Source code for pyspark.ml.stat
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from pyspark import since, SparkContext
from pyspark.ml.common import _java2py, _py2java
from pyspark.ml.wrapper import _jvm
[docs]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")
[docs] 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))
[docs]class Correlation(object):
"""
.. note:: Experimental
Compute the correlation matrix for the input dataset of Vectors using the specified method.
Methods currently supported: `pearson` (default), `spearman`.
.. note:: For Spearman, a rank correlation, we need to create an RDD[Double] for each column
and sort it in order to retrieve the ranks and then join the columns back into an RDD[Vector],
which is fairly costly. Cache the input Dataset before calling corr with `method = 'spearman'`
to avoid recomputing the common lineage.
:param dataset:
A dataset or a dataframe.
:param column:
The name of the column of vectors for which the correlation coefficient needs
to be computed. This must be a column of the dataset, and it must contain
Vector objects.
:param method:
String specifying the method to use for computing correlation.
Supported: `pearson` (default), `spearman`.
:return:
A dataframe that contains the correlation matrix of the column of vectors. This
dataframe contains a single row and a single column of name
'$METHODNAME($COLUMN)'.
>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml.stat import Correlation
>>> dataset = [[Vectors.dense([1, 0, 0, -2])],
... [Vectors.dense([4, 5, 0, 3])],
... [Vectors.dense([6, 7, 0, 8])],
... [Vectors.dense([9, 0, 0, 1])]]
>>> dataset = spark.createDataFrame(dataset, ['features'])
>>> pearsonCorr = Correlation.corr(dataset, 'features', 'pearson').collect()[0][0]
>>> print(str(pearsonCorr).replace('nan', 'NaN'))
DenseMatrix([[ 1. , 0.0556..., NaN, 0.4004...],
[ 0.0556..., 1. , NaN, 0.9135...],
[ NaN, NaN, 1. , NaN],
[ 0.4004..., 0.9135..., NaN, 1. ]])
>>> spearmanCorr = Correlation.corr(dataset, 'features', method='spearman').collect()[0][0]
>>> print(str(spearmanCorr).replace('nan', 'NaN'))
DenseMatrix([[ 1. , 0.1054..., NaN, 0.4 ],
[ 0.1054..., 1. , NaN, 0.9486... ],
[ NaN, NaN, 1. , NaN],
[ 0.4 , 0.9486... , NaN, 1. ]])
.. versionadded:: 2.2.0
"""
@staticmethod
@since("2.2.0")
[docs] def corr(dataset, column, method="pearson"):
"""
Compute the correlation matrix with specified method using dataset.
"""
sc = SparkContext._active_spark_context
javaCorrObj = _jvm().org.apache.spark.ml.stat.Correlation
args = [_py2java(sc, arg) for arg in (dataset, column, method)]
return _java2py(sc, javaCorrObj.corr(*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)
