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# The ASF licenses this file to You under the Apache License, Version 2.0
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# http://www.apache.org/licenses/LICENSE-2.0
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import json
import sys
import os
import time
import uuid
import warnings
if sys.version > '3':
basestring = str
unicode = str
long = int
from pyspark import SparkContext, since
from pyspark.ml.common import inherit_doc
from pyspark.sql import SparkSession
from pyspark.util import VersionUtils
def _jvm():
"""
Returns the JVM view associated with SparkContext. Must be called
after SparkContext is initialized.
"""
jvm = SparkContext._jvm
if jvm:
return jvm
else:
raise AttributeError("Cannot load _jvm from SparkContext. Is SparkContext initialized?")
[docs]class Identifiable(object):
"""
Object with a unique ID.
"""
def __init__(self):
#: A unique id for the object.
self.uid = self._randomUID()
def __repr__(self):
return self.uid
@classmethod
def _randomUID(cls):
"""
Generate a unique unicode id for the object. The default implementation
concatenates the class name, "_", and 12 random hex chars.
"""
return unicode(cls.__name__ + "_" + uuid.uuid4().hex[-12:])
[docs]@inherit_doc
class BaseReadWrite(object):
"""
Base class for MLWriter and MLReader. Stores information about the SparkContext
and SparkSession.
.. versionadded:: 2.3.0
"""
def __init__(self):
self._sparkSession = None
[docs] def session(self, sparkSession):
"""
Sets the Spark Session to use for saving/loading.
"""
self._sparkSession = sparkSession
return self
@property
def sparkSession(self):
"""
Returns the user-specified Spark Session or the default.
"""
if self._sparkSession is None:
self._sparkSession = SparkSession.builder.getOrCreate()
return self._sparkSession
@property
def sc(self):
"""
Returns the underlying `SparkContext`.
"""
return self.sparkSession.sparkContext
[docs]@inherit_doc
class MLWriter(BaseReadWrite):
"""
Utility class that can save ML instances.
.. versionadded:: 2.0.0
"""
def __init__(self):
super(MLWriter, self).__init__()
self.shouldOverwrite = False
def _handleOverwrite(self, path):
from pyspark.ml.wrapper import JavaWrapper
_java_obj = JavaWrapper._new_java_obj("org.apache.spark.ml.util.FileSystemOverwrite")
wrapper = JavaWrapper(_java_obj)
wrapper._call_java("handleOverwrite", path, True, self.sparkSession._jsparkSession)
[docs] def save(self, path):
"""Save the ML instance to the input path."""
if self.shouldOverwrite:
self._handleOverwrite(path)
self.saveImpl(path)
[docs] def saveImpl(self, path):
"""
save() handles overwriting and then calls this method. Subclasses should override this
method to implement the actual saving of the instance.
"""
raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
[docs] def overwrite(self):
"""Overwrites if the output path already exists."""
self.shouldOverwrite = True
return self
[docs]@inherit_doc
class GeneralMLWriter(MLWriter):
"""
Utility class that can save ML instances in different formats.
.. versionadded:: 2.4.0
"""
[docs]@inherit_doc
class JavaMLWriter(MLWriter):
"""
(Private) Specialization of :py:class:`MLWriter` for :py:class:`JavaParams` types
"""
def __init__(self, instance):
super(JavaMLWriter, self).__init__()
_java_obj = instance._to_java()
self._jwrite = _java_obj.write()
[docs] def save(self, path):
"""Save the ML instance to the input path."""
if not isinstance(path, basestring):
raise TypeError("path should be a basestring, got type %s" % type(path))
self._jwrite.save(path)
[docs] def overwrite(self):
"""Overwrites if the output path already exists."""
self._jwrite.overwrite()
return self
[docs] def option(self, key, value):
self._jwrite.option(key, value)
return self
[docs] def session(self, sparkSession):
"""Sets the Spark Session to use for saving."""
self._jwrite.session(sparkSession._jsparkSession)
return self
[docs]@inherit_doc
class GeneralJavaMLWriter(JavaMLWriter):
"""
(Private) Specialization of :py:class:`GeneralMLWriter` for :py:class:`JavaParams` types
"""
def __init__(self, instance):
super(GeneralJavaMLWriter, self).__init__(instance)
[docs]@inherit_doc
class MLWritable(object):
"""
Mixin for ML instances that provide :py:class:`MLWriter`.
.. versionadded:: 2.0.0
"""
[docs] def write(self):
"""Returns an MLWriter instance for this ML instance."""
raise NotImplementedError("MLWritable is not yet implemented for type: %r" % type(self))
[docs] def save(self, path):
"""Save this ML instance to the given path, a shortcut of 'write().save(path)'."""
self.write().save(path)
[docs]@inherit_doc
class JavaMLWritable(MLWritable):
"""
(Private) Mixin for ML instances that provide :py:class:`JavaMLWriter`.
"""
[docs] def write(self):
"""Returns an MLWriter instance for this ML instance."""
return JavaMLWriter(self)
[docs]@inherit_doc
class GeneralJavaMLWritable(JavaMLWritable):
"""
(Private) Mixin for ML instances that provide :py:class:`GeneralJavaMLWriter`.
"""
[docs] def write(self):
"""Returns an GeneralMLWriter instance for this ML instance."""
return GeneralJavaMLWriter(self)
[docs]@inherit_doc
class MLReader(BaseReadWrite):
"""
Utility class that can load ML instances.
.. versionadded:: 2.0.0
"""
def __init__(self):
super(MLReader, self).__init__()
[docs] def load(self, path):
"""Load the ML instance from the input path."""
raise NotImplementedError("MLReader is not yet implemented for type: %s" % type(self))
[docs]@inherit_doc
class JavaMLReader(MLReader):
"""
(Private) Specialization of :py:class:`MLReader` for :py:class:`JavaParams` types
"""
def __init__(self, clazz):
super(JavaMLReader, self).__init__()
self._clazz = clazz
self._jread = self._load_java_obj(clazz).read()
[docs] def load(self, path):
"""Load the ML instance from the input path."""
if not isinstance(path, basestring):
raise TypeError("path should be a basestring, got type %s" % type(path))
java_obj = self._jread.load(path)
if not hasattr(self._clazz, "_from_java"):
raise NotImplementedError("This Java ML type cannot be loaded into Python currently: %r"
% self._clazz)
return self._clazz._from_java(java_obj)
[docs] def session(self, sparkSession):
"""Sets the Spark Session to use for loading."""
self._jread.session(sparkSession._jsparkSession)
return self
@classmethod
def _java_loader_class(cls, clazz):
"""
Returns the full class name of the Java ML instance. The default
implementation replaces "pyspark" by "org.apache.spark" in
the Python full class name.
"""
java_package = clazz.__module__.replace("pyspark", "org.apache.spark")
if clazz.__name__ in ("Pipeline", "PipelineModel"):
# Remove the last package name "pipeline" for Pipeline and PipelineModel.
java_package = ".".join(java_package.split(".")[0:-1])
return java_package + "." + clazz.__name__
@classmethod
def _load_java_obj(cls, clazz):
"""Load the peer Java object of the ML instance."""
java_class = cls._java_loader_class(clazz)
java_obj = _jvm()
for name in java_class.split("."):
java_obj = getattr(java_obj, name)
return java_obj
[docs]@inherit_doc
class MLReadable(object):
"""
Mixin for instances that provide :py:class:`MLReader`.
.. versionadded:: 2.0.0
"""
[docs] @classmethod
def read(cls):
"""Returns an MLReader instance for this class."""
raise NotImplementedError("MLReadable.read() not implemented for type: %r" % cls)
[docs] @classmethod
def load(cls, path):
"""Reads an ML instance from the input path, a shortcut of `read().load(path)`."""
return cls.read().load(path)
[docs]@inherit_doc
class JavaMLReadable(MLReadable):
"""
(Private) Mixin for instances that provide JavaMLReader.
"""
[docs] @classmethod
def read(cls):
"""Returns an MLReader instance for this class."""
return JavaMLReader(cls)
[docs]@inherit_doc
class DefaultParamsWritable(MLWritable):
"""
Helper trait for making simple :py:class:`Params` types writable. If a :py:class:`Params`
class stores all data as :py:class:`Param` values, then extending this trait will provide
a default implementation of writing saved instances of the class.
This only handles simple :py:class:`Param` types; e.g., it will not handle
:py:class:`Dataset`. See :py:class:`DefaultParamsReadable`, the counterpart to this trait.
.. versionadded:: 2.3.0
"""
[docs] def write(self):
"""Returns a DefaultParamsWriter instance for this class."""
from pyspark.ml.param import Params
if isinstance(self, Params):
return DefaultParamsWriter(self)
else:
raise TypeError("Cannot use DefautParamsWritable with type %s because it does not " +
" extend Params.", type(self))
[docs]@inherit_doc
class DefaultParamsWriter(MLWriter):
"""
Specialization of :py:class:`MLWriter` for :py:class:`Params` types
Class for writing Estimators and Transformers whose parameters are JSON-serializable.
.. versionadded:: 2.3.0
"""
def __init__(self, instance):
super(DefaultParamsWriter, self).__init__()
self.instance = instance
[docs] def saveImpl(self, path):
DefaultParamsWriter.saveMetadata(self.instance, path, self.sc)
@staticmethod
def _get_metadata_to_save(instance, sc, extraMetadata=None, paramMap=None):
"""
Helper for :py:meth:`DefaultParamsWriter.saveMetadata` which extracts the JSON to save.
This is useful for ensemble models which need to save metadata for many sub-models.
.. note:: :py:meth:`DefaultParamsWriter.saveMetadata` for details on what this includes.
"""
uid = instance.uid
cls = instance.__module__ + '.' + instance.__class__.__name__
# User-supplied param values
params = instance._paramMap
jsonParams = {}
if paramMap is not None:
jsonParams = paramMap
else:
for p in params:
jsonParams[p.name] = params[p]
# Default param values
jsonDefaultParams = {}
for p in instance._defaultParamMap:
jsonDefaultParams[p.name] = instance._defaultParamMap[p]
basicMetadata = {"class": cls, "timestamp": long(round(time.time() * 1000)),
"sparkVersion": sc.version, "uid": uid, "paramMap": jsonParams,
"defaultParamMap": jsonDefaultParams}
if extraMetadata is not None:
basicMetadata.update(extraMetadata)
return json.dumps(basicMetadata, separators=[',', ':'])
[docs]@inherit_doc
class DefaultParamsReadable(MLReadable):
"""
Helper trait for making simple :py:class:`Params` types readable.
If a :py:class:`Params` class stores all data as :py:class:`Param` values,
then extending this trait will provide a default implementation of reading saved
instances of the class. This only handles simple :py:class:`Param` types;
e.g., it will not handle :py:class:`Dataset`. See :py:class:`DefaultParamsWritable`,
the counterpart to this trait.
.. versionadded:: 2.3.0
"""
[docs] @classmethod
def read(cls):
"""Returns a DefaultParamsReader instance for this class."""
return DefaultParamsReader(cls)
[docs]@inherit_doc
class DefaultParamsReader(MLReader):
"""
Specialization of :py:class:`MLReader` for :py:class:`Params` types
Default :py:class:`MLReader` implementation for transformers and estimators that
contain basic (json-serializable) params and no data. This will not handle
more complex params or types with data (e.g., models with coefficients).
.. versionadded:: 2.3.0
"""
def __init__(self, cls):
super(DefaultParamsReader, self).__init__()
self.cls = cls
@staticmethod
def __get_class(clazz):
"""
Loads Python class from its name.
"""
parts = clazz.split('.')
module = ".".join(parts[:-1])
m = __import__(module)
for comp in parts[1:]:
m = getattr(m, comp)
return m
[docs] def load(self, path):
metadata = DefaultParamsReader.loadMetadata(path, self.sc)
py_type = DefaultParamsReader.__get_class(metadata['class'])
instance = py_type()
instance._resetUid(metadata['uid'])
DefaultParamsReader.getAndSetParams(instance, metadata)
return instance
@staticmethod
def _parseMetaData(metadataStr, expectedClassName=""):
"""
Parse metadata JSON string produced by :py:meth`DefaultParamsWriter._get_metadata_to_save`.
This is a helper function for :py:meth:`DefaultParamsReader.loadMetadata`.
:param metadataStr: JSON string of metadata
:param expectedClassName: If non empty, this is checked against the loaded metadata.
"""
metadata = json.loads(metadataStr)
className = metadata['class']
if len(expectedClassName) > 0:
assert className == expectedClassName, "Error loading metadata: Expected " + \
"class name {} but found class name {}".format(expectedClassName, className)
return metadata
[docs] @staticmethod
def getAndSetParams(instance, metadata):
"""
Extract Params from metadata, and set them in the instance.
"""
# Set user-supplied param values
for paramName in metadata['paramMap']:
param = instance.getParam(paramName)
paramValue = metadata['paramMap'][paramName]
instance.set(param, paramValue)
# Set default param values
majorAndMinorVersions = VersionUtils.majorMinorVersion(metadata['sparkVersion'])
major = majorAndMinorVersions[0]
minor = majorAndMinorVersions[1]
# For metadata file prior to Spark 2.4, there is no default section.
if major > 2 or (major == 2 and minor >= 4):
assert 'defaultParamMap' in metadata, "Error loading metadata: Expected " + \
"`defaultParamMap` section not found"
for paramName in metadata['defaultParamMap']:
paramValue = metadata['defaultParamMap'][paramName]
instance._setDefault(**{paramName: paramValue})
[docs] @staticmethod
def loadParamsInstance(path, sc):
"""
Load a :py:class:`Params` instance from the given path, and return it.
This assumes the instance inherits from :py:class:`MLReadable`.
"""
metadata = DefaultParamsReader.loadMetadata(path, sc)
pythonClassName = metadata['class'].replace("org.apache.spark", "pyspark")
py_type = DefaultParamsReader.__get_class(pythonClassName)
instance = py_type.load(path)
return instance
[docs]@inherit_doc
class HasTrainingSummary(object):
"""
Base class for models that provides Training summary.
.. versionadded:: 3.0.0
"""
@property
@since("2.1.0")
def hasSummary(self):
"""
Indicates whether a training summary exists for this model
instance.
"""
return self._call_java("hasSummary")
@property
@since("2.1.0")
def summary(self):
"""
Gets summary of the model trained on the training set. An exception is thrown if
no summary exists.
"""
return (self._call_java("summary"))
