Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/6795#discussion_r33840439
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/SignificantSelector.scala
---
@@ -0,0 +1,127 @@
+/*
+ * 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.mllib.feature
+
+import scala.collection.mutable
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector,
Vectors}
+import org.apache.spark.rdd.RDD
+
+/**
+ * :: Experimental ::
+ * Model to extract significant indices from vector.
+ *
+ * Significant indices is vector's index that has different value for
different vectors.
+ *
+ * For example, when you use HashingTF they create big sparse vector,
+ * and this code convert to smallest vector that don't include same values
indices for all vectors.
+ *
+ * @param indices array of significant indices.
+ */
+@Experimental
+class SignificantSelectorModel(val indices: Array[Int]) extends
VectorTransformer {
+
+ /**
+ * Applies transformation on a vector.
+ *
+ * @param vector vector to be transformed.
+ * @return transformed vector.
+ */
+ override def transform(vector: Vector): Vector = vector match {
+ case DenseVector(vs) =>
+ Vectors.dense(indices.map(vs))
+
+ case SparseVector(s, ids, vs) =>
+ var sv_idx = 0
+ var new_idx = 0
+ val elements = new mutable.ListBuffer[(Int, Double)]()
+
+ for (idx <- indices) {
+ while (sv_idx < ids.length && ids(sv_idx) < idx) {
+ sv_idx += 1
+ }
+ if (sv_idx < ids.length && ids(sv_idx) == idx) {
+ elements += ((new_idx, vs(sv_idx)))
+ sv_idx += 1
+ }
+ new_idx += 1
+ }
+
+ Vectors.sparse(indices.length, elements)
+
+ case v =>
+ throw new IllegalArgumentException("Don't support vector type " +
v.getClass)
+ }
+}
+
+/**
+ * :: Experimental ::
+ * Specialized model for equivalent vectors
+ */
+@Experimental
+class SignificantSelectorEmptyModel extends
SignificantSelectorModel(Array[Int]()) {
+
+ val empty_vector = Vectors.dense(Array[Double]())
+
+ override def transform(vector: Vector): Vector = empty_vector
+}
+
+/**
+ * :: Experimental ::
+ * Create Significant selector.
+ */
+@Experimental
+class SignificantSelector() {
+
+ /**
+ * Returns a significant vector indices selector.
+ *
+ * @param sources an `RDD[Vector]` containing the vectors.
+ */
+ def fit(sources: RDD[Vector]): SignificantSelectorModel = {
+ val sources_count = sources.count()
+ val significant_indices = sources.flatMap {
+ case DenseVector(vs) =>
+ vs.zipWithIndex
+ case SparseVector(_, ids, vs) =>
+ vs.zip(ids)
+ case v =>
+ throw new IllegalArgumentException("Don't support vector type "
+ v.getClass)
+ }
+ .map(e => (e.swap, 1))
+ .reduceByKey(_ + _)
+ .map { case ((idx, value), count) => (idx, (value, count))}
+ .groupByKey()
+ .mapValues { e =>
+ val values = e.groupBy(_._1)
+ val sum = e.map(_._2).sum
+
+ values.size + (if (sum == sources_count || values.contains(0.0)) 0
else 1)
+ }
+ .filter(_._2 > 1)
+ .keys
+ .collect()
+ .sorted
+
+ if (significant_indices.nonEmpty)
+ new SignificantSelectorModel(significant_indices)
+ else
+ new SignificantSelectorEmptyModel()
--- End diff --
Can just construct a `SignificantSelectorModel(Array())` instead
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