Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/17421#discussion_r108286819
--- Diff: python/pyspark/ml/stat.py ---
@@ -0,0 +1,104 @@
+#
+# 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
+ import tempfile
+
+ temp_path = tempfile.mkdtemp()
--- End diff --
I don't think this test is using the temp path?
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