511d4929c8
## What changes were proposed in this pull request? The changes proposed were to add train-validation-split to pyspark.ml.tuning. ## How was the this patch tested? This patch was tested through unit tests located in pyspark/ml/test.py. This is my original work and I license it to Spark. Author: JeremyNixon <jnixon2@gmail.com> Closes #11335 from JeremyNixon/tvs_pyspark.
498 lines
17 KiB
Python
498 lines
17 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import itertools
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import numpy as np
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from pyspark import since
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from pyspark.ml import Estimator, Model
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from pyspark.ml.param import Params, Param
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from pyspark.ml.param.shared import HasSeed
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from pyspark.ml.util import keyword_only
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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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class ParamGridBuilder(object):
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r"""
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Builder for a param grid used in grid search-based model selection.
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>>> from pyspark.ml.classification import LogisticRegression
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>>> lr = LogisticRegression()
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>>> output = ParamGridBuilder() \
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... .baseOn({lr.labelCol: 'l'}) \
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... .baseOn([lr.predictionCol, 'p']) \
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... .addGrid(lr.regParam, [1.0, 2.0]) \
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... .addGrid(lr.maxIter, [1, 5]) \
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... .build()
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>>> expected = [
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... {lr.regParam: 1.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
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... {lr.regParam: 2.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
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... {lr.regParam: 1.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'},
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... {lr.regParam: 2.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}]
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>>> len(output) == len(expected)
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True
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>>> all([m in expected for m in output])
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True
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.. versionadded:: 1.4.0
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"""
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def __init__(self):
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self._param_grid = {}
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@since("1.4.0")
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def addGrid(self, param, values):
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"""
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Sets the given parameters in this grid to fixed values.
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"""
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self._param_grid[param] = values
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return self
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@since("1.4.0")
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def baseOn(self, *args):
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"""
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Sets the given parameters in this grid to fixed values.
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Accepts either a parameter dictionary or a list of (parameter, value) pairs.
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"""
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if isinstance(args[0], dict):
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self.baseOn(*args[0].items())
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else:
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for (param, value) in args:
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self.addGrid(param, [value])
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return self
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@since("1.4.0")
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def build(self):
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"""
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Builds and returns all combinations of parameters specified
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by the param grid.
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"""
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keys = self._param_grid.keys()
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grid_values = self._param_grid.values()
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return [dict(zip(keys, prod)) for prod in itertools.product(*grid_values)]
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class CrossValidator(Estimator, HasSeed):
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"""
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K-fold cross validation.
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>>> from pyspark.ml.classification import LogisticRegression
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>>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
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>>> from pyspark.mllib.linalg import Vectors
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>>> dataset = sqlContext.createDataFrame(
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... [(Vectors.dense([0.0]), 0.0),
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... (Vectors.dense([0.4]), 1.0),
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... (Vectors.dense([0.5]), 0.0),
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... (Vectors.dense([0.6]), 1.0),
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... (Vectors.dense([1.0]), 1.0)] * 10,
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... ["features", "label"])
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>>> lr = LogisticRegression()
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>>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
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>>> evaluator = BinaryClassificationEvaluator()
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>>> cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
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>>> cvModel = cv.fit(dataset)
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>>> evaluator.evaluate(cvModel.transform(dataset))
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0.8333...
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.. versionadded:: 1.4.0
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"""
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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 cross-validated metric")
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numFolds = Param(Params._dummy(), "numFolds", "number of folds for cross validation")
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@keyword_only
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def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,
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seed=None):
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"""
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__init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\
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seed=None)
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"""
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super(CrossValidator, self).__init__()
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self._setDefault(numFolds=3)
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kwargs = self.__init__._input_kwargs
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self._set(**kwargs)
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@keyword_only
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@since("1.4.0")
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def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,
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seed=None):
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"""
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setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\
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seed=None):
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Sets params for cross validator.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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@since("1.4.0")
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def 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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self._paramMap[self.estimator] = value
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return self
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@since("1.4.0")
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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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@since("1.4.0")
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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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self._paramMap[self.estimatorParamMaps] = value
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return self
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@since("1.4.0")
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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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@since("1.4.0")
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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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self._paramMap[self.evaluator] = value
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return self
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@since("1.4.0")
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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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@since("1.4.0")
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def setNumFolds(self, value):
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"""
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Sets the value of :py:attr:`numFolds`.
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"""
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self._paramMap[self.numFolds] = value
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return self
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@since("1.4.0")
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def getNumFolds(self):
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"""
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Gets the value of numFolds or its default value.
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"""
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return self.getOrDefault(self.numFolds)
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def _fit(self, dataset):
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est = self.getOrDefault(self.estimator)
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epm = self.getOrDefault(self.estimatorParamMaps)
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numModels = len(epm)
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eva = self.getOrDefault(self.evaluator)
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nFolds = self.getOrDefault(self.numFolds)
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seed = self.getOrDefault(self.seed)
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h = 1.0 / nFolds
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randCol = self.uid + "_rand"
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df = dataset.select("*", rand(seed).alias(randCol))
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metrics = np.zeros(numModels)
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for i in range(nFolds):
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validateLB = i * h
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validateUB = (i + 1) * h
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condition = (df[randCol] >= validateLB) & (df[randCol] < validateUB)
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validation = df.filter(condition)
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train = df.filter(~condition)
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for j in range(numModels):
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model = est.fit(train, epm[j])
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# TODO: duplicate evaluator to take extra params from input
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metric = eva.evaluate(model.transform(validation, epm[j]))
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metrics[j] += metric
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if eva.isLargerBetter():
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bestIndex = np.argmax(metrics)
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else:
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bestIndex = np.argmin(metrics)
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bestModel = est.fit(dataset, epm[bestIndex])
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return CrossValidatorModel(bestModel)
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@since("1.4.0")
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def copy(self, extra=None):
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"""
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Creates a copy of this instance with a randomly generated uid
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and some extra params. This copies creates a deep copy of
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the embedded paramMap, and copies the embedded and extra parameters over.
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:param extra: Extra parameters to copy to the new instance
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:return: Copy of this instance
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"""
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if extra is None:
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extra = dict()
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newCV = Params.copy(self, extra)
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if self.isSet(self.estimator):
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newCV.setEstimator(self.getEstimator().copy(extra))
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# estimatorParamMaps remain the same
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if self.isSet(self.evaluator):
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newCV.setEvaluator(self.getEvaluator().copy(extra))
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return newCV
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class CrossValidatorModel(Model):
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"""
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Model from k-fold cross validation.
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.. versionadded:: 1.4.0
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"""
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def __init__(self, bestModel):
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super(CrossValidatorModel, self).__init__()
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#: best model from cross validation
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self.bestModel = bestModel
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def _transform(self, dataset):
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return self.bestModel.transform(dataset)
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@since("1.4.0")
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def copy(self, extra=None):
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"""
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Creates a copy of this instance with a randomly generated uid
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and some extra params. This copies the underlying bestModel,
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creates a deep copy of the embedded paramMap, and
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copies the embedded and extra parameters over.
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:param extra: Extra parameters to copy to the new instance
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:return: Copy of this instance
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"""
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if extra is None:
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extra = dict()
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return CrossValidatorModel(self.bestModel.copy(extra))
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class TrainValidationSplit(Estimator, HasSeed):
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"""
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Train-Validation-Split.
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>>> from pyspark.ml.classification import LogisticRegression
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>>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
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>>> from pyspark.mllib.linalg import Vectors
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>>> dataset = sqlContext.createDataFrame(
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... [(Vectors.dense([0.0]), 0.0),
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... (Vectors.dense([0.4]), 1.0),
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... (Vectors.dense([0.5]), 0.0),
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... (Vectors.dense([0.6]), 1.0),
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... (Vectors.dense([1.0]), 1.0)] * 10,
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... ["features", "label"])
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>>> lr = LogisticRegression()
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>>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
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>>> evaluator = BinaryClassificationEvaluator()
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>>> tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
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>>> tvsModel = tvs.fit(dataset)
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>>> evaluator.evaluate(tvsModel.transform(dataset))
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0.8333...
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.. versionadded:: 2.0.0
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"""
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estimator = Param(Params._dummy(), "estimator", "estimator to be tested")
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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 validated metric")
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trainRatio = Param(Params._dummy(), "trainRatio", "Param for ratio between train and\
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validation data. Must be between 0 and 1.")
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@keyword_only
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def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
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seed=None):
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"""
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__init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
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seed=None)
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"""
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super(TrainValidationSplit, self).__init__()
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self._setDefault(trainRatio=0.75)
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kwargs = self.__init__._input_kwargs
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self._set(**kwargs)
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@since("2.0.0")
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@keyword_only
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def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
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seed=None):
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"""
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setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
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seed=None):
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Sets params for the train validation split.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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@since("2.0.0")
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def 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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self._paramMap[self.estimator] = value
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return self
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@since("2.0.0")
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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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@since("2.0.0")
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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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self._paramMap[self.estimatorParamMaps] = value
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return self
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@since("2.0.0")
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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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@since("2.0.0")
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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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self._paramMap[self.evaluator] = value
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return self
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@since("2.0.0")
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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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@since("2.0.0")
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def setTrainRatio(self, value):
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"""
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Sets the value of :py:attr:`trainRatio`.
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"""
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self._paramMap[self.trainRatio] = value
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return self
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@since("2.0.0")
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def getTrainRatio(self):
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"""
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Gets the value of trainRatio or its default value.
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"""
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return self.getOrDefault(self.trainRatio)
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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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tRatio = self.getOrDefault(self.trainRatio)
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seed = self.getOrDefault(self.seed)
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randCol = self.uid + "_rand"
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df = dataset.select("*", rand(seed).alias(randCol))
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metrics = np.zeros(numModels)
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condition = (df[randCol] >= tRatio)
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validation = df.filter(condition)
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train = df.filter(~condition)
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for j in range(numModels):
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model = est.fit(train, epm[j])
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metric = eva.evaluate(model.transform(validation, epm[j]))
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metrics[j] += metric
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if eva.isLargerBetter():
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bestIndex = np.argmax(metrics)
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else:
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bestIndex = np.argmin(metrics)
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bestModel = est.fit(dataset, epm[bestIndex])
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return TrainValidationSplitModel(bestModel)
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@since("2.0.0")
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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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newTVS = Params.copy(self, extra)
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if self.isSet(self.estimator):
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newTVS.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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newTVS.setEvaluator(self.getEvaluator().copy(extra))
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return newTVS
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class TrainValidationSplitModel(Model):
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"""
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Model from train validation split.
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"""
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def __init__(self, bestModel):
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super(TrainValidationSplitModel, self).__init__()
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#: best model from cross validation
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self.bestModel = bestModel
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def _transform(self, dataset):
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return self.bestModel.transform(dataset)
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@since("2.0.0")
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def copy(self, extra=None):
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"""
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Creates a copy of this instance with a randomly generated uid
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and some extra params. This copies the underlying bestModel,
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creates a deep copy of the embedded paramMap, and
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copies the embedded and extra parameters over.
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:param extra: Extra parameters to copy to the new instance
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:return: Copy of this instance
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"""
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if extra is None:
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extra = dict()
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return TrainValidationSplitModel(self.bestModel.copy(extra))
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if __name__ == "__main__":
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import doctest
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from pyspark.context import SparkContext
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from pyspark.sql import SQLContext
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globs = globals().copy()
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# The small batch size here ensures that we see multiple batches,
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# even in these small test examples:
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sc = SparkContext("local[2]", "ml.tuning tests")
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sqlContext = SQLContext(sc)
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globs['sc'] = sc
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globs['sqlContext'] = sqlContext
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(failure_count, test_count) = doctest.testmod(
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globs=globs, optionflags=doctest.ELLIPSIS)
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sc.stop()
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if failure_count:
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exit(-1)
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