Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/18305#discussion_r125675084
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregatorSuite.scala
---
@@ -0,0 +1,254 @@
+/*
+ * 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.optim.aggregator
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.feature.Instance
+import org.apache.spark.ml.linalg.{BLAS, Matrices, Vector, Vectors}
+import org.apache.spark.ml.util.TestingUtils._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+
+class LogisticAggregatorSuite extends SparkFunSuite with
MLlibTestSparkContext {
+
+ import DifferentiableLossAggregatorSuite.getClassificationSummarizers
+
+ @transient var instances: Array[Instance] = _
+ @transient var instancesConstantFeature: Array[Instance] = _
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+ instances = Array(
+ Instance(0.0, 0.1, Vectors.dense(1.0, 2.0)),
+ Instance(1.0, 0.5, Vectors.dense(1.5, 1.0)),
+ Instance(2.0, 0.3, Vectors.dense(4.0, 0.5))
+ )
+ instancesConstantFeature = Array(
+ Instance(0.0, 0.1, Vectors.dense(1.0, 2.0)),
+ Instance(1.0, 0.5, Vectors.dense(1.0, 1.0)),
+ Instance(2.0, 0.3, Vectors.dense(1.0, 0.5))
+ )
+ }
+
+
+ /** Get summary statistics for some data and create a new
LogisticAggregator. */
+ private def getNewAggregator(
+ instances: Array[Instance],
+ coefficients: Vector,
+ fitIntercept: Boolean,
+ isMultinomial: Boolean): LogisticAggregator = {
+ val (featuresSummarizer, ySummarizer) =
+
DifferentiableLossAggregatorSuite.getClassificationSummarizers(instances)
+ val numClasses = ySummarizer.histogram.length
+ val featuresStd = featuresSummarizer.variance.toArray.map(math.sqrt)
+ val bcFeaturesStd = spark.sparkContext.broadcast(featuresStd)
+ val bcCoefficients = spark.sparkContext.broadcast(coefficients)
--- End diff --
It's better to destroy these broadcast variable explicitly even in test
suites.
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