Github user thunterdb commented on a diff in the pull request:
https://github.com/apache/spark/pull/17108#discussion_r107505185
--- Diff: mllib/src/main/scala/org/apache/spark/ml/stat/Correlations.scala
---
@@ -0,0 +1,88 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.stat
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.linalg.{SQLDataTypes, Vector}
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
+import org.apache.spark.mllib.stat.{Statistics => OldStatistics}
+import org.apache.spark.sql.{DataFrame, Dataset, Row}
+import org.apache.spark.sql.types.{StructField, StructType}
+
+/**
+ * API for statistical functions in MLlib, compatible with Dataframes and
Datasets.
+ *
+ * The functions in this package generalize the functions in
[[org.apache.spark.sql.Dataset.stat]]
+ * to spark.ml's Vector types.
+ */
+@Since("2.2.0")
+@Experimental
+object Correlations {
+
+ /**
+ * Compute the correlation matrix for the input RDD of Vectors using the
specified method.
+ * Methods currently supported: `pearson` (default), `spearman`.
+ *
+ * @param dataset A dataset or a dataframe
+ * @param column 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.
+ * @param method String specifying the method to use for computing
correlation.
+ * Supported: `pearson` (default), `spearman`
+ * @return 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)'.
+ * @throws IllegalArgumentException if the column is not a valid column
in the dataset, or if
+ * the content of this column is not of
type Vector.
+ *
+ * Here is how to access the correlation coefficient:
+ * {{{
+ * val data: Dataset[Vector] = ...
+ * val Row(coeff: Matrix) = Statistics.corr(data, "value").head
+ * // coeff now contains the Pearson correlation matrix.
+ * }}}
+ *
+ * @note 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 RDD before calling corr with
`method = "spearman"` to
+ * avoid recomputing the common lineage.
+ */
+ @Since("2.2.0")
+ def corr(dataset: Dataset[_], column: String, method: String): DataFrame
= {
+ val rdd = dataset.select(column).rdd.map {
+ case Row(v: Vector) => OldVectors.fromML(v)
+ }
+ val oldM = OldStatistics.corr(rdd, method)
+ val name = s"$method($column)"
+ val schema = StructType(Array(StructField(name,
SQLDataTypes.MatrixType, nullable = true)))
--- End diff --
Good point. It seems that Spark can be quite liberal with the nullability.
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