#
# 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.
#
"""
Python package for statistical functions in MLlib.
"""
from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
from pyspark.mllib.linalg import _convert_to_vector
__all__ = ['MultivariateStatisticalSummary', 'Statistics']
class MultivariateStatisticalSummary(JavaModelWrapper):
"""
Trait for multivariate statistical summary of a data matrix.
"""
def mean(self):
return self.call("mean").toArray()
def variance(self):
return self.call("variance").toArray()
def count(self):
return self.call("count")
def numNonzeros(self):
return self.call("numNonzeros").toArray()
def max(self):
return self.call("max").toArray()
def min(self):
return self.call("min").toArray()
class Statistics(object):
@staticmethod
def colStats(rdd):
"""
Computes column-wise summary statistics for the input RDD[Vector].
>>> from pyspark.mllib.linalg import Vectors
>>> rdd = sc.parallelize([Vectors.dense([2, 0, 0, -2]),
... Vectors.dense([4, 5, 0, 3]),
... Vectors.dense([6, 7, 0, 8])])
>>> cStats = Statistics.colStats(rdd)
>>> cStats.mean()
array([ 4., 4., 0., 3.])
>>> cStats.variance()
array([ 4., 13., 0., 25.])
>>> cStats.count()
3L
>>> cStats.numNonzeros()
array([ 3., 2., 0., 3.])
>>> cStats.max()
array([ 6., 7., 0., 8.])
>>> cStats.min()
array([ 2., 0., 0., -2.])
"""
cStats = callMLlibFunc("colStats", rdd.map(_convert_to_vector))
return MultivariateStatisticalSummary(cStats)
@staticmethod
def corr(x, y=None, method=None):
"""
Compute the correlation (matrix) for the input RDD(s) using the
specified method.
Methods currently supported: I{pearson (default), spearman}.
If a single RDD of Vectors is passed in, a correlation matrix
comparing the columns in the input RDD is returned. Use C{method=}
to specify the method to be used for single RDD inout.
If two RDDs of floats are passed in, a single float is returned.
>>> x = sc.parallelize([1.0, 0.0, -2.0], 2)
>>> y = sc.parallelize([4.0, 5.0, 3.0], 2)
>>> zeros = sc.parallelize([0.0, 0.0, 0.0], 2)
>>> abs(Statistics.corr(x, y) - 0.6546537) < 1e-7
True
>>> Statistics.corr(x, y) == Statistics.corr(x, y, "pearson")
True
>>> Statistics.corr(x, y, "spearman")
0.5
>>> from math import isnan
>>> isnan(Statistics.corr(x, zeros))
True
>>> from pyspark.mllib.linalg import Vectors
>>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]),
... Vectors.dense([6, 7, 0, 8]), Vectors.dense([9, 0, 0, 1])])
>>> pearsonCorr = Statistics.corr(rdd)
>>> print str(pearsonCorr).replace('nan', 'NaN')
[[ 1. 0.05564149 NaN 0.40047142]
[ 0.05564149 1. NaN 0.91359586]
[ NaN NaN 1. NaN]
[ 0.40047142 0.91359586 NaN 1. ]]
>>> spearmanCorr = Statistics.corr(rdd, method="spearman")
>>> print str(spearmanCorr).replace('nan', 'NaN')
[[ 1. 0.10540926 NaN 0.4 ]
[ 0.10540926 1. NaN 0.9486833 ]
[ NaN NaN 1. NaN]
[ 0.4 0.9486833 NaN 1. ]]
>>> try:
... Statistics.corr(rdd, "spearman")
... print "Method name as second argument without 'method=' shouldn't be allowed."
... except TypeError:
... pass
"""
# Check inputs to determine whether a single value or a matrix is needed for output.
# Since it's legal for users to use the method name as the second argument, we need to
# check if y is used to specify the method name instead.
if type(y) == str:
raise TypeError("Use 'method=' to specify method name.")
if not y:
return callMLlibFunc("corr", x.map(_convert_to_vector), method).toArray()
else:
return callMLlibFunc("corr", x.map(float), y.map(float), method)
def _test():
import doctest
from pyspark import SparkContext
globs = globals().copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
