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[SPARK-12905] [ML] [PySpark] PCAModel return eigenvalues for PySpark #10830

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[SPARK-12905] [ML] [PySpark] PCAModel return eigenvalues for PySpark #10830

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2 changes: 2 additions & 0 deletions mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
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Expand Up @@ -102,6 +102,8 @@ object PCA extends DefaultParamsReadable[PCA] {
* Model fitted by [[PCA]].
*
* @param pc A principal components Matrix. Each column is one principal component.
* @param explainedVariance A vector of proportions of variance explained by
* each principal component.
*/
@Experimental
class PCAModel private[ml] (
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11 changes: 11 additions & 0 deletions python/pyspark/ml/feature.py
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Expand Up @@ -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
"""
Expand Down Expand Up @@ -2052,6 +2054,15 @@ def pc(self):
"""
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):
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