[SPARK-20040][ML][PYTHON] pyspark wrapper for ChiSquareTest

## What changes were proposed in this pull request?

A pyspark wrapper for spark.ml.stat.ChiSquareTest

## How was this patch tested?

unit tests
doctests

Author: Bago Amirbekian <bago@databricks.com>

Closes #17421 from MrBago/chiSquareTestWrapper.

5 files changed, 127 insertions, 12 deletions

diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index eaf1f3a1db..246f5188a5 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -431,6 +431,7 @@ pyspark_ml = Module(
         "pyspark.ml.linalg.__init__",
         "pyspark.ml.recommendation",
         "pyspark.ml.regression",
+        "pyspark.ml.stat",
         "pyspark.ml.tuning",
         "pyspark.ml.tests",
     ],
diff --git a/mllib/src/main/scala/org/apache/spark/ml/stat/ChiSquareTest.scala b/mllib/src/main/scala/org/apache/spark/ml/stat/ChiSquareTest.scala
index 21eba9a498..5b38ca73e8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/stat/ChiSquareTest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/stat/ChiSquareTest.scala
@@ -46,9 +46,9 @@ object ChiSquareTest {
       statistics: Vector)
 
   /**
-   * Conduct Pearson's independence test for every feature against the label across the input RDD.
-   * For each feature, the (feature, label) pairs are converted into a contingency matrix for which
-   * the Chi-squared statistic is computed. All label and feature values must be categorical.
+   * Conduct Pearson's independence test for every feature against the label. For each feature, the
+   * (feature, label) pairs are converted into a contingency matrix for which the Chi-squared
+   * statistic is computed. All label and feature values must be categorical.
    *
    * The null hypothesis is that the occurrence of the outcomes is statistically independent.
    *
diff --git a/python/docs/pyspark.ml.rst b/python/docs/pyspark.ml.rst
index a68183445d..930646de9c 100644
--- a/python/docs/pyspark.ml.rst
+++ b/python/docs/pyspark.ml.rst
@@ -65,6 +65,14 @@ pyspark.ml.regression module
     :undoc-members:
     :inherited-members:
 
+pyspark.ml.stat module
+----------------------
+
+.. automodule:: pyspark.ml.stat
+    :members:
+    :undoc-members:
+    :inherited-members:
+
 pyspark.ml.tuning module
 ------------------------
 
diff --git a/python/pyspark/ml/stat.py b/python/pyspark/ml/stat.py
new file mode 100644
index 0000000000..db043ff68f
--- /dev/null
+++ b/python/pyspark/ml/stat.py
@@ -0,0 +1,93 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from pyspark import since, SparkContext
+from pyspark.ml.common import _java2py, _py2java
+from pyspark.ml.wrapper import _jvm
+
+
+class ChiSquareTest(object):
+    """
+    .. note:: Experimental
+
+    Conduct Pearson's independence test for every feature against the label. For each feature,
+    the (feature, label) pairs are converted into a contingency matrix for which the Chi-squared
+    statistic is computed. All label and feature values must be categorical.
+
+    The null hypothesis is that the occurrence of the outcomes is statistically independent.
+
+    :param dataset:
+      DataFrame of categorical labels and categorical features.
+      Real-valued features will be treated as categorical for each distinct value.
+    :param featuresCol:
+      Name of features column in dataset, of type `Vector` (`VectorUDT`).
+    :param labelCol:
+      Name of label column in dataset, of any numerical type.
+    :return:
+      DataFrame containing the test result for every feature against the label.
+      This DataFrame will contain a single Row with the following fields:
+      - `pValues: Vector`
+      - `degreesOfFreedom: Array[Int]`
+      - `statistics: Vector`
+      Each of these fields has one value per feature.
+
+    >>> from pyspark.ml.linalg import Vectors
+    >>> from pyspark.ml.stat import ChiSquareTest
+    >>> dataset = [[0, Vectors.dense([0, 0, 1])],
+    ...            [0, Vectors.dense([1, 0, 1])],
+    ...            [1, Vectors.dense([2, 1, 1])],
+    ...            [1, Vectors.dense([3, 1, 1])]]
+    >>> dataset = spark.createDataFrame(dataset, ["label", "features"])
+    >>> chiSqResult = ChiSquareTest.test(dataset, 'features', 'label')
+    >>> chiSqResult.select("degreesOfFreedom").collect()[0]
+    Row(degreesOfFreedom=[3, 1, 0])
+
+    .. versionadded:: 2.2.0
+
+    """
+    @staticmethod
+    @since("2.2.0")
+    def test(dataset, featuresCol, labelCol):
+        """
+        Perform a Pearson's independence test using dataset.
+        """
+        sc = SparkContext._active_spark_context
+        javaTestObj = _jvm().org.apache.spark.ml.stat.ChiSquareTest
+        args = [_py2java(sc, arg) for arg in (dataset, featuresCol, labelCol)]
+        return _java2py(sc, javaTestObj.test(*args))
+
+
+if __name__ == "__main__":
+    import doctest
+    import pyspark.ml.stat
+    from pyspark.sql import SparkSession
+
+    globs = pyspark.ml.stat.__dict__.copy()
+    # The small batch size here ensures that we see multiple batches,
+    # even in these small test examples:
+    spark = SparkSession.builder \
+        .master("local[2]") \
+        .appName("ml.stat tests") \
+        .getOrCreate()
+    sc = spark.sparkContext
+    globs['sc'] = sc
+    globs['spark'] = spark
+
+    failure_count, test_count = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
+    spark.stop()
+    if failure_count:
+        exit(-1)
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index 527db9b667..571ac4bc1c 100755
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -41,9 +41,7 @@ from shutil import rmtree
 import tempfile
 import array as pyarray
 import numpy as np
-from numpy import (
-    abs, all, arange, array, array_equal, dot, exp, inf, mean, ones, random, tile, zeros)
-from numpy import sum as array_sum
+from numpy import abs, all, arange, array, array_equal, inf, ones, tile, zeros
 import inspect
 
 from pyspark import keyword_only, SparkContext
@@ -54,20 +52,19 @@ from pyspark.ml.common import _java2py, _py2java
 from pyspark.ml.evaluation import BinaryClassificationEvaluator, RegressionEvaluator
 from pyspark.ml.feature import *
 from pyspark.ml.fpm import FPGrowth, FPGrowthModel
-from pyspark.ml.linalg import (
-    DenseMatrix, DenseMatrix, DenseVector, Matrices, MatrixUDT,
-    SparseMatrix, SparseVector, Vector, VectorUDT, Vectors, _convert_to_vector)
+from pyspark.ml.linalg import DenseMatrix, DenseMatrix, DenseVector, Matrices, MatrixUDT, \
+    SparseMatrix, SparseVector, Vector, VectorUDT, Vectors
 from pyspark.ml.param import Param, Params, TypeConverters
 from pyspark.ml.param.shared import HasInputCol, HasMaxIter, HasSeed
 from pyspark.ml.recommendation import ALS
-from pyspark.ml.regression import (
-    DecisionTreeRegressor, GeneralizedLinearRegression, LinearRegression)
+from pyspark.ml.regression import DecisionTreeRegressor, GeneralizedLinearRegression, \
+    LinearRegression
+from pyspark.ml.stat import ChiSquareTest
 from pyspark.ml.tuning import *
 from pyspark.ml.wrapper import JavaParams, JavaWrapper
 from pyspark.serializers import PickleSerializer
 from pyspark.sql import DataFrame, Row, SparkSession
 from pyspark.sql.functions import rand
-from pyspark.sql.utils import IllegalArgumentException
 from pyspark.storagelevel import *
 from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
 
@@ -1741,6 +1738,22 @@ class WrapperTests(MLlibTestCase):
         self.assertEqual(_java2py(self.sc, java_array), [])
 
 
+class ChiSquareTestTests(SparkSessionTestCase):
+
+    def test_chisquaretest(self):
+        data = [[0, Vectors.dense([0, 1, 2])],
+                [1, Vectors.dense([1, 1, 1])],
+                [2, Vectors.dense([2, 1, 0])]]
+        df = self.spark.createDataFrame(data, ['label', 'feat'])
+        res = ChiSquareTest.test(df, 'feat', 'label')
+        # This line is hitting the collect bug described in #17218, commented for now.
+        # pValues = res.select("degreesOfFreedom").collect())
+        self.assertIsInstance(res, DataFrame)
+        fieldNames = set(field.name for field in res.schema.fields)
+        expectedFields = ["pValues", "degreesOfFreedom", "statistics"]
+        self.assertTrue(all(field in fieldNames for field in expectedFields))
+
+
 if __name__ == "__main__":
     from pyspark.ml.tests import *
     if xmlrunner: