diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 93ffcd40949b37903798824e87ac94e3cc68361d..b5814f76de00060db78c411577eb017c434441a7 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -31,7 +31,7 @@ __all__ = ['LogisticRegression', 'LogisticRegressionModel', 'DecisionTreeClassif
@inherit_doc
class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
- HasRegParam, HasTol, HasProbabilityCol):
+ HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol):
"""
Logistic regression.
@@ -42,13 +42,18 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
... Row(label=0.0, features=Vectors.sparse(1, [], []))]).toDF()
>>> lr = LogisticRegression(maxIter=5, regParam=0.01)
>>> model = lr.fit(df)
- >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
- >>> model.transform(test0).head().prediction
- 0.0
>>> model.weights
DenseVector([5.5...])
>>> model.intercept
-2.68...
+ >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
+ >>> result = model.transform(test0).head()
+ >>> result.prediction
+ 0.0
+ >>> result.probability
+ DenseVector([0.99..., 0.00...])
+ >>> result.rawPrediction
+ DenseVector([8.22..., -8.22...])
>>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF()
>>> model.transform(test1).head().prediction
1.0
@@ -70,11 +75,11 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
@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, probabilityCol="probability"):
+ threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction"):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
- threshold=0.5, probabilityCol="probability")
+ threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction")
"""
super(LogisticRegression, self).__init__()
self._java_obj = self._new_java_obj(
@@ -98,11 +103,11 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
@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, probabilityCol="probability"):
+ threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction"):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
- threshold=0.5, probabilityCol="probability")
+ threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction")
Sets params for logistic regression.
"""
kwargs = self.setParams._input_kwargs
@@ -187,7 +192,8 @@ class GBTParams(object):
@inherit_doc
class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
- DecisionTreeParams, HasCheckpointInterval):
+ HasProbabilityCol, HasRawPredictionCol, DecisionTreeParams,
+ HasCheckpointInterval):
"""
`http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree`
learning algorithm for classification.
@@ -209,8 +215,13 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
>>> model.depth
1
>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
- >>> model.transform(test0).head().prediction
+ >>> result = model.transform(test0).head()
+ >>> result.prediction
0.0
+ >>> result.probability
+ DenseVector([1.0, 0.0])
+ >>> result.rawPrediction
+ DenseVector([1.0, 0.0])
>>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
>>> model.transform(test1).head().prediction
1.0
@@ -223,10 +234,12 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
@keyword_only
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+ probabilityCol="probability", rawPredictionCol="rawPrediction",
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini"):
"""
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+ probabilityCol="probability", rawPredictionCol="rawPrediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini")
"""
@@ -246,11 +259,13 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
@keyword_only
def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+ probabilityCol="probability", rawPredictionCol="rawPrediction",
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
impurity="gini"):
"""
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+ probabilityCol="probability", rawPredictionCol="rawPrediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini")
Sets params for the DecisionTreeClassifier.
@@ -578,7 +593,8 @@ class GBTClassificationModel(TreeEnsembleModels):
@inherit_doc
-class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol):
+class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasProbabilityCol,
+ HasRawPredictionCol):
"""
Naive Bayes Classifiers.
@@ -595,8 +611,13 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol):
>>> model.theta
DenseMatrix(2, 2, [-1.09..., -0.40..., -0.40..., -1.09...], 1)
>>> test0 = sc.parallelize([Row(features=Vectors.dense([1.0, 0.0]))]).toDF()
- >>> model.transform(test0).head().prediction
+ >>> result = model.transform(test0).head()
+ >>> result.prediction
1.0
+ >>> result.probability
+ DenseVector([0.42..., 0.57...])
+ >>> result.rawPrediction
+ DenseVector([-1.60..., -1.32...])
>>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF()
>>> model.transform(test1).head().prediction
1.0
@@ -610,10 +631,12 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol):
@keyword_only
def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
- smoothing=1.0, modelType="multinomial"):
+ probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0,
+ modelType="multinomial"):
"""
- __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
- smoothing=1.0, modelType="multinomial")
+ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+ probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0, \
+ modelType="multinomial")
"""
super(NaiveBayes, self).__init__()
self._java_obj = self._new_java_obj(
@@ -631,10 +654,12 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol):
@keyword_only
def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
- smoothing=1.0, modelType="multinomial"):
+ probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0,
+ modelType="multinomial"):
"""
- setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
- smoothing=1.0, modelType="multinomial")
+ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+ probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0, \
+ modelType="multinomial")
Sets params for Naive Bayes.
"""
kwargs = self.setParams._input_kwargs