[SPARK-19336][ML][PYSPARK] LinearSVC Python API

## What changes were proposed in this pull request?

Add Python API for the newly added LinearSVC algorithm.

## How was this patch tested?

Add new doc string test.

Author: wm624@hotmail.com <wm624@hotmail.com>

Closes #16694 from wangmiao1981/ser.

3 files changed, 156 insertions, 1 deletions

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 5fe4bab186..f10556ca92 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -31,7 +31,8 @@ from pyspark.sql.functions import udf, when
 from pyspark.sql.types import ArrayType, DoubleType
 from pyspark.storagelevel import StorageLevel
 
-__all__ = ['LogisticRegression', 'LogisticRegressionModel',
+__all__ = ['LinearSVC', 'LinearSVCModel',
+           'LogisticRegression', 'LogisticRegressionModel',
            'LogisticRegressionSummary', 'LogisticRegressionTrainingSummary',
            'BinaryLogisticRegressionSummary', 'BinaryLogisticRegressionTrainingSummary',
            'DecisionTreeClassifier', 'DecisionTreeClassificationModel',
@@ -60,6 +61,134 @@ class JavaClassificationModel(JavaPredictionModel):
 
 
 @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
+    >>> df = sc.parallelize([
+    ...     Row(label=1.0, features=Vectors.dense(1.0, 1.0, 1.0)),
+    ...     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.coefficients
+    DenseVector([0.0, -0.2792, -0.1833])
+    >>> model.intercept
+    1.0206118982229047
+    >>> model.numClasses
+    2
+    >>> model.numFeatures
+    3
+    >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, -1.0, -1.0))]).toDF()
+    >>> result = model.transform(test0).head()
+    >>> result.prediction
+    1.0
+    >>> result.rawPrediction
+    DenseVector([-1.4831, 1.4831])
+    >>> svm.setParams("vector")
+    Traceback (most recent call last):
+        ...
+    TypeError: Method setParams forces keyword arguments.
+    >>> svm_path = temp_path + "/svm"
+    >>> svm.save(svm_path)
+    >>> svm2 = LinearSVC.load(svm_path)
+    >>> svm2.getMaxIter()
+    5
+    >>> model_path = temp_path + "/svm_model"
+    >>> model.save(model_path)
+    >>> model2 = LinearSVCModel.load(model_path)
+    >>> model.coefficients[0] == model2.coefficients[0]
+    True
+    >>> model.intercept == model2.intercept
+    True
+
+    .. versionadded:: 2.2.0
+    """
+
+    @keyword_only
+    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                 maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol="rawPrediction",
+                 fitIntercept=True, standardization=True, threshold=0.0, weightCol=None,
+                 aggregationDepth=2):
+        """
+        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol="rawPrediction", \
+                 fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, \
+                 aggregationDepth=2):
+        """
+        super(LinearSVC, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.classification.LinearSVC", self.uid)
+        self._setDefault(maxIter=100, regParam=0.0, tol=1e-6, fitIntercept=True,
+                         standardization=True, threshold=0.0, aggregationDepth=2)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    @since("2.2.0")
+    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                  maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol="rawPrediction",
+                  fitIntercept=True, standardization=True, threshold=0.0, weightCol=None,
+                  aggregationDepth=2):
+        """
+        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                  maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol="rawPrediction", \
+                  fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, \
+                  aggregationDepth=2):
+        Sets params for Linear SVM Classifier.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def _create_model(self, java_model):
+        return LinearSVCModel(java_model)
+
+
+class LinearSVCModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable):
+    """
+    Model fitted by LinearSVC.
+
+    .. versionadded:: 2.2.0
+    """
+
+    @property
+    @since("2.2.0")
+    def coefficients(self):
+        """
+        Model coefficients of Linear SVM Classifier.
+        """
+        return self._call_java("coefficients")
+
+    @property
+    @since("2.2.0")
+    def intercept(self):
+        """
+        Model intercept of Linear SVM Classifier.
+        """
+        return self._call_java("intercept")
+
+    @property
+    @since("2.2.0")
+    def numClasses(self):
+        """
+        Number of classes.
+        """
+        return self._call_java("numClasses")
+
+    @property
+    @since("2.2.0")
+    def numFeatures(self):
+        """
+        Number of features.
+        """
+        return self._call_java("numFeatures")
+
+
+@inherit_doc
 class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
                          HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol,
                          HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds,
diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py
index 929591236d..51d49b524c 100644
--- a/python/pyspark/ml/param/_shared_params_code_gen.py
+++ b/python/pyspark/ml/param/_shared_params_code_gen.py
@@ -143,6 +143,8 @@ if __name__ == "__main__":
          "The class with largest value p/t is predicted, where p is the original " +
          "probability of that class and t is the class's threshold.", None,
          "TypeConverters.toListFloat"),
+        ("threshold", "threshold in binary classification prediction, in range [0, 1]",
+         "0.5", "TypeConverters.toFloat"),
         ("weightCol", "weight column name. If this is not set or empty, we treat " +
          "all instance weights as 1.0.", None, "TypeConverters.toString"),
         ("solver", "the solver algorithm for optimization. If this is not set or empty, " +
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index cc596936d8..163a0e2b3a 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -490,6 +490,30 @@ class HasThresholds(Params):
         return self.getOrDefault(self.thresholds)
 
 
+class HasThreshold(Params):
+    """
+    Mixin for param threshold: threshold in binary classification prediction, in range [0, 1]
+    """
+
+    threshold = Param(Params._dummy(), "threshold", "threshold in binary classification prediction, in range [0, 1]", typeConverter=TypeConverters.toFloat)
+
+    def __init__(self):
+        super(HasThreshold, self).__init__()
+        self._setDefault(threshold=0.5)
+
+    def setThreshold(self, value):
+        """
+        Sets the value of :py:attr:`threshold`.
+        """
+        return self._set(threshold=value)
+
+    def getThreshold(self):
+        """
+        Gets the value of threshold or its default value.
+        """
+        return self.getOrDefault(self.threshold)
+
+
 class HasWeightCol(Params):
     """
     Mixin for param weightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0.