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From yinxusen <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-11922][PYSPARK][ML] Python api for ml.f...
Date Fri, 15 Jan 2016 08:28:24 GMT
Github user yinxusen commented on a diff in the pull request:

    https://github.com/apache/spark/pull/10085#discussion_r49829992
  
    --- Diff: python/pyspark/ml/feature.py ---
    @@ -972,6 +973,88 @@ def getDegree(self):
     
     
     @inherit_doc
    +class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol):
    +    """
    +    .. note:: Experimental
    +
    +    `QuantileDiscretizer` takes a column with continuous features and outputs a column
with binned
    +    categorical features. The bin ranges are chosen by taking a sample of the data and
dividing it
    +    into roughly equal parts. The lower and upper bin bounds will be -Infinity and +Infinity,
    +    covering all real values. This attempts to find numBuckets partitions based on a
sample of data,
    +    but it may find fewer depending on the data sample values.
    +
    +    >>> df = sqlContext.createDataFrame([(0.1,), (0.4,), (1.2,), (1.5,)], ["values"])
    +    >>> qds = QuantileDiscretizer(numBuckets=2,
    +    ...     inputCol="values", outputCol="buckets")
    +    >>> bucketizer = qds.fit(df)
    +    >>> splits = bucketizer.getSplits()
    +    >>> splits[0]
    +    -inf
    +    >>> int(splits[1]*10)
    +    4
    +    >>> bucketed = bucketizer.transform(df).collect()
    +    >>> bucketed[0].buckets
    +    0.0
    +
    +    .. versionadded:: 1.6.0
    +    """
    +
    +    # a placeholder to make it appear in the generated doc
    +    numBuckets = Param(Params._dummy(), "numBuckets",
    +                       "Maximum number of buckets (quantiles, or " +
    +                       "categories) into which data points are grouped. Must be >=
2.")
    --- End diff --
    
    Should we add a `default 2` here?


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