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Diffstat (limited to 'python/pyspark/mllib/stat.py')
| -rw-r--r-- | python/pyspark/mllib/stat.py | 137 |
1 files changed, 135 insertions, 2 deletions
diff --git a/python/pyspark/mllib/stat.py b/python/pyspark/mllib/stat.py index 15f0652f83..0700f8a8e5 100644 --- a/python/pyspark/mllib/stat.py +++ b/python/pyspark/mllib/stat.py @@ -19,11 +19,12 @@ Python package for statistical functions in MLlib. """ +from pyspark import RDD from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper -from pyspark.mllib.linalg import _convert_to_vector +from pyspark.mllib.linalg import Matrix, _convert_to_vector -__all__ = ['MultivariateStatisticalSummary', 'Statistics'] +__all__ = ['MultivariateStatisticalSummary', 'ChiSqTestResult', 'Statistics'] class MultivariateStatisticalSummary(JavaModelWrapper): @@ -51,6 +52,54 @@ class MultivariateStatisticalSummary(JavaModelWrapper): return self.call("min").toArray() +class ChiSqTestResult(JavaModelWrapper): + """ + :: Experimental :: + + Object containing the test results for the chi-squared hypothesis test. + """ + @property + def method(self): + """ + Name of the test method + """ + return self._java_model.method() + + @property + def pValue(self): + """ + The probability of obtaining a test statistic result at least as + extreme as the one that was actually observed, assuming that the + null hypothesis is true. + """ + return self._java_model.pValue() + + @property + def degreesOfFreedom(self): + """ + Returns the degree(s) of freedom of the hypothesis test. + Return type should be Number(e.g. Int, Double) or tuples of Numbers. + """ + return self._java_model.degreesOfFreedom() + + @property + def statistic(self): + """ + Test statistic. + """ + return self._java_model.statistic() + + @property + def nullHypothesis(self): + """ + Null hypothesis of the test. + """ + return self._java_model.nullHypothesis() + + def __str__(self): + return self._java_model.toString() + + class Statistics(object): @staticmethod @@ -135,6 +184,90 @@ class Statistics(object): else: return callMLlibFunc("corr", x.map(float), y.map(float), method) + @staticmethod + def chiSqTest(observed, expected=None): + """ + :: Experimental :: + + If `observed` is Vector, conduct Pearson's chi-squared goodness + of fit test of the observed data against the expected distribution, + or againt the uniform distribution (by default), with each category + having an expected frequency of `1 / len(observed)`. + (Note: `observed` cannot contain negative values) + + If `observed` is matrix, conduct Pearson's independence test on the + input contingency matrix, which cannot contain negative entries or + columns or rows that sum up to 0. + + If `observed` is an RDD of LabeledPoint, conduct Pearson's independence + test for every feature against the label across the input RDD. + 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. + + :param observed: it could be a vector containing the observed categorical + counts/relative frequencies, or the contingency matrix + (containing either counts or relative frequencies), + or an RDD of LabeledPoint containing the labeled dataset + with categorical features. Real-valued features will be + treated as categorical for each distinct value. + :param expected: Vector containing the expected categorical counts/relative + frequencies. `expected` is rescaled if the `expected` sum + differs from the `observed` sum. + :return: ChiSquaredTest object containing the test statistic, degrees + of freedom, p-value, the method used, and the null hypothesis. + + >>> from pyspark.mllib.linalg import Vectors, Matrices + >>> observed = Vectors.dense([4, 6, 5]) + >>> pearson = Statistics.chiSqTest(observed) + >>> print pearson.statistic + 0.4 + >>> pearson.degreesOfFreedom + 2 + >>> print round(pearson.pValue, 4) + 0.8187 + >>> pearson.method + u'pearson' + >>> pearson.nullHypothesis + u'observed follows the same distribution as expected.' + + >>> observed = Vectors.dense([21, 38, 43, 80]) + >>> expected = Vectors.dense([3, 5, 7, 20]) + >>> pearson = Statistics.chiSqTest(observed, expected) + >>> print round(pearson.pValue, 4) + 0.0027 + + >>> 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] + >>> chi = Statistics.chiSqTest(Matrices.dense(3, 4, data)) + >>> print round(chi.statistic, 4) + 21.9958 + + >>> from pyspark.mllib.regression import LabeledPoint + >>> data = [LabeledPoint(0.0, Vectors.dense([0.5, 10.0])), + ... LabeledPoint(0.0, Vectors.dense([1.5, 20.0])), + ... LabeledPoint(1.0, Vectors.dense([1.5, 30.0])), + ... LabeledPoint(0.0, Vectors.dense([3.5, 30.0])), + ... LabeledPoint(0.0, Vectors.dense([3.5, 40.0])), + ... LabeledPoint(1.0, Vectors.dense([3.5, 40.0])),] + >>> rdd = sc.parallelize(data, 4) + >>> chi = Statistics.chiSqTest(rdd) + >>> print chi[0].statistic + 0.75 + >>> print chi[1].statistic + 1.5 + """ + if isinstance(observed, RDD): + jmodels = callMLlibFunc("chiSqTest", observed) + return [ChiSqTestResult(m) for m in jmodels] + + if isinstance(observed, Matrix): + jmodel = callMLlibFunc("chiSqTest", observed) + else: + if expected and len(expected) != len(observed): + raise ValueError("`expected` should have same length with `observed`") + jmodel = callMLlibFunc("chiSqTest", _convert_to_vector(observed), expected) + return ChiSqTestResult(jmodel) + def _test(): import doctest |