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From: MLnick <git@git.apache.org>
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Subject: [GitHub] spark pull request #17494: [SPARK-20076][ML][PySpark] Add Python interface f...
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Message-Id: <20170403183747.71272DFBC6@git1-us-west.apache.org>
Date: Mon,  3 Apr 2017 18:37:47 +0000 (UTC)
archived-at: Mon, 03 Apr 2017 18:37:49 -0000

Github user MLnick commented on a diff in the pull request:

    https://github.com/apache/spark/pull/17494#discussion_r109492883
  
    --- Diff: python/pyspark/ml/stat.py ---
    @@ -71,6 +71,62 @@ def test(dataset, featuresCol, labelCol):
             return _java2py(sc, javaTestObj.test(*args))
     
     
    +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`.
    +
    +    :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.        ,  0.05564149,         NaN,  0.40047142],
    +                 [ 0.05564149,  1.        ,         NaN,  0.91359586],
    +                 [        NaN,         NaN,  1.        ,         NaN],
    +                 [ 0.40047142,  0.91359586,         NaN,  1.        ]])
    +    >>> spearmanCorr = Correlation.corr(dataset, 'features', method="spearman").collect()[0][0]
    --- End diff --
    
    Super minor nit - but let's use single `'` everywhere here rather than have a mix of single & double.


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