a3dc618486
There is only a single `stat.py` file for the `mllib.stat` package. We recently added `MultivariateGaussian` under `mllib.stat.distribution` in Scala/Java. It would be nice to refactor `stat.py` and make it easy to expand. Note that `ChiSqTestResult` is moved from `mllib.stat` to `mllib.stat.test`. The latter is used in Scala/Java. It is only used in the return value of `Statistics.chiSqTest`, so this should be an okay change. davies Author: Xiangrui Meng <meng@databricks.com> Closes #4266 from mengxr/py-stat-refactor and squashes the following commits: 1a5e1db [Xiangrui Meng] refactor stat.py
248 lines
10 KiB
Python
248 lines
10 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from pyspark import RDD
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from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
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from pyspark.mllib.linalg import Matrix, _convert_to_vector
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from pyspark.mllib.regression import LabeledPoint
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from pyspark.mllib.stat.test import ChiSqTestResult
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__all__ = ['MultivariateStatisticalSummary', 'Statistics']
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class MultivariateStatisticalSummary(JavaModelWrapper):
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"""
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Trait for multivariate statistical summary of a data matrix.
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"""
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def mean(self):
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return self.call("mean").toArray()
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def variance(self):
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return self.call("variance").toArray()
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def count(self):
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return self.call("count")
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def numNonzeros(self):
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return self.call("numNonzeros").toArray()
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def max(self):
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return self.call("max").toArray()
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def min(self):
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return self.call("min").toArray()
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class Statistics(object):
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@staticmethod
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def colStats(rdd):
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"""
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Computes column-wise summary statistics for the input RDD[Vector].
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:param rdd: an RDD[Vector] for which column-wise summary statistics
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are to be computed.
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:return: :class:`MultivariateStatisticalSummary` object containing
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column-wise summary statistics.
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>>> from pyspark.mllib.linalg import Vectors
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>>> rdd = sc.parallelize([Vectors.dense([2, 0, 0, -2]),
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... Vectors.dense([4, 5, 0, 3]),
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... Vectors.dense([6, 7, 0, 8])])
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>>> cStats = Statistics.colStats(rdd)
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>>> cStats.mean()
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array([ 4., 4., 0., 3.])
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>>> cStats.variance()
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array([ 4., 13., 0., 25.])
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>>> cStats.count()
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3L
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>>> cStats.numNonzeros()
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array([ 3., 2., 0., 3.])
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>>> cStats.max()
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array([ 6., 7., 0., 8.])
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>>> cStats.min()
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array([ 2., 0., 0., -2.])
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"""
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cStats = callMLlibFunc("colStats", rdd.map(_convert_to_vector))
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return MultivariateStatisticalSummary(cStats)
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@staticmethod
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def corr(x, y=None, method=None):
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"""
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Compute the correlation (matrix) for the input RDD(s) using the
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specified method.
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Methods currently supported: I{pearson (default), spearman}.
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If a single RDD of Vectors is passed in, a correlation matrix
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comparing the columns in the input RDD is returned. Use C{method=}
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to specify the method to be used for single RDD inout.
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If two RDDs of floats are passed in, a single float is returned.
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:param x: an RDD of vector for which the correlation matrix is to be computed,
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or an RDD of float of the same cardinality as y when y is specified.
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:param y: an RDD of float of the same cardinality as x.
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:param method: String specifying the method to use for computing correlation.
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Supported: `pearson` (default), `spearman`
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:return: Correlation matrix comparing columns in x.
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>>> x = sc.parallelize([1.0, 0.0, -2.0], 2)
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>>> y = sc.parallelize([4.0, 5.0, 3.0], 2)
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>>> zeros = sc.parallelize([0.0, 0.0, 0.0], 2)
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>>> abs(Statistics.corr(x, y) - 0.6546537) < 1e-7
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True
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>>> Statistics.corr(x, y) == Statistics.corr(x, y, "pearson")
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True
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>>> Statistics.corr(x, y, "spearman")
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0.5
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>>> from math import isnan
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>>> isnan(Statistics.corr(x, zeros))
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True
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>>> from pyspark.mllib.linalg import Vectors
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>>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]),
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... Vectors.dense([6, 7, 0, 8]), Vectors.dense([9, 0, 0, 1])])
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>>> pearsonCorr = Statistics.corr(rdd)
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>>> print str(pearsonCorr).replace('nan', 'NaN')
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[[ 1. 0.05564149 NaN 0.40047142]
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[ 0.05564149 1. NaN 0.91359586]
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[ NaN NaN 1. NaN]
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[ 0.40047142 0.91359586 NaN 1. ]]
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>>> spearmanCorr = Statistics.corr(rdd, method="spearman")
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>>> print str(spearmanCorr).replace('nan', 'NaN')
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[[ 1. 0.10540926 NaN 0.4 ]
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[ 0.10540926 1. NaN 0.9486833 ]
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[ NaN NaN 1. NaN]
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[ 0.4 0.9486833 NaN 1. ]]
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>>> try:
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... Statistics.corr(rdd, "spearman")
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... print "Method name as second argument without 'method=' shouldn't be allowed."
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... except TypeError:
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... pass
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"""
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# Check inputs to determine whether a single value or a matrix is needed for output.
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# Since it's legal for users to use the method name as the second argument, we need to
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# check if y is used to specify the method name instead.
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if type(y) == str:
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raise TypeError("Use 'method=' to specify method name.")
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if not y:
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return callMLlibFunc("corr", x.map(_convert_to_vector), method).toArray()
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else:
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return callMLlibFunc("corr", x.map(float), y.map(float), method)
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@staticmethod
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def chiSqTest(observed, expected=None):
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"""
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.. note:: Experimental
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If `observed` is Vector, conduct Pearson's chi-squared goodness
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of fit test of the observed data against the expected distribution,
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or againt the uniform distribution (by default), with each category
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having an expected frequency of `1 / len(observed)`.
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(Note: `observed` cannot contain negative values)
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If `observed` is matrix, conduct Pearson's independence test on the
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input contingency matrix, which cannot contain negative entries or
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columns or rows that sum up to 0.
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If `observed` is an RDD of LabeledPoint, conduct Pearson's independence
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test for every feature against the label across the input RDD.
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For each feature, the (feature, label) pairs are converted into a
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contingency matrix for which the chi-squared statistic is computed.
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All label and feature values must be categorical.
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:param observed: it could be a vector containing the observed categorical
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counts/relative frequencies, or the contingency matrix
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(containing either counts or relative frequencies),
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or an RDD of LabeledPoint containing the labeled dataset
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with categorical features. Real-valued features will be
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treated as categorical for each distinct value.
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:param expected: Vector containing the expected categorical counts/relative
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frequencies. `expected` is rescaled if the `expected` sum
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differs from the `observed` sum.
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:return: ChiSquaredTest object containing the test statistic, degrees
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of freedom, p-value, the method used, and the null hypothesis.
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>>> from pyspark.mllib.linalg import Vectors, Matrices
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>>> observed = Vectors.dense([4, 6, 5])
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>>> pearson = Statistics.chiSqTest(observed)
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>>> print pearson.statistic
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0.4
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>>> pearson.degreesOfFreedom
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2
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>>> print round(pearson.pValue, 4)
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0.8187
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>>> pearson.method
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u'pearson'
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>>> pearson.nullHypothesis
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u'observed follows the same distribution as expected.'
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>>> observed = Vectors.dense([21, 38, 43, 80])
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>>> expected = Vectors.dense([3, 5, 7, 20])
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>>> pearson = Statistics.chiSqTest(observed, expected)
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>>> print round(pearson.pValue, 4)
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0.0027
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>>> data = [40.0, 24.0, 29.0, 56.0, 32.0, 42.0, 31.0, 10.0, 0.0, 30.0, 15.0, 12.0]
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>>> chi = Statistics.chiSqTest(Matrices.dense(3, 4, data))
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>>> print round(chi.statistic, 4)
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21.9958
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>>> data = [LabeledPoint(0.0, Vectors.dense([0.5, 10.0])),
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... LabeledPoint(0.0, Vectors.dense([1.5, 20.0])),
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... LabeledPoint(1.0, Vectors.dense([1.5, 30.0])),
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... LabeledPoint(0.0, Vectors.dense([3.5, 30.0])),
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... LabeledPoint(0.0, Vectors.dense([3.5, 40.0])),
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... LabeledPoint(1.0, Vectors.dense([3.5, 40.0])),]
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>>> rdd = sc.parallelize(data, 4)
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>>> chi = Statistics.chiSqTest(rdd)
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>>> print chi[0].statistic
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0.75
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>>> print chi[1].statistic
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1.5
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"""
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if isinstance(observed, RDD):
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if not isinstance(observed.first(), LabeledPoint):
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raise ValueError("observed should be an RDD of LabeledPoint")
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jmodels = callMLlibFunc("chiSqTest", observed)
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return [ChiSqTestResult(m) for m in jmodels]
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if isinstance(observed, Matrix):
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jmodel = callMLlibFunc("chiSqTest", observed)
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else:
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if expected and len(expected) != len(observed):
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raise ValueError("`expected` should have same length with `observed`")
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jmodel = callMLlibFunc("chiSqTest", _convert_to_vector(observed), expected)
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return ChiSqTestResult(jmodel)
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def _test():
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import doctest
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from pyspark import SparkContext
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globs = globals().copy()
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globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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globs['sc'].stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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