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[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 <[email protected]> Closes apache#17421 from MrBago/chiSquareTestWrapper.
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# | ||
# 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. | ||
# | ||
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from pyspark import since, SparkContext | ||
from pyspark.ml.common import _java2py, _py2java | ||
from pyspark.ml.wrapper import _jvm | ||
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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)) | ||
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if __name__ == "__main__": | ||
import doctest | ||
import pyspark.ml.stat | ||
from pyspark.sql import SparkSession | ||
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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 | ||
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failure_count, test_count = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) | ||
spark.stop() | ||
if failure_count: | ||
exit(-1) |
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