[SPARK-3964] [MLlib] [PySpark] add Hypothesis test Python API

```
pyspark.mllib.stat.StatisticschiSqTest(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.
```

Author: Davies Liu <davies@databricks.com>

Closes #3091 from davies/his and squashes the following commits:

145d16c [Davies Liu] address comments
0ab0764 [Davies Liu] fix float
5097d54 [Davies Liu] add Hypothesis test Python API

1 files changed, 40 insertions, 0 deletions

diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md
index 10a5131c07..ca8c29218f 100644
--- a/docs/mllib-statistics.md
+++ b/docs/mllib-statistics.md
@@ -380,6 +380,46 @@ for (ChiSqTestResult result : featureTestResults) {
 {% endhighlight %}
 </div>
 
+<div data-lang="python" markdown="1">
+[`Statistics`](api/python/index.html#pyspark.mllib.stat.Statistics$) provides methods to
+run Pearson's chi-squared tests. The following example demonstrates how to run and interpret
+hypothesis tests.
+
+{% highlight python %}
+from pyspark import SparkContext
+from pyspark.mllib.linalg import Vectors, Matrices
+from pyspark.mllib.regresssion import LabeledPoint
+from pyspark.mllib.stat import Statistics
+
+sc = SparkContext()
+
+vec = Vectors.dense(...) # a vector composed of the frequencies of events
+
+# compute the goodness of fit. If a second vector to test against is not supplied as a parameter,
+# the test runs against a uniform distribution.
+goodnessOfFitTestResult = Statistics.chiSqTest(vec)
+print goodnessOfFitTestResult # summary of the test including the p-value, degrees of freedom,
+                              # test statistic, the method used, and the null hypothesis.
+
+mat = Matrices.dense(...) # a contingency matrix
+
+# conduct Pearson's independence test on the input contingency matrix
+independenceTestResult = Statistics.chiSqTest(mat)
+print independenceTestResult  # summary of the test including the p-value, degrees of freedom...
+
+obs = sc.parallelize(...)  # LabeledPoint(feature, label) .
+
+# The contingency table is constructed from an RDD of LabeledPoint and used to conduct
+# the independence test. Returns an array containing the ChiSquaredTestResult for every feature
+# against the label.
+featureTestResults = Statistics.chiSqTest(obs)
+
+for i, result in enumerate(featureTestResults):
+    print "Column $d:" % (i + 1)
+    print result
+{% endhighlight %}
+</div>
+
 </div>
 
 ## Random data generation