fb4967b5fc
Temporarily disable flaky doctest for CrossValidator. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes #5962 from mengxr/disable-pyspark-cv-test and squashes the following commits:
5db7e5b [Xiangrui Meng] disable cv doctest
(cherry picked from commit 773aa25252
)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
264 lines
9.5 KiB
Python
264 lines
9.5 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.ml.param import Params, Param
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from pyspark.ml import Estimator, Model
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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']
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class ParamGridBuilder(object):
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"""
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Builder for a param grid used in grid search-based model selection.
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>>> from classification import LogisticRegression
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>>> lr = LogisticRegression()
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>>> output = ParamGridBuilder().baseOn({lr.labelCol: 'l'}) \
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.baseOn([lr.predictionCol, 'p']) \
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.addGrid(lr.regParam, [1.0, 2.0, 3.0]) \
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.addGrid(lr.maxIter, [1, 5]) \
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.addGrid(lr.featuresCol, ['f']) \
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.build()
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>>> expected = [ \
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{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
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{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
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{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
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{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
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{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
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{lr.regParam: 3.0, lr.featuresCol: 'f', 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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"""
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def __init__(self):
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self._param_grid = {}
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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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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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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):
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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, 1.0]), 0.0),
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... (Vectors.dense([1.0, 2.0]), 1.0),
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... (Vectors.dense([0.55, 3.0]), 0.0),
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... (Vectors.dense([0.45, 4.0]), 1.0),
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... (Vectors.dense([0.51, 5.0]), 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, 5]).build()
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>>> evaluator = BinaryClassificationEvaluator()
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>>> cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
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>>> # SPARK-7432: The following test is flaky.
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>>> # cvModel = cv.fit(dataset)
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>>> # expected = lr.fit(dataset, {lr.maxIter: 5}).transform(dataset)
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>>> # cvModel.transform(dataset).collect() == expected.collect()
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"""
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# a placeholder to make it appear in the generated doc
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estimator = Param(Params._dummy(), "estimator", "estimator to be cross-validated")
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# a placeholder to make it appear in the generated doc
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estimatorParamMaps = Param(Params._dummy(), "estimatorParamMaps", "estimator param maps")
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# a placeholder to make it appear in the generated doc
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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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# a placeholder to make it appear in the generated doc
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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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"""
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__init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3)
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"""
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super(CrossValidator, self).__init__()
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#: param for estimator to be cross-validated
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self.estimator = Param(self, "estimator", "estimator to be cross-validated")
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#: param for estimator param maps
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self.estimatorParamMaps = Param(self, "estimatorParamMaps", "estimator param maps")
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#: param for the evaluator used to select hyper-parameters that
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#: maximize the cross-validated metric
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self.evaluator = Param(
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self, "evaluator",
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"evaluator used to select hyper-parameters that maximize the cross-validated metric")
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#: param for number of folds for cross validation
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self.numFolds = Param(self, "numFolds", "number of folds for cross validation")
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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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def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3):
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"""
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setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3):
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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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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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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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self.paramMap[self.estimatorParamMaps] = value
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return self
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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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self.paramMap[self.evaluator] = value
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return self
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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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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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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, params={}):
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paramMap = self.extractParamMap(params)
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est = paramMap[self.estimator]
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epm = paramMap[self.estimatorParamMaps]
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numModels = len(epm)
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eva = paramMap[self.evaluator]
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nFolds = paramMap[self.numFolds]
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h = 1.0 / nFolds
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randCol = self.uid + "_rand"
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df = dataset.select("*", rand(0).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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bestIndex = np.argmax(metrics)
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bestModel = est.fit(dataset, epm[bestIndex])
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return CrossValidatorModel(bestModel)
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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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"""
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def __init__(self, bestModel):
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#: best model from cross validation
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self.bestModel = bestModel
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def transform(self, dataset, params={}):
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return self.bestModel.transform(dataset, params)
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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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