db0b06c6ea
## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-13786 Add save/load for Python CrossValidator/Model and TrainValidationSplit/Model. ## How was this patch tested? Test with Python doctest. Author: Xusen Yin <yinxusen@gmail.com> Closes #12020 from yinxusen/SPARK-13786.
688 lines
24 KiB
Python
688 lines
24 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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import itertools
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import numpy as np
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from pyspark import SparkContext
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from pyspark import since
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from pyspark.ml import Estimator, Model
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from pyspark.ml.param import Params, Param, TypeConverters
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from pyspark.ml.param.shared import HasSeed
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from pyspark.ml.util import keyword_only, JavaMLWriter, JavaMLReader, MLReadable, MLWritable
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from pyspark.ml.wrapper import JavaWrapper
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from pyspark.sql.functions import rand
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from pyspark.mllib.common import inherit_doc, _py2java
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__all__ = ['ParamGridBuilder', 'CrossValidator', 'CrossValidatorModel', 'TrainValidationSplit',
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'TrainValidationSplitModel']
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class ParamGridBuilder(object):
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r"""
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Builder for a param grid used in grid search-based model selection.
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>>> from pyspark.ml.classification import LogisticRegression
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>>> lr = LogisticRegression()
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>>> output = ParamGridBuilder() \
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... .baseOn({lr.labelCol: 'l'}) \
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... .baseOn([lr.predictionCol, 'p']) \
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... .addGrid(lr.regParam, [1.0, 2.0]) \
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... .addGrid(lr.maxIter, [1, 5]) \
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... .build()
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>>> expected = [
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... {lr.regParam: 1.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
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... {lr.regParam: 2.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
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... {lr.regParam: 1.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'},
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... {lr.regParam: 2.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}]
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>>> len(output) == len(expected)
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True
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>>> all([m in expected for m in output])
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True
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.. versionadded:: 1.4.0
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"""
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def __init__(self):
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self._param_grid = {}
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@since("1.4.0")
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def addGrid(self, param, values):
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"""
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Sets the given parameters in this grid to fixed values.
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"""
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self._param_grid[param] = values
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return self
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@since("1.4.0")
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def baseOn(self, *args):
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"""
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Sets the given parameters in this grid to fixed values.
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Accepts either a parameter dictionary or a list of (parameter, value) pairs.
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"""
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if isinstance(args[0], dict):
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self.baseOn(*args[0].items())
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else:
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for (param, value) in args:
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self.addGrid(param, [value])
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return self
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@since("1.4.0")
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def build(self):
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"""
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Builds and returns all combinations of parameters specified
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by the param grid.
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"""
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keys = self._param_grid.keys()
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grid_values = self._param_grid.values()
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return [dict(zip(keys, prod)) for prod in itertools.product(*grid_values)]
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class ValidatorParams(HasSeed):
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"""
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Common params for TrainValidationSplit and CrossValidator.
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"""
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estimator = Param(Params._dummy(), "estimator", "estimator to be cross-validated")
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estimatorParamMaps = Param(Params._dummy(), "estimatorParamMaps", "estimator param maps")
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evaluator = Param(
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Params._dummy(), "evaluator",
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"evaluator used to select hyper-parameters that maximize the validator metric")
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def setEstimator(self, value):
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"""
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Sets the value of :py:attr:`estimator`.
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"""
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return self._set(estimator=value)
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def getEstimator(self):
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"""
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Gets the value of estimator or its default value.
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"""
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return self.getOrDefault(self.estimator)
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def setEstimatorParamMaps(self, value):
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"""
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Sets the value of :py:attr:`estimatorParamMaps`.
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"""
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return self._set(estimatorParamMaps=value)
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def getEstimatorParamMaps(self):
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"""
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Gets the value of estimatorParamMaps or its default value.
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"""
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return self.getOrDefault(self.estimatorParamMaps)
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def setEvaluator(self, value):
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"""
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Sets the value of :py:attr:`evaluator`.
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"""
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return self._set(evaluator=value)
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def getEvaluator(self):
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"""
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Gets the value of evaluator or its default value.
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"""
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return self.getOrDefault(self.evaluator)
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@classmethod
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def _from_java_impl(cls, java_stage):
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"""
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Return Python estimator, estimatorParamMaps, and evaluator from a Java ValidatorParams.
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"""
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# Load information from java_stage to the instance.
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estimator = JavaWrapper._from_java(java_stage.getEstimator())
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evaluator = JavaWrapper._from_java(java_stage.getEvaluator())
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epms = [estimator._transfer_param_map_from_java(epm)
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for epm in java_stage.getEstimatorParamMaps()]
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return estimator, epms, evaluator
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def _to_java_impl(self):
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"""
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Return Java estimator, estimatorParamMaps, and evaluator from this Python instance.
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"""
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gateway = SparkContext._gateway
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cls = SparkContext._jvm.org.apache.spark.ml.param.ParamMap
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java_epms = gateway.new_array(cls, len(self.getEstimatorParamMaps()))
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for idx, epm in enumerate(self.getEstimatorParamMaps()):
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java_epms[idx] = self.getEstimator()._transfer_param_map_to_java(epm)
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java_estimator = self.getEstimator()._to_java()
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java_evaluator = self.getEvaluator()._to_java()
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return java_estimator, java_epms, java_evaluator
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class CrossValidator(Estimator, ValidatorParams, MLReadable, MLWritable):
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"""
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K-fold cross validation.
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>>> from pyspark.ml.classification import LogisticRegression
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>>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
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>>> from pyspark.mllib.linalg import Vectors
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>>> dataset = sqlContext.createDataFrame(
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... [(Vectors.dense([0.0]), 0.0),
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... (Vectors.dense([0.4]), 1.0),
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... (Vectors.dense([0.5]), 0.0),
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... (Vectors.dense([0.6]), 1.0),
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... (Vectors.dense([1.0]), 1.0)] * 10,
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... ["features", "label"])
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>>> lr = LogisticRegression()
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>>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
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>>> evaluator = BinaryClassificationEvaluator()
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>>> cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
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>>> cvModel = cv.fit(dataset)
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>>> evaluator.evaluate(cvModel.transform(dataset))
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0.8333...
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.. versionadded:: 1.4.0
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"""
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numFolds = Param(Params._dummy(), "numFolds", "number of folds for cross validation",
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typeConverter=TypeConverters.toInt)
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@keyword_only
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def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,
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seed=None):
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"""
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__init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\
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seed=None)
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"""
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super(CrossValidator, self).__init__()
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self._setDefault(numFolds=3)
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kwargs = self.__init__._input_kwargs
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self._set(**kwargs)
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@keyword_only
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@since("1.4.0")
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def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,
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seed=None):
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"""
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setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\
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seed=None):
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Sets params for cross validator.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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@since("1.4.0")
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def setNumFolds(self, value):
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"""
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Sets the value of :py:attr:`numFolds`.
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"""
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self._paramMap[self.numFolds] = value
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return self
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@since("1.4.0")
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def getNumFolds(self):
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"""
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Gets the value of numFolds or its default value.
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"""
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return self.getOrDefault(self.numFolds)
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def _fit(self, dataset):
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est = self.getOrDefault(self.estimator)
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epm = self.getOrDefault(self.estimatorParamMaps)
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numModels = len(epm)
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eva = self.getOrDefault(self.evaluator)
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nFolds = self.getOrDefault(self.numFolds)
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seed = self.getOrDefault(self.seed)
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h = 1.0 / nFolds
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randCol = self.uid + "_rand"
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df = dataset.select("*", rand(seed).alias(randCol))
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metrics = np.zeros(numModels)
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for i in range(nFolds):
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validateLB = i * h
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validateUB = (i + 1) * h
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condition = (df[randCol] >= validateLB) & (df[randCol] < validateUB)
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validation = df.filter(condition)
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train = df.filter(~condition)
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for j in range(numModels):
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model = est.fit(train, epm[j])
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# TODO: duplicate evaluator to take extra params from input
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metric = eva.evaluate(model.transform(validation, epm[j]))
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metrics[j] += metric
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if eva.isLargerBetter():
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bestIndex = np.argmax(metrics)
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else:
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bestIndex = np.argmin(metrics)
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bestModel = est.fit(dataset, epm[bestIndex])
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return self._copyValues(CrossValidatorModel(bestModel))
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@since("1.4.0")
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def copy(self, extra=None):
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"""
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Creates a copy of this instance with a randomly generated uid
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and some extra params. This copies creates a deep copy of
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the embedded paramMap, and copies the embedded and extra parameters over.
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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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newCV = Params.copy(self, extra)
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if self.isSet(self.estimator):
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newCV.setEstimator(self.getEstimator().copy(extra))
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# estimatorParamMaps remain the same
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if self.isSet(self.evaluator):
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newCV.setEvaluator(self.getEvaluator().copy(extra))
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return newCV
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@since("2.0.0")
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def write(self):
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"""Returns an MLWriter instance for this ML instance."""
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return JavaMLWriter(self)
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@since("2.0.0")
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def save(self, path):
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"""Save this ML instance to the given path, a shortcut of `write().save(path)`."""
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self.write().save(path)
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@classmethod
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@since("2.0.0")
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def read(cls):
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"""Returns an MLReader instance for this class."""
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return JavaMLReader(cls)
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@classmethod
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def _from_java(cls, java_stage):
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"""
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Given a Java CrossValidator, create and return a Python wrapper of it.
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Used for ML persistence.
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"""
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estimator, epms, evaluator = super(CrossValidator, cls)._from_java_impl(java_stage)
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numFolds = java_stage.getNumFolds()
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seed = java_stage.getSeed()
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# Create a new instance of this stage.
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py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
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numFolds=numFolds, seed=seed)
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py_stage._resetUid(java_stage.uid())
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return py_stage
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def _to_java(self):
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"""
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Transfer this instance to a Java CrossValidator. Used for ML persistence.
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:return: Java object equivalent to this instance.
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"""
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estimator, epms, evaluator = super(CrossValidator, self)._to_java_impl()
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_java_obj = JavaWrapper._new_java_obj("org.apache.spark.ml.tuning.CrossValidator", self.uid)
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_java_obj.setEstimatorParamMaps(epms)
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_java_obj.setEvaluator(evaluator)
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_java_obj.setEstimator(estimator)
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_java_obj.setSeed(self.getSeed())
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_java_obj.setNumFolds(self.getNumFolds())
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return _java_obj
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class CrossValidatorModel(Model, ValidatorParams, MLReadable, MLWritable):
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"""
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Model from k-fold cross validation.
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.. versionadded:: 1.4.0
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"""
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def __init__(self, bestModel):
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super(CrossValidatorModel, self).__init__()
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#: best model from cross validation
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self.bestModel = bestModel
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def _transform(self, dataset):
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return self.bestModel.transform(dataset)
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@since("1.4.0")
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def copy(self, extra=None):
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"""
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Creates a copy of this instance with a randomly generated uid
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and some extra params. This copies the underlying bestModel,
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creates a deep copy of the embedded paramMap, and
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copies the embedded and extra parameters over.
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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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return CrossValidatorModel(self.bestModel.copy(extra))
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@since("2.0.0")
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def write(self):
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"""Returns an MLWriter instance for this ML instance."""
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return JavaMLWriter(self)
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@since("2.0.0")
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def save(self, path):
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"""Save this ML instance to the given path, a shortcut of `write().save(path)`."""
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self.write().save(path)
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@classmethod
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@since("2.0.0")
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def read(cls):
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"""Returns an MLReader instance for this class."""
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return JavaMLReader(cls)
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@classmethod
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def _from_java(cls, java_stage):
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"""
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Given a Java CrossValidatorModel, create and return a Python wrapper of it.
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Used for ML persistence.
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"""
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# Load information from java_stage to the instance.
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bestModel = JavaWrapper._from_java(java_stage.bestModel())
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estimator, epms, evaluator = super(CrossValidatorModel, cls)._from_java_impl(java_stage)
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# Create a new instance of this stage.
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py_stage = cls(bestModel=bestModel)\
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.setEstimator(estimator).setEstimatorParamMaps(epms).setEvaluator(evaluator)
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py_stage._resetUid(java_stage.uid())
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return py_stage
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def _to_java(self):
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"""
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Transfer this instance to a Java CrossValidatorModel. Used for ML persistence.
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:return: Java object equivalent to this instance.
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"""
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sc = SparkContext._active_spark_context
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_java_obj = JavaWrapper._new_java_obj("org.apache.spark.ml.tuning.CrossValidatorModel",
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self.uid,
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self.bestModel._to_java(),
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_py2java(sc, []))
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estimator, epms, evaluator = super(CrossValidatorModel, self)._to_java_impl()
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_java_obj.set("evaluator", evaluator)
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_java_obj.set("estimator", estimator)
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_java_obj.set("estimatorParamMaps", epms)
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return _java_obj
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class TrainValidationSplit(Estimator, ValidatorParams, MLReadable, MLWritable):
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"""
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Train-Validation-Split.
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>>> from pyspark.ml.classification import LogisticRegression
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>>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
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>>> from pyspark.mllib.linalg import Vectors
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>>> dataset = sqlContext.createDataFrame(
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... [(Vectors.dense([0.0]), 0.0),
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... (Vectors.dense([0.4]), 1.0),
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... (Vectors.dense([0.5]), 0.0),
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... (Vectors.dense([0.6]), 1.0),
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... (Vectors.dense([1.0]), 1.0)] * 10,
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... ["features", "label"])
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>>> lr = LogisticRegression()
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>>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
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>>> evaluator = BinaryClassificationEvaluator()
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>>> tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
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>>> tvsModel = tvs.fit(dataset)
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>>> evaluator.evaluate(tvsModel.transform(dataset))
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0.8333...
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.. versionadded:: 2.0.0
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"""
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trainRatio = Param(Params._dummy(), "trainRatio", "Param for ratio between train and\
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validation data. Must be between 0 and 1.")
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@keyword_only
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def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
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seed=None):
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"""
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__init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
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seed=None)
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"""
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super(TrainValidationSplit, self).__init__()
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self._setDefault(trainRatio=0.75)
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kwargs = self.__init__._input_kwargs
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self._set(**kwargs)
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@since("2.0.0")
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@keyword_only
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def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
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seed=None):
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"""
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setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
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seed=None):
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Sets params for the train validation split.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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@since("2.0.0")
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def setTrainRatio(self, value):
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"""
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Sets the value of :py:attr:`trainRatio`.
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"""
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self._paramMap[self.trainRatio] = value
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return self
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@since("2.0.0")
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def getTrainRatio(self):
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"""
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Gets the value of trainRatio or its default value.
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"""
|
|
return self.getOrDefault(self.trainRatio)
|
|
|
|
def _fit(self, dataset):
|
|
est = self.getOrDefault(self.estimator)
|
|
epm = self.getOrDefault(self.estimatorParamMaps)
|
|
numModels = len(epm)
|
|
eva = self.getOrDefault(self.evaluator)
|
|
tRatio = self.getOrDefault(self.trainRatio)
|
|
seed = self.getOrDefault(self.seed)
|
|
randCol = self.uid + "_rand"
|
|
df = dataset.select("*", rand(seed).alias(randCol))
|
|
metrics = np.zeros(numModels)
|
|
condition = (df[randCol] >= tRatio)
|
|
validation = df.filter(condition)
|
|
train = df.filter(~condition)
|
|
for j in range(numModels):
|
|
model = est.fit(train, epm[j])
|
|
metric = eva.evaluate(model.transform(validation, epm[j]))
|
|
metrics[j] += metric
|
|
if eva.isLargerBetter():
|
|
bestIndex = np.argmax(metrics)
|
|
else:
|
|
bestIndex = np.argmin(metrics)
|
|
bestModel = est.fit(dataset, epm[bestIndex])
|
|
return self._copyValues(TrainValidationSplitModel(bestModel))
|
|
|
|
@since("2.0.0")
|
|
def copy(self, extra=None):
|
|
"""
|
|
Creates a copy of this instance with a randomly generated uid
|
|
and some extra params. This copies creates a deep copy of
|
|
the embedded paramMap, and copies the embedded and extra parameters over.
|
|
|
|
:param extra: Extra parameters to copy to the new instance
|
|
:return: Copy of this instance
|
|
"""
|
|
if extra is None:
|
|
extra = dict()
|
|
newTVS = Params.copy(self, extra)
|
|
if self.isSet(self.estimator):
|
|
newTVS.setEstimator(self.getEstimator().copy(extra))
|
|
# estimatorParamMaps remain the same
|
|
if self.isSet(self.evaluator):
|
|
newTVS.setEvaluator(self.getEvaluator().copy(extra))
|
|
return newTVS
|
|
|
|
@since("2.0.0")
|
|
def write(self):
|
|
"""Returns an MLWriter instance for this ML instance."""
|
|
return JavaMLWriter(self)
|
|
|
|
@since("2.0.0")
|
|
def save(self, path):
|
|
"""Save this ML instance to the given path, a shortcut of `write().save(path)`."""
|
|
self.write().save(path)
|
|
|
|
@classmethod
|
|
@since("2.0.0")
|
|
def read(cls):
|
|
"""Returns an MLReader instance for this class."""
|
|
return JavaMLReader(cls)
|
|
|
|
@classmethod
|
|
def _from_java(cls, java_stage):
|
|
"""
|
|
Given a Java TrainValidationSplit, create and return a Python wrapper of it.
|
|
Used for ML persistence.
|
|
"""
|
|
|
|
estimator, epms, evaluator = super(TrainValidationSplit, cls)._from_java_impl(java_stage)
|
|
trainRatio = java_stage.getTrainRatio()
|
|
seed = java_stage.getSeed()
|
|
# Create a new instance of this stage.
|
|
py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
|
|
trainRatio=trainRatio, seed=seed)
|
|
py_stage._resetUid(java_stage.uid())
|
|
return py_stage
|
|
|
|
def _to_java(self):
|
|
"""
|
|
Transfer this instance to a Java TrainValidationSplit. Used for ML persistence.
|
|
|
|
:return: Java object equivalent to this instance.
|
|
"""
|
|
|
|
estimator, epms, evaluator = super(TrainValidationSplit, self)._to_java_impl()
|
|
|
|
_java_obj = JavaWrapper._new_java_obj("org.apache.spark.ml.tuning.TrainValidationSplit",
|
|
self.uid)
|
|
_java_obj.setEstimatorParamMaps(epms)
|
|
_java_obj.setEvaluator(evaluator)
|
|
_java_obj.setEstimator(estimator)
|
|
_java_obj.setTrainRatio(self.getTrainRatio())
|
|
_java_obj.setSeed(self.getSeed())
|
|
|
|
return _java_obj
|
|
|
|
|
|
class TrainValidationSplitModel(Model, ValidatorParams, MLReadable, MLWritable):
|
|
"""
|
|
Model from train validation split.
|
|
"""
|
|
|
|
def __init__(self, bestModel):
|
|
super(TrainValidationSplitModel, self).__init__()
|
|
#: best model from cross validation
|
|
self.bestModel = bestModel
|
|
|
|
def _transform(self, dataset):
|
|
return self.bestModel.transform(dataset)
|
|
|
|
@since("2.0.0")
|
|
def copy(self, extra=None):
|
|
"""
|
|
Creates a copy of this instance with a randomly generated uid
|
|
and some extra params. This copies the underlying bestModel,
|
|
creates a deep copy of the embedded paramMap, and
|
|
copies the embedded and extra parameters over.
|
|
|
|
:param extra: Extra parameters to copy to the new instance
|
|
:return: Copy of this instance
|
|
"""
|
|
if extra is None:
|
|
extra = dict()
|
|
return TrainValidationSplitModel(self.bestModel.copy(extra))
|
|
|
|
@since("2.0.0")
|
|
def write(self):
|
|
"""Returns an MLWriter instance for this ML instance."""
|
|
return JavaMLWriter(self)
|
|
|
|
@since("2.0.0")
|
|
def save(self, path):
|
|
"""Save this ML instance to the given path, a shortcut of `write().save(path)`."""
|
|
self.write().save(path)
|
|
|
|
@classmethod
|
|
@since("2.0.0")
|
|
def read(cls):
|
|
"""Returns an MLReader instance for this class."""
|
|
return JavaMLReader(cls)
|
|
|
|
@classmethod
|
|
def _from_java(cls, java_stage):
|
|
"""
|
|
Given a Java TrainValidationSplitModel, create and return a Python wrapper of it.
|
|
Used for ML persistence.
|
|
"""
|
|
|
|
# Load information from java_stage to the instance.
|
|
bestModel = JavaWrapper._from_java(java_stage.bestModel())
|
|
estimator, epms, evaluator = \
|
|
super(TrainValidationSplitModel, cls)._from_java_impl(java_stage)
|
|
# Create a new instance of this stage.
|
|
py_stage = cls(bestModel=bestModel)\
|
|
.setEstimator(estimator).setEstimatorParamMaps(epms).setEvaluator(evaluator)
|
|
py_stage._resetUid(java_stage.uid())
|
|
return py_stage
|
|
|
|
def _to_java(self):
|
|
"""
|
|
Transfer this instance to a Java TrainValidationSplitModel. Used for ML persistence.
|
|
|
|
:return: Java object equivalent to this instance.
|
|
"""
|
|
|
|
sc = SparkContext._active_spark_context
|
|
|
|
_java_obj = JavaWrapper._new_java_obj(
|
|
"org.apache.spark.ml.tuning.TrainValidationSplitModel",
|
|
self.uid,
|
|
self.bestModel._to_java(),
|
|
_py2java(sc, []))
|
|
estimator, epms, evaluator = super(TrainValidationSplitModel, self)._to_java_impl()
|
|
|
|
_java_obj.set("evaluator", evaluator)
|
|
_java_obj.set("estimator", estimator)
|
|
_java_obj.set("estimatorParamMaps", epms)
|
|
return _java_obj
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import doctest
|
|
|
|
from pyspark.context import SparkContext
|
|
from pyspark.sql import SQLContext
|
|
globs = globals().copy()
|
|
|
|
# The small batch size here ensures that we see multiple batches,
|
|
# even in these small test examples:
|
|
sc = SparkContext("local[2]", "ml.tuning tests")
|
|
sqlContext = SQLContext(sc)
|
|
globs['sc'] = sc
|
|
globs['sqlContext'] = sqlContext
|
|
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
|
|
sc.stop()
|
|
if failure_count:
|
|
exit(-1)
|