Source code for pyspark.ml.util
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import sys
import uuid
import warnings
if sys.version > '3':
basestring = str
unicode = str
from pyspark import SparkContext, since
from pyspark.ml.common import inherit_doc
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 MLWriter(object):
"""
Utility class that can save ML instances.
.. versionadded:: 2.0.0
"""
[docs] def save(self, path):
"""Save the ML instance to the input path."""
raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
[docs] def overwrite(self):
"""Overwrites if the output path already exists."""
raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
[docs] def context(self, sqlContext):
"""
Sets the SQL context to use for saving.
.. note:: Deprecated in 2.1 and will be removed in 3.0, use session instead.
"""
raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
[docs] def session(self, sparkSession):
"""Sets the Spark Session to use for saving."""
raise NotImplementedError("MLWriter is not yet implemented for type: %s" % type(self))
[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 context(self, sqlContext):
"""
Sets the SQL context to use for saving.
.. note:: Deprecated in 2.1 and will be removed in 3.0, use session instead.
"""
warnings.warn("Deprecated in 2.1 and will be removed in 3.0, use session instead.")
self._jwrite.context(sqlContext._ssql_ctx)
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 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 MLReader(object):
"""
Utility class that can load ML instances.
.. versionadded:: 2.0.0
"""
[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] def context(self, sqlContext):
"""
Sets the SQL context to use for loading.
.. note:: Deprecated in 2.1 and will be removed in 3.0, use session instead.
"""
raise NotImplementedError("MLReader is not yet implemented for type: %s" % type(self))
[docs] def session(self, sparkSession):
"""Sets the Spark Session to use for loading."""
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):
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 context(self, sqlContext):
"""
Sets the SQL context to use for loading.
.. note:: Deprecated in 2.1 and will be removed in 3.0, use session instead.
"""
warnings.warn("Deprecated in 2.1 and will be removed in 3.0, use session instead.")
self._jread.context(sqlContext._ssql_ctx)
return self
[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 JavaPredictionModel():
"""
(Private) Java Model for prediction tasks (regression and classification).
To be mixed in with class:`pyspark.ml.JavaModel`
"""
@property
@since("2.1.0")
def numFeatures(self):
"""
Returns the number of features the model was trained on. If unknown, returns -1
"""
return self._call_java("numFeatures")