20fa456932
## What changes were proposed in this pull request? When the grid of the parameters is created in `ParamGridBuilder`, the implicit type coercion is not enforced. So using an integer in the list of parameters to set for a parameter accepting a double can cause a class cast exception. The PR proposes to enforce the type coercion when building the parameters. ## How was this patch tested? added UT Closes #22076 from mgaido91/SPARK-25090. Authored-by: Marco Gaido <marcogaido91@gmail.com> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
786 lines
29 KiB
Python
786 lines
29 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 sys
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from multiprocessing.pool import ThreadPool
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import numpy as np
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from pyspark import since, keyword_only
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from pyspark.ml import Estimator, Model
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from pyspark.ml.common import _py2java
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from pyspark.ml.param import Params, Param, TypeConverters
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from pyspark.ml.param.shared import HasCollectSubModels, HasParallelism, HasSeed
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from pyspark.ml.util import *
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from pyspark.ml.wrapper import JavaParams
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from pyspark.sql.functions import rand
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__all__ = ['ParamGridBuilder', 'CrossValidator', 'CrossValidatorModel', 'TrainValidationSplit',
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'TrainValidationSplitModel']
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def _parallelFitTasks(est, train, eva, validation, epm, collectSubModel):
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"""
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Creates a list of callables which can be called from different threads to fit and evaluate
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an estimator in parallel. Each callable returns an `(index, metric)` pair.
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:param est: Estimator, the estimator to be fit.
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:param train: DataFrame, training data set, used for fitting.
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:param eva: Evaluator, used to compute `metric`
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:param validation: DataFrame, validation data set, used for evaluation.
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:param epm: Sequence of ParamMap, params maps to be used during fitting & evaluation.
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:param collectSubModel: Whether to collect sub model.
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:return: (int, float, subModel), an index into `epm` and the associated metric value.
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"""
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modelIter = est.fitMultiple(train, epm)
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def singleTask():
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index, model = next(modelIter)
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metric = eva.evaluate(model.transform(validation, epm[index]))
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return index, metric, model if collectSubModel else None
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return [singleTask] * len(epm)
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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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def to_key_value_pairs(keys, values):
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return [(key, key.typeConverter(value)) for key, value in zip(keys, values)]
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return [dict(to_key_value_pairs(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 = JavaParams._from_java(java_stage.getEstimator())
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evaluator = JavaParams._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, HasParallelism, HasCollectSubModels,
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MLReadable, MLWritable):
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"""
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K-fold cross validation performs model selection by splitting the dataset into a set of
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non-overlapping randomly partitioned folds which are used as separate training and test datasets
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e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs,
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each of which uses 2/3 of the data for training and 1/3 for testing. Each fold is used as the
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test set exactly once.
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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.ml.linalg import Vectors
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>>> dataset = spark.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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... parallelism=2)
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>>> cvModel = cv.fit(dataset)
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>>> cvModel.avgMetrics[0]
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0.5
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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, parallelism=1, collectSubModels=False):
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"""
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__init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\
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seed=None, parallelism=1, collectSubModels=False)
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"""
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super(CrossValidator, self).__init__()
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self._setDefault(numFolds=3, parallelism=1)
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kwargs = self._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, parallelism=1, collectSubModels=False):
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"""
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setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\
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seed=None, parallelism=1, collectSubModels=False):
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Sets params for cross validator.
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"""
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kwargs = self._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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return self._set(numFolds=value)
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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 = [0.0] * numModels
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pool = ThreadPool(processes=min(self.getParallelism(), numModels))
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subModels = None
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collectSubModelsParam = self.getCollectSubModels()
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if collectSubModelsParam:
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subModels = [[None for j in range(numModels)] for i in range(nFolds)]
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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).cache()
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train = df.filter(~condition).cache()
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tasks = _parallelFitTasks(est, train, eva, validation, epm, collectSubModelsParam)
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for j, metric, subModel in pool.imap_unordered(lambda f: f(), tasks):
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metrics[j] += (metric / nFolds)
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if collectSubModelsParam:
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subModels[i][j] = subModel
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validation.unpersist()
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train.unpersist()
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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, metrics, subModels))
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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("2.3.0")
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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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@classmethod
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@since("2.3.0")
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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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parallelism = java_stage.getParallelism()
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collectSubModels = java_stage.getCollectSubModels()
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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, parallelism=parallelism,
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collectSubModels=collectSubModels)
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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 = JavaParams._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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_java_obj.setParallelism(self.getParallelism())
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_java_obj.setCollectSubModels(self.getCollectSubModels())
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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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CrossValidatorModel contains the model with the highest average cross-validation
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metric across folds and uses this model to transform input data. CrossValidatorModel
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also tracks the metrics for each param map evaluated.
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.. versionadded:: 1.4.0
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"""
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def __init__(self, bestModel, avgMetrics=[], subModels=None):
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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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#: Average cross-validation metrics for each paramMap in
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#: CrossValidator.estimatorParamMaps, in the corresponding order.
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self.avgMetrics = avgMetrics
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#: sub model list from cross validation
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self.subModels = subModels
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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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It does not copy the extra Params into the subModels.
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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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bestModel = self.bestModel.copy(extra)
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avgMetrics = self.avgMetrics
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subModels = self.subModels
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return CrossValidatorModel(bestModel, avgMetrics, subModels)
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@since("2.3.0")
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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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@classmethod
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@since("2.3.0")
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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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bestModel = JavaParams._from_java(java_stage.bestModel())
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estimator, epms, evaluator = super(CrossValidatorModel, cls)._from_java_impl(java_stage)
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py_stage = cls(bestModel=bestModel).setEstimator(estimator)
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py_stage = py_stage.setEstimatorParamMaps(epms).setEvaluator(evaluator)
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if java_stage.hasSubModels():
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py_stage.subModels = [[JavaParams._from_java(sub_model)
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for sub_model in fold_sub_models]
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for fold_sub_models in java_stage.subModels()]
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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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# TODO: persist average metrics as well
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_java_obj = JavaParams._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)
|
|
_java_obj.set("estimatorParamMaps", epms)
|
|
|
|
if self.subModels is not None:
|
|
java_sub_models = [[sub_model._to_java() for sub_model in fold_sub_models]
|
|
for fold_sub_models in self.subModels]
|
|
_java_obj.setSubModels(java_sub_models)
|
|
return _java_obj
|
|
|
|
|
|
class TrainValidationSplit(Estimator, ValidatorParams, HasParallelism, HasCollectSubModels,
|
|
MLReadable, MLWritable):
|
|
"""
|
|
.. note:: Experimental
|
|
|
|
Validation for hyper-parameter tuning. Randomly splits the input dataset into train and
|
|
validation sets, and uses evaluation metric on the validation set to select the best model.
|
|
Similar to :class:`CrossValidator`, but only splits the set once.
|
|
|
|
>>> from pyspark.ml.classification import LogisticRegression
|
|
>>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> dataset = spark.createDataFrame(
|
|
... [(Vectors.dense([0.0]), 0.0),
|
|
... (Vectors.dense([0.4]), 1.0),
|
|
... (Vectors.dense([0.5]), 0.0),
|
|
... (Vectors.dense([0.6]), 1.0),
|
|
... (Vectors.dense([1.0]), 1.0)] * 10,
|
|
... ["features", "label"])
|
|
>>> lr = LogisticRegression()
|
|
>>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
|
|
>>> evaluator = BinaryClassificationEvaluator()
|
|
>>> tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator,
|
|
... parallelism=2)
|
|
>>> tvsModel = tvs.fit(dataset)
|
|
>>> evaluator.evaluate(tvsModel.transform(dataset))
|
|
0.8333...
|
|
|
|
.. versionadded:: 2.0.0
|
|
"""
|
|
|
|
trainRatio = Param(Params._dummy(), "trainRatio", "Param for ratio between train and\
|
|
validation data. Must be between 0 and 1.", typeConverter=TypeConverters.toFloat)
|
|
|
|
@keyword_only
|
|
def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
|
|
parallelism=1, collectSubModels=False, seed=None):
|
|
"""
|
|
__init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
|
|
parallelism=1, collectSubModels=False, seed=None)
|
|
"""
|
|
super(TrainValidationSplit, self).__init__()
|
|
self._setDefault(trainRatio=0.75, parallelism=1)
|
|
kwargs = self._input_kwargs
|
|
self._set(**kwargs)
|
|
|
|
@since("2.0.0")
|
|
@keyword_only
|
|
def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
|
|
parallelism=1, collectSubModels=False, seed=None):
|
|
"""
|
|
setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
|
|
parallelism=1, collectSubModels=False, seed=None):
|
|
Sets params for the train validation split.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("2.0.0")
|
|
def setTrainRatio(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`trainRatio`.
|
|
"""
|
|
return self._set(trainRatio=value)
|
|
|
|
@since("2.0.0")
|
|
def getTrainRatio(self):
|
|
"""
|
|
Gets the value of trainRatio or its default value.
|
|
"""
|
|
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))
|
|
condition = (df[randCol] >= tRatio)
|
|
validation = df.filter(condition).cache()
|
|
train = df.filter(~condition).cache()
|
|
|
|
subModels = None
|
|
collectSubModelsParam = self.getCollectSubModels()
|
|
if collectSubModelsParam:
|
|
subModels = [None for i in range(numModels)]
|
|
|
|
tasks = _parallelFitTasks(est, train, eva, validation, epm, collectSubModelsParam)
|
|
pool = ThreadPool(processes=min(self.getParallelism(), numModels))
|
|
metrics = [None] * numModels
|
|
for j, metric, subModel in pool.imap_unordered(lambda f: f(), tasks):
|
|
metrics[j] = metric
|
|
if collectSubModelsParam:
|
|
subModels[j] = subModel
|
|
|
|
train.unpersist()
|
|
validation.unpersist()
|
|
|
|
if eva.isLargerBetter():
|
|
bestIndex = np.argmax(metrics)
|
|
else:
|
|
bestIndex = np.argmin(metrics)
|
|
bestModel = est.fit(dataset, epm[bestIndex])
|
|
return self._copyValues(TrainValidationSplitModel(bestModel, metrics, subModels))
|
|
|
|
@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("2.3.0")
|
|
def write(self):
|
|
"""Returns an MLWriter instance for this ML instance."""
|
|
return JavaMLWriter(self)
|
|
|
|
@classmethod
|
|
@since("2.3.0")
|
|
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()
|
|
parallelism = java_stage.getParallelism()
|
|
collectSubModels = java_stage.getCollectSubModels()
|
|
# Create a new instance of this stage.
|
|
py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
|
|
trainRatio=trainRatio, seed=seed, parallelism=parallelism,
|
|
collectSubModels=collectSubModels)
|
|
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 = JavaParams._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())
|
|
_java_obj.setParallelism(self.getParallelism())
|
|
_java_obj.setCollectSubModels(self.getCollectSubModels())
|
|
return _java_obj
|
|
|
|
|
|
class TrainValidationSplitModel(Model, ValidatorParams, MLReadable, MLWritable):
|
|
"""
|
|
.. note:: Experimental
|
|
|
|
Model from train validation split.
|
|
|
|
.. versionadded:: 2.0.0
|
|
"""
|
|
|
|
def __init__(self, bestModel, validationMetrics=[], subModels=None):
|
|
super(TrainValidationSplitModel, self).__init__()
|
|
#: best model from train validation split
|
|
self.bestModel = bestModel
|
|
#: evaluated validation metrics
|
|
self.validationMetrics = validationMetrics
|
|
#: sub models from train validation split
|
|
self.subModels = subModels
|
|
|
|
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.
|
|
And, this creates a shallow copy of the validationMetrics.
|
|
It does not copy the extra Params into the subModels.
|
|
|
|
:param extra: Extra parameters to copy to the new instance
|
|
:return: Copy of this instance
|
|
"""
|
|
if extra is None:
|
|
extra = dict()
|
|
bestModel = self.bestModel.copy(extra)
|
|
validationMetrics = list(self.validationMetrics)
|
|
subModels = self.subModels
|
|
return TrainValidationSplitModel(bestModel, validationMetrics, subModels)
|
|
|
|
@since("2.3.0")
|
|
def write(self):
|
|
"""Returns an MLWriter instance for this ML instance."""
|
|
return JavaMLWriter(self)
|
|
|
|
@classmethod
|
|
@since("2.3.0")
|
|
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 = JavaParams._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)
|
|
py_stage = py_stage.setEstimatorParamMaps(epms).setEvaluator(evaluator)
|
|
|
|
if java_stage.hasSubModels():
|
|
py_stage.subModels = [JavaParams._from_java(sub_model)
|
|
for sub_model in java_stage.subModels()]
|
|
|
|
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
|
|
# TODO: persst validation metrics as well
|
|
_java_obj = JavaParams._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)
|
|
|
|
if self.subModels is not None:
|
|
java_sub_models = [sub_model._to_java() for sub_model in self.subModels]
|
|
_java_obj.setSubModels(java_sub_models)
|
|
|
|
return _java_obj
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import doctest
|
|
|
|
from pyspark.sql import SparkSession
|
|
globs = globals().copy()
|
|
|
|
# The small batch size here ensures that we see multiple batches,
|
|
# even in these small test examples:
|
|
spark = SparkSession.builder\
|
|
.master("local[2]")\
|
|
.appName("ml.tuning tests")\
|
|
.getOrCreate()
|
|
sc = spark.sparkContext
|
|
globs['sc'] = sc
|
|
globs['spark'] = spark
|
|
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
|
|
spark.stop()
|
|
if failure_count:
|
|
sys.exit(-1)
|