Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/10186#discussion_r49677620
--- Diff: python/pyspark/ml/feature.py ---
@@ -2093,6 +2093,101 @@ class RFormulaModel(JavaModel):
"""
+@inherit_doc
+class ChiSqSelector(JavaEstimator, HasFeaturesCol, HasOutputCol,
HasLabelCol):
+ """
+ .. note:: Experimental
+
+ Chi-Squared feature selection, which selects categorical features to
use for predicting a
+ categorical label.
+
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> df = sqlContext.createDataFrame(
+ ... [(Vectors.dense([0.0, 0.0, 18.0, 1.0]), 1.0),
+ ... (Vectors.dense([0.0, 1.0, 12.0, 0.0]), 0.0),
+ ... (Vectors.dense([1.0, 0.0, 15.0, 0.1]), 0.0)],
+ ... ["features", "label"])
+ >>> selector = ChiSqSelector(numTopFeatures=1,
outputCol="selectedFeatures")
+ >>> model = selector.fit(df)
+ >>> model.transform(df).head().selectedFeatures
+ DenseVector([1.0])
+ >>> model.selectedFeatures
+ [3]
+
+ .. versionadded:: 2.0.0
+ """
+
+ # a placeholder to make it appear in the generated doc
+ numTopFeatures = \
+ Param(Params._dummy(), "numTopFeatures",
+ "Number of features that selector will select, ordered by
statistics value " +
+ "descending. If the number of features is < numTopFeatures,
then this will select " +
+ "all features.")
+
+ @keyword_only
+ def __init__(self, numTopFeatures=50, featuresCol="features",
outputCol=None, labelCol="label"):
+ """
+ __init__(self, numTopFeatures=50, featuresCol="features",
outputCol=None, labelCol="label")
+ """
+ super(ChiSqSelector, self).__init__()
+ self._java_obj =
self._new_java_obj("org.apache.spark.ml.feature.ChiSqSelector", self.uid)
+ self.numTopFeatures = \
+ Param(self, "numTopFeatures",
+ "Number of features that selector will select, ordered
by statistics value " +
+ "descending. If the number of features is <
numTopFeatures, then this will " +
+ "select all features.")
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ @since("2.0.0")
+ def setParams(self, numTopFeatures=50, featuresCol="features",
outputCol=None,
+ labelCol="labels"):
+ """
+ setParams(self, numTopFeatures=50, featuresCol="features",
outputCol=None,\
+ labelCol="labels")
+ Sets params for this ChiSqSelector.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
+
+ @since("2.0.0")
+ def setNumTopFeatures(self, value):
+ """
+ Sets the value of :py:attr:`numTopFeatures`.
+ """
+ self._paramMap[self.numTopFeatures] = value
+ return self
+
+ @since("2.0.0")
+ def getNumTopFeatures(self):
+ """
+ Gets the value of numTopFeatures or its default value.
+ """
+ return self.getOrDefault(self.numTopFeatures)
+
+ def _create_model(self, java_model):
+ return ChiSqSelectorModel(java_model)
+
+
+class ChiSqSelectorModel(JavaModel):
+ """
+ .. note:: Experimental
+
+ Model fitted by ChiSqSelector.
+
+ .. versionadded:: 2.0.0
+ """
+
+ @property
+ @since("2.0.0")
+ def selectedFeatures(self):
+ """
+ Standard deviation of the StandardScalerModel.
--- End diff --
update doc
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