9f4ff95524
## What changes were proposed in this pull request? clean up after big test move ## How was this patch tested? unit tests, jenkins Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #18267 from felixcheung/rtestset2.
86 lines
2.6 KiB
R
86 lines
2.6 KiB
R
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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library(testthat)
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context("MLlib frequent pattern mining")
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# Tests for MLlib frequent pattern mining algorithms in SparkR
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sparkSession <- sparkR.session(master = sparkRTestMaster, enableHiveSupport = FALSE)
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test_that("spark.fpGrowth", {
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data <- selectExpr(createDataFrame(data.frame(items = c(
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"1,2",
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"1,2",
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"1,2,3",
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"1,3"
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))), "split(items, ',') as items")
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model <- spark.fpGrowth(data, minSupport = 0.3, minConfidence = 0.8, numPartitions = 1)
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itemsets <- collect(spark.freqItemsets(model))
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expected_itemsets <- data.frame(
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items = I(list(list("3"), list("3", "1"), list("2"), list("2", "1"), list("1"))),
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freq = c(2, 2, 3, 3, 4)
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)
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expect_equivalent(expected_itemsets, itemsets)
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expected_association_rules <- data.frame(
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antecedent = I(list(list("2"), list("3"))),
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consequent = I(list(list("1"), list("1"))),
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confidence = c(1, 1)
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)
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expect_equivalent(expected_association_rules, collect(spark.associationRules(model)))
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new_data <- selectExpr(createDataFrame(data.frame(items = c(
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"1,2",
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"1,3",
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"2,3"
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))), "split(items, ',') as items")
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expected_predictions <- data.frame(
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items = I(list(list("1", "2"), list("1", "3"), list("2", "3"))),
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prediction = I(list(list(), list(), list("1")))
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)
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expect_equivalent(expected_predictions, collect(predict(model, new_data)))
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if (windows_with_hadoop()) {
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modelPath <- tempfile(pattern = "spark-fpm", fileext = ".tmp")
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write.ml(model, modelPath, overwrite = TRUE)
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loaded_model <- read.ml(modelPath)
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expect_equivalent(
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itemsets,
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collect(spark.freqItemsets(loaded_model)))
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unlink(modelPath)
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}
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model_without_numpartitions <- spark.fpGrowth(data, minSupport = 0.3, minConfidence = 0.8)
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expect_equal(
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count(spark.freqItemsets(model_without_numpartitions)),
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count(spark.freqItemsets(model))
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)
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})
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sparkR.session.stop()
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