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[SPARK-28985][PYTHON][ML] Add common classes (JavaPredictor/JavaClassificationModel/JavaProbabilisticClassifier) in PYTHON #25776

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[SPARK-28985][PYTHON][ML] Add common classes (JavaPredictor/JavaClassificationModel/JavaProbabilisticClassifier) in PYTHON #25776

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[SPARK-28985][PYTHON][ML] Add common classes (JavaPredictor/JavaClass…
huaxingao Sep 12, 2019
d6c7da2
add a few changes
huaxingao Sep 12, 2019
de6f8e8
address comments
huaxingao Sep 16, 2019
95f88f5
add simple doc test for RandomForestClassificationModel
huaxingao Sep 17, 2019
bc1d9e1
address comments and add a few more doctests
huaxingao Sep 18, 2019
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170 changes: 135 additions & 35 deletions python/pyspark/ml/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,8 +26,8 @@
DecisionTreeRegressionModel, GBTParams, HasVarianceImpurity, RandomForestParams, \
TreeEnsembleModel
from pyspark.ml.util import *
from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams
from pyspark.ml.wrapper import JavaWrapper
from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams, \
JavaPredictor, JavaPredictorParams, JavaPredictionModel, JavaWrapper
from pyspark.ml.common import inherit_doc, _java2py, _py2java
from pyspark.ml.linalg import Vectors
from pyspark.sql import DataFrame
Expand All @@ -47,14 +47,43 @@
'OneVsRest', 'OneVsRestModel']


class JavaClassifierParams(HasRawPredictionCol, JavaPredictorParams):
"""
(Private) Java Classifier Params for classification tasks.
"""
pass


@inherit_doc
class JavaClassifier(JavaPredictor, JavaClassifierParams):
"""
Java Classifier for classification tasks.
Classes are indexed {0, 1, ..., numClasses - 1}.
"""

@since("3.0.0")
def setRawPredictionCol(self, value):
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"""
Sets the value of :py:attr:`rawPredictionCol`.
"""
return self._set(rawPredictionCol=value)


@inherit_doc
class JavaClassificationModel(JavaPredictionModel):
class JavaClassificationModel(JavaPredictionModel, JavaClassifierParams):
"""
(Private) Java Model produced by a ``Classifier``.
Java Model produced by a ``Classifier``.
Classes are indexed {0, 1, ..., numClasses - 1}.
To be mixed in with class:`pyspark.ml.JavaModel`
"""

@since("3.0.0")
def setRawPredictionCol(self, value):

@zhengruifeng zhengruifeng Sep 16, 2019

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ditto

"""
Sets the value of :py:attr:`rawPredictionCol`.
"""
return self._set(rawPredictionCol=value)

@property
@since("2.1.0")
def numClasses(self):
Expand All @@ -64,10 +93,60 @@ def numClasses(self):
return self._call_java("numClasses")


class JavaProbabilisticClassifierParams(HasProbabilityCol, HasThresholds, JavaClassifierParams):
"""
(Private) Java Probabilistic Classifier Params for classification tasks.
"""
pass
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@inherit_doc
class LinearSVC(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
HasRegParam, HasTol, HasRawPredictionCol, HasFitIntercept, HasStandardization,
HasWeightCol, HasAggregationDepth, HasThreshold, JavaMLWritable, JavaMLReadable):
class JavaProbabilisticClassifier(JavaClassifier, JavaProbabilisticClassifierParams):
"""
Java Probabilistic Classifier for classification tasks.
"""

@since("3.0.0")
def setProbabilityCol(self, value):
"""
Sets the value of :py:attr:`probabilityCol`.
"""
return self._set(probabilityCol=value)

@since("3.0.0")
def setThresholds(self, value):

@zhengruifeng zhengruifeng Sep 16, 2019

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ditto

"""
Sets the value of :py:attr:`thresholds`.
"""
return self._set(thresholds=value)


@inherit_doc
class JavaProbabilisticClassificationModel(JavaClassificationModel,
JavaProbabilisticClassifierParams):
"""
Java Model produced by a ``ProbabilisticClassifier``.
"""

@since("3.0.0")
def setProbabilityCol(self, value):
"""
Sets the value of :py:attr:`probabilityCol`.
"""
return self._set(probabilityCol=value)

@since("3.0.0")
def setThresholds(self, value):
"""
Sets the value of :py:attr:`thresholds`.
"""
return self._set(thresholds=value)


@inherit_doc
class LinearSVC(JavaClassifier, HasMaxIter, HasRegParam, HasTol,
HasFitIntercept, HasStandardization, HasWeightCol, HasAggregationDepth,
HasThreshold, JavaMLWritable, JavaMLReadable):
"""
`Linear SVM Classifier <https://en.wikipedia.org/wiki/Support_vector_machine#Linear_SVM>`_

Expand All @@ -81,6 +160,8 @@ class LinearSVC(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, Ha
... Row(label=0.0, features=Vectors.dense(1.0, 2.0, 3.0))]).toDF()
>>> svm = LinearSVC(maxIter=5, regParam=0.01)
>>> model = svm.fit(df)
>>> model.setPredictionCol("prediction")

@zhengruifeng zhengruifeng Sep 18, 2019 •
edited
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What about changing the value to a non-default value like "newPrediction", and making sure that the ouput dataframe/row has changed column name?

LinearSVC...
>>> model.coefficients
DenseVector([0.0, -0.2792, -0.1833])
>>> model.intercept
Expand All @@ -90,6 +171,8 @@ class LinearSVC(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, Ha
>>> model.numFeatures
3
>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, -1.0, -1.0))]).toDF()
>>> model.predict(test0.head().features)
1.0
>>> result = model.transform(test0).head()
>>> result.prediction
1.0
Expand Down Expand Up @@ -156,7 +239,7 @@ def _create_model(self, java_model):
return LinearSVCModel(java_model)


class LinearSVCModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable):
class LinearSVCModel(JavaClassificationModel, JavaMLWritable, JavaMLReadable):
"""
Model fitted by LinearSVC.

Expand All @@ -181,8 +264,7 @@ def intercept(self):


@inherit_doc
class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol,
class LogisticRegression(JavaProbabilisticClassifier, HasMaxIter, HasRegParam, HasTol,
HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds,
HasWeightCol, HasAggregationDepth, JavaMLWritable, JavaMLReadable):
"""
Expand All @@ -198,6 +280,8 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
... Row(label=0.0, weight=4.0, features=Vectors.dense(3.0, 3.0))]).toDF()
>>> blor = LogisticRegression(regParam=0.01, weightCol="weight")
>>> blorModel = blor.fit(bdf)
>>> blorModel.setFeaturesCol("features")
LogisticRegressionModel...
>>> blorModel.coefficients
DenseVector([-1.080..., -0.646...])
>>> blorModel.intercept
Expand All @@ -211,6 +295,8 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
>>> mlorModel.interceptVector
DenseVector([0.04..., -0.42..., 0.37...])
>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, 1.0))]).toDF()
>>> blorModel.predict(test0.head().features)
1.0
>>> result = blorModel.transform(test0).head()
>>> result.prediction
1.0
Expand Down Expand Up @@ -481,7 +567,7 @@ def getUpperBoundsOnIntercepts(self):
return self.getOrDefault(self.upperBoundsOnIntercepts)


class LogisticRegressionModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable,
class LogisticRegressionModel(JavaProbabilisticClassificationModel, JavaMLWritable, JavaMLReadable,
HasTrainingSummary):
"""
Model fitted by LogisticRegression.
Expand Down Expand Up @@ -872,8 +958,7 @@ def getImpurity(self):


@inherit_doc
class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasWeightCol,
HasPredictionCol, HasProbabilityCol, HasRawPredictionCol,
class DecisionTreeClassifier(JavaProbabilisticClassifier, HasWeightCol,
DecisionTreeParams, TreeClassifierParams, HasCheckpointInterval,
HasSeed, JavaMLWritable, JavaMLReadable):
"""
Expand All @@ -892,6 +977,10 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasWeig
>>> td = si_model.transform(df)
>>> dt = DecisionTreeClassifier(maxDepth=2, labelCol="indexed", leafCol="leafId")
>>> model = dt.fit(td)
>>> model.getLabelCol()
'indexed'
>>> model.setFeaturesCol("features")
DecisionTreeClassificationModel...
>>> model.numNodes
3
>>> model.depth
Expand All @@ -905,6 +994,8 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasWeig
>>> print(model.toDebugString)
DecisionTreeClassificationModel (uid=...) of depth 1 with 3 nodes...
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> model.predict(test0.head().features)
0.0
>>> result = model.transform(test0).head()
>>> result.prediction
0.0
Expand Down Expand Up @@ -1031,8 +1122,8 @@ def setImpurity(self, value):


@inherit_doc
class DecisionTreeClassificationModel(DecisionTreeModel, JavaClassificationModel, JavaMLWritable,
JavaMLReadable):
class DecisionTreeClassificationModel(DecisionTreeModel, JavaProbabilisticClassificationModel,
JavaMLWritable, JavaMLReadable):
"""
Model fitted by DecisionTreeClassifier.

Expand Down Expand Up @@ -1062,9 +1153,8 @@ def featureImportances(self):


@inherit_doc
class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
HasRawPredictionCol, HasProbabilityCol,
RandomForestParams, TreeClassifierParams, HasCheckpointInterval,
class RandomForestClassifier(JavaProbabilisticClassifier, HasSeed, RandomForestParams,
TreeClassifierParams, HasCheckpointInterval,
JavaMLWritable, JavaMLReadable):

@zhengruifeng zhengruifeng Sep 17, 2019

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What about adding some simple tests for it?

"""
`Random Forest <http://en.wikipedia.org/wiki/Random_forest>`_
Expand Down Expand Up @@ -1231,8 +1321,8 @@ def setFeatureSubsetStrategy(self, value):
return self._set(featureSubsetStrategy=value)


class RandomForestClassificationModel(TreeEnsembleModel, JavaClassificationModel, JavaMLWritable,
JavaMLReadable):
class RandomForestClassificationModel(TreeEnsembleModel, JavaProbabilisticClassificationModel,
JavaMLWritable, JavaMLReadable):
"""
Model fitted by RandomForestClassifier.

Expand Down Expand Up @@ -1284,9 +1374,8 @@ def getLossType(self):


@inherit_doc
class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
GBTClassifierParams, HasCheckpointInterval, HasSeed, JavaMLWritable,
JavaMLReadable):
class GBTClassifier(JavaProbabilisticClassifier, GBTClassifierParams, HasCheckpointInterval,
HasSeed, JavaMLWritable, JavaMLReadable):
"""
`Gradient-Boosted Trees (GBTs) <http://en.wikipedia.org/wiki/Gradient_boosting>`_
learning algorithm for classification.
Expand Down Expand Up @@ -1318,11 +1407,17 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
>>> gbt.getFeatureSubsetStrategy()
'all'
>>> model = gbt.fit(td)
>>> model.getLabelCol()
'indexed'
>>> model.setFeaturesCol("features")
GBTClassificationModel...
>>> model.featureImportances
SparseVector(1, {0: 1.0})
>>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1])
True
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> model.predict(test0.head().features)
0.0
>>> result = model.transform(test0).head()
>>> result.prediction
0.0
Expand Down Expand Up @@ -1485,8 +1580,8 @@ def setValidationIndicatorCol(self, value):
return self._set(validationIndicatorCol=value)


class GBTClassificationModel(TreeEnsembleModel, JavaClassificationModel, JavaMLWritable,
JavaMLReadable):
class GBTClassificationModel(TreeEnsembleModel, JavaProbabilisticClassificationModel,
JavaMLWritable, JavaMLReadable):
"""
Model fitted by GBTClassifier.

Expand Down Expand Up @@ -1527,8 +1622,8 @@ def evaluateEachIteration(self, dataset):


@inherit_doc
class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasProbabilityCol,
HasRawPredictionCol, HasThresholds, HasWeightCol, JavaMLWritable, JavaMLReadable):
class NaiveBayes(JavaProbabilisticClassifier, HasThresholds, HasWeightCol,
JavaMLWritable, JavaMLReadable):
"""
Naive Bayes Classifiers.
It supports both Multinomial and Bernoulli NB. `Multinomial NB
Expand All @@ -1547,11 +1642,15 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
... Row(label=1.0, weight=1.0, features=Vectors.dense([1.0, 0.0]))])
>>> nb = NaiveBayes(smoothing=1.0, modelType="multinomial", weightCol="weight")
>>> model = nb.fit(df)
>>> model.setFeaturesCol("features")
NaiveBayes_...
>>> model.pi
DenseVector([-0.81..., -0.58...])
>>> model.theta
DenseMatrix(2, 2, [-0.91..., -0.51..., -0.40..., -1.09...], 1)
>>> test0 = sc.parallelize([Row(features=Vectors.dense([1.0, 0.0]))]).toDF()
>>> model.predict(test0.head().features)
1.0
>>> result = model.transform(test0).head()
>>> result.prediction
1.0
Expand Down Expand Up @@ -1651,7 +1750,7 @@ def getModelType(self):
return self.getOrDefault(self.modelType)


class NaiveBayesModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable):
class NaiveBayesModel(JavaProbabilisticClassificationModel, JavaMLWritable, JavaMLReadable):
"""
Model fitted by NaiveBayes.

Expand All @@ -1676,10 +1775,8 @@ def theta(self):


@inherit_doc
class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
HasMaxIter, HasTol, HasSeed, HasStepSize, HasSolver,
JavaMLWritable, JavaMLReadable, HasProbabilityCol,
HasRawPredictionCol):
class MultilayerPerceptronClassifier(JavaProbabilisticClassifier, HasMaxIter, HasTol, HasSeed,
HasStepSize, HasSolver, JavaMLWritable, JavaMLReadable):
"""
Classifier trainer based on the Multilayer Perceptron.
Each layer has sigmoid activation function, output layer has softmax.
Expand All @@ -1694,13 +1791,17 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
... (0.0, Vectors.dense([1.0, 1.0]))], ["label", "features"])
>>> mlp = MultilayerPerceptronClassifier(maxIter=100, layers=[2, 2, 2], blockSize=1, seed=123)
>>> model = mlp.fit(df)
>>> model.setFeaturesCol("features")
MultilayerPerceptronClassifier...
>>> model.layers
[2, 2, 2]
>>> model.weights.size
12
>>> testDF = spark.createDataFrame([
... (Vectors.dense([1.0, 0.0]),),
... (Vectors.dense([0.0, 0.0]),)], ["features"])
>>> model.predict(testDF.head().features)
1.0
>>> model.transform(testDF).select("features", "prediction").show()
+---------+----------+
| features|prediction|
Expand Down Expand Up @@ -1839,7 +1940,7 @@ def getInitialWeights(self):
return self.getOrDefault(self.initialWeights)


class MultilayerPerceptronClassificationModel(JavaModel, JavaClassificationModel, JavaMLWritable,
class MultilayerPerceptronClassificationModel(JavaProbabilisticClassificationModel, JavaMLWritable,
JavaMLReadable):
"""
Model fitted by MultilayerPerceptronClassifier.
Expand All @@ -1864,8 +1965,7 @@ def weights(self):
return self._call_java("weights")


class OneVsRestParams(HasFeaturesCol, HasLabelCol, HasWeightCol, HasPredictionCol,
HasRawPredictionCol):
class OneVsRestParams(JavaClassifierParams, HasWeightCol):
"""
Parameters for OneVsRest and OneVsRestModel.
"""
Expand Down
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