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import sys
from pyspark import since, SparkContext
from pyspark.ml.common import _java2py, _py2java
from pyspark.ml.wrapper import JavaWrapper, _jvm
from pyspark.sql.column import Column, _to_seq
from pyspark.sql.functions import lit
[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.
.. versionadded:: 2.2.0
"""
[docs] @staticmethod
@since("2.2.0")
def test(dataset, featuresCol, labelCol):
"""
Perform a Pearson's independence test using dataset.
: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])
"""
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.
.. versionadded:: 2.2.0
"""
[docs] @staticmethod
@since("2.2.0")
def corr(dataset, column, method="pearson"):
"""
Compute the correlation matrix with specified method using dataset.
: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. ]])
"""
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))
[docs]class KolmogorovSmirnovTest(object):
"""
.. note:: Experimental
Conduct the two-sided Kolmogorov Smirnov (KS) test for data sampled from a continuous
distribution.
By comparing the largest difference between the empirical cumulative
distribution of the sample data and the theoretical distribution we can provide a test for the
the null hypothesis that the sample data comes from that theoretical distribution.
.. versionadded:: 2.4.0
"""
[docs] @staticmethod
@since("2.4.0")
def test(dataset, sampleCol, distName, *params):
"""
Conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution
equality. Currently supports the normal distribution, taking as parameters the mean and
standard deviation.
:param dataset:
a Dataset or a DataFrame containing the sample of data to test.
:param sampleCol:
Name of sample column in dataset, of any numerical type.
:param distName:
a `string` name for a theoretical distribution, currently only support "norm".
:param params:
a list of `Double` values specifying the parameters to be used for the theoretical
distribution. For "norm" distribution, the parameters includes mean and variance.
:return:
A DataFrame that contains the Kolmogorov-Smirnov test result for the input sampled data.
This DataFrame will contain a single Row with the following fields:
- `pValue: Double`
- `statistic: Double`
>>> from pyspark.ml.stat import KolmogorovSmirnovTest
>>> dataset = [[-1.0], [0.0], [1.0]]
>>> dataset = spark.createDataFrame(dataset, ['sample'])
>>> ksResult = KolmogorovSmirnovTest.test(dataset, 'sample', 'norm', 0.0, 1.0).first()
>>> round(ksResult.pValue, 3)
1.0
>>> round(ksResult.statistic, 3)
0.175
>>> dataset = [[2.0], [3.0], [4.0]]
>>> dataset = spark.createDataFrame(dataset, ['sample'])
>>> ksResult = KolmogorovSmirnovTest.test(dataset, 'sample', 'norm', 3.0, 1.0).first()
>>> round(ksResult.pValue, 3)
1.0
>>> round(ksResult.statistic, 3)
0.175
"""
sc = SparkContext._active_spark_context
javaTestObj = _jvm().org.apache.spark.ml.stat.KolmogorovSmirnovTest
dataset = _py2java(sc, dataset)
params = [float(param) for param in params]
return _java2py(sc, javaTestObj.test(dataset, sampleCol, distName,
_jvm().PythonUtils.toSeq(params)))
[docs]class Summarizer(object):
"""
.. note:: Experimental
Tools for vectorized statistics on MLlib Vectors.
The methods in this package provide various statistics for Vectors contained inside DataFrames.
This class lets users pick the statistics they would like to extract for a given column.
>>> from pyspark.ml.stat import Summarizer
>>> from pyspark.sql import Row
>>> from pyspark.ml.linalg import Vectors
>>> summarizer = Summarizer.metrics("mean", "count")
>>> df = sc.parallelize([Row(weight=1.0, features=Vectors.dense(1.0, 1.0, 1.0)),
... Row(weight=0.0, features=Vectors.dense(1.0, 2.0, 3.0))]).toDF()
>>> df.select(summarizer.summary(df.features, df.weight)).show(truncate=False)
+-----------------------------------+
|aggregate_metrics(features, weight)|
+-----------------------------------+
|[[1.0,1.0,1.0], 1] |
+-----------------------------------+
<BLANKLINE>
>>> df.select(summarizer.summary(df.features)).show(truncate=False)
+--------------------------------+
|aggregate_metrics(features, 1.0)|
+--------------------------------+
|[[1.0,1.5,2.0], 2] |
+--------------------------------+
<BLANKLINE>
>>> df.select(Summarizer.mean(df.features, df.weight)).show(truncate=False)
+--------------+
|mean(features)|
+--------------+
|[1.0,1.0,1.0] |
+--------------+
<BLANKLINE>
>>> df.select(Summarizer.mean(df.features)).show(truncate=False)
+--------------+
|mean(features)|
+--------------+
|[1.0,1.5,2.0] |
+--------------+
<BLANKLINE>
.. versionadded:: 2.4.0
"""
[docs] @staticmethod
@since("2.4.0")
def mean(col, weightCol=None):
"""
return a column of mean summary
"""
return Summarizer._get_single_metric(col, weightCol, "mean")
[docs] @staticmethod
@since("2.4.0")
def variance(col, weightCol=None):
"""
return a column of variance summary
"""
return Summarizer._get_single_metric(col, weightCol, "variance")
[docs] @staticmethod
@since("2.4.0")
def count(col, weightCol=None):
"""
return a column of count summary
"""
return Summarizer._get_single_metric(col, weightCol, "count")
[docs] @staticmethod
@since("2.4.0")
def numNonZeros(col, weightCol=None):
"""
return a column of numNonZero summary
"""
return Summarizer._get_single_metric(col, weightCol, "numNonZeros")
[docs] @staticmethod
@since("2.4.0")
def max(col, weightCol=None):
"""
return a column of max summary
"""
return Summarizer._get_single_metric(col, weightCol, "max")
[docs] @staticmethod
@since("2.4.0")
def min(col, weightCol=None):
"""
return a column of min summary
"""
return Summarizer._get_single_metric(col, weightCol, "min")
[docs] @staticmethod
@since("2.4.0")
def normL1(col, weightCol=None):
"""
return a column of normL1 summary
"""
return Summarizer._get_single_metric(col, weightCol, "normL1")
[docs] @staticmethod
@since("2.4.0")
def normL2(col, weightCol=None):
"""
return a column of normL2 summary
"""
return Summarizer._get_single_metric(col, weightCol, "normL2")
@staticmethod
def _check_param(featuresCol, weightCol):
if weightCol is None:
weightCol = lit(1.0)
if not isinstance(featuresCol, Column) or not isinstance(weightCol, Column):
raise TypeError("featureCol and weightCol should be a Column")
return featuresCol, weightCol
@staticmethod
def _get_single_metric(col, weightCol, metric):
col, weightCol = Summarizer._check_param(col, weightCol)
return Column(JavaWrapper._new_java_obj("org.apache.spark.ml.stat.Summarizer." + metric,
col._jc, weightCol._jc))
[docs] @staticmethod
@since("2.4.0")
def metrics(*metrics):
"""
Given a list of metrics, provides a builder that it turns computes metrics from a column.
See the documentation of [[Summarizer]] for an example.
The following metrics are accepted (case sensitive):
- mean: a vector that contains the coefficient-wise mean.
- variance: a vector tha contains the coefficient-wise variance.
- count: the count of all vectors seen.
- numNonzeros: a vector with the number of non-zeros for each coefficients
- max: the maximum for each coefficient.
- min: the minimum for each coefficient.
- normL2: the Euclidian norm for each coefficient.
- normL1: the L1 norm of each coefficient (sum of the absolute values).
:param metrics:
metrics that can be provided.
:return:
an object of :py:class:`pyspark.ml.stat.SummaryBuilder`
Note: Currently, the performance of this interface is about 2x~3x slower then using the RDD
interface.
"""
sc = SparkContext._active_spark_context
js = JavaWrapper._new_java_obj("org.apache.spark.ml.stat.Summarizer.metrics",
_to_seq(sc, metrics))
return SummaryBuilder(js)
[docs]class SummaryBuilder(JavaWrapper):
"""
.. note:: Experimental
A builder object that provides summary statistics about a given column.
Users should not directly create such builders, but instead use one of the methods in
:py:class:`pyspark.ml.stat.Summarizer`
.. versionadded:: 2.4.0
"""
def __init__(self, jSummaryBuilder):
super(SummaryBuilder, self).__init__(jSummaryBuilder)
[docs] @since("2.4.0")
def summary(self, featuresCol, weightCol=None):
"""
Returns an aggregate object that contains the summary of the column with the requested
metrics.
:param featuresCol:
a column that contains features Vector object.
:param weightCol:
a column that contains weight value. Default weight is 1.0.
:return:
an aggregate column that contains the statistics. The exact content of this
structure is determined during the creation of the builder.
"""
featuresCol, weightCol = Summarizer._check_param(featuresCol, weightCol)
return Column(self._java_obj.summary(featuresCol._jc, weightCol._jc))
if __name__ == "__main__":
import doctest
import numpy
import pyspark.ml.stat
from pyspark.sql import SparkSession
try:
# Numpy 1.14+ changed it's string format.
numpy.set_printoptions(legacy='1.13')
except TypeError:
pass
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:
sys.exit(-1)
