bm25
writing output... [ 78%] summarization/commons
writing output... [ 79%] summarization/graph
writing output... [ 80%] summarization/keywords
writing output... [ 81%] summarization/pagerank_weighted
writing output... [ 82%] summarization/summariser
writing output... [ 84%] summarization/syntactic_unit
writing output... [ 85%] summarization/textcleaner
writing output... [ 86%] support
writing output... [ 87%] topic_coherence/aggregation
writing output... [ 89%] topic_coherence/direct_confirmation_measure
writing output... [ 90%] topic_coherence/indirect_confirmation_measure
writing output... [ 91%] topic_coherence/probability_estimation
writing output... [ 92%] topic_coherence/segmentation
writing output... [ 93%] tut1
writing output... [ 95%] tut2
writing output... [ 96%] tut3
writing output... [ 97%] tutorial
writing output... [ 98%] utils
writing output... [100%] wiki
generating indices... genindex
writing additional pages... index search
copying static files... WARNING: favicon file 'favicon.ico' does not exist
done
copying extra files... done
dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 112 warnings.
rm -r _build/html/_sources
cp -r _build/html/* ../
Build finished. The HTML pages are in ../
|
ImportError
|
def load_fasttext_format(cls, model_file, encoding="utf8"):
"""
Load the input-hidden weight matrix from the fast text output files.
Note that due to limitations in the FastText API, you cannot continue training
with a model loaded this way, though you can query for word similarity etc.
`model_file` is the path to the FastText output files.
FastText outputs two model files - `/path/to/model.vec` and `/path/to/model.bin`
Expected value for this example: `/path/to/model` or `/path/to/model.bin`,
as gensim requires only `.bin` file to load entire fastText model.
"""
model = cls()
if not model_file.endswith(".bin"):
model_file += ".bin"
model.file_name = model_file
model.load_binary_data(encoding=encoding)
return model
|
def load_fasttext_format(cls, model_file, encoding="utf8"):
"""
Load the input-hidden weight matrix from the fast text output files.
Note that due to limitations in the FastText API, you cannot continue training
with a model loaded this way, though you can query for word similarity etc.
`model_file` is the path to the FastText output files.
FastText outputs two training files - `/path/to/train.vec` and `/path/to/train.bin`
Expected value for this example: `/path/to/train`
"""
model = cls()
model.wv = cls.load_word2vec_format("%s.vec" % model_file, encoding=encoding)
model.load_binary_data("%s.bin" % model_file, encoding=encoding)
return model
|
https://github.com/RaRe-Technologies/gensim/issues/1236
|
Traceback (most recent call last):
File "app.py", line 18, in <module>
model = FastText.load_fasttext_format(os.path.abspath('./wiki.fr'))
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 238, in load_fasttext_format
model.load_binary_data('%s.bin' % model_file)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 255, in load_binary_data
self.load_dict(f)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 277, in load_dict
assert len(self.wv.vocab) == vocab_size, 'mismatch between vocab sizes'
AssertionError: mismatch between vocab sizes
|
AssertionError
|
def load_binary_data(self, encoding="utf8"):
"""Loads data from the output binary file created by FastText training"""
with utils.smart_open(self.file_name, "rb") as f:
self.load_model_params(f)
self.load_dict(f, encoding=encoding)
self.load_vectors(f)
|
def load_binary_data(self, model_binary_file, encoding="utf8"):
"""Loads data from the output binary file created by FastText training"""
with utils.smart_open(model_binary_file, "rb") as f:
self.load_model_params(f)
self.load_dict(f, encoding=encoding)
self.load_vectors(f)
|
https://github.com/RaRe-Technologies/gensim/issues/1236
|
Traceback (most recent call last):
File "app.py", line 18, in <module>
model = FastText.load_fasttext_format(os.path.abspath('./wiki.fr'))
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 238, in load_fasttext_format
model.load_binary_data('%s.bin' % model_file)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 255, in load_binary_data
self.load_dict(f)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 277, in load_dict
assert len(self.wv.vocab) == vocab_size, 'mismatch between vocab sizes'
AssertionError: mismatch between vocab sizes
|
AssertionError
|
def load_dict(self, file_handle, encoding="utf8"):
vocab_size, nwords, _ = self.struct_unpack(file_handle, "@3i")
# Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc)
logger.info(
"loading %s words for fastText model from %s", vocab_size, self.file_name
)
self.struct_unpack(file_handle, "@1q") # number of tokens
if self.new_format:
(pruneidx_size,) = self.struct_unpack(file_handle, "@q")
for i in range(vocab_size):
word_bytes = b""
char_byte = file_handle.read(1)
# Read vocab word
while char_byte != b"\x00":
word_bytes += char_byte
char_byte = file_handle.read(1)
word = word_bytes.decode(encoding)
count, _ = self.struct_unpack(file_handle, "@qb")
if i == nwords and i < vocab_size:
# To handle the error in pretrained vector wiki.fr (French).
# For more info : https://github.com/facebookresearch/fastText/issues/218
assert word == "__label__", (
'mismatched vocab_size ({}) and nwords ({}), extra word "{}"'.format(
vocab_size, nwords, word
)
)
continue # don't add word to vocab
self.wv.vocab[word] = Vocab(index=i, count=count)
self.wv.index2word.append(word)
assert len(self.wv.vocab) == nwords, (
"mismatch between final vocab size ({} words), "
"and expected number of words ({} words)".format(len(self.wv.vocab), nwords)
)
if len(self.wv.vocab) != vocab_size:
# expecting to log this warning only for pretrained french vector, wiki.fr
logger.warning(
"mismatch between final vocab size (%s words), and expected vocab size (%s words)",
len(self.wv.vocab),
vocab_size,
)
if self.new_format:
for j in range(pruneidx_size):
self.struct_unpack(file_handle, "@2i")
|
def load_dict(self, file_handle, encoding="utf8"):
vocab_size, nwords, _ = self.struct_unpack(file_handle, "@3i")
# Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc)
assert len(self.wv.vocab) == nwords, "mismatch between vocab sizes"
assert len(self.wv.vocab) == vocab_size, "mismatch between vocab sizes"
self.struct_unpack(file_handle, "@1q") # number of tokens
if self.new_format:
(pruneidx_size,) = self.struct_unpack(file_handle, "@q")
for i in range(nwords):
word_bytes = b""
char_byte = file_handle.read(1)
# Read vocab word
while char_byte != b"\x00":
word_bytes += char_byte
char_byte = file_handle.read(1)
word = word_bytes.decode(encoding)
count, _ = self.struct_unpack(file_handle, "@qb")
assert self.wv.vocab[word].index == i, (
"mismatch between gensim word index and fastText word index"
)
self.wv.vocab[word].count = count
if self.new_format:
for j in range(pruneidx_size):
self.struct_unpack(file_handle, "@2i")
|
https://github.com/RaRe-Technologies/gensim/issues/1236
|
Traceback (most recent call last):
File "app.py", line 18, in <module>
model = FastText.load_fasttext_format(os.path.abspath('./wiki.fr'))
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 238, in load_fasttext_format
model.load_binary_data('%s.bin' % model_file)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 255, in load_binary_data
self.load_dict(f)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 277, in load_dict
assert len(self.wv.vocab) == vocab_size, 'mismatch between vocab sizes'
AssertionError: mismatch between vocab sizes
|
AssertionError
|
def load_vectors(self, file_handle):
if self.new_format:
self.struct_unpack(file_handle, "@?") # bool quant_input in fasttext.cc
num_vectors, dim = self.struct_unpack(file_handle, "@2q")
# Vectors stored by [Matrix::save](https://github.com/facebookresearch/fastText/blob/master/src/matrix.cc)
assert self.vector_size == dim, (
"mismatch between vector size in model params ({}) and model vectors ({})".format(
self.vector_size, dim
)
)
float_size = struct.calcsize("@f")
if float_size == 4:
dtype = np.dtype(np.float32)
elif float_size == 8:
dtype = np.dtype(np.float64)
self.num_original_vectors = num_vectors
self.wv.syn0_all = np.fromfile(file_handle, dtype=dtype, count=num_vectors * dim)
self.wv.syn0_all = self.wv.syn0_all.reshape((num_vectors, dim))
assert self.wv.syn0_all.shape == (
self.bucket + len(self.wv.vocab),
self.vector_size,
), "mismatch between actual weight matrix shape {} and expected shape {}".format(
self.wv.syn0_all.shape, (self.bucket + len(self.wv.vocab), self.vector_size)
)
self.init_ngrams()
|
def load_vectors(self, file_handle):
if self.new_format:
self.struct_unpack(file_handle, "@?") # bool quant_input in fasttext.cc
num_vectors, dim = self.struct_unpack(file_handle, "@2q")
# Vectors stored by [Matrix::save](https://github.com/facebookresearch/fastText/blob/master/src/matrix.cc)
assert self.vector_size == dim, "mismatch between model sizes"
float_size = struct.calcsize("@f")
if float_size == 4:
dtype = np.dtype(np.float32)
elif float_size == 8:
dtype = np.dtype(np.float64)
self.num_original_vectors = num_vectors
self.wv.syn0_all = np.fromfile(file_handle, dtype=dtype, count=num_vectors * dim)
self.wv.syn0_all = self.wv.syn0_all.reshape((num_vectors, dim))
assert self.wv.syn0_all.shape == (
self.bucket + len(self.wv.vocab),
self.vector_size,
), "mismatch between weight matrix shape and vocab/model size"
self.init_ngrams()
|
https://github.com/RaRe-Technologies/gensim/issues/1236
|
Traceback (most recent call last):
File "app.py", line 18, in <module>
model = FastText.load_fasttext_format(os.path.abspath('./wiki.fr'))
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 238, in load_fasttext_format
model.load_binary_data('%s.bin' % model_file)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 255, in load_binary_data
self.load_dict(f)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 277, in load_dict
assert len(self.wv.vocab) == vocab_size, 'mismatch between vocab sizes'
AssertionError: mismatch between vocab sizes
|
AssertionError
|
def init_ngrams(self):
"""
Computes ngrams of all words present in vocabulary and stores vectors for only those ngrams.
Vectors for other ngrams are initialized with a random uniform distribution in FastText. These
vectors are discarded here to save space.
"""
self.wv.ngrams = {}
all_ngrams = []
self.wv.syn0 = np.zeros((len(self.wv.vocab), self.vector_size), dtype=REAL)
for w, vocab in self.wv.vocab.items():
all_ngrams += self.compute_ngrams(w, self.wv.min_n, self.wv.max_n)
self.wv.syn0[vocab.index] += np.array(self.wv.syn0_all[vocab.index])
all_ngrams = set(all_ngrams)
self.num_ngram_vectors = len(all_ngrams)
ngram_indices = []
for i, ngram in enumerate(all_ngrams):
ngram_hash = self.ft_hash(ngram)
ngram_indices.append(len(self.wv.vocab) + ngram_hash % self.bucket)
self.wv.ngrams[ngram] = i
self.wv.syn0_all = self.wv.syn0_all.take(ngram_indices, axis=0)
ngram_weights = self.wv.syn0_all
logger.info(
"loading weights for %s words for fastText model from %s",
len(self.wv.vocab),
self.file_name,
)
for w, vocab in self.wv.vocab.items():
word_ngrams = self.compute_ngrams(w, self.wv.min_n, self.wv.max_n)
for word_ngram in word_ngrams:
self.wv.syn0[vocab.index] += np.array(
ngram_weights[self.wv.ngrams[word_ngram]]
)
self.wv.syn0[vocab.index] /= len(word_ngrams) + 1
logger.info(
"loaded %s weight matrix for fastText model from %s",
self.wv.syn0.shape,
self.file_name,
)
|
def init_ngrams(self):
"""
Computes ngrams of all words present in vocabulary and stores vectors for only those ngrams.
Vectors for other ngrams are initialized with a random uniform distribution in FastText. These
vectors are discarded here to save space.
"""
self.wv.ngrams = {}
all_ngrams = []
for w, v in self.wv.vocab.items():
all_ngrams += self.compute_ngrams(w, self.wv.min_n, self.wv.max_n)
all_ngrams = set(all_ngrams)
self.num_ngram_vectors = len(all_ngrams)
ngram_indices = []
for i, ngram in enumerate(all_ngrams):
ngram_hash = self.ft_hash(ngram)
ngram_indices.append(len(self.wv.vocab) + ngram_hash % self.bucket)
self.wv.ngrams[ngram] = i
self.wv.syn0_all = self.wv.syn0_all.take(ngram_indices, axis=0)
|
https://github.com/RaRe-Technologies/gensim/issues/1236
|
Traceback (most recent call last):
File "app.py", line 18, in <module>
model = FastText.load_fasttext_format(os.path.abspath('./wiki.fr'))
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 238, in load_fasttext_format
model.load_binary_data('%s.bin' % model_file)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 255, in load_binary_data
self.load_dict(f)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/gensim/models/wrappers/fasttext.py", line 277, in load_dict
assert len(self.wv.vocab) == vocab_size, 'mismatch between vocab sizes'
AssertionError: mismatch between vocab sizes
|
AssertionError
|
def inference(self, chunk, collect_sstats=False):
"""
Given a chunk of sparse document vectors, estimate gamma (parameters
controlling the topic weights) for each document in the chunk.
This function does not modify the model (=is read-only aka const). The
whole input chunk of document is assumed to fit in RAM; chunking of a
large corpus must be done earlier in the pipeline.
If `collect_sstats` is True, also collect sufficient statistics needed
to update the model's topic-word distributions, and return a 2-tuple
`(gamma, sstats)`. Otherwise, return `(gamma, None)`. `gamma` is of shape
`len(chunk) x self.num_topics`.
Avoids computing the `phi` variational parameter directly using the
optimization presented in **Lee, Seung: Algorithms for non-negative matrix factorization, NIPS 2001**.
"""
try:
_ = len(chunk)
except:
# convert iterators/generators to plain list, so we have len() etc.
chunk = list(chunk)
if len(chunk) > 1:
logger.debug("performing inference on a chunk of %i documents", len(chunk))
# Initialize the variational distribution q(theta|gamma) for the chunk
gamma = self.random_state.gamma(100.0, 1.0 / 100.0, (len(chunk), self.num_topics))
Elogtheta = dirichlet_expectation(gamma)
expElogtheta = np.exp(Elogtheta)
if collect_sstats:
sstats = np.zeros_like(self.expElogbeta)
else:
sstats = None
converged = 0
# Now, for each document d update that document's gamma and phi
# Inference code copied from Hoffman's `onlineldavb.py` (esp. the
# Lee&Seung trick which speeds things up by an order of magnitude, compared
# to Blei's original LDA-C code, cool!).
for d, doc in enumerate(chunk):
if len(doc) > 0 and not isinstance(doc[0][0], six.integer_types):
# make sure the term IDs are ints, otherwise np will get upset
ids = [int(id) for id, _ in doc]
else:
ids = [id for id, _ in doc]
cts = np.array([cnt for _, cnt in doc])
gammad = gamma[d, :]
Elogthetad = Elogtheta[d, :]
expElogthetad = expElogtheta[d, :]
expElogbetad = self.expElogbeta[:, ids]
# The optimal phi_{dwk} is proportional to expElogthetad_k * expElogbetad_w.
# phinorm is the normalizer.
# TODO treat zeros explicitly, instead of adding 1e-100?
phinorm = np.dot(expElogthetad, expElogbetad) + 1e-100
# Iterate between gamma and phi until convergence
for _ in xrange(self.iterations):
lastgamma = gammad
# We represent phi implicitly to save memory and time.
# Substituting the value of the optimal phi back into
# the update for gamma gives this update. Cf. Lee&Seung 2001.
gammad = self.alpha + expElogthetad * np.dot(cts / phinorm, expElogbetad.T)
Elogthetad = dirichlet_expectation(gammad)
expElogthetad = np.exp(Elogthetad)
phinorm = np.dot(expElogthetad, expElogbetad) + 1e-100
# If gamma hasn't changed much, we're done.
meanchange = np.mean(abs(gammad - lastgamma))
if meanchange < self.gamma_threshold:
converged += 1
break
gamma[d, :] = gammad
if collect_sstats:
# Contribution of document d to the expected sufficient
# statistics for the M step.
sstats[:, ids] += np.outer(expElogthetad.T, cts / phinorm)
if len(chunk) > 1:
logger.debug(
"%i/%i documents converged within %i iterations",
converged,
len(chunk),
self.iterations,
)
if collect_sstats:
# This step finishes computing the sufficient statistics for the
# M step, so that
# sstats[k, w] = \sum_d n_{dw} * phi_{dwk}
# = \sum_d n_{dw} * exp{Elogtheta_{dk} + Elogbeta_{kw}} / phinorm_{dw}.
sstats *= self.expElogbeta
return gamma, sstats
|
def inference(self, chunk, collect_sstats=False):
"""
Given a chunk of sparse document vectors, estimate gamma (parameters
controlling the topic weights) for each document in the chunk.
This function does not modify the model (=is read-only aka const). The
whole input chunk of document is assumed to fit in RAM; chunking of a
large corpus must be done earlier in the pipeline.
If `collect_sstats` is True, also collect sufficient statistics needed
to update the model's topic-word distributions, and return a 2-tuple
`(gamma, sstats)`. Otherwise, return `(gamma, None)`. `gamma` is of shape
`len(chunk) x self.num_topics`.
Avoids computing the `phi` variational parameter directly using the
optimization presented in **Lee, Seung: Algorithms for non-negative matrix factorization, NIPS 2001**.
"""
try:
_ = len(chunk)
except:
# convert iterators/generators to plain list, so we have len() etc.
chunk = list(chunk)
if len(chunk) > 1:
logger.debug("performing inference on a chunk of %i documents", len(chunk))
# Initialize the variational distribution q(theta|gamma) for the chunk
gamma = self.random_state.gamma(100.0, 1.0 / 100.0, (len(chunk), self.num_topics))
Elogtheta = dirichlet_expectation(gamma)
expElogtheta = np.exp(Elogtheta)
if collect_sstats:
sstats = np.zeros_like(self.expElogbeta)
else:
sstats = None
converged = 0
# Now, for each document d update that document's gamma and phi
# Inference code copied from Hoffman's `onlineldavb.py` (esp. the
# Lee&Seung trick which speeds things up by an order of magnitude, compared
# to Blei's original LDA-C code, cool!).
for d, doc in enumerate(chunk):
if doc and not isinstance(doc[0][0], six.integer_types):
# make sure the term IDs are ints, otherwise np will get upset
ids = [int(id) for id, _ in doc]
else:
ids = [id for id, _ in doc]
cts = np.array([cnt for _, cnt in doc])
gammad = gamma[d, :]
Elogthetad = Elogtheta[d, :]
expElogthetad = expElogtheta[d, :]
expElogbetad = self.expElogbeta[:, ids]
# The optimal phi_{dwk} is proportional to expElogthetad_k * expElogbetad_w.
# phinorm is the normalizer.
# TODO treat zeros explicitly, instead of adding 1e-100?
phinorm = np.dot(expElogthetad, expElogbetad) + 1e-100
# Iterate between gamma and phi until convergence
for _ in xrange(self.iterations):
lastgamma = gammad
# We represent phi implicitly to save memory and time.
# Substituting the value of the optimal phi back into
# the update for gamma gives this update. Cf. Lee&Seung 2001.
gammad = self.alpha + expElogthetad * np.dot(cts / phinorm, expElogbetad.T)
Elogthetad = dirichlet_expectation(gammad)
expElogthetad = np.exp(Elogthetad)
phinorm = np.dot(expElogthetad, expElogbetad) + 1e-100
# If gamma hasn't changed much, we're done.
meanchange = np.mean(abs(gammad - lastgamma))
if meanchange < self.gamma_threshold:
converged += 1
break
gamma[d, :] = gammad
if collect_sstats:
# Contribution of document d to the expected sufficient
# statistics for the M step.
sstats[:, ids] += np.outer(expElogthetad.T, cts / phinorm)
if len(chunk) > 1:
logger.debug(
"%i/%i documents converged within %i iterations",
converged,
len(chunk),
self.iterations,
)
if collect_sstats:
# This step finishes computing the sufficient statistics for the
# M step, so that
# sstats[k, w] = \sum_d n_{dw} * phi_{dwk}
# = \sum_d n_{dw} * exp{Elogtheta_{dk} + Elogbeta_{kw}} / phinorm_{dw}.
sstats *= self.expElogbeta
return gamma, sstats
|
https://github.com/RaRe-Technologies/gensim/issues/911
|
In [16]: model = models.LdaModel(corpus, id2word=dictionary, num_topics=2, distributed=True)
2016-10-03 09:21:04,056 : INFO : using symmetric alpha at 0.5
2016-10-03 09:21:04,056 : INFO : using symmetric eta at 0.5
2016-10-03 09:21:04,057 : DEBUG : looking for dispatcher at PYRO:gensim.lda_dispatcher@127.0.0.1:41212
2016-10-03 09:21:04,089 : INFO : using distributed version with 3 workers
2016-10-03 09:21:04,090 : INFO : running online LDA training, 2 topics, 1 passes over the supplied corpus of 9 documents, updating model once every 9 documents, evaluating perplexity every 9 documents, iterating 50x with a convergence threshold of 0.001000
2016-10-03 09:21:04,090 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy
2016-10-03 09:21:04,090 : INFO : initializing 3 workers
2016-10-03 09:21:04,097 : DEBUG : bound: at document #0
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-16-46e2552e21a7> in <module>()
----> 1 model = models.LdaModel(corpus, id2word=dictionary, num_topics=2, distributed=True)
/home/lev/Dropbox/raretech/os/release/gensim/gensim/models/ldamodel.py in __init__(self, corpus, num_topics, id2word, distributed, chunksize, passes, update_every, alpha, eta, decay, offset, eval_every, iterations, gamma_threshold, minimum_probability, random_state, ns_conf)
344 if corpus is not None:
345 use_numpy = self.dispatcher is not None
--> 346 self.update(corpus, chunks_as_numpy=use_numpy)
347
348 def init_dir_prior(self, prior, name):
/home/lev/Dropbox/raretech/os/release/gensim/gensim/models/ldamodel.py in update(self, corpus, chunksize, decay, offset, passes, update_every, eval_every, iterations, gamma_threshold, chunks_as_numpy)
648
649 if eval_every and ((reallen == lencorpus) or ((chunk_no + 1) % (eval_every * self.numworkers) == 0)):
--> 650 self.log_perplexity(chunk, total_docs=lencorpus)
651
652 if self.dispatcher:
/home/lev/Dropbox/raretech/os/release/gensim/gensim/models/ldamodel.py in log_perplexity(self, chunk, total_docs)
539 corpus_words = sum(cnt for document in chunk for _, cnt in document)
540 subsample_ratio = 1.0 * total_docs / len(chunk)
--> 541 perwordbound = self.bound(chunk, subsample_ratio=subsample_ratio) / (subsample_ratio * corpus_words)
542 logger.info("%.3f per-word bound, %.1f perplexity estimate based on a held-out corpus of %i documents with %i words" %
543 (perwordbound, numpy.exp2(-perwordbound), len(chunk), corpus_words))
/home/lev/Dropbox/raretech/os/release/gensim/gensim/models/ldamodel.py in bound(self, corpus, gamma, subsample_ratio)
740 logger.debug("bound: at document #%i", d)
741 if gamma is None:
--> 742 gammad, _ = self.inference([doc])
743 else:
744 gammad = gamma[d]
/home/lev/Dropbox/raretech/os/release/gensim/gensim/models/ldamodel.py in inference(self, chunk, collect_sstats)
438 # to Blei's original LDA-C code, cool!).
439 for d, doc in enumerate(chunk):
--> 440 if doc and not isinstance(doc[0][0], six.integer_types):
441 # make sure the term IDs are ints, otherwise numpy will get upset
442 ids = [int(id) for id, _ in doc]
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
|
ValueError
|
def bound(self, corpus, gamma=None, subsample_ratio=1.0):
"""
Estimate the variational bound of documents from `corpus`:
E_q[log p(corpus)] - E_q[log q(corpus)]
`gamma` are the variational parameters on topic weights for each `corpus`
document (=2d matrix=what comes out of `inference()`).
If not supplied, will be inferred from the model.
"""
score = 0.0
_lambda = self.state.get_lambda()
Elogbeta = dirichlet_expectation(_lambda)
for d, doc in enumerate(
corpus
): # stream the input doc-by-doc, in case it's too large to fit in RAM
if d % self.chunksize == 0:
logger.debug("bound: at document #%i", d)
if gamma is None:
gammad, _ = self.inference([doc])
else:
gammad = gamma[d]
Elogthetad = dirichlet_expectation(gammad)
# E[log p(doc | theta, beta)]
score += np.sum(
cnt * logsumexp(Elogthetad + Elogbeta[:, int(id)]) for id, cnt in doc
)
# E[log p(theta | alpha) - log q(theta | gamma)]; assumes alpha is a vector
score += np.sum((self.alpha - gammad) * Elogthetad)
score += np.sum(gammaln(gammad) - gammaln(self.alpha))
score += gammaln(np.sum(self.alpha)) - gammaln(np.sum(gammad))
# Compensate likelihood for when `corpus` above is only a sample of the whole corpus. This ensures
# that the likelihood is always rougly on the same scale.
score *= subsample_ratio
# E[log p(beta | eta) - log q (beta | lambda)]; assumes eta is a scalar
score += np.sum((self.eta - _lambda) * Elogbeta)
score += np.sum(gammaln(_lambda) - gammaln(self.eta))
if np.ndim(self.eta) == 0:
sum_eta = self.eta * self.num_terms
else:
sum_eta = np.sum(self.eta)
score += np.sum(gammaln(sum_eta) - gammaln(np.sum(_lambda, 1)))
return score
|
def bound(self, corpus, gamma=None, subsample_ratio=1.0):
"""
Estimate the variational bound of documents from `corpus`:
E_q[log p(corpus)] - E_q[log q(corpus)]
`gamma` are the variational parameters on topic weights for each `corpus`
document (=2d matrix=what comes out of `inference()`).
If not supplied, will be inferred from the model.
"""
score = 0.0
_lambda = self.state.get_lambda()
Elogbeta = dirichlet_expectation(_lambda)
for d, doc in enumerate(
corpus
): # stream the input doc-by-doc, in case it's too large to fit in RAM
if d % self.chunksize == 0:
logger.debug("bound: at document #%i", d)
if gamma is None:
gammad, _ = self.inference([doc])
else:
gammad = gamma[d]
Elogthetad = dirichlet_expectation(gammad)
# E[log p(doc | theta, beta)]
score += np.sum(
cnt * logsumexp(Elogthetad + Elogbeta[:, id]) for id, cnt in doc
)
# E[log p(theta | alpha) - log q(theta | gamma)]; assumes alpha is a vector
score += np.sum((self.alpha - gammad) * Elogthetad)
score += np.sum(gammaln(gammad) - gammaln(self.alpha))
score += gammaln(np.sum(self.alpha)) - gammaln(np.sum(gammad))
# Compensate likelihood for when `corpus` above is only a sample of the whole corpus. This ensures
# that the likelihood is always rougly on the same scale.
score *= subsample_ratio
# E[log p(beta | eta) - log q (beta | lambda)]; assumes eta is a scalar
score += np.sum((self.eta - _lambda) * Elogbeta)
score += np.sum(gammaln(_lambda) - gammaln(self.eta))
if np.ndim(self.eta) == 0:
sum_eta = self.eta * self.num_terms
else:
sum_eta = np.sum(self.eta)
score += np.sum(gammaln(sum_eta) - gammaln(np.sum(_lambda, 1)))
return score
|
https://github.com/RaRe-Technologies/gensim/issues/911
|
In [16]: model = models.LdaModel(corpus, id2word=dictionary, num_topics=2, distributed=True)
2016-10-03 09:21:04,056 : INFO : using symmetric alpha at 0.5
2016-10-03 09:21:04,056 : INFO : using symmetric eta at 0.5
2016-10-03 09:21:04,057 : DEBUG : looking for dispatcher at PYRO:gensim.lda_dispatcher@127.0.0.1:41212
2016-10-03 09:21:04,089 : INFO : using distributed version with 3 workers
2016-10-03 09:21:04,090 : INFO : running online LDA training, 2 topics, 1 passes over the supplied corpus of 9 documents, updating model once every 9 documents, evaluating perplexity every 9 documents, iterating 50x with a convergence threshold of 0.001000
2016-10-03 09:21:04,090 : WARNING : too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy
2016-10-03 09:21:04,090 : INFO : initializing 3 workers
2016-10-03 09:21:04,097 : DEBUG : bound: at document #0
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-16-46e2552e21a7> in <module>()
----> 1 model = models.LdaModel(corpus, id2word=dictionary, num_topics=2, distributed=True)
/home/lev/Dropbox/raretech/os/release/gensim/gensim/models/ldamodel.py in __init__(self, corpus, num_topics, id2word, distributed, chunksize, passes, update_every, alpha, eta, decay, offset, eval_every, iterations, gamma_threshold, minimum_probability, random_state, ns_conf)
344 if corpus is not None:
345 use_numpy = self.dispatcher is not None
--> 346 self.update(corpus, chunks_as_numpy=use_numpy)
347
348 def init_dir_prior(self, prior, name):
/home/lev/Dropbox/raretech/os/release/gensim/gensim/models/ldamodel.py in update(self, corpus, chunksize, decay, offset, passes, update_every, eval_every, iterations, gamma_threshold, chunks_as_numpy)
648
649 if eval_every and ((reallen == lencorpus) or ((chunk_no + 1) % (eval_every * self.numworkers) == 0)):
--> 650 self.log_perplexity(chunk, total_docs=lencorpus)
651
652 if self.dispatcher:
/home/lev/Dropbox/raretech/os/release/gensim/gensim/models/ldamodel.py in log_perplexity(self, chunk, total_docs)
539 corpus_words = sum(cnt for document in chunk for _, cnt in document)
540 subsample_ratio = 1.0 * total_docs / len(chunk)
--> 541 perwordbound = self.bound(chunk, subsample_ratio=subsample_ratio) / (subsample_ratio * corpus_words)
542 logger.info("%.3f per-word bound, %.1f perplexity estimate based on a held-out corpus of %i documents with %i words" %
543 (perwordbound, numpy.exp2(-perwordbound), len(chunk), corpus_words))
/home/lev/Dropbox/raretech/os/release/gensim/gensim/models/ldamodel.py in bound(self, corpus, gamma, subsample_ratio)
740 logger.debug("bound: at document #%i", d)
741 if gamma is None:
--> 742 gammad, _ = self.inference([doc])
743 else:
744 gammad = gamma[d]
/home/lev/Dropbox/raretech/os/release/gensim/gensim/models/ldamodel.py in inference(self, chunk, collect_sstats)
438 # to Blei's original LDA-C code, cool!).
439 for d, doc in enumerate(chunk):
--> 440 if doc and not isinstance(doc[0][0], six.integer_types):
441 # make sure the term IDs are ints, otherwise numpy will get upset
442 ids = [int(id) for id, _ in doc]
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
|
ValueError
|
def _get_combined_keywords(_keywords, split_text):
"""
:param keywords:dict of keywords:scores
:param split_text: list of strings
:return: combined_keywords:list
"""
result = []
_keywords = _keywords.copy()
len_text = len(split_text)
for i in xrange(len_text):
word = _strip_word(split_text[i])
if word in _keywords:
combined_word = [word]
if i + 1 == len_text:
result.append(word) # appends last word if keyword and doesn't iterate
for j in xrange(i + 1, len_text):
other_word = _strip_word(split_text[j])
if (
other_word in _keywords
and other_word == split_text[j]
and not other_word in combined_word
):
combined_word.append(other_word)
else:
for keyword in combined_word:
_keywords.pop(keyword)
result.append(" ".join(combined_word))
break
return result
|
def _get_combined_keywords(_keywords, split_text):
"""
:param keywords:dict of keywords:scores
:param split_text: list of strings
:return: combined_keywords:list
"""
result = []
_keywords = _keywords.copy()
len_text = len(split_text)
for i in xrange(len_text):
word = _strip_word(split_text[i])
if word in _keywords:
combined_word = [word]
if i + 1 == len_text:
result.append(word) # appends last word if keyword and doesn't iterate
for j in xrange(i + 1, len_text):
other_word = _strip_word(split_text[j])
if other_word in _keywords and other_word == split_text[j]:
combined_word.append(other_word)
else:
for keyword in combined_word:
_keywords.pop(keyword)
result.append(" ".join(combined_word))
break
return result
|
https://github.com/RaRe-Technologies/gensim/issues/667
|
import gensim.summarization
t = "Victor S. Sage Compare Sage 50c Editions Find accounting software that's right for your business Every product comes with anytime, anywhere online access; automatic updates; access to unlimited support; access to built-in credit card processing and payroll; and advanced reporting. Three solutions for your business 1 user From $249/year Buy now Free Trial 1-5 users From $299/year Buy now Free Trial 3-40 users From $1,199/year Buy now Free Trial Essential Accounting Accounts payable, accounts receivable, cash management check check check open check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check check Advanced Accounting Automated tasks, audit trail, budgeting, change order processing check check open check check check check check check check check check check check check check check check check check check check check check check check check check check check check In-depth Accounting Fast processing, industry-specific features, workflow management check open check check check check check check check check check check check Disclaimers open * This product is backed by a no-risk guarantee for first-time Sage 50 customers. If, within 60 days of purchase, you are not convinced that Sage 50 is the best accounting program for your business, we will refund your money (less and rebate you have received for this purchase). Dated proof of purchase and return of product is required. For details, call 877-481-0341."
import gensim.summarization
keywords = gensim.summarization.keywords(t, pos_filter=[], ratio=0.2, lemmatize=True, scores=True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/site-packages/gensim/summarization/keywords.py", line 229, in keywords
combined_keywords = _get_combined_keywords(keywords, text.split())
File "/usr/local/lib/python2.7/site-packages/gensim/summarization/keywords.py", line 171, in _get_combined_keywords
_keywords.pop(keyword)
KeyError: u'check'
|
KeyError
|
def get_probability_map(self, subject: Subject) -> torch.Tensor:
label_map_tensor = self.get_probability_map_image(subject).data.float()
if self.label_probabilities_dict is None:
return label_map_tensor > 0
probability_map = self.get_probabilities_from_label_map(
label_map_tensor,
self.label_probabilities_dict,
self.patch_size,
)
return probability_map
|
def get_probability_map(self, subject: Subject) -> torch.Tensor:
label_map_tensor = self.get_probability_map_image(subject).data
label_map_tensor = label_map_tensor.float()
if self.label_probabilities_dict is None:
return label_map_tensor > 0
probability_map = self.get_probabilities_from_label_map(
label_map_tensor,
self.label_probabilities_dict,
)
return probability_map
|
https://github.com/fepegar/torchio/issues/458
|
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Test label sampler.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyNrlDa49HLQ/jzNDBnNeFDL",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/fepegar/9e029c85827f48360ecc1b62b2530fa7/test-label-sampler.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "KrbGyxQCb62F"
},
"source": [
"!pip install --quiet torchio"
],
"execution_count": 85,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ETNalyapb8t3",
"outputId": "3cb5991a-7d4f-41a8-a1e5-52391274dcbc"
},
"source": [
"import torch\n",
"import torchio as tio\n",
"import matplotlib.pyplot as plt\n",
"\n",
"size = 5\n",
"tensor = torch.zeros(1, size, size, 1)\n",
"tensor[0, 2, 2] = 1\n",
"label = tio.LabelMap(tensor=tensor)\n",
"\n",
"subject = tio.Subject(label=label)\n",
"patch_size = 2, 2, 1\n",
"sampler = tio.LabelSampler(patch_size, label_probabilities={0: 1, 1:1})\n",
"values = torch.as_tensor([patch.label.data[0, 1, 1, 0] for patch in sampler(subject, 10000)])\n",
"print(values.mean())"
],
"execution_count": 86,
"outputs": [
{
"output_type": "stream",
"text": [
"tensor(0.6166)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 287
},
"id": "qWNBeroSctgu",
"outputId": "f06fa6be-b3b0-46b0-e32b-c73505a50ee9"
},
"source": [
"ax = plt.imshow(tensor[0, ..., 0])\n",
"plt.colorbar(ax)"
],
"execution_count": 87,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f6b2e792630>"
]
},
"metadata": {
"tags": []
},
"execution_count": 87
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 323
},
"id": "foNGhpRikpXN",
"outputId": "50c8769b-f836-45d5-d0e6-45b75e85d54c"
},
"source": [
"pmap = sampler.get_probability_map(subject)\n",
"print(pmap.shape)\n",
"ax = plt.imshow(pmap[0, ..., 0])\n",
"plt.colorbar(ax)\n",
"print(pmap.min())\n",
"print(pmap.max())\n",
"p0, p1 = pmap[tensor == 0].sum(), pmap[tensor == 1].sum()\n",
"assert p0 == p1"
],
"execution_count": 88,
"outputs": [
{
"output_type": "stream",
"text": [
"torch.Size([1, 5, 5, 1])\n",
"tensor(0.0208)\n",
"tensor(0.5000)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 496
},
"id": "T91VpRClktEP",
"outputId": "c6a43c3c-9ec3-4feb-9e25-e33a7bff4c30"
},
"source": [
"noborder = pmap.clone()[0]\n",
"sampler.clear_probability_borders(noborder, torch.as_tensor(patch_size))\n",
"ax = plt.imshow(noborder[..., 0])\n",
"plt.colorbar(ax)\n",
"print(noborder.min())\n",
"print(noborder.max())\n",
"p0, p1 = noborder[tensor[0] == 0].sum(), noborder[tensor[0] == 1].sum()\n",
"assert p0 == p1, (p0, p1)"
],
"execution_count": 89,
"outputs": [
{
"output_type": "stream",
"text": [
"tensor(0.)\n",
"tensor(0.5000)\n"
],
"name": "stdout"
},
{
"output_type": "error",
"ename": "AssertionError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-89-7d8d55210a52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoborder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mp0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnoborder\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoborder\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mp0\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mp1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m: (tensor(0.3125), tensor(0.5000))"
]
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
}
]
}
|
AssertionError
|
def get_probabilities_from_label_map(
label_map: torch.Tensor,
label_probabilities_dict: Dict[int, float],
patch_size: np.ndarray,
) -> torch.Tensor:
"""Create probability map according to label map probabilities."""
patch_size = patch_size.astype(int)
ini_i, ini_j, ini_k = patch_size // 2
spatial_shape = np.array(label_map.shape[1:])
if np.any(patch_size > spatial_shape):
message = f"Patch size {patch_size}larger than label map {spatial_shape}"
raise RuntimeError(message)
crop_fin_i, crop_fin_j, crop_fin_k = crop_fin = (patch_size - 1) // 2
fin_i, fin_j, fin_k = spatial_shape - crop_fin
# See https://github.com/fepegar/torchio/issues/458
label_map = label_map[:, ini_i:fin_i, ini_j:fin_j, ini_k:fin_k]
multichannel = label_map.shape[0] > 1
probability_map = torch.zeros_like(label_map)
label_probs = torch.Tensor(list(label_probabilities_dict.values()))
normalized_probs = label_probs / label_probs.sum()
iterable = zip(label_probabilities_dict, normalized_probs)
for label, label_probability in iterable:
if multichannel:
mask = label_map[label]
else:
mask = label_map == label
label_size = mask.sum()
if not label_size:
continue
prob_voxels = label_probability / label_size
if multichannel:
probability_map[label] = prob_voxels * mask
else:
probability_map[mask] = prob_voxels
if multichannel:
probability_map = probability_map.sum(dim=0, keepdim=True)
# See https://github.com/fepegar/torchio/issues/458
padding = ini_k, crop_fin_k, ini_j, crop_fin_j, ini_i, crop_fin_i
probability_map = torch.nn.functional.pad(
probability_map,
padding,
)
return probability_map
|
def get_probabilities_from_label_map(
label_map: torch.Tensor,
label_probabilities_dict: Dict[int, float],
) -> torch.Tensor:
"""Create probability map according to label map probabilities."""
multichannel = label_map.shape[0] > 1
probability_map = torch.zeros_like(label_map)
label_probs = torch.Tensor(list(label_probabilities_dict.values()))
normalized_probs = label_probs / label_probs.sum()
iterable = zip(label_probabilities_dict, normalized_probs)
for label, label_probability in iterable:
if multichannel:
mask = label_map[label]
else:
mask = label_map == label
label_size = mask.sum()
if not label_size:
continue
prob_voxels = label_probability / label_size
if multichannel:
probability_map[label] = prob_voxels * mask
else:
probability_map[mask] = prob_voxels
if multichannel:
probability_map = probability_map.sum(dim=0, keepdim=True)
return probability_map
|
https://github.com/fepegar/torchio/issues/458
|
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Test label sampler.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyNrlDa49HLQ/jzNDBnNeFDL",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/fepegar/9e029c85827f48360ecc1b62b2530fa7/test-label-sampler.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "KrbGyxQCb62F"
},
"source": [
"!pip install --quiet torchio"
],
"execution_count": 85,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ETNalyapb8t3",
"outputId": "3cb5991a-7d4f-41a8-a1e5-52391274dcbc"
},
"source": [
"import torch\n",
"import torchio as tio\n",
"import matplotlib.pyplot as plt\n",
"\n",
"size = 5\n",
"tensor = torch.zeros(1, size, size, 1)\n",
"tensor[0, 2, 2] = 1\n",
"label = tio.LabelMap(tensor=tensor)\n",
"\n",
"subject = tio.Subject(label=label)\n",
"patch_size = 2, 2, 1\n",
"sampler = tio.LabelSampler(patch_size, label_probabilities={0: 1, 1:1})\n",
"values = torch.as_tensor([patch.label.data[0, 1, 1, 0] for patch in sampler(subject, 10000)])\n",
"print(values.mean())"
],
"execution_count": 86,
"outputs": [
{
"output_type": "stream",
"text": [
"tensor(0.6166)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 287
},
"id": "qWNBeroSctgu",
"outputId": "f06fa6be-b3b0-46b0-e32b-c73505a50ee9"
},
"source": [
"ax = plt.imshow(tensor[0, ..., 0])\n",
"plt.colorbar(ax)"
],
"execution_count": 87,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f6b2e792630>"
]
},
"metadata": {
"tags": []
},
"execution_count": 87
},
{
"output_type": "display_data",
"data": {
"image/png": 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Bt9mkUap3lGqF0aq3qVo/sNgGXueVPd/xg6tqnj4uaarr+YTtic7jS4Dnu947Cmya9fm3z7F9StJrwM/Q4++mkXCyfdGg25Q0ZXvDoNttyijVO0q1wmjVW3KttrcMu4ZeclkXEYv1ArCm6/nqzmtzniPpTOC9wEu9Gk04RcRi7QPWSbpU0llUuzRNzjpnEvidzuPfpNrFqedtwFGa5zTR/5SijFK9o1QrjFa9o1Tru9IZQ7oF2AOMAbtsH5B0FzBlexK4B/iapMPAy1QB1pP6hFdExFDksi4iipRwiogijUQ49ZsaXxJJuyQdk/TssGvpR9IaSXslHZR0QNKtw65pPpLGJf1A0r93av2TYddUh6QxSU9LemjYtYya4sOp5tT4ktwLFD1/pMsp4Dbb64ErgU8V/Hf7FnC17V8BLge2SLpyyDXVcStwaNhFjKLiw4l6U+OLYftRqrsRxbP9ou2nOo9fp/qf6JLhVjU3V97oPF3ROYq+myNpNfBR4CvDrmUUjUI4zTU1vsj/gUZZ51fiVwBPDLeS+XUukZ4BjgEP2y621o4vAZ8Dyl2XpGCjEE7RMEnnAd8EPm37J8OuZz62p21fTjUDeaOky4Zd03wkfQw4ZvvJYdcyqkYhnOpMjY93SdIKqmD6hu1vDbueOmy/Cuyl7LG9zcBWSf9NNRRxtaSvD7ek0TIK4VRnany8C50lK+4BDtm+e9j19CLpIkkXdB6/B7gO+OFwq5qf7Ttsr7a9luq/2e/ZvmnIZY2U4sPJ9ing9NT4Q8ADtg8Mt6r5SboPeAz4oKSjkm4edk09bAY+QfWv+jOd44ZhFzWPi4G9kvZT/YP1sO3cnl/G8vOViChS8T2niGinhFNEFCnhFBFFSjhFRJESThFRpIRTRBQp4RQRRfp/9slH0Kzqm7cAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 323
},
"id": "foNGhpRikpXN",
"outputId": "50c8769b-f836-45d5-d0e6-45b75e85d54c"
},
"source": [
"pmap = sampler.get_probability_map(subject)\n",
"print(pmap.shape)\n",
"ax = plt.imshow(pmap[0, ..., 0])\n",
"plt.colorbar(ax)\n",
"print(pmap.min())\n",
"print(pmap.max())\n",
"p0, p1 = pmap[tensor == 0].sum(), pmap[tensor == 1].sum()\n",
"assert p0 == p1"
],
"execution_count": 88,
"outputs": [
{
"output_type": "stream",
"text": [
"torch.Size([1, 5, 5, 1])\n",
"tensor(0.0208)\n",
"tensor(0.5000)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 496
},
"id": "T91VpRClktEP",
"outputId": "c6a43c3c-9ec3-4feb-9e25-e33a7bff4c30"
},
"source": [
"noborder = pmap.clone()[0]\n",
"sampler.clear_probability_borders(noborder, torch.as_tensor(patch_size))\n",
"ax = plt.imshow(noborder[..., 0])\n",
"plt.colorbar(ax)\n",
"print(noborder.min())\n",
"print(noborder.max())\n",
"p0, p1 = noborder[tensor[0] == 0].sum(), noborder[tensor[0] == 1].sum()\n",
"assert p0 == p1, (p0, p1)"
],
"execution_count": 89,
"outputs": [
{
"output_type": "stream",
"text": [
"tensor(0.)\n",
"tensor(0.5000)\n"
],
"name": "stdout"
},
{
"output_type": "error",
"ename": "AssertionError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-89-7d8d55210a52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoborder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mp0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnoborder\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoborder\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mp0\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mp1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m: (tensor(0.3125), tensor(0.5000))"
]
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
}
]
}
|
AssertionError
|
def __init__(
self,
subjects_dataset: SubjectsDataset,
max_length: int,
samples_per_volume: int,
sampler: PatchSampler,
num_workers: int = 0,
shuffle_subjects: bool = True,
shuffle_patches: bool = True,
start_background: bool = True,
verbose: bool = False,
):
self.subjects_dataset = subjects_dataset
self.max_length = max_length
self.shuffle_subjects = shuffle_subjects
self.shuffle_patches = shuffle_patches
self.samples_per_volume = samples_per_volume
self.sampler = sampler
self.num_workers = num_workers
self.verbose = verbose
self._subjects_iterable = None
if start_background:
self.initialize_subjects_iterable()
self.patches_list: List[Subject] = []
self.num_sampled_patches = 0
|
def __init__(
self,
subjects_dataset: SubjectsDataset,
max_length: int,
samples_per_volume: int,
sampler: PatchSampler,
num_workers: int = 0,
pin_memory: bool = True,
shuffle_subjects: bool = True,
shuffle_patches: bool = True,
start_background: bool = True,
verbose: bool = False,
):
self.subjects_dataset = subjects_dataset
self.max_length = max_length
self.shuffle_subjects = shuffle_subjects
self.shuffle_patches = shuffle_patches
self.samples_per_volume = samples_per_volume
self.sampler = sampler
self.num_workers = num_workers
self.pin_memory = pin_memory
self.verbose = verbose
self._subjects_iterable = None
if start_background:
self.initialize_subjects_iterable()
self.patches_list: List[Subject] = []
self.num_sampled_patches = 0
|
https://github.com/fepegar/torchio/issues/431
|
AttributeError Traceback (most recent call last)
<ipython-input-28-bf125a1bde76> in <module>
----> 1 batch = next(iter(patches_loader))
~/miniconda3/envs/master/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)
343
344 def __next__(self):
--> 345 data = self._next_data()
346 self._num_yielded += 1
347 if self._dataset_kind == _DatasetKind.Iterable and \
~/miniconda3/envs/master/lib/python3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self)
383 def _next_data(self):
384 index = self._next_index() # may raise StopIteration
--> 385 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
386 if self._pin_memory:
387 data = _utils.pin_memory.pin_memory(data)
~/miniconda3/envs/master/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
~/miniconda3/envs/master/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
~/miniconda3/envs/master/lib/python3.7/site-packages/torchio/data/queue.py in __getitem__(self, _)
166 if not self.patches_list:
167 self._print('Patches list is empty.')
--> 168 self.fill()
169 sample_patch = self.patches_list.pop()
170 self.num_sampled_patches += 1
~/miniconda3/envs/master/lib/python3.7/site-packages/torchio/data/queue.py in fill(self)
232 subject = self.get_next_subject()
233 iterable = self.sampler(subject)
--> 234 patches = list(islice(iterable, self.samples_per_volume))
235 self.patches_list.extend(patches)
236 if self.shuffle_patches:
~/miniconda3/envs/master/lib/python3.7/site-packages/torchio/data/sampler/uniform.py in __call__(self, subject, num_patches)
24 num_patches: int = None,
25 ) -> Generator[Subject, None, None]:
---> 26 subject.check_consistent_spatial_shape()
27
28 if np.any(self.patch_size > subject.spatial_shape):
AttributeError: 'dict' object has no attribute 'check_consistent_spatial_shape'
|
AttributeError
|
def get_subjects_iterable(self) -> Iterator:
# I need a DataLoader to handle parallelism
# But this loader is always expected to yield single subject samples
self._print(f"\nCreating subjects loader with {self.num_workers} workers")
subjects_loader = DataLoader(
self.subjects_dataset,
num_workers=self.num_workers,
batch_size=1,
collate_fn=self.get_first_item,
shuffle=self.shuffle_subjects,
)
return iter(subjects_loader)
|
def get_subjects_iterable(self) -> Iterator:
# I need a DataLoader to handle parallelism
# But this loader is always expected to yield single subject samples
self._print(f"\nCreating subjects loader with {self.num_workers} workers")
subjects_loader = DataLoader(
self.subjects_dataset,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
batch_size=1,
collate_fn=self.get_first_item,
shuffle=self.shuffle_subjects,
)
return iter(subjects_loader)
|
https://github.com/fepegar/torchio/issues/431
|
AttributeError Traceback (most recent call last)
<ipython-input-28-bf125a1bde76> in <module>
----> 1 batch = next(iter(patches_loader))
~/miniconda3/envs/master/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)
343
344 def __next__(self):
--> 345 data = self._next_data()
346 self._num_yielded += 1
347 if self._dataset_kind == _DatasetKind.Iterable and \
~/miniconda3/envs/master/lib/python3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self)
383 def _next_data(self):
384 index = self._next_index() # may raise StopIteration
--> 385 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
386 if self._pin_memory:
387 data = _utils.pin_memory.pin_memory(data)
~/miniconda3/envs/master/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
~/miniconda3/envs/master/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
~/miniconda3/envs/master/lib/python3.7/site-packages/torchio/data/queue.py in __getitem__(self, _)
166 if not self.patches_list:
167 self._print('Patches list is empty.')
--> 168 self.fill()
169 sample_patch = self.patches_list.pop()
170 self.num_sampled_patches += 1
~/miniconda3/envs/master/lib/python3.7/site-packages/torchio/data/queue.py in fill(self)
232 subject = self.get_next_subject()
233 iterable = self.sampler(subject)
--> 234 patches = list(islice(iterable, self.samples_per_volume))
235 self.patches_list.extend(patches)
236 if self.shuffle_patches:
~/miniconda3/envs/master/lib/python3.7/site-packages/torchio/data/sampler/uniform.py in __call__(self, subject, num_patches)
24 num_patches: int = None,
25 ) -> Generator[Subject, None, None]:
---> 26 subject.check_consistent_spatial_shape()
27
28 if np.any(self.patch_size > subject.spatial_shape):
AttributeError: 'dict' object has no attribute 'check_consistent_spatial_shape'
|
AttributeError
|
def __init__(
self,
subjects_dataset: SubjectsDataset,
max_length: int,
samples_per_volume: int,
sampler: PatchSampler,
num_workers: int = 0,
pin_memory: bool = True,
shuffle_subjects: bool = True,
shuffle_patches: bool = True,
start_background: bool = True,
verbose: bool = False,
):
self.subjects_dataset = subjects_dataset
self.max_length = max_length
self.shuffle_subjects = shuffle_subjects
self.shuffle_patches = shuffle_patches
self.samples_per_volume = samples_per_volume
self.sampler = sampler
self.num_workers = num_workers
self.pin_memory = pin_memory
self.verbose = verbose
self._subjects_iterable = None
if start_background:
self.initialize_subjects_iterable()
self.patches_list: List[Subject] = []
self.num_sampled_patches = 0
|
def __init__(
self,
subjects_dataset: SubjectsDataset,
max_length: int,
samples_per_volume: int,
sampler: PatchSampler,
num_workers: int = 0,
shuffle_subjects: bool = True,
shuffle_patches: bool = True,
verbose: bool = False,
):
self.subjects_dataset = subjects_dataset
self.max_length = max_length
self.shuffle_subjects = shuffle_subjects
self.shuffle_patches = shuffle_patches
self.samples_per_volume = samples_per_volume
self.sampler = sampler
self.num_workers = num_workers
self.verbose = verbose
self.subjects_iterable = self.get_subjects_iterable()
self.patches_list: List[dict] = []
self.num_sampled_patches = 0
|
https://github.com/fepegar/torchio/issues/422
|
Traceback (most recent call last):
File "C:\Users\fzj\anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 93, in __init__
reduction.dump(process_obj, to_child)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'Queue.get_subjects_iterable.<locals>.<lambda>'
python-BaseException
Traceback (most recent call last):
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\fzj\Google Drive\PHD project\3_src\DL\sli_net\main_loader.py", line 10, in <module>
patches_queue = tio.Queue(
File "C:\Users\fzj\anaconda3\lib\site-packages\torchio\data\queue.py", line 146, in __init__
self.subjects_iterable = self.get_subjects_iterable()
File "C:\Users\fzj\anaconda3\lib\site-packages\torchio\data\queue.py", line 240, in get_subjects_iterable
return iter(subjects_loader)
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 352, in __iter__
return self._get_iterator()
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 294, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 801, in __init__
w.start()
File "C:\Users\fzj\anaconda3\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\context.py", line 326, in _Popen
return Popen(process_obj)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
python-BaseException
Process finished with exit code -1
|
AttributeError
|
def get_next_subject(self) -> Subject:
# A StopIteration exception is expected when the queue is empty
try:
subject = next(self.subjects_iterable)
except StopIteration as exception:
self._print("Queue is empty:", exception)
self.initialize_subjects_iterable()
subject = next(self.subjects_iterable)
return subject
|
def get_next_subject(self) -> Subject:
# A StopIteration exception is expected when the queue is empty
try:
subject = next(self.subjects_iterable)
except StopIteration as exception:
self._print("Queue is empty:", exception)
self.subjects_iterable = self.get_subjects_iterable()
subject = next(self.subjects_iterable)
return subject
|
https://github.com/fepegar/torchio/issues/422
|
Traceback (most recent call last):
File "C:\Users\fzj\anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 93, in __init__
reduction.dump(process_obj, to_child)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'Queue.get_subjects_iterable.<locals>.<lambda>'
python-BaseException
Traceback (most recent call last):
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\fzj\Google Drive\PHD project\3_src\DL\sli_net\main_loader.py", line 10, in <module>
patches_queue = tio.Queue(
File "C:\Users\fzj\anaconda3\lib\site-packages\torchio\data\queue.py", line 146, in __init__
self.subjects_iterable = self.get_subjects_iterable()
File "C:\Users\fzj\anaconda3\lib\site-packages\torchio\data\queue.py", line 240, in get_subjects_iterable
return iter(subjects_loader)
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 352, in __iter__
return self._get_iterator()
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 294, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 801, in __init__
w.start()
File "C:\Users\fzj\anaconda3\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\context.py", line 326, in _Popen
return Popen(process_obj)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
python-BaseException
Process finished with exit code -1
|
AttributeError
|
def get_subjects_iterable(self) -> Iterator:
# I need a DataLoader to handle parallelism
# But this loader is always expected to yield single subject samples
self._print(f"\nCreating subjects loader with {self.num_workers} workers")
subjects_loader = DataLoader(
self.subjects_dataset,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
batch_size=1,
collate_fn=self.get_first_item,
shuffle=self.shuffle_subjects,
)
return iter(subjects_loader)
|
def get_subjects_iterable(self) -> Iterator:
# I need a DataLoader to handle parallelism
# But this loader is always expected to yield single subject samples
self._print("\nCreating subjects loader with", self.num_workers, "workers")
subjects_loader = DataLoader(
self.subjects_dataset,
num_workers=self.num_workers,
collate_fn=lambda x: x[0],
shuffle=self.shuffle_subjects,
)
return iter(subjects_loader)
|
https://github.com/fepegar/torchio/issues/422
|
Traceback (most recent call last):
File "C:\Users\fzj\anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 93, in __init__
reduction.dump(process_obj, to_child)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'Queue.get_subjects_iterable.<locals>.<lambda>'
python-BaseException
Traceback (most recent call last):
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Users\fzj\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\fzj\Google Drive\PHD project\3_src\DL\sli_net\main_loader.py", line 10, in <module>
patches_queue = tio.Queue(
File "C:\Users\fzj\anaconda3\lib\site-packages\torchio\data\queue.py", line 146, in __init__
self.subjects_iterable = self.get_subjects_iterable()
File "C:\Users\fzj\anaconda3\lib\site-packages\torchio\data\queue.py", line 240, in get_subjects_iterable
return iter(subjects_loader)
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 352, in __iter__
return self._get_iterator()
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 294, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\fzj\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 801, in __init__
w.start()
File "C:\Users\fzj\anaconda3\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\context.py", line 326, in _Popen
return Popen(process_obj)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "C:\Users\fzj\anaconda3\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
python-BaseException
Process finished with exit code -1
|
AttributeError
|
def add_transform_to_subject_history(self, subject):
from .augmentation import RandomTransform
from . import Compose, OneOf, CropOrPad, EnsureShapeMultiple
from .preprocessing import SequentialLabels
call_others = (
RandomTransform,
Compose,
OneOf,
CropOrPad,
EnsureShapeMultiple,
SequentialLabels,
)
if not isinstance(self, call_others):
subject.add_transform(self, self._get_reproducing_arguments())
|
def add_transform_to_subject_history(self, subject):
from .augmentation import RandomTransform
from . import Compose, OneOf, CropOrPad, EnsureShapeMultiple
from .preprocessing.label import SequentialLabels
call_others = (
RandomTransform,
Compose,
OneOf,
CropOrPad,
EnsureShapeMultiple,
SequentialLabels,
)
if not isinstance(self, call_others):
subject.add_transform(self, self._get_reproducing_arguments())
|
https://github.com/fepegar/torchio/issues/419
|
assert logits.shape == target.shape #1,4,16,16,16 vs. 1,1,16,16,16
AssertionError
Exception ignored in: <function tqdm.__del__ at 0x7f2fef73a550>
Traceback (most recent call last):
File "/home/nico/.local/lib/python3.8/site-packages/tqdm/std.py", line 1090, in __del__
File "/home/nico/.local/lib/python3.8/site-packages/tqdm/std.py", line 1303, in close
File "/home/nico/.local/lib/python3.8/site-packages/tqdm/std.py", line 1481, in display
File "/home/nico/.local/lib/python3.8/site-packages/tqdm/std.py", line 1093, in __repr__
File "/home/nico/.local/lib/python3.8/site-packages/tqdm/std.py", line 1443, in format_dict
TypeError: cannot unpack non-iterable NoneType object
Process finished with exit code 1
|
TypeError
|
def train(
cls,
images_paths: Sequence[TypePath],
cutoff: Optional[Tuple[float, float]] = None,
mask_path: Optional[TypePath] = None,
masking_function: Optional[Callable] = None,
output_path: Optional[TypePath] = None,
) -> np.ndarray:
"""Extract average histogram landmarks from images used for training.
Args:
images_paths: List of image paths used to train.
cutoff: Optional minimum and maximum quantile values,
respectively, that are used to select a range of intensity of
interest. Equivalent to :math:`pc_1` and :math:`pc_2` in
`Nyúl and Udupa's paper <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.204.102&rep=rep1&type=pdf>`_.
mask_path: Optional path to a mask image to extract voxels used for
training.
masking_function: Optional function used to extract voxels used for
training.
output_path: Optional file path with extension ``.txt`` or
``.npy``, where the landmarks will be saved.
Example:
>>> import torch
>>> import numpy as np
>>> from pathlib import Path
>>> from torchio.transforms import HistogramStandardization
>>>
>>> t1_paths = ['subject_a_t1.nii', 'subject_b_t1.nii.gz']
>>> t2_paths = ['subject_a_t2.nii', 'subject_b_t2.nii.gz']
>>>
>>> t1_landmarks_path = Path('t1_landmarks.npy')
>>> t2_landmarks_path = Path('t2_landmarks.npy')
>>>
>>> t1_landmarks = (
... t1_landmarks_path
... if t1_landmarks_path.is_file()
... else HistogramStandardization.train(t1_paths)
... )
>>> torch.save(t1_landmarks, t1_landmarks_path)
>>>
>>> t2_landmarks = (
... t2_landmarks_path
... if t2_landmarks_path.is_file()
... else HistogramStandardization.train(t2_paths)
... )
>>> torch.save(t2_landmarks, t2_landmarks_path)
>>>
>>> landmarks_dict = {
... 't1': t1_landmarks,
... 't2': t2_landmarks,
... }
>>>
>>> transform = HistogramStandardization(landmarks_dict)
""" # noqa: E501
quantiles_cutoff = DEFAULT_CUTOFF if cutoff is None else cutoff
percentiles_cutoff = 100 * np.array(quantiles_cutoff)
percentiles_database = []
percentiles = _get_percentiles(percentiles_cutoff)
for image_file_path in tqdm(images_paths):
tensor, _ = read_image(image_file_path)
if masking_function is not None:
mask = masking_function(tensor)
else:
if mask_path is not None:
mask, _ = read_image(mask_path)
mask = mask.numpy() > 0
else:
mask = np.ones_like(tensor, dtype=np.bool)
array = tensor.numpy()
percentile_values = np.percentile(array[mask], percentiles)
percentiles_database.append(percentile_values)
percentiles_database = np.vstack(percentiles_database)
mapping = _get_average_mapping(percentiles_database)
if output_path is not None:
output_path = Path(output_path).expanduser()
extension = output_path.suffix
if extension == ".txt":
modality = "image"
text = f"{modality} {' '.join(map(str, mapping))}"
output_path.write_text(text)
elif extension == ".npy":
np.save(output_path, mapping)
return mapping
|
def train(
cls,
images_paths: Sequence[TypePath],
cutoff: Optional[Tuple[float, float]] = None,
mask_path: Optional[TypePath] = None,
masking_function: Optional[Callable] = None,
output_path: Optional[TypePath] = None,
) -> np.ndarray:
"""Extract average histogram landmarks from images used for training.
Args:
images_paths: List of image paths used to train.
cutoff: Optional minimum and maximum quantile values,
respectively, that are used to select a range of intensity of
interest. Equivalent to :math:`pc_1` and :math:`pc_2` in
`Nyúl and Udupa's paper <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.204.102&rep=rep1&type=pdf>`_.
mask_path: Optional path to a mask image to extract voxels used for
training.
masking_function: Optional function used to extract voxels used for
training.
output_path: Optional file path with extension ``.txt`` or
``.npy``, where the landmarks will be saved.
Example:
>>> import torch
>>> import numpy as np
>>> from pathlib import Path
>>> from torchio.transforms import HistogramStandardization
>>>
>>> t1_paths = ['subject_a_t1.nii', 'subject_b_t1.nii.gz']
>>> t2_paths = ['subject_a_t2.nii', 'subject_b_t2.nii.gz']
>>>
>>> t1_landmarks_path = Path('t1_landmarks.npy')
>>> t2_landmarks_path = Path('t2_landmarks.npy')
>>>
>>> t1_landmarks = (
... t1_landmarks_path
... if t1_landmarks_path.is_file()
... else HistogramStandardization.train(t1_paths)
... )
>>> torch.save(t1_landmarks, t1_landmarks_path)
>>>
>>> t2_landmarks = (
... t2_landmarks_path
... if t2_landmarks_path.is_file()
... else HistogramStandardization.train(t2_paths)
... )
>>> torch.save(t2_landmarks, t2_landmarks_path)
>>>
>>> landmarks_dict = {
... 't1': t1_landmarks,
... 't2': t2_landmarks,
... }
>>>
>>> transform = HistogramStandardization(landmarks_dict)
""" # noqa: E501
quantiles_cutoff = DEFAULT_CUTOFF if cutoff is None else cutoff
percentiles_cutoff = 100 * np.array(quantiles_cutoff)
percentiles_database = []
percentiles = _get_percentiles(percentiles_cutoff)
for image_file_path in tqdm(images_paths):
tensor, _ = read_image(image_file_path)
data = tensor.numpy()
if masking_function is not None:
mask = masking_function(data)
else:
if mask_path is not None:
mask, _ = read_image(mask_path)
mask = mask.numpy() > 0
else:
mask = np.ones_like(data, dtype=np.bool)
percentile_values = np.percentile(data[mask], percentiles)
percentiles_database.append(percentile_values)
percentiles_database = np.vstack(percentiles_database)
mapping = _get_average_mapping(percentiles_database)
if output_path is not None:
output_path = Path(output_path).expanduser()
extension = output_path.suffix
if extension == ".txt":
modality = "image"
text = f"{modality} {' '.join(map(str, mapping))}"
output_path.write_text(text)
elif extension == ".npy":
np.save(output_path, mapping)
return mapping
|
https://github.com/fepegar/torchio/issues/407
|
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-5-f7eeffa30bdc> in <module>()
10 ]
11 transform = tio.Compose(transforms)
---> 12 preprocessed = transform(subject)
5 frames
/usr/local/lib/python3.6/dist-packages/torchio/transforms/transform.py in __call__(self, data)
100 subject = copy.copy(subject)
101 with np.errstate(all='raise'):
--> 102 transformed = self.apply_transform(subject)
103 self.add_transform_to_subject_history(transformed)
104 for image in transformed.get_images(intensity_only=False):
/usr/local/lib/python3.6/dist-packages/torchio/transforms/augmentation/composition.py in apply_transform(self, subject)
44 def apply_transform(self, subject: Subject) -> Subject:
45 for transform in self.transforms:
---> 46 subject = transform(subject)
47 return subject
48
/usr/local/lib/python3.6/dist-packages/torchio/transforms/transform.py in __call__(self, data)
100 subject = copy.copy(subject)
101 with np.errstate(all='raise'):
--> 102 transformed = self.apply_transform(subject)
103 self.add_transform_to_subject_history(transformed)
104 for image in transformed.get_images(intensity_only=False):
/usr/local/lib/python3.6/dist-packages/torchio/transforms/preprocessing/intensity/normalization_transform.py in apply_transform(self, subject)
46 def apply_transform(self, subject: Subject) -> Subject:
47 for image_name, image in self.get_images_dict(subject).items():
---> 48 mask = Transform.get_mask(self.masking_method, subject, image.data)
49 self.apply_normalization(subject, image_name, mask)
50 return subject
/usr/local/lib/python3.6/dist-packages/torchio/transforms/transform.py in get_mask(masking_method, subject, tensor)
394 return Transform.ones(tensor)
395 elif callable(masking_method):
--> 396 return masking_method(tensor)
397 elif type(masking_method) is str:
398 if masking_method in subject and isinstance(subject[masking_method], LabelMap):
/usr/local/lib/python3.6/dist-packages/torchio/transforms/transform.py in mean(tensor)
386 @staticmethod
387 def mean(tensor: torch.Tensor) -> torch.Tensor:
--> 388 mask = tensor > tensor.mean()
389 return mask
390
RuntimeError: Can only calculate the mean of floating types. Got Short instead.
|
RuntimeError
|
def mean(tensor: torch.Tensor) -> torch.Tensor:
mask = tensor > tensor.float().mean()
return mask
|
def mean(tensor: torch.Tensor) -> torch.Tensor:
mask = tensor > tensor.mean()
return mask
|
https://github.com/fepegar/torchio/issues/407
|
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-5-f7eeffa30bdc> in <module>()
10 ]
11 transform = tio.Compose(transforms)
---> 12 preprocessed = transform(subject)
5 frames
/usr/local/lib/python3.6/dist-packages/torchio/transforms/transform.py in __call__(self, data)
100 subject = copy.copy(subject)
101 with np.errstate(all='raise'):
--> 102 transformed = self.apply_transform(subject)
103 self.add_transform_to_subject_history(transformed)
104 for image in transformed.get_images(intensity_only=False):
/usr/local/lib/python3.6/dist-packages/torchio/transforms/augmentation/composition.py in apply_transform(self, subject)
44 def apply_transform(self, subject: Subject) -> Subject:
45 for transform in self.transforms:
---> 46 subject = transform(subject)
47 return subject
48
/usr/local/lib/python3.6/dist-packages/torchio/transforms/transform.py in __call__(self, data)
100 subject = copy.copy(subject)
101 with np.errstate(all='raise'):
--> 102 transformed = self.apply_transform(subject)
103 self.add_transform_to_subject_history(transformed)
104 for image in transformed.get_images(intensity_only=False):
/usr/local/lib/python3.6/dist-packages/torchio/transforms/preprocessing/intensity/normalization_transform.py in apply_transform(self, subject)
46 def apply_transform(self, subject: Subject) -> Subject:
47 for image_name, image in self.get_images_dict(subject).items():
---> 48 mask = Transform.get_mask(self.masking_method, subject, image.data)
49 self.apply_normalization(subject, image_name, mask)
50 return subject
/usr/local/lib/python3.6/dist-packages/torchio/transforms/transform.py in get_mask(masking_method, subject, tensor)
394 return Transform.ones(tensor)
395 elif callable(masking_method):
--> 396 return masking_method(tensor)
397 elif type(masking_method) is str:
398 if masking_method in subject and isinstance(subject[masking_method], LabelMap):
/usr/local/lib/python3.6/dist-packages/torchio/transforms/transform.py in mean(tensor)
386 @staticmethod
387 def mean(tensor: torch.Tensor) -> torch.Tensor:
--> 388 mask = tensor > tensor.mean()
389 return mask
390
RuntimeError: Can only calculate the mean of floating types. Got Short instead.
|
RuntimeError
|
def generate_bias_field(
data: TypeData,
order: int,
coefficients: TypeData,
) -> np.ndarray:
# Create the bias field map using a linear combination of polynomial
# functions and the coefficients previously sampled
shape = np.array(data.shape[1:]) # first axis is channels
half_shape = shape.astype(float) / 2
ranges = [np.arange(-n, n) + 0.5 for n in half_shape]
bias_field = np.zeros(shape)
x_mesh, y_mesh, z_mesh = np.asarray(np.meshgrid(*ranges))
x_mesh /= x_mesh.max()
y_mesh /= y_mesh.max()
z_mesh /= z_mesh.max()
i = 0
for x_order in range(order + 1):
for y_order in range(order + 1 - x_order):
for z_order in range(order + 1 - (x_order + y_order)):
coefficient = coefficients[i]
new_map = (
coefficient * x_mesh**x_order * y_mesh**y_order * z_mesh**z_order
)
bias_field += np.transpose(new_map, (1, 0, 2)) # why?
i += 1
bias_field = np.exp(bias_field).astype(np.float32)
return bias_field
|
def generate_bias_field(
data: TypeData,
order: int,
coefficients: TypeData,
) -> np.ndarray:
# Create the bias field map using a linear combination of polynomial
# functions and the coefficients previously sampled
shape = np.array(data.shape[1:]) # first axis is channels
half_shape = shape / 2
ranges = [np.arange(-n, n) for n in half_shape]
bias_field = np.zeros(shape)
x_mesh, y_mesh, z_mesh = np.asarray(np.meshgrid(*ranges))
x_mesh /= x_mesh.max()
y_mesh /= y_mesh.max()
z_mesh /= z_mesh.max()
i = 0
for x_order in range(order + 1):
for y_order in range(order + 1 - x_order):
for z_order in range(order + 1 - (x_order + y_order)):
coefficient = coefficients[i]
new_map = (
coefficient * x_mesh**x_order * y_mesh**y_order * z_mesh**z_order
)
bias_field += np.transpose(new_map, (1, 0, 2)) # why?
i += 1
bias_field = np.exp(bias_field).astype(np.float32)
return bias_field
|
https://github.com/fepegar/torchio/issues/300
|
---------------------------------------------------------------------------
FloatingPointError Traceback (most recent call last)
<ipython-input-3-fb6790e7e65a> in <module>
----> 1 tio.RandomBiasField()(torch.rand(1, 2, 3, 4))
~/git/torchio/torchio/transforms/augmentation/random_transform.py in __call__(self, sample)
32 def __call__(self, sample: Subject):
33 self.check_seed()
---> 34 return super().__call__(sample)
35
36 def parse_degrees(
~/git/torchio/torchio/transforms/transform.py in __call__(self, data)
93
94 with np.errstate(all='raise'):
---> 95 transformed = self.apply_transform(sample)
96
97 for image in transformed.get_images(intensity_only=False):
~/git/torchio/torchio/transforms/augmentation/intensity/random_bias_field.py in apply_transform(self, sample)
56 random_parameters_images_dict[image_name] = random_parameters_dict
57
---> 58 bias_field = self.generate_bias_field(
59 image_dict[DATA], self.order, coefficients)
60 image_dict[DATA] = image_dict[DATA] * torch.from_numpy(bias_field)
~/git/torchio/torchio/transforms/augmentation/intensity/random_bias_field.py in generate_bias_field(data, order, coefficients)
93 x_mesh, y_mesh, z_mesh = np.asarray(np.meshgrid(*ranges))
94
---> 95 x_mesh /= x_mesh.max()
96 y_mesh /= y_mesh.max()
97 z_mesh /= z_mesh.max()
FloatingPointError: divide by zero encountered in true_divide
|
FloatingPointError
|
def _parse_path(
self, path: Union[TypePath, Sequence[TypePath]]
) -> Optional[Union[Path, List[Path]]]:
if path is None:
return None
if isinstance(path, (str, Path)):
return self._parse_single_path(path)
else:
return [self._parse_single_path(p) for p in path]
|
def _parse_path(
self, path: Union[TypePath, Sequence[TypePath]]
) -> Union[Path, List[Path]]:
if path is None:
return None
if isinstance(path, (str, Path)):
return self._parse_single_path(path)
else:
return [self._parse_single_path(p) for p in path]
|
https://github.com/fepegar/torchio/issues/383
|
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-bf55f6468bbb> in <module>
----> 1 x.as_pil()
AttributeError: 'numpy.ndarray' object has no attribute 'as_pil'
In [5]: im = tio.ScalarImage(tensor=x)
In [6]: im.as_pil()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/usr/local/Caskroom/miniconda/base/envs/torchio/lib/python3.8/site-packages/PIL/Image.py in open(fp, mode)
2881 try:
-> 2882 fp.seek(0)
2883 except (AttributeError, io.UnsupportedOperation):
AttributeError: 'NoneType' object has no attribute 'seek'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-6-bc29f2434f0a> in <module>
----> 1 im.as_pil()
~/git/torchio/torchio/data/image.py in as_pil(self)
466 """Get the image as an instance of :class:`PIL.Image`."""
467 self.check_is_2d()
--> 468 return ImagePIL.open(self.path)
469
470 def get_center(self, lps: bool = False) -> TypeTripletFloat:
/usr/local/Caskroom/miniconda/base/envs/torchio/lib/python3.8/site-packages/PIL/Image.py in open(fp, mode)
2882 fp.seek(0)
2883 except (AttributeError, io.UnsupportedOperation):
-> 2884 fp = io.BytesIO(fp.read())
2885 exclusive_fp = True
2886
|
AttributeError
|
def as_pil(self) -> ImagePIL:
"""Get the image as an instance of :class:`PIL.Image`.
.. note:: Values will be clamped to 0-255 and cast to uint8.
"""
self.check_is_2d()
tensor = self.data
if len(tensor) == 1:
tensor = torch.cat(3 * [tensor])
if len(tensor) != 3:
raise RuntimeError("The image must have 1 or 3 channels")
tensor = tensor.permute(3, 1, 2, 0)[0]
array = tensor.clamp(0, 255).numpy()
return ImagePIL.fromarray(array.astype(np.uint8))
|
def as_pil(self) -> ImagePIL:
"""Get the image as an instance of :class:`PIL.Image`."""
self.check_is_2d()
return ImagePIL.open(self.path)
|
https://github.com/fepegar/torchio/issues/383
|
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-bf55f6468bbb> in <module>
----> 1 x.as_pil()
AttributeError: 'numpy.ndarray' object has no attribute 'as_pil'
In [5]: im = tio.ScalarImage(tensor=x)
In [6]: im.as_pil()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/usr/local/Caskroom/miniconda/base/envs/torchio/lib/python3.8/site-packages/PIL/Image.py in open(fp, mode)
2881 try:
-> 2882 fp.seek(0)
2883 except (AttributeError, io.UnsupportedOperation):
AttributeError: 'NoneType' object has no attribute 'seek'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-6-bc29f2434f0a> in <module>
----> 1 im.as_pil()
~/git/torchio/torchio/data/image.py in as_pil(self)
466 """Get the image as an instance of :class:`PIL.Image`."""
467 self.check_is_2d()
--> 468 return ImagePIL.open(self.path)
469
470 def get_center(self, lps: bool = False) -> TypeTripletFloat:
/usr/local/Caskroom/miniconda/base/envs/torchio/lib/python3.8/site-packages/PIL/Image.py in open(fp, mode)
2882 fp.seek(0)
2883 except (AttributeError, io.UnsupportedOperation):
-> 2884 fp = io.BytesIO(fp.read())
2885 exclusive_fp = True
2886
|
AttributeError
|
def __init__(self, *args, **kwargs: Dict[str, Any]):
if args:
if len(args) == 1 and isinstance(args[0], dict):
kwargs.update(args[0])
else:
message = "Only one dictionary as positional argument is allowed"
raise ValueError(message)
super().__init__(**kwargs)
self._parse_images(self.get_images(intensity_only=False))
self.update_attributes() # this allows me to do e.g. subject.t1
self.history = []
|
def __init__(self, *args, **kwargs: Dict[str, Any]):
if args:
if len(args) == 1 and isinstance(args[0], dict):
kwargs.update(args[0])
else:
message = "Only one dictionary as positional argument is allowed"
raise ValueError(message)
super().__init__(**kwargs)
self.images = [(k, v) for (k, v) in self.items() if isinstance(v, Image)]
self._parse_images(self.images)
self.update_attributes() # this allows me to do e.g. subject.t1
self.history = []
|
https://github.com/fepegar/torchio/issues/265
|
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-6b7dc2edb3cc> in <module>
----> 1 icbm.spatial_shape
~/git/torchio/torchio/data/subject.py in spatial_shape(self)
95 Consistency of shapes across images in the subject is checked first.
96 """
---> 97 return self.shape[1:]
98
99 @property
~/git/torchio/torchio/data/subject.py in shape(self)
85 Consistency of shapes across images in the subject is checked first.
86 """
---> 87 self.check_consistent_shape()
88 image = self.get_images(intensity_only=False)[0]
89 return image.shape
~/git/torchio/torchio/data/subject.py in check_consistent_shape(self)
135 f'\n{pprint.pformat(shapes_dict)}'
136 )
--> 137 raise ValueError(message)
138
139 def check_consistent_orientation(self) -> None:
ValueError: Images in subject have inconsistent shapes:
{'brain': (1, 193, 229, 193),
'eyes': (1, 193, 229, 193),
'pd': (1, 193, 229, 193),
't1': (1, 193, 229, 193),
't2': (1, 193, 229, 193),
'tissues': (3, 193, 229, 193)}
|
ValueError
|
def __repr__(self):
num_images = len(self.get_images(intensity_only=False))
string = (
f"{self.__class__.__name__}(Keys: {tuple(self.keys())}; images: {num_images})"
)
return string
|
def __repr__(self):
string = (
f"{self.__class__.__name__}"
f"(Keys: {tuple(self.keys())}; images: {len(self.images)})"
)
return string
|
https://github.com/fepegar/torchio/issues/265
|
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-6b7dc2edb3cc> in <module>
----> 1 icbm.spatial_shape
~/git/torchio/torchio/data/subject.py in spatial_shape(self)
95 Consistency of shapes across images in the subject is checked first.
96 """
---> 97 return self.shape[1:]
98
99 @property
~/git/torchio/torchio/data/subject.py in shape(self)
85 Consistency of shapes across images in the subject is checked first.
86 """
---> 87 self.check_consistent_shape()
88 image = self.get_images(intensity_only=False)[0]
89 return image.shape
~/git/torchio/torchio/data/subject.py in check_consistent_shape(self)
135 f'\n{pprint.pformat(shapes_dict)}'
136 )
--> 137 raise ValueError(message)
138
139 def check_consistent_orientation(self) -> None:
ValueError: Images in subject have inconsistent shapes:
{'brain': (1, 193, 229, 193),
'eyes': (1, 193, 229, 193),
'pd': (1, 193, 229, 193),
't1': (1, 193, 229, 193),
't2': (1, 193, 229, 193),
'tissues': (3, 193, 229, 193)}
|
ValueError
|
def shape(self):
"""Return shape of first image in subject.
Consistency of shapes across images in the subject is checked first.
"""
self.check_consistent_attribute("shape")
return self.get_first_image().shape
|
def shape(self):
"""Return shape of first image in subject.
Consistency of shapes across images in the subject is checked first.
"""
self.check_consistent_shape()
image = self.get_images(intensity_only=False)[0]
return image.shape
|
https://github.com/fepegar/torchio/issues/265
|
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-6b7dc2edb3cc> in <module>
----> 1 icbm.spatial_shape
~/git/torchio/torchio/data/subject.py in spatial_shape(self)
95 Consistency of shapes across images in the subject is checked first.
96 """
---> 97 return self.shape[1:]
98
99 @property
~/git/torchio/torchio/data/subject.py in shape(self)
85 Consistency of shapes across images in the subject is checked first.
86 """
---> 87 self.check_consistent_shape()
88 image = self.get_images(intensity_only=False)[0]
89 return image.shape
~/git/torchio/torchio/data/subject.py in check_consistent_shape(self)
135 f'\n{pprint.pformat(shapes_dict)}'
136 )
--> 137 raise ValueError(message)
138
139 def check_consistent_orientation(self) -> None:
ValueError: Images in subject have inconsistent shapes:
{'brain': (1, 193, 229, 193),
'eyes': (1, 193, 229, 193),
'pd': (1, 193, 229, 193),
't1': (1, 193, 229, 193),
't2': (1, 193, 229, 193),
'tissues': (3, 193, 229, 193)}
|
ValueError
|
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