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#
# 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.
#
library(testthat)
context("MLlib clustering algorithms")
# Tests for MLlib clustering algorithms in SparkR
sparkSession <- sparkR.session(enableHiveSupport = FALSE)
absoluteSparkPath <- function(x) {
sparkHome <- sparkR.conf("spark.home")
file.path(sparkHome, x)
}
test_that("spark.gaussianMixture", {
# R code to reproduce the result.
# nolint start
#' library(mvtnorm)
#' set.seed(1)
#' a <- rmvnorm(7, c(0, 0))
#' b <- rmvnorm(8, c(10, 10))
#' data <- rbind(a, b)
#' model <- mvnormalmixEM(data, k = 2)
#' model$lambda
#
# [1] 0.4666667 0.5333333
#
#' model$mu
#
# [1] 0.11731091 -0.06192351
# [1] 10.363673 9.897081
#
#' model$sigma
#
# [[1]]
# [,1] [,2]
# [1,] 0.62049934 0.06880802
# [2,] 0.06880802 1.27431874
#
# [[2]]
# [,1] [,2]
# [1,] 0.2961543 0.160783
# [2,] 0.1607830 1.008878
# nolint end
data <- list(list(-0.6264538, 0.1836433), list(-0.8356286, 1.5952808),
list(0.3295078, -0.8204684), list(0.4874291, 0.7383247),
list(0.5757814, -0.3053884), list(1.5117812, 0.3898432),
list(-0.6212406, -2.2146999), list(11.1249309, 9.9550664),
list(9.9838097, 10.9438362), list(10.8212212, 10.5939013),
list(10.9189774, 10.7821363), list(10.0745650, 8.0106483),
list(10.6198257, 9.9438713), list(9.8442045, 8.5292476),
list(9.5218499, 10.4179416))
df <- createDataFrame(data, c("x1", "x2"))
model <- spark.gaussianMixture(df, ~ x1 + x2, k = 2)
stats <- summary(model)
rLambda <- c(0.4666667, 0.5333333)
rMu <- c(0.11731091, -0.06192351, 10.363673, 9.897081)
rSigma <- c(0.62049934, 0.06880802, 0.06880802, 1.27431874,
0.2961543, 0.160783, 0.1607830, 1.008878)
expect_equal(stats$lambda, rLambda, tolerance = 1e-3)
expect_equal(unlist(stats$mu), rMu, tolerance = 1e-3)
expect_equal(unlist(stats$sigma), rSigma, tolerance = 1e-3)
p <- collect(select(predict(model, df), "prediction"))
expect_equal(p$prediction, c(0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1))
# Test model save/load
modelPath <- tempfile(pattern = "spark-gaussianMixture", fileext = ".tmp")
write.ml(model, modelPath)
expect_error(write.ml(model, modelPath))
write.ml(model, modelPath, overwrite = TRUE)
model2 <- read.ml(modelPath)
stats2 <- summary(model2)
expect_equal(stats$lambda, stats2$lambda)
expect_equal(unlist(stats$mu), unlist(stats2$mu))
expect_equal(unlist(stats$sigma), unlist(stats2$sigma))
unlink(modelPath)
})
test_that("spark.kmeans", {
newIris <- iris
newIris$Species <- NULL
training <- suppressWarnings(createDataFrame(newIris))
take(training, 1)
model <- spark.kmeans(data = training, ~ ., k = 2, maxIter = 10, initMode = "random")
sample <- take(select(predict(model, training), "prediction"), 1)
expect_equal(typeof(sample$prediction), "integer")
expect_equal(sample$prediction, 1)
# Test stats::kmeans is working
statsModel <- kmeans(x = newIris, centers = 2)
expect_equal(sort(unique(statsModel$cluster)), c(1, 2))
# Test fitted works on KMeans
fitted.model <- fitted(model)
expect_equal(sort(collect(distinct(select(fitted.model, "prediction")))$prediction), c(0, 1))
# Test summary works on KMeans
summary.model <- summary(model)
cluster <- summary.model$cluster
k <- summary.model$k
expect_equal(k, 2)
expect_equal(sort(collect(distinct(select(cluster, "prediction")))$prediction), c(0, 1))
# Test model save/load
modelPath <- tempfile(pattern = "spark-kmeans", fileext = ".tmp")
write.ml(model, modelPath)
expect_error(write.ml(model, modelPath))
write.ml(model, modelPath, overwrite = TRUE)
model2 <- read.ml(modelPath)
summary2 <- summary(model2)
expect_equal(sort(unlist(summary.model$size)), sort(unlist(summary2$size)))
expect_equal(summary.model$coefficients, summary2$coefficients)
expect_true(!summary.model$is.loaded)
expect_true(summary2$is.loaded)
unlink(modelPath)
# Test Kmeans on dataset that is sensitive to seed value
col1 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
col2 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
col3 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
cols <- as.data.frame(cbind(col1, col2, col3))
df <- createDataFrame(cols)
model1 <- spark.kmeans(data = df, ~ ., k = 5, maxIter = 10,
initMode = "random", seed = 1, tol = 1E-5)
model2 <- spark.kmeans(data = df, ~ ., k = 5, maxIter = 10,
initMode = "random", seed = 22222, tol = 1E-5)
fitted.model1 <- fitted(model1)
fitted.model2 <- fitted(model2)
# The predicted clusters are different
expect_equal(sort(collect(distinct(select(fitted.model1, "prediction")))$prediction),
c(0, 1, 2, 3))
expect_equal(sort(collect(distinct(select(fitted.model2, "prediction")))$prediction),
c(0, 1, 2))
})
test_that("spark.lda with libsvm", {
text <- read.df(absoluteSparkPath("data/mllib/sample_lda_libsvm_data.txt"), source = "libsvm")
model <- spark.lda(text, optimizer = "em")
stats <- summary(model, 10)
isDistributed <- stats$isDistributed
logLikelihood <- stats$logLikelihood
logPerplexity <- stats$logPerplexity
vocabSize <- stats$vocabSize
topics <- stats$topicTopTerms
weights <- stats$topicTopTermsWeights
vocabulary <- stats$vocabulary
trainingLogLikelihood <- stats$trainingLogLikelihood
logPrior <- stats$logPrior
expect_true(isDistributed)
expect_true(logLikelihood <= 0 & is.finite(logLikelihood))
expect_true(logPerplexity >= 0 & is.finite(logPerplexity))
expect_equal(vocabSize, 11)
expect_true(is.null(vocabulary))
expect_true(trainingLogLikelihood <= 0 & !is.na(trainingLogLikelihood))
expect_true(logPrior <= 0 & !is.na(logPrior))
# Test model save/load
modelPath <- tempfile(pattern = "spark-lda", fileext = ".tmp")
write.ml(model, modelPath)
expect_error(write.ml(model, modelPath))
write.ml(model, modelPath, overwrite = TRUE)
model2 <- read.ml(modelPath)
stats2 <- summary(model2)
expect_true(stats2$isDistributed)
expect_equal(logLikelihood, stats2$logLikelihood)
expect_equal(logPerplexity, stats2$logPerplexity)
expect_equal(vocabSize, stats2$vocabSize)
expect_equal(vocabulary, stats2$vocabulary)
expect_equal(trainingLogLikelihood, stats2$trainingLogLikelihood)
expect_equal(logPrior, stats2$logPrior)
unlink(modelPath)
})
test_that("spark.lda with text input", {
text <- read.text(absoluteSparkPath("data/mllib/sample_lda_data.txt"))
model <- spark.lda(text, optimizer = "online", features = "value")
stats <- summary(model)
isDistributed <- stats$isDistributed
logLikelihood <- stats$logLikelihood
logPerplexity <- stats$logPerplexity
vocabSize <- stats$vocabSize
topics <- stats$topicTopTerms
weights <- stats$topicTopTermsWeights
vocabulary <- stats$vocabulary
trainingLogLikelihood <- stats$trainingLogLikelihood
logPrior <- stats$logPrior
expect_false(isDistributed)
expect_true(logLikelihood <= 0 & is.finite(logLikelihood))
expect_true(logPerplexity >= 0 & is.finite(logPerplexity))
expect_equal(vocabSize, 10)
expect_true(setequal(stats$vocabulary, c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9")))
expect_true(is.na(trainingLogLikelihood))
expect_true(is.na(logPrior))
# Test model save/load
modelPath <- tempfile(pattern = "spark-lda-text", fileext = ".tmp")
write.ml(model, modelPath)
expect_error(write.ml(model, modelPath))
write.ml(model, modelPath, overwrite = TRUE)
model2 <- read.ml(modelPath)
stats2 <- summary(model2)
expect_false(stats2$isDistributed)
expect_equal(logLikelihood, stats2$logLikelihood)
expect_equal(logPerplexity, stats2$logPerplexity)
expect_equal(vocabSize, stats2$vocabSize)
expect_true(all.equal(vocabulary, stats2$vocabulary))
expect_true(is.na(stats2$trainingLogLikelihood))
expect_true(is.na(stats2$logPrior))
unlink(modelPath)
})
test_that("spark.posterior and spark.perplexity", {
text <- read.text(absoluteSparkPath("data/mllib/sample_lda_data.txt"))
model <- spark.lda(text, features = "value", k = 3)
# Assert perplexities are equal
stats <- summary(model)
logPerplexity <- spark.perplexity(model, text)
expect_equal(logPerplexity, stats$logPerplexity)
# Assert the sum of every topic distribution is equal to 1
posterior <- spark.posterior(model, text)
local.posterior <- collect(posterior)$topicDistribution
expect_equal(length(local.posterior), sum(unlist(local.posterior)))
})
sparkR.session.stop()
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