Code stringlengths 103 85.9k | Summary listlengths 0 94 |
|---|---|
Please provide a description of the function:def get_for_accounts(self, accounts: List[Account]):
''' Get all splits for the given accounts '''
account_ids = [acc.guid for acc in accounts]
query = (
self.query
.filter(Split.account_guid.in_(account_ids))
)
... | [] |
Please provide a description of the function:def __get_model_for_portfolio_value(input_model: PortfolioValueInputModel
) -> PortfolioValueViewModel:
result = PortfolioValueViewModel()
result.filter = input_model
ref_datum = Datum()
ref_datum.from_datetime(input_model.as_of_date)
ref_da... | [
" loads the data for portfolio value "
] |
Please provide a description of the function:def __load_settings(self):
#file_path = path.relpath(settings_file_path)
#file_path = path.abspath(settings_file_path)
file_path = self.file_path
try:
self.data = json.load(open(file_path))
except FileNotFoundErro... | [
" Load settings from .json file "
] |
Please provide a description of the function:def file_exists(self) -> bool:
cfg_path = self.file_path
assert cfg_path
return path.isfile(cfg_path) | [
" Check if the settings file exists or not "
] |
Please provide a description of the function:def save(self):
content = self.dumps()
fileutils.save_text_to_file(content, self.file_path) | [
" Saves the settings contents "
] |
Please provide a description of the function:def database_path(self):
filename = self.database_filename
db_path = ":memory:" if filename == ":memory:" else (
path.abspath(path.join(__file__, "../..", "..", "data", filename)))
return db_path | [
"\n Full database path. Includes the default location + the database filename.\n "
] |
Please provide a description of the function:def file_path(self) -> str:
user_dir = self.__get_user_path()
file_path = path.abspath(path.join(user_dir, self.FILENAME))
return file_path | [
" Settings file absolute path"
] |
Please provide a description of the function:def dumps(self) -> str:
return json.dumps(self.data, sort_keys=True, indent=4) | [
" Dumps the json content as a string "
] |
Please provide a description of the function:def __copy_template(self):
import shutil
template_filename = "settings.json.template"
template_path = path.abspath(
path.join(__file__, "..", "..", "config", template_filename))
settings_path = self.file_path
shut... | [
" Copy the settings template into the user's directory "
] |
Please provide a description of the function:def is_not_empty(self, value, strict=False):
value = stringify(value)
if value is not None:
return
self.shout('Value %r is empty', strict, value) | [
"if value is not empty"
] |
Please provide a description of the function:def is_numeric(self, value, strict=False):
value = stringify(value)
if value is not None:
if value.isnumeric():
return
self.shout('value %r is not numeric', strict, value) | [
"if value is numeric"
] |
Please provide a description of the function:def is_integer(self, value, strict=False):
if value is not None:
if isinstance(value, numbers.Number):
return
value = stringify(value)
if value is not None and value.isnumeric():
return
self.sho... | [
"if value is an integer"
] |
Please provide a description of the function:def match_date(self, value, strict=False):
value = stringify(value)
try:
parse(value)
except Exception:
self.shout('Value %r is not a valid date', strict, value) | [
"if value is a date"
] |
Please provide a description of the function:def match_regexp(self, value, q, strict=False):
value = stringify(value)
mr = re.compile(q)
if value is not None:
if mr.match(value):
return
self.shout('%r not matching the regexp %r', strict, value, q) | [
"if value matches a regexp q"
] |
Please provide a description of the function:def has_length(self, value, q, strict=False):
value = stringify(value)
if value is not None:
if len(value) == q:
return
self.shout('Value %r not matching length %r', strict, value, q) | [
"if value has a length of q"
] |
Please provide a description of the function:def must_contain(self, value, q, strict=False):
if value is not None:
if value.find(q) != -1:
return
self.shout('Value %r does not contain %r', strict, value, q) | [
"if value must contain q"
] |
Please provide a description of the function:def extract(context, data):
with context.http.rehash(data) as result:
file_path = result.file_path
content_type = result.content_type
extract_dir = random_filename(context.work_path)
if content_type in ZIP_MIME_TYPES:
extr... | [
"Extract a compressed file"
] |
Please provide a description of the function:def size(cls, crawler):
key = make_key('queue_pending', crawler)
return unpack_int(conn.get(key)) | [
"Total operations pending for this crawler"
] |
Please provide a description of the function:def read_word(image, whitelist=None, chars=None, spaces=False):
from tesserocr import PyTessBaseAPI
api = PyTessBaseAPI()
api.SetPageSegMode(8)
if whitelist is not None:
api.SetVariable("tessedit_char_whitelist", whitelist)
api.SetImage(image... | [
" OCR a single word from an image. Useful for captchas.\n Image should be pre-processed to remove noise etc. "
] |
Please provide a description of the function:def read_char(image, whitelist=None):
from tesserocr import PyTessBaseAPI
api = PyTessBaseAPI()
api.SetPageSegMode(10)
if whitelist is not None:
api.SetVariable("tessedit_char_whitelist", whitelist)
api.SetImage(image)
api.Recognize()
... | [
" OCR a single character from an image. Useful for captchas."
] |
Please provide a description of the function:def get(self, name, default=None):
value = self.params.get(name, default)
if isinstance(value, str):
value = os.path.expandvars(value)
return value | [
"Get a configuration value and expand environment variables."
] |
Please provide a description of the function:def emit(self, rule='pass', stage=None, data={}, delay=None,
optional=False):
if stage is None:
stage = self.stage.handlers.get(rule)
if optional and stage is None:
return
if stage is None or stage not in ... | [
"Invoke the next stage, either based on a handling rule, or by calling\n the `pass` rule by default."
] |
Please provide a description of the function:def recurse(self, data={}, delay=None):
return self.emit(stage=self.stage.name,
data=data,
delay=delay) | [
"Have a stage invoke itself with a modified set of arguments."
] |
Please provide a description of the function:def execute(self, data):
if Crawl.is_aborted(self.crawler, self.run_id):
return
try:
Crawl.operation_start(self.crawler, self.stage, self.run_id)
self.log.info('[%s->%s(%s)]: %s',
self.cr... | [
"Execute the crawler and create a database record of having done\n so."
] |
Please provide a description of the function:def skip_incremental(self, *criteria):
if not self.incremental:
return False
# this is pure convenience, and will probably backfire at some point.
key = make_key(*criteria)
if key is None:
return False
... | [
"Perform an incremental check on a set of criteria.\n\n This can be used to execute a part of a crawler only once per an\n interval (which is specified by the ``expire`` setting). If the\n operation has already been performed (and should thus be skipped),\n this will return ``True``. If ... |
Please provide a description of the function:def store_data(self, data, encoding='utf-8'):
path = random_filename(self.work_path)
try:
with open(path, 'wb') as fh:
if isinstance(data, str):
data = data.encode(encoding)
if data is n... | [
"Put the given content into a file, possibly encoding it as UTF-8\n in the process."
] |
Please provide a description of the function:def check_due(self):
if self.disabled:
return False
if self.is_running:
return False
if self.delta is None:
return False
last_run = self.last_run
if last_run is None:
return True... | [
"Check if the last execution of this crawler is older than\n the scheduled interval."
] |
Please provide a description of the function:def flush(self):
Queue.flush(self)
Event.delete(self)
Crawl.flush(self) | [
"Delete all run-time data generated by this crawler."
] |
Please provide a description of the function:def run(self, incremental=None, run_id=None):
state = {
'crawler': self.name,
'run_id': run_id,
'incremental': settings.INCREMENTAL
}
if incremental is not None:
state['incremental'] = increment... | [
"Queue the execution of a particular crawler."
] |
Please provide a description of the function:def fetch(context, data):
url = data.get('url')
attempt = data.pop('retry_attempt', 1)
try:
result = context.http.get(url, lazy=True)
rules = context.get('rules', {'match_all': {}})
if not Rule.get_rule(rules).apply(result):
... | [
"Do an HTTP GET on the ``url`` specified in the inbound data."
] |
Please provide a description of the function:def dav_index(context, data):
# This is made to work with ownCloud/nextCloud, but some rumor has
# it they are "standards compliant" and it should thus work for
# other DAV servers.
url = data.get('url')
result = context.http.request('PROPFIND', url)... | [
"List files in a WebDAV directory."
] |
Please provide a description of the function:def session(context, data):
context.http.reset()
user = context.get('user')
password = context.get('password')
if user is not None and password is not None:
context.http.session.auth = (user, password)
user_agent = context.get('user_agent'... | [
"Set some HTTP parameters for all subsequent requests.\n\n This includes ``user`` and ``password`` for HTTP basic authentication,\n and ``user_agent`` as a header.\n "
] |
Please provide a description of the function:def save(cls, crawler, stage, level, run_id, error=None, message=None):
event = {
'stage': stage.name,
'level': level,
'timestamp': pack_now(),
'error': error,
'message': message
}
d... | [
"Create an event, possibly based on an exception."
] |
Please provide a description of the function:def get_stage_events(cls, crawler, stage_name, start, end, level=None):
key = make_key(crawler, "events", stage_name, level)
return cls.event_list(key, start, end) | [
"events from a particular stage"
] |
Please provide a description of the function:def get_run_events(cls, crawler, run_id, start, end, level=None):
key = make_key(crawler, "events", run_id, level)
return cls.event_list(key, start, end) | [
"Events from a particular run"
] |
Please provide a description of the function:def soviet_checksum(code):
def sum_digits(code, offset=1):
total = 0
for digit, index in zip(code[:7], count(offset)):
total += int(digit) * index
summed = (total / 11 * 11)
return total - summed
check = sum_digits(co... | [
"Courtesy of Sir Vlad Lavrov."
] |
Please provide a description of the function:def search_results_total(html, xpath, check, delimiter):
for container in html.findall(xpath):
if check in container.findtext('.'):
text = container.findtext('.').split(delimiter)
total = int(text[-1].strip())
return total | [
" Get the total number of results from the DOM of a search index. "
] |
Please provide a description of the function:def search_results_last_url(html, xpath, label):
for container in html.findall(xpath):
if container.text_content().strip() == label:
return container.find('.//a').get('href') | [
" Get the URL of the 'last' button in a search results listing. "
] |
Please provide a description of the function:def op_count(cls, crawler, stage=None):
if stage:
total_ops = conn.get(make_key(crawler, stage))
else:
total_ops = conn.get(make_key(crawler, "total_ops"))
return unpack_int(total_ops) | [
"Total operations performed for this crawler"
] |
Please provide a description of the function:def index():
crawlers = []
for crawler in manager:
data = Event.get_counts(crawler)
data['last_active'] = crawler.last_run
data['total_ops'] = crawler.op_count
data['running'] = crawler.is_running
data['crawler'] = crawler... | [
"Generate a list of all crawlers, alphabetically, with op counts."
] |
Please provide a description of the function:def clean_html(context, data):
doc = _get_html_document(context, data)
if doc is None:
context.emit(data=data)
return
remove_paths = context.params.get('remove_paths')
for path in ensure_list(remove_paths):
for el in doc.findall(... | [
"Clean an HTML DOM and store the changed version."
] |
Please provide a description of the function:def execute(cls, stage, state, data, next_allowed_exec_time=None):
try:
context = Context.from_state(state, stage)
now = datetime.utcnow()
if next_allowed_exec_time and now < next_allowed_exec_time:
# task ... | [
"Execute the operation, rate limiting allowing."
] |
Please provide a description of the function:def _upsert(context, params, data):
table = params.get("table")
table = datastore.get_table(table, primary_id=False)
unique_keys = ensure_list(params.get("unique"))
data["__last_seen"] = datetime.datetime.utcnow()
if len(unique_keys):
updated... | [
"Insert or update data and add/update appropriate timestamps"
] |
Please provide a description of the function:def _recursive_upsert(context, params, data):
children = params.get("children", {})
nested_calls = []
for child_params in children:
key = child_params.get("key")
child_data_list = ensure_list(data.pop(key))
if isinstance(child_data_li... | [
"Insert or update nested dicts recursively into db tables"
] |
Please provide a description of the function:def db(context, data):
table = context.params.get("table", context.crawler.name)
params = context.params
params["table"] = table
_recursive_upsert(context, params, data) | [
"Insert or update `data` as a row into specified db table"
] |
Please provide a description of the function:def cli(debug, cache, incremental):
settings.HTTP_CACHE = cache
settings.INCREMENTAL = incremental
settings.DEBUG = debug
if settings.DEBUG:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.INFO)
in... | [
"Crawler framework for documents and structured scrapers."
] |
Please provide a description of the function:def run(crawler):
crawler = get_crawler(crawler)
crawler.run()
if is_sync_mode():
TaskRunner.run_sync() | [
"Run a specified crawler."
] |
Please provide a description of the function:def index():
crawler_list = []
for crawler in manager:
is_due = 'yes' if crawler.check_due() else 'no'
if crawler.disabled:
is_due = 'off'
crawler_list.append([crawler.name,
crawler.description,
... | [
"List the available crawlers."
] |
Please provide a description of the function:def scheduled(wait=False):
manager.run_scheduled()
while wait:
# Loop and try to run scheduled crawlers at short intervals
manager.run_scheduled()
time.sleep(settings.SCHEDULER_INTERVAL) | [
"Run crawlers that are due."
] |
Please provide a description of the function:def _get_directory_path(context):
path = os.path.join(settings.BASE_PATH, 'store')
path = context.params.get('path', path)
path = os.path.join(path, context.crawler.name)
path = os.path.abspath(os.path.expandvars(path))
try:
os.makedirs(path)... | [
"Get the storage path fro the output."
] |
Please provide a description of the function:def directory(context, data):
with context.http.rehash(data) as result:
if not result.ok:
return
content_hash = data.get('content_hash')
if content_hash is None:
context.emit_warning("No content hash in data.")
... | [
"Store the collected files to a given directory."
] |
Please provide a description of the function:def seed(context, data):
for key in ('url', 'urls'):
for url in ensure_list(context.params.get(key)):
url = url % data
context.emit(data={'url': url}) | [
"Initialize a crawler with a set of seed URLs.\n\n The URLs are given as a list or single value to the ``urls`` parameter.\n\n If this is called as a second stage in a crawler, the URL will be formatted\n against the supplied ``data`` values, e.g.:\n\n https://crawl.site/entries/%(number)s.html\n ... |
Please provide a description of the function:def enumerate(context, data):
items = ensure_list(context.params.get('items'))
for item in items:
data['item'] = item
context.emit(data=data) | [
"Iterate through a set of items and emit each one of them."
] |
Please provide a description of the function:def sequence(context, data):
number = data.get('number', context.params.get('start', 1))
stop = context.params.get('stop')
step = context.params.get('step', 1)
delay = context.params.get('delay')
prefix = context.params.get('tag')
while True:
... | [
"Generate a sequence of numbers.\n\n It is the memorious equivalent of the xrange function, accepting the\n ``start``, ``stop`` and ``step`` parameters.\n\n This can run in two ways:\n * As a single function generating all numbers in the given range.\n * Recursively, generating numbers one by one wit... |
Please provide a description of the function:def fetch(self):
if self._file_path is not None:
return self._file_path
temp_path = self.context.work_path
if self._content_hash is not None:
self._file_path = storage.load_file(self._content_hash,
... | [
"Lazily trigger download of the data when requested."
] |
Please provide a description of the function:def make_key(*criteria):
criteria = [stringify(c) for c in criteria]
criteria = [c for c in criteria if c is not None]
if len(criteria):
return ':'.join(criteria) | [
"Make a string key out of many criteria."
] |
Please provide a description of the function:def random_filename(path=None):
filename = uuid4().hex
if path is not None:
filename = os.path.join(path, filename)
return filename | [
"Make a UUID-based file name which is extremely unlikely\n to exist already."
] |
Please provide a description of the function:def box(self, bottom_left_corner, top_right_corner, paint=None, blank=False):
''' creates the visual frame/box in which we place the graph '''
path = [
bottom_left_corner,
Point(bottom_left_corner.x, top_right_corner.y),
to... | [] |
Please provide a description of the function:def get_terminal_size():
current_os = platform.system()
tuple_xy = None
if current_os == 'Windows':
tuple_xy = _get_terminal_size_windows()
if tuple_xy is None:
tuple_xy = _get_terminal_size_tput()
# needed for window'... | [
" getTerminalSize()\n - get width and height of console\n - works on linux,os x,windows,cygwin(windows)\n originally retrieved from:\n http://stackoverflow.com/questions/566746/how-to-get-console-window-width-in-python\n "
] |
Please provide a description of the function:def sample_vMF(mu, kappa, num_samples):
dim = len(mu)
result = np.zeros((num_samples, dim))
for nn in range(num_samples):
# sample offset from center (on sphere) with spread kappa
w = _sample_weight(kappa, dim)
# sample a point v on ... | [
"Generate num_samples N-dimensional samples from von Mises Fisher\n distribution around center mu \\in R^N with concentration kappa.\n "
] |
Please provide a description of the function:def _sample_weight(kappa, dim):
dim = dim - 1 # since S^{n-1}
b = dim / (np.sqrt(4. * kappa ** 2 + dim ** 2) + 2 * kappa)
x = (1. - b) / (1. + b)
c = kappa * x + dim * np.log(1 - x ** 2)
while True:
z = np.random.beta(dim / 2., dim / 2.)
... | [
"Rejection sampling scheme for sampling distance from center on\n surface of the sphere.\n "
] |
Please provide a description of the function:def _sample_orthonormal_to(mu):
v = np.random.randn(mu.shape[0])
proj_mu_v = mu * np.dot(mu, v) / np.linalg.norm(mu)
orthto = v - proj_mu_v
return orthto / np.linalg.norm(orthto) | [
"Sample point on sphere orthogonal to mu."
] |
Please provide a description of the function:def _spherical_kmeans_single_lloyd(
X,
n_clusters,
sample_weight=None,
max_iter=300,
init="k-means++",
verbose=False,
x_squared_norms=None,
random_state=None,
tol=1e-4,
precompute_distances=True,
):
random_state = check_random... | [
"\n Modified from sklearn.cluster.k_means_.k_means_single_lloyd.\n "
] |
Please provide a description of the function:def spherical_k_means(
X,
n_clusters,
sample_weight=None,
init="k-means++",
n_init=10,
max_iter=300,
verbose=False,
tol=1e-4,
random_state=None,
copy_x=True,
n_jobs=1,
algorithm="auto",
return_n_iter=False,
):
if n... | [
"Modified from sklearn.cluster.k_means_.k_means.\n "
] |
Please provide a description of the function:def fit(self, X, y=None, sample_weight=None):
if self.normalize:
X = normalize(X)
random_state = check_random_state(self.random_state)
# TODO: add check that all data is unit-normalized
self.cluster_centers_, self.label... | [
"Compute k-means clustering.\n\n Parameters\n ----------\n\n X : array-like or sparse matrix, shape=(n_samples, n_features)\n\n y : Ignored\n not used, present here for API consistency by convention.\n\n sample_weight : array-like, shape (n_samples,), optional\n ... |
Please provide a description of the function:def _inertia_from_labels(X, centers, labels):
n_examples, n_features = X.shape
inertia = np.zeros((n_examples,))
for ee in range(n_examples):
inertia[ee] = 1 - X[ee, :].dot(centers[int(labels[ee]), :].T)
return np.sum(inertia) | [
"Compute inertia with cosine distance using known labels.\n "
] |
Please provide a description of the function:def _labels_inertia(X, centers):
n_examples, n_features = X.shape
n_clusters, n_features = centers.shape
labels = np.zeros((n_examples,))
inertia = np.zeros((n_examples,))
for ee in range(n_examples):
dists = np.zeros((n_clusters,))
... | [
"Compute labels and inertia with cosine distance.\n "
] |
Please provide a description of the function:def _vmf_log(X, kappa, mu):
n_examples, n_features = X.shape
return np.log(_vmf_normalize(kappa, n_features) * np.exp(kappa * X.dot(mu).T)) | [
"Computs log(vMF(X, kappa, mu)) using built-in numpy/scipy Bessel\n approximations.\n\n Works well on small kappa and mu.\n "
] |
Please provide a description of the function:def _vmf_normalize(kappa, dim):
num = np.power(kappa, dim / 2. - 1.)
if dim / 2. - 1. < 1e-15:
denom = np.power(2. * np.pi, dim / 2.) * i0(kappa)
else:
denom = np.power(2. * np.pi, dim / 2.) * iv(dim / 2. - 1., kappa)
if np.isinf(num):
... | [
"Compute normalization constant using built-in numpy/scipy Bessel\n approximations.\n\n Works well on small kappa and mu.\n "
] |
Please provide a description of the function:def _log_H_asymptotic(nu, kappa):
beta = np.sqrt((nu + 0.5) ** 2)
kappa_l = np.min([kappa, np.sqrt((3. * nu + 11. / 2.) * (nu + 3. / 2.))])
return _S(kappa, nu + 0.5, beta) + (
_S(kappa_l, nu, nu + 2.) - _S(kappa_l, nu + 0.5, beta)
) | [
"Compute the Amos-type upper bound asymptotic approximation on H where\n log(H_\\nu)(\\kappa) = \\int_0^\\kappa R_\\nu(t) dt.\n\n See \"lH_asymptotic <-\" in movMF.R and utility function implementation notes\n from https://cran.r-project.org/web/packages/movMF/index.html\n "
] |
Please provide a description of the function:def _S(kappa, alpha, beta):
kappa = 1. * np.abs(kappa)
alpha = 1. * alpha
beta = 1. * np.abs(beta)
a_plus_b = alpha + beta
u = np.sqrt(kappa ** 2 + beta ** 2)
if alpha == 0:
alpha_scale = 0
else:
alpha_scale = alpha * np.log((... | [
"Compute the antiderivative of the Amos-type bound G on the modified\n Bessel function ratio.\n\n Note: Handles scalar kappa, alpha, and beta only.\n\n See \"S <-\" in movMF.R and utility function implementation notes from\n https://cran.r-project.org/web/packages/movMF/index.html\n "
] |
Please provide a description of the function:def _vmf_log_asymptotic(X, kappa, mu):
n_examples, n_features = X.shape
log_vfm = kappa * X.dot(mu).T + -_log_H_asymptotic(n_features / 2. - 1., kappa)
return log_vfm | [
"Compute log(f(x|theta)) via Amos approximation\n\n log(f(x|theta)) = theta' x - log(H_{d/2-1})(\\|theta\\|)\n\n where theta = kappa * X, \\|theta\\| = kappa.\n\n Computing _vmf_log helps with numerical stability / loss of precision for\n for large values of kappa and n_features.\n\n See utility ... |
Please provide a description of the function:def _init_unit_centers(X, n_clusters, random_state, init):
n_examples, n_features = np.shape(X)
if isinstance(init, np.ndarray):
n_init_clusters, n_init_features = init.shape
assert n_init_clusters == n_clusters
assert n_init_features == ... | [
"Initializes unit norm centers.\n\n Parameters\n ----------\n X : array-like or sparse matrix, shape=(n_samples, n_features)\n\n n_clusters : int, optional, default: 8\n The number of clusters to form as well as the number of\n centroids to generate.\n\n random_state : integer or numpy.... |
Please provide a description of the function:def _expectation(X, centers, weights, concentrations, posterior_type="soft"):
n_examples, n_features = np.shape(X)
n_clusters, _ = centers.shape
if n_features <= 50: # works up to about 50 before numrically unstable
vmf_f = _vmf_log
else:
... | [
"Compute the log-likelihood of each datapoint being in each cluster.\n\n Parameters\n ----------\n centers (mu) : array, [n_centers x n_features]\n weights (alpha) : array, [n_centers, ] (alpha)\n concentrations (kappa) : array, [n_centers, ]\n\n Returns\n ----------\n posterior : array, [n_... |
Please provide a description of the function:def _maximization(X, posterior, force_weights=None):
n_examples, n_features = X.shape
n_clusters, n_examples = posterior.shape
concentrations = np.zeros((n_clusters,))
centers = np.zeros((n_clusters, n_features))
if force_weights is None:
wei... | [
"Estimate new centers, weights, and concentrations from\n\n Parameters\n ----------\n posterior : array, [n_centers, n_examples]\n The posterior matrix from the expectation step.\n\n force_weights : None or array, [n_centers, ]\n If None is passed, will estimate weights.\n If an arr... |
Please provide a description of the function:def _movMF(
X,
n_clusters,
posterior_type="soft",
force_weights=None,
max_iter=300,
verbose=False,
init="random-class",
random_state=None,
tol=1e-6,
):
random_state = check_random_state(random_state)
n_examples, n_features = n... | [
"Mixture of von Mises Fisher clustering.\n\n Implements the algorithms (i) and (ii) from\n\n \"Clustering on the Unit Hypersphere using von Mises-Fisher Distributions\"\n by Banerjee, Dhillon, Ghosh, and Sra.\n\n TODO: Currently only supports Banerjee et al 2005 approximation of kappa,\n ho... |
Please provide a description of the function:def movMF(
X,
n_clusters,
posterior_type="soft",
force_weights=None,
n_init=10,
n_jobs=1,
max_iter=300,
verbose=False,
init="random-class",
random_state=None,
tol=1e-6,
copy_x=True,
):
if n_init <= 0:
raise Val... | [
"Wrapper for parallelization of _movMF and running n_init times.\n "
] |
Please provide a description of the function:def _check_fit_data(self, X):
X = check_array(X, accept_sparse="csr", dtype=[np.float64, np.float32])
n_samples, n_features = X.shape
if X.shape[0] < self.n_clusters:
raise ValueError(
"n_samples=%d should be >= n_... | [
"Verify that the number of samples given is larger than k"
] |
Please provide a description of the function:def fit(self, X, y=None):
if self.normalize:
X = normalize(X)
self._check_force_weights()
random_state = check_random_state(self.random_state)
X = self._check_fit_data(X)
(
self.cluster_centers_,
... | [
"Compute mixture of von Mises Fisher clustering.\n\n Parameters\n ----------\n X : array-like or sparse matrix, shape=(n_samples, n_features)\n "
] |
Please provide a description of the function:def transform(self, X, y=None):
if self.normalize:
X = normalize(X)
check_is_fitted(self, "cluster_centers_")
X = self._check_test_data(X)
return self._transform(X) | [
"Transform X to a cluster-distance space.\n In the new space, each dimension is the cosine distance to the cluster\n centers. Note that even if X is sparse, the array returned by\n `transform` will typically be dense.\n\n Parameters\n ----------\n X : {array-like, sparse m... |
Please provide a description of the function:def predict(self, X):
if self.normalize:
X = normalize(X)
check_is_fitted(self, "cluster_centers_")
X = self._check_test_data(X)
return _labels_inertia(X, self.cluster_centers_)[0] | [
"Predict the closest cluster each sample in X belongs to.\n In the vector quantization literature, `cluster_centers_` is called\n the code book and each value returned by `predict` is the index of\n the closest code in the code book.\n\n Note: Does not check that each point is on the sp... |
Please provide a description of the function:def score(self, X, y=None):
if self.normalize:
X = normalize(X)
check_is_fitted(self, "cluster_centers_")
X = self._check_test_data(X)
return -_labels_inertia(X, self.cluster_centers_)[1] | [
"Inertia score (sum of all distances to closest cluster).\n\n Parameters\n ----------\n X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n New data.\n\n Returns\n -------\n score : float\n Larger score is better.\n "
] |
Please provide a description of the function:def log_likelihood(covariance, precision):
assert covariance.shape == precision.shape
dim, _ = precision.shape
log_likelihood_ = (
-np.sum(covariance * precision)
+ fast_logdet(precision)
- dim * np.log(2 * np.pi)
)
log_likeli... | [
"Computes the log-likelihood between the covariance and precision\n estimate.\n\n Parameters\n ----------\n covariance : 2D ndarray (n_features, n_features)\n Maximum Likelihood Estimator of covariance\n\n precision : 2D ndarray (n_features, n_features)\n The precision matrix of the cov... |
Please provide a description of the function:def kl_loss(covariance, precision):
assert covariance.shape == precision.shape
dim, _ = precision.shape
logdet_p_dot_c = fast_logdet(np.dot(precision, covariance))
return 0.5 * (np.sum(precision * covariance) - logdet_p_dot_c - dim) | [
"Computes the KL divergence between precision estimate and\n reference covariance.\n\n The loss is computed as:\n\n Trace(Theta_1 * Sigma_0) - log(Theta_0 * Sigma_1) - dim(Sigma)\n\n Parameters\n ----------\n covariance : 2D ndarray (n_features, n_features)\n Maximum Likelihood Estimato... |
Please provide a description of the function:def quadratic_loss(covariance, precision):
assert covariance.shape == precision.shape
dim, _ = precision.shape
return np.trace((np.dot(covariance, precision) - np.eye(dim)) ** 2) | [
"Computes ...\n\n Parameters\n ----------\n covariance : 2D ndarray (n_features, n_features)\n Maximum Likelihood Estimator of covariance\n\n precision : 2D ndarray (n_features, n_features)\n The precision matrix of the model to be tested\n\n Returns\n -------\n Quadratic loss\n ... |
Please provide a description of the function:def ebic(covariance, precision, n_samples, n_features, gamma=0):
l_theta = -np.sum(covariance * precision) + fast_logdet(precision)
l_theta *= n_features / 2.
# is something goes wrong with fast_logdet, return large value
if np.isinf(l_theta) or np.isna... | [
"\n Extended Bayesian Information Criteria for model selection.\n\n When using path mode, use this as an alternative to cross-validation for\n finding lambda.\n\n See:\n \"Extended Bayesian Information Criteria for Gaussian Graphical Models\"\n R. Foygel and M. Drton, NIPS 2010\n\n Para... |
Please provide a description of the function:def lattice(prng, n_features, alpha, random_sign=False, low=0.3, high=0.7):
degree = int(1 + np.round(alpha * n_features / 2.))
if random_sign:
sign_row = -1.0 * np.ones(degree) + 2 * (
prng.uniform(low=0, high=1, size=degree) > .5
)... | [
"Returns the adjacency matrix for a lattice network.\n\n The resulting network is a Toeplitz matrix with random values summing\n between -1 and 1 and zeros along the diagonal.\n\n The range of the values can be controlled via the parameters low and high.\n If random_sign is false, all entries will be ne... |
Please provide a description of the function:def blocks(prng, block, n_blocks=2, chain_blocks=True):
n_block_features, _ = block.shape
n_features = n_block_features * n_blocks
adjacency = np.zeros((n_features, n_features))
dep_groups = np.eye(n_blocks)
if chain_blocks:
chain_alpha = np... | [
"Replicates `block` matrix n_blocks times diagonally to create a\n square matrix of size n_features = block.size[0] * n_blocks and with zeros\n along the diagonal.\n\n The graph can be made fully connected using chaining assumption when\n chain_blocks=True (default).\n\n This utility can be used to g... |
Please provide a description of the function:def _to_diagonally_dominant(mat):
mat += np.diag(np.sum(mat != 0, axis=1) + 0.01)
return mat | [
"Make matrix unweighted diagonally dominant using the Laplacian."
] |
Please provide a description of the function:def _to_diagonally_dominant_weighted(mat):
mat += np.diag(np.sum(np.abs(mat), axis=1) + 0.01)
return mat | [
"Make matrix weighted diagonally dominant using the Laplacian."
] |
Please provide a description of the function:def _rescale_to_unit_diagonals(mat):
d = np.sqrt(np.diag(mat))
mat /= d
mat /= d[:, np.newaxis]
return mat | [
"Rescale matrix to have unit diagonals.\n\n Note: Call only after diagonal dominance is ensured.\n "
] |
Please provide a description of the function:def create(self, n_features, alpha):
n_block_features = int(np.floor(1. * n_features / self.n_blocks))
if n_block_features * self.n_blocks != n_features:
raise ValueError(
(
"Error: n_features {} not di... | [
"Build a new graph with block structure.\n\n Parameters\n -----------\n n_features : int\n\n alpha : float (0,1)\n The complexity / sparsity factor for each graph type.\n\n Returns\n -----------\n (n_features, n_features) matrices: covariance, precision, a... |
Please provide a description of the function:def trace_plot(precisions, path, n_edges=20, ground_truth=None, edges=[]):
_check_path(path)
assert len(path) == len(precisions)
assert len(precisions) > 0
path = np.array(path)
dim, _ = precisions[0].shape
# determine which indices to track
... | [
"Plot the change in precision (or covariance) coefficients as a function\n of changing lambda and l1-norm. Always ignores diagonals.\n\n Parameters\n -----------\n precisions : array of len(path) 2D ndarray, shape (n_features, n_features)\n This is either precision_ or covariance_ from an Invers... |
Please provide a description of the function:def fit(self, X, y=None):
# default to QuicGraphicalLassoCV
estimator = self.estimator or QuicGraphicalLassoCV()
self.lam_ = None
self.estimator_ = None
X = check_array(X, ensure_min_features=2, estimator=self)
X = a... | [
"Estimate the precision using an adaptive maximum likelihood estimator.\n Parameters\n ----------\n X : ndarray, shape (n_samples, n_features)\n Data from which to compute the proportion matrix.\n "
] |
Please provide a description of the function:def _sample_mvn(n_samples, cov, prng):
n_features, _ = cov.shape
return prng.multivariate_normal(np.zeros(n_features), cov, size=n_samples) | [
"Draw a multivariate normal sample from the graph defined by cov.\n\n Parameters\n -----------\n n_samples : int\n\n cov : matrix of shape (n_features, n_features)\n Covariance matrix of the graph.\n\n prng : np.random.RandomState instance.\n "
] |
Please provide a description of the function:def _fully_random_weights(n_features, lam_scale, prng):
weights = np.zeros((n_features, n_features))
n_off_diag = int((n_features ** 2 - n_features) / 2)
weights[np.triu_indices(n_features, k=1)] = 0.1 * lam_scale * prng.randn(
n_off_diag
) + (0.... | [
"Generate a symmetric random matrix with zeros along the diagonal."
] |
Please provide a description of the function:def _fix_weights(weight_fun, *args):
weights = weight_fun(*args)
# TODO: fix this
# disable checks for now
return weights
# if positive semidefinite, then we're good as is
if _check_psd(weights):
return weights
# make diagonally do... | [
"Ensure random weight matrix is valid.\n\n TODO: The diagonally dominant tuning currently doesn't make sense.\n Our weight matrix has zeros along the diagonal, so multiplying by\n a diagonal matrix results in a zero-matrix.\n "
] |
Please provide a description of the function:def _fit(
indexed_params,
penalization,
lam,
lam_perturb,
lam_scale_,
estimator,
penalty_name,
subsample,
bootstrap,
prng,
X=None,
):
index = indexed_params
if isinstance(X, np.ndarray):
local_X = X
else:
... | [
"Wrapper function outside of instance for fitting a single model average\n trial.\n\n If X is None, then we assume we are using a broadcast spark object. Else,\n we expect X to get passed into this function.\n "
] |
Please provide a description of the function:def _spark_map(fun, indexed_param_grid, sc, seed, X_bc):
def _wrap_random_state(split_index, partition):
prng = np.random.RandomState(seed + split_index)
yield map(partial(fun, prng=prng, X=X_bc), partition)
par_param_grid = sc.parallelize(inde... | [
"We cannot pass a RandomState instance to each spark worker since it will\n behave identically across partitions. Instead, we explictly handle the\n partitions with a newly seeded instance.\n\n The seed for each partition will be the \"seed\" (MonteCarloProfile.seed) +\n \"split_index\" which is the pa... |
Please provide a description of the function:def fit(self, X, y=None):
# default to QuicGraphicalLasso
estimator = self.estimator or QuicGraphicalLasso()
if self.penalization != "subsampling" and not hasattr(
estimator, self.penalty_name
):
raise ValueEr... | [
"Learn a model averaged proportion matrix for X.\n Parameters\n ----------\n X : ndarray, shape (n_samples, n_features)\n Data from which to compute the proportion matrix.\n "
] |
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