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[SPARK-10026] [ML] [PySpark] Implement some common Params for regression in PySpark #8508

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[SPARK-10026] [ML] [PySpark] Implement some common Params for regression in PySpark #8508

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730b0a7
Implement some common Params for regression in PySpark
yanboliang Aug 28, 2015
d44ac06
fix typo
yanboliang Aug 28, 2015
093bbe2
fix typos
yanboliang Sep 11, 2015
962692b
resolve merge conflicts
yanboliang Sep 11, 2015
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75 changes: 11 additions & 64 deletions python/pyspark/ml/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,8 @@

@inherit_doc
class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol):
HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol,
HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds):
"""
Logistic regression.
Currently, this class only supports binary classification.
Expand Down Expand Up @@ -65,72 +66,44 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
"""

# 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.")
fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.")
thresholds = Param(Params._dummy(), "thresholds",
"Thresholds in multi-class classification" +
" to adjust the probability of predicting each class." +
" Array must have length equal to the number of classes, with values >= 0." +
" The class with largest value p/t is predicted, where p is the original" +
" probability of that class and t is the class' threshold.")
threshold = Param(Params._dummy(), "threshold",
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Perhaps we should also extract a HasThreshold mixin for binary classifier thresholds

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threshold is a deprecated parameter, it is replaced by thresholds. LogisticRegression still reserve threshold is just for binary compatibility. So I think we don't need to extract HasThreshold as shared Param. @jkbradley

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My understanding is that the HasThresholds trait mixin in ml.LogisticRegression is actually an artifact resulting from transient dependency through ProbabilisticClassifier. We don't actually support multi-class classification in ml.LogisticRegression ATM and did quite a bit of work to make the API less confusing.

After mutli-class is supported I think it makes sense to use HasThresholds, but for the time being I would prefer we only use HasThreshold in the Python API.

"Threshold in binary classification prediction, in range [0, 1]." +
" If threshold and thresholds are both set, they must match.")

@keyword_only
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
threshold=0.5, thresholds=None,
probabilityCol="probability", rawPredictionCol="rawPrediction"):
threshold=0.5, thresholds=None, probabilityCol="probability",
rawPredictionCol="rawPrediction", standardization=True):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
threshold=0.5, thresholds=None, \
probabilityCol="probability", rawPredictionCol="rawPrediction")
threshold=0.5, thresholds=None, probabilityCol="probability", \
rawPredictionCol="rawPrediction", standardization=True)
If the threshold and thresholds Params are both set, they must be equivalent.
"""
super(LogisticRegression, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.classification.LogisticRegression", 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.")
#: param for whether to fit an intercept term.
self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
#: param for threshold in binary classification, in range [0, 1].
self.threshold = Param(self, "threshold",
"Threshold in binary classification prediction, in range [0, 1]." +
" If threshold and thresholds are both set, they must match.")
#: param for thresholds or cutoffs in binary or multiclass classification
self.thresholds = \
Param(self, "thresholds",
"Thresholds in multi-class classification" +
" to adjust the probability of predicting each class." +
" Array must have length equal to the number of classes, with values >= 0." +
" The class with largest value p/t is predicted, where p is the original" +
" probability of that class and t is the class' threshold.")
self._setDefault(maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1E-6,
fitIntercept=True, threshold=0.5)
self._setDefault(maxIter=100, regParam=0.1, tol=1E-6, threshold=0.5)
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
self._checkThresholdConsistency()

@keyword_only
def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
threshold=0.5, thresholds=None,
probabilityCol="probability", rawPredictionCol="rawPrediction"):
threshold=0.5, thresholds=None, probabilityCol="probability",
rawPredictionCol="rawPrediction", standardization=True):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
threshold=0.5, thresholds=None, \
probabilityCol="probability", rawPredictionCol="rawPrediction")
threshold=0.5, thresholds=None, probabilityCol="probability", \
rawPredictionCol="rawPrediction", standardization=True)
Sets params for logistic regression.
If the threshold and thresholds Params are both set, they must be equivalent.
"""
Expand All @@ -142,32 +115,6 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
def _create_model(self, java_model):
return LogisticRegressionModel(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)

def setFitIntercept(self, value):
"""
Sets the value of :py:attr:`fitIntercept`.
"""
self._paramMap[self.fitIntercept] = value
return self

def getFitIntercept(self):
"""
Gets the value of fitIntercept or its default value.
"""
return self.getOrDefault(self.fitIntercept)

def setThreshold(self, value):
"""
Sets the value of :py:attr:`threshold`.
Expand Down
11 changes: 10 additions & 1 deletion python/pyspark/ml/param/_shared_params_code_gen.py
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Expand Up @@ -124,7 +124,16 @@ def get$Name(self):
("stepSize", "Step size to be used for each iteration of optimization.", None),
("handleInvalid", "how to handle invalid entries. Options are skip (which will filter " +
"out rows with bad values), or error (which will throw an errror). More options may be " +
"added later.", None)]
"added later.", None),
("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.", "0.0"),
("fitIntercept", "whether to fit an intercept term.", "True"),
("standardization", "whether to standardize the training features before fitting the " +
"model.", "True"),
("thresholds", "Thresholds in multi-class classification to adjust the probability of " +
"predicting each class. Array must have length equal to the number of classes, with " +
"values >= 0. The class with largest value p/t is predicted, where p is the original " +
"probability of that class and t is the class' threshold.", None)]
code = []
for name, doc, defaultValueStr in shared:
param_code = _gen_param_header(name, doc, defaultValueStr)
Expand Down
111 changes: 111 additions & 0 deletions python/pyspark/ml/param/shared.py
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Expand Up @@ -459,6 +459,117 @@ def getHandleInvalid(self):
return self.getOrDefault(self.handleInvalid)


class HasElasticNetParam(Params):
"""
Mixin for param 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..
"""

# 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.")

def __init__(self):
super(HasElasticNetParam, self).__init__()
#: 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(elasticNetParam=0.0)

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 HasFitIntercept(Params):
"""
Mixin for param fitIntercept: whether to fit an intercept term..
"""

# a placeholder to make it appear in the generated doc
fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.")

def __init__(self):
super(HasFitIntercept, self).__init__()
#: param for whether to fit an intercept term.
self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
self._setDefault(fitIntercept=True)

def setFitIntercept(self, value):
"""
Sets the value of :py:attr:`fitIntercept`.
"""
self._paramMap[self.fitIntercept] = value
return self

def getFitIntercept(self):
"""
Gets the value of fitIntercept or its default value.
"""
return self.getOrDefault(self.fitIntercept)


class HasStandardization(Params):
"""
Mixin for param standardization: whether to standardize the training features before fitting the model..
"""

# a placeholder to make it appear in the generated doc
standardization = Param(Params._dummy(), "standardization", "whether to standardize the training features before fitting the model.")

def __init__(self):
super(HasStandardization, self).__init__()
#: param for whether to standardize the training features before fitting the model.
self.standardization = Param(self, "standardization", "whether to standardize the training features before fitting the model.")
self._setDefault(standardization=True)

def setStandardization(self, value):
"""
Sets the value of :py:attr:`standardization`.
"""
self._paramMap[self.standardization] = value
return self

def getStandardization(self):
"""
Gets the value of standardization or its default value.
"""
return self.getOrDefault(self.standardization)


class HasThresholds(Params):
"""
Mixin for param thresholds: Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold..
"""

# a placeholder to make it appear in the generated doc
thresholds = Param(Params._dummy(), "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")

def __init__(self):
super(HasThresholds, self).__init__()
#: param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.
self.thresholds = Param(self, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")

def setThresholds(self, value):
"""
Sets the value of :py:attr:`thresholds`.
"""
self._paramMap[self.thresholds] = value
return self

def getThresholds(self):
"""
Gets the value of thresholds or its default value.
"""
return self.getOrDefault(self.thresholds)


class DecisionTreeParams(Params):
"""
Mixin for Decision Tree parameters.
Expand Down
42 changes: 11 additions & 31 deletions python/pyspark/ml/regression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,8 @@

@inherit_doc
class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
HasRegParam, HasTol):
HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept,
HasStandardization):
"""
Linear regression.

Expand Down Expand Up @@ -63,38 +64,30 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
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):
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
standardization=True):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
standardization=True)
"""
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)
self._setDefault(maxIter=100, regParam=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):
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
standardization=True):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
standardization=True)
Sets params for linear regression.
"""
kwargs = self.setParams._input_kwargs
Expand All @@ -103,19 +96,6 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
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):
"""
Expand Down