Skip to content

[SPARK-30358][ML][PySpark][FOLLOWUP] ML expose predictRaw and predictProbability on Python side #27082

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
huaxingao wants to merge 1 commit into from
Closed

[SPARK-30358][ML][PySpark][FOLLOWUP] ML expose predictRaw and predictProbability on Python side #27082

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
44 changes: 44 additions & 0 deletions python/pyspark/ml/classification.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -96,6 +96,13 @@ def numClasses(self):
"""
return self._call_java("numClasses")

@since("3.0.0")
def predictRaw(self, value):
"""
Raw prediction for each possible label.
"""
return self._call_java("predictRaw", value)


class _JavaProbabilisticClassifierParams(HasProbabilityCol, HasThresholds, _JavaClassifierParams):
"""
Expand Down Expand Up @@ -149,6 +156,13 @@ def setThresholds(self, value):
"""
return self._set(thresholds=value)

@since("3.0.0")
def predictProbability(self, value):
"""
Predict the probability of each class given the features.
"""
return self._call_java("predictProbability", value)


class _LinearSVCParams(_JavaClassifierParams, HasRegParam, HasMaxIter, HasFitIntercept, HasTol,
HasStandardization, HasWeightCol, HasAggregationDepth, HasThreshold):
Expand Down Expand Up @@ -211,6 +225,8 @@ class LinearSVC(JavaClassifier, _LinearSVCParams, JavaMLWritable, JavaMLReadable
>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, -1.0, -1.0))]).toDF()
>>> model.predict(test0.head().features)
1.0
>>> model.predictRaw(test0.head().features)
DenseVector([-1.4831, 1.4831])
>>> result = model.transform(test0).head()
>>> result.newPrediction
1.0
Expand Down Expand Up @@ -568,6 +584,10 @@ class LogisticRegression(JavaProbabilisticClassifier, _LogisticRegressionParams,
>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, 1.0))]).toDF()
>>> blorModel.predict(test0.head().features)
1.0
>>> blorModel.predictRaw(test0.head().features)
DenseVector([-3.54..., 3.54...])
>>> blorModel.predictProbability(test0.head().features)
DenseVector([0.028, 0.972])
>>> result = blorModel.transform(test0).head()
>>> result.prediction
1.0
Expand Down Expand Up @@ -1148,6 +1168,10 @@ class DecisionTreeClassifier(JavaProbabilisticClassifier, _DecisionTreeClassifie
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> model.predict(test0.head().features)
0.0
>>> model.predictRaw(test0.head().features)
DenseVector([1.0, 0.0])
>>> model.predictProbability(test0.head().features)
DenseVector([1.0, 0.0])
>>> result = model.transform(test0).head()
>>> result.prediction
0.0
Expand Down Expand Up @@ -1379,6 +1403,10 @@ class RandomForestClassifier(JavaProbabilisticClassifier, _RandomForestClassifie
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> model.predict(test0.head().features)
0.0
>>> model.predictRaw(test0.head().features)
DenseVector([2.0, 0.0])
>>> model.predictProbability(test0.head().features)
DenseVector([1.0, 0.0])
>>> result = model.transform(test0).head()
>>> result.prediction
0.0
Expand Down Expand Up @@ -1640,6 +1668,10 @@ class GBTClassifier(JavaProbabilisticClassifier, _GBTClassifierParams,
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> model.predict(test0.head().features)
0.0
>>> model.predictRaw(test0.head().features)
DenseVector([1.1697, -1.1697])
>>> model.predictProbability(test0.head().features)
DenseVector([0.9121, 0.0879])
>>> result = model.transform(test0).head()
>>> result.prediction
0.0
Expand Down Expand Up @@ -1959,6 +1991,10 @@ class NaiveBayes(JavaProbabilisticClassifier, _NaiveBayesParams, HasThresholds,
>>> test0 = sc.parallelize([Row(features=Vectors.dense([1.0, 0.0]))]).toDF()
>>> model.predict(test0.head().features)
1.0
>>> model.predictRaw(test0.head().features)
DenseVector([-1.72..., -0.99...])
>>> model.predictProbability(test0.head().features)
DenseVector([0.32..., 0.67...])
>>> result = model.transform(test0).head()
>>> result.prediction
1.0
Expand Down Expand Up @@ -2174,6 +2210,10 @@ class MultilayerPerceptronClassifier(JavaProbabilisticClassifier, _MultilayerPer
... (Vectors.dense([0.0, 0.0]),)], ["features"])
>>> model.predict(testDF.head().features)
1.0
>>> model.predictRaw(testDF.head().features)
DenseVector([-16.208, 16.344])
>>> model.predictProbability(testDF.head().features)
DenseVector([0.0, 1.0])
>>> model.transform(testDF).select("features", "prediction").show()
+---------+----------+
| features|prediction|
Expand Down Expand Up @@ -2791,6 +2831,10 @@ class FMClassifier(JavaProbabilisticClassifier, _FactorizationMachinesParams, Ja
... (Vectors.dense(0.5),),
... (Vectors.dense(1.0),),
... (Vectors.dense(2.0),)], ["features"])
>>> model.predictRaw(test0.head().features)
DenseVector([22.13..., -22.13...])
>>> model.predictProbability(test0.head().features)
DenseVector([1.0, 0.0])
>>> model.transform(test0).select("features", "probability").show(10, False)
+--------+------------------------------------------+
|features|probability |
Expand Down