Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15415#discussion_r102273266
--- Diff: mllib/src/test/scala/org/apache/spark/ml/fpm/FPGrowthSuite.scala
---
@@ -0,0 +1,130 @@
+/*
+ * 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.fpm
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+
+class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext with
DefaultReadWriteTest {
+
+ @transient var dataset: Dataset[_] = _
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+ dataset = FPGrowthSuite.getFPGrowthData(spark)
+ }
+
+ test("FPGrowth fit and transform with different data types") {
+ Array(IntegerType, StringType, ShortType, LongType, ByteType).foreach
{ dt =>
+ val intData = dataset.withColumn("features",
col("features").cast(ArrayType(dt)))
+ val model = new FPGrowth().setMinSupport(0.5).fit(intData)
+ val generatedRules = model.setMinConfidence(0.5).getAssociationRules
+ val expectedRules = spark.createDataFrame(Seq(
+ (Array("2"), Array("1"), 1.0),
+ (Array("1"), Array("2"), 0.75)
+ )).toDF("antecedent", "consequent", "confidence")
+ .withColumn("antecedent", col("antecedent").cast(ArrayType(dt)))
+ .withColumn("consequent", col("consequent").cast(ArrayType(dt)))
+ assert(expectedRules.sort("antecedent").rdd.collect().sameElements(
+ generatedRules.sort("antecedent").rdd.collect()))
+
+ val transformed = model.transform(intData)
+ val expectedTransformed = spark.createDataFrame(Seq(
+ (0, Array("1", "2"), Array.emptyIntArray),
+ (0, Array("1", "2"), Array.emptyIntArray),
+ (0, Array("1", "2"), Array.emptyIntArray),
+ (0, Array("1", "3"), Array(2))
+ )).toDF("id", "features", "prediction")
+ .withColumn("features", col("features").cast(ArrayType(dt)))
+ .withColumn("prediction", col("prediction").cast(ArrayType(dt)))
+ assert(expectedTransformed.sort("id").rdd.collect().sameElements(
+ transformed.sort("id").rdd.collect()))
+ }
+ }
+
+ test("FPGrowth getFreqItems") {
+ val model = new FPGrowth().setMinSupport(0.7).fit(dataset)
+ val expectedFreq = spark.createDataFrame(Seq(
+ (Array("1"), 4L),
+ (Array("2"), 3L),
+ (Array("1", "2"), 3L),
+ (Array("2", "1"), 3L)
+ )).toDF("items", "freqExp")
+ val freqItems = model.getFreqItemsets
+
+ val checkDF = freqItems.join(expectedFreq, "items")
+ assert(checkDF.count() == 3 && checkDF.filter(col("freq") ===
col("freqExp")).count() == 3)
+ }
+
+ test("FPGrowth getFreqItems with Null") {
--- End diff --
In FPGrowth, document that null values are treated as empty sequences.
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