Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/16694#discussion_r98141079
--- Diff: python/pyspark/ml/classification.py ---
@@ -60,6 +61,137 @@ def numClasses(self):
@inherit_doc
+class LinearSVC(JavaEstimator, HasFeaturesCol, HasLabelCol,
HasPredictionCol, HasMaxIter,
+ HasRegParam, HasTol, HasRawPredictionCol, HasFitIntercept,
HasStandardization,
+ HasThreshold, HasWeightCol, HasAggregationDepth,
JavaMLWritable, JavaMLReadable):
+ """
+ Linear SVM Classifier
(https://en.wikipedia.org/wiki/Support_vector_machine#Linear_SVM)
+ This binary classifier optimizes the Hinge Loss using the OWLQN
optimizer.
+
+ >>> from pyspark.sql import Row
+ >>> from pyspark.ml.linalg import Vectors
+ >>> bdf = sc.parallelize([
+ ... Row(label=1.0, weight=2.0, features=Vectors.dense(1.0)),
+ ... Row(label=0.0, weight=2.0, features=Vectors.sparse(1, [],
[]))]).toDF()
+ >>> svm = LinearSVC(maxIter=5, regParam=0.01, weightCol="weight")
+ >>> model = svm.fit(bdf)
+ >>> model.coefficients
+ DenseVector([1.909])
+ >>> model.intercept
+ -1.0045358384178
+ >>> model.numClasses
+ 2
+ >>> model.numFeatures
+ 1
+ >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
+ >>> result = model.transform(test0).head()
+ >>> result.prediction
+ 0.0
+ >>> result.rawPrediction
+ DenseVector([2.9135, -2.9135])
+ >>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0],
[1.0]))]).toDF()
+ >>> model.transform(test1).head().prediction
+ 1.0
+ >>> svm.setParams("vector")
--- End diff --
Put this in a unit test (tests.py), not here in the doc tests (though I
also don't think you really need this test)
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