Source code for pyspark.ml.regression
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# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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from pyspark.ml.util import keyword_only
from pyspark.ml.wrapper import JavaEstimator, JavaModel
from pyspark.ml.param.shared import *
from pyspark.mllib.common import inherit_doc
__all__ = ['DecisionTreeRegressor', 'DecisionTreeRegressionModel', 'GBTRegressor',
'GBTRegressionModel', 'LinearRegression', 'LinearRegressionModel',
'RandomForestRegressor', 'RandomForestRegressionModel']
@inherit_doc
[docs]class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
HasRegParam, HasTol):
"""
Linear regression.
The learning objective is to minimize the squared error, with regularization.
The specific squared error loss function used is: L = 1/2n ||A weights - y||^2^
This support multiple types of regularization:
- none (a.k.a. ordinary least squares)
- L2 (ridge regression)
- L1 (Lasso)
- L2 + L1 (elastic net)
>>> from pyspark.mllib.linalg import Vectors
>>> df = sqlContext.createDataFrame([
... (1.0, Vectors.dense(1.0)),
... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
>>> lr = LinearRegression(maxIter=5, regParam=0.0)
>>> model = lr.fit(df)
>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> model.transform(test0).head().prediction
-1.0
>>> model.weights
DenseVector([1.0])
>>> model.intercept
0.0
>>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
>>> model.transform(test1).head().prediction
1.0
>>> lr.setParams("vector")
Traceback (most recent call last):
...
TypeError: Method setParams forces keyword arguments.
"""
# a placeholder to make it appear in the generated doc
elasticNetParam = \
Param(Params._dummy(), "elasticNetParam",
"the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
"the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
@keyword_only
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
"""
super(LinearRegression, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.regression.LinearRegression", self.uid)
#: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty
# is an L2 penalty. For alpha = 1, it is an L1 penalty.
self.elasticNetParam = \
Param(self, "elasticNetParam",
"the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty " +
"is an L2 penalty. For alpha = 1, it is an L1 penalty.")
self._setDefault(maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
[docs] def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
Sets params for linear regression.
"""
kwargs = self.setParams._input_kwargs
return self._set(**kwargs)
def _create_model(self, java_model):
return LinearRegressionModel(java_model)
[docs] def setElasticNetParam(self, value):
"""
Sets the value of :py:attr:`elasticNetParam`.
"""
self._paramMap[self.elasticNetParam] = value
return self
[docs] def getElasticNetParam(self):
"""
Gets the value of elasticNetParam or its default value.
"""
return self.getOrDefault(self.elasticNetParam)
[docs]class LinearRegressionModel(JavaModel):
"""
Model fitted by LinearRegression.
"""
@property
@property
class TreeRegressorParams(object):
"""
Private class to track supported impurity measures.
"""
supportedImpurities = ["variance"]
class RandomForestParams(object):
"""
Private class to track supported random forest parameters.
"""
supportedFeatureSubsetStrategies = ["auto", "all", "onethird", "sqrt", "log2"]
class GBTParams(object):
"""
Private class to track supported GBT params.
"""
supportedLossTypes = ["squared", "absolute"]
@inherit_doc
[docs]class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
DecisionTreeParams, HasCheckpointInterval):
"""
`http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree`
learning algorithm for regression.
It supports both continuous and categorical features.
>>> from pyspark.mllib.linalg import Vectors
>>> df = sqlContext.createDataFrame([
... (1.0, Vectors.dense(1.0)),
... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
>>> dt = DecisionTreeRegressor(maxDepth=2)
>>> model = dt.fit(df)
>>> 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
impurity = Param(Params._dummy(), "impurity",
"Criterion used for information gain calculation (case-insensitive). " +
"Supported options: " + ", ".join(TreeRegressorParams.supportedImpurities))
@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, impurity="variance"):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance")
"""
super(DecisionTreeRegressor, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.regression.DecisionTreeRegressor", self.uid)
#: param for Criterion used for information gain calculation (case-insensitive).
self.impurity = \
Param(self, "impurity",
"Criterion used for information gain calculation (case-insensitive). " +
"Supported options: " + ", ".join(TreeRegressorParams.supportedImpurities))
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
impurity="variance")
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
[docs] def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
impurity="variance"):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance")
Sets params for the DecisionTreeRegressor.
"""
kwargs = self.setParams._input_kwargs
return self._set(**kwargs)
def _create_model(self, java_model):
return DecisionTreeRegressionModel(java_model)
[docs] def setImpurity(self, value):
"""
Sets the value of :py:attr:`impurity`.
"""
self._paramMap[self.impurity] = value
return self
[docs] def getImpurity(self):
"""
Gets the value of impurity or its default value.
"""
return self.getOrDefault(self.impurity)
@inherit_doc
[docs]class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
DecisionTreeParams, HasCheckpointInterval):
"""
`http://en.wikipedia.org/wiki/Random_forest Random Forest`
learning algorithm for regression.
It supports both continuous and categorical features.
>>> from pyspark.mllib.linalg import Vectors
>>> df = sqlContext.createDataFrame([
... (1.0, Vectors.dense(1.0)),
... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
>>> rf = RandomForestRegressor(numTrees=2, maxDepth=2, seed=42)
>>> model = rf.fit(df)
>>> 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
0.5
"""
# a placeholder to make it appear in the generated doc
impurity = Param(Params._dummy(), "impurity",
"Criterion used for information gain calculation (case-insensitive). " +
"Supported options: " + ", ".join(TreeRegressorParams.supportedImpurities))
subsamplingRate = Param(Params._dummy(), "subsamplingRate",
"Fraction of the training data used for learning each decision tree, " +
"in range (0, 1].")
numTrees = Param(Params._dummy(), "numTrees", "Number of trees to train (>= 1)")
featureSubsetStrategy = \
Param(Params._dummy(), "featureSubsetStrategy",
"The number of features to consider for splits at each tree node. Supported " +
"options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies))
@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, impurity="variance",
numTrees=20, featureSubsetStrategy="auto", seed=None):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
impurity="variance", numTrees=20, \
featureSubsetStrategy="auto", seed=None)
"""
super(RandomForestRegressor, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.regression.RandomForestRegressor", self.uid)
#: param for Criterion used for information gain calculation (case-insensitive).
self.impurity = \
Param(self, "impurity",
"Criterion used for information gain calculation (case-insensitive). " +
"Supported options: " + ", ".join(TreeRegressorParams.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="variance", numTrees=20, featureSubsetStrategy="auto")
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
[docs] def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
impurity="variance", numTrees=20, featureSubsetStrategy="auto"):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, \
impurity="variance", numTrees=20, featureSubsetStrategy="auto")
Sets params for linear regression.
"""
kwargs = self.setParams._input_kwargs
return self._set(**kwargs)
def _create_model(self, java_model):
return RandomForestRegressionModel(java_model)
[docs] def setImpurity(self, value):
"""
Sets the value of :py:attr:`impurity`.
"""
self._paramMap[self.impurity] = value
return self
[docs] def getImpurity(self):
"""
Gets the value of impurity or its default value.
"""
return self.getOrDefault(self.impurity)
[docs] def setSubsamplingRate(self, value):
"""
Sets the value of :py:attr:`subsamplingRate`.
"""
self._paramMap[self.subsamplingRate] = value
return self
[docs] def getSubsamplingRate(self):
"""
Gets the value of subsamplingRate or its default value.
"""
return self.getOrDefault(self.subsamplingRate)
[docs] def setNumTrees(self, value):
"""
Sets the value of :py:attr:`numTrees`.
"""
self._paramMap[self.numTrees] = value
return self
[docs] def getNumTrees(self):
"""
Gets the value of numTrees or its default value.
"""
return self.getOrDefault(self.numTrees)
[docs] def setFeatureSubsetStrategy(self, value):
"""
Sets the value of :py:attr:`featureSubsetStrategy`.
"""
self._paramMap[self.featureSubsetStrategy] = value
return self
[docs] def getFeatureSubsetStrategy(self):
"""
Gets the value of featureSubsetStrategy or its default value.
"""
return self.getOrDefault(self.featureSubsetStrategy)
@inherit_doc
[docs]class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
DecisionTreeParams, HasCheckpointInterval):
"""
`http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
learning algorithm for regression.
It supports both continuous and categorical features.
>>> from pyspark.mllib.linalg import Vectors
>>> df = sqlContext.createDataFrame([
... (1.0, Vectors.dense(1.0)),
... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
>>> gbt = GBTRegressor(maxIter=5, maxDepth=2)
>>> model = gbt.fit(df)
>>> 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="squared",
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="squared", maxIter=20, stepSize=0.1)
"""
super(GBTRegressor, self).__init__()
self._java_obj = self._new_java_obj("org.apache.spark.ml.regression.GBTRegressor", 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="squared", maxIter=20, stepSize=0.1)
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
[docs] 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)
[docs] def setLossType(self, value):
"""
Sets the value of :py:attr:`lossType`.
"""
self._paramMap[self.lossType] = value
return self
[docs] def getLossType(self):
"""
Gets the value of lossType or its default value.
"""
return self.getOrDefault(self.lossType)
[docs] def setSubsamplingRate(self, value):
"""
Sets the value of :py:attr:`subsamplingRate`.
"""
self._paramMap[self.subsamplingRate] = value
return self
[docs] def getSubsamplingRate(self):
"""
Gets the value of subsamplingRate or its default value.
"""
return self.getOrDefault(self.subsamplingRate)
[docs] def setStepSize(self, value):
"""
Sets the value of :py:attr:`stepSize`.
"""
self._paramMap[self.stepSize] = value
return self
[docs] def getStepSize(self):
"""
Gets the value of stepSize or its default value.
"""
return self.getOrDefault(self.stepSize)
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)
