repo stringclasses 4 values | path stringlengths 20 56 | func_name stringlengths 3 41 | original_string stringlengths 140 10k | language stringclasses 1 value | code stringlengths 140 10k | code_tokens listlengths 22 929 | docstring stringlengths 18 8.4k | docstring_tokens listlengths 2 122 | sha stringclasses 4 values | url stringlengths 108 167 | partition stringclasses 1 value | summary stringlengths 17 285 | input_ids listlengths 502 502 | token_type_ids listlengths 502 502 | attention_mask listlengths 502 502 | labels listlengths 502 502 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
pandas-dev/pandas | pandas/core/groupby/groupby.py | GroupBy.head | def head(self, n=5):
"""
Return first n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.head(n))``,
except ignores as_index flag.
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
columns=['A', 'B'])
>>> df.groupby('A', as_index=False).head(1)
A B
0 1 2
2 5 6
>>> df.groupby('A').head(1)
A B
0 1 2
2 5 6
"""
self._reset_group_selection()
mask = self._cumcount_array() < n
return self._selected_obj[mask] | python | def head(self, n=5):
"""
Return first n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.head(n))``,
except ignores as_index flag.
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
columns=['A', 'B'])
>>> df.groupby('A', as_index=False).head(1)
A B
0 1 2
2 5 6
>>> df.groupby('A').head(1)
A B
0 1 2
2 5 6
"""
self._reset_group_selection()
mask = self._cumcount_array() < n
return self._selected_obj[mask] | [
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apache/spark | python/pyspark/mllib/util.py | MLUtils.appendBias | def appendBias(data):
"""
Returns a new vector with `1.0` (bias) appended to
the end of the input vector.
"""
vec = _convert_to_vector(data)
if isinstance(vec, SparseVector):
newIndices = np.append(vec.indices, len(vec))
newValues = np.append(vec.values, 1.0)
return SparseVector(len(vec) + 1, newIndices, newValues)
else:
return _convert_to_vector(np.append(vec.toArray(), 1.0)) | python | def appendBias(data):
"""
Returns a new vector with `1.0` (bias) appended to
the end of the input vector.
"""
vec = _convert_to_vector(data)
if isinstance(vec, SparseVector):
newIndices = np.append(vec.indices, len(vec))
newValues = np.append(vec.values, 1.0)
return SparseVector(len(vec) + 1, newIndices, newValues)
else:
return _convert_to_vector(np.append(vec.toArray(), 1.0)) | [
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apache/spark | python/pyspark/shuffle.py | ExternalGroupBy._spill | def _spill(self):
"""
dump already partitioned data into disks.
"""
global MemoryBytesSpilled, DiskBytesSpilled
path = self._get_spill_dir(self.spills)
if not os.path.exists(path):
os.makedirs(path)
used_memory = get_used_memory()
if not self.pdata:
# The data has not been partitioned, it will iterator the
# data once, write them into different files, has no
# additional memory. It only called when the memory goes
# above limit at the first time.
# open all the files for writing
streams = [open(os.path.join(path, str(i)), 'wb')
for i in range(self.partitions)]
# If the number of keys is small, then the overhead of sort is small
# sort them before dumping into disks
self._sorted = len(self.data) < self.SORT_KEY_LIMIT
if self._sorted:
self.serializer = self.flattened_serializer()
for k in sorted(self.data.keys()):
h = self._partition(k)
self.serializer.dump_stream([(k, self.data[k])], streams[h])
else:
for k, v in self.data.items():
h = self._partition(k)
self.serializer.dump_stream([(k, v)], streams[h])
for s in streams:
DiskBytesSpilled += s.tell()
s.close()
self.data.clear()
# self.pdata is cached in `mergeValues` and `mergeCombiners`
self.pdata.extend([{} for i in range(self.partitions)])
else:
for i in range(self.partitions):
p = os.path.join(path, str(i))
with open(p, "wb") as f:
# dump items in batch
if self._sorted:
# sort by key only (stable)
sorted_items = sorted(self.pdata[i].items(), key=operator.itemgetter(0))
self.serializer.dump_stream(sorted_items, f)
else:
self.serializer.dump_stream(self.pdata[i].items(), f)
self.pdata[i].clear()
DiskBytesSpilled += os.path.getsize(p)
self.spills += 1
gc.collect() # release the memory as much as possible
MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20 | python | def _spill(self):
"""
dump already partitioned data into disks.
"""
global MemoryBytesSpilled, DiskBytesSpilled
path = self._get_spill_dir(self.spills)
if not os.path.exists(path):
os.makedirs(path)
used_memory = get_used_memory()
if not self.pdata:
# The data has not been partitioned, it will iterator the
# data once, write them into different files, has no
# additional memory. It only called when the memory goes
# above limit at the first time.
# open all the files for writing
streams = [open(os.path.join(path, str(i)), 'wb')
for i in range(self.partitions)]
# If the number of keys is small, then the overhead of sort is small
# sort them before dumping into disks
self._sorted = len(self.data) < self.SORT_KEY_LIMIT
if self._sorted:
self.serializer = self.flattened_serializer()
for k in sorted(self.data.keys()):
h = self._partition(k)
self.serializer.dump_stream([(k, self.data[k])], streams[h])
else:
for k, v in self.data.items():
h = self._partition(k)
self.serializer.dump_stream([(k, v)], streams[h])
for s in streams:
DiskBytesSpilled += s.tell()
s.close()
self.data.clear()
# self.pdata is cached in `mergeValues` and `mergeCombiners`
self.pdata.extend([{} for i in range(self.partitions)])
else:
for i in range(self.partitions):
p = os.path.join(path, str(i))
with open(p, "wb") as f:
# dump items in batch
if self._sorted:
# sort by key only (stable)
sorted_items = sorted(self.pdata[i].items(), key=operator.itemgetter(0))
self.serializer.dump_stream(sorted_items, f)
else:
self.serializer.dump_stream(self.pdata[i].items(), f)
self.pdata[i].clear()
DiskBytesSpilled += os.path.getsize(p)
self.spills += 1
gc.collect() # release the memory as much as possible
MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20 | [
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huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/modeling_transfo_xl.py | TransfoXLPreTrainedModel.from_pretrained | def from_pretrained(cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None,
from_tf=False, *inputs, **kwargs):
"""
Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict.
Download and cache the pre-trained model file if needed.
Params:
pretrained_model_name_or_path: either:
- a str with the name of a pre-trained model to load selected in the list of:
. `transfo-xl`
- a path or url to a pretrained model archive containing:
. `transfo_xl_config.json` a configuration file for the model
. `pytorch_model.bin` a PyTorch dump of a TransfoXLModel instance
- a path or url to a pretrained model archive containing:
. `bert_config.json` a configuration file for the model
. `model.chkpt` a TensorFlow checkpoint
from_tf: should we load the weights from a locally saved TensorFlow checkpoint
cache_dir: an optional path to a folder in which the pre-trained models will be cached.
state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models
*inputs, **kwargs: additional input for the specific Bert class
(ex: num_labels for BertForSequenceClassification)
"""
if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
else:
archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find files {} and {} "
"at this path or url.".format(
pretrained_model_name_or_path,
', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
pretrained_model_name_or_path,
archive_file, config_file))
return None
if resolved_archive_file == archive_file and resolved_config_file == config_file:
logger.info("loading weights file {}".format(archive_file))
logger.info("loading configuration file {}".format(config_file))
else:
logger.info("loading weights file {} from cache at {}".format(
archive_file, resolved_archive_file))
logger.info("loading configuration file {} from cache at {}".format(
config_file, resolved_config_file))
# Load config
config = TransfoXLConfig.from_json_file(resolved_config_file)
logger.info("Model config {}".format(config))
# Instantiate model.
model = cls(config, *inputs, **kwargs)
if state_dict is None and not from_tf:
state_dict = torch.load(resolved_archive_file, map_location='cpu')
if from_tf:
# Directly load from a TensorFlow checkpoint
return load_tf_weights_in_transfo_xl(model, config, pretrained_model_name_or_path)
missing_keys = []
unexpected_keys = []
error_msgs = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
def load(module, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
module._load_from_state_dict(
state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
start_prefix = ''
if not hasattr(model, 'transformer') and any(s.startswith('transformer.') for s in state_dict.keys()):
start_prefix = 'transformer.'
load(model, prefix=start_prefix)
if len(missing_keys) > 0:
logger.info("Weights of {} not initialized from pretrained model: {}".format(
model.__class__.__name__, missing_keys))
if len(unexpected_keys) > 0:
logger.info("Weights from pretrained model not used in {}: {}".format(
model.__class__.__name__, unexpected_keys))
if len(error_msgs) > 0:
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
model.__class__.__name__, "\n\t".join(error_msgs)))
# Make sure we are still sharing the input and output embeddings
if hasattr(model, 'tie_weights'):
model.tie_weights()
return model | python | def from_pretrained(cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None,
from_tf=False, *inputs, **kwargs):
"""
Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict.
Download and cache the pre-trained model file if needed.
Params:
pretrained_model_name_or_path: either:
- a str with the name of a pre-trained model to load selected in the list of:
. `transfo-xl`
- a path or url to a pretrained model archive containing:
. `transfo_xl_config.json` a configuration file for the model
. `pytorch_model.bin` a PyTorch dump of a TransfoXLModel instance
- a path or url to a pretrained model archive containing:
. `bert_config.json` a configuration file for the model
. `model.chkpt` a TensorFlow checkpoint
from_tf: should we load the weights from a locally saved TensorFlow checkpoint
cache_dir: an optional path to a folder in which the pre-trained models will be cached.
state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models
*inputs, **kwargs: additional input for the specific Bert class
(ex: num_labels for BertForSequenceClassification)
"""
if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
else:
archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find files {} and {} "
"at this path or url.".format(
pretrained_model_name_or_path,
', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
pretrained_model_name_or_path,
archive_file, config_file))
return None
if resolved_archive_file == archive_file and resolved_config_file == config_file:
logger.info("loading weights file {}".format(archive_file))
logger.info("loading configuration file {}".format(config_file))
else:
logger.info("loading weights file {} from cache at {}".format(
archive_file, resolved_archive_file))
logger.info("loading configuration file {} from cache at {}".format(
config_file, resolved_config_file))
# Load config
config = TransfoXLConfig.from_json_file(resolved_config_file)
logger.info("Model config {}".format(config))
# Instantiate model.
model = cls(config, *inputs, **kwargs)
if state_dict is None and not from_tf:
state_dict = torch.load(resolved_archive_file, map_location='cpu')
if from_tf:
# Directly load from a TensorFlow checkpoint
return load_tf_weights_in_transfo_xl(model, config, pretrained_model_name_or_path)
missing_keys = []
unexpected_keys = []
error_msgs = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
def load(module, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
module._load_from_state_dict(
state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
start_prefix = ''
if not hasattr(model, 'transformer') and any(s.startswith('transformer.') for s in state_dict.keys()):
start_prefix = 'transformer.'
load(model, prefix=start_prefix)
if len(missing_keys) > 0:
logger.info("Weights of {} not initialized from pretrained model: {}".format(
model.__class__.__name__, missing_keys))
if len(unexpected_keys) > 0:
logger.info("Weights from pretrained model not used in {}: {}".format(
model.__class__.__name__, unexpected_keys))
if len(error_msgs) > 0:
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
model.__class__.__name__, "\n\t".join(error_msgs)))
# Make sure we are still sharing the input and output embeddings
if hasattr(model, 'tie_weights'):
model.tie_weights()
return model | [
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apache/spark | python/pyspark/rdd.py | RDD.aggregate | def aggregate(self, zeroValue, seqOp, combOp):
"""
Aggregate the elements of each partition, and then the results for all
the partitions, using a given combine functions and a neutral "zero
value."
The functions C{op(t1, t2)} is allowed to modify C{t1} and return it
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>>> seqOp = (lambda x, y: (x[0] + y, x[1] + 1))
>>> combOp = (lambda x, y: (x[0] + y[0], x[1] + y[1]))
>>> sc.parallelize([1, 2, 3, 4]).aggregate((0, 0), seqOp, combOp)
(10, 4)
>>> sc.parallelize([]).aggregate((0, 0), seqOp, combOp)
(0, 0)
"""
seqOp = fail_on_stopiteration(seqOp)
combOp = fail_on_stopiteration(combOp)
def func(iterator):
acc = zeroValue
for obj in iterator:
acc = seqOp(acc, obj)
yield acc
# collecting result of mapPartitions here ensures that the copy of
# zeroValue provided to each partition is unique from the one provided
# to the final reduce call
vals = self.mapPartitions(func).collect()
return reduce(combOp, vals, zeroValue) | python | def aggregate(self, zeroValue, seqOp, combOp):
"""
Aggregate the elements of each partition, and then the results for all
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value."
The functions C{op(t1, t2)} is allowed to modify C{t1} and return it
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modify C{t2}.
The first function (seqOp) can return a different result type, U, than
the type of this RDD. Thus, we need one operation for merging a T into
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>>> seqOp = (lambda x, y: (x[0] + y, x[1] + 1))
>>> combOp = (lambda x, y: (x[0] + y[0], x[1] + y[1]))
>>> sc.parallelize([1, 2, 3, 4]).aggregate((0, 0), seqOp, combOp)
(10, 4)
>>> sc.parallelize([]).aggregate((0, 0), seqOp, combOp)
(0, 0)
"""
seqOp = fail_on_stopiteration(seqOp)
combOp = fail_on_stopiteration(combOp)
def func(iterator):
acc = zeroValue
for obj in iterator:
acc = seqOp(acc, obj)
yield acc
# collecting result of mapPartitions here ensures that the copy of
# zeroValue provided to each partition is unique from the one provided
# to the final reduce call
vals = self.mapPartitions(func).collect()
return reduce(combOp, vals, zeroValue) | [
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apache/spark | python/pyspark/sql/functions.py | array_position | def array_position(col, value):
"""
Collection function: Locates the position of the first occurrence of the given value
in the given array. Returns null if either of the arguments are null.
.. note:: The position is not zero based, but 1 based index. Returns 0 if the given
value could not be found in the array.
>>> df = spark.createDataFrame([(["c", "b", "a"],), ([],)], ['data'])
>>> df.select(array_position(df.data, "a")).collect()
[Row(array_position(data, a)=3), Row(array_position(data, a)=0)]
"""
sc = SparkContext._active_spark_context
return Column(sc._jvm.functions.array_position(_to_java_column(col), value)) | python | def array_position(col, value):
"""
Collection function: Locates the position of the first occurrence of the given value
in the given array. Returns null if either of the arguments are null.
.. note:: The position is not zero based, but 1 based index. Returns 0 if the given
value could not be found in the array.
>>> df = spark.createDataFrame([(["c", "b", "a"],), ([],)], ['data'])
>>> df.select(array_position(df.data, "a")).collect()
[Row(array_position(data, a)=3), Row(array_position(data, a)=0)]
"""
sc = SparkContext._active_spark_context
return Column(sc._jvm.functions.array_position(_to_java_column(col), value)) | [
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apache/spark | python/pyspark/ml/regression.py | GeneralizedLinearRegressionModel.summary | def summary(self):
"""
Gets summary (e.g. residuals, deviance, pValues) of model on
training set. An exception is thrown if
`trainingSummary is None`.
"""
if self.hasSummary:
return GeneralizedLinearRegressionTrainingSummary(
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"""
Gets summary (e.g. residuals, deviance, pValues) of model on
training set. An exception is thrown if
`trainingSummary is None`.
"""
if self.hasSummary:
return GeneralizedLinearRegressionTrainingSummary(
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame._repr_html_ | def _repr_html_(self):
"""Returns a dataframe with html code when you enabled eager evaluation
by 'spark.sql.repl.eagerEval.enabled', this only called by REPL you are
using support eager evaluation with HTML.
"""
import cgi
if not self._support_repr_html:
self._support_repr_html = True
if self.sql_ctx._conf.isReplEagerEvalEnabled():
max_num_rows = max(self.sql_ctx._conf.replEagerEvalMaxNumRows(), 0)
sock_info = self._jdf.getRowsToPython(
max_num_rows, self.sql_ctx._conf.replEagerEvalTruncate())
rows = list(_load_from_socket(sock_info, BatchedSerializer(PickleSerializer())))
head = rows[0]
row_data = rows[1:]
has_more_data = len(row_data) > max_num_rows
row_data = row_data[:max_num_rows]
html = "<table border='1'>\n"
# generate table head
html += "<tr><th>%s</th></tr>\n" % "</th><th>".join(map(lambda x: cgi.escape(x), head))
# generate table rows
for row in row_data:
html += "<tr><td>%s</td></tr>\n" % "</td><td>".join(
map(lambda x: cgi.escape(x), row))
html += "</table>\n"
if has_more_data:
html += "only showing top %d %s\n" % (
max_num_rows, "row" if max_num_rows == 1 else "rows")
return html
else:
return None | python | def _repr_html_(self):
"""Returns a dataframe with html code when you enabled eager evaluation
by 'spark.sql.repl.eagerEval.enabled', this only called by REPL you are
using support eager evaluation with HTML.
"""
import cgi
if not self._support_repr_html:
self._support_repr_html = True
if self.sql_ctx._conf.isReplEagerEvalEnabled():
max_num_rows = max(self.sql_ctx._conf.replEagerEvalMaxNumRows(), 0)
sock_info = self._jdf.getRowsToPython(
max_num_rows, self.sql_ctx._conf.replEagerEvalTruncate())
rows = list(_load_from_socket(sock_info, BatchedSerializer(PickleSerializer())))
head = rows[0]
row_data = rows[1:]
has_more_data = len(row_data) > max_num_rows
row_data = row_data[:max_num_rows]
html = "<table border='1'>\n"
# generate table head
html += "<tr><th>%s</th></tr>\n" % "</th><th>".join(map(lambda x: cgi.escape(x), head))
# generate table rows
for row in row_data:
html += "<tr><td>%s</td></tr>\n" % "</td><td>".join(
map(lambda x: cgi.escape(x), row))
html += "</table>\n"
if has_more_data:
html += "only showing top %d %s\n" % (
max_num_rows, "row" if max_num_rows == 1 else "rows")
return html
else:
return None | [
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apache/spark | python/pyspark/rdd.py | RDD.collect | def collect(self):
"""
Return a list that contains all of the elements in this RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
"""
with SCCallSiteSync(self.context) as css:
sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
return list(_load_from_socket(sock_info, self._jrdd_deserializer)) | python | def collect(self):
"""
Return a list that contains all of the elements in this RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
"""
with SCCallSiteSync(self.context) as css:
sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
return list(_load_from_socket(sock_info, self._jrdd_deserializer)) | [
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apache/spark | python/pyspark/sql/utils.py | require_minimum_pandas_version | def require_minimum_pandas_version():
""" Raise ImportError if minimum version of Pandas is not installed
"""
# TODO(HyukjinKwon): Relocate and deduplicate the version specification.
minimum_pandas_version = "0.19.2"
from distutils.version import LooseVersion
try:
import pandas
have_pandas = True
except ImportError:
have_pandas = False
if not have_pandas:
raise ImportError("Pandas >= %s must be installed; however, "
"it was not found." % minimum_pandas_version)
if LooseVersion(pandas.__version__) < LooseVersion(minimum_pandas_version):
raise ImportError("Pandas >= %s must be installed; however, "
"your version was %s." % (minimum_pandas_version, pandas.__version__)) | python | def require_minimum_pandas_version():
""" Raise ImportError if minimum version of Pandas is not installed
"""
# TODO(HyukjinKwon): Relocate and deduplicate the version specification.
minimum_pandas_version = "0.19.2"
from distutils.version import LooseVersion
try:
import pandas
have_pandas = True
except ImportError:
have_pandas = False
if not have_pandas:
raise ImportError("Pandas >= %s must be installed; however, "
"it was not found." % minimum_pandas_version)
if LooseVersion(pandas.__version__) < LooseVersion(minimum_pandas_version):
raise ImportError("Pandas >= %s must be installed; however, "
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apache/spark | python/pyspark/mllib/linalg/__init__.py | DenseMatrix.asML | def asML(self):
"""
Convert this matrix to the new mllib-local representation.
This does NOT copy the data; it copies references.
:return: :py:class:`pyspark.ml.linalg.DenseMatrix`
.. versionadded:: 2.0.0
"""
return newlinalg.DenseMatrix(self.numRows, self.numCols, self.values, self.isTransposed) | python | def asML(self):
"""
Convert this matrix to the new mllib-local representation.
This does NOT copy the data; it copies references.
:return: :py:class:`pyspark.ml.linalg.DenseMatrix`
.. versionadded:: 2.0.0
"""
return newlinalg.DenseMatrix(self.numRows, self.numCols, self.values, self.isTransposed) | [
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apache/spark | python/pyspark/heapq3.py | nsmallest | def nsmallest(n, iterable, key=None):
"""Find the n smallest elements in a dataset.
Equivalent to: sorted(iterable, key=key)[:n]
"""
# Short-cut for n==1 is to use min()
if n == 1:
it = iter(iterable)
sentinel = object()
if key is None:
result = min(it, default=sentinel)
else:
result = min(it, default=sentinel, key=key)
return [] if result is sentinel else [result]
# When n>=size, it's faster to use sorted()
try:
size = len(iterable)
except (TypeError, AttributeError):
pass
else:
if n >= size:
return sorted(iterable, key=key)[:n]
# When key is none, use simpler decoration
if key is None:
it = iter(iterable)
# put the range(n) first so that zip() doesn't
# consume one too many elements from the iterator
result = [(elem, i) for i, elem in zip(range(n), it)]
if not result:
return result
_heapify_max(result)
top = result[0][0]
order = n
_heapreplace = _heapreplace_max
for elem in it:
if elem < top:
_heapreplace(result, (elem, order))
top = result[0][0]
order += 1
result.sort()
return [r[0] for r in result]
# General case, slowest method
it = iter(iterable)
result = [(key(elem), i, elem) for i, elem in zip(range(n), it)]
if not result:
return result
_heapify_max(result)
top = result[0][0]
order = n
_heapreplace = _heapreplace_max
for elem in it:
k = key(elem)
if k < top:
_heapreplace(result, (k, order, elem))
top = result[0][0]
order += 1
result.sort()
return [r[2] for r in result] | python | def nsmallest(n, iterable, key=None):
"""Find the n smallest elements in a dataset.
Equivalent to: sorted(iterable, key=key)[:n]
"""
# Short-cut for n==1 is to use min()
if n == 1:
it = iter(iterable)
sentinel = object()
if key is None:
result = min(it, default=sentinel)
else:
result = min(it, default=sentinel, key=key)
return [] if result is sentinel else [result]
# When n>=size, it's faster to use sorted()
try:
size = len(iterable)
except (TypeError, AttributeError):
pass
else:
if n >= size:
return sorted(iterable, key=key)[:n]
# When key is none, use simpler decoration
if key is None:
it = iter(iterable)
# put the range(n) first so that zip() doesn't
# consume one too many elements from the iterator
result = [(elem, i) for i, elem in zip(range(n), it)]
if not result:
return result
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result.sort()
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apache/spark | python/pyspark/sql/context.py | SQLContext.getOrCreate | def getOrCreate(cls, sc):
"""
Get the existing SQLContext or create a new one with given SparkContext.
:param sc: SparkContext
"""
if cls._instantiatedContext is None:
jsqlContext = sc._jvm.SQLContext.getOrCreate(sc._jsc.sc())
sparkSession = SparkSession(sc, jsqlContext.sparkSession())
cls(sc, sparkSession, jsqlContext)
return cls._instantiatedContext | python | def getOrCreate(cls, sc):
"""
Get the existing SQLContext or create a new one with given SparkContext.
:param sc: SparkContext
"""
if cls._instantiatedContext is None:
jsqlContext = sc._jvm.SQLContext.getOrCreate(sc._jsc.sc())
sparkSession = SparkSession(sc, jsqlContext.sparkSession())
cls(sc, sparkSession, jsqlContext)
return cls._instantiatedContext | [
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apache/spark | python/pyspark/serializers.py | ArrowStreamPandasSerializer.load_stream | def load_stream(self, stream):
"""
Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series.
"""
batches = super(ArrowStreamPandasSerializer, self).load_stream(stream)
import pyarrow as pa
for batch in batches:
yield [self.arrow_to_pandas(c) for c in pa.Table.from_batches([batch]).itercolumns()] | python | def load_stream(self, stream):
"""
Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series.
"""
batches = super(ArrowStreamPandasSerializer, self).load_stream(stream)
import pyarrow as pa
for batch in batches:
yield [self.arrow_to_pandas(c) for c in pa.Table.from_batches([batch]).itercolumns()] | [
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apache/spark | python/pyspark/heapq3.py | heappush | def heappush(heap, item):
"""Push item onto heap, maintaining the heap invariant."""
heap.append(item)
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"""Push item onto heap, maintaining the heap invariant."""
heap.append(item)
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apache/spark | python/pyspark/streaming/context.py | StreamingContext.queueStream | def queueStream(self, rdds, oneAtATime=True, default=None):
"""
Create an input stream from a queue of RDDs or list. In each batch,
it will process either one or all of the RDDs returned by the queue.
.. note:: Changes to the queue after the stream is created will not be recognized.
@param rdds: Queue of RDDs
@param oneAtATime: pick one rdd each time or pick all of them once.
@param default: The default rdd if no more in rdds
"""
if default and not isinstance(default, RDD):
default = self._sc.parallelize(default)
if not rdds and default:
rdds = [rdds]
if rdds and not isinstance(rdds[0], RDD):
rdds = [self._sc.parallelize(input) for input in rdds]
self._check_serializers(rdds)
queue = self._jvm.PythonDStream.toRDDQueue([r._jrdd for r in rdds])
if default:
default = default._reserialize(rdds[0]._jrdd_deserializer)
jdstream = self._jssc.queueStream(queue, oneAtATime, default._jrdd)
else:
jdstream = self._jssc.queueStream(queue, oneAtATime)
return DStream(jdstream, self, rdds[0]._jrdd_deserializer) | python | def queueStream(self, rdds, oneAtATime=True, default=None):
"""
Create an input stream from a queue of RDDs or list. In each batch,
it will process either one or all of the RDDs returned by the queue.
.. note:: Changes to the queue after the stream is created will not be recognized.
@param rdds: Queue of RDDs
@param oneAtATime: pick one rdd each time or pick all of them once.
@param default: The default rdd if no more in rdds
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if default and not isinstance(default, RDD):
default = self._sc.parallelize(default)
if not rdds and default:
rdds = [rdds]
if rdds and not isinstance(rdds[0], RDD):
rdds = [self._sc.parallelize(input) for input in rdds]
self._check_serializers(rdds)
queue = self._jvm.PythonDStream.toRDDQueue([r._jrdd for r in rdds])
if default:
default = default._reserialize(rdds[0]._jrdd_deserializer)
jdstream = self._jssc.queueStream(queue, oneAtATime, default._jrdd)
else:
jdstream = self._jssc.queueStream(queue, oneAtATime)
return DStream(jdstream, self, rdds[0]._jrdd_deserializer) | [
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apache/spark | python/pyspark/serializers.py | _hijack_namedtuple | def _hijack_namedtuple():
""" Hack namedtuple() to make it picklable """
# hijack only one time
if hasattr(collections.namedtuple, "__hijack"):
return
global _old_namedtuple # or it will put in closure
global _old_namedtuple_kwdefaults # or it will put in closure too
def _copy_func(f):
return types.FunctionType(f.__code__, f.__globals__, f.__name__,
f.__defaults__, f.__closure__)
def _kwdefaults(f):
# __kwdefaults__ contains the default values of keyword-only arguments which are
# introduced from Python 3. The possible cases for __kwdefaults__ in namedtuple
# are as below:
#
# - Does not exist in Python 2.
# - Returns None in <= Python 3.5.x.
# - Returns a dictionary containing the default values to the keys from Python 3.6.x
# (See https://bugs.python.org/issue25628).
kargs = getattr(f, "__kwdefaults__", None)
if kargs is None:
return {}
else:
return kargs
_old_namedtuple = _copy_func(collections.namedtuple)
_old_namedtuple_kwdefaults = _kwdefaults(collections.namedtuple)
def namedtuple(*args, **kwargs):
for k, v in _old_namedtuple_kwdefaults.items():
kwargs[k] = kwargs.get(k, v)
cls = _old_namedtuple(*args, **kwargs)
return _hack_namedtuple(cls)
# replace namedtuple with the new one
collections.namedtuple.__globals__["_old_namedtuple_kwdefaults"] = _old_namedtuple_kwdefaults
collections.namedtuple.__globals__["_old_namedtuple"] = _old_namedtuple
collections.namedtuple.__globals__["_hack_namedtuple"] = _hack_namedtuple
collections.namedtuple.__code__ = namedtuple.__code__
collections.namedtuple.__hijack = 1
# hack the cls already generated by namedtuple.
# Those created in other modules can be pickled as normal,
# so only hack those in __main__ module
for n, o in sys.modules["__main__"].__dict__.items():
if (type(o) is type and o.__base__ is tuple
and hasattr(o, "_fields")
and "__reduce__" not in o.__dict__):
_hack_namedtuple(o) | python | def _hijack_namedtuple():
""" Hack namedtuple() to make it picklable """
# hijack only one time
if hasattr(collections.namedtuple, "__hijack"):
return
global _old_namedtuple # or it will put in closure
global _old_namedtuple_kwdefaults # or it will put in closure too
def _copy_func(f):
return types.FunctionType(f.__code__, f.__globals__, f.__name__,
f.__defaults__, f.__closure__)
def _kwdefaults(f):
# __kwdefaults__ contains the default values of keyword-only arguments which are
# introduced from Python 3. The possible cases for __kwdefaults__ in namedtuple
# are as below:
#
# - Does not exist in Python 2.
# - Returns None in <= Python 3.5.x.
# - Returns a dictionary containing the default values to the keys from Python 3.6.x
# (See https://bugs.python.org/issue25628).
kargs = getattr(f, "__kwdefaults__", None)
if kargs is None:
return {}
else:
return kargs
_old_namedtuple = _copy_func(collections.namedtuple)
_old_namedtuple_kwdefaults = _kwdefaults(collections.namedtuple)
def namedtuple(*args, **kwargs):
for k, v in _old_namedtuple_kwdefaults.items():
kwargs[k] = kwargs.get(k, v)
cls = _old_namedtuple(*args, **kwargs)
return _hack_namedtuple(cls)
# replace namedtuple with the new one
collections.namedtuple.__globals__["_old_namedtuple_kwdefaults"] = _old_namedtuple_kwdefaults
collections.namedtuple.__globals__["_old_namedtuple"] = _old_namedtuple
collections.namedtuple.__globals__["_hack_namedtuple"] = _hack_namedtuple
collections.namedtuple.__code__ = namedtuple.__code__
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# hack the cls already generated by namedtuple.
# Those created in other modules can be pickled as normal,
# so only hack those in __main__ module
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if (type(o) is type and o.__base__ is tuple
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and "__reduce__" not in o.__dict__):
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apache/spark | python/pyspark/sql/readwriter.py | DataFrameReader.json | def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None,
allowComments=None, allowUnquotedFieldNames=None, allowSingleQuotes=None,
allowNumericLeadingZero=None, allowBackslashEscapingAnyCharacter=None,
mode=None, columnNameOfCorruptRecord=None, dateFormat=None, timestampFormat=None,
multiLine=None, allowUnquotedControlChars=None, lineSep=None, samplingRatio=None,
dropFieldIfAllNull=None, encoding=None, locale=None):
"""
Loads JSON files and returns the results as a :class:`DataFrame`.
`JSON Lines <http://jsonlines.org/>`_ (newline-delimited JSON) is supported by default.
For JSON (one record per file), set the ``multiLine`` parameter to ``true``.
If the ``schema`` parameter is not specified, this function goes
through the input once to determine the input schema.
:param path: string represents path to the JSON dataset, or a list of paths,
or RDD of Strings storing JSON objects.
:param schema: an optional :class:`pyspark.sql.types.StructType` for the input schema or
a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).
:param primitivesAsString: infers all primitive values as a string type. If None is set,
it uses the default value, ``false``.
:param prefersDecimal: infers all floating-point values as a decimal type. If the values
do not fit in decimal, then it infers them as doubles. If None is
set, it uses the default value, ``false``.
:param allowComments: ignores Java/C++ style comment in JSON records. If None is set,
it uses the default value, ``false``.
:param allowUnquotedFieldNames: allows unquoted JSON field names. If None is set,
it uses the default value, ``false``.
:param allowSingleQuotes: allows single quotes in addition to double quotes. If None is
set, it uses the default value, ``true``.
:param allowNumericLeadingZero: allows leading zeros in numbers (e.g. 00012). If None is
set, it uses the default value, ``false``.
:param allowBackslashEscapingAnyCharacter: allows accepting quoting of all character
using backslash quoting mechanism. If None is
set, it uses the default value, ``false``.
:param mode: allows a mode for dealing with corrupt records during parsing. If None is
set, it uses the default value, ``PERMISSIVE``.
* ``PERMISSIVE`` : when it meets a corrupted record, puts the malformed string \
into a field configured by ``columnNameOfCorruptRecord``, and sets malformed \
fields to ``null``. To keep corrupt records, an user can set a string type \
field named ``columnNameOfCorruptRecord`` in an user-defined schema. If a \
schema does not have the field, it drops corrupt records during parsing. \
When inferring a schema, it implicitly adds a ``columnNameOfCorruptRecord`` \
field in an output schema.
* ``DROPMALFORMED`` : ignores the whole corrupted records.
* ``FAILFAST`` : throws an exception when it meets corrupted records.
:param columnNameOfCorruptRecord: allows renaming the new field having malformed string
created by ``PERMISSIVE`` mode. This overrides
``spark.sql.columnNameOfCorruptRecord``. If None is set,
it uses the value specified in
``spark.sql.columnNameOfCorruptRecord``.
:param dateFormat: sets the string that indicates a date format. Custom date formats
follow the formats at ``java.time.format.DateTimeFormatter``. This
applies to date type. If None is set, it uses the
default value, ``yyyy-MM-dd``.
:param timestampFormat: sets the string that indicates a timestamp format.
Custom date formats follow the formats at
``java.time.format.DateTimeFormatter``.
This applies to timestamp type. If None is set, it uses the
default value, ``yyyy-MM-dd'T'HH:mm:ss.SSSXXX``.
:param multiLine: parse one record, which may span multiple lines, per file. If None is
set, it uses the default value, ``false``.
:param allowUnquotedControlChars: allows JSON Strings to contain unquoted control
characters (ASCII characters with value less than 32,
including tab and line feed characters) or not.
:param encoding: allows to forcibly set one of standard basic or extended encoding for
the JSON files. For example UTF-16BE, UTF-32LE. If None is set,
the encoding of input JSON will be detected automatically
when the multiLine option is set to ``true``.
:param lineSep: defines the line separator that should be used for parsing. If None is
set, it covers all ``\\r``, ``\\r\\n`` and ``\\n``.
:param samplingRatio: defines fraction of input JSON objects used for schema inferring.
If None is set, it uses the default value, ``1.0``.
:param dropFieldIfAllNull: whether to ignore column of all null values or empty
array/struct during schema inference. If None is set, it
uses the default value, ``false``.
:param locale: sets a locale as language tag in IETF BCP 47 format. If None is set,
it uses the default value, ``en-US``. For instance, ``locale`` is used while
parsing dates and timestamps.
>>> df1 = spark.read.json('python/test_support/sql/people.json')
>>> df1.dtypes
[('age', 'bigint'), ('name', 'string')]
>>> rdd = sc.textFile('python/test_support/sql/people.json')
>>> df2 = spark.read.json(rdd)
>>> df2.dtypes
[('age', 'bigint'), ('name', 'string')]
"""
self._set_opts(
schema=schema, primitivesAsString=primitivesAsString, prefersDecimal=prefersDecimal,
allowComments=allowComments, allowUnquotedFieldNames=allowUnquotedFieldNames,
allowSingleQuotes=allowSingleQuotes, allowNumericLeadingZero=allowNumericLeadingZero,
allowBackslashEscapingAnyCharacter=allowBackslashEscapingAnyCharacter,
mode=mode, columnNameOfCorruptRecord=columnNameOfCorruptRecord, dateFormat=dateFormat,
timestampFormat=timestampFormat, multiLine=multiLine,
allowUnquotedControlChars=allowUnquotedControlChars, lineSep=lineSep,
samplingRatio=samplingRatio, dropFieldIfAllNull=dropFieldIfAllNull, encoding=encoding,
locale=locale)
if isinstance(path, basestring):
path = [path]
if type(path) == list:
return self._df(self._jreader.json(self._spark._sc._jvm.PythonUtils.toSeq(path)))
elif isinstance(path, RDD):
def func(iterator):
for x in iterator:
if not isinstance(x, basestring):
x = unicode(x)
if isinstance(x, unicode):
x = x.encode("utf-8")
yield x
keyed = path.mapPartitions(func)
keyed._bypass_serializer = True
jrdd = keyed._jrdd.map(self._spark._jvm.BytesToString())
return self._df(self._jreader.json(jrdd))
else:
raise TypeError("path can be only string, list or RDD") | python | def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None,
allowComments=None, allowUnquotedFieldNames=None, allowSingleQuotes=None,
allowNumericLeadingZero=None, allowBackslashEscapingAnyCharacter=None,
mode=None, columnNameOfCorruptRecord=None, dateFormat=None, timestampFormat=None,
multiLine=None, allowUnquotedControlChars=None, lineSep=None, samplingRatio=None,
dropFieldIfAllNull=None, encoding=None, locale=None):
"""
Loads JSON files and returns the results as a :class:`DataFrame`.
`JSON Lines <http://jsonlines.org/>`_ (newline-delimited JSON) is supported by default.
For JSON (one record per file), set the ``multiLine`` parameter to ``true``.
If the ``schema`` parameter is not specified, this function goes
through the input once to determine the input schema.
:param path: string represents path to the JSON dataset, or a list of paths,
or RDD of Strings storing JSON objects.
:param schema: an optional :class:`pyspark.sql.types.StructType` for the input schema or
a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).
:param primitivesAsString: infers all primitive values as a string type. If None is set,
it uses the default value, ``false``.
:param prefersDecimal: infers all floating-point values as a decimal type. If the values
do not fit in decimal, then it infers them as doubles. If None is
set, it uses the default value, ``false``.
:param allowComments: ignores Java/C++ style comment in JSON records. If None is set,
it uses the default value, ``false``.
:param allowUnquotedFieldNames: allows unquoted JSON field names. If None is set,
it uses the default value, ``false``.
:param allowSingleQuotes: allows single quotes in addition to double quotes. If None is
set, it uses the default value, ``true``.
:param allowNumericLeadingZero: allows leading zeros in numbers (e.g. 00012). If None is
set, it uses the default value, ``false``.
:param allowBackslashEscapingAnyCharacter: allows accepting quoting of all character
using backslash quoting mechanism. If None is
set, it uses the default value, ``false``.
:param mode: allows a mode for dealing with corrupt records during parsing. If None is
set, it uses the default value, ``PERMISSIVE``.
* ``PERMISSIVE`` : when it meets a corrupted record, puts the malformed string \
into a field configured by ``columnNameOfCorruptRecord``, and sets malformed \
fields to ``null``. To keep corrupt records, an user can set a string type \
field named ``columnNameOfCorruptRecord`` in an user-defined schema. If a \
schema does not have the field, it drops corrupt records during parsing. \
When inferring a schema, it implicitly adds a ``columnNameOfCorruptRecord`` \
field in an output schema.
* ``DROPMALFORMED`` : ignores the whole corrupted records.
* ``FAILFAST`` : throws an exception when it meets corrupted records.
:param columnNameOfCorruptRecord: allows renaming the new field having malformed string
created by ``PERMISSIVE`` mode. This overrides
``spark.sql.columnNameOfCorruptRecord``. If None is set,
it uses the value specified in
``spark.sql.columnNameOfCorruptRecord``.
:param dateFormat: sets the string that indicates a date format. Custom date formats
follow the formats at ``java.time.format.DateTimeFormatter``. This
applies to date type. If None is set, it uses the
default value, ``yyyy-MM-dd``.
:param timestampFormat: sets the string that indicates a timestamp format.
Custom date formats follow the formats at
``java.time.format.DateTimeFormatter``.
This applies to timestamp type. If None is set, it uses the
default value, ``yyyy-MM-dd'T'HH:mm:ss.SSSXXX``.
:param multiLine: parse one record, which may span multiple lines, per file. If None is
set, it uses the default value, ``false``.
:param allowUnquotedControlChars: allows JSON Strings to contain unquoted control
characters (ASCII characters with value less than 32,
including tab and line feed characters) or not.
:param encoding: allows to forcibly set one of standard basic or extended encoding for
the JSON files. For example UTF-16BE, UTF-32LE. If None is set,
the encoding of input JSON will be detected automatically
when the multiLine option is set to ``true``.
:param lineSep: defines the line separator that should be used for parsing. If None is
set, it covers all ``\\r``, ``\\r\\n`` and ``\\n``.
:param samplingRatio: defines fraction of input JSON objects used for schema inferring.
If None is set, it uses the default value, ``1.0``.
:param dropFieldIfAllNull: whether to ignore column of all null values or empty
array/struct during schema inference. If None is set, it
uses the default value, ``false``.
:param locale: sets a locale as language tag in IETF BCP 47 format. If None is set,
it uses the default value, ``en-US``. For instance, ``locale`` is used while
parsing dates and timestamps.
>>> df1 = spark.read.json('python/test_support/sql/people.json')
>>> df1.dtypes
[('age', 'bigint'), ('name', 'string')]
>>> rdd = sc.textFile('python/test_support/sql/people.json')
>>> df2 = spark.read.json(rdd)
>>> df2.dtypes
[('age', 'bigint'), ('name', 'string')]
"""
self._set_opts(
schema=schema, primitivesAsString=primitivesAsString, prefersDecimal=prefersDecimal,
allowComments=allowComments, allowUnquotedFieldNames=allowUnquotedFieldNames,
allowSingleQuotes=allowSingleQuotes, allowNumericLeadingZero=allowNumericLeadingZero,
allowBackslashEscapingAnyCharacter=allowBackslashEscapingAnyCharacter,
mode=mode, columnNameOfCorruptRecord=columnNameOfCorruptRecord, dateFormat=dateFormat,
timestampFormat=timestampFormat, multiLine=multiLine,
allowUnquotedControlChars=allowUnquotedControlChars, lineSep=lineSep,
samplingRatio=samplingRatio, dropFieldIfAllNull=dropFieldIfAllNull, encoding=encoding,
locale=locale)
if isinstance(path, basestring):
path = [path]
if type(path) == list:
return self._df(self._jreader.json(self._spark._sc._jvm.PythonUtils.toSeq(path)))
elif isinstance(path, RDD):
def func(iterator):
for x in iterator:
if not isinstance(x, basestring):
x = unicode(x)
if isinstance(x, unicode):
x = x.encode("utf-8")
yield x
keyed = path.mapPartitions(func)
keyed._bypass_serializer = True
jrdd = keyed._jrdd.map(self._spark._jvm.BytesToString())
return self._df(self._jreader.json(jrdd))
else:
raise TypeError("path can be only string, list or RDD") | [
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If the ``schema`` parameter is not specified, this function goes
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:param mode: allows a mode for dealing with corrupt records during parsing. If None is
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* ``PERMISSIVE`` : when it meets a corrupted record, puts the malformed string \
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schema does not have the field, it drops corrupt records during parsing. \
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:param columnNameOfCorruptRecord: allows renaming the new field having malformed string
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default value, ``yyyy-MM-dd``.
:param timestampFormat: sets the string that indicates a timestamp format.
Custom date formats follow the formats at
``java.time.format.DateTimeFormatter``.
This applies to timestamp type. If None is set, it uses the
default value, ``yyyy-MM-dd'T'HH:mm:ss.SSSXXX``.
:param multiLine: parse one record, which may span multiple lines, per file. If None is
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:param allowUnquotedControlChars: allows JSON Strings to contain unquoted control
characters (ASCII characters with value less than 32,
including tab and line feed characters) or not.
:param encoding: allows to forcibly set one of standard basic or extended encoding for
the JSON files. For example UTF-16BE, UTF-32LE. If None is set,
the encoding of input JSON will be detected automatically
when the multiLine option is set to ``true``.
:param lineSep: defines the line separator that should be used for parsing. If None is
set, it covers all ``\\r``, ``\\r\\n`` and ``\\n``.
:param samplingRatio: defines fraction of input JSON objects used for schema inferring.
If None is set, it uses the default value, ``1.0``.
:param dropFieldIfAllNull: whether to ignore column of all null values or empty
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:param locale: sets a locale as language tag in IETF BCP 47 format. If None is set,
it uses the default value, ``en-US``. For instance, ``locale`` is used while
parsing dates and timestamps.
>>> df1 = spark.read.json('python/test_support/sql/people.json')
>>> df1.dtypes
[('age', 'bigint'), ('name', 'string')]
>>> rdd = sc.textFile('python/test_support/sql/people.json')
>>> df2 = spark.read.json(rdd)
>>> df2.dtypes
[('age', 'bigint'), ('name', 'string')] | [
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apache/spark | python/pyspark/ml/param/__init__.py | Params.set | def set(self, param, value):
"""
Sets a parameter in the embedded param map.
"""
self._shouldOwn(param)
try:
value = param.typeConverter(value)
except ValueError as e:
raise ValueError('Invalid param value given for param "%s". %s' % (param.name, e))
self._paramMap[param] = value | python | def set(self, param, value):
"""
Sets a parameter in the embedded param map.
"""
self._shouldOwn(param)
try:
value = param.typeConverter(value)
except ValueError as e:
raise ValueError('Invalid param value given for param "%s". %s' % (param.name, e))
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huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/modeling_transfo_xl_utilities.py | LogUniformSampler.sample | def sample(self, labels):
"""
labels: [b1, b2]
Return
true_log_probs: [b1, b2]
samp_log_probs: [n_sample]
neg_samples: [n_sample]
"""
# neg_samples = torch.empty(0).long()
n_sample = self.n_sample
n_tries = 2 * n_sample
with torch.no_grad():
neg_samples = torch.multinomial(self.dist, n_tries, replacement=True).unique()
device = labels.device
neg_samples = neg_samples.to(device)
true_log_probs = self.log_q[labels].to(device)
samp_log_probs = self.log_q[neg_samples].to(device)
return true_log_probs, samp_log_probs, neg_samples | python | def sample(self, labels):
"""
labels: [b1, b2]
Return
true_log_probs: [b1, b2]
samp_log_probs: [n_sample]
neg_samples: [n_sample]
"""
# neg_samples = torch.empty(0).long()
n_sample = self.n_sample
n_tries = 2 * n_sample
with torch.no_grad():
neg_samples = torch.multinomial(self.dist, n_tries, replacement=True).unique()
device = labels.device
neg_samples = neg_samples.to(device)
true_log_probs = self.log_q[labels].to(device)
samp_log_probs = self.log_q[neg_samples].to(device)
return true_log_probs, samp_log_probs, neg_samples | [
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pandas-dev/pandas | pandas/core/missing.py | _interp_limit | def _interp_limit(invalid, fw_limit, bw_limit):
"""
Get indexers of values that won't be filled
because they exceed the limits.
Parameters
----------
invalid : boolean ndarray
fw_limit : int or None
forward limit to index
bw_limit : int or None
backward limit to index
Returns
-------
set of indexers
Notes
-----
This is equivalent to the more readable, but slower
.. code-block:: python
def _interp_limit(invalid, fw_limit, bw_limit):
for x in np.where(invalid)[0]:
if invalid[max(0, x - fw_limit):x + bw_limit + 1].all():
yield x
"""
# handle forward first; the backward direction is the same except
# 1. operate on the reversed array
# 2. subtract the returned indices from N - 1
N = len(invalid)
f_idx = set()
b_idx = set()
def inner(invalid, limit):
limit = min(limit, N)
windowed = _rolling_window(invalid, limit + 1).all(1)
idx = (set(np.where(windowed)[0] + limit) |
set(np.where((~invalid[:limit + 1]).cumsum() == 0)[0]))
return idx
if fw_limit is not None:
if fw_limit == 0:
f_idx = set(np.where(invalid)[0])
else:
f_idx = inner(invalid, fw_limit)
if bw_limit is not None:
if bw_limit == 0:
# then we don't even need to care about backwards
# just use forwards
return f_idx
else:
b_idx = list(inner(invalid[::-1], bw_limit))
b_idx = set(N - 1 - np.asarray(b_idx))
if fw_limit == 0:
return b_idx
return f_idx & b_idx | python | def _interp_limit(invalid, fw_limit, bw_limit):
"""
Get indexers of values that won't be filled
because they exceed the limits.
Parameters
----------
invalid : boolean ndarray
fw_limit : int or None
forward limit to index
bw_limit : int or None
backward limit to index
Returns
-------
set of indexers
Notes
-----
This is equivalent to the more readable, but slower
.. code-block:: python
def _interp_limit(invalid, fw_limit, bw_limit):
for x in np.where(invalid)[0]:
if invalid[max(0, x - fw_limit):x + bw_limit + 1].all():
yield x
"""
# handle forward first; the backward direction is the same except
# 1. operate on the reversed array
# 2. subtract the returned indices from N - 1
N = len(invalid)
f_idx = set()
b_idx = set()
def inner(invalid, limit):
limit = min(limit, N)
windowed = _rolling_window(invalid, limit + 1).all(1)
idx = (set(np.where(windowed)[0] + limit) |
set(np.where((~invalid[:limit + 1]).cumsum() == 0)[0]))
return idx
if fw_limit is not None:
if fw_limit == 0:
f_idx = set(np.where(invalid)[0])
else:
f_idx = inner(invalid, fw_limit)
if bw_limit is not None:
if bw_limit == 0:
# then we don't even need to care about backwards
# just use forwards
return f_idx
else:
b_idx = list(inner(invalid[::-1], bw_limit))
b_idx = set(N - 1 - np.asarray(b_idx))
if fw_limit == 0:
return b_idx
return f_idx & b_idx | [
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apache/spark | python/pyspark/conf.py | SparkConf.get | def get(self, key, defaultValue=None):
"""Get the configured value for some key, or return a default otherwise."""
if defaultValue is None: # Py4J doesn't call the right get() if we pass None
if self._jconf is not None:
if not self._jconf.contains(key):
return None
return self._jconf.get(key)
else:
if key not in self._conf:
return None
return self._conf[key]
else:
if self._jconf is not None:
return self._jconf.get(key, defaultValue)
else:
return self._conf.get(key, defaultValue) | python | def get(self, key, defaultValue=None):
"""Get the configured value for some key, or return a default otherwise."""
if defaultValue is None: # Py4J doesn't call the right get() if we pass None
if self._jconf is not None:
if not self._jconf.contains(key):
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return self._jconf.get(key)
else:
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return None
return self._conf[key]
else:
if self._jconf is not None:
return self._jconf.get(key, defaultValue)
else:
return self._conf.get(key, defaultValue) | [
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pandas-dev/pandas | pandas/core/groupby/groupby.py | GroupBy.shift | def shift(self, periods=1, freq=None, axis=0, fill_value=None):
"""
Shift each group by periods observations.
Parameters
----------
periods : integer, default 1
number of periods to shift
freq : frequency string
axis : axis to shift, default 0
fill_value : optional
.. versionadded:: 0.24.0
"""
if freq is not None or axis != 0 or not isna(fill_value):
return self.apply(lambda x: x.shift(periods, freq,
axis, fill_value))
return self._get_cythonized_result('group_shift_indexer',
self.grouper, cython_dtype=np.int64,
needs_ngroups=True,
result_is_index=True,
periods=periods) | python | def shift(self, periods=1, freq=None, axis=0, fill_value=None):
"""
Shift each group by periods observations.
Parameters
----------
periods : integer, default 1
number of periods to shift
freq : frequency string
axis : axis to shift, default 0
fill_value : optional
.. versionadded:: 0.24.0
"""
if freq is not None or axis != 0 or not isna(fill_value):
return self.apply(lambda x: x.shift(periods, freq,
axis, fill_value))
return self._get_cythonized_result('group_shift_indexer',
self.grouper, cython_dtype=np.int64,
needs_ngroups=True,
result_is_index=True,
periods=periods) | [
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.rdd | def rdd(self):
"""Returns the content as an :class:`pyspark.RDD` of :class:`Row`.
"""
if self._lazy_rdd is None:
jrdd = self._jdf.javaToPython()
self._lazy_rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
return self._lazy_rdd | python | def rdd(self):
"""Returns the content as an :class:`pyspark.RDD` of :class:`Row`.
"""
if self._lazy_rdd is None:
jrdd = self._jdf.javaToPython()
self._lazy_rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
return self._lazy_rdd | [
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.hint | def hint(self, name, *parameters):
"""Specifies some hint on the current DataFrame.
:param name: A name of the hint.
:param parameters: Optional parameters.
:return: :class:`DataFrame`
>>> df.join(df2.hint("broadcast"), "name").show()
+----+---+------+
|name|age|height|
+----+---+------+
| Bob| 5| 85|
+----+---+------+
"""
if len(parameters) == 1 and isinstance(parameters[0], list):
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if not isinstance(name, str):
raise TypeError("name should be provided as str, got {0}".format(type(name)))
allowed_types = (basestring, list, float, int)
for p in parameters:
if not isinstance(p, allowed_types):
raise TypeError(
"all parameters should be in {0}, got {1} of type {2}".format(
allowed_types, p, type(p)))
jdf = self._jdf.hint(name, self._jseq(parameters))
return DataFrame(jdf, self.sql_ctx) | python | def hint(self, name, *parameters):
"""Specifies some hint on the current DataFrame.
:param name: A name of the hint.
:param parameters: Optional parameters.
:return: :class:`DataFrame`
>>> df.join(df2.hint("broadcast"), "name").show()
+----+---+------+
|name|age|height|
+----+---+------+
| Bob| 5| 85|
+----+---+------+
"""
if len(parameters) == 1 and isinstance(parameters[0], list):
parameters = parameters[0]
if not isinstance(name, str):
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allowed_types = (basestring, list, float, int)
for p in parameters:
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raise TypeError(
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jdf = self._jdf.hint(name, self._jseq(parameters))
return DataFrame(jdf, self.sql_ctx) | [
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huggingface/pytorch-pretrained-BERT | examples/lm_finetuning/pregenerate_training_data.py | create_masked_lm_predictions | def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_list):
"""Creates the predictions for the masked LM objective. This is mostly copied from the Google BERT repo, but
with several refactors to clean it up and remove a lot of unnecessary variables."""
cand_indices = []
for (i, token) in enumerate(tokens):
if token == "[CLS]" or token == "[SEP]":
continue
cand_indices.append(i)
num_to_mask = min(max_predictions_per_seq,
max(1, int(round(len(tokens) * masked_lm_prob))))
shuffle(cand_indices)
mask_indices = sorted(sample(cand_indices, num_to_mask))
masked_token_labels = []
for index in mask_indices:
# 80% of the time, replace with [MASK]
if random() < 0.8:
masked_token = "[MASK]"
else:
# 10% of the time, keep original
if random() < 0.5:
masked_token = tokens[index]
# 10% of the time, replace with random word
else:
masked_token = choice(vocab_list)
masked_token_labels.append(tokens[index])
# Once we've saved the true label for that token, we can overwrite it with the masked version
tokens[index] = masked_token
return tokens, mask_indices, masked_token_labels | python | def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_list):
"""Creates the predictions for the masked LM objective. This is mostly copied from the Google BERT repo, but
with several refactors to clean it up and remove a lot of unnecessary variables."""
cand_indices = []
for (i, token) in enumerate(tokens):
if token == "[CLS]" or token == "[SEP]":
continue
cand_indices.append(i)
num_to_mask = min(max_predictions_per_seq,
max(1, int(round(len(tokens) * masked_lm_prob))))
shuffle(cand_indices)
mask_indices = sorted(sample(cand_indices, num_to_mask))
masked_token_labels = []
for index in mask_indices:
# 80% of the time, replace with [MASK]
if random() < 0.8:
masked_token = "[MASK]"
else:
# 10% of the time, keep original
if random() < 0.5:
masked_token = tokens[index]
# 10% of the time, replace with random word
else:
masked_token = choice(vocab_list)
masked_token_labels.append(tokens[index])
# Once we've saved the true label for that token, we can overwrite it with the masked version
tokens[index] = masked_token
return tokens, mask_indices, masked_token_labels | [
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apache/spark | python/pyspark/mllib/linalg/distributed.py | IndexedRowMatrix.multiply | def multiply(self, matrix):
"""
Multiply this matrix by a local dense matrix on the right.
:param matrix: a local dense matrix whose number of rows must match the number of columns
of this matrix
:returns: :py:class:`IndexedRowMatrix`
>>> mat = IndexedRowMatrix(sc.parallelize([(0, (0, 1)), (1, (2, 3))]))
>>> mat.multiply(DenseMatrix(2, 2, [0, 2, 1, 3])).rows.collect()
[IndexedRow(0, [2.0,3.0]), IndexedRow(1, [6.0,11.0])]
"""
if not isinstance(matrix, DenseMatrix):
raise ValueError("Only multiplication with DenseMatrix "
"is supported.")
return IndexedRowMatrix(self._java_matrix_wrapper.call("multiply", matrix)) | python | def multiply(self, matrix):
"""
Multiply this matrix by a local dense matrix on the right.
:param matrix: a local dense matrix whose number of rows must match the number of columns
of this matrix
:returns: :py:class:`IndexedRowMatrix`
>>> mat = IndexedRowMatrix(sc.parallelize([(0, (0, 1)), (1, (2, 3))]))
>>> mat.multiply(DenseMatrix(2, 2, [0, 2, 1, 3])).rows.collect()
[IndexedRow(0, [2.0,3.0]), IndexedRow(1, [6.0,11.0])]
"""
if not isinstance(matrix, DenseMatrix):
raise ValueError("Only multiplication with DenseMatrix "
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return IndexedRowMatrix(self._java_matrix_wrapper.call("multiply", matrix)) | [
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apache/spark | python/pyspark/mllib/classification.py | LogisticRegressionWithLBFGS.train | def train(cls, data, iterations=100, initialWeights=None, regParam=0.0, regType="l2",
intercept=False, corrections=10, tolerance=1e-6, validateData=True, numClasses=2):
"""
Train a logistic regression model on the given data.
:param data:
The training data, an RDD of LabeledPoint.
:param iterations:
The number of iterations.
(default: 100)
:param initialWeights:
The initial weights.
(default: None)
:param regParam:
The regularizer parameter.
(default: 0.0)
:param regType:
The type of regularizer used for training our model.
Supported values:
- "l1" for using L1 regularization
- "l2" for using L2 regularization (default)
- None for no regularization
:param intercept:
Boolean parameter which indicates the use or not of the
augmented representation for training data (i.e., whether bias
features are activated or not).
(default: False)
:param corrections:
The number of corrections used in the LBFGS update.
If a known updater is used for binary classification,
it calls the ml implementation and this parameter will
have no effect. (default: 10)
:param tolerance:
The convergence tolerance of iterations for L-BFGS.
(default: 1e-6)
:param validateData:
Boolean parameter which indicates if the algorithm should
validate data before training.
(default: True)
:param numClasses:
The number of classes (i.e., outcomes) a label can take in
Multinomial Logistic Regression.
(default: 2)
>>> data = [
... LabeledPoint(0.0, [0.0, 1.0]),
... LabeledPoint(1.0, [1.0, 0.0]),
... ]
>>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=10)
>>> lrm.predict([1.0, 0.0])
1
>>> lrm.predict([0.0, 1.0])
0
"""
def train(rdd, i):
return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i,
float(regParam), regType, bool(intercept), int(corrections),
float(tolerance), bool(validateData), int(numClasses))
if initialWeights is None:
if numClasses == 2:
initialWeights = [0.0] * len(data.first().features)
else:
if intercept:
initialWeights = [0.0] * (len(data.first().features) + 1) * (numClasses - 1)
else:
initialWeights = [0.0] * len(data.first().features) * (numClasses - 1)
return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights) | python | def train(cls, data, iterations=100, initialWeights=None, regParam=0.0, regType="l2",
intercept=False, corrections=10, tolerance=1e-6, validateData=True, numClasses=2):
"""
Train a logistic regression model on the given data.
:param data:
The training data, an RDD of LabeledPoint.
:param iterations:
The number of iterations.
(default: 100)
:param initialWeights:
The initial weights.
(default: None)
:param regParam:
The regularizer parameter.
(default: 0.0)
:param regType:
The type of regularizer used for training our model.
Supported values:
- "l1" for using L1 regularization
- "l2" for using L2 regularization (default)
- None for no regularization
:param intercept:
Boolean parameter which indicates the use or not of the
augmented representation for training data (i.e., whether bias
features are activated or not).
(default: False)
:param corrections:
The number of corrections used in the LBFGS update.
If a known updater is used for binary classification,
it calls the ml implementation and this parameter will
have no effect. (default: 10)
:param tolerance:
The convergence tolerance of iterations for L-BFGS.
(default: 1e-6)
:param validateData:
Boolean parameter which indicates if the algorithm should
validate data before training.
(default: True)
:param numClasses:
The number of classes (i.e., outcomes) a label can take in
Multinomial Logistic Regression.
(default: 2)
>>> data = [
... LabeledPoint(0.0, [0.0, 1.0]),
... LabeledPoint(1.0, [1.0, 0.0]),
... ]
>>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=10)
>>> lrm.predict([1.0, 0.0])
1
>>> lrm.predict([0.0, 1.0])
0
"""
def train(rdd, i):
return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i,
float(regParam), regType, bool(intercept), int(corrections),
float(tolerance), bool(validateData), int(numClasses))
if initialWeights is None:
if numClasses == 2:
initialWeights = [0.0] * len(data.first().features)
else:
if intercept:
initialWeights = [0.0] * (len(data.first().features) + 1) * (numClasses - 1)
else:
initialWeights = [0.0] * len(data.first().features) * (numClasses - 1)
return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights) | [
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>>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=10)
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apache/spark | python/pyspark/ml/fpm.py | PrefixSpan.setParams | def setParams(self, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000,
sequenceCol="sequence"):
"""
setParams(self, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000, \
sequenceCol="sequence")
"""
kwargs = self._input_kwargs
return self._set(**kwargs) | python | def setParams(self, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000,
sequenceCol="sequence"):
"""
setParams(self, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000, \
sequenceCol="sequence")
"""
kwargs = self._input_kwargs
return self._set(**kwargs) | [
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apache/spark | python/pyspark/mllib/fpm.py | FPGrowth.train | def train(cls, data, minSupport=0.3, numPartitions=-1):
"""
Computes an FP-Growth model that contains frequent itemsets.
:param data:
The input data set, each element contains a transaction.
:param minSupport:
The minimal support level.
(default: 0.3)
:param numPartitions:
The number of partitions used by parallel FP-growth. A value
of -1 will use the same number as input data.
(default: -1)
"""
model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions))
return FPGrowthModel(model) | python | def train(cls, data, minSupport=0.3, numPartitions=-1):
"""
Computes an FP-Growth model that contains frequent itemsets.
:param data:
The input data set, each element contains a transaction.
:param minSupport:
The minimal support level.
(default: 0.3)
:param numPartitions:
The number of partitions used by parallel FP-growth. A value
of -1 will use the same number as input data.
(default: -1)
"""
model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions))
return FPGrowthModel(model) | [
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ageitgey/face_recognition | examples/face_recognition_knn.py | show_prediction_labels_on_image | def show_prediction_labels_on_image(img_path, predictions):
"""
Shows the face recognition results visually.
:param img_path: path to image to be recognized
:param predictions: results of the predict function
:return:
"""
pil_image = Image.open(img_path).convert("RGB")
draw = ImageDraw.Draw(pil_image)
for name, (top, right, bottom, left) in predictions:
# Draw a box around the face using the Pillow module
draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))
# There's a bug in Pillow where it blows up with non-UTF-8 text
# when using the default bitmap font
name = name.encode("UTF-8")
# Draw a label with a name below the face
text_width, text_height = draw.textsize(name)
draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))
draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))
# Remove the drawing library from memory as per the Pillow docs
del draw
# Display the resulting image
pil_image.show() | python | def show_prediction_labels_on_image(img_path, predictions):
"""
Shows the face recognition results visually.
:param img_path: path to image to be recognized
:param predictions: results of the predict function
:return:
"""
pil_image = Image.open(img_path).convert("RGB")
draw = ImageDraw.Draw(pil_image)
for name, (top, right, bottom, left) in predictions:
# Draw a box around the face using the Pillow module
draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))
# There's a bug in Pillow where it blows up with non-UTF-8 text
# when using the default bitmap font
name = name.encode("UTF-8")
# Draw a label with a name below the face
text_width, text_height = draw.textsize(name)
draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))
draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))
# Remove the drawing library from memory as per the Pillow docs
del draw
# Display the resulting image
pil_image.show() | [
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apache/spark | python/pyspark/rdd.py | RDD.sumApprox | def sumApprox(self, timeout, confidence=0.95):
"""
.. note:: Experimental
Approximate operation to return the sum within a timeout
or meet the confidence.
>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(range(1000))
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"""
jrdd = self.mapPartitions(lambda it: [float(sum(it))])._to_java_object_rdd()
jdrdd = self.ctx._jvm.JavaDoubleRDD.fromRDD(jrdd.rdd())
r = jdrdd.sumApprox(timeout, confidence).getFinalValue()
return BoundedFloat(r.mean(), r.confidence(), r.low(), r.high()) | python | def sumApprox(self, timeout, confidence=0.95):
"""
.. note:: Experimental
Approximate operation to return the sum within a timeout
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>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(range(1000))
>>> abs(rdd.sumApprox(1000) - r) / r < 0.05
True
"""
jrdd = self.mapPartitions(lambda it: [float(sum(it))])._to_java_object_rdd()
jdrdd = self.ctx._jvm.JavaDoubleRDD.fromRDD(jrdd.rdd())
r = jdrdd.sumApprox(timeout, confidence).getFinalValue()
return BoundedFloat(r.mean(), r.confidence(), r.low(), r.high()) | [
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apache/spark | python/pyspark/streaming/dstream.py | DStream.groupByKeyAndWindow | def groupByKeyAndWindow(self, windowDuration, slideDuration, numPartitions=None):
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ls = self.mapValues(lambda x: [x])
grouped = ls.reduceByKeyAndWindow(lambda a, b: a.extend(b) or a, lambda a, b: a[len(b):],
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return grouped.mapValues(ResultIterable) | python | def groupByKeyAndWindow(self, windowDuration, slideDuration, numPartitions=None):
"""
Return a new DStream by applying `groupByKey` over a sliding window.
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pandas-dev/pandas | pandas/core/sorting.py | decons_obs_group_ids | def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull):
"""
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Parameters
----------
xnull: boolean,
if nulls are excluded; i.e. -1 labels are passed through
"""
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shape = np.asarray(shape, dtype='i8') + lift
if not is_int64_overflow_possible(shape):
# obs ids are deconstructable! take the fast route!
out = decons_group_index(obs_ids, shape)
return out if xnull or not lift.any() \
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i = unique_label_indices(comp_ids)
i8copy = lambda a: a.astype('i8', subok=False, copy=True)
return [i8copy(lab[i]) for lab in labels] | python | def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull):
"""
reconstruct labels from observed group ids
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----------
xnull: boolean,
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lift = np.fromiter(((a == -1).any() for a in labels), dtype='i8')
shape = np.asarray(shape, dtype='i8') + lift
if not is_int64_overflow_possible(shape):
# obs ids are deconstructable! take the fast route!
out = decons_group_index(obs_ids, shape)
return out if xnull or not lift.any() \
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i8copy = lambda a: a.astype('i8', subok=False, copy=True)
return [i8copy(lab[i]) for lab in labels] | [
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apache/spark | python/pyspark/mllib/feature.py | ChiSqSelector.fit | def fit(self, data):
"""
Returns a ChiSquared feature selector.
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jmodel = callMLlibFunc("fitChiSqSelector", self.selectorType, self.numTopFeatures,
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return ChiSqSelectorModel(jmodel) | python | def fit(self, data):
"""
Returns a ChiSquared feature selector.
:param data: an `RDD[LabeledPoint]` containing the labeled dataset
with categorical features. Real-valued features will be
treated as categorical for each distinct value.
Apply feature discretizer before using this function.
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jmodel = callMLlibFunc("fitChiSqSelector", self.selectorType, self.numTopFeatures,
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return ChiSqSelectorModel(jmodel) | [
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pandas-dev/pandas | pandas/tseries/holiday.py | weekend_to_monday | def weekend_to_monday(dt):
"""
If holiday falls on Sunday or Saturday,
use day thereafter (Monday) instead.
Needed for holidays such as Christmas observation in Europe
"""
if dt.weekday() == 6:
return dt + timedelta(1)
elif dt.weekday() == 5:
return dt + timedelta(2)
return dt | python | def weekend_to_monday(dt):
"""
If holiday falls on Sunday or Saturday,
use day thereafter (Monday) instead.
Needed for holidays such as Christmas observation in Europe
"""
if dt.weekday() == 6:
return dt + timedelta(1)
elif dt.weekday() == 5:
return dt + timedelta(2)
return dt | [
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apache/spark | python/pyspark/rdd.py | RDD.take | def take(self, num):
"""
Take the first num elements of the RDD.
It works by first scanning one partition, and use the results from
that partition to estimate the number of additional partitions needed
to satisfy the limit.
Translated from the Scala implementation in RDD#take().
.. note:: this method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
>>> sc.parallelize([2, 3, 4, 5, 6]).cache().take(2)
[2, 3]
>>> sc.parallelize([2, 3, 4, 5, 6]).take(10)
[2, 3, 4, 5, 6]
>>> sc.parallelize(range(100), 100).filter(lambda x: x > 90).take(3)
[91, 92, 93]
"""
items = []
totalParts = self.getNumPartitions()
partsScanned = 0
while len(items) < num and partsScanned < totalParts:
# The number of partitions to try in this iteration.
# It is ok for this number to be greater than totalParts because
# we actually cap it at totalParts in runJob.
numPartsToTry = 1
if partsScanned > 0:
# If we didn't find any rows after the previous iteration,
# quadruple and retry. Otherwise, interpolate the number of
# partitions we need to try, but overestimate it by 50%.
# We also cap the estimation in the end.
if len(items) == 0:
numPartsToTry = partsScanned * 4
else:
# the first parameter of max is >=1 whenever partsScanned >= 2
numPartsToTry = int(1.5 * num * partsScanned / len(items)) - partsScanned
numPartsToTry = min(max(numPartsToTry, 1), partsScanned * 4)
left = num - len(items)
def takeUpToNumLeft(iterator):
iterator = iter(iterator)
taken = 0
while taken < left:
try:
yield next(iterator)
except StopIteration:
return
taken += 1
p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
res = self.context.runJob(self, takeUpToNumLeft, p)
items += res
partsScanned += numPartsToTry
return items[:num] | python | def take(self, num):
"""
Take the first num elements of the RDD.
It works by first scanning one partition, and use the results from
that partition to estimate the number of additional partitions needed
to satisfy the limit.
Translated from the Scala implementation in RDD#take().
.. note:: this method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
>>> sc.parallelize([2, 3, 4, 5, 6]).cache().take(2)
[2, 3]
>>> sc.parallelize([2, 3, 4, 5, 6]).take(10)
[2, 3, 4, 5, 6]
>>> sc.parallelize(range(100), 100).filter(lambda x: x > 90).take(3)
[91, 92, 93]
"""
items = []
totalParts = self.getNumPartitions()
partsScanned = 0
while len(items) < num and partsScanned < totalParts:
# The number of partitions to try in this iteration.
# It is ok for this number to be greater than totalParts because
# we actually cap it at totalParts in runJob.
numPartsToTry = 1
if partsScanned > 0:
# If we didn't find any rows after the previous iteration,
# quadruple and retry. Otherwise, interpolate the number of
# partitions we need to try, but overestimate it by 50%.
# We also cap the estimation in the end.
if len(items) == 0:
numPartsToTry = partsScanned * 4
else:
# the first parameter of max is >=1 whenever partsScanned >= 2
numPartsToTry = int(1.5 * num * partsScanned / len(items)) - partsScanned
numPartsToTry = min(max(numPartsToTry, 1), partsScanned * 4)
left = num - len(items)
def takeUpToNumLeft(iterator):
iterator = iter(iterator)
taken = 0
while taken < left:
try:
yield next(iterator)
except StopIteration:
return
taken += 1
p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
res = self.context.runJob(self, takeUpToNumLeft, p)
items += res
partsScanned += numPartsToTry
return items[:num] | [
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pandas-dev/pandas | pandas/core/panel.py | Panel.xs | def xs(self, key, axis=1):
"""
Return slice of panel along selected axis.
Parameters
----------
key : object
Label
axis : {'items', 'major', 'minor}, default 1/'major'
Returns
-------
y : ndim(self)-1
Notes
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self._consolidate_inplace()
axis_number = self._get_axis_number(axis)
new_data = self._data.xs(key, axis=axis_number, copy=False)
result = self._construct_return_type(new_data)
copy = new_data.is_mixed_type
result._set_is_copy(self, copy=copy)
return result | python | def xs(self, key, axis=1):
"""
Return slice of panel along selected axis.
Parameters
----------
key : object
Label
axis : {'items', 'major', 'minor}, default 1/'major'
Returns
-------
y : ndim(self)-1
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-----
xs is only for getting, not setting values.
MultiIndex Slicers is a generic way to get/set values on any level or
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axis = self._get_axis_number(axis)
if axis == 0:
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self._consolidate_inplace()
axis_number = self._get_axis_number(axis)
new_data = self._data.xs(key, axis=axis_number, copy=False)
result = self._construct_return_type(new_data)
copy = new_data.is_mixed_type
result._set_is_copy(self, copy=copy)
return result | [
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apache/spark | python/pyspark/sql/readwriter.py | DataFrameWriter.parquet | def parquet(self, path, mode=None, partitionBy=None, compression=None):
"""Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
:param path: the path in any Hadoop supported file system
:param mode: specifies the behavior of the save operation when data already exists.
* ``append``: Append contents of this :class:`DataFrame` to existing data.
* ``overwrite``: Overwrite existing data.
* ``ignore``: Silently ignore this operation if data already exists.
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lzo, brotli, lz4, and zstd). This will override
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>>> df.write.parquet(os.path.join(tempfile.mkdtemp(), 'data'))
"""
self.mode(mode)
if partitionBy is not None:
self.partitionBy(partitionBy)
self._set_opts(compression=compression)
self._jwrite.parquet(path) | python | def parquet(self, path, mode=None, partitionBy=None, compression=None):
"""Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
:param path: the path in any Hadoop supported file system
:param mode: specifies the behavior of the save operation when data already exists.
* ``append``: Append contents of this :class:`DataFrame` to existing data.
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value specified in ``spark.sql.parquet.compression.codec``.
>>> df.write.parquet(os.path.join(tempfile.mkdtemp(), 'data'))
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self.mode(mode)
if partitionBy is not None:
self.partitionBy(partitionBy)
self._set_opts(compression=compression)
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apache/spark | python/pyspark/profiler.py | Profiler.dump | def dump(self, id, path):
""" Dump the profile into path, id is the RDD id """
if not os.path.exists(path):
os.makedirs(path)
stats = self.stats()
if stats:
p = os.path.join(path, "rdd_%d.pstats" % id)
stats.dump_stats(p) | python | def dump(self, id, path):
""" Dump the profile into path, id is the RDD id """
if not os.path.exists(path):
os.makedirs(path)
stats = self.stats()
if stats:
p = os.path.join(path, "rdd_%d.pstats" % id)
stats.dump_stats(p) | [
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apache/spark | python/pyspark/sql/readwriter.py | DataFrameReader.schema | def schema(self, schema):
"""Specifies the input schema.
Some data sources (e.g. JSON) can infer the input schema automatically from data.
By specifying the schema here, the underlying data source can skip the schema
inference step, and thus speed up data loading.
:param schema: a :class:`pyspark.sql.types.StructType` object or a DDL-formatted string
(For example ``col0 INT, col1 DOUBLE``).
>>> s = spark.read.schema("col0 INT, col1 DOUBLE")
"""
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
if isinstance(schema, StructType):
jschema = spark._jsparkSession.parseDataType(schema.json())
self._jreader = self._jreader.schema(jschema)
elif isinstance(schema, basestring):
self._jreader = self._jreader.schema(schema)
else:
raise TypeError("schema should be StructType or string")
return self | python | def schema(self, schema):
"""Specifies the input schema.
Some data sources (e.g. JSON) can infer the input schema automatically from data.
By specifying the schema here, the underlying data source can skip the schema
inference step, and thus speed up data loading.
:param schema: a :class:`pyspark.sql.types.StructType` object or a DDL-formatted string
(For example ``col0 INT, col1 DOUBLE``).
>>> s = spark.read.schema("col0 INT, col1 DOUBLE")
"""
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
if isinstance(schema, StructType):
jschema = spark._jsparkSession.parseDataType(schema.json())
self._jreader = self._jreader.schema(jschema)
elif isinstance(schema, basestring):
self._jreader = self._jreader.schema(schema)
else:
raise TypeError("schema should be StructType or string")
return self | [
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apache/spark | python/pyspark/mllib/feature.py | Word2VecModel.findSynonyms | def findSynonyms(self, word, num):
"""
Find synonyms of a word
:param word: a word or a vector representation of word
:param num: number of synonyms to find
:return: array of (word, cosineSimilarity)
.. note:: Local use only
"""
if not isinstance(word, basestring):
word = _convert_to_vector(word)
words, similarity = self.call("findSynonyms", word, num)
return zip(words, similarity) | python | def findSynonyms(self, word, num):
"""
Find synonyms of a word
:param word: a word or a vector representation of word
:param num: number of synonyms to find
:return: array of (word, cosineSimilarity)
.. note:: Local use only
"""
if not isinstance(word, basestring):
word = _convert_to_vector(word)
words, similarity = self.call("findSynonyms", word, num)
return zip(words, similarity) | [
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apache/spark | python/pyspark/sql/readwriter.py | DataFrameWriter.save | def save(self, path=None, format=None, mode=None, partitionBy=None, **options):
"""Saves the contents of the :class:`DataFrame` to a data source.
The data source is specified by the ``format`` and a set of ``options``.
If ``format`` is not specified, the default data source configured by
``spark.sql.sources.default`` will be used.
:param path: the path in a Hadoop supported file system
:param format: the format used to save
:param mode: specifies the behavior of the save operation when data already exists.
* ``append``: Append contents of this :class:`DataFrame` to existing data.
* ``overwrite``: Overwrite existing data.
* ``ignore``: Silently ignore this operation if data already exists.
* ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
exists.
:param partitionBy: names of partitioning columns
:param options: all other string options
>>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
"""
self.mode(mode).options(**options)
if partitionBy is not None:
self.partitionBy(partitionBy)
if format is not None:
self.format(format)
if path is None:
self._jwrite.save()
else:
self._jwrite.save(path) | python | def save(self, path=None, format=None, mode=None, partitionBy=None, **options):
"""Saves the contents of the :class:`DataFrame` to a data source.
The data source is specified by the ``format`` and a set of ``options``.
If ``format`` is not specified, the default data source configured by
``spark.sql.sources.default`` will be used.
:param path: the path in a Hadoop supported file system
:param format: the format used to save
:param mode: specifies the behavior of the save operation when data already exists.
* ``append``: Append contents of this :class:`DataFrame` to existing data.
* ``overwrite``: Overwrite existing data.
* ``ignore``: Silently ignore this operation if data already exists.
* ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
exists.
:param partitionBy: names of partitioning columns
:param options: all other string options
>>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
"""
self.mode(mode).options(**options)
if partitionBy is not None:
self.partitionBy(partitionBy)
if format is not None:
self.format(format)
if path is None:
self._jwrite.save()
else:
self._jwrite.save(path) | [
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pandas-dev/pandas | pandas/core/groupby/groupby.py | GroupBy.nth | def nth(self, n, dropna=None):
"""
Take the nth row from each group if n is an int, or a subset of rows
if n is a list of ints.
If dropna, will take the nth non-null row, dropna is either
Truthy (if a Series) or 'all', 'any' (if a DataFrame);
this is equivalent to calling dropna(how=dropna) before the
groupby.
Parameters
----------
n : int or list of ints
a single nth value for the row or a list of nth values
dropna : None or str, optional
apply the specified dropna operation before counting which row is
the nth row. Needs to be None, 'any' or 'all'
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
>>> g = df.groupby('A')
>>> g.nth(0)
B
A
1 NaN
2 3.0
>>> g.nth(1)
B
A
1 2.0
2 5.0
>>> g.nth(-1)
B
A
1 4.0
2 5.0
>>> g.nth([0, 1])
B
A
1 NaN
1 2.0
2 3.0
2 5.0
Specifying `dropna` allows count ignoring ``NaN``
>>> g.nth(0, dropna='any')
B
A
1 2.0
2 3.0
NaNs denote group exhausted when using dropna
>>> g.nth(3, dropna='any')
B
A
1 NaN
2 NaN
Specifying `as_index=False` in `groupby` keeps the original index.
>>> df.groupby('A', as_index=False).nth(1)
A B
1 1 2.0
4 2 5.0
"""
if isinstance(n, int):
nth_values = [n]
elif isinstance(n, (set, list, tuple)):
nth_values = list(set(n))
if dropna is not None:
raise ValueError(
"dropna option with a list of nth values is not supported")
else:
raise TypeError("n needs to be an int or a list/set/tuple of ints")
nth_values = np.array(nth_values, dtype=np.intp)
self._set_group_selection()
if not dropna:
mask_left = np.in1d(self._cumcount_array(), nth_values)
mask_right = np.in1d(self._cumcount_array(ascending=False) + 1,
-nth_values)
mask = mask_left | mask_right
out = self._selected_obj[mask]
if not self.as_index:
return out
ids, _, _ = self.grouper.group_info
out.index = self.grouper.result_index[ids[mask]]
return out.sort_index() if self.sort else out
if dropna not in ['any', 'all']:
if isinstance(self._selected_obj, Series) and dropna is True:
warnings.warn("the dropna={dropna} keyword is deprecated,"
"use dropna='all' instead. "
"For a Series groupby, dropna must be "
"either None, 'any' or 'all'.".format(
dropna=dropna),
FutureWarning,
stacklevel=2)
dropna = 'all'
else:
# Note: when agg-ing picker doesn't raise this,
# just returns NaN
raise ValueError("For a DataFrame groupby, dropna must be "
"either None, 'any' or 'all', "
"(was passed {dropna}).".format(
dropna=dropna))
# old behaviour, but with all and any support for DataFrames.
# modified in GH 7559 to have better perf
max_len = n if n >= 0 else - 1 - n
dropped = self.obj.dropna(how=dropna, axis=self.axis)
# get a new grouper for our dropped obj
if self.keys is None and self.level is None:
# we don't have the grouper info available
# (e.g. we have selected out
# a column that is not in the current object)
axis = self.grouper.axis
grouper = axis[axis.isin(dropped.index)]
else:
# create a grouper with the original parameters, but on the dropped
# object
from pandas.core.groupby.grouper import _get_grouper
grouper, _, _ = _get_grouper(dropped, key=self.keys,
axis=self.axis, level=self.level,
sort=self.sort,
mutated=self.mutated)
grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort)
sizes, result = grb.size(), grb.nth(n)
mask = (sizes < max_len).values
# set the results which don't meet the criteria
if len(result) and mask.any():
result.loc[mask] = np.nan
# reset/reindex to the original groups
if (len(self.obj) == len(dropped) or
len(result) == len(self.grouper.result_index)):
result.index = self.grouper.result_index
else:
result = result.reindex(self.grouper.result_index)
return result | python | def nth(self, n, dropna=None):
"""
Take the nth row from each group if n is an int, or a subset of rows
if n is a list of ints.
If dropna, will take the nth non-null row, dropna is either
Truthy (if a Series) or 'all', 'any' (if a DataFrame);
this is equivalent to calling dropna(how=dropna) before the
groupby.
Parameters
----------
n : int or list of ints
a single nth value for the row or a list of nth values
dropna : None or str, optional
apply the specified dropna operation before counting which row is
the nth row. Needs to be None, 'any' or 'all'
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
>>> g = df.groupby('A')
>>> g.nth(0)
B
A
1 NaN
2 3.0
>>> g.nth(1)
B
A
1 2.0
2 5.0
>>> g.nth(-1)
B
A
1 4.0
2 5.0
>>> g.nth([0, 1])
B
A
1 NaN
1 2.0
2 3.0
2 5.0
Specifying `dropna` allows count ignoring ``NaN``
>>> g.nth(0, dropna='any')
B
A
1 2.0
2 3.0
NaNs denote group exhausted when using dropna
>>> g.nth(3, dropna='any')
B
A
1 NaN
2 NaN
Specifying `as_index=False` in `groupby` keeps the original index.
>>> df.groupby('A', as_index=False).nth(1)
A B
1 1 2.0
4 2 5.0
"""
if isinstance(n, int):
nth_values = [n]
elif isinstance(n, (set, list, tuple)):
nth_values = list(set(n))
if dropna is not None:
raise ValueError(
"dropna option with a list of nth values is not supported")
else:
raise TypeError("n needs to be an int or a list/set/tuple of ints")
nth_values = np.array(nth_values, dtype=np.intp)
self._set_group_selection()
if not dropna:
mask_left = np.in1d(self._cumcount_array(), nth_values)
mask_right = np.in1d(self._cumcount_array(ascending=False) + 1,
-nth_values)
mask = mask_left | mask_right
out = self._selected_obj[mask]
if not self.as_index:
return out
ids, _, _ = self.grouper.group_info
out.index = self.grouper.result_index[ids[mask]]
return out.sort_index() if self.sort else out
if dropna not in ['any', 'all']:
if isinstance(self._selected_obj, Series) and dropna is True:
warnings.warn("the dropna={dropna} keyword is deprecated,"
"use dropna='all' instead. "
"For a Series groupby, dropna must be "
"either None, 'any' or 'all'.".format(
dropna=dropna),
FutureWarning,
stacklevel=2)
dropna = 'all'
else:
# Note: when agg-ing picker doesn't raise this,
# just returns NaN
raise ValueError("For a DataFrame groupby, dropna must be "
"either None, 'any' or 'all', "
"(was passed {dropna}).".format(
dropna=dropna))
# old behaviour, but with all and any support for DataFrames.
# modified in GH 7559 to have better perf
max_len = n if n >= 0 else - 1 - n
dropped = self.obj.dropna(how=dropna, axis=self.axis)
# get a new grouper for our dropped obj
if self.keys is None and self.level is None:
# we don't have the grouper info available
# (e.g. we have selected out
# a column that is not in the current object)
axis = self.grouper.axis
grouper = axis[axis.isin(dropped.index)]
else:
# create a grouper with the original parameters, but on the dropped
# object
from pandas.core.groupby.grouper import _get_grouper
grouper, _, _ = _get_grouper(dropped, key=self.keys,
axis=self.axis, level=self.level,
sort=self.sort,
mutated=self.mutated)
grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort)
sizes, result = grb.size(), grb.nth(n)
mask = (sizes < max_len).values
# set the results which don't meet the criteria
if len(result) and mask.any():
result.loc[mask] = np.nan
# reset/reindex to the original groups
if (len(self.obj) == len(dropped) or
len(result) == len(self.grouper.result_index)):
result.index = self.grouper.result_index
else:
result = result.reindex(self.grouper.result_index)
return result | [
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... | Take the nth row from each group if n is an int, or a subset of rows
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If dropna, will take the nth non-null row, dropna is either
Truthy (if a Series) or 'all', 'any' (if a DataFrame);
this is equivalent to calling dropna(how=dropna) before the
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Parameters
----------
n : int or list of ints
a single nth value for the row or a list of nth values
dropna : None or str, optional
apply the specified dropna operation before counting which row is
the nth row. Needs to be None, 'any' or 'all'
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
>>> g = df.groupby('A')
>>> g.nth(0)
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>>> g.nth(1)
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Specifying `dropna` allows count ignoring ``NaN``
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2 3.0
NaNs denote group exhausted when using dropna
>>> g.nth(3, dropna='any')
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1 NaN
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Specifying `as_index=False` in `groupby` keeps the original index.
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1 1 2.0
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apache/spark | python/pyspark/sql/types.py | _check_series_convert_timestamps_internal | def _check_series_convert_timestamps_internal(s, timezone):
"""
Convert a tz-naive timestamp in the specified timezone or local timezone to UTC normalized for
Spark internal storage
:param s: a pandas.Series
:param timezone: the timezone to convert. if None then use local timezone
:return pandas.Series where if it is a timestamp, has been UTC normalized without a time zone
"""
from pyspark.sql.utils import require_minimum_pandas_version
require_minimum_pandas_version()
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
# TODO: handle nested timestamps, such as ArrayType(TimestampType())?
if is_datetime64_dtype(s.dtype):
# When tz_localize a tz-naive timestamp, the result is ambiguous if the tz-naive
# timestamp is during the hour when the clock is adjusted backward during due to
# daylight saving time (dst).
# E.g., for America/New_York, the clock is adjusted backward on 2015-11-01 2:00 to
# 2015-11-01 1:00 from dst-time to standard time, and therefore, when tz_localize
# a tz-naive timestamp 2015-11-01 1:30 with America/New_York timezone, it can be either
# dst time (2015-01-01 1:30-0400) or standard time (2015-11-01 1:30-0500).
#
# Here we explicit choose to use standard time. This matches the default behavior of
# pytz.
#
# Here are some code to help understand this behavior:
# >>> import datetime
# >>> import pandas as pd
# >>> import pytz
# >>>
# >>> t = datetime.datetime(2015, 11, 1, 1, 30)
# >>> ts = pd.Series([t])
# >>> tz = pytz.timezone('America/New_York')
# >>>
# >>> ts.dt.tz_localize(tz, ambiguous=True)
# 0 2015-11-01 01:30:00-04:00
# dtype: datetime64[ns, America/New_York]
# >>>
# >>> ts.dt.tz_localize(tz, ambiguous=False)
# 0 2015-11-01 01:30:00-05:00
# dtype: datetime64[ns, America/New_York]
# >>>
# >>> str(tz.localize(t))
# '2015-11-01 01:30:00-05:00'
tz = timezone or _get_local_timezone()
return s.dt.tz_localize(tz, ambiguous=False).dt.tz_convert('UTC')
elif is_datetime64tz_dtype(s.dtype):
return s.dt.tz_convert('UTC')
else:
return s | python | def _check_series_convert_timestamps_internal(s, timezone):
"""
Convert a tz-naive timestamp in the specified timezone or local timezone to UTC normalized for
Spark internal storage
:param s: a pandas.Series
:param timezone: the timezone to convert. if None then use local timezone
:return pandas.Series where if it is a timestamp, has been UTC normalized without a time zone
"""
from pyspark.sql.utils import require_minimum_pandas_version
require_minimum_pandas_version()
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
# TODO: handle nested timestamps, such as ArrayType(TimestampType())?
if is_datetime64_dtype(s.dtype):
# When tz_localize a tz-naive timestamp, the result is ambiguous if the tz-naive
# timestamp is during the hour when the clock is adjusted backward during due to
# daylight saving time (dst).
# E.g., for America/New_York, the clock is adjusted backward on 2015-11-01 2:00 to
# 2015-11-01 1:00 from dst-time to standard time, and therefore, when tz_localize
# a tz-naive timestamp 2015-11-01 1:30 with America/New_York timezone, it can be either
# dst time (2015-01-01 1:30-0400) or standard time (2015-11-01 1:30-0500).
#
# Here we explicit choose to use standard time. This matches the default behavior of
# pytz.
#
# Here are some code to help understand this behavior:
# >>> import datetime
# >>> import pandas as pd
# >>> import pytz
# >>>
# >>> t = datetime.datetime(2015, 11, 1, 1, 30)
# >>> ts = pd.Series([t])
# >>> tz = pytz.timezone('America/New_York')
# >>>
# >>> ts.dt.tz_localize(tz, ambiguous=True)
# 0 2015-11-01 01:30:00-04:00
# dtype: datetime64[ns, America/New_York]
# >>>
# >>> ts.dt.tz_localize(tz, ambiguous=False)
# 0 2015-11-01 01:30:00-05:00
# dtype: datetime64[ns, America/New_York]
# >>>
# >>> str(tz.localize(t))
# '2015-11-01 01:30:00-05:00'
tz = timezone or _get_local_timezone()
return s.dt.tz_localize(tz, ambiguous=False).dt.tz_convert('UTC')
elif is_datetime64tz_dtype(s.dtype):
return s.dt.tz_convert('UTC')
else:
return s | [
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pandas-dev/pandas | pandas/core/indexes/base.py | Index._validate_for_numeric_unaryop | def _validate_for_numeric_unaryop(self, op, opstr):
"""
Validate if we can perform a numeric unary operation.
"""
if not self._is_numeric_dtype:
raise TypeError("cannot evaluate a numeric op "
"{opstr} for type: {typ}"
.format(opstr=opstr, typ=type(self).__name__)) | python | def _validate_for_numeric_unaryop(self, op, opstr):
"""
Validate if we can perform a numeric unary operation.
"""
if not self._is_numeric_dtype:
raise TypeError("cannot evaluate a numeric op "
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.format(opstr=opstr, typ=type(self).__name__)) | [
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apache/spark | python/pyspark/sql/dataframe.py | _to_corrected_pandas_type | def _to_corrected_pandas_type(dt):
"""
When converting Spark SQL records to Pandas DataFrame, the inferred data type may be wrong.
This method gets the corrected data type for Pandas if that type may be inferred uncorrectly.
"""
import numpy as np
if type(dt) == ByteType:
return np.int8
elif type(dt) == ShortType:
return np.int16
elif type(dt) == IntegerType:
return np.int32
elif type(dt) == FloatType:
return np.float32
else:
return None | python | def _to_corrected_pandas_type(dt):
"""
When converting Spark SQL records to Pandas DataFrame, the inferred data type may be wrong.
This method gets the corrected data type for Pandas if that type may be inferred uncorrectly.
"""
import numpy as np
if type(dt) == ByteType:
return np.int8
elif type(dt) == ShortType:
return np.int16
elif type(dt) == IntegerType:
return np.int32
elif type(dt) == FloatType:
return np.float32
else:
return None | [
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apache/spark | python/pyspark/rdd.py | RDD.reduce | def reduce(self, f):
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>>> sc.parallelize([]).reduce(add)
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...
ValueError: Can not reduce() empty RDD
"""
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raise ValueError("Can not reduce() empty RDD") | python | def reduce(self, f):
"""
Reduces the elements of this RDD using the specified commutative and
associative binary operator. Currently reduces partitions locally.
>>> from operator import add
>>> sc.parallelize([1, 2, 3, 4, 5]).reduce(add)
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10
>>> sc.parallelize([]).reduce(add)
Traceback (most recent call last):
...
ValueError: Can not reduce() empty RDD
"""
f = fail_on_stopiteration(f)
def func(iterator):
iterator = iter(iterator)
try:
initial = next(iterator)
except StopIteration:
return
yield reduce(f, iterator, initial)
vals = self.mapPartitions(func).collect()
if vals:
return reduce(f, vals)
raise ValueError("Can not reduce() empty RDD") | [
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apache/spark | python/pyspark/sql/functions.py | _create_window_function | def _create_window_function(name, doc=''):
""" Create a window function by name """
def _():
sc = SparkContext._active_spark_context
jc = getattr(sc._jvm.functions, name)()
return Column(jc)
_.__name__ = name
_.__doc__ = 'Window function: ' + doc
return _ | python | def _create_window_function(name, doc=''):
""" Create a window function by name """
def _():
sc = SparkContext._active_spark_context
jc = getattr(sc._jvm.functions, name)()
return Column(jc)
_.__name__ = name
_.__doc__ = 'Window function: ' + doc
return _ | [
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pandas-dev/pandas | pandas/core/panel.py | Panel._construct_return_type | def _construct_return_type(self, result, axes=None):
"""
Return the type for the ndim of the result.
"""
ndim = getattr(result, 'ndim', None)
# need to assume they are the same
if ndim is None:
if isinstance(result, dict):
ndim = getattr(list(result.values())[0], 'ndim', 0)
# have a dict, so top-level is +1 dim
if ndim != 0:
ndim += 1
# scalar
if ndim == 0:
return Series(result)
# same as self
elif self.ndim == ndim:
# return the construction dictionary for these axes
if axes is None:
return self._constructor(result)
return self._constructor(result, **self._construct_axes_dict())
# sliced
elif self.ndim == ndim + 1:
if axes is None:
return self._constructor_sliced(result)
return self._constructor_sliced(
result, **self._extract_axes_for_slice(self, axes))
raise ValueError('invalid _construct_return_type [self->{self}] '
'[result->{result}]'.format(self=self, result=result)) | python | def _construct_return_type(self, result, axes=None):
"""
Return the type for the ndim of the result.
"""
ndim = getattr(result, 'ndim', None)
# need to assume they are the same
if ndim is None:
if isinstance(result, dict):
ndim = getattr(list(result.values())[0], 'ndim', 0)
# have a dict, so top-level is +1 dim
if ndim != 0:
ndim += 1
# scalar
if ndim == 0:
return Series(result)
# same as self
elif self.ndim == ndim:
# return the construction dictionary for these axes
if axes is None:
return self._constructor(result)
return self._constructor(result, **self._construct_axes_dict())
# sliced
elif self.ndim == ndim + 1:
if axes is None:
return self._constructor_sliced(result)
return self._constructor_sliced(
result, **self._extract_axes_for_slice(self, axes))
raise ValueError('invalid _construct_return_type [self->{self}] '
'[result->{result}]'.format(self=self, result=result)) | [
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apache/spark | python/pyspark/cloudpickle.py | _fill_function | def _fill_function(*args):
"""Fills in the rest of function data into the skeleton function object
The skeleton itself is create by _make_skel_func().
"""
if len(args) == 2:
func = args[0]
state = args[1]
elif len(args) == 5:
# Backwards compat for cloudpickle v0.4.0, after which the `module`
# argument was introduced
func = args[0]
keys = ['globals', 'defaults', 'dict', 'closure_values']
state = dict(zip(keys, args[1:]))
elif len(args) == 6:
# Backwards compat for cloudpickle v0.4.1, after which the function
# state was passed as a dict to the _fill_function it-self.
func = args[0]
keys = ['globals', 'defaults', 'dict', 'module', 'closure_values']
state = dict(zip(keys, args[1:]))
else:
raise ValueError('Unexpected _fill_value arguments: %r' % (args,))
# - At pickling time, any dynamic global variable used by func is
# serialized by value (in state['globals']).
# - At unpickling time, func's __globals__ attribute is initialized by
# first retrieving an empty isolated namespace that will be shared
# with other functions pickled from the same original module
# by the same CloudPickler instance and then updated with the
# content of state['globals'] to populate the shared isolated
# namespace with all the global variables that are specifically
# referenced for this function.
func.__globals__.update(state['globals'])
func.__defaults__ = state['defaults']
func.__dict__ = state['dict']
if 'annotations' in state:
func.__annotations__ = state['annotations']
if 'doc' in state:
func.__doc__ = state['doc']
if 'name' in state:
func.__name__ = state['name']
if 'module' in state:
func.__module__ = state['module']
if 'qualname' in state:
func.__qualname__ = state['qualname']
cells = func.__closure__
if cells is not None:
for cell, value in zip(cells, state['closure_values']):
if value is not _empty_cell_value:
cell_set(cell, value)
return func | python | def _fill_function(*args):
"""Fills in the rest of function data into the skeleton function object
The skeleton itself is create by _make_skel_func().
"""
if len(args) == 2:
func = args[0]
state = args[1]
elif len(args) == 5:
# Backwards compat for cloudpickle v0.4.0, after which the `module`
# argument was introduced
func = args[0]
keys = ['globals', 'defaults', 'dict', 'closure_values']
state = dict(zip(keys, args[1:]))
elif len(args) == 6:
# Backwards compat for cloudpickle v0.4.1, after which the function
# state was passed as a dict to the _fill_function it-self.
func = args[0]
keys = ['globals', 'defaults', 'dict', 'module', 'closure_values']
state = dict(zip(keys, args[1:]))
else:
raise ValueError('Unexpected _fill_value arguments: %r' % (args,))
# - At pickling time, any dynamic global variable used by func is
# serialized by value (in state['globals']).
# - At unpickling time, func's __globals__ attribute is initialized by
# first retrieving an empty isolated namespace that will be shared
# with other functions pickled from the same original module
# by the same CloudPickler instance and then updated with the
# content of state['globals'] to populate the shared isolated
# namespace with all the global variables that are specifically
# referenced for this function.
func.__globals__.update(state['globals'])
func.__defaults__ = state['defaults']
func.__dict__ = state['dict']
if 'annotations' in state:
func.__annotations__ = state['annotations']
if 'doc' in state:
func.__doc__ = state['doc']
if 'name' in state:
func.__name__ = state['name']
if 'module' in state:
func.__module__ = state['module']
if 'qualname' in state:
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cells = func.__closure__
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pandas-dev/pandas | pandas/core/indexes/base.py | Index._add_numeric_methods_binary | def _add_numeric_methods_binary(cls):
"""
Add in numeric methods.
"""
cls.__add__ = _make_arithmetic_op(operator.add, cls)
cls.__radd__ = _make_arithmetic_op(ops.radd, cls)
cls.__sub__ = _make_arithmetic_op(operator.sub, cls)
cls.__rsub__ = _make_arithmetic_op(ops.rsub, cls)
cls.__rpow__ = _make_arithmetic_op(ops.rpow, cls)
cls.__pow__ = _make_arithmetic_op(operator.pow, cls)
cls.__truediv__ = _make_arithmetic_op(operator.truediv, cls)
cls.__rtruediv__ = _make_arithmetic_op(ops.rtruediv, cls)
# TODO: rmod? rdivmod?
cls.__mod__ = _make_arithmetic_op(operator.mod, cls)
cls.__floordiv__ = _make_arithmetic_op(operator.floordiv, cls)
cls.__rfloordiv__ = _make_arithmetic_op(ops.rfloordiv, cls)
cls.__divmod__ = _make_arithmetic_op(divmod, cls)
cls.__mul__ = _make_arithmetic_op(operator.mul, cls)
cls.__rmul__ = _make_arithmetic_op(ops.rmul, cls) | python | def _add_numeric_methods_binary(cls):
"""
Add in numeric methods.
"""
cls.__add__ = _make_arithmetic_op(operator.add, cls)
cls.__radd__ = _make_arithmetic_op(ops.radd, cls)
cls.__sub__ = _make_arithmetic_op(operator.sub, cls)
cls.__rsub__ = _make_arithmetic_op(ops.rsub, cls)
cls.__rpow__ = _make_arithmetic_op(ops.rpow, cls)
cls.__pow__ = _make_arithmetic_op(operator.pow, cls)
cls.__truediv__ = _make_arithmetic_op(operator.truediv, cls)
cls.__rtruediv__ = _make_arithmetic_op(ops.rtruediv, cls)
# TODO: rmod? rdivmod?
cls.__mod__ = _make_arithmetic_op(operator.mod, cls)
cls.__floordiv__ = _make_arithmetic_op(operator.floordiv, cls)
cls.__rfloordiv__ = _make_arithmetic_op(ops.rfloordiv, cls)
cls.__divmod__ = _make_arithmetic_op(divmod, cls)
cls.__mul__ = _make_arithmetic_op(operator.mul, cls)
cls.__rmul__ = _make_arithmetic_op(ops.rmul, cls) | [
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pandas-dev/pandas | pandas/core/sorting.py | nargsort | def nargsort(items, kind='quicksort', ascending=True, na_position='last'):
"""
This is intended to be a drop-in replacement for np.argsort which
handles NaNs. It adds ascending and na_position parameters.
GH #6399, #5231
"""
# specially handle Categorical
if is_categorical_dtype(items):
if na_position not in {'first', 'last'}:
raise ValueError('invalid na_position: {!r}'.format(na_position))
mask = isna(items)
cnt_null = mask.sum()
sorted_idx = items.argsort(ascending=ascending, kind=kind)
if ascending and na_position == 'last':
# NaN is coded as -1 and is listed in front after sorting
sorted_idx = np.roll(sorted_idx, -cnt_null)
elif not ascending and na_position == 'first':
# NaN is coded as -1 and is listed in the end after sorting
sorted_idx = np.roll(sorted_idx, cnt_null)
return sorted_idx
with warnings.catch_warnings():
# https://github.com/pandas-dev/pandas/issues/25439
# can be removed once ExtensionArrays are properly handled by nargsort
warnings.filterwarnings(
"ignore", category=FutureWarning,
message="Converting timezone-aware DatetimeArray to")
items = np.asanyarray(items)
idx = np.arange(len(items))
mask = isna(items)
non_nans = items[~mask]
non_nan_idx = idx[~mask]
nan_idx = np.nonzero(mask)[0]
if not ascending:
non_nans = non_nans[::-1]
non_nan_idx = non_nan_idx[::-1]
indexer = non_nan_idx[non_nans.argsort(kind=kind)]
if not ascending:
indexer = indexer[::-1]
# Finally, place the NaNs at the end or the beginning according to
# na_position
if na_position == 'last':
indexer = np.concatenate([indexer, nan_idx])
elif na_position == 'first':
indexer = np.concatenate([nan_idx, indexer])
else:
raise ValueError('invalid na_position: {!r}'.format(na_position))
return indexer | python | def nargsort(items, kind='quicksort', ascending=True, na_position='last'):
"""
This is intended to be a drop-in replacement for np.argsort which
handles NaNs. It adds ascending and na_position parameters.
GH #6399, #5231
"""
# specially handle Categorical
if is_categorical_dtype(items):
if na_position not in {'first', 'last'}:
raise ValueError('invalid na_position: {!r}'.format(na_position))
mask = isna(items)
cnt_null = mask.sum()
sorted_idx = items.argsort(ascending=ascending, kind=kind)
if ascending and na_position == 'last':
# NaN is coded as -1 and is listed in front after sorting
sorted_idx = np.roll(sorted_idx, -cnt_null)
elif not ascending and na_position == 'first':
# NaN is coded as -1 and is listed in the end after sorting
sorted_idx = np.roll(sorted_idx, cnt_null)
return sorted_idx
with warnings.catch_warnings():
# https://github.com/pandas-dev/pandas/issues/25439
# can be removed once ExtensionArrays are properly handled by nargsort
warnings.filterwarnings(
"ignore", category=FutureWarning,
message="Converting timezone-aware DatetimeArray to")
items = np.asanyarray(items)
idx = np.arange(len(items))
mask = isna(items)
non_nans = items[~mask]
non_nan_idx = idx[~mask]
nan_idx = np.nonzero(mask)[0]
if not ascending:
non_nans = non_nans[::-1]
non_nan_idx = non_nan_idx[::-1]
indexer = non_nan_idx[non_nans.argsort(kind=kind)]
if not ascending:
indexer = indexer[::-1]
# Finally, place the NaNs at the end or the beginning according to
# na_position
if na_position == 'last':
indexer = np.concatenate([indexer, nan_idx])
elif na_position == 'first':
indexer = np.concatenate([nan_idx, indexer])
else:
raise ValueError('invalid na_position: {!r}'.format(na_position))
return indexer | [
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apache/spark | python/pyspark/rdd.py | _parse_memory | def _parse_memory(s):
"""
Parse a memory string in the format supported by Java (e.g. 1g, 200m) and
return the value in MiB
>>> _parse_memory("256m")
256
>>> _parse_memory("2g")
2048
"""
units = {'g': 1024, 'm': 1, 't': 1 << 20, 'k': 1.0 / 1024}
if s[-1].lower() not in units:
raise ValueError("invalid format: " + s)
return int(float(s[:-1]) * units[s[-1].lower()]) | python | def _parse_memory(s):
"""
Parse a memory string in the format supported by Java (e.g. 1g, 200m) and
return the value in MiB
>>> _parse_memory("256m")
256
>>> _parse_memory("2g")
2048
"""
units = {'g': 1024, 'm': 1, 't': 1 << 20, 'k': 1.0 / 1024}
if s[-1].lower() not in units:
raise ValueError("invalid format: " + s)
return int(float(s[:-1]) * units[s[-1].lower()]) | [
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apache/spark | python/pyspark/ml/tuning.py | TrainValidationSplit.setParams | def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
parallelism=1, collectSubModels=False, seed=None):
"""
setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
parallelism=1, collectSubModels=False, seed=None):
Sets params for the train validation split.
"""
kwargs = self._input_kwargs
return self._set(**kwargs) | python | def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
parallelism=1, collectSubModels=False, seed=None):
"""
setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
parallelism=1, collectSubModels=False, seed=None):
Sets params for the train validation split.
"""
kwargs = self._input_kwargs
return self._set(**kwargs) | [
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... | setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
parallelism=1, collectSubModels=False, seed=None):
Sets params for the train validation split. | [
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apache/spark | python/pyspark/mllib/stat/KernelDensity.py | KernelDensity.setSample | def setSample(self, sample):
"""Set sample points from the population. Should be a RDD"""
if not isinstance(sample, RDD):
raise TypeError("samples should be a RDD, received %s" % type(sample))
self._sample = sample | python | def setSample(self, sample):
"""Set sample points from the population. Should be a RDD"""
if not isinstance(sample, RDD):
raise TypeError("samples should be a RDD, received %s" % type(sample))
self._sample = sample | [
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apache/spark | python/pyspark/sql/functions.py | map_concat | def map_concat(*cols):
"""Returns the union of all the given maps.
:param cols: list of column names (string) or list of :class:`Column` expressions
>>> from pyspark.sql.functions import map_concat
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as map1, map(3, 'c', 1, 'd') as map2")
>>> df.select(map_concat("map1", "map2").alias("map3")).show(truncate=False)
+------------------------+
|map3 |
+------------------------+
|[1 -> d, 2 -> b, 3 -> c]|
+------------------------+
"""
sc = SparkContext._active_spark_context
if len(cols) == 1 and isinstance(cols[0], (list, set)):
cols = cols[0]
jc = sc._jvm.functions.map_concat(_to_seq(sc, cols, _to_java_column))
return Column(jc) | python | def map_concat(*cols):
"""Returns the union of all the given maps.
:param cols: list of column names (string) or list of :class:`Column` expressions
>>> from pyspark.sql.functions import map_concat
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as map1, map(3, 'c', 1, 'd') as map2")
>>> df.select(map_concat("map1", "map2").alias("map3")).show(truncate=False)
+------------------------+
|map3 |
+------------------------+
|[1 -> d, 2 -> b, 3 -> c]|
+------------------------+
"""
sc = SparkContext._active_spark_context
if len(cols) == 1 and isinstance(cols[0], (list, set)):
cols = cols[0]
jc = sc._jvm.functions.map_concat(_to_seq(sc, cols, _to_java_column))
return Column(jc) | [
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pandas-dev/pandas | pandas/_config/config.py | register_option | def register_option(key, defval, doc='', validator=None, cb=None):
"""Register an option in the package-wide pandas config object
Parameters
----------
key - a fully-qualified key, e.g. "x.y.option - z".
defval - the default value of the option
doc - a string description of the option
validator - a function of a single argument, should raise `ValueError` if
called with a value which is not a legal value for the option.
cb - a function of a single argument "key", which is called
immediately after an option value is set/reset. key is
the full name of the option.
Returns
-------
Nothing.
Raises
------
ValueError if `validator` is specified and `defval` is not a valid value.
"""
import tokenize
import keyword
key = key.lower()
if key in _registered_options:
msg = "Option '{key}' has already been registered"
raise OptionError(msg.format(key=key))
if key in _reserved_keys:
msg = "Option '{key}' is a reserved key"
raise OptionError(msg.format(key=key))
# the default value should be legal
if validator:
validator(defval)
# walk the nested dict, creating dicts as needed along the path
path = key.split('.')
for k in path:
if not bool(re.match('^' + tokenize.Name + '$', k)):
raise ValueError("{k} is not a valid identifier".format(k=k))
if keyword.iskeyword(k):
raise ValueError("{k} is a python keyword".format(k=k))
cursor = _global_config
msg = "Path prefix to option '{option}' is already an option"
for i, p in enumerate(path[:-1]):
if not isinstance(cursor, dict):
raise OptionError(msg.format(option='.'.join(path[:i])))
if p not in cursor:
cursor[p] = {}
cursor = cursor[p]
if not isinstance(cursor, dict):
raise OptionError(msg.format(option='.'.join(path[:-1])))
cursor[path[-1]] = defval # initialize
# save the option metadata
_registered_options[key] = RegisteredOption(key=key, defval=defval,
doc=doc, validator=validator,
cb=cb) | python | def register_option(key, defval, doc='', validator=None, cb=None):
"""Register an option in the package-wide pandas config object
Parameters
----------
key - a fully-qualified key, e.g. "x.y.option - z".
defval - the default value of the option
doc - a string description of the option
validator - a function of a single argument, should raise `ValueError` if
called with a value which is not a legal value for the option.
cb - a function of a single argument "key", which is called
immediately after an option value is set/reset. key is
the full name of the option.
Returns
-------
Nothing.
Raises
------
ValueError if `validator` is specified and `defval` is not a valid value.
"""
import tokenize
import keyword
key = key.lower()
if key in _registered_options:
msg = "Option '{key}' has already been registered"
raise OptionError(msg.format(key=key))
if key in _reserved_keys:
msg = "Option '{key}' is a reserved key"
raise OptionError(msg.format(key=key))
# the default value should be legal
if validator:
validator(defval)
# walk the nested dict, creating dicts as needed along the path
path = key.split('.')
for k in path:
if not bool(re.match('^' + tokenize.Name + '$', k)):
raise ValueError("{k} is not a valid identifier".format(k=k))
if keyword.iskeyword(k):
raise ValueError("{k} is a python keyword".format(k=k))
cursor = _global_config
msg = "Path prefix to option '{option}' is already an option"
for i, p in enumerate(path[:-1]):
if not isinstance(cursor, dict):
raise OptionError(msg.format(option='.'.join(path[:i])))
if p not in cursor:
cursor[p] = {}
cursor = cursor[p]
if not isinstance(cursor, dict):
raise OptionError(msg.format(option='.'.join(path[:-1])))
cursor[path[-1]] = defval # initialize
# save the option metadata
_registered_options[key] = RegisteredOption(key=key, defval=defval,
doc=doc, validator=validator,
cb=cb) | [
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apache/spark | python/pyspark/rdd.py | RDD.pipe | def pipe(self, command, env=None, checkCode=False):
"""
Return an RDD created by piping elements to a forked external process.
>>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect()
[u'1', u'2', u'', u'3']
:param checkCode: whether or not to check the return value of the shell command.
"""
if env is None:
env = dict()
def func(iterator):
pipe = Popen(
shlex.split(command), env=env, stdin=PIPE, stdout=PIPE)
def pipe_objs(out):
for obj in iterator:
s = unicode(obj).rstrip('\n') + '\n'
out.write(s.encode('utf-8'))
out.close()
Thread(target=pipe_objs, args=[pipe.stdin]).start()
def check_return_code():
pipe.wait()
if checkCode and pipe.returncode:
raise Exception("Pipe function `%s' exited "
"with error code %d" % (command, pipe.returncode))
else:
for i in range(0):
yield i
return (x.rstrip(b'\n').decode('utf-8') for x in
chain(iter(pipe.stdout.readline, b''), check_return_code()))
return self.mapPartitions(func) | python | def pipe(self, command, env=None, checkCode=False):
"""
Return an RDD created by piping elements to a forked external process.
>>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect()
[u'1', u'2', u'', u'3']
:param checkCode: whether or not to check the return value of the shell command.
"""
if env is None:
env = dict()
def func(iterator):
pipe = Popen(
shlex.split(command), env=env, stdin=PIPE, stdout=PIPE)
def pipe_objs(out):
for obj in iterator:
s = unicode(obj).rstrip('\n') + '\n'
out.write(s.encode('utf-8'))
out.close()
Thread(target=pipe_objs, args=[pipe.stdin]).start()
def check_return_code():
pipe.wait()
if checkCode and pipe.returncode:
raise Exception("Pipe function `%s' exited "
"with error code %d" % (command, pipe.returncode))
else:
for i in range(0):
yield i
return (x.rstrip(b'\n').decode('utf-8') for x in
chain(iter(pipe.stdout.readline, b''), check_return_code()))
return self.mapPartitions(func) | [
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pandas-dev/pandas | pandas/core/window.py | Window._apply_window | def _apply_window(self, mean=True, **kwargs):
"""
Applies a moving window of type ``window_type`` on the data.
Parameters
----------
mean : bool, default True
If True computes weighted mean, else weighted sum
Returns
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window = self._prep_window(**kwargs)
center = self.center
blocks, obj, index = self._create_blocks()
results = []
for b in blocks:
try:
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except TypeError:
results.append(b.values.copy())
continue
if values.size == 0:
results.append(values.copy())
continue
offset = _offset(window, center)
additional_nans = np.array([np.NaN] * offset)
def f(arg, *args, **kwargs):
minp = _use_window(self.min_periods, len(window))
return libwindow.roll_window(np.concatenate((arg,
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if center else arg, window, minp,
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result = np.apply_along_axis(f, self.axis, values)
if center:
result = self._center_window(result, window)
results.append(result)
return self._wrap_results(results, blocks, obj) | python | def _apply_window(self, mean=True, **kwargs):
"""
Applies a moving window of type ``window_type`` on the data.
Parameters
----------
mean : bool, default True
If True computes weighted mean, else weighted sum
Returns
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y : same type as input argument
"""
window = self._prep_window(**kwargs)
center = self.center
blocks, obj, index = self._create_blocks()
results = []
for b in blocks:
try:
values = self._prep_values(b.values)
except TypeError:
results.append(b.values.copy())
continue
if values.size == 0:
results.append(values.copy())
continue
offset = _offset(window, center)
additional_nans = np.array([np.NaN] * offset)
def f(arg, *args, **kwargs):
minp = _use_window(self.min_periods, len(window))
return libwindow.roll_window(np.concatenate((arg,
additional_nans))
if center else arg, window, minp,
avg=mean)
result = np.apply_along_axis(f, self.axis, values)
if center:
result = self._center_window(result, window)
results.append(result)
return self._wrap_results(results, blocks, obj) | [
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.randomSplit | def randomSplit(self, weights, seed=None):
"""Randomly splits this :class:`DataFrame` with the provided weights.
:param weights: list of doubles as weights with which to split the DataFrame. Weights will
be normalized if they don't sum up to 1.0.
:param seed: The seed for sampling.
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2
"""
for w in weights:
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seed = seed if seed is not None else random.randint(0, sys.maxsize)
rdd_array = self._jdf.randomSplit(_to_list(self.sql_ctx._sc, weights), long(seed))
return [DataFrame(rdd, self.sql_ctx) for rdd in rdd_array] | python | def randomSplit(self, weights, seed=None):
"""Randomly splits this :class:`DataFrame` with the provided weights.
:param weights: list of doubles as weights with which to split the DataFrame. Weights will
be normalized if they don't sum up to 1.0.
:param seed: The seed for sampling.
>>> splits = df4.randomSplit([1.0, 2.0], 24)
>>> splits[0].count()
2
>>> splits[1].count()
2
"""
for w in weights:
if w < 0.0:
raise ValueError("Weights must be positive. Found weight value: %s" % w)
seed = seed if seed is not None else random.randint(0, sys.maxsize)
rdd_array = self._jdf.randomSplit(_to_list(self.sql_ctx._sc, weights), long(seed))
return [DataFrame(rdd, self.sql_ctx) for rdd in rdd_array] | [
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pandas-dev/pandas | pandas/core/indexes/base.py | Index.to_native_types | def to_native_types(self, slicer=None, **kwargs):
"""
Format specified values of `self` and return them.
Parameters
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slicer : int, array-like
An indexer into `self` that specifies which values
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kwargs : dict
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Whether or not there are quoted values in `self`
3) date_format : str
The format used to represent date-like values
"""
values = self
if slicer is not None:
values = values[slicer]
return values._format_native_types(**kwargs) | python | def to_native_types(self, slicer=None, **kwargs):
"""
Format specified values of `self` and return them.
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----------
slicer : int, array-like
An indexer into `self` that specifies which values
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values = self
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values = values[slicer]
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apache/spark | python/pyspark/rdd.py | RDD.meanApprox | def meanApprox(self, timeout, confidence=0.95):
"""
.. note:: Experimental
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or meet the confidence.
>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(range(1000)) / 1000.0
>>> abs(rdd.meanApprox(1000) - r) / r < 0.05
True
"""
jrdd = self.map(float)._to_java_object_rdd()
jdrdd = self.ctx._jvm.JavaDoubleRDD.fromRDD(jrdd.rdd())
r = jdrdd.meanApprox(timeout, confidence).getFinalValue()
return BoundedFloat(r.mean(), r.confidence(), r.low(), r.high()) | python | def meanApprox(self, timeout, confidence=0.95):
"""
.. note:: Experimental
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or meet the confidence.
>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(range(1000)) / 1000.0
>>> abs(rdd.meanApprox(1000) - r) / r < 0.05
True
"""
jrdd = self.map(float)._to_java_object_rdd()
jdrdd = self.ctx._jvm.JavaDoubleRDD.fromRDD(jrdd.rdd())
r = jdrdd.meanApprox(timeout, confidence).getFinalValue()
return BoundedFloat(r.mean(), r.confidence(), r.low(), r.high()) | [
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apache/spark | python/pyspark/context.py | SparkContext.dump_profiles | def dump_profiles(self, path):
""" Dump the profile stats into directory `path`
"""
if self.profiler_collector is not None:
self.profiler_collector.dump_profiles(path)
else:
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""" Dump the profile stats into directory `path`
"""
if self.profiler_collector is not None:
self.profiler_collector.dump_profiles(path)
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apache/spark | python/pyspark/sql/functions.py | broadcast | def broadcast(df):
"""Marks a DataFrame as small enough for use in broadcast joins."""
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"""Marks a DataFrame as small enough for use in broadcast joins."""
sc = SparkContext._active_spark_context
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apache/spark | python/pyspark/sql/functions.py | months_between | def months_between(date1, date2, roundOff=True):
"""
Returns number of months between dates date1 and date2.
If date1 is later than date2, then the result is positive.
If date1 and date2 are on the same day of month, or both are the last day of month,
returns an integer (time of day will be ignored).
The result is rounded off to 8 digits unless `roundOff` is set to `False`.
>>> df = spark.createDataFrame([('1997-02-28 10:30:00', '1996-10-30')], ['date1', 'date2'])
>>> df.select(months_between(df.date1, df.date2).alias('months')).collect()
[Row(months=3.94959677)]
>>> df.select(months_between(df.date1, df.date2, False).alias('months')).collect()
[Row(months=3.9495967741935485)]
"""
sc = SparkContext._active_spark_context
return Column(sc._jvm.functions.months_between(
_to_java_column(date1), _to_java_column(date2), roundOff)) | python | def months_between(date1, date2, roundOff=True):
"""
Returns number of months between dates date1 and date2.
If date1 is later than date2, then the result is positive.
If date1 and date2 are on the same day of month, or both are the last day of month,
returns an integer (time of day will be ignored).
The result is rounded off to 8 digits unless `roundOff` is set to `False`.
>>> df = spark.createDataFrame([('1997-02-28 10:30:00', '1996-10-30')], ['date1', 'date2'])
>>> df.select(months_between(df.date1, df.date2).alias('months')).collect()
[Row(months=3.94959677)]
>>> df.select(months_between(df.date1, df.date2, False).alias('months')).collect()
[Row(months=3.9495967741935485)]
"""
sc = SparkContext._active_spark_context
return Column(sc._jvm.functions.months_between(
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[Row(months=3.94959677)]
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[Row(months=3.9495967741935485)] | [
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apache/spark | python/pyspark/mllib/linalg/__init__.py | Vectors.parse | def parse(s):
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>>> Vectors.parse(' ( 100, [0], [2])')
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elif s.find('(') != -1:
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"""Parse a string representation back into the Vector.
>>> Vectors.parse('[2,1,2 ]')
DenseVector([2.0, 1.0, 2.0])
>>> Vectors.parse(' ( 100, [0], [2])')
SparseVector(100, {0: 2.0})
"""
if s.find('(') == -1 and s.find('[') != -1:
return DenseVector.parse(s)
elif s.find('(') != -1:
return SparseVector.parse(s)
else:
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apache/spark | python/pyspark/streaming/dstream.py | DStream.slice | def slice(self, begin, end):
"""
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`begin`, `end` could be datetime.datetime() or unix_timestamp
"""
jrdds = self._jdstream.slice(self._jtime(begin), self._jtime(end))
return [RDD(jrdd, self._sc, self._jrdd_deserializer) for jrdd in jrdds] | python | def slice(self, begin, end):
"""
Return all the RDDs between 'begin' to 'end' (both included)
`begin`, `end` could be datetime.datetime() or unix_timestamp
"""
jrdds = self._jdstream.slice(self._jtime(begin), self._jtime(end))
return [RDD(jrdd, self._sc, self._jrdd_deserializer) for jrdd in jrdds] | [
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pandas-dev/pandas | pandas/core/panel.py | Panel.round | def round(self, decimals=0, *args, **kwargs):
"""
Round each value in Panel to a specified number of decimal places.
.. versionadded:: 0.18.0
Parameters
----------
decimals : int
Number of decimal places to round to (default: 0).
If decimals is negative, it specifies the number of
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Returns
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Panel object
See Also
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numpy.around
"""
nv.validate_round(args, kwargs)
if is_integer(decimals):
result = np.apply_along_axis(np.round, 0, self.values)
return self._wrap_result(result, axis=0)
raise TypeError("decimals must be an integer") | python | def round(self, decimals=0, *args, **kwargs):
"""
Round each value in Panel to a specified number of decimal places.
.. versionadded:: 0.18.0
Parameters
----------
decimals : int
Number of decimal places to round to (default: 0).
If decimals is negative, it specifies the number of
positions to the left of the decimal point.
Returns
-------
Panel object
See Also
--------
numpy.around
"""
nv.validate_round(args, kwargs)
if is_integer(decimals):
result = np.apply_along_axis(np.round, 0, self.values)
return self._wrap_result(result, axis=0)
raise TypeError("decimals must be an integer") | [
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apache/spark | python/pyspark/ml/feature.py | StringIndexerModel.from_arrays_of_labels | def from_arrays_of_labels(cls, arrayOfLabels, inputCols, outputCols=None,
handleInvalid=None):
"""
Construct the model directly from an array of array of label strings,
requires an active SparkContext.
"""
sc = SparkContext._active_spark_context
java_class = sc._gateway.jvm.java.lang.String
jlabels = StringIndexerModel._new_java_array(arrayOfLabels, java_class)
model = StringIndexerModel._create_from_java_class(
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model.setInputCols(inputCols)
if outputCols is not None:
model.setOutputCols(outputCols)
if handleInvalid is not None:
model.setHandleInvalid(handleInvalid)
return model | python | def from_arrays_of_labels(cls, arrayOfLabels, inputCols, outputCols=None,
handleInvalid=None):
"""
Construct the model directly from an array of array of label strings,
requires an active SparkContext.
"""
sc = SparkContext._active_spark_context
java_class = sc._gateway.jvm.java.lang.String
jlabels = StringIndexerModel._new_java_array(arrayOfLabels, java_class)
model = StringIndexerModel._create_from_java_class(
"org.apache.spark.ml.feature.StringIndexerModel", jlabels)
model.setInputCols(inputCols)
if outputCols is not None:
model.setOutputCols(outputCols)
if handleInvalid is not None:
model.setHandleInvalid(handleInvalid)
return model | [
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apache/spark | python/pyspark/sql/functions.py | lpad | def lpad(col, len, pad):
"""
Left-pad the string column to width `len` with `pad`.
>>> df = spark.createDataFrame([('abcd',)], ['s',])
>>> df.select(lpad(df.s, 6, '#').alias('s')).collect()
[Row(s=u'##abcd')]
"""
sc = SparkContext._active_spark_context
return Column(sc._jvm.functions.lpad(_to_java_column(col), len, pad)) | python | def lpad(col, len, pad):
"""
Left-pad the string column to width `len` with `pad`.
>>> df = spark.createDataFrame([('abcd',)], ['s',])
>>> df.select(lpad(df.s, 6, '#').alias('s')).collect()
[Row(s=u'##abcd')]
"""
sc = SparkContext._active_spark_context
return Column(sc._jvm.functions.lpad(_to_java_column(col), len, pad)) | [
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pandas-dev/pandas | pandas/core/indexes/base.py | Index.asof_locs | def asof_locs(self, where, mask):
"""
Find the locations (indices) of the labels from the index for
every entry in the `where` argument.
As in the `asof` function, if the label (a particular entry in
`where`) is not in the index, the latest index label upto the
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If all of the labels in the index are later than a label in `where`,
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`mask` is used to ignore NA values in the index during calculation.
Parameters
----------
where : Index
An Index consisting of an array of timestamps.
mask : array-like
Array of booleans denoting where values in the original
data are not NA.
Returns
-------
numpy.ndarray
An array of locations (indices) of the labels from the Index
which correspond to the return values of the `asof` function
for every element in `where`.
"""
locs = self.values[mask].searchsorted(where.values, side='right')
locs = np.where(locs > 0, locs - 1, 0)
result = np.arange(len(self))[mask].take(locs)
first = mask.argmax()
result[(locs == 0) & (where.values < self.values[first])] = -1
return result | python | def asof_locs(self, where, mask):
"""
Find the locations (indices) of the labels from the index for
every entry in the `where` argument.
As in the `asof` function, if the label (a particular entry in
`where`) is not in the index, the latest index label upto the
passed label is chosen and its index returned.
If all of the labels in the index are later than a label in `where`,
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`mask` is used to ignore NA values in the index during calculation.
Parameters
----------
where : Index
An Index consisting of an array of timestamps.
mask : array-like
Array of booleans denoting where values in the original
data are not NA.
Returns
-------
numpy.ndarray
An array of locations (indices) of the labels from the Index
which correspond to the return values of the `asof` function
for every element in `where`.
"""
locs = self.values[mask].searchsorted(where.values, side='right')
locs = np.where(locs > 0, locs - 1, 0)
result = np.arange(len(self))[mask].take(locs)
first = mask.argmax()
result[(locs == 0) & (where.values < self.values[first])] = -1
return result | [
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ageitgey/face_recognition | face_recognition/api.py | _raw_face_locations | def _raw_face_locations(img, number_of_times_to_upsample=1, model="hog"):
"""
Returns an array of bounding boxes of human faces in a image
:param img: An image (as a numpy array)
:param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
:param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate
deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog".
:return: A list of dlib 'rect' objects of found face locations
"""
if model == "cnn":
return cnn_face_detector(img, number_of_times_to_upsample)
else:
return face_detector(img, number_of_times_to_upsample) | python | def _raw_face_locations(img, number_of_times_to_upsample=1, model="hog"):
"""
Returns an array of bounding boxes of human faces in a image
:param img: An image (as a numpy array)
:param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
:param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate
deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog".
:return: A list of dlib 'rect' objects of found face locations
"""
if model == "cnn":
return cnn_face_detector(img, number_of_times_to_upsample)
else:
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pandas-dev/pandas | pandas/core/computation/engines.py | _check_ne_builtin_clash | def _check_ne_builtin_clash(expr):
"""Attempt to prevent foot-shooting in a helpful way.
Parameters
----------
terms : Term
Terms can contain
"""
names = expr.names
overlap = names & _ne_builtins
if overlap:
s = ', '.join(map(repr, overlap))
raise NumExprClobberingError('Variables in expression "{expr}" '
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.format(expr=expr, s=s)) | python | def _check_ne_builtin_clash(expr):
"""Attempt to prevent foot-shooting in a helpful way.
Parameters
----------
terms : Term
Terms can contain
"""
names = expr.names
overlap = names & _ne_builtins
if overlap:
s = ', '.join(map(repr, overlap))
raise NumExprClobberingError('Variables in expression "{expr}" '
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apache/spark | python/pyspark/mllib/linalg/__init__.py | DenseVector.dot | def dot(self, other):
"""
Compute the dot product of two Vectors. We support
(Numpy array, list, SparseVector, or SciPy sparse)
and a target NumPy array that is either 1- or 2-dimensional.
Equivalent to calling numpy.dot of the two vectors.
>>> dense = DenseVector(array.array('d', [1., 2.]))
>>> dense.dot(dense)
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>>> dense.dot(range(1, 3))
5.0
>>> dense.dot(np.array(range(1, 3)))
5.0
>>> dense.dot([1.,])
Traceback (most recent call last):
...
AssertionError: dimension mismatch
>>> dense.dot(np.reshape([1., 2., 3., 4.], (2, 2), order='F'))
array([ 5., 11.])
>>> dense.dot(np.reshape([1., 2., 3.], (3, 1), order='F'))
Traceback (most recent call last):
...
AssertionError: dimension mismatch
"""
if type(other) == np.ndarray:
if other.ndim > 1:
assert len(self) == other.shape[0], "dimension mismatch"
return np.dot(self.array, other)
elif _have_scipy and scipy.sparse.issparse(other):
assert len(self) == other.shape[0], "dimension mismatch"
return other.transpose().dot(self.toArray())
else:
assert len(self) == _vector_size(other), "dimension mismatch"
if isinstance(other, SparseVector):
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elif isinstance(other, Vector):
return np.dot(self.toArray(), other.toArray())
else:
return np.dot(self.toArray(), other) | python | def dot(self, other):
"""
Compute the dot product of two Vectors. We support
(Numpy array, list, SparseVector, or SciPy sparse)
and a target NumPy array that is either 1- or 2-dimensional.
Equivalent to calling numpy.dot of the two vectors.
>>> dense = DenseVector(array.array('d', [1., 2.]))
>>> dense.dot(dense)
5.0
>>> dense.dot(SparseVector(2, [0, 1], [2., 1.]))
4.0
>>> dense.dot(range(1, 3))
5.0
>>> dense.dot(np.array(range(1, 3)))
5.0
>>> dense.dot([1.,])
Traceback (most recent call last):
...
AssertionError: dimension mismatch
>>> dense.dot(np.reshape([1., 2., 3., 4.], (2, 2), order='F'))
array([ 5., 11.])
>>> dense.dot(np.reshape([1., 2., 3.], (3, 1), order='F'))
Traceback (most recent call last):
...
AssertionError: dimension mismatch
"""
if type(other) == np.ndarray:
if other.ndim > 1:
assert len(self) == other.shape[0], "dimension mismatch"
return np.dot(self.array, other)
elif _have_scipy and scipy.sparse.issparse(other):
assert len(self) == other.shape[0], "dimension mismatch"
return other.transpose().dot(self.toArray())
else:
assert len(self) == _vector_size(other), "dimension mismatch"
if isinstance(other, SparseVector):
return other.dot(self)
elif isinstance(other, Vector):
return np.dot(self.toArray(), other.toArray())
else:
return np.dot(self.toArray(), other) | [
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apache/spark | python/pyspark/sql/functions.py | to_date | def to_date(col, format=None):
"""Converts a :class:`Column` of :class:`pyspark.sql.types.StringType` or
:class:`pyspark.sql.types.TimestampType` into :class:`pyspark.sql.types.DateType`
using the optionally specified format. Specify formats according to
`DateTimeFormatter <https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html>`_. # noqa
By default, it follows casting rules to :class:`pyspark.sql.types.DateType` if the format
is omitted (equivalent to ``col.cast("date")``).
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(to_date(df.t).alias('date')).collect()
[Row(date=datetime.date(1997, 2, 28))]
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(to_date(df.t, 'yyyy-MM-dd HH:mm:ss').alias('date')).collect()
[Row(date=datetime.date(1997, 2, 28))]
"""
sc = SparkContext._active_spark_context
if format is None:
jc = sc._jvm.functions.to_date(_to_java_column(col))
else:
jc = sc._jvm.functions.to_date(_to_java_column(col), format)
return Column(jc) | python | def to_date(col, format=None):
"""Converts a :class:`Column` of :class:`pyspark.sql.types.StringType` or
:class:`pyspark.sql.types.TimestampType` into :class:`pyspark.sql.types.DateType`
using the optionally specified format. Specify formats according to
`DateTimeFormatter <https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html>`_. # noqa
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>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(to_date(df.t).alias('date')).collect()
[Row(date=datetime.date(1997, 2, 28))]
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(to_date(df.t, 'yyyy-MM-dd HH:mm:ss').alias('date')).collect()
[Row(date=datetime.date(1997, 2, 28))]
"""
sc = SparkContext._active_spark_context
if format is None:
jc = sc._jvm.functions.to_date(_to_java_column(col))
else:
jc = sc._jvm.functions.to_date(_to_java_column(col), format)
return Column(jc) | [
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.sample | def sample(self, withReplacement=None, fraction=None, seed=None):
"""Returns a sampled subset of this :class:`DataFrame`.
:param withReplacement: Sample with replacement or not (default False).
:param fraction: Fraction of rows to generate, range [0.0, 1.0].
:param seed: Seed for sampling (default a random seed).
.. note:: This is not guaranteed to provide exactly the fraction specified of the total
count of the given :class:`DataFrame`.
.. note:: `fraction` is required and, `withReplacement` and `seed` are optional.
>>> df = spark.range(10)
>>> df.sample(0.5, 3).count()
7
>>> df.sample(fraction=0.5, seed=3).count()
7
>>> df.sample(withReplacement=True, fraction=0.5, seed=3).count()
1
>>> df.sample(1.0).count()
10
>>> df.sample(fraction=1.0).count()
10
>>> df.sample(False, fraction=1.0).count()
10
"""
# For the cases below:
# sample(True, 0.5 [, seed])
# sample(True, fraction=0.5 [, seed])
# sample(withReplacement=False, fraction=0.5 [, seed])
is_withReplacement_set = \
type(withReplacement) == bool and isinstance(fraction, float)
# For the case below:
# sample(faction=0.5 [, seed])
is_withReplacement_omitted_kwargs = \
withReplacement is None and isinstance(fraction, float)
# For the case below:
# sample(0.5 [, seed])
is_withReplacement_omitted_args = isinstance(withReplacement, float)
if not (is_withReplacement_set
or is_withReplacement_omitted_kwargs
or is_withReplacement_omitted_args):
argtypes = [
str(type(arg)) for arg in [withReplacement, fraction, seed] if arg is not None]
raise TypeError(
"withReplacement (optional), fraction (required) and seed (optional)"
" should be a bool, float and number; however, "
"got [%s]." % ", ".join(argtypes))
if is_withReplacement_omitted_args:
if fraction is not None:
seed = fraction
fraction = withReplacement
withReplacement = None
seed = long(seed) if seed is not None else None
args = [arg for arg in [withReplacement, fraction, seed] if arg is not None]
jdf = self._jdf.sample(*args)
return DataFrame(jdf, self.sql_ctx) | python | def sample(self, withReplacement=None, fraction=None, seed=None):
"""Returns a sampled subset of this :class:`DataFrame`.
:param withReplacement: Sample with replacement or not (default False).
:param fraction: Fraction of rows to generate, range [0.0, 1.0].
:param seed: Seed for sampling (default a random seed).
.. note:: This is not guaranteed to provide exactly the fraction specified of the total
count of the given :class:`DataFrame`.
.. note:: `fraction` is required and, `withReplacement` and `seed` are optional.
>>> df = spark.range(10)
>>> df.sample(0.5, 3).count()
7
>>> df.sample(fraction=0.5, seed=3).count()
7
>>> df.sample(withReplacement=True, fraction=0.5, seed=3).count()
1
>>> df.sample(1.0).count()
10
>>> df.sample(fraction=1.0).count()
10
>>> df.sample(False, fraction=1.0).count()
10
"""
# For the cases below:
# sample(True, 0.5 [, seed])
# sample(True, fraction=0.5 [, seed])
# sample(withReplacement=False, fraction=0.5 [, seed])
is_withReplacement_set = \
type(withReplacement) == bool and isinstance(fraction, float)
# For the case below:
# sample(faction=0.5 [, seed])
is_withReplacement_omitted_kwargs = \
withReplacement is None and isinstance(fraction, float)
# For the case below:
# sample(0.5 [, seed])
is_withReplacement_omitted_args = isinstance(withReplacement, float)
if not (is_withReplacement_set
or is_withReplacement_omitted_kwargs
or is_withReplacement_omitted_args):
argtypes = [
str(type(arg)) for arg in [withReplacement, fraction, seed] if arg is not None]
raise TypeError(
"withReplacement (optional), fraction (required) and seed (optional)"
" should be a bool, float and number; however, "
"got [%s]." % ", ".join(argtypes))
if is_withReplacement_omitted_args:
if fraction is not None:
seed = fraction
fraction = withReplacement
withReplacement = None
seed = long(seed) if seed is not None else None
args = [arg for arg in [withReplacement, fraction, seed] if arg is not None]
jdf = self._jdf.sample(*args)
return DataFrame(jdf, self.sql_ctx) | [
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:param seed: Seed for sampling (default a random seed).
.. note:: This is not guaranteed to provide exactly the fraction specified of the total
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.. note:: `fraction` is required and, `withReplacement` and `seed` are optional.
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pandas-dev/pandas | pandas/core/indexes/base.py | Index._reindex_non_unique | def _reindex_non_unique(self, target):
"""
Create a new index with target's values (move/add/delete values as
necessary) use with non-unique Index and a possibly non-unique target.
Parameters
----------
target : an iterable
Returns
-------
new_index : pd.Index
Resulting index.
indexer : np.ndarray or None
Indices of output values in original index.
"""
target = ensure_index(target)
indexer, missing = self.get_indexer_non_unique(target)
check = indexer != -1
new_labels = self.take(indexer[check])
new_indexer = None
if len(missing):
length = np.arange(len(indexer))
missing = ensure_platform_int(missing)
missing_labels = target.take(missing)
missing_indexer = ensure_int64(length[~check])
cur_labels = self.take(indexer[check]).values
cur_indexer = ensure_int64(length[check])
new_labels = np.empty(tuple([len(indexer)]), dtype=object)
new_labels[cur_indexer] = cur_labels
new_labels[missing_indexer] = missing_labels
# a unique indexer
if target.is_unique:
# see GH5553, make sure we use the right indexer
new_indexer = np.arange(len(indexer))
new_indexer[cur_indexer] = np.arange(len(cur_labels))
new_indexer[missing_indexer] = -1
# we have a non_unique selector, need to use the original
# indexer here
else:
# need to retake to have the same size as the indexer
indexer[~check] = -1
# reset the new indexer to account for the new size
new_indexer = np.arange(len(self.take(indexer)))
new_indexer[~check] = -1
new_index = self._shallow_copy_with_infer(new_labels, freq=None)
return new_index, indexer, new_indexer | python | def _reindex_non_unique(self, target):
"""
Create a new index with target's values (move/add/delete values as
necessary) use with non-unique Index and a possibly non-unique target.
Parameters
----------
target : an iterable
Returns
-------
new_index : pd.Index
Resulting index.
indexer : np.ndarray or None
Indices of output values in original index.
"""
target = ensure_index(target)
indexer, missing = self.get_indexer_non_unique(target)
check = indexer != -1
new_labels = self.take(indexer[check])
new_indexer = None
if len(missing):
length = np.arange(len(indexer))
missing = ensure_platform_int(missing)
missing_labels = target.take(missing)
missing_indexer = ensure_int64(length[~check])
cur_labels = self.take(indexer[check]).values
cur_indexer = ensure_int64(length[check])
new_labels = np.empty(tuple([len(indexer)]), dtype=object)
new_labels[cur_indexer] = cur_labels
new_labels[missing_indexer] = missing_labels
# a unique indexer
if target.is_unique:
# see GH5553, make sure we use the right indexer
new_indexer = np.arange(len(indexer))
new_indexer[cur_indexer] = np.arange(len(cur_labels))
new_indexer[missing_indexer] = -1
# we have a non_unique selector, need to use the original
# indexer here
else:
# need to retake to have the same size as the indexer
indexer[~check] = -1
# reset the new indexer to account for the new size
new_indexer = np.arange(len(self.take(indexer)))
new_indexer[~check] = -1
new_index = self._shallow_copy_with_infer(new_labels, freq=None)
return new_index, indexer, new_indexer | [
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apache/spark | python/pyspark/heapq3.py | _siftup_max | def _siftup_max(heap, pos):
'Maxheap variant of _siftup'
endpos = len(heap)
startpos = pos
newitem = heap[pos]
# Bubble up the larger child until hitting a leaf.
childpos = 2*pos + 1 # leftmost child position
while childpos < endpos:
# Set childpos to index of larger child.
rightpos = childpos + 1
if rightpos < endpos and not heap[rightpos] < heap[childpos]:
childpos = rightpos
# Move the larger child up.
heap[pos] = heap[childpos]
pos = childpos
childpos = 2*pos + 1
# The leaf at pos is empty now. Put newitem there, and bubble it up
# to its final resting place (by sifting its parents down).
heap[pos] = newitem
_siftdown_max(heap, startpos, pos) | python | def _siftup_max(heap, pos):
'Maxheap variant of _siftup'
endpos = len(heap)
startpos = pos
newitem = heap[pos]
# Bubble up the larger child until hitting a leaf.
childpos = 2*pos + 1 # leftmost child position
while childpos < endpos:
# Set childpos to index of larger child.
rightpos = childpos + 1
if rightpos < endpos and not heap[rightpos] < heap[childpos]:
childpos = rightpos
# Move the larger child up.
heap[pos] = heap[childpos]
pos = childpos
childpos = 2*pos + 1
# The leaf at pos is empty now. Put newitem there, and bubble it up
# to its final resting place (by sifting its parents down).
heap[pos] = newitem
_siftdown_max(heap, startpos, pos) | [
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pandas-dev/pandas | pandas/core/groupby/groupby.py | GroupBy.ngroup | def ngroup(self, ascending=True):
"""
Number each group from 0 to the number of groups - 1.
This is the enumerative complement of cumcount. Note that the
numbers given to the groups match the order in which the groups
would be seen when iterating over the groupby object, not the
order they are first observed.
.. versionadded:: 0.20.2
Parameters
----------
ascending : bool, default True
If False, number in reverse, from number of group - 1 to 0.
See Also
--------
.cumcount : Number the rows in each group.
Examples
--------
>>> df = pd.DataFrame({"A": list("aaabba")})
>>> df
A
0 a
1 a
2 a
3 b
4 b
5 a
>>> df.groupby('A').ngroup()
0 0
1 0
2 0
3 1
4 1
5 0
dtype: int64
>>> df.groupby('A').ngroup(ascending=False)
0 1
1 1
2 1
3 0
4 0
5 1
dtype: int64
>>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup()
0 0
1 0
2 1
3 3
4 2
5 0
dtype: int64
"""
with _group_selection_context(self):
index = self._selected_obj.index
result = Series(self.grouper.group_info[0], index)
if not ascending:
result = self.ngroups - 1 - result
return result | python | def ngroup(self, ascending=True):
"""
Number each group from 0 to the number of groups - 1.
This is the enumerative complement of cumcount. Note that the
numbers given to the groups match the order in which the groups
would be seen when iterating over the groupby object, not the
order they are first observed.
.. versionadded:: 0.20.2
Parameters
----------
ascending : bool, default True
If False, number in reverse, from number of group - 1 to 0.
See Also
--------
.cumcount : Number the rows in each group.
Examples
--------
>>> df = pd.DataFrame({"A": list("aaabba")})
>>> df
A
0 a
1 a
2 a
3 b
4 b
5 a
>>> df.groupby('A').ngroup()
0 0
1 0
2 0
3 1
4 1
5 0
dtype: int64
>>> df.groupby('A').ngroup(ascending=False)
0 1
1 1
2 1
3 0
4 0
5 1
dtype: int64
>>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup()
0 0
1 0
2 1
3 3
4 2
5 0
dtype: int64
"""
with _group_selection_context(self):
index = self._selected_obj.index
result = Series(self.grouper.group_info[0], index)
if not ascending:
result = self.ngroups - 1 - result
return result | [
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order they are first observed.
.. versionadded:: 0.20.2
Parameters
----------
ascending : bool, default True
If False, number in reverse, from number of group - 1 to 0.
See Also
--------
.cumcount : Number the rows in each group.
Examples
--------
>>> df = pd.DataFrame({"A": list("aaabba")})
>>> df
A
0 a
1 a
2 a
3 b
4 b
5 a
>>> df.groupby('A').ngroup()
0 0
1 0
2 0
3 1
4 1
5 0
dtype: int64
>>> df.groupby('A').ngroup(ascending=False)
0 1
1 1
2 1
3 0
4 0
5 1
dtype: int64
>>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup()
0 0
1 0
2 1
3 3
4 2
5 0
dtype: int64 | [
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apache/spark | python/pyspark/rdd.py | RDD.treeReduce | def treeReduce(self, f, depth=2):
"""
Reduces the elements of this RDD in a multi-level tree pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, 4], 10)
>>> rdd.treeReduce(add)
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>>> rdd.treeReduce(add, 10)
-5
"""
if depth < 1:
raise ValueError("Depth cannot be smaller than 1 but got %d." % depth)
zeroValue = None, True # Use the second entry to indicate whether this is a dummy value.
def op(x, y):
if x[1]:
return y
elif y[1]:
return x
else:
return f(x[0], y[0]), False
reduced = self.map(lambda x: (x, False)).treeAggregate(zeroValue, op, op, depth)
if reduced[1]:
raise ValueError("Cannot reduce empty RDD.")
return reduced[0] | python | def treeReduce(self, f, depth=2):
"""
Reduces the elements of this RDD in a multi-level tree pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, 4], 10)
>>> rdd.treeReduce(add)
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>>> rdd.treeReduce(add, 1)
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>>> rdd.treeReduce(add, 2)
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>>> rdd.treeReduce(add, 5)
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>>> rdd.treeReduce(add, 10)
-5
"""
if depth < 1:
raise ValueError("Depth cannot be smaller than 1 but got %d." % depth)
zeroValue = None, True # Use the second entry to indicate whether this is a dummy value.
def op(x, y):
if x[1]:
return y
elif y[1]:
return x
else:
return f(x[0], y[0]), False
reduced = self.map(lambda x: (x, False)).treeAggregate(zeroValue, op, op, depth)
if reduced[1]:
raise ValueError("Cannot reduce empty RDD.")
return reduced[0] | [
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pandas-dev/pandas | pandas/tseries/holiday.py | AbstractHolidayCalendar.merge_class | def merge_class(base, other):
"""
Merge holiday calendars together. The base calendar
will take precedence to other. The merge will be done
based on each holiday's name.
Parameters
----------
base : AbstractHolidayCalendar
instance/subclass or array of Holiday objects
other : AbstractHolidayCalendar
instance/subclass or array of Holiday objects
"""
try:
other = other.rules
except AttributeError:
pass
if not isinstance(other, list):
other = [other]
other_holidays = {holiday.name: holiday for holiday in other}
try:
base = base.rules
except AttributeError:
pass
if not isinstance(base, list):
base = [base]
base_holidays = {holiday.name: holiday for holiday in base}
other_holidays.update(base_holidays)
return list(other_holidays.values()) | python | def merge_class(base, other):
"""
Merge holiday calendars together. The base calendar
will take precedence to other. The merge will be done
based on each holiday's name.
Parameters
----------
base : AbstractHolidayCalendar
instance/subclass or array of Holiday objects
other : AbstractHolidayCalendar
instance/subclass or array of Holiday objects
"""
try:
other = other.rules
except AttributeError:
pass
if not isinstance(other, list):
other = [other]
other_holidays = {holiday.name: holiday for holiday in other}
try:
base = base.rules
except AttributeError:
pass
if not isinstance(base, list):
base = [base]
base_holidays = {holiday.name: holiday for holiday in base}
other_holidays.update(base_holidays)
return list(other_holidays.values()) | [
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apache/spark | python/pyspark/rdd.py | RDD.partitionBy | def partitionBy(self, numPartitions, partitionFunc=portable_hash):
"""
Return a copy of the RDD partitioned using the specified partitioner.
>>> pairs = sc.parallelize([1, 2, 3, 4, 2, 4, 1]).map(lambda x: (x, x))
>>> sets = pairs.partitionBy(2).glom().collect()
>>> len(set(sets[0]).intersection(set(sets[1])))
0
"""
if numPartitions is None:
numPartitions = self._defaultReducePartitions()
partitioner = Partitioner(numPartitions, partitionFunc)
if self.partitioner == partitioner:
return self
# Transferring O(n) objects to Java is too expensive.
# Instead, we'll form the hash buckets in Python,
# transferring O(numPartitions) objects to Java.
# Each object is a (splitNumber, [objects]) pair.
# In order to avoid too huge objects, the objects are
# grouped into chunks.
outputSerializer = self.ctx._unbatched_serializer
limit = (_parse_memory(self.ctx._conf.get(
"spark.python.worker.memory", "512m")) / 2)
def add_shuffle_key(split, iterator):
buckets = defaultdict(list)
c, batch = 0, min(10 * numPartitions, 1000)
for k, v in iterator:
buckets[partitionFunc(k) % numPartitions].append((k, v))
c += 1
# check used memory and avg size of chunk of objects
if (c % 1000 == 0 and get_used_memory() > limit
or c > batch):
n, size = len(buckets), 0
for split in list(buckets.keys()):
yield pack_long(split)
d = outputSerializer.dumps(buckets[split])
del buckets[split]
yield d
size += len(d)
avg = int(size / n) >> 20
# let 1M < avg < 10M
if avg < 1:
batch *= 1.5
elif avg > 10:
batch = max(int(batch / 1.5), 1)
c = 0
for split, items in buckets.items():
yield pack_long(split)
yield outputSerializer.dumps(items)
keyed = self.mapPartitionsWithIndex(add_shuffle_key, preservesPartitioning=True)
keyed._bypass_serializer = True
with SCCallSiteSync(self.context) as css:
pairRDD = self.ctx._jvm.PairwiseRDD(
keyed._jrdd.rdd()).asJavaPairRDD()
jpartitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
id(partitionFunc))
jrdd = self.ctx._jvm.PythonRDD.valueOfPair(pairRDD.partitionBy(jpartitioner))
rdd = RDD(jrdd, self.ctx, BatchedSerializer(outputSerializer))
rdd.partitioner = partitioner
return rdd | python | def partitionBy(self, numPartitions, partitionFunc=portable_hash):
"""
Return a copy of the RDD partitioned using the specified partitioner.
>>> pairs = sc.parallelize([1, 2, 3, 4, 2, 4, 1]).map(lambda x: (x, x))
>>> sets = pairs.partitionBy(2).glom().collect()
>>> len(set(sets[0]).intersection(set(sets[1])))
0
"""
if numPartitions is None:
numPartitions = self._defaultReducePartitions()
partitioner = Partitioner(numPartitions, partitionFunc)
if self.partitioner == partitioner:
return self
# Transferring O(n) objects to Java is too expensive.
# Instead, we'll form the hash buckets in Python,
# transferring O(numPartitions) objects to Java.
# Each object is a (splitNumber, [objects]) pair.
# In order to avoid too huge objects, the objects are
# grouped into chunks.
outputSerializer = self.ctx._unbatched_serializer
limit = (_parse_memory(self.ctx._conf.get(
"spark.python.worker.memory", "512m")) / 2)
def add_shuffle_key(split, iterator):
buckets = defaultdict(list)
c, batch = 0, min(10 * numPartitions, 1000)
for k, v in iterator:
buckets[partitionFunc(k) % numPartitions].append((k, v))
c += 1
# check used memory and avg size of chunk of objects
if (c % 1000 == 0 and get_used_memory() > limit
or c > batch):
n, size = len(buckets), 0
for split in list(buckets.keys()):
yield pack_long(split)
d = outputSerializer.dumps(buckets[split])
del buckets[split]
yield d
size += len(d)
avg = int(size / n) >> 20
# let 1M < avg < 10M
if avg < 1:
batch *= 1.5
elif avg > 10:
batch = max(int(batch / 1.5), 1)
c = 0
for split, items in buckets.items():
yield pack_long(split)
yield outputSerializer.dumps(items)
keyed = self.mapPartitionsWithIndex(add_shuffle_key, preservesPartitioning=True)
keyed._bypass_serializer = True
with SCCallSiteSync(self.context) as css:
pairRDD = self.ctx._jvm.PairwiseRDD(
keyed._jrdd.rdd()).asJavaPairRDD()
jpartitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
id(partitionFunc))
jrdd = self.ctx._jvm.PythonRDD.valueOfPair(pairRDD.partitionBy(jpartitioner))
rdd = RDD(jrdd, self.ctx, BatchedSerializer(outputSerializer))
rdd.partitioner = partitioner
return rdd | [
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apache/spark | python/pyspark/shuffle.py | ExternalList._spill | def _spill(self):
""" dump the values into disk """
global MemoryBytesSpilled, DiskBytesSpilled
if self._file is None:
self._open_file()
used_memory = get_used_memory()
pos = self._file.tell()
self._ser.dump_stream(self.values, self._file)
self.values = []
gc.collect()
DiskBytesSpilled += self._file.tell() - pos
MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20 | python | def _spill(self):
""" dump the values into disk """
global MemoryBytesSpilled, DiskBytesSpilled
if self._file is None:
self._open_file()
used_memory = get_used_memory()
pos = self._file.tell()
self._ser.dump_stream(self.values, self._file)
self.values = []
gc.collect()
DiskBytesSpilled += self._file.tell() - pos
MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20 | [
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apache/spark | python/pyspark/ml/tuning.py | CrossValidator._to_java | def _to_java(self):
"""
Transfer this instance to a Java CrossValidator. Used for ML persistence.
:return: Java object equivalent to this instance.
"""
estimator, epms, evaluator = super(CrossValidator, self)._to_java_impl()
_java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.CrossValidator", self.uid)
_java_obj.setEstimatorParamMaps(epms)
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_java_obj.setParallelism(self.getParallelism())
_java_obj.setCollectSubModels(self.getCollectSubModels())
return _java_obj | python | def _to_java(self):
"""
Transfer this instance to a Java CrossValidator. Used for ML persistence.
:return: Java object equivalent to this instance.
"""
estimator, epms, evaluator = super(CrossValidator, self)._to_java_impl()
_java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.CrossValidator", self.uid)
_java_obj.setEstimatorParamMaps(epms)
_java_obj.setEvaluator(evaluator)
_java_obj.setEstimator(estimator)
_java_obj.setSeed(self.getSeed())
_java_obj.setNumFolds(self.getNumFolds())
_java_obj.setParallelism(self.getParallelism())
_java_obj.setCollectSubModels(self.getCollectSubModels())
return _java_obj | [
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pandas-dev/pandas | pandas/core/window.py | _GroupByMixin._apply | def _apply(self, func, name, window=None, center=None,
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return self._groupby.apply(f) | python | def _apply(self, func, name, window=None, center=None,
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"""
Dispatch to apply; we are stripping all of the _apply kwargs and
performing the original function call on the grouped object.
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def f(x, name=name, *args):
x = self._shallow_copy(x)
if isinstance(name, str):
return getattr(x, name)(*args, **kwargs)
return x.apply(name, *args, **kwargs)
return self._groupby.apply(f) | [
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.sampleBy | def sampleBy(self, col, fractions, seed=None):
"""
Returns a stratified sample without replacement based on the
fraction given on each stratum.
:param col: column that defines strata
:param fractions:
sampling fraction for each stratum. If a stratum is not
specified, we treat its fraction as zero.
:param seed: random seed
:return: a new DataFrame that represents the stratified sample
>>> from pyspark.sql.functions import col
>>> dataset = sqlContext.range(0, 100).select((col("id") % 3).alias("key"))
>>> sampled = dataset.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0)
>>> sampled.groupBy("key").count().orderBy("key").show()
+---+-----+
|key|count|
+---+-----+
| 0| 3|
| 1| 6|
+---+-----+
>>> dataset.sampleBy(col("key"), fractions={2: 1.0}, seed=0).count()
33
.. versionchanged:: 3.0
Added sampling by a column of :class:`Column`
"""
if isinstance(col, basestring):
col = Column(col)
elif not isinstance(col, Column):
raise ValueError("col must be a string or a column, but got %r" % type(col))
if not isinstance(fractions, dict):
raise ValueError("fractions must be a dict but got %r" % type(fractions))
for k, v in fractions.items():
if not isinstance(k, (float, int, long, basestring)):
raise ValueError("key must be float, int, long, or string, but got %r" % type(k))
fractions[k] = float(v)
col = col._jc
seed = seed if seed is not None else random.randint(0, sys.maxsize)
return DataFrame(self._jdf.stat().sampleBy(col, self._jmap(fractions), seed), self.sql_ctx) | python | def sampleBy(self, col, fractions, seed=None):
"""
Returns a stratified sample without replacement based on the
fraction given on each stratum.
:param col: column that defines strata
:param fractions:
sampling fraction for each stratum. If a stratum is not
specified, we treat its fraction as zero.
:param seed: random seed
:return: a new DataFrame that represents the stratified sample
>>> from pyspark.sql.functions import col
>>> dataset = sqlContext.range(0, 100).select((col("id") % 3).alias("key"))
>>> sampled = dataset.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0)
>>> sampled.groupBy("key").count().orderBy("key").show()
+---+-----+
|key|count|
+---+-----+
| 0| 3|
| 1| 6|
+---+-----+
>>> dataset.sampleBy(col("key"), fractions={2: 1.0}, seed=0).count()
33
.. versionchanged:: 3.0
Added sampling by a column of :class:`Column`
"""
if isinstance(col, basestring):
col = Column(col)
elif not isinstance(col, Column):
raise ValueError("col must be a string or a column, but got %r" % type(col))
if not isinstance(fractions, dict):
raise ValueError("fractions must be a dict but got %r" % type(fractions))
for k, v in fractions.items():
if not isinstance(k, (float, int, long, basestring)):
raise ValueError("key must be float, int, long, or string, but got %r" % type(k))
fractions[k] = float(v)
col = col._jc
seed = seed if seed is not None else random.randint(0, sys.maxsize)
return DataFrame(self._jdf.stat().sampleBy(col, self._jmap(fractions), seed), self.sql_ctx) | [
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.. versionchanged:: 3.0
Added sampling by a column of :class:`Column` | [
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apache/spark | python/pyspark/sql/functions.py | slice | def slice(x, start, length):
"""
Collection function: returns an array containing all the elements in `x` from index `start`
(or starting from the end if `start` is negative) with the specified `length`.
>>> df = spark.createDataFrame([([1, 2, 3],), ([4, 5],)], ['x'])
>>> df.select(slice(df.x, 2, 2).alias("sliced")).collect()
[Row(sliced=[2, 3]), Row(sliced=[5])]
"""
sc = SparkContext._active_spark_context
return Column(sc._jvm.functions.slice(_to_java_column(x), start, length)) | python | def slice(x, start, length):
"""
Collection function: returns an array containing all the elements in `x` from index `start`
(or starting from the end if `start` is negative) with the specified `length`.
>>> df = spark.createDataFrame([([1, 2, 3],), ([4, 5],)], ['x'])
>>> df.select(slice(df.x, 2, 2).alias("sliced")).collect()
[Row(sliced=[2, 3]), Row(sliced=[5])]
"""
sc = SparkContext._active_spark_context
return Column(sc._jvm.functions.slice(_to_java_column(x), start, length)) | [
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apache/spark | python/pyspark/ml/tuning.py | TrainValidationSplit._from_java | def _from_java(cls, java_stage):
"""
Given a Java TrainValidationSplit, create and return a Python wrapper of it.
Used for ML persistence.
"""
estimator, epms, evaluator = super(TrainValidationSplit, cls)._from_java_impl(java_stage)
trainRatio = java_stage.getTrainRatio()
seed = java_stage.getSeed()
parallelism = java_stage.getParallelism()
collectSubModels = java_stage.getCollectSubModels()
# Create a new instance of this stage.
py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
trainRatio=trainRatio, seed=seed, parallelism=parallelism,
collectSubModels=collectSubModels)
py_stage._resetUid(java_stage.uid())
return py_stage | python | def _from_java(cls, java_stage):
"""
Given a Java TrainValidationSplit, create and return a Python wrapper of it.
Used for ML persistence.
"""
estimator, epms, evaluator = super(TrainValidationSplit, cls)._from_java_impl(java_stage)
trainRatio = java_stage.getTrainRatio()
seed = java_stage.getSeed()
parallelism = java_stage.getParallelism()
collectSubModels = java_stage.getCollectSubModels()
# Create a new instance of this stage.
py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
trainRatio=trainRatio, seed=seed, parallelism=parallelism,
collectSubModels=collectSubModels)
py_stage._resetUid(java_stage.uid())
return py_stage | [
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apache/spark | python/pyspark/mllib/clustering.py | GaussianMixture.train | def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None):
"""
Train a Gaussian Mixture clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
Number of independent Gaussians in the mixture model.
:param convergenceTol:
Maximum change in log-likelihood at which convergence is
considered to have occurred.
(default: 1e-3)
:param maxIterations:
Maximum number of iterations allowed.
(default: 100)
:param seed:
Random seed for initial Gaussian distribution. Set as None to
generate seed based on system time.
(default: None)
:param initialModel:
Initial GMM starting point, bypassing the random
initialization.
(default: None)
"""
initialModelWeights = None
initialModelMu = None
initialModelSigma = None
if initialModel is not None:
if initialModel.k != k:
raise Exception("Mismatched cluster count, initialModel.k = %s, however k = %s"
% (initialModel.k, k))
initialModelWeights = list(initialModel.weights)
initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)]
initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)]
java_model = callMLlibFunc("trainGaussianMixtureModel", rdd.map(_convert_to_vector),
k, convergenceTol, maxIterations, seed,
initialModelWeights, initialModelMu, initialModelSigma)
return GaussianMixtureModel(java_model) | python | def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None):
"""
Train a Gaussian Mixture clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
Number of independent Gaussians in the mixture model.
:param convergenceTol:
Maximum change in log-likelihood at which convergence is
considered to have occurred.
(default: 1e-3)
:param maxIterations:
Maximum number of iterations allowed.
(default: 100)
:param seed:
Random seed for initial Gaussian distribution. Set as None to
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(default: None)
:param initialModel:
Initial GMM starting point, bypassing the random
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(default: None)
"""
initialModelWeights = None
initialModelMu = None
initialModelSigma = None
if initialModel is not None:
if initialModel.k != k:
raise Exception("Mismatched cluster count, initialModel.k = %s, however k = %s"
% (initialModel.k, k))
initialModelWeights = list(initialModel.weights)
initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)]
initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)]
java_model = callMLlibFunc("trainGaussianMixtureModel", rdd.map(_convert_to_vector),
k, convergenceTol, maxIterations, seed,
initialModelWeights, initialModelMu, initialModelSigma)
return GaussianMixtureModel(java_model) | [
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apache/spark | python/pyspark/mllib/util.py | MLUtils.saveAsLibSVMFile | def saveAsLibSVMFile(data, dir):
"""
Save labeled data in LIBSVM format.
:param data: an RDD of LabeledPoint to be saved
:param dir: directory to save the data
>>> from tempfile import NamedTemporaryFile
>>> from fileinput import input
>>> from pyspark.mllib.regression import LabeledPoint
>>> from glob import glob
>>> from pyspark.mllib.util import MLUtils
>>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])),
... LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
>>> tempFile = NamedTemporaryFile(delete=True)
>>> tempFile.close()
>>> MLUtils.saveAsLibSVMFile(sc.parallelize(examples), tempFile.name)
>>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*"))))
'0.0 1:1.01 2:2.02 3:3.03\\n1.1 1:1.23 3:4.56\\n'
"""
lines = data.map(lambda p: MLUtils._convert_labeled_point_to_libsvm(p))
lines.saveAsTextFile(dir) | python | def saveAsLibSVMFile(data, dir):
"""
Save labeled data in LIBSVM format.
:param data: an RDD of LabeledPoint to be saved
:param dir: directory to save the data
>>> from tempfile import NamedTemporaryFile
>>> from fileinput import input
>>> from pyspark.mllib.regression import LabeledPoint
>>> from glob import glob
>>> from pyspark.mllib.util import MLUtils
>>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])),
... LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
>>> tempFile = NamedTemporaryFile(delete=True)
>>> tempFile.close()
>>> MLUtils.saveAsLibSVMFile(sc.parallelize(examples), tempFile.name)
>>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*"))))
'0.0 1:1.01 2:2.02 3:3.03\\n1.1 1:1.23 3:4.56\\n'
"""
lines = data.map(lambda p: MLUtils._convert_labeled_point_to_libsvm(p))
lines.saveAsTextFile(dir) | [
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>>> from fileinput import input
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>>> from glob import glob
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apache/spark | python/pyspark/sql/readwriter.py | DataFrameReader.parquet | def parquet(self, *paths):
"""Loads Parquet files, returning the result as a :class:`DataFrame`.
You can set the following Parquet-specific option(s) for reading Parquet files:
* ``mergeSchema``: sets whether we should merge schemas collected from all \
Parquet part-files. This will override ``spark.sql.parquet.mergeSchema``. \
The default value is specified in ``spark.sql.parquet.mergeSchema``.
>>> df = spark.read.parquet('python/test_support/sql/parquet_partitioned')
>>> df.dtypes
[('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
"""
return self._df(self._jreader.parquet(_to_seq(self._spark._sc, paths))) | python | def parquet(self, *paths):
"""Loads Parquet files, returning the result as a :class:`DataFrame`.
You can set the following Parquet-specific option(s) for reading Parquet files:
* ``mergeSchema``: sets whether we should merge schemas collected from all \
Parquet part-files. This will override ``spark.sql.parquet.mergeSchema``. \
The default value is specified in ``spark.sql.parquet.mergeSchema``.
>>> df = spark.read.parquet('python/test_support/sql/parquet_partitioned')
>>> df.dtypes
[('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
"""
return self._df(self._jreader.parquet(_to_seq(self._spark._sc, paths))) | [
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.dropDuplicates | def dropDuplicates(self, subset=None):
"""Return a new :class:`DataFrame` with duplicate rows removed,
optionally only considering certain columns.
For a static batch :class:`DataFrame`, it just drops duplicate rows. For a streaming
:class:`DataFrame`, it will keep all data across triggers as intermediate state to drop
duplicates rows. You can use :func:`withWatermark` to limit how late the duplicate data can
be and system will accordingly limit the state. In addition, too late data older than
watermark will be dropped to avoid any possibility of duplicates.
:func:`drop_duplicates` is an alias for :func:`dropDuplicates`.
>>> from pyspark.sql import Row
>>> df = sc.parallelize([ \\
... Row(name='Alice', age=5, height=80), \\
... Row(name='Alice', age=5, height=80), \\
... Row(name='Alice', age=10, height=80)]).toDF()
>>> df.dropDuplicates().show()
+---+------+-----+
|age|height| name|
+---+------+-----+
| 5| 80|Alice|
| 10| 80|Alice|
+---+------+-----+
>>> df.dropDuplicates(['name', 'height']).show()
+---+------+-----+
|age|height| name|
+---+------+-----+
| 5| 80|Alice|
+---+------+-----+
"""
if subset is None:
jdf = self._jdf.dropDuplicates()
else:
jdf = self._jdf.dropDuplicates(self._jseq(subset))
return DataFrame(jdf, self.sql_ctx) | python | def dropDuplicates(self, subset=None):
"""Return a new :class:`DataFrame` with duplicate rows removed,
optionally only considering certain columns.
For a static batch :class:`DataFrame`, it just drops duplicate rows. For a streaming
:class:`DataFrame`, it will keep all data across triggers as intermediate state to drop
duplicates rows. You can use :func:`withWatermark` to limit how late the duplicate data can
be and system will accordingly limit the state. In addition, too late data older than
watermark will be dropped to avoid any possibility of duplicates.
:func:`drop_duplicates` is an alias for :func:`dropDuplicates`.
>>> from pyspark.sql import Row
>>> df = sc.parallelize([ \\
... Row(name='Alice', age=5, height=80), \\
... Row(name='Alice', age=5, height=80), \\
... Row(name='Alice', age=10, height=80)]).toDF()
>>> df.dropDuplicates().show()
+---+------+-----+
|age|height| name|
+---+------+-----+
| 5| 80|Alice|
| 10| 80|Alice|
+---+------+-----+
>>> df.dropDuplicates(['name', 'height']).show()
+---+------+-----+
|age|height| name|
+---+------+-----+
| 5| 80|Alice|
+---+------+-----+
"""
if subset is None:
jdf = self._jdf.dropDuplicates()
else:
jdf = self._jdf.dropDuplicates(self._jseq(subset))
return DataFrame(jdf, self.sql_ctx) | [
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... Row(name='Alice', age=10, height=80)]).toDF()
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+---+------+-----+
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+---+------+-----+
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apache/spark | python/pyspark/sql/catalog.py | Catalog.listTables | def listTables(self, dbName=None):
"""Returns a list of tables/views in the specified database.
If no database is specified, the current database is used.
This includes all temporary views.
"""
if dbName is None:
dbName = self.currentDatabase()
iter = self._jcatalog.listTables(dbName).toLocalIterator()
tables = []
while iter.hasNext():
jtable = iter.next()
tables.append(Table(
name=jtable.name(),
database=jtable.database(),
description=jtable.description(),
tableType=jtable.tableType(),
isTemporary=jtable.isTemporary()))
return tables | python | def listTables(self, dbName=None):
"""Returns a list of tables/views in the specified database.
If no database is specified, the current database is used.
This includes all temporary views.
"""
if dbName is None:
dbName = self.currentDatabase()
iter = self._jcatalog.listTables(dbName).toLocalIterator()
tables = []
while iter.hasNext():
jtable = iter.next()
tables.append(Table(
name=jtable.name(),
database=jtable.database(),
description=jtable.description(),
tableType=jtable.tableType(),
isTemporary=jtable.isTemporary()))
return tables | [
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apache/spark | python/pyspark/mllib/util.py | MLUtils._convert_labeled_point_to_libsvm | def _convert_labeled_point_to_libsvm(p):
"""Converts a LabeledPoint to a string in LIBSVM format."""
from pyspark.mllib.regression import LabeledPoint
assert isinstance(p, LabeledPoint)
items = [str(p.label)]
v = _convert_to_vector(p.features)
if isinstance(v, SparseVector):
nnz = len(v.indices)
for i in xrange(nnz):
items.append(str(v.indices[i] + 1) + ":" + str(v.values[i]))
else:
for i in xrange(len(v)):
items.append(str(i + 1) + ":" + str(v[i]))
return " ".join(items) | python | def _convert_labeled_point_to_libsvm(p):
"""Converts a LabeledPoint to a string in LIBSVM format."""
from pyspark.mllib.regression import LabeledPoint
assert isinstance(p, LabeledPoint)
items = [str(p.label)]
v = _convert_to_vector(p.features)
if isinstance(v, SparseVector):
nnz = len(v.indices)
for i in xrange(nnz):
items.append(str(v.indices[i] + 1) + ":" + str(v.values[i]))
else:
for i in xrange(len(v)):
items.append(str(i + 1) + ":" + str(v[i]))
return " ".join(items) | [
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apache/spark | python/pyspark/heapq3.py | heappushpop | def heappushpop(heap, item):
"""Fast version of a heappush followed by a heappop."""
if heap and heap[0] < item:
item, heap[0] = heap[0], item
_siftup(heap, 0)
return item | python | def heappushpop(heap, item):
"""Fast version of a heappush followed by a heappop."""
if heap and heap[0] < item:
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_siftup(heap, 0)
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apache/spark | python/pyspark/ml/param/__init__.py | Params._resetUid | def _resetUid(self, newUid):
"""
Changes the uid of this instance. This updates both
the stored uid and the parent uid of params and param maps.
This is used by persistence (loading).
:param newUid: new uid to use, which is converted to unicode
:return: same instance, but with the uid and Param.parent values
updated, including within param maps
"""
newUid = unicode(newUid)
self.uid = newUid
newDefaultParamMap = dict()
newParamMap = dict()
for param in self.params:
newParam = copy.copy(param)
newParam.parent = newUid
if param in self._defaultParamMap:
newDefaultParamMap[newParam] = self._defaultParamMap[param]
if param in self._paramMap:
newParamMap[newParam] = self._paramMap[param]
param.parent = newUid
self._defaultParamMap = newDefaultParamMap
self._paramMap = newParamMap
return self | python | def _resetUid(self, newUid):
"""
Changes the uid of this instance. This updates both
the stored uid and the parent uid of params and param maps.
This is used by persistence (loading).
:param newUid: new uid to use, which is converted to unicode
:return: same instance, but with the uid and Param.parent values
updated, including within param maps
"""
newUid = unicode(newUid)
self.uid = newUid
newDefaultParamMap = dict()
newParamMap = dict()
for param in self.params:
newParam = copy.copy(param)
newParam.parent = newUid
if param in self._defaultParamMap:
newDefaultParamMap[newParam] = self._defaultParamMap[param]
if param in self._paramMap:
newParamMap[newParam] = self._paramMap[param]
param.parent = newUid
self._defaultParamMap = newDefaultParamMap
self._paramMap = newParamMap
return self | [
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pandas-dev/pandas | pandas/core/dtypes/inference.py | is_nested_list_like | def is_nested_list_like(obj):
"""
Check if the object is list-like, and that all of its elements
are also list-like.
.. versionadded:: 0.20.0
Parameters
----------
obj : The object to check
Returns
-------
is_list_like : bool
Whether `obj` has list-like properties.
Examples
--------
>>> is_nested_list_like([[1, 2, 3]])
True
>>> is_nested_list_like([{1, 2, 3}, {1, 2, 3}])
True
>>> is_nested_list_like(["foo"])
False
>>> is_nested_list_like([])
False
>>> is_nested_list_like([[1, 2, 3], 1])
False
Notes
-----
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a generator) is a nested-list-like without consuming the iterator.
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doesn't define `__len__`.
See Also
--------
is_list_like
"""
return (is_list_like(obj) and hasattr(obj, '__len__') and
len(obj) > 0 and all(is_list_like(item) for item in obj)) | python | def is_nested_list_like(obj):
"""
Check if the object is list-like, and that all of its elements
are also list-like.
.. versionadded:: 0.20.0
Parameters
----------
obj : The object to check
Returns
-------
is_list_like : bool
Whether `obj` has list-like properties.
Examples
--------
>>> is_nested_list_like([[1, 2, 3]])
True
>>> is_nested_list_like([{1, 2, 3}, {1, 2, 3}])
True
>>> is_nested_list_like(["foo"])
False
>>> is_nested_list_like([])
False
>>> is_nested_list_like([[1, 2, 3], 1])
False
Notes
-----
This won't reliably detect whether a consumable iterator (e. g.
a generator) is a nested-list-like without consuming the iterator.
To avoid consuming it, we always return False if the outer container
doesn't define `__len__`.
See Also
--------
is_list_like
"""
return (is_list_like(obj) and hasattr(obj, '__len__') and
len(obj) > 0 and all(is_list_like(item) for item in obj)) | [
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.summary | def summary(self, *statistics):
"""Computes specified statistics for numeric and string columns. Available statistics are:
- count
- mean
- stddev
- min
- max
- arbitrary approximate percentiles specified as a percentage (eg, 75%)
If no statistics are given, this function computes count, mean, stddev, min,
approximate quartiles (percentiles at 25%, 50%, and 75%), and max.
.. note:: This function is meant for exploratory data analysis, as we make no
guarantee about the backward compatibility of the schema of the resulting DataFrame.
>>> df.summary().show()
+-------+------------------+-----+
|summary| age| name|
+-------+------------------+-----+
| count| 2| 2|
| mean| 3.5| null|
| stddev|2.1213203435596424| null|
| min| 2|Alice|
| 25%| 2| null|
| 50%| 2| null|
| 75%| 5| null|
| max| 5| Bob|
+-------+------------------+-----+
>>> df.summary("count", "min", "25%", "75%", "max").show()
+-------+---+-----+
|summary|age| name|
+-------+---+-----+
| count| 2| 2|
| min| 2|Alice|
| 25%| 2| null|
| 75%| 5| null|
| max| 5| Bob|
+-------+---+-----+
To do a summary for specific columns first select them:
>>> df.select("age", "name").summary("count").show()
+-------+---+----+
|summary|age|name|
+-------+---+----+
| count| 2| 2|
+-------+---+----+
See also describe for basic statistics.
"""
if len(statistics) == 1 and isinstance(statistics[0], list):
statistics = statistics[0]
jdf = self._jdf.summary(self._jseq(statistics))
return DataFrame(jdf, self.sql_ctx) | python | def summary(self, *statistics):
"""Computes specified statistics for numeric and string columns. Available statistics are:
- count
- mean
- stddev
- min
- max
- arbitrary approximate percentiles specified as a percentage (eg, 75%)
If no statistics are given, this function computes count, mean, stddev, min,
approximate quartiles (percentiles at 25%, 50%, and 75%), and max.
.. note:: This function is meant for exploratory data analysis, as we make no
guarantee about the backward compatibility of the schema of the resulting DataFrame.
>>> df.summary().show()
+-------+------------------+-----+
|summary| age| name|
+-------+------------------+-----+
| count| 2| 2|
| mean| 3.5| null|
| stddev|2.1213203435596424| null|
| min| 2|Alice|
| 25%| 2| null|
| 50%| 2| null|
| 75%| 5| null|
| max| 5| Bob|
+-------+------------------+-----+
>>> df.summary("count", "min", "25%", "75%", "max").show()
+-------+---+-----+
|summary|age| name|
+-------+---+-----+
| count| 2| 2|
| min| 2|Alice|
| 25%| 2| null|
| 75%| 5| null|
| max| 5| Bob|
+-------+---+-----+
To do a summary for specific columns first select them:
>>> df.select("age", "name").summary("count").show()
+-------+---+----+
|summary|age|name|
+-------+---+----+
| count| 2| 2|
+-------+---+----+
See also describe for basic statistics.
"""
if len(statistics) == 1 and isinstance(statistics[0], list):
statistics = statistics[0]
jdf = self._jdf.summary(self._jseq(statistics))
return DataFrame(jdf, self.sql_ctx) | [
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.intersectAll | def intersectAll(self, other):
""" Return a new :class:`DataFrame` containing rows in both this dataframe and other
dataframe while preserving duplicates.
This is equivalent to `INTERSECT ALL` in SQL.
>>> df1 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3), ("c", 4)], ["C1", "C2"])
>>> df2 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3)], ["C1", "C2"])
>>> df1.intersectAll(df2).sort("C1", "C2").show()
+---+---+
| C1| C2|
+---+---+
| a| 1|
| a| 1|
| b| 3|
+---+---+
Also as standard in SQL, this function resolves columns by position (not by name).
"""
return DataFrame(self._jdf.intersectAll(other._jdf), self.sql_ctx) | python | def intersectAll(self, other):
""" Return a new :class:`DataFrame` containing rows in both this dataframe and other
dataframe while preserving duplicates.
This is equivalent to `INTERSECT ALL` in SQL.
>>> df1 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3), ("c", 4)], ["C1", "C2"])
>>> df2 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3)], ["C1", "C2"])
>>> df1.intersectAll(df2).sort("C1", "C2").show()
+---+---+
| C1| C2|
+---+---+
| a| 1|
| a| 1|
| b| 3|
+---+---+
Also as standard in SQL, this function resolves columns by position (not by name).
"""
return DataFrame(self._jdf.intersectAll(other._jdf), self.sql_ctx) | [
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