Github user thunterdb commented on a diff in the pull request:
https://github.com/apache/spark/pull/18798#discussion_r131970836
--- Diff: mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala ---
@@ -0,0 +1,587 @@
+/*
+ * 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 java.io._
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.internal.Logging
+import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
+import org.apache.spark.sql.Column
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Expression,
UnsafeArrayData}
+import
org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression,
Complete, TypedImperativeAggregate}
+import org.apache.spark.sql.catalyst.util.ArrayData
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.sql.types._
+
+/**
+ * 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
+ * [[Summarizer]].
+ */
+@Since("2.3.0")
+abstract class SummaryBuilder {
+ /**
+ * Returns an aggregate object that contains the summary of the column
with the requested metrics.
+ * @param featuresCol a column that contains features Vector object.
+ * @param weightCol a column that contains weight value.
+ * @return an aggregate column that contains the statistics. The exact
content of this
+ * structure is determined during the creation of the builder.
+ */
+ @Since("2.3.0")
+ def summary(featuresCol: Column, weightCol: Column): Column
+
+ @Since("2.3.0")
+ def summary(featuresCol: Column): Column = summary(featuresCol, lit(1.0))
+}
+
+/**
+ * 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. Here is
+ * an example in Scala:
+ * {{{
+ * val dataframe = ... // Some dataframe containing a feature column
+ * val allStats = dataframe.select(Summarizer.metrics("min",
"max").summary($"features"))
+ * val Row(min_, max_) = allStats.first()
+ * }}}
+ *
+ * If one wants to get a single metric, shortcuts are also available:
+ * {{{
+ * val meanDF = dataframe.select(Summarizer.mean($"features"))
+ * val Row(mean_) = meanDF.first()
+ * }}}
+ */
+@Since("2.3.0")
+object Summarizer extends Logging {
+
+ import SummaryBuilderImpl._
+
+ /**
+ * Given a list of metrics, provides a builder that it turns computes
metrics from a column.
+ *
+ * See the documentation of [[Summarizer]] for an example.
+ *
+ * The following metrics are accepted (case sensitive):
+ * - mean: a vector that contains the coefficient-wise mean.
+ * - variance: a vector tha contains the coefficient-wise variance.
+ * - 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 Euclidian norm for each coefficient.
+ * - normL1: the L1 norm of each coefficient (sum of the absolute
values).
+ * @param firstMetric the metric being provided
+ * @param metrics additional metrics that can be provided.
+ * @return a builder.
+ * @throws IllegalArgumentException if one of the metric names is not
understood.
+ */
+ @Since("2.3.0")
--- End diff --
Let's put a comment about performance to indicate that it is about 3x
slower than using the RDD interface.
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