d7af736b2c
## What changes were proposed in this pull request? Cleanups to documentation. No changes to code. * GBT docs: Move Scala doc for private object GradientBoostedTrees to public docs for GBTClassifier,Regressor * GLM regParam: needs doc saying it is for L2 only * TrainValidationSplitModel: add .. versionadded:: 2.0.0 * Rename “_transformer_params_from_java” to “_transfer_params_from_java” * LogReg Summary classes: “probability” col should not say “calibrated” * LR summaries: coefficientStandardErrors —> document that intercept stderr comes last. Same for t,p-values * approxCountDistinct: Document meaning of “rsd" argument. * LDA: note which params are for online LDA only ## How was this patch tested? Doc build Author: Joseph K. Bradley <joseph@databricks.com> Closes #12266 from jkbradley/ml-doc-cleanups.
284 lines
9.9 KiB
Python
284 lines
9.9 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from abc import ABCMeta, abstractmethod
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from pyspark import SparkContext
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from pyspark.sql import DataFrame
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from pyspark.ml import Estimator, Transformer, Model
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from pyspark.ml.param import Params
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from pyspark.ml.util import _jvm
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from pyspark.mllib.common import inherit_doc, _java2py, _py2java
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@inherit_doc
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class JavaWrapper(Params):
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"""
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Utility class to help create wrapper classes from Java/Scala
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implementations of pipeline components.
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"""
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__metaclass__ = ABCMeta
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def __init__(self):
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"""
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Initialize the wrapped java object to None
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"""
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super(JavaWrapper, self).__init__()
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#: The wrapped Java companion object. Subclasses should initialize
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#: it properly. The param values in the Java object should be
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#: synced with the Python wrapper in fit/transform/evaluate/copy.
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self._java_obj = None
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@staticmethod
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def _new_java_obj(java_class, *args):
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"""
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Construct a new Java object.
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"""
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sc = SparkContext._active_spark_context
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java_obj = _jvm()
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for name in java_class.split("."):
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java_obj = getattr(java_obj, name)
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java_args = [_py2java(sc, arg) for arg in args]
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return java_obj(*java_args)
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def _make_java_param_pair(self, param, value):
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"""
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Makes a Java parm pair.
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"""
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sc = SparkContext._active_spark_context
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param = self._resolveParam(param)
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java_param = self._java_obj.getParam(param.name)
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java_value = _py2java(sc, value)
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return java_param.w(java_value)
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def _transfer_params_to_java(self):
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"""
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Transforms the embedded params to the companion Java object.
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"""
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paramMap = self.extractParamMap()
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for param in self.params:
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if param in paramMap:
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pair = self._make_java_param_pair(param, paramMap[param])
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self._java_obj.set(pair)
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def _transfer_param_map_to_java(self, pyParamMap):
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"""
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Transforms a Python ParamMap into a Java ParamMap.
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"""
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paramMap = JavaWrapper._new_java_obj("org.apache.spark.ml.param.ParamMap")
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for param in self.params:
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if param in pyParamMap:
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pair = self._make_java_param_pair(param, pyParamMap[param])
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paramMap.put([pair])
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return paramMap
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def _transfer_params_from_java(self):
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"""
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Transforms the embedded params from the companion Java object.
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"""
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sc = SparkContext._active_spark_context
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for param in self.params:
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if self._java_obj.hasParam(param.name):
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java_param = self._java_obj.getParam(param.name)
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if self._java_obj.isDefined(java_param):
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value = _java2py(sc, self._java_obj.getOrDefault(java_param))
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self._paramMap[param] = value
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def _transfer_param_map_from_java(self, javaParamMap):
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"""
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Transforms a Java ParamMap into a Python ParamMap.
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"""
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sc = SparkContext._active_spark_context
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paramMap = dict()
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for pair in javaParamMap.toList():
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param = pair.param()
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if self.hasParam(str(param.name())):
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paramMap[self.getParam(param.name())] = _java2py(sc, pair.value())
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return paramMap
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@staticmethod
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def _empty_java_param_map():
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"""
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Returns an empty Java ParamMap reference.
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"""
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return _jvm().org.apache.spark.ml.param.ParamMap()
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def _to_java(self):
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"""
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Transfer this instance's Params to the wrapped Java object, and return the Java object.
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Used for ML persistence.
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Meta-algorithms such as Pipeline should override this method.
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:return: Java object equivalent to this instance.
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"""
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self._transfer_params_to_java()
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return self._java_obj
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@staticmethod
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def _from_java(java_stage):
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"""
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Given a Java object, create and return a Python wrapper of it.
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Used for ML persistence.
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Meta-algorithms such as Pipeline should override this method as a classmethod.
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"""
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def __get_class(clazz):
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"""
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Loads Python class from its name.
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"""
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parts = clazz.split('.')
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module = ".".join(parts[:-1])
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m = __import__(module)
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for comp in parts[1:]:
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m = getattr(m, comp)
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return m
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stage_name = java_stage.getClass().getName().replace("org.apache.spark", "pyspark")
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# Generate a default new instance from the stage_name class.
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py_type = __get_class(stage_name)
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if issubclass(py_type, JavaWrapper):
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# Load information from java_stage to the instance.
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py_stage = py_type()
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py_stage._java_obj = java_stage
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py_stage._resetUid(java_stage.uid())
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py_stage._transfer_params_from_java()
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elif hasattr(py_type, "_from_java"):
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py_stage = py_type._from_java(java_stage)
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else:
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raise NotImplementedError("This Java stage cannot be loaded into Python currently: %r"
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% stage_name)
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return py_stage
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@inherit_doc
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class JavaEstimator(Estimator, JavaWrapper):
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"""
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Base class for :py:class:`Estimator`s that wrap Java/Scala
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implementations.
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"""
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__metaclass__ = ABCMeta
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@abstractmethod
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def _create_model(self, java_model):
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"""
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Creates a model from the input Java model reference.
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"""
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raise NotImplementedError()
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def _fit_java(self, dataset):
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"""
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Fits a Java model to the input dataset.
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:param dataset: input dataset, which is an instance of
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:py:class:`pyspark.sql.DataFrame`
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:param params: additional params (overwriting embedded values)
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:return: fitted Java model
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"""
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self._transfer_params_to_java()
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return self._java_obj.fit(dataset._jdf)
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def _fit(self, dataset):
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java_model = self._fit_java(dataset)
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return self._create_model(java_model)
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@inherit_doc
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class JavaTransformer(Transformer, JavaWrapper):
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"""
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Base class for :py:class:`Transformer`s that wrap Java/Scala
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implementations. Subclasses should ensure they have the transformer Java object
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available as _java_obj.
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"""
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__metaclass__ = ABCMeta
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def _transform(self, dataset):
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self._transfer_params_to_java()
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return DataFrame(self._java_obj.transform(dataset._jdf), dataset.sql_ctx)
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class JavaCallable(object):
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"""
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Wrapper for a plain object in JVM to make Java calls, can be used
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as a mixin to another class that defines a _java_obj wrapper
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"""
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def __init__(self, java_obj=None, sc=None):
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super(JavaCallable, self).__init__()
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self._sc = sc if sc is not None else SparkContext._active_spark_context
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# if this class is a mixin and _java_obj is already defined then don't initialize
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if java_obj is not None or not hasattr(self, "_java_obj"):
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self._java_obj = java_obj
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def __del__(self):
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if self._java_obj is not None:
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self._sc._gateway.detach(self._java_obj)
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def _call_java(self, name, *args):
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m = getattr(self._java_obj, name)
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java_args = [_py2java(self._sc, arg) for arg in args]
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return _java2py(self._sc, m(*java_args))
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@inherit_doc
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class JavaModel(Model, JavaCallable, JavaTransformer):
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"""
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Base class for :py:class:`Model`s that wrap Java/Scala
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implementations. Subclasses should inherit this class before
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param mix-ins, because this sets the UID from the Java model.
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"""
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__metaclass__ = ABCMeta
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def __init__(self, java_model=None):
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"""
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Initialize this instance with a Java model object.
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Subclasses should call this constructor, initialize params,
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and then call _transfer_params_from_java.
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This instance can be instantiated without specifying java_model,
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it will be assigned after that, but this scenario only used by
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:py:class:`JavaMLReader` to load models. This is a bit of a
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hack, but it is easiest since a proper fix would require
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MLReader (in pyspark.ml.util) to depend on these wrappers, but
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these wrappers depend on pyspark.ml.util (both directly and via
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other ML classes).
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"""
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super(JavaModel, self).__init__()
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if java_model is not None:
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self._java_obj = java_model
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self.uid = java_model.uid()
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def copy(self, extra=None):
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"""
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Creates a copy of this instance with the same uid and some
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extra params. This implementation first calls Params.copy and
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then make a copy of the companion Java model with extra params.
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So both the Python wrapper and the Java model get copied.
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:param extra: Extra parameters to copy to the new instance
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:return: Copy of this instance
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"""
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if extra is None:
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extra = dict()
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that = super(JavaModel, self).copy(extra)
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if self._java_obj is not None:
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that._java_obj = self._java_obj.copy(self._empty_java_param_map())
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that._transfer_params_to_java()
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return that
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