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19,300 | apache/spark | python/pyspark/mllib/random.py | RandomRDDs.gammaRDD | def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d. samples from the Gamma
distribution with the input shape and scale.
:param sc: SparkContext used to create the RDD.
:param shape: shape (> 0) parameter for the Gamma dis... | python | def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d. samples from the Gamma
distribution with the input shape and scale.
:param sc: SparkContext used to create the RDD.
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19,301 | apache/spark | python/pyspark/mllib/random.py | RandomRDDs.normalVectorRDD | def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the standard normal distribution.
:param sc: SparkContext used to create the RDD.
:param numRows: Number of Vectors in the RDD... | python | def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
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:param sc: SparkContext used to create the RDD.
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19,302 | apache/spark | python/pyspark/mllib/random.py | RandomRDDs.logNormalVectorRDD | def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the log normal distribution.
:param sc: SparkContext used to create the RDD.
:param mean: Mean of the log normal... | python | def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
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:param sc: SparkContext used to create the RDD.
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19,303 | apache/spark | python/pyspark/mllib/random.py | RandomRDDs.poissonVectorRDD | def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the Poisson distribution with the input mean.
:param sc: SparkContext used to create the RDD.
:param mean: Mean, or lam... | python | def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
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:param sc: SparkContext used to create the RDD.
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19,304 | apache/spark | python/pyspark/mllib/random.py | RandomRDDs.gammaVectorRDD | def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the Gamma distribution.
:param sc: SparkContext used to create the RDD.
:param shape: Shape (> 0) of the Gamma di... | python | def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
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:param sc: SparkContext used to create the RDD.
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19,305 | apache/spark | python/pyspark/sql/session.py | SparkSession.conf | def conf(self):
"""Runtime configuration interface for Spark.
This is the interface through which the user can get and set all Spark and Hadoop
configurations that are relevant to Spark SQL. When getting the value of a config,
this defaults to the value set in the underlying :class:`Spa... | python | def conf(self):
"""Runtime configuration interface for Spark.
This is the interface through which the user can get and set all Spark and Hadoop
configurations that are relevant to Spark SQL. When getting the value of a config,
this defaults to the value set in the underlying :class:`Spa... | [
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19,306 | apache/spark | python/pyspark/sql/session.py | SparkSession.catalog | def catalog(self):
"""Interface through which the user may create, drop, alter or query underlying
databases, tables, functions etc.
:return: :class:`Catalog`
"""
from pyspark.sql.catalog import Catalog
if not hasattr(self, "_catalog"):
self._catalog = Catalo... | python | def catalog(self):
"""Interface through which the user may create, drop, alter or query underlying
databases, tables, functions etc.
:return: :class:`Catalog`
"""
from pyspark.sql.catalog import Catalog
if not hasattr(self, "_catalog"):
self._catalog = Catalo... | [
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19,307 | apache/spark | python/pyspark/sql/session.py | SparkSession._inferSchemaFromList | def _inferSchemaFromList(self, data, names=None):
"""
Infer schema from list of Row or tuple.
:param data: list of Row or tuple
:param names: list of column names
:return: :class:`pyspark.sql.types.StructType`
"""
if not data:
raise ValueError("can no... | python | def _inferSchemaFromList(self, data, names=None):
"""
Infer schema from list of Row or tuple.
:param data: list of Row or tuple
:param names: list of column names
:return: :class:`pyspark.sql.types.StructType`
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19,308 | apache/spark | python/pyspark/sql/session.py | SparkSession._inferSchema | def _inferSchema(self, rdd, samplingRatio=None, names=None):
"""
Infer schema from an RDD of Row or tuple.
:param rdd: an RDD of Row or tuple
:param samplingRatio: sampling ratio, or no sampling (default)
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"""
Infer schema from an RDD of Row or tuple.
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:param samplingRatio: sampling ratio, or no sampling (default)
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19,309 | apache/spark | python/pyspark/sql/session.py | SparkSession._createFromRDD | def _createFromRDD(self, rdd, schema, samplingRatio):
"""
Create an RDD for DataFrame from an existing RDD, returns the RDD and schema.
"""
if schema is None or isinstance(schema, (list, tuple)):
struct = self._inferSchema(rdd, samplingRatio, names=schema)
convert... | python | def _createFromRDD(self, rdd, schema, samplingRatio):
"""
Create an RDD for DataFrame from an existing RDD, returns the RDD and schema.
"""
if schema is None or isinstance(schema, (list, tuple)):
struct = self._inferSchema(rdd, samplingRatio, names=schema)
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19,310 | apache/spark | python/pyspark/sql/session.py | SparkSession._createFromLocal | def _createFromLocal(self, data, schema):
"""
Create an RDD for DataFrame from a list or pandas.DataFrame, returns
the RDD and schema.
"""
# make sure data could consumed multiple times
if not isinstance(data, list):
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"""
Create an RDD for DataFrame from a list or pandas.DataFrame, returns
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19,311 | apache/spark | python/pyspark/sql/session.py | SparkSession._create_from_pandas_with_arrow | def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
"""
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"""
Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
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19,312 | apache/spark | python/pyspark/sql/session.py | SparkSession._create_shell_session | def _create_shell_session():
"""
Initialize a SparkSession for a pyspark shell session. This is called from shell.py
to make error handling simpler without needing to declare local variables in that
script, which would expose those to users.
"""
import py4j
from p... | python | def _create_shell_session():
"""
Initialize a SparkSession for a pyspark shell session. This is called from shell.py
to make error handling simpler without needing to declare local variables in that
script, which would expose those to users.
"""
import py4j
from p... | [
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19,313 | apache/spark | python/pyspark/serializers.py | _restore | def _restore(name, fields, value):
""" Restore an object of namedtuple"""
k = (name, fields)
cls = __cls.get(k)
if cls is None:
cls = collections.namedtuple(name, fields)
__cls[k] = cls
return cls(*value) | python | def _restore(name, fields, value):
""" Restore an object of namedtuple"""
k = (name, fields)
cls = __cls.get(k)
if cls is None:
cls = collections.namedtuple(name, fields)
__cls[k] = cls
return cls(*value) | [
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19,314 | apache/spark | python/pyspark/serializers.py | _hack_namedtuple | def _hack_namedtuple(cls):
""" Make class generated by namedtuple picklable """
name = cls.__name__
fields = cls._fields
def __reduce__(self):
return (_restore, (name, fields, tuple(self)))
cls.__reduce__ = __reduce__
cls._is_namedtuple_ = True
return cls | python | def _hack_namedtuple(cls):
""" Make class generated by namedtuple picklable """
name = cls.__name__
fields = cls._fields
def __reduce__(self):
return (_restore, (name, fields, tuple(self)))
cls.__reduce__ = __reduce__
cls._is_namedtuple_ = True
return cls | [
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19,315 | apache/spark | python/pyspark/serializers.py | ArrowCollectSerializer.load_stream | def load_stream(self, stream):
"""
Load a stream of un-ordered Arrow RecordBatches, where the last iteration yields
a list of indices that can be used to put the RecordBatches in the correct order.
"""
# load the batches
for batch in self.serializer.load_stream(stream):
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19,316 | apache/spark | python/pyspark/serializers.py | ArrowStreamPandasSerializer._create_batch | def _create_batch(self, series):
"""
Create an Arrow record batch from the given pandas.Series or list of Series,
with optional type.
:param series: A single pandas.Series, list of Series, or list of (series, arrow_type)
:return: Arrow RecordBatch
"""
import pand... | python | def _create_batch(self, series):
"""
Create an Arrow record batch from the given pandas.Series or list of Series,
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:param series: A single pandas.Series, list of Series, or list of (series, arrow_type)
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19,317 | apache/spark | python/pyspark/serializers.py | ArrowStreamPandasSerializer.dump_stream | def dump_stream(self, iterator, stream):
"""
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a list of series accompanied by an optional pyarrow type to coerce the data to.
"""
batches = (self._create_batch(series) for series in iterator)
... | python | def dump_stream(self, iterator, stream):
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19,318 | 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_t... | python | def load_stream(self, stream):
"""
Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series.
"""
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19,319 | apache/spark | python/pyspark/serializers.py | ArrowStreamPandasUDFSerializer.dump_stream | def dump_stream(self, iterator, stream):
"""
Override because Pandas UDFs require a START_ARROW_STREAM before the Arrow stream is sent.
This should be sent after creating the first record batch so in case of an error, it can
be sent back to the JVM before the Arrow stream starts.
... | python | def dump_stream(self, iterator, stream):
"""
Override because Pandas UDFs require a START_ARROW_STREAM before the Arrow stream is sent.
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be sent back to the JVM before the Arrow stream starts.
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19,320 | apache/spark | python/pyspark/sql/streaming.py | DataStreamWriter.trigger | def trigger(self, processingTime=None, once=None, continuous=None):
"""Set the trigger for the stream query. If this is not set it will run the query as fast
as possible, which is equivalent to setting the trigger to ``processingTime='0 seconds'``.
.. note:: Evolving.
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19,321 | apache/spark | python/pyspark/sql/streaming.py | DataStreamWriter.foreach | def foreach(self, f):
"""
Sets the output of the streaming query to be processed using the provided writer ``f``.
This is often used to write the output of a streaming query to arbitrary storage systems.
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#. A **function** that t... | python | def foreach(self, f):
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19,322 | apache/spark | python/pyspark/cloudpickle.py | dumps | def dumps(obj, protocol=None):
"""Serialize obj as a string of bytes allocated in memory
protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
pickle.HIGHEST_PROTOCOL. This setting favors maximum communication speed
between processes running the same Python version.
Set protocol=p... | python | def dumps(obj, protocol=None):
"""Serialize obj as a string of bytes allocated in memory
protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
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19,323 | 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 ... | python | def _fill_function(*args):
"""Fills in the rest of function data into the skeleton function object
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func = args[0]
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19,324 | apache/spark | python/pyspark/cloudpickle.py | _is_dynamic | def _is_dynamic(module):
"""
Return True if the module is special module that cannot be imported by its
name.
"""
# Quick check: module that have __file__ attribute are not dynamic modules.
if hasattr(module, '__file__'):
return False
if hasattr(module, '__spec__'):
return m... | python | def _is_dynamic(module):
"""
Return True if the module is special module that cannot be imported by its
name.
"""
# Quick check: module that have __file__ attribute are not dynamic modules.
if hasattr(module, '__file__'):
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19,325 | apache/spark | python/pyspark/cloudpickle.py | CloudPickler.save_function | def save_function(self, obj, name=None):
""" Registered with the dispatch to handle all function types.
Determines what kind of function obj is (e.g. lambda, defined at
interactive prompt, etc) and handles the pickling appropriately.
"""
try:
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19,326 | apache/spark | python/pyspark/cloudpickle.py | CloudPickler.save_inst | def save_inst(self, obj):
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cls = obj.__class__
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f = self.dispatch.get(cls)
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f(self, obj) # Call unbound method with explicit self
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cls = obj.__class__
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f = self.dispatch.get(cls)
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19,327 | apache/spark | python/pyspark/ml/param/__init__.py | Param._copy_new_parent | def _copy_new_parent(self, parent):
"""Copy the current param to a new parent, must be a dummy param."""
if self.parent == "undefined":
param = copy.copy(self)
param.parent = parent.uid
return param
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raise ValueError("Cannot copy from non-dumm... | python | def _copy_new_parent(self, parent):
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19,328 | apache/spark | python/pyspark/ml/param/__init__.py | TypeConverters.toList | def toList(value):
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"""
Convert a value to a list, if possible.
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19,332 | apache/spark | python/pyspark/ml/param/__init__.py | TypeConverters.toVector | def toVector(value):
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19,334 | apache/spark | python/pyspark/ml/param/__init__.py | Params._copy_params | def _copy_params(self):
"""
Copy all params defined on the class to current object.
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src_params = list(filter(lambda nameAttr: isinstance(nameAttr[1], Param), src_name_attrs))
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"""
Copy all params defined on the class to current object.
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cls = type(self)
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19,335 | apache/spark | python/pyspark/ml/param/__init__.py | Params.explainParam | def explainParam(self, param):
"""
Explains a single param and returns its name, doc, and optional
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"""
param = self._resolveParam(param)
values = []
if self.isDefined(param):
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Explains a single param and returns its name, doc, and optional
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19,336 | apache/spark | python/pyspark/ml/param/__init__.py | Params.getParam | def getParam(self, paramName):
"""
Gets a param by its name.
"""
param = getattr(self, paramName)
if isinstance(param, Param):
return param
else:
raise ValueError("Cannot find param with name %s." % paramName) | python | def getParam(self, paramName):
"""
Gets a param by its name.
"""
param = getattr(self, paramName)
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return param
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19,337 | apache/spark | python/pyspark/ml/param/__init__.py | Params.isSet | def isSet(self, param):
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Checks whether a param is explicitly set by user.
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19,338 | apache/spark | python/pyspark/ml/param/__init__.py | Params.hasDefault | def hasDefault(self, param):
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Checks whether a param has a default value.
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19,339 | apache/spark | python/pyspark/ml/param/__init__.py | Params.getOrDefault | def getOrDefault(self, param):
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param = self._resolveParam(param)
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19,340 | apache/spark | python/pyspark/ml/param/__init__.py | Params.set | def set(self, param, value):
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19,343 | apache/spark | python/pyspark/ml/param/__init__.py | Params._set | def _set(self, **kwargs):
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19,344 | apache/spark | python/pyspark/ml/param/__init__.py | Params._setDefault | def _setDefault(self, **kwargs):
"""
Sets default params.
"""
for param, value in kwargs.items():
p = getattr(self, param)
if value is not None and not isinstance(value, JavaObject):
try:
value = p.typeConverter(value)
... | python | def _setDefault(self, **kwargs):
"""
Sets default params.
"""
for param, value in kwargs.items():
p = getattr(self, param)
if value is not None and not isinstance(value, JavaObject):
try:
value = p.typeConverter(value)
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19,345 | apache/spark | python/pyspark/ml/param/__init__.py | Params._copyValues | def _copyValues(self, to, extra=None):
"""
Copies param values from this instance to another instance for
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:param to: the target instance
:param extra: extra params to be copied
:return: the target instance with param values copied
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19,346 | apache/spark | python/pyspark/ml/common.py | _to_java_object_rdd | def _to_java_object_rdd(rdd):
""" Return an JavaRDD of Object by unpickling
It will convert each Python object into Java object by Pyrolite, whenever the
RDD is serialized in batch or not.
"""
rdd = rdd._reserialize(AutoBatchedSerializer(PickleSerializer()))
return rdd.ctx._jvm.org.apache.spark... | python | def _to_java_object_rdd(rdd):
""" Return an JavaRDD of Object by unpickling
It will convert each Python object into Java object by Pyrolite, whenever the
RDD is serialized in batch or not.
"""
rdd = rdd._reserialize(AutoBatchedSerializer(PickleSerializer()))
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19,347 | apache/spark | python/pyspark/broadcast.py | Broadcast.value | def value(self):
""" Return the broadcasted value
"""
if not hasattr(self, "_value") and self._path is not None:
# we only need to decrypt it here when encryption is enabled and
# if its on the driver, since executor decryption is handled already
if self._sc i... | python | def value(self):
""" Return the broadcasted value
"""
if not hasattr(self, "_value") and self._path is not None:
# we only need to decrypt it here when encryption is enabled and
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19,348 | apache/spark | python/pyspark/broadcast.py | Broadcast.unpersist | def unpersist(self, blocking=False):
"""
Delete cached copies of this broadcast on the executors. If the
broadcast is used after this is called, it will need to be
re-sent to each executor.
:param blocking: Whether to block until unpersisting has completed
"""
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Delete cached copies of this broadcast on the executors. If the
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:param blocking: Whether to block until unpersisting has completed
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19,349 | apache/spark | python/pyspark/broadcast.py | Broadcast.destroy | def destroy(self, blocking=False):
"""
Destroy all data and metadata related to this broadcast variable.
Use this with caution; once a broadcast variable has been destroyed,
it cannot be used again.
.. versionchanged:: 3.0.0
Added optional argument `blocking` to speci... | python | def destroy(self, blocking=False):
"""
Destroy all data and metadata related to this broadcast variable.
Use this with caution; once a broadcast variable has been destroyed,
it cannot be used again.
.. versionchanged:: 3.0.0
Added optional argument `blocking` to speci... | [
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19,350 | apache/spark | python/pyspark/sql/udf.py | UserDefinedFunction._wrapped | def _wrapped(self):
"""
Wrap this udf with a function and attach docstring from func
"""
# It is possible for a callable instance without __name__ attribute or/and
# __module__ attribute to be wrapped here. For example, functools.partial. In this case,
# we should avoid ... | python | def _wrapped(self):
"""
Wrap this udf with a function and attach docstring from func
"""
# It is possible for a callable instance without __name__ attribute or/and
# __module__ attribute to be wrapped here. For example, functools.partial. In this case,
# we should avoid ... | [
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19,351 | apache/spark | python/pyspark/sql/udf.py | UDFRegistration.registerJavaFunction | def registerJavaFunction(self, name, javaClassName, returnType=None):
"""Register a Java user-defined function as a SQL function.
In addition to a name and the function itself, the return type can be optionally specified.
When the return type is not specified we would infer it via reflection.
... | python | def registerJavaFunction(self, name, javaClassName, returnType=None):
"""Register a Java user-defined function as a SQL function.
In addition to a name and the function itself, the return type can be optionally specified.
When the return type is not specified we would infer it via reflection.
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19,352 | apache/spark | python/pyspark/sql/udf.py | UDFRegistration.registerJavaUDAF | def registerJavaUDAF(self, name, javaClassName):
"""Register a Java user-defined aggregate function as a SQL function.
:param name: name of the user-defined aggregate function
:param javaClassName: fully qualified name of java class
>>> spark.udf.registerJavaUDAF("javaUDAF", "test.org.... | python | def registerJavaUDAF(self, name, javaClassName):
"""Register a Java user-defined aggregate function as a SQL function.
:param name: name of the user-defined aggregate function
:param javaClassName: fully qualified name of java class
>>> spark.udf.registerJavaUDAF("javaUDAF", "test.org.... | [
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19,353 | apache/spark | python/pyspark/streaming/context.py | StreamingContext.getOrCreate | def getOrCreate(cls, checkpointPath, setupFunc):
"""
Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be
recreated from the checkpoint data. If the data does no... | python | def getOrCreate(cls, checkpointPath, setupFunc):
"""
Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
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19,354 | apache/spark | python/pyspark/streaming/context.py | StreamingContext.awaitTermination | def awaitTermination(self, timeout=None):
"""
Wait for the execution to stop.
@param timeout: time to wait in seconds
"""
if timeout is None:
self._jssc.awaitTermination()
else:
self._jssc.awaitTerminationOrTimeout(int(timeout * 1000)) | python | def awaitTermination(self, timeout=None):
"""
Wait for the execution to stop.
@param timeout: time to wait in seconds
"""
if timeout is None:
self._jssc.awaitTermination()
else:
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19,355 | apache/spark | python/pyspark/streaming/context.py | StreamingContext.stop | def stop(self, stopSparkContext=True, stopGraceFully=False):
"""
Stop the execution of the streams, with option of ensuring all
received data has been processed.
@param stopSparkContext: Stop the associated SparkContext or not
@param stopGracefully: Stop gracefully by waiting fo... | python | def stop(self, stopSparkContext=True, stopGraceFully=False):
"""
Stop the execution of the streams, with option of ensuring all
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@param stopSparkContext: Stop the associated SparkContext or not
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19,356 | apache/spark | python/pyspark/streaming/context.py | StreamingContext.textFileStream | def textFileStream(self, directory):
"""
Create an input stream that monitors a Hadoop-compatible file system
for new files and reads them as text files. Files must be wrriten to the
monitored directory by "moving" them from another location within the same
file system. File name... | python | def textFileStream(self, directory):
"""
Create an input stream that monitors a Hadoop-compatible file system
for new files and reads them as text files. Files must be wrriten to the
monitored directory by "moving" them from another location within the same
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19,357 | apache/spark | python/pyspark/streaming/context.py | StreamingContext.binaryRecordsStream | def binaryRecordsStream(self, directory, recordLength):
"""
Create an input stream that monitors a Hadoop-compatible file system
for new files and reads them as flat binary files with records of
fixed length. Files must be written to the monitored directory by "moving"
them from ... | python | def binaryRecordsStream(self, directory, recordLength):
"""
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19,358 | 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.
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19,360 | apache/spark | python/pyspark/streaming/context.py | StreamingContext.union | def union(self, *dstreams):
"""
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"""
if not dstreams:
raise ValueError("should have at least one DStream to union")
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"""
Create a unified DStream from multiple DStreams of the same
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if not dstreams:
raise ValueError("should have at least one DStream to union")
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19,361 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/modeling_gpt2.py | GPT2Config.from_json_file | def from_json_file(cls, json_file):
"""Constructs a `GPT2Config` from a json file of parameters."""
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return cls.from_dict(json.loads(text)) | python | def from_json_file(cls, json_file):
"""Constructs a `GPT2Config` from a json file of parameters."""
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return cls.from_dict(json.loads(text)) | [
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19,362 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/modeling_gpt2.py | GPT2Config.to_json_file | def to_json_file(self, json_file_path):
""" Save this instance to a json file."""
with open(json_file_path, "w", encoding='utf-8') as writer:
writer.write(self.to_json_string()) | python | def to_json_file(self, json_file_path):
""" Save this instance to a json file."""
with open(json_file_path, "w", encoding='utf-8') as writer:
writer.write(self.to_json_string()) | [
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19,363 | huggingface/pytorch-pretrained-BERT | examples/extract_features.py | convert_examples_to_features | def convert_examples_to_features(examples, seq_length, tokenizer):
"""Loads a data file into a list of `InputFeature`s."""
features = []
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
toke... | python | def convert_examples_to_features(examples, seq_length, tokenizer):
"""Loads a data file into a list of `InputFeature`s."""
features = []
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
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19,364 | huggingface/pytorch-pretrained-BERT | examples/extract_features.py | read_examples | def read_examples(input_file):
"""Read a list of `InputExample`s from an input file."""
examples = []
unique_id = 0
with open(input_file, "r", encoding='utf-8') as reader:
while True:
line = reader.readline()
if not line:
break
line = line.stri... | python | def read_examples(input_file):
"""Read a list of `InputExample`s from an input file."""
examples = []
unique_id = 0
with open(input_file, "r", encoding='utf-8') as reader:
while True:
line = reader.readline()
if not line:
break
line = line.stri... | [
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19,365 | huggingface/pytorch-pretrained-BERT | examples/run_squad.py | read_squad_examples | def read_squad_examples(input_file, is_training, version_2_with_negative):
"""Read a SQuAD json file into a list of SquadExample."""
with open(input_file, "r", encoding='utf-8') as reader:
input_data = json.load(reader)["data"]
def is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or ... | python | def read_squad_examples(input_file, is_training, version_2_with_negative):
"""Read a SQuAD json file into a list of SquadExample."""
with open(input_file, "r", encoding='utf-8') as reader:
input_data = json.load(reader)["data"]
def is_whitespace(c):
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19,366 | huggingface/pytorch-pretrained-BERT | examples/run_squad.py | _improve_answer_span | def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
# The SQuAD annotations are character based. We first project them to
# whitespace-tokenized words. But then after... | python | def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
# The SQuAD annotations are character based. We first project them to
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19,367 | huggingface/pytorch-pretrained-BERT | examples/run_squad.py | _check_is_max_context | def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# Because of the sliding window approach taken to scoring documents, a single
# token can appear in multiple documents. E.g.
# Doc: the man went to the store and bought a ga... | python | def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# Because of the sliding window approach taken to scoring documents, a single
# token can appear in multiple documents. E.g.
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19,368 | huggingface/pytorch-pretrained-BERT | examples/run_squad.py | _get_best_indexes | def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and... | python | def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
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19,369 | huggingface/pytorch-pretrained-BERT | examples/run_squad.py | _compute_softmax | def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
... | python | def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
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19,370 | huggingface/pytorch-pretrained-BERT | examples/run_classifier.py | DataProcessor._read_tsv | def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r", encoding="utf-8") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
if sys.version_info[0]... | python | def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r", encoding="utf-8") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
if sys.version_info[0]... | [
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19,371 | huggingface/pytorch-pretrained-BERT | examples/run_classifier.py | MrpcProcessor._create_examples | def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[3]
text_b = line[4]... | python | def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
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19,372 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/modeling_openai.py | OpenAIGPTConfig.from_dict | def from_dict(cls, json_object):
"""Constructs a `OpenAIGPTConfig` from a Python dictionary of parameters."""
config = OpenAIGPTConfig(vocab_size_or_config_json_file=-1)
for key, value in json_object.items():
config.__dict__[key] = value
return config | python | def from_dict(cls, json_object):
"""Constructs a `OpenAIGPTConfig` from a Python dictionary of parameters."""
config = OpenAIGPTConfig(vocab_size_or_config_json_file=-1)
for key, value in json_object.items():
config.__dict__[key] = value
return config | [
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19,373 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/modeling_openai.py | OpenAIGPTModel.set_num_special_tokens | def set_num_special_tokens(self, num_special_tokens):
" Update input embeddings with new embedding matrice if needed "
if self.config.n_special == num_special_tokens:
return
# Update config
self.config.n_special = num_special_tokens
# Build new embeddings and initiali... | python | def set_num_special_tokens(self, num_special_tokens):
" Update input embeddings with new embedding matrice if needed "
if self.config.n_special == num_special_tokens:
return
# Update config
self.config.n_special = num_special_tokens
# Build new embeddings and initiali... | [
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19,374 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/modeling_openai.py | OpenAIGPTLMHeadModel.set_num_special_tokens | def set_num_special_tokens(self, num_special_tokens):
""" Update input and output embeddings with new embedding matrice
Make sure we are sharing the embeddings
"""
self.transformer.set_num_special_tokens(num_special_tokens)
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""" Update input and output embeddings with new embedding matrice
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"""
self.transformer.set_num_special_tokens(num_special_tokens)
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19,375 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/tokenization.py | BertTokenizer.convert_tokens_to_ids | def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
ids.append(self.vocab[token])
if len(ids) > self.max_len:
logger.warning(
"Token indices sequence length is longer ... | python | def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
ids.append(self.vocab[token])
if len(ids) > self.max_len:
logger.warning(
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19,376 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/tokenization.py | BertTokenizer.convert_ids_to_tokens | def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens | python | def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens | [
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19,377 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/tokenization.py | BertTokenizer.save_vocabulary | def save_vocabulary(self, vocab_path):
"""Save the tokenizer vocabulary to a directory or file."""
index = 0
if os.path.isdir(vocab_path):
vocab_file = os.path.join(vocab_path, VOCAB_NAME)
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_in... | python | def save_vocabulary(self, vocab_path):
"""Save the tokenizer vocabulary to a directory or file."""
index = 0
if os.path.isdir(vocab_path):
vocab_file = os.path.join(vocab_path, VOCAB_NAME)
with open(vocab_file, "w", encoding="utf-8") as writer:
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19,378 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/tokenization.py | BertTokenizer.from_pretrained | def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
"""
Instantiate a PreTrainedBertModel from a pre-trained model file.
Download and cache the pre-trained model file if needed.
"""
if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHI... | python | def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
"""
Instantiate a PreTrainedBertModel from a pre-trained model file.
Download and cache the pre-trained model file if needed.
"""
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19,379 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/tokenization.py | BasicTokenizer._run_strip_accents | def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
... | python | def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
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19,380 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/tokenization.py | BasicTokenizer._tokenize_chinese_chars | def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
... | python | def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
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19,381 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/tokenization.py | BasicTokenizer._is_chinese_char | def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is ... | python | def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
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19,382 | huggingface/pytorch-pretrained-BERT | examples/lm_finetuning/simple_lm_finetuning.py | BERTDataset.get_next_line | def get_next_line(self):
""" Gets next line of random_file and starts over when reaching end of file"""
try:
line = next(self.random_file).strip()
#keep track of which document we are currently looking at to later avoid having the same doc as t1
if line == "":
... | python | def get_next_line(self):
""" Gets next line of random_file and starts over when reaching end of file"""
try:
line = next(self.random_file).strip()
#keep track of which document we are currently looking at to later avoid having the same doc as t1
if line == "":
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19,383 | 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 = []
... | 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 = []
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19,384 | huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/modeling_transfo_xl.py | build_tf_to_pytorch_map | def build_tf_to_pytorch_map(model, config):
""" A map of modules from TF to PyTorch.
This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible.
"""
tf_to_pt_map = {}
if hasattr(model, 'transformer'):
# We are loading in a TransfoXLLMHeadModel... | python | def build_tf_to_pytorch_map(model, config):
""" A map of modules from TF to PyTorch.
This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible.
"""
tf_to_pt_map = {}
if hasattr(model, 'transformer'):
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19,385 | pandas-dev/pandas | pandas/tseries/frequencies.py | to_offset | def to_offset(freq):
"""
Return DateOffset object from string or tuple representation
or datetime.timedelta object
Parameters
----------
freq : str, tuple, datetime.timedelta, DateOffset or None
Returns
-------
DateOffset
None if freq is None.
Raises
------
Val... | python | def to_offset(freq):
"""
Return DateOffset object from string or tuple representation
or datetime.timedelta object
Parameters
----------
freq : str, tuple, datetime.timedelta, DateOffset or None
Returns
-------
DateOffset
None if freq is None.
Raises
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19,386 | pandas-dev/pandas | pandas/tseries/frequencies.py | get_offset | def get_offset(name):
"""
Return DateOffset object associated with rule name
Examples
--------
get_offset('EOM') --> BMonthEnd(1)
"""
if name not in libfreqs._dont_uppercase:
name = name.upper()
name = libfreqs._lite_rule_alias.get(name, name)
name = libfreqs._lite_r... | python | def get_offset(name):
"""
Return DateOffset object associated with rule name
Examples
--------
get_offset('EOM') --> BMonthEnd(1)
"""
if name not in libfreqs._dont_uppercase:
name = name.upper()
name = libfreqs._lite_rule_alias.get(name, name)
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Examples
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get_offset('EOM') --> BMonthEnd(1) | [
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] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/tseries/frequencies.py#L167-L195 |
19,387 | pandas-dev/pandas | pandas/tseries/frequencies.py | infer_freq | def infer_freq(index, warn=True):
"""
Infer the most likely frequency given the input index. If the frequency is
uncertain, a warning will be printed.
Parameters
----------
index : DatetimeIndex or TimedeltaIndex
if passed a Series will use the values of the series (NOT THE INDEX)
war... | python | def infer_freq(index, warn=True):
"""
Infer the most likely frequency given the input index. If the frequency is
uncertain, a warning will be printed.
Parameters
----------
index : DatetimeIndex or TimedeltaIndex
if passed a Series will use the values of the series (NOT THE INDEX)
war... | [
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19,388 | pandas-dev/pandas | pandas/tseries/frequencies.py | _FrequencyInferer.get_freq | def get_freq(self):
"""
Find the appropriate frequency string to describe the inferred
frequency of self.values
Returns
-------
str or None
"""
if not self.is_monotonic or not self.index._is_unique:
return None
delta = self.deltas[0]
... | python | def get_freq(self):
"""
Find the appropriate frequency string to describe the inferred
frequency of self.values
Returns
-------
str or None
"""
if not self.is_monotonic or not self.index._is_unique:
return None
delta = self.deltas[0]
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19,389 | pandas-dev/pandas | pandas/compat/pickle_compat.py | load | def load(fh, encoding=None, is_verbose=False):
"""load a pickle, with a provided encoding
if compat is True:
fake the old class hierarchy
if it works, then return the new type objects
Parameters
----------
fh : a filelike object
encoding : an optional encoding
is_verbose : sh... | python | def load(fh, encoding=None, is_verbose=False):
"""load a pickle, with a provided encoding
if compat is True:
fake the old class hierarchy
if it works, then return the new type objects
Parameters
----------
fh : a filelike object
encoding : an optional encoding
is_verbose : sh... | [
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if compat is True:
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if it works, then return the new type objects
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fh : a filelike object
encoding : an optional encoding
is_verbose : show exception output | [
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19,390 | pandas-dev/pandas | pandas/core/indexes/base.py | ensure_index_from_sequences | def ensure_index_from_sequences(sequences, names=None):
"""
Construct an index from sequences of data.
A single sequence returns an Index. Many sequences returns a
MultiIndex.
Parameters
----------
sequences : sequence of sequences
names : sequence of str
Returns
-------
i... | python | def ensure_index_from_sequences(sequences, names=None):
"""
Construct an index from sequences of data.
A single sequence returns an Index. Many sequences returns a
MultiIndex.
Parameters
----------
sequences : sequence of sequences
names : sequence of str
Returns
-------
i... | [
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A single sequence returns an Index. Many sequences returns a
MultiIndex.
Parameters
----------
sequences : sequence of sequences
names : sequence of str
Returns
-------
index : Index or MultiIndex
Examples
--------
>>> ensure... | [
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19,391 | pandas-dev/pandas | pandas/core/indexes/base.py | ensure_index | def ensure_index(index_like, copy=False):
"""
Ensure that we have an index from some index-like object.
Parameters
----------
index : sequence
An Index or other sequence
copy : bool
Returns
-------
index : Index or MultiIndex
Examples
--------
>>> ensure_index(... | python | def ensure_index(index_like, copy=False):
"""
Ensure that we have an index from some index-like object.
Parameters
----------
index : sequence
An Index or other sequence
copy : bool
Returns
-------
index : Index or MultiIndex
Examples
--------
>>> ensure_index(... | [
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index : sequence
An Index or other sequence
copy : bool
Returns
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index : Index or MultiIndex
Examples
--------
>>> ensure_index(['a', 'b'])
Index(['a', 'b'], dtype='object')
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19,392 | pandas-dev/pandas | pandas/core/indexes/base.py | Index._simple_new | def _simple_new(cls, values, name=None, dtype=None, **kwargs):
"""
We require that we have a dtype compat for the values. If we are passed
a non-dtype compat, then coerce using the constructor.
Must be careful not to recurse.
"""
if not hasattr(values, 'dtype'):
... | python | def _simple_new(cls, values, name=None, dtype=None, **kwargs):
"""
We require that we have a dtype compat for the values. If we are passed
a non-dtype compat, then coerce using the constructor.
Must be careful not to recurse.
"""
if not hasattr(values, 'dtype'):
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19,393 | pandas-dev/pandas | pandas/core/indexes/base.py | Index._shallow_copy_with_infer | def _shallow_copy_with_infer(self, values, **kwargs):
"""
Create a new Index inferring the class with passed value, don't copy
the data, use the same object attributes with passed in attributes
taking precedence.
*this is an internal non-public method*
Parameters
... | python | def _shallow_copy_with_infer(self, values, **kwargs):
"""
Create a new Index inferring the class with passed value, don't copy
the data, use the same object attributes with passed in attributes
taking precedence.
*this is an internal non-public method*
Parameters
... | [
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19,394 | pandas-dev/pandas | pandas/core/indexes/base.py | Index.is_ | def is_(self, other):
"""
More flexible, faster check like ``is`` but that works through views.
Note: this is *not* the same as ``Index.identical()``, which checks
that metadata is also the same.
Parameters
----------
other : object
other object to c... | python | def is_(self, other):
"""
More flexible, faster check like ``is`` but that works through views.
Note: this is *not* the same as ``Index.identical()``, which checks
that metadata is also the same.
Parameters
----------
other : object
other object to c... | [
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Note: this is *not* the same as ``Index.identical()``, which checks
that metadata is also the same.
Parameters
----------
other : object
other object to compare against.
Returns
-... | [
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19,395 | pandas-dev/pandas | pandas/core/indexes/base.py | Index._assert_take_fillable | def _assert_take_fillable(self, values, indices, allow_fill=True,
fill_value=None, na_value=np.nan):
"""
Internal method to handle NA filling of take.
"""
indices = ensure_platform_int(indices)
# only fill if we are passing a non-None fill_value
... | python | def _assert_take_fillable(self, values, indices, allow_fill=True,
fill_value=None, na_value=np.nan):
"""
Internal method to handle NA filling of take.
"""
indices = ensure_platform_int(indices)
# only fill if we are passing a non-None fill_value
... | [
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19,396 | pandas-dev/pandas | pandas/core/indexes/base.py | Index._format_data | def _format_data(self, name=None):
"""
Return the formatted data as a unicode string.
"""
# do we want to justify (only do so for non-objects)
is_justify = not (self.inferred_type in ('string', 'unicode') or
(self.inferred_type == 'categorical' and
... | python | def _format_data(self, name=None):
"""
Return the formatted data as a unicode string.
"""
# do we want to justify (only do so for non-objects)
is_justify = not (self.inferred_type in ('string', 'unicode') or
(self.inferred_type == 'categorical' and
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19,397 | pandas-dev/pandas | pandas/core/indexes/base.py | Index.format | def format(self, name=False, formatter=None, **kwargs):
"""
Render a string representation of the Index.
"""
header = []
if name:
header.append(pprint_thing(self.name,
escape_chars=('\t', '\r', '\n')) if
... | python | def format(self, name=False, formatter=None, **kwargs):
"""
Render a string representation of the Index.
"""
header = []
if name:
header.append(pprint_thing(self.name,
escape_chars=('\t', '\r', '\n')) if
... | [
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19,398 | 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
----------
slicer : int, array-like
An indexer into `self` that specifies which values
are used in the formatting process.
kwargs :... | python | def to_native_types(self, slicer=None, **kwargs):
"""
Format specified values of `self` and return them.
Parameters
----------
slicer : int, array-like
An indexer into `self` that specifies which values
are used in the formatting process.
kwargs :... | [
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19,399 | pandas-dev/pandas | pandas/core/indexes/base.py | Index._format_native_types | def _format_native_types(self, na_rep='', quoting=None, **kwargs):
"""
Actually format specific types of the index.
"""
mask = isna(self)
if not self.is_object() and not quoting:
values = np.asarray(self).astype(str)
else:
values = np.array(self, d... | python | def _format_native_types(self, na_rep='', quoting=None, **kwargs):
"""
Actually format specific types of the index.
"""
mask = isna(self)
if not self.is_object() and not quoting:
values = np.asarray(self).astype(str)
else:
values = np.array(self, d... | [
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