a238c23b02
Just realized that we need `\` at the end of the docstring. brkyvz
Author: Xiangrui Meng <meng@databricks.com>
Closes #6161 from mengxr/SPARK-7619 and squashes the following commits:
e44495f [Xiangrui Meng] fix docstring signature
(cherry picked from commit 48fc38f584)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
537 lines
22 KiB
Python
537 lines
22 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.mllib.common import inherit_doc
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__all__ = ['DecisionTreeRegressor', 'DecisionTreeRegressionModel', 'GBTRegressor',
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'GBTRegressionModel', 'LinearRegression', 'LinearRegressionModel',
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'RandomForestRegressor', 'RandomForestRegressionModel']
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@inherit_doc
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class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
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HasRegParam, HasTol):
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"""
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Linear regression.
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The learning objective is to minimize the squared error, with regularization.
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The specific squared error loss function used is: L = 1/2n ||A weights - y||^2^
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This support multiple types of regularization:
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- none (a.k.a. ordinary least squares)
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- L2 (ridge regression)
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- L1 (Lasso)
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- L2 + L1 (elastic net)
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>>> from pyspark.mllib.linalg import Vectors
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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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>>> lr = LinearRegression(maxIter=5, regParam=0.0)
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>>> model = lr.fit(df)
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>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
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>>> model.transform(test0).head().prediction
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-1.0
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>>> model.weights
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DenseVector([1.0])
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>>> model.intercept
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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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>>> 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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_java_class = "org.apache.spark.ml.regression.LinearRegression"
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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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@keyword_only
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def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6):
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"""
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__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
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"""
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super(LinearRegression, self).__init__()
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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, the penalty " +
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"is an L2 penalty. For alpha = 1, it is an L1 penalty.")
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self._setDefault(maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
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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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maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6):
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"""
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setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
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Sets params for linear regression.
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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 LinearRegressionModel(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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class LinearRegressionModel(JavaModel):
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"""
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Model fitted by LinearRegression.
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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 TreeRegressorParams(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 = ["variance"]
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class RandomForestParams(object):
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"""
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Private class to track supported random forest parameters.
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"""
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supportedFeatureSubsetStrategies = ["auto", "all", "onethird", "sqrt", "log2"]
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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 = ["squared", "absolute"]
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@inherit_doc
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class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
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DecisionTreeParams, 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 regression.
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It supports both continuous and categorical features.
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>>> from pyspark.mllib.linalg import Vectors
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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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>>> dt = DecisionTreeRegressor(maxDepth=2)
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>>> model = dt.fit(df)
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>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
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>>> model.transform(test0).head().prediction
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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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_java_class = "org.apache.spark.ml.regression.DecisionTreeRegressor"
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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(TreeRegressorParams.supportedImpurities))
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@keyword_only
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def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance"):
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"""
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__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance")
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"""
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super(DecisionTreeRegressor, self).__init__()
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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(TreeRegressorParams.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="variance")
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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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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
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impurity="variance"):
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"""
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setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance")
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Sets params for the DecisionTreeRegressor.
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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 DecisionTreeRegressionModel(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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class DecisionTreeRegressionModel(JavaModel):
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"""
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Model fitted by DecisionTreeRegressor.
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"""
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@inherit_doc
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class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
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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 regression.
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It supports both continuous and categorical features.
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>>> from pyspark.mllib.linalg import Vectors
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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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>>> rf = RandomForestRegressor(numTrees=2, maxDepth=2)
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>>> model = rf.fit(df)
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>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
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>>> model.transform(test0).head().prediction
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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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0.5
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"""
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_java_class = "org.apache.spark.ml.regression.RandomForestRegressor"
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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(TreeRegressorParams.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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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance",
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numTrees=20, featureSubsetStrategy="auto", seed=42):
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"""
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__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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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="variance", numTrees=20, featureSubsetStrategy="auto", seed=42)
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"""
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super(RandomForestRegressor, self).__init__()
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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(TreeRegressorParams.supportedImpurities))
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#: param for Fraction of the training data used for learning each decision tree,
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# in range (0, 1]
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self.subsamplingRate = Param(self, "subsamplingRate",
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"Fraction of the training data used for learning each " +
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"decision tree, in range (0, 1].")
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#: param for Number of trees to train (>= 1)
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self.numTrees = Param(self, "numTrees", "Number of trees to train (>= 1)")
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#: param for The number of features to consider for splits at each tree node
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self.featureSubsetStrategy = \
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Param(self, "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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self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42,
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impurity="variance", numTrees=20, featureSubsetStrategy="auto")
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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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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42,
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impurity="variance", numTrees=20, featureSubsetStrategy="auto"):
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"""
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setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42, \
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impurity="variance", numTrees=20, featureSubsetStrategy="auto")
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Sets params for linear regression.
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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 RandomForestRegressionModel(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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def setSubsamplingRate(self, value):
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"""
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Sets the value of :py:attr:`subsamplingRate`.
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"""
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self.paramMap[self.subsamplingRate] = value
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return self
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def getSubsamplingRate(self):
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"""
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Gets the value of subsamplingRate or its default value.
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"""
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return self.getOrDefault(self.subsamplingRate)
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def setNumTrees(self, value):
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"""
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Sets the value of :py:attr:`numTrees`.
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"""
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self.paramMap[self.numTrees] = value
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return self
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def getNumTrees(self):
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"""
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Gets the value of numTrees or its default value.
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"""
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return self.getOrDefault(self.numTrees)
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def setFeatureSubsetStrategy(self, value):
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"""
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Sets the value of :py:attr:`featureSubsetStrategy`.
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"""
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self.paramMap[self.featureSubsetStrategy] = value
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return self
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def getFeatureSubsetStrategy(self):
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"""
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Gets the value of featureSubsetStrategy or its default value.
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"""
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return self.getOrDefault(self.featureSubsetStrategy)
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class RandomForestRegressionModel(JavaModel):
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"""
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Model fitted by RandomForestRegressor.
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"""
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@inherit_doc
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class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
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DecisionTreeParams, HasCheckpointInterval):
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"""
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`http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
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learning algorithm for regression.
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It supports both continuous and categorical features.
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>>> from pyspark.mllib.linalg import Vectors
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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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>>> gbt = GBTRegressor(maxIter=5, maxDepth=2)
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>>> model = gbt.fit(df)
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>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
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>>> model.transform(test0).head().prediction
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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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_java_class = "org.apache.spark.ml.regression.GBTRegressor"
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# a placeholder to make it appear in the generated doc
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lossType = Param(Params._dummy(), "lossType",
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"Loss function which GBT tries to minimize (case-insensitive). " +
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"Supported options: " + ", ".join(GBTParams.supportedLossTypes))
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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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stepSize = Param(Params._dummy(), "stepSize",
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"Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the " +
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"contribution of each estimator")
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@keyword_only
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def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
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maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
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maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="squared",
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maxIter=20, stepSize=0.1):
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"""
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__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
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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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lossType="squared", maxIter=20, stepSize=0.1)
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"""
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super(GBTRegressor, self).__init__()
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#: param for Loss function which GBT tries to minimize (case-insensitive).
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self.lossType = Param(self, "lossType",
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"Loss function which GBT tries to minimize (case-insensitive). " +
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"Supported options: " + ", ".join(GBTParams.supportedLossTypes))
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#: Fraction of the training data used for learning each decision tree, in range (0, 1].
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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="squared", 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="squared", 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="squared", maxIter=20, stepSize=0.1)
|
|
Sets params for Gradient Boosted Tree Regression.
|
|
"""
|
|
kwargs = self.setParams._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
def _create_model(self, java_model):
|
|
return GBTRegressionModel(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 GBTRegressionModel(JavaModel):
|
|
"""
|
|
Model fitted by GBTRegressor.
|
|
"""
|
|
|
|
|
|
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.regression 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)
|