Source code for pyspark.ml.regression
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# this work for additional information regarding copyright ownership.
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# http://www.apache.org/licenses/LICENSE-2.0
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#
import warnings
from pyspark import since
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__ = ['AFTSurvivalRegression', 'AFTSurvivalRegressionModel',
'DecisionTreeRegressor', 'DecisionTreeRegressionModel',
'GBTRegressor', 'GBTRegressionModel',
'IsotonicRegression', 'IsotonicRegressionModel',
'LinearRegression', 'LinearRegressionModel',
'RandomForestRegressor', 'RandomForestRegressionModel']
@inherit_doc
[docs]class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept,
HasStandardization, HasSolver, HasWeightCol):
"""
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 coefficients - 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, 2.0, Vectors.dense(1.0)),
... (0.0, 2.0, Vectors.sparse(1, [], []))], ["label", "weight", "features"])
>>> lr = LinearRegression(maxIter=5, regParam=0.0, solver="normal", weightCol="weight")
>>> model = lr.fit(df)
>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> abs(model.transform(test0).head().prediction - (-1.0)) < 0.001
True
>>> abs(model.coefficients[0] - 1.0) < 0.001
True
>>> abs(model.intercept - 0.0) < 0.001
True
>>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
>>> abs(model.transform(test1).head().prediction - 1.0) < 0.001
True
>>> lr.setParams("vector")
Traceback (most recent call last):
...
TypeError: Method setParams forces keyword arguments.
.. versionadded:: 1.4.0
"""
@keyword_only
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
standardization=True, solver="auto", weightCol=None):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
standardization=True, solver="auto", weightCol=None)
"""
super(LinearRegression, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.regression.LinearRegression", self.uid)
self._setDefault(maxIter=100, regParam=0.0, tol=1e-6)
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
@since("1.4.0")
[docs] def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
standardization=True, solver="auto", weightCol=None):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
standardization=True, solver="auto", weightCol=None)
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]class LinearRegressionModel(JavaModel):
"""
Model fitted by LinearRegression.
.. versionadded:: 1.4.0
"""
@property
@since("1.4.0")
[docs] def weights(self):
"""
Model weights.
"""
warnings.warn("weights is deprecated. Use coefficients instead.")
return self._call_java("weights")
@property
@since("1.6.0")
@property
@since("1.4.0")
@inherit_doc
[docs]class IsotonicRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
HasWeightCol):
"""
.. note:: Experimental
Currently implemented using parallelized pool adjacent violators algorithm.
Only univariate (single feature) algorithm supported.
>>> from pyspark.mllib.linalg import Vectors
>>> df = sqlContext.createDataFrame([
... (1.0, Vectors.dense(1.0)),
... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
>>> ir = IsotonicRegression()
>>> model = ir.fit(df)
>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> model.transform(test0).head().prediction
0.0
>>> model.boundaries
DenseVector([0.0, 1.0])
"""
# a placeholder to make it appear in the generated doc
isotonic = \
Param(Params._dummy(), "isotonic",
"whether the output sequence should be isotonic/increasing (true) or" +
"antitonic/decreasing (false).")
featureIndex = \
Param(Params._dummy(), "featureIndex",
"The index of the feature if featuresCol is a vector column, no effect otherwise.")
@keyword_only
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
weightCol=None, isotonic=True, featureIndex=0):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
weightCol=None, isotonic=True, featureIndex=0):
"""
super(IsotonicRegression, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.regression.IsotonicRegression", self.uid)
self.isotonic = \
Param(self, "isotonic",
"whether the output sequence should be isotonic/increasing (true) or" +
"antitonic/decreasing (false).")
self.featureIndex = \
Param(self, "featureIndex",
"The index of the feature if featuresCol is a vector column, no effect " +
"otherwise.")
self._setDefault(isotonic=True, featureIndex=0)
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
[docs] def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
weightCol=None, isotonic=True, featureIndex=0):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
weightCol=None, isotonic=True, featureIndex=0):
Set the params for IsotonicRegression.
"""
kwargs = self.setParams._input_kwargs
return self._set(**kwargs)
def _create_model(self, java_model):
return IsotonicRegressionModel(java_model)
[docs] def setIsotonic(self, value):
"""
Sets the value of :py:attr:`isotonic`.
"""
self._paramMap[self.isotonic] = value
return self
[docs] def getIsotonic(self):
"""
Gets the value of isotonic or its default value.
"""
return self.getOrDefault(self.isotonic)
[docs] def setFeatureIndex(self, value):
"""
Sets the value of :py:attr:`featureIndex`.
"""
self._paramMap[self.featureIndex] = value
return self
[docs] def getFeatureIndex(self):
"""
Gets the value of featureIndex or its default value.
"""
return self.getOrDefault(self.featureIndex)
[docs]class IsotonicRegressionModel(JavaModel):
"""
.. note:: Experimental
Model fitted by IsotonicRegression.
"""
@property
@property
[docs] def predictions(self):
"""
Predictions associated with the boundaries at the same index, monotone because of isotonic
regression.
"""
return self._call_java("predictions")
class TreeEnsembleParams(DecisionTreeParams):
"""
Mixin for Decision Tree-based ensemble algorithms parameters.
"""
# a placeholder to make it appear in the generated doc
subsamplingRate = Param(Params._dummy(), "subsamplingRate", "Fraction of the training data " +
"used for learning each decision tree, in range (0, 1].")
def __init__(self):
super(TreeEnsembleParams, self).__init__()
#: param for Fraction of the training data, in range (0, 1].
self.subsamplingRate = Param(self, "subsamplingRate", "Fraction of the training data " +
"used for learning each decision tree, in range (0, 1].")
@since("1.4.0")
def setSubsamplingRate(self, value):
"""
Sets the value of :py:attr:`subsamplingRate`.
"""
self._paramMap[self.subsamplingRate] = value
return self
@since("1.4.0")
def getSubsamplingRate(self):
"""
Gets the value of subsamplingRate or its default value.
"""
return self.getOrDefault(self.subsamplingRate)
class TreeRegressorParams(Params):
"""
Private class to track supported impurity measures.
"""
supportedImpurities = ["variance"]
# 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(supportedImpurities))
def __init__(self):
super(TreeRegressorParams, self).__init__()
#: 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(self.supportedImpurities))
@since("1.4.0")
def setImpurity(self, value):
"""
Sets the value of :py:attr:`impurity`.
"""
self._paramMap[self.impurity] = value
return self
@since("1.4.0")
def getImpurity(self):
"""
Gets the value of impurity or its default value.
"""
return self.getOrDefault(self.impurity)
class RandomForestParams(TreeEnsembleParams):
"""
Private class to track supported random forest parameters.
"""
supportedFeatureSubsetStrategies = ["auto", "all", "onethird", "sqrt", "log2"]
# a placeholder to make it appear in the generated doc
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(supportedFeatureSubsetStrategies))
def __init__(self):
super(RandomForestParams, self).__init__()
#: 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(self.supportedFeatureSubsetStrategies))
@since("1.4.0")
def setNumTrees(self, value):
"""
Sets the value of :py:attr:`numTrees`.
"""
self._paramMap[self.numTrees] = value
return self
@since("1.4.0")
def getNumTrees(self):
"""
Gets the value of numTrees or its default value.
"""
return self.getOrDefault(self.numTrees)
@since("1.4.0")
def setFeatureSubsetStrategy(self, value):
"""
Sets the value of :py:attr:`featureSubsetStrategy`.
"""
self._paramMap[self.featureSubsetStrategy] = value
return self
@since("1.4.0")
def getFeatureSubsetStrategy(self):
"""
Gets the value of featureSubsetStrategy or its default value.
"""
return self.getOrDefault(self.featureSubsetStrategy)
class GBTParams(TreeEnsembleParams):
"""
Private class to track supported GBT params.
"""
supportedLossTypes = ["squared", "absolute"]
@inherit_doc
[docs]class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
DecisionTreeParams, TreeRegressorParams, 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)
>>> model.depth
1
>>> model.numNodes
3
>>> 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
.. versionadded:: 1.4.0
"""
@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)
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
@since("1.4.0")
[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)
@inherit_doc
class DecisionTreeModel(JavaModel):
"""Abstraction for Decision Tree models.
.. versionadded:: 1.5.0
"""
@property
@since("1.5.0")
def numNodes(self):
"""Return number of nodes of the decision tree."""
return self._call_java("numNodes")
@property
@since("1.5.0")
def depth(self):
"""Return depth of the decision tree."""
return self._call_java("depth")
def __repr__(self):
return self._call_java("toString")
@inherit_doc
class TreeEnsembleModels(JavaModel):
"""Represents a tree ensemble model.
.. versionadded:: 1.5.0
"""
@property
@since("1.5.0")
def treeWeights(self):
"""Return the weights for each tree"""
return list(self._call_java("javaTreeWeights"))
def __repr__(self):
return self._call_java("toString")
@inherit_doc
[docs]class DecisionTreeRegressionModel(DecisionTreeModel):
"""
Model fitted by DecisionTreeRegressor.
.. versionadded:: 1.4.0
"""
@inherit_doc
[docs]class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
RandomForestParams, TreeRegressorParams, HasCheckpointInterval):
"""
`http://en.wikipedia.org/wiki/Random_forest Random Forest`
learning algorithm for regression.
It supports both continuous and categorical features.
>>> from numpy import allclose
>>> 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)
>>> allclose(model.treeWeights, [1.0, 1.0])
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
0.5
.. versionadded:: 1.4.0
"""
@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", subsamplingRate=1.0, seed=None, numTrees=20,
featureSubsetStrategy="auto"):
"""
__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", subsamplingRate=1.0, seed=None, numTrees=20, \
featureSubsetStrategy="auto")
"""
super(RandomForestRegressor, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.regression.RandomForestRegressor", self.uid)
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20,
featureSubsetStrategy="auto")
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
@since("1.4.0")
[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", subsamplingRate=1.0, seed=None, 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, \
impurity="variance", subsamplingRate=1.0, seed=None, 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]class RandomForestRegressionModel(TreeEnsembleModels):
"""
Model fitted by RandomForestRegressor.
.. versionadded:: 1.4.0
"""
@inherit_doc
[docs]class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
GBTParams, HasCheckpointInterval, HasStepSize, HasSeed):
"""
`http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
learning algorithm for regression.
It supports both continuous and categorical features.
>>> from numpy import allclose
>>> 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)
>>> 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
.. versionadded:: 1.4.0
"""
# 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))
@keyword_only
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
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, subsamplingRate=1.0, \
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))
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1)
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
@since("1.4.0")
[docs] def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
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, subsamplingRate=1.0, \
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)
@since("1.4.0")
[docs] def setLossType(self, value):
"""
Sets the value of :py:attr:`lossType`.
"""
self._paramMap[self.lossType] = value
return self
@since("1.4.0")
[docs] def getLossType(self):
"""
Gets the value of lossType or its default value.
"""
return self.getOrDefault(self.lossType)
[docs]class GBTRegressionModel(TreeEnsembleModels):
"""
Model fitted by GBTRegressor.
.. versionadded:: 1.4.0
"""
@inherit_doc
[docs]class AFTSurvivalRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
HasFitIntercept, HasMaxIter, HasTol):
"""
Accelerated Failure Time (AFT) Model Survival Regression
Fit a parametric AFT survival regression model based on the Weibull distribution
of the survival time.
.. seealso:: `AFT Model <https://en.wikipedia.org/wiki/Accelerated_failure_time_model>`_
>>> from pyspark.mllib.linalg import Vectors
>>> df = sqlContext.createDataFrame([
... (1.0, Vectors.dense(1.0), 1.0),
... (0.0, Vectors.sparse(1, [], []), 0.0)], ["label", "features", "censor"])
>>> aftsr = AFTSurvivalRegression()
>>> model = aftsr.fit(df)
>>> model.predict(Vectors.dense(6.3))
1.0
>>> model.predictQuantiles(Vectors.dense(6.3))
DenseVector([0.0101, 0.0513, 0.1054, 0.2877, 0.6931, 1.3863, 2.3026, 2.9957, 4.6052])
>>> model.transform(df).show()
+-----+---------+------+----------+
|label| features|censor|prediction|
+-----+---------+------+----------+
| 1.0| [1.0]| 1.0| 1.0|
| 0.0|(1,[],[])| 0.0| 1.0|
+-----+---------+------+----------+
...
.. versionadded:: 1.6.0
"""
# a placeholder to make it appear in the generated doc
censorCol = Param(Params._dummy(), "censorCol",
"censor column name. The value of this column could be 0 or 1. " +
"If the value is 1, it means the event has occurred i.e. " +
"uncensored; otherwise censored.")
quantileProbabilities = \
Param(Params._dummy(), "quantileProbabilities",
"quantile probabilities array. Values of the quantile probabilities array " +
"should be in the range (0, 1) and the array should be non-empty.")
quantilesCol = Param(Params._dummy(), "quantilesCol",
"quantiles column name. This column will output quantiles of " +
"corresponding quantileProbabilities if it is set.")
@keyword_only
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor",
quantileProbabilities=None, quantilesCol=None):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor", \
quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], \
quantilesCol=None)
"""
super(AFTSurvivalRegression, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.regression.AFTSurvivalRegression", self.uid)
#: Param for censor column name
self.censorCol = Param(self, "censorCol",
"censor column name. The value of this column could be 0 or 1. " +
"If the value is 1, it means the event has occurred i.e. " +
"uncensored; otherwise censored.")
#: Param for quantile probabilities array
self.quantileProbabilities = \
Param(self, "quantileProbabilities",
"quantile probabilities array. Values of the quantile probabilities array " +
"should be in the range (0, 1) and the array should be non-empty.")
#: Param for quantiles column name
self.quantilesCol = Param(self, "quantilesCol",
"quantiles column name. This column will output quantiles of " +
"corresponding quantileProbabilities if it is set.")
self._setDefault(censorCol="censor",
quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99])
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
@since("1.6.0")
[docs] def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor",
quantileProbabilities=None, quantilesCol=None):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor", \
quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], \
quantilesCol=None):
"""
kwargs = self.setParams._input_kwargs
return self._set(**kwargs)
def _create_model(self, java_model):
return AFTSurvivalRegressionModel(java_model)
@since("1.6.0")
[docs] def setCensorCol(self, value):
"""
Sets the value of :py:attr:`censorCol`.
"""
self._paramMap[self.censorCol] = value
return self
@since("1.6.0")
[docs] def getCensorCol(self):
"""
Gets the value of censorCol or its default value.
"""
return self.getOrDefault(self.censorCol)
@since("1.6.0")
[docs] def setQuantileProbabilities(self, value):
"""
Sets the value of :py:attr:`quantileProbabilities`.
"""
self._paramMap[self.quantileProbabilities] = value
return self
@since("1.6.0")
[docs] def getQuantileProbabilities(self):
"""
Gets the value of quantileProbabilities or its default value.
"""
return self.getOrDefault(self.quantileProbabilities)
@since("1.6.0")
[docs] def setQuantilesCol(self, value):
"""
Sets the value of :py:attr:`quantilesCol`.
"""
self._paramMap[self.quantilesCol] = value
return self
@since("1.6.0")
[docs] def getQuantilesCol(self):
"""
Gets the value of quantilesCol or its default value.
"""
return self.getOrDefault(self.quantilesCol)
[docs]class AFTSurvivalRegressionModel(JavaModel):
"""
Model fitted by AFTSurvivalRegression.
.. versionadded:: 1.6.0
"""
@property
@since("1.6.0")
@property
@since("1.6.0")
@property
@since("1.6.0")
[docs] def predictQuantiles(self, features):
"""
Predicted Quantiles
"""
return self._call_java("predictQuantiles", features)
[docs] def predict(self, features):
"""
Predicted value
"""
return self._call_java("predict", features)
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)
