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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.
#
"""
Python package for statistical functions in MLlib.
"""
from pyspark.mllib._common import \
_get_unmangled_double_vector_rdd, _get_unmangled_rdd, \
_serialize_double, _serialize_double_vector, \
_deserialize_double, _deserialize_double_matrix
class Statistics(object):
@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 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])])
>>> Statistics.corr(rdd)
array([[ 1. , 0.05564149, nan, 0.40047142],
[ 0.05564149, 1. , nan, 0.91359586],
[ nan, nan, 1. , nan],
[ 0.40047142, 0.91359586, nan, 1. ]])
>>> Statistics.corr(rdd, method="spearman")
array([[ 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
"""
sc = x.ctx
# 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:
try:
Xser = _get_unmangled_double_vector_rdd(x)
except TypeError:
raise TypeError("corr called on a single RDD not consisted of Vectors.")
resultMat = sc._jvm.PythonMLLibAPI().corr(Xser._jrdd, method)
return _deserialize_double_matrix(resultMat)
else:
xSer = _get_unmangled_rdd(x, _serialize_double)
ySer = _get_unmangled_rdd(y, _serialize_double)
result = sc._jvm.PythonMLLibAPI().corr(xSer._jrdd, ySer._jrdd, method)
return result
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()
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