Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6016#discussion_r30112906
--- Diff: python/pyspark/ml/regression.py ---
@@ -0,0 +1,509 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# 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
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+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', 'GBTModel',
+ 'LinearRegression', 'LinearRegressionModel',
'RandomForestRegressor',
+ 'RandomForestRegressionModel']
+
+
+@inherit_doc
+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
+ >>> 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.
+ """
+ _java_class = "org.apache.spark.ml.regression.LinearRegression"
+ # 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.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
+ 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)
+
+ def setElasticNetParam(self, value):
+ """
+ Sets the value of :py:attr:`elasticNetParam`.
+ """
+ self.paramMap[self.elasticNetParam] = value
+ return self
+
+ def getElasticNetParam(self):
+ """
+ Gets the value of elasticNetParam or its default value.
+ """
+ return self.getOrDefault(self.elasticNetParam)
+
+
+class LinearRegressionModel(JavaModel):
+ """
+ Model fitted by LinearRegression.
+ """
+
+
+class TreeRegressorParams():
+ """
+ Private class to track supported impurity measures.
+ """
+ supportedImpurities = ["variance"]
+
+
+class RandomForestParams():
+ """
+ Private class to track supported random forest parameters.
+ """
+ supportedFeatureSubsetStrategies = ["auto", "all", "onethird", "sqrt",
"log2"]
+
+
+class GBTParams():
+ """
+ Private class to track supported GBT params.
+ """
+ supportedLossTypes = ["squared", "absolute"]
+
+
+@inherit_doc
+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
+ """
+
+ _java_class = "org.apache.spark.ml.regression.DecisionTreeRegressor"
+ # 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.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
+ 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)
+
+ 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)
+
+
+class DecisionTreeRegressionModel(JavaModel):
+ """
+ Model fitted by DecisionTreeRegressor.
+ """
+
+
+@inherit_doc
+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)
+ >>> 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
+ """
+
+ _java_class = "org.apache.spark.ml.regression.RandomForestRegressor"
+ # 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=42):
+ """
+ __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=42)
+ """
+ super(RandomForestRegressor, self).__init__()
+ self.impurity = Param(self, "impurity",
+ "Criterion used for information gain
calculation " +
+ "(case-insensitive). Supported options: " +
+ ",
".join(TreeRegressorParams.supportedImpurities))
+ self.subsamplingRate = Param(self, "subsamplingRate",
+ "Fraction of the training data used
for learning each " +
+ "decision tree, in range (0, 1].")
+ self.numTrees = Param(self, "numTrees", "Number of trees to train
(>= 1)")
+ 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=42,
+ impurity="variance", numTrees=20,
featureSubsetStrategy="auto")
+ 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, seed=42,
+ 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=42,
+ 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)
+
+ 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 RandomForestRegressionModel(JavaModel):
+ """
+ Model fitted by RandomForestRegressor.
+ """
+
+
+@inherit_doc
+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
+ """
+
+ _java_class = "org.apache.spark.ml.regression.GBTRegressor"
+ # 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.lossType = Param(self, "lossType",
+ "Loss function which GBT tries to minimize
(case-insensitive). " +
+ "Supported options: " + ",
".join(GBTParams.supportedLossTypes))
+ self.subsamplingRate = Param(self, "subsamplingRate",
+ "Fraction of the training data used
for learning each " +
+ "decision tree, in range (0, 1].")
+ 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 GBTModel(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 GBTModel(JavaModel):
--- End diff --
GBTRegressionModel
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
