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  import sys
  
  from pyspark import since, SparkContext
  from pyspark.ml.common import _java2py, _py2java
  from pyspark.ml.wrapper import JavaWrapper, _jvm
  from pyspark.sql.column import Column, _to_seq
  from pyspark.sql.functions import lit
  
  
  class ChiSquareTest(object):
      """
      Conduct Pearson's independence test for every feature against the label. 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.
  
      The null hypothesis is that the occurrence of the outcomes is statistically independent.
  
      .. versionadded:: 2.2.0
  
      """
  
      @staticmethod
      def test(dataset, featuresCol, labelCol, flatten=False):
          """
          Perform a Pearson's independence test using dataset.
  
          .. versionadded:: 2.2.0
          .. versionchanged:: 3.1.0
             Added optional ``flatten`` argument.
  
          Parameters
          ----------
          dataset : :py:class:`pyspark.sql.DataFrame`
              DataFrame of categorical labels and categorical features.
              Real-valued features will be treated as categorical for each distinct value.
          featuresCol : str
              Name of features column in dataset, of type `Vector` (`VectorUDT`).
          labelCol : str
              Name of label column in dataset, of any numerical type.
          flatten : bool, optional
              if True, flattens the returned dataframe.
  
          Returns
          -------
          :py:class:`pyspark.sql.DataFrame`
              DataFrame containing the test result for every feature against the label.
              If flatten is True, this DataFrame will contain one row per feature with the following
              fields:
  
              - `featureIndex: int`
              - `pValue: float`
              - `degreesOfFreedom: int`
              - `statistic: float`
  
              If flatten is False, this DataFrame will contain a single Row with the following fields:
  
              - `pValues: Vector`
              - `degreesOfFreedom: Array[int]`
              - `statistics: Vector`
  
              Each of these fields has one value per feature.
  
          Examples
          --------
          >>> from pyspark.ml.linalg import Vectors
          >>> from pyspark.ml.stat import ChiSquareTest
          >>> dataset = [[0, Vectors.dense([0, 0, 1])],
          ...            [0, Vectors.dense([1, 0, 1])],
          ...            [1, Vectors.dense([2, 1, 1])],
          ...            [1, Vectors.dense([3, 1, 1])]]
          >>> dataset = spark.createDataFrame(dataset, ["label", "features"])
          >>> chiSqResult = ChiSquareTest.test(dataset, 'features', 'label')
          >>> chiSqResult.select("degreesOfFreedom").collect()[0]
          Row(degreesOfFreedom=[3, 1, 0])
          >>> chiSqResult = ChiSquareTest.test(dataset, 'features', 'label', True)
          >>> row = chiSqResult.orderBy("featureIndex").collect()
          >>> row[0].statistic
          4.0
          """
          sc = SparkContext._active_spark_context
          javaTestObj = _jvm().org.apache.spark.ml.stat.ChiSquareTest
          args = [_py2java(sc, arg) for arg in (dataset, featuresCol, labelCol, flatten)]
          return _java2py(sc, javaTestObj.test(*args))
  
  
  class Correlation(object):
      """
      Compute the correlation matrix for the input dataset of Vectors using the specified method.
      Methods currently supported: `pearson` (default), `spearman`.
  
      .. versionadded:: 2.2.0
  
      Notes
      -----
      For Spearman, a rank correlation, we need to create an RDD[Double] for each column
      and sort it in order to retrieve the ranks and then join the columns back into an RDD[Vector],
      which is fairly costly. Cache the input Dataset before calling corr with `method = 'spearman'`
      to avoid recomputing the common lineage.
      """
  
      @staticmethod
      def corr(dataset, column, method="pearson"):
          """
          Compute the correlation matrix with specified method using dataset.
  
          .. versionadded:: 2.2.0
  
          Parameters
          ----------
          dataset : :py:class:`pyspark.sql.DataFrame`
              A DataFrame.
          column : str
              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.
          method : str, optional
              String specifying the method to use for computing correlation.
              Supported: `pearson` (default), `spearman`.
  
          Returns
          -------
          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)`.
  
          Examples
          --------
          >>> from pyspark.ml.linalg import DenseMatrix, 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.        ,  0.0556...,         NaN,  0.4004...],
                       [ 0.0556...,  1.        ,         NaN,  0.9135...],
                       [        NaN,         NaN,  1.        ,         NaN],
                       [ 0.4004...,  0.9135...,         NaN,  1.        ]])
          >>> spearmanCorr = Correlation.corr(dataset, 'features', method='spearman').collect()[0][0]
          >>> print(str(spearmanCorr).replace('nan', 'NaN'))
          DenseMatrix([[ 1.        ,  0.1054...,         NaN,  0.4       ],
                       [ 0.1054...,  1.        ,         NaN,  0.9486... ],
                       [        NaN,         NaN,  1.        ,         NaN],
                       [ 0.4       ,  0.9486... ,         NaN,  1.        ]])
          """
          sc = SparkContext._active_spark_context
          javaCorrObj = _jvm().org.apache.spark.ml.stat.Correlation
          args = [_py2java(sc, arg) for arg in (dataset, column, method)]
          return _java2py(sc, javaCorrObj.corr(*args))
  
  
  class KolmogorovSmirnovTest(object):
      """
      Conduct the two-sided Kolmogorov Smirnov (KS) test for data sampled from a continuous
      distribution.
  
      By comparing the largest difference between the empirical cumulative
      distribution of the sample data and the theoretical distribution we can provide a test for the
      the null hypothesis that the sample data comes from that theoretical distribution.
  
      .. versionadded:: 2.4.0
  
      """
  
      @staticmethod
      def test(dataset, sampleCol, distName, *params):
          """
          Conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution
          equality. Currently supports the normal distribution, taking as parameters the mean and
          standard deviation.
  
          .. versionadded:: 2.4.0
  
          Parameters
          ----------
          dataset : :py:class:`pyspark.sql.DataFrame`
              a Dataset or a DataFrame containing the sample of data to test.
          sampleCol : str
              Name of sample column in dataset, of any numerical type.
          distName : str
              a `string` name for a theoretical distribution, currently only support "norm".
          params : float
              a list of `float` values specifying the parameters to be used for the theoretical
              distribution. For "norm" distribution, the parameters includes mean and variance.
  
          Returns
          -------
          A DataFrame that contains the Kolmogorov-Smirnov test result for the input sampled data.
          This DataFrame will contain a single Row with the following fields:
  
          - `pValue: Double`
          - `statistic: Double`
  
          Examples
          --------
          >>> from pyspark.ml.stat import KolmogorovSmirnovTest
          >>> dataset = [[-1.0], [0.0], [1.0]]
          >>> dataset = spark.createDataFrame(dataset, ['sample'])
          >>> ksResult = KolmogorovSmirnovTest.test(dataset, 'sample', 'norm', 0.0, 1.0).first()
          >>> round(ksResult.pValue, 3)
          1.0
          >>> round(ksResult.statistic, 3)
          0.175
          >>> dataset = [[2.0], [3.0], [4.0]]
          >>> dataset = spark.createDataFrame(dataset, ['sample'])
          >>> ksResult = KolmogorovSmirnovTest.test(dataset, 'sample', 'norm', 3.0, 1.0).first()
          >>> round(ksResult.pValue, 3)
          1.0
          >>> round(ksResult.statistic, 3)
          0.175
          """
          sc = SparkContext._active_spark_context
          javaTestObj = _jvm().org.apache.spark.ml.stat.KolmogorovSmirnovTest
          dataset = _py2java(sc, dataset)
          params = [float(param) for param in params]
          return _java2py(
              sc, javaTestObj.test(dataset, sampleCol, distName, _jvm().PythonUtils.toSeq(params))
          )
  
  
  class Summarizer(object):
      """
      Tools for vectorized statistics on MLlib Vectors.
      The methods in this package provide various statistics for Vectors contained inside DataFrames.
      This class lets users pick the statistics they would like to extract for a given column.
  
      .. versionadded:: 2.4.0
  
      Examples
      --------
      >>> from pyspark.ml.stat import Summarizer
      >>> from pyspark.sql import Row
      >>> from pyspark.ml.linalg import Vectors
      >>> summarizer = Summarizer.metrics("mean", "count")
      >>> df = sc.parallelize([Row(weight=1.0, features=Vectors.dense(1.0, 1.0, 1.0)),
      ...                      Row(weight=0.0, features=Vectors.dense(1.0, 2.0, 3.0))]).toDF()
      >>> df.select(summarizer.summary(df.features, df.weight)).show(truncate=False)
      +-----------------------------------+
      |aggregate_metrics(features, weight)|
      +-----------------------------------+
      |{[1.0,1.0,1.0], 1}                 |
      +-----------------------------------+
      <BLANKLINE>
      >>> df.select(summarizer.summary(df.features)).show(truncate=False)
      +--------------------------------+
      |aggregate_metrics(features, 1.0)|
      +--------------------------------+
      |{[1.0,1.5,2.0], 2}              |
      +--------------------------------+
      <BLANKLINE>
      >>> df.select(Summarizer.mean(df.features, df.weight)).show(truncate=False)
      +--------------+
      |mean(features)|
      +--------------+
      |[1.0,1.0,1.0] |
      +--------------+
      <BLANKLINE>
      >>> df.select(Summarizer.mean(df.features)).show(truncate=False)
      +--------------+
      |mean(features)|
      +--------------+
      |[1.0,1.5,2.0] |
      +--------------+
      <BLANKLINE>
      """
  
      @staticmethod
      @since("2.4.0")
      def mean(col, weightCol=None):
          """
          return a column of mean summary
          """
          return Summarizer._get_single_metric(col, weightCol, "mean")
  
      @staticmethod
      @since("3.0.0")
      def sum(col, weightCol=None):
          """
          return a column of sum summary
          """
          return Summarizer._get_single_metric(col, weightCol, "sum")
  
      @staticmethod
      @since("2.4.0")
      def variance(col, weightCol=None):
          """
          return a column of variance summary
          """
          return Summarizer._get_single_metric(col, weightCol, "variance")
  
      @staticmethod
      @since("3.0.0")
      def std(col, weightCol=None):
          """
          return a column of std summary
          """
          return Summarizer._get_single_metric(col, weightCol, "std")
  
      @staticmethod
      @since("2.4.0")
      def count(col, weightCol=None):
          """
          return a column of count summary
          """
          return Summarizer._get_single_metric(col, weightCol, "count")
  
      @staticmethod
      @since("2.4.0")
      def numNonZeros(col, weightCol=None):
          """
          return a column of numNonZero summary
          """
          return Summarizer._get_single_metric(col, weightCol, "numNonZeros")
  
      @staticmethod
      @since("2.4.0")
      def max(col, weightCol=None):
          """
          return a column of max summary
          """
          return Summarizer._get_single_metric(col, weightCol, "max")
  
      @staticmethod
      @since("2.4.0")
      def min(col, weightCol=None):
          """
          return a column of min summary
          """
          return Summarizer._get_single_metric(col, weightCol, "min")
  
      @staticmethod
      @since("2.4.0")
      def normL1(col, weightCol=None):
          """
          return a column of normL1 summary
          """
          return Summarizer._get_single_metric(col, weightCol, "normL1")
  
      @staticmethod
      @since("2.4.0")
      def normL2(col, weightCol=None):
          """
          return a column of normL2 summary
          """
          return Summarizer._get_single_metric(col, weightCol, "normL2")
  
      @staticmethod
      def _check_param(featuresCol, weightCol):
          if weightCol is None:
              weightCol = lit(1.0)
          if not isinstance(featuresCol, Column) or not isinstance(weightCol, Column):
              raise TypeError("featureCol and weightCol should be a Column")
          return featuresCol, weightCol
  
      @staticmethod
      def _get_single_metric(col, weightCol, metric):
          col, weightCol = Summarizer._check_param(col, weightCol)
          return Column(
              JavaWrapper._new_java_obj(
                  "org.apache.spark.ml.stat.Summarizer." + metric, col._jc, weightCol._jc
              )
          )
  
      @staticmethod
      def metrics(*metrics):
          """
          Given a list of metrics, provides a builder that it turns computes metrics from a column.
  
          See the documentation of :py:class:`Summarizer` for an example.
  
          The following metrics are accepted (case sensitive):
           - mean: a vector that contains the coefficient-wise mean.
           - sum: a vector that contains the coefficient-wise sum.
           - variance: a vector tha contains the coefficient-wise variance.
           - std: a vector tha contains the coefficient-wise standard deviation.
           - count: the count of all vectors seen.
           - numNonzeros: a vector with the number of non-zeros for each coefficients
           - max: the maximum for each coefficient.
           - min: the minimum for each coefficient.
           - normL2: the Euclidean norm for each coefficient.
           - normL1: the L1 norm of each coefficient (sum of the absolute values).
  
          .. versionadded:: 2.4.0
  
          Notes
          -----
          Currently, the performance of this interface is about 2x~3x slower than using the RDD
          interface.
  
          Examples
          --------
          metrics : str
              metrics that can be provided.
  
          Returns
          -------
          :py:class:`pyspark.ml.stat.SummaryBuilder`
          """
          sc = SparkContext._active_spark_context
          js = JavaWrapper._new_java_obj(
              "org.apache.spark.ml.stat.Summarizer.metrics", _to_seq(sc, metrics)
          )
          return SummaryBuilder(js)
  
  
  class SummaryBuilder(JavaWrapper):
      """
      A builder object that provides summary statistics about a given column.
  
      Users should not directly create such builders, but instead use one of the methods in
      :py:class:`pyspark.ml.stat.Summarizer`
  
      .. versionadded:: 2.4.0
  
      """
  
      def __init__(self, jSummaryBuilder):
          super(SummaryBuilder, self).__init__(jSummaryBuilder)
  
      def summary(self, featuresCol, weightCol=None):
          """
          Returns an aggregate object that contains the summary of the column with the requested
          metrics.
  
          .. versionadded:: 2.4.0
  
          Parameters
          ----------
          featuresCol : str
              a column that contains features Vector object.
          weightCol : str, optional
              a column that contains weight value. Default weight is 1.0.
  
          Returns
          -------
          :py:class:`pyspark.sql.Column`
              an aggregate column that contains the statistics. The exact content of this
              structure is determined during the creation of the builder.
          """
          featuresCol, weightCol = Summarizer._check_param(featuresCol, weightCol)
          return Column(self._java_obj.summary(featuresCol._jc, weightCol._jc))
  
  
  class MultivariateGaussian(object):
      """Represents a (mean, cov) tuple
  
      .. versionadded:: 3.0.0
  
      Examples
      --------
      >>> from pyspark.ml.linalg import DenseMatrix, Vectors
      >>> from pyspark.ml.stat import MultivariateGaussian
      >>> m = MultivariateGaussian(Vectors.dense([11,12]), DenseMatrix(2, 2, (1.0, 3.0, 5.0, 2.0)))
      >>> (m.mean, m.cov.toArray())
      (DenseVector([11.0, 12.0]), array([[ 1.,  5.],
             [ 3.,  2.]]))
      """
  
      def __init__(self, mean, cov):
          self.mean = mean
          self.cov = cov
  
  
  if __name__ == "__main__":
      import doctest
      import numpy
      import pyspark.ml.stat
      from pyspark.sql import SparkSession
  
      try:
          # Numpy 1.14+ changed it's string format.
          numpy.set_printoptions(legacy="1.13")
      except TypeError:
          pass
  
      globs = pyspark.ml.stat.__dict__.copy()
      # The small batch size here ensures that we see multiple batches,
      # even in these small test examples:
      spark = SparkSession.builder.master("local[2]").appName("ml.stat tests").getOrCreate()
      sc = spark.sparkContext
      globs["sc"] = sc
      globs["spark"] = spark
  
      failure_count, test_count = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
      spark.stop()
      if failure_count:
          sys.exit(-1)
  