027ed2d11b
## What changes were proposed in this pull request? The hashSeed method allocates 64 bytes instead of 8. Other bytes are always zeros (thanks to default behavior of ByteBuffer). And they could be excluded from hash calculation because they don't differentiate inputs. ## How was this patch tested? By running the existing tests - XORShiftRandomSuite Closes #20793 from MaxGekk/hash-buff-size. Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com> Co-authored-by: Maxim Gekk <max.gekk@gmail.com> Signed-off-by: Sean Owen <sean.owen@databricks.com>
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)
|
|
_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"]).repartition(1)
|
|
>>> lr = LogisticRegression()
|
|
>>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
|
|
>>> evaluator = BinaryClassificationEvaluator()
|
|
>>> tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator,
|
|
... parallelism=1, seed=42)
|
|
>>> tvsModel = tvs.fit(dataset)
|
|
>>> evaluator.evaluate(tvsModel.transform(dataset))
|
|
0.833...
|
|
|
|
.. 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)
|