551def5d69
Reinstated LogisticRegression.threshold Param for binary compatibility. Param thresholds overrides threshold, if set. CC: mengxr dbtsai feynmanliang Author: Joseph K. Bradley <joseph@databricks.com> Closes #8079 from jkbradley/logreg-reinstate-threshold.
827 lines
35 KiB
Python
827 lines
35 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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from pyspark.ml.util import keyword_only
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from pyspark.ml.wrapper import JavaEstimator, JavaModel
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from pyspark.ml.param.shared import *
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from pyspark.ml.regression import (
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RandomForestParams, DecisionTreeModel, TreeEnsembleModels)
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from pyspark.mllib.common import inherit_doc
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__all__ = ['LogisticRegression', 'LogisticRegressionModel', 'DecisionTreeClassifier',
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'DecisionTreeClassificationModel', 'GBTClassifier', 'GBTClassificationModel',
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'RandomForestClassifier', 'RandomForestClassificationModel', 'NaiveBayes',
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'NaiveBayesModel']
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@inherit_doc
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class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
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HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol):
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"""
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Logistic regression.
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Currently, this class only supports binary classification.
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>>> from pyspark.sql import Row
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>>> from pyspark.mllib.linalg import Vectors
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>>> df = sc.parallelize([
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... Row(label=1.0, features=Vectors.dense(1.0)),
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... Row(label=0.0, features=Vectors.sparse(1, [], []))]).toDF()
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>>> lr = LogisticRegression(maxIter=5, regParam=0.01)
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>>> model = lr.fit(df)
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>>> model.weights
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DenseVector([5.5...])
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>>> model.intercept
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-2.68...
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>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
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>>> result = model.transform(test0).head()
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>>> result.prediction
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0.0
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>>> result.probability
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DenseVector([0.99..., 0.00...])
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>>> result.rawPrediction
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DenseVector([8.22..., -8.22...])
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>>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF()
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>>> model.transform(test1).head().prediction
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1.0
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>>> lr.setParams("vector")
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Traceback (most recent call last):
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...
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TypeError: Method setParams forces keyword arguments.
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"""
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# a placeholder to make it appear in the generated doc
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elasticNetParam = \
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Param(Params._dummy(), "elasticNetParam",
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"the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
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"the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
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fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.")
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thresholds = Param(Params._dummy(), "thresholds",
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"Thresholds in multi-class classification" +
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" to adjust the probability of predicting each class." +
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" Array must have length equal to the number of classes, with values >= 0." +
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" The class with largest value p/t is predicted, where p is the original" +
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" probability of that class and t is the class' threshold.")
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threshold = Param(Params._dummy(), "threshold",
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"Threshold in binary classification prediction, in range [0, 1]." +
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" If threshold and thresholds are both set, they must match.")
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@keyword_only
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def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
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threshold=0.5, thresholds=None,
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probabilityCol="probability", rawPredictionCol="rawPrediction"):
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"""
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__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
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threshold=0.5, thresholds=None, \
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probabilityCol="probability", rawPredictionCol="rawPrediction")
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If the threshold and thresholds Params are both set, they must be equivalent.
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"""
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super(LogisticRegression, self).__init__()
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self._java_obj = self._new_java_obj(
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"org.apache.spark.ml.classification.LogisticRegression", self.uid)
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#: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty
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# is an L2 penalty. For alpha = 1, it is an L1 penalty.
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self.elasticNetParam = \
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Param(self, "elasticNetParam",
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"the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
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"the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
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#: param for whether to fit an intercept term.
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self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
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#: param for threshold in binary classification, in range [0, 1].
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self.threshold = Param(self, "threshold",
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"Threshold in binary classification prediction, in range [0, 1]." +
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" If threshold and thresholds are both set, they must match.")
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#: param for thresholds or cutoffs in binary or multiclass classification
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self.thresholds = \
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Param(self, "thresholds",
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"Thresholds in multi-class classification" +
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" to adjust the probability of predicting each class." +
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" Array must have length equal to the number of classes, with values >= 0." +
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" The class with largest value p/t is predicted, where p is the original" +
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" probability of that class and t is the class' threshold.")
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self._setDefault(maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1E-6,
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fitIntercept=True, threshold=0.5)
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kwargs = self.__init__._input_kwargs
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self.setParams(**kwargs)
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self._checkThresholdConsistency()
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@keyword_only
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def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
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threshold=0.5, thresholds=None,
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probabilityCol="probability", rawPredictionCol="rawPrediction"):
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"""
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setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
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threshold=0.5, thresholds=None, \
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probabilityCol="probability", rawPredictionCol="rawPrediction")
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Sets params for logistic regression.
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If the threshold and thresholds Params are both set, they must be equivalent.
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"""
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kwargs = self.setParams._input_kwargs
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self._set(**kwargs)
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self._checkThresholdConsistency()
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return self
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def _create_model(self, java_model):
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return LogisticRegressionModel(java_model)
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def setElasticNetParam(self, value):
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"""
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Sets the value of :py:attr:`elasticNetParam`.
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"""
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self._paramMap[self.elasticNetParam] = value
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return self
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def getElasticNetParam(self):
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"""
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Gets the value of elasticNetParam or its default value.
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"""
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return self.getOrDefault(self.elasticNetParam)
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def setFitIntercept(self, value):
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"""
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Sets the value of :py:attr:`fitIntercept`.
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"""
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self._paramMap[self.fitIntercept] = value
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return self
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def getFitIntercept(self):
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"""
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Gets the value of fitIntercept or its default value.
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"""
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return self.getOrDefault(self.fitIntercept)
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def setThreshold(self, value):
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"""
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Sets the value of :py:attr:`threshold`.
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Clears value of :py:attr:`thresholds` if it has been set.
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"""
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self._paramMap[self.threshold] = value
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if self.isSet(self.thresholds):
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del self._paramMap[self.thresholds]
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return self
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def getThreshold(self):
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"""
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Gets the value of threshold or its default value.
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"""
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self._checkThresholdConsistency()
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if self.isSet(self.thresholds):
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ts = self.getOrDefault(self.thresholds)
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if len(ts) != 2:
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raise ValueError("Logistic Regression getThreshold only applies to" +
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" binary classification, but thresholds has length != 2." +
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" thresholds: " + ",".join(ts))
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return 1.0/(1.0 + ts[0]/ts[1])
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else:
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return self.getOrDefault(self.threshold)
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def setThresholds(self, value):
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"""
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Sets the value of :py:attr:`thresholds`.
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Clears value of :py:attr:`threshold` if it has been set.
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"""
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self._paramMap[self.thresholds] = value
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if self.isSet(self.threshold):
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del self._paramMap[self.threshold]
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return self
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def getThresholds(self):
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"""
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If :py:attr:`thresholds` is set, return its value.
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Otherwise, if :py:attr:`threshold` is set, return the equivalent thresholds for binary
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classification: (1-threshold, threshold).
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If neither are set, throw an error.
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"""
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self._checkThresholdConsistency()
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if not self.isSet(self.thresholds) and self.isSet(self.threshold):
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t = self.getOrDefault(self.threshold)
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return [1.0-t, t]
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else:
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return self.getOrDefault(self.thresholds)
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def _checkThresholdConsistency(self):
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if self.isSet(self.threshold) and self.isSet(self.thresholds):
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ts = self.getParam(self.thresholds)
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if len(ts) != 2:
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raise ValueError("Logistic Regression getThreshold only applies to" +
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" binary classification, but thresholds has length != 2." +
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" thresholds: " + ",".join(ts))
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t = 1.0/(1.0 + ts[0]/ts[1])
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t2 = self.getParam(self.threshold)
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if abs(t2 - t) >= 1E-5:
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raise ValueError("Logistic Regression getThreshold found inconsistent values for" +
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" threshold (%g) and thresholds (equivalent to %g)" % (t2, t))
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class LogisticRegressionModel(JavaModel):
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"""
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Model fitted by LogisticRegression.
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"""
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@property
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def weights(self):
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"""
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Model weights.
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"""
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return self._call_java("weights")
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@property
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def intercept(self):
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"""
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Model intercept.
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"""
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return self._call_java("intercept")
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class TreeClassifierParams(object):
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"""
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Private class to track supported impurity measures.
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"""
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supportedImpurities = ["entropy", "gini"]
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class GBTParams(object):
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"""
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Private class to track supported GBT params.
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"""
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supportedLossTypes = ["logistic"]
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@inherit_doc
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class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
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HasProbabilityCol, HasRawPredictionCol, DecisionTreeParams,
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HasCheckpointInterval):
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"""
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`http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree`
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learning algorithm for classification.
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It supports both binary and multiclass labels, as well as both continuous and categorical
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features.
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>>> from pyspark.mllib.linalg import Vectors
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>>> from pyspark.ml.feature import StringIndexer
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>>> df = sqlContext.createDataFrame([
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... (1.0, Vectors.dense(1.0)),
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... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
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>>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
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>>> si_model = stringIndexer.fit(df)
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>>> td = si_model.transform(df)
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>>> dt = DecisionTreeClassifier(maxDepth=2, labelCol="indexed")
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>>> model = dt.fit(td)
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>>> model.numNodes
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3
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>>> model.depth
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1
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>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
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>>> result = model.transform(test0).head()
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>>> result.prediction
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0.0
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>>> result.probability
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DenseVector([1.0, 0.0])
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>>> result.rawPrediction
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DenseVector([1.0, 0.0])
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>>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
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>>> model.transform(test1).head().prediction
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1.0
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"""
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# a placeholder to make it appear in the generated doc
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impurity = Param(Params._dummy(), "impurity",
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"Criterion used for information gain calculation (case-insensitive). " +
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"Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities))
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@keyword_only
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def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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probabilityCol="probability", rawPredictionCol="rawPrediction",
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini"):
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"""
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__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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probabilityCol="probability", rawPredictionCol="rawPrediction", \
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini")
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"""
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super(DecisionTreeClassifier, self).__init__()
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self._java_obj = self._new_java_obj(
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"org.apache.spark.ml.classification.DecisionTreeClassifier", self.uid)
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#: param for Criterion used for information gain calculation (case-insensitive).
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self.impurity = \
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Param(self, "impurity",
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"Criterion used for information gain calculation (case-insensitive). " +
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"Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities))
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self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
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impurity="gini")
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kwargs = self.__init__._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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probabilityCol="probability", rawPredictionCol="rawPrediction",
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
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impurity="gini"):
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"""
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setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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probabilityCol="probability", rawPredictionCol="rawPrediction", \
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini")
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Sets params for the DecisionTreeClassifier.
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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 _create_model(self, java_model):
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return DecisionTreeClassificationModel(java_model)
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def setImpurity(self, value):
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"""
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Sets the value of :py:attr:`impurity`.
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"""
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self._paramMap[self.impurity] = value
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return self
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def getImpurity(self):
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"""
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Gets the value of impurity or its default value.
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"""
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return self.getOrDefault(self.impurity)
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@inherit_doc
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class DecisionTreeClassificationModel(DecisionTreeModel):
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"""
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Model fitted by DecisionTreeClassifier.
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"""
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@inherit_doc
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class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
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HasRawPredictionCol, HasProbabilityCol,
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DecisionTreeParams, HasCheckpointInterval):
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"""
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`http://en.wikipedia.org/wiki/Random_forest Random Forest`
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learning algorithm for classification.
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It supports both binary and multiclass labels, as well as both continuous and categorical
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features.
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>>> import numpy
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>>> from numpy import allclose
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>>> from pyspark.mllib.linalg import Vectors
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>>> from pyspark.ml.feature import StringIndexer
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>>> df = sqlContext.createDataFrame([
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... (1.0, Vectors.dense(1.0)),
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... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
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>>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
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>>> si_model = stringIndexer.fit(df)
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>>> td = si_model.transform(df)
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>>> rf = RandomForestClassifier(numTrees=3, maxDepth=2, labelCol="indexed", seed=42)
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>>> model = rf.fit(td)
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>>> allclose(model.treeWeights, [1.0, 1.0, 1.0])
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True
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>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
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>>> result = model.transform(test0).head()
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>>> result.prediction
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0.0
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>>> numpy.argmax(result.probability)
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0
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>>> numpy.argmax(result.rawPrediction)
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0
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>>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
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>>> model.transform(test1).head().prediction
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1.0
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"""
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# a placeholder to make it appear in the generated doc
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impurity = Param(Params._dummy(), "impurity",
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"Criterion used for information gain calculation (case-insensitive). " +
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"Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities))
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subsamplingRate = Param(Params._dummy(), "subsamplingRate",
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"Fraction of the training data used for learning each decision tree, " +
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"in range (0, 1].")
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numTrees = Param(Params._dummy(), "numTrees", "Number of trees to train (>= 1)")
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featureSubsetStrategy = \
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Param(Params._dummy(), "featureSubsetStrategy",
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"The number of features to consider for splits at each tree node. Supported " +
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"options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies))
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@keyword_only
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def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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probabilityCol="probability", rawPredictionCol="rawPrediction",
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini",
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numTrees=20, featureSubsetStrategy="auto", seed=None):
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"""
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__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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probabilityCol="probability", rawPredictionCol="rawPrediction", \
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini", \
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numTrees=20, featureSubsetStrategy="auto", seed=None)
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"""
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super(RandomForestClassifier, self).__init__()
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self._java_obj = self._new_java_obj(
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"org.apache.spark.ml.classification.RandomForestClassifier", self.uid)
|
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#: param for Criterion used for information gain calculation (case-insensitive).
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|
self.impurity = \
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|
Param(self, "impurity",
|
|
"Criterion used for information gain calculation (case-insensitive). " +
|
|
"Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities))
|
|
#: param for Fraction of the training data used for learning each decision tree,
|
|
# in range (0, 1]
|
|
self.subsamplingRate = Param(self, "subsamplingRate",
|
|
"Fraction of the training data used for learning each " +
|
|
"decision tree, in range (0, 1].")
|
|
#: param for Number of trees to train (>= 1)
|
|
self.numTrees = Param(self, "numTrees", "Number of trees to train (>= 1)")
|
|
#: param for The number of features to consider for splits at each tree node
|
|
self.featureSubsetStrategy = \
|
|
Param(self, "featureSubsetStrategy",
|
|
"The number of features to consider for splits at each tree node. Supported " +
|
|
"options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies))
|
|
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
|
|
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
|
|
impurity="gini", numTrees=20, featureSubsetStrategy="auto")
|
|
kwargs = self.__init__._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
|
|
probabilityCol="probability", rawPredictionCol="rawPrediction",
|
|
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
|
|
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
|
|
impurity="gini", numTrees=20, featureSubsetStrategy="auto"):
|
|
"""
|
|
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
|
|
probabilityCol="probability", rawPredictionCol="rawPrediction", \
|
|
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
|
|
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, \
|
|
impurity="gini", numTrees=20, featureSubsetStrategy="auto")
|
|
Sets params for linear classification.
|
|
"""
|
|
kwargs = self.setParams._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
def _create_model(self, java_model):
|
|
return RandomForestClassificationModel(java_model)
|
|
|
|
def setImpurity(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`impurity`.
|
|
"""
|
|
self._paramMap[self.impurity] = value
|
|
return self
|
|
|
|
def getImpurity(self):
|
|
"""
|
|
Gets the value of impurity or its default value.
|
|
"""
|
|
return self.getOrDefault(self.impurity)
|
|
|
|
def setSubsamplingRate(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`subsamplingRate`.
|
|
"""
|
|
self._paramMap[self.subsamplingRate] = value
|
|
return self
|
|
|
|
def getSubsamplingRate(self):
|
|
"""
|
|
Gets the value of subsamplingRate or its default value.
|
|
"""
|
|
return self.getOrDefault(self.subsamplingRate)
|
|
|
|
def setNumTrees(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`numTrees`.
|
|
"""
|
|
self._paramMap[self.numTrees] = value
|
|
return self
|
|
|
|
def getNumTrees(self):
|
|
"""
|
|
Gets the value of numTrees or its default value.
|
|
"""
|
|
return self.getOrDefault(self.numTrees)
|
|
|
|
def setFeatureSubsetStrategy(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`featureSubsetStrategy`.
|
|
"""
|
|
self._paramMap[self.featureSubsetStrategy] = value
|
|
return self
|
|
|
|
def getFeatureSubsetStrategy(self):
|
|
"""
|
|
Gets the value of featureSubsetStrategy or its default value.
|
|
"""
|
|
return self.getOrDefault(self.featureSubsetStrategy)
|
|
|
|
|
|
class RandomForestClassificationModel(TreeEnsembleModels):
|
|
"""
|
|
Model fitted by RandomForestClassifier.
|
|
"""
|
|
|
|
|
|
@inherit_doc
|
|
class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
|
|
DecisionTreeParams, HasCheckpointInterval):
|
|
"""
|
|
`http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
|
|
learning algorithm for classification.
|
|
It supports binary labels, as well as both continuous and categorical features.
|
|
Note: Multiclass labels are not currently supported.
|
|
|
|
>>> from numpy import allclose
|
|
>>> from pyspark.mllib.linalg import Vectors
|
|
>>> from pyspark.ml.feature import StringIndexer
|
|
>>> df = sqlContext.createDataFrame([
|
|
... (1.0, Vectors.dense(1.0)),
|
|
... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
|
|
>>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
|
|
>>> si_model = stringIndexer.fit(df)
|
|
>>> td = si_model.transform(df)
|
|
>>> gbt = GBTClassifier(maxIter=5, maxDepth=2, labelCol="indexed")
|
|
>>> model = gbt.fit(td)
|
|
>>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1])
|
|
True
|
|
>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
|
|
>>> model.transform(test0).head().prediction
|
|
0.0
|
|
>>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
|
|
>>> model.transform(test1).head().prediction
|
|
1.0
|
|
"""
|
|
|
|
# a placeholder to make it appear in the generated doc
|
|
lossType = Param(Params._dummy(), "lossType",
|
|
"Loss function which GBT tries to minimize (case-insensitive). " +
|
|
"Supported options: " + ", ".join(GBTParams.supportedLossTypes))
|
|
subsamplingRate = Param(Params._dummy(), "subsamplingRate",
|
|
"Fraction of the training data used for learning each decision tree, " +
|
|
"in range (0, 1].")
|
|
stepSize = Param(Params._dummy(), "stepSize",
|
|
"Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the " +
|
|
"contribution of each estimator")
|
|
|
|
@keyword_only
|
|
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
|
|
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
|
|
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="logistic",
|
|
maxIter=20, stepSize=0.1):
|
|
"""
|
|
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
|
|
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
|
|
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
|
|
lossType="logistic", maxIter=20, stepSize=0.1)
|
|
"""
|
|
super(GBTClassifier, self).__init__()
|
|
self._java_obj = self._new_java_obj(
|
|
"org.apache.spark.ml.classification.GBTClassifier", self.uid)
|
|
#: param for Loss function which GBT tries to minimize (case-insensitive).
|
|
self.lossType = Param(self, "lossType",
|
|
"Loss function which GBT tries to minimize (case-insensitive). " +
|
|
"Supported options: " + ", ".join(GBTParams.supportedLossTypes))
|
|
#: Fraction of the training data used for learning each decision tree, in range (0, 1].
|
|
self.subsamplingRate = Param(self, "subsamplingRate",
|
|
"Fraction of the training data used for learning each " +
|
|
"decision tree, in range (0, 1].")
|
|
#: Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of
|
|
# each estimator
|
|
self.stepSize = Param(self, "stepSize",
|
|
"Step size (a.k.a. learning rate) in interval (0, 1] for shrinking " +
|
|
"the contribution of each estimator")
|
|
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
|
|
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
|
|
lossType="logistic", maxIter=20, stepSize=0.1)
|
|
kwargs = self.__init__._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
|
|
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
|
|
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
|
|
lossType="logistic", maxIter=20, stepSize=0.1):
|
|
"""
|
|
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
|
|
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
|
|
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
|
|
lossType="logistic", maxIter=20, stepSize=0.1)
|
|
Sets params for Gradient Boosted Tree Classification.
|
|
"""
|
|
kwargs = self.setParams._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
def _create_model(self, java_model):
|
|
return GBTClassificationModel(java_model)
|
|
|
|
def setLossType(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`lossType`.
|
|
"""
|
|
self._paramMap[self.lossType] = value
|
|
return self
|
|
|
|
def getLossType(self):
|
|
"""
|
|
Gets the value of lossType or its default value.
|
|
"""
|
|
return self.getOrDefault(self.lossType)
|
|
|
|
def setSubsamplingRate(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`subsamplingRate`.
|
|
"""
|
|
self._paramMap[self.subsamplingRate] = value
|
|
return self
|
|
|
|
def getSubsamplingRate(self):
|
|
"""
|
|
Gets the value of subsamplingRate or its default value.
|
|
"""
|
|
return self.getOrDefault(self.subsamplingRate)
|
|
|
|
def setStepSize(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`stepSize`.
|
|
"""
|
|
self._paramMap[self.stepSize] = value
|
|
return self
|
|
|
|
def getStepSize(self):
|
|
"""
|
|
Gets the value of stepSize or its default value.
|
|
"""
|
|
return self.getOrDefault(self.stepSize)
|
|
|
|
|
|
class GBTClassificationModel(TreeEnsembleModels):
|
|
"""
|
|
Model fitted by GBTClassifier.
|
|
"""
|
|
|
|
|
|
@inherit_doc
|
|
class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasProbabilityCol,
|
|
HasRawPredictionCol):
|
|
"""
|
|
Naive Bayes Classifiers.
|
|
It supports both Multinomial and Bernoulli NB. Multinomial NB
|
|
(`http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html`)
|
|
can handle finitely supported discrete data. For example, by converting documents into
|
|
TF-IDF vectors, it can be used for document classification. By making every vector a
|
|
binary (0/1) data, it can also be used as Bernoulli NB
|
|
(`http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html`).
|
|
The input feature values must be nonnegative.
|
|
|
|
>>> from pyspark.sql import Row
|
|
>>> from pyspark.mllib.linalg import Vectors
|
|
>>> df = sqlContext.createDataFrame([
|
|
... Row(label=0.0, features=Vectors.dense([0.0, 0.0])),
|
|
... Row(label=0.0, features=Vectors.dense([0.0, 1.0])),
|
|
... Row(label=1.0, features=Vectors.dense([1.0, 0.0]))])
|
|
>>> nb = NaiveBayes(smoothing=1.0, modelType="multinomial")
|
|
>>> model = nb.fit(df)
|
|
>>> model.pi
|
|
DenseVector([-0.51..., -0.91...])
|
|
>>> model.theta
|
|
DenseMatrix(2, 2, [-1.09..., -0.40..., -0.40..., -1.09...], 1)
|
|
>>> test0 = sc.parallelize([Row(features=Vectors.dense([1.0, 0.0]))]).toDF()
|
|
>>> result = model.transform(test0).head()
|
|
>>> result.prediction
|
|
1.0
|
|
>>> result.probability
|
|
DenseVector([0.42..., 0.57...])
|
|
>>> result.rawPrediction
|
|
DenseVector([-1.60..., -1.32...])
|
|
>>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF()
|
|
>>> model.transform(test1).head().prediction
|
|
1.0
|
|
"""
|
|
|
|
# a placeholder to make it appear in the generated doc
|
|
smoothing = Param(Params._dummy(), "smoothing", "The smoothing parameter, should be >= 0, " +
|
|
"default is 1.0")
|
|
modelType = Param(Params._dummy(), "modelType", "The model type which is a string " +
|
|
"(case-sensitive). Supported options: multinomial (default) and bernoulli.")
|
|
|
|
@keyword_only
|
|
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
|
|
probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0,
|
|
modelType="multinomial"):
|
|
"""
|
|
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
|
|
probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0, \
|
|
modelType="multinomial")
|
|
"""
|
|
super(NaiveBayes, self).__init__()
|
|
self._java_obj = self._new_java_obj(
|
|
"org.apache.spark.ml.classification.NaiveBayes", self.uid)
|
|
#: param for the smoothing parameter.
|
|
self.smoothing = Param(self, "smoothing", "The smoothing parameter, should be >= 0, " +
|
|
"default is 1.0")
|
|
#: param for the model type.
|
|
self.modelType = Param(self, "modelType", "The model type which is a string " +
|
|
"(case-sensitive). Supported options: multinomial (default) " +
|
|
"and bernoulli.")
|
|
self._setDefault(smoothing=1.0, modelType="multinomial")
|
|
kwargs = self.__init__._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
|
|
probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0,
|
|
modelType="multinomial"):
|
|
"""
|
|
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
|
|
probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0, \
|
|
modelType="multinomial")
|
|
Sets params for Naive Bayes.
|
|
"""
|
|
kwargs = self.setParams._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
def _create_model(self, java_model):
|
|
return NaiveBayesModel(java_model)
|
|
|
|
def setSmoothing(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`smoothing`.
|
|
"""
|
|
self._paramMap[self.smoothing] = value
|
|
return self
|
|
|
|
def getSmoothing(self):
|
|
"""
|
|
Gets the value of smoothing or its default value.
|
|
"""
|
|
return self.getOrDefault(self.smoothing)
|
|
|
|
def setModelType(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`modelType`.
|
|
"""
|
|
self._paramMap[self.modelType] = value
|
|
return self
|
|
|
|
def getModelType(self):
|
|
"""
|
|
Gets the value of modelType or its default value.
|
|
"""
|
|
return self.getOrDefault(self.modelType)
|
|
|
|
|
|
class NaiveBayesModel(JavaModel):
|
|
"""
|
|
Model fitted by NaiveBayes.
|
|
"""
|
|
|
|
@property
|
|
def pi(self):
|
|
"""
|
|
log of class priors.
|
|
"""
|
|
return self._call_java("pi")
|
|
|
|
@property
|
|
def theta(self):
|
|
"""
|
|
log of class conditional probabilities.
|
|
"""
|
|
return self._call_java("theta")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import doctest
|
|
from pyspark.context import SparkContext
|
|
from pyspark.sql import SQLContext
|
|
globs = globals().copy()
|
|
# The small batch size here ensures that we see multiple batches,
|
|
# even in these small test examples:
|
|
sc = SparkContext("local[2]", "ml.classification tests")
|
|
sqlContext = SQLContext(sc)
|
|
globs['sc'] = sc
|
|
globs['sqlContext'] = sqlContext
|
|
(failure_count, test_count) = doctest.testmod(
|
|
globs=globs, optionflags=doctest.ELLIPSIS)
|
|
sc.stop()
|
|
if failure_count:
|
|
exit(-1)
|