Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15415#discussion_r101698347
--- Diff: mllib/src/test/scala/org/apache/spark/ml/fpm/FPGrowthSuite.scala
---
@@ -0,0 +1,120 @@
+/*
+ * 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, 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(0.8).fit(intData)
+ val generatedRules = model.setMinConfidence(0.8).getAssociationRules
+ val expectedRules = spark.createDataFrame(Seq(
+ (Array("2"), Array("1"), 1.0),
+ (Array("1"), Array("2"), 1.0)
+ )).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)
+ assert(transformed.count() == 3)
+ }
+ }
+
+ test("FPGrowth getFreqItems") {
+ val model = new FPGrowth().setMinSupport(0.8).fit(dataset)
+ val expectedFreq = spark.createDataFrame(Seq(
+ (Array("1"), 3L),
+ (Array("2"), 3L),
+ (Array("1", "2"), 3L)
+ )).toDF("items", "freq")
+ val freqItems = model.getFreqItemsets
+ assert(freqItems.sort("items").rdd.collect()
+ .sameElements(expectedFreq.sort("items").rdd.collect()))
+ }
+
+ test("FPGrowth get Association Rules") {
+ val model = new FPGrowth().setMinSupport(0.8).fit(dataset)
+ val expectedRules = spark.createDataFrame(Seq(
+ (Array("2"), Array("1"), 1.0),
+ (Array("1"), Array("2"), 1.0)
+ )).toDF("antecedent", "consequent", "confidence")
+ val associationRules = model.getAssociationRules
+
+ assert(associationRules.sort("antecedent").rdd.collect()
+ .sameElements(expectedRules.sort("antecedent").rdd.collect()))
+ }
+
+ test("FPGrowth parameter check") {
+ val fpGrowth = new FPGrowth().setMinSupport(0.4567)
+ val model = fpGrowth.fit(dataset)
+ .setMinConfidence(0.5678)
+ assert(fpGrowth.getMinSupport === 0.4567)
+ assert(model.getMinConfidence === 0.5678)
+ }
+
+ test("read/write") {
+ def checkModelData(model: FPGrowthModel, model2: FPGrowthModel): Unit
= {
+ assert(model.freqItemsets.sort("items").collect() ===
+ model2.freqItemsets.sort("items").collect())
+ }
+ val fPGrowth = new FPGrowth()
+ testEstimatorAndModelReadWrite(
+ fPGrowth, dataset, FPGrowthSuite.allParamSettings, checkModelData)
+ }
+
+}
+
+object FPGrowthSuite {
+
+ def getFPGrowthData(spark: SparkSession): DataFrame = {
+ spark.createDataFrame(Seq(
+ (0, Array("1", "2", "3", "5")),
+ (0, Array("1", "2", "3", "6")),
+ (0, Array("1", "2", "7"))
+ )).toDF("id", "features")
+ }
+
+ /**
+ * Mapping from all Params to valid settings which differ from the
defaults.
+ * This is useful for tests which need to exercise all Params, such as
save/load.
+ * This excludes input columns to simplify some tests.
+ */
+ val allParamSettings: Map[String, Any] = Map(
+ "minSupport" -> 0.321,
+ "minConfidence" -> 0.456,
+ "numPartitions" -> 5,
+ "featuresCol" -> "features",
--- End diff --
Exclude input columns here; it complicates the standard tests if we don't
use default values for inputs.
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