[SPARK-12905][ML][PYSPARK] PCAModel return eigenvalues for PySpark

```PCAModel```  can output ```explainedVariance``` at Python side.

cc mengxr srowen

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #10830 from yanboliang/spark-12905.

1 files changed, 11 insertions, 0 deletions

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 141ec3492a..1fa0eab384 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -1987,6 +1987,8 @@ class PCA(JavaEstimator, HasInputCol, HasOutputCol):
     >>> model = pca.fit(df)
     >>> model.transform(df).collect()[0].pca_features
     DenseVector([1.648..., -4.013...])
+    >>> model.explainedVariance
+    DenseVector([0.794..., 0.205...])
 
     .. versionadded:: 1.5.0
     """
@@ -2052,6 +2054,15 @@ class PCAModel(JavaModel):
         """
         return self._call_java("pc")
 
+    @property
+    @since("2.0.0")
+    def explainedVariance(self):
+        """
+        Returns a vector of proportions of variance
+        explained by each principal component.
+        """
+        return self._call_java("explainedVariance")
+
 
 @inherit_doc
 class RFormula(JavaEstimator, HasFeaturesCol, HasLabelCol):