6902edab7e
## What changes were proposed in this pull request? #14881 added Kolmogorov-Smirnov Test wrapper to SparkR. I found that ```print.summary.KSTest``` was implemented inappropriately and result in no effect. Running the following code for KSTest: ```Scala data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25, -1, -0.5)) df <- createDataFrame(data) testResult <- spark.kstest(df, "test", "norm") summary(testResult) ``` Before this PR:  After this PR:  The new implementation is similar with [```print.summary.GeneralizedLinearRegressionModel```](https://github.com/apache/spark/blob/master/R/pkg/R/mllib.R#L284) of SparkR and [```print.summary.glm```](https://svn.r-project.org/R/trunk/src/library/stats/R/glm.R) of native R. BTW, I removed the comparison of ```print.summary.KSTest``` in unit test, since it's only wrappers of the summary output which has been checked. Another reason is that these comparison will output summary information to the test console, it will make the test output in a mess. ## How was this patch tested? Existing test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15139 from yanboliang/spark-17315.
776 lines
29 KiB
R
776 lines
29 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 functions")
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# Tests for MLlib functions in SparkR
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sparkSession <- sparkR.session(enableHiveSupport = FALSE)
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absoluteSparkPath <- function(x) {
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sparkHome <- sparkR.conf("spark.home")
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file.path(sparkHome, x)
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}
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test_that("formula of spark.glm", {
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training <- suppressWarnings(createDataFrame(iris))
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# directly calling the spark API
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# dot minus and intercept vs native glm
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model <- spark.glm(training, Sepal_Width ~ . - Species + 0)
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vals <- collect(select(predict(model, training), "prediction"))
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rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# feature interaction vs native glm
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model <- spark.glm(training, Sepal_Width ~ Species:Sepal_Length)
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vals <- collect(select(predict(model, training), "prediction"))
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rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# glm should work with long formula
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training <- suppressWarnings(createDataFrame(iris))
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training$LongLongLongLongLongName <- training$Sepal_Width
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training$VeryLongLongLongLonLongName <- training$Sepal_Length
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training$AnotherLongLongLongLongName <- training$Species
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model <- spark.glm(training, LongLongLongLongLongName ~ VeryLongLongLongLonLongName +
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AnotherLongLongLongLongName)
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vals <- collect(select(predict(model, training), "prediction"))
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rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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})
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test_that("spark.glm and predict", {
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training <- suppressWarnings(createDataFrame(iris))
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# gaussian family
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model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species)
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prediction <- predict(model, training)
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expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
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vals <- collect(select(prediction, "prediction"))
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rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# poisson family
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model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species,
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family = poisson(link = identity))
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prediction <- predict(model, training)
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expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
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vals <- collect(select(prediction, "prediction"))
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rVals <- suppressWarnings(predict(glm(Sepal.Width ~ Sepal.Length + Species,
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data = iris, family = poisson(link = identity)), iris))
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# Test stats::predict is working
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x <- rnorm(15)
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y <- x + rnorm(15)
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expect_equal(length(predict(lm(y ~ x))), 15)
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})
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test_that("spark.glm summary", {
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# gaussian family
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training <- suppressWarnings(createDataFrame(iris))
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stats <- summary(spark.glm(training, Sepal_Width ~ Sepal_Length + Species))
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rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
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coefs <- unlist(stats$coefficients)
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rCoefs <- unlist(rStats$coefficients)
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expect_true(all(abs(rCoefs - coefs) < 1e-4))
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expect_true(all(
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rownames(stats$coefficients) ==
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c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
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expect_equal(stats$dispersion, rStats$dispersion)
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expect_equal(stats$null.deviance, rStats$null.deviance)
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expect_equal(stats$deviance, rStats$deviance)
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expect_equal(stats$df.null, rStats$df.null)
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expect_equal(stats$df.residual, rStats$df.residual)
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expect_equal(stats$aic, rStats$aic)
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out <- capture.output(print(stats))
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expect_match(out[2], "Deviance Residuals:")
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expect_true(any(grepl("AIC: 59.22", out)))
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# binomial family
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df <- suppressWarnings(createDataFrame(iris))
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training <- df[df$Species %in% c("versicolor", "virginica"), ]
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stats <- summary(spark.glm(training, Species ~ Sepal_Length + Sepal_Width,
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family = binomial(link = "logit")))
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rTraining <- iris[iris$Species %in% c("versicolor", "virginica"), ]
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rStats <- summary(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
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family = binomial(link = "logit")))
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coefs <- unlist(stats$coefficients)
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rCoefs <- unlist(rStats$coefficients)
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expect_true(all(abs(rCoefs - coefs) < 1e-4))
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expect_true(all(
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rownames(stats$coefficients) ==
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c("(Intercept)", "Sepal_Length", "Sepal_Width")))
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expect_equal(stats$dispersion, rStats$dispersion)
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expect_equal(stats$null.deviance, rStats$null.deviance)
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expect_equal(stats$deviance, rStats$deviance)
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expect_equal(stats$df.null, rStats$df.null)
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expect_equal(stats$df.residual, rStats$df.residual)
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expect_equal(stats$aic, rStats$aic)
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# Test spark.glm works with weighted dataset
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a1 <- c(0, 1, 2, 3)
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a2 <- c(5, 2, 1, 3)
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w <- c(1, 2, 3, 4)
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b <- c(1, 0, 1, 0)
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data <- as.data.frame(cbind(a1, a2, w, b))
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df <- suppressWarnings(createDataFrame(data))
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stats <- summary(spark.glm(df, b ~ a1 + a2, family = "binomial", weightCol = "w"))
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rStats <- summary(glm(b ~ a1 + a2, family = "binomial", data = data, weights = w))
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coefs <- unlist(stats$coefficients)
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rCoefs <- unlist(rStats$coefficients)
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expect_true(all(abs(rCoefs - coefs) < 1e-3))
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expect_true(all(rownames(stats$coefficients) == c("(Intercept)", "a1", "a2")))
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expect_equal(stats$dispersion, rStats$dispersion)
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expect_equal(stats$null.deviance, rStats$null.deviance)
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expect_equal(stats$deviance, rStats$deviance)
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expect_equal(stats$df.null, rStats$df.null)
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expect_equal(stats$df.residual, rStats$df.residual)
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expect_equal(stats$aic, rStats$aic)
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# Test summary works on base GLM models
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baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris)
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baseSummary <- summary(baseModel)
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expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4)
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# Test spark.glm works with regularization parameter
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data <- as.data.frame(cbind(a1, a2, b))
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df <- suppressWarnings(createDataFrame(data))
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regStats <- summary(spark.glm(df, b ~ a1 + a2, regParam = 1.0))
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expect_equal(regStats$aic, 13.32836, tolerance = 1e-4) # 13.32836 is from summary() result
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})
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test_that("spark.glm save/load", {
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training <- suppressWarnings(createDataFrame(iris))
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m <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species)
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s <- summary(m)
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modelPath <- tempfile(pattern = "spark-glm", fileext = ".tmp")
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write.ml(m, modelPath)
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expect_error(write.ml(m, modelPath))
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write.ml(m, modelPath, overwrite = TRUE)
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m2 <- read.ml(modelPath)
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s2 <- summary(m2)
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expect_equal(s$coefficients, s2$coefficients)
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expect_equal(rownames(s$coefficients), rownames(s2$coefficients))
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expect_equal(s$dispersion, s2$dispersion)
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expect_equal(s$null.deviance, s2$null.deviance)
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expect_equal(s$deviance, s2$deviance)
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expect_equal(s$df.null, s2$df.null)
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expect_equal(s$df.residual, s2$df.residual)
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expect_equal(s$aic, s2$aic)
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expect_equal(s$iter, s2$iter)
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expect_true(!s$is.loaded)
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expect_true(s2$is.loaded)
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unlink(modelPath)
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})
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test_that("formula of glm", {
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training <- suppressWarnings(createDataFrame(iris))
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# dot minus and intercept vs native glm
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model <- glm(Sepal_Width ~ . - Species + 0, data = training)
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vals <- collect(select(predict(model, training), "prediction"))
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rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# feature interaction vs native glm
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model <- glm(Sepal_Width ~ Species:Sepal_Length, data = training)
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vals <- collect(select(predict(model, training), "prediction"))
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rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# glm should work with long formula
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training <- suppressWarnings(createDataFrame(iris))
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training$LongLongLongLongLongName <- training$Sepal_Width
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training$VeryLongLongLongLonLongName <- training$Sepal_Length
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training$AnotherLongLongLongLongName <- training$Species
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model <- glm(LongLongLongLongLongName ~ VeryLongLongLongLonLongName + AnotherLongLongLongLongName,
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data = training)
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vals <- collect(select(predict(model, training), "prediction"))
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rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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})
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test_that("glm and predict", {
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training <- suppressWarnings(createDataFrame(iris))
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# gaussian family
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model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
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prediction <- predict(model, training)
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expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
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vals <- collect(select(prediction, "prediction"))
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rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# poisson family
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model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training,
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family = poisson(link = identity))
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prediction <- predict(model, training)
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expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
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vals <- collect(select(prediction, "prediction"))
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rVals <- suppressWarnings(predict(glm(Sepal.Width ~ Sepal.Length + Species,
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data = iris, family = poisson(link = identity)), iris))
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# Test stats::predict is working
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x <- rnorm(15)
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y <- x + rnorm(15)
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expect_equal(length(predict(lm(y ~ x))), 15)
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})
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test_that("glm summary", {
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# gaussian family
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training <- suppressWarnings(createDataFrame(iris))
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stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training))
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rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
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coefs <- unlist(stats$coefficients)
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rCoefs <- unlist(rStats$coefficients)
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expect_true(all(abs(rCoefs - coefs) < 1e-4))
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expect_true(all(
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rownames(stats$coefficients) ==
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c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
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expect_equal(stats$dispersion, rStats$dispersion)
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expect_equal(stats$null.deviance, rStats$null.deviance)
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expect_equal(stats$deviance, rStats$deviance)
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expect_equal(stats$df.null, rStats$df.null)
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expect_equal(stats$df.residual, rStats$df.residual)
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expect_equal(stats$aic, rStats$aic)
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# binomial family
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df <- suppressWarnings(createDataFrame(iris))
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training <- df[df$Species %in% c("versicolor", "virginica"), ]
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stats <- summary(glm(Species ~ Sepal_Length + Sepal_Width, data = training,
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family = binomial(link = "logit")))
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rTraining <- iris[iris$Species %in% c("versicolor", "virginica"), ]
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rStats <- summary(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
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family = binomial(link = "logit")))
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coefs <- unlist(stats$coefficients)
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rCoefs <- unlist(rStats$coefficients)
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expect_true(all(abs(rCoefs - coefs) < 1e-4))
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expect_true(all(
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rownames(stats$coefficients) ==
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c("(Intercept)", "Sepal_Length", "Sepal_Width")))
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expect_equal(stats$dispersion, rStats$dispersion)
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expect_equal(stats$null.deviance, rStats$null.deviance)
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expect_equal(stats$deviance, rStats$deviance)
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expect_equal(stats$df.null, rStats$df.null)
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expect_equal(stats$df.residual, rStats$df.residual)
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expect_equal(stats$aic, rStats$aic)
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# Test summary works on base GLM models
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baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris)
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baseSummary <- summary(baseModel)
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expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4)
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})
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test_that("glm save/load", {
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training <- suppressWarnings(createDataFrame(iris))
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m <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
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s <- summary(m)
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modelPath <- tempfile(pattern = "glm", fileext = ".tmp")
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write.ml(m, modelPath)
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expect_error(write.ml(m, modelPath))
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write.ml(m, modelPath, overwrite = TRUE)
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m2 <- read.ml(modelPath)
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s2 <- summary(m2)
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expect_equal(s$coefficients, s2$coefficients)
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expect_equal(rownames(s$coefficients), rownames(s2$coefficients))
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expect_equal(s$dispersion, s2$dispersion)
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expect_equal(s$null.deviance, s2$null.deviance)
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expect_equal(s$deviance, s2$deviance)
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expect_equal(s$df.null, s2$df.null)
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expect_equal(s$df.residual, s2$df.residual)
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expect_equal(s$aic, s2$aic)
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expect_equal(s$iter, s2$iter)
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expect_true(!s$is.loaded)
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expect_true(s2$is.loaded)
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unlink(modelPath)
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})
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test_that("spark.kmeans", {
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newIris <- iris
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newIris$Species <- NULL
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training <- suppressWarnings(createDataFrame(newIris))
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take(training, 1)
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model <- spark.kmeans(data = training, ~ ., k = 2, maxIter = 10, initMode = "random")
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sample <- take(select(predict(model, training), "prediction"), 1)
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expect_equal(typeof(sample$prediction), "integer")
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expect_equal(sample$prediction, 1)
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# Test stats::kmeans is working
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statsModel <- kmeans(x = newIris, centers = 2)
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expect_equal(sort(unique(statsModel$cluster)), c(1, 2))
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# Test fitted works on KMeans
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fitted.model <- fitted(model)
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expect_equal(sort(collect(distinct(select(fitted.model, "prediction")))$prediction), c(0, 1))
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# Test summary works on KMeans
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summary.model <- summary(model)
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cluster <- summary.model$cluster
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expect_equal(sort(collect(distinct(select(cluster, "prediction")))$prediction), c(0, 1))
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# Test model save/load
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modelPath <- tempfile(pattern = "spark-kmeans", fileext = ".tmp")
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write.ml(model, modelPath)
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expect_error(write.ml(model, modelPath))
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write.ml(model, modelPath, overwrite = TRUE)
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model2 <- read.ml(modelPath)
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summary2 <- summary(model2)
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expect_equal(sort(unlist(summary.model$size)), sort(unlist(summary2$size)))
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expect_equal(summary.model$coefficients, summary2$coefficients)
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expect_true(!summary.model$is.loaded)
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expect_true(summary2$is.loaded)
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unlink(modelPath)
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})
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test_that("spark.mlp", {
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df <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
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source = "libsvm")
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model <- spark.mlp(df, blockSize = 128, layers = c(4, 5, 4, 3), solver = "l-bfgs", maxIter = 100,
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tol = 0.5, stepSize = 1, seed = 1)
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# Test summary method
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summary <- summary(model)
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expect_equal(summary$labelCount, 3)
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expect_equal(summary$layers, c(4, 5, 4, 3))
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expect_equal(length(summary$weights), 64)
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expect_equal(head(summary$weights, 5), list(-0.878743, 0.2154151, -1.16304, -0.6583214, 1.009825),
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tolerance = 1e-6)
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# Test predict method
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mlpTestDF <- df
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mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
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expect_equal(head(mlpPredictions$prediction, 6), c(0, 1, 1, 1, 1, 1))
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# Test model save/load
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modelPath <- tempfile(pattern = "spark-mlp", fileext = ".tmp")
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write.ml(model, modelPath)
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expect_error(write.ml(model, modelPath))
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write.ml(model, modelPath, overwrite = TRUE)
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model2 <- read.ml(modelPath)
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summary2 <- summary(model2)
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expect_equal(summary2$labelCount, 3)
|
|
expect_equal(summary2$layers, c(4, 5, 4, 3))
|
|
expect_equal(length(summary2$weights), 64)
|
|
|
|
unlink(modelPath)
|
|
|
|
})
|
|
|
|
test_that("spark.naiveBayes", {
|
|
# R code to reproduce the result.
|
|
# We do not support instance weights yet. So we ignore the frequencies.
|
|
#
|
|
#' library(e1071)
|
|
#' t <- as.data.frame(Titanic)
|
|
#' t1 <- t[t$Freq > 0, -5]
|
|
#' m <- naiveBayes(Survived ~ ., data = t1)
|
|
#' m
|
|
#' predict(m, t1)
|
|
#
|
|
# -- output of 'm'
|
|
#
|
|
# A-priori probabilities:
|
|
# Y
|
|
# No Yes
|
|
# 0.4166667 0.5833333
|
|
#
|
|
# Conditional probabilities:
|
|
# Class
|
|
# Y 1st 2nd 3rd Crew
|
|
# No 0.2000000 0.2000000 0.4000000 0.2000000
|
|
# Yes 0.2857143 0.2857143 0.2857143 0.1428571
|
|
#
|
|
# Sex
|
|
# Y Male Female
|
|
# No 0.5 0.5
|
|
# Yes 0.5 0.5
|
|
#
|
|
# Age
|
|
# Y Child Adult
|
|
# No 0.2000000 0.8000000
|
|
# Yes 0.4285714 0.5714286
|
|
#
|
|
# -- output of 'predict(m, t1)'
|
|
#
|
|
# Yes Yes Yes Yes No No Yes Yes No No Yes Yes Yes Yes Yes Yes Yes Yes No No Yes Yes No No
|
|
#
|
|
|
|
t <- as.data.frame(Titanic)
|
|
t1 <- t[t$Freq > 0, -5]
|
|
df <- suppressWarnings(createDataFrame(t1))
|
|
m <- spark.naiveBayes(df, Survived ~ ., smoothing = 0.0)
|
|
s <- summary(m)
|
|
expect_equal(as.double(s$apriori[1, "Yes"]), 0.5833333, tolerance = 1e-6)
|
|
expect_equal(sum(s$apriori), 1)
|
|
expect_equal(as.double(s$tables["Yes", "Age_Adult"]), 0.5714286, tolerance = 1e-6)
|
|
p <- collect(select(predict(m, df), "prediction"))
|
|
expect_equal(p$prediction, c("Yes", "Yes", "Yes", "Yes", "No", "No", "Yes", "Yes", "No", "No",
|
|
"Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "No",
|
|
"Yes", "Yes", "No", "No"))
|
|
|
|
# Test model save/load
|
|
modelPath <- tempfile(pattern = "spark-naiveBayes", fileext = ".tmp")
|
|
write.ml(m, modelPath)
|
|
expect_error(write.ml(m, modelPath))
|
|
write.ml(m, modelPath, overwrite = TRUE)
|
|
m2 <- read.ml(modelPath)
|
|
s2 <- summary(m2)
|
|
expect_equal(s$apriori, s2$apriori)
|
|
expect_equal(s$tables, s2$tables)
|
|
|
|
unlink(modelPath)
|
|
|
|
# Test e1071::naiveBayes
|
|
if (requireNamespace("e1071", quietly = TRUE)) {
|
|
expect_error(m <- e1071::naiveBayes(Survived ~ ., data = t1), NA)
|
|
expect_equal(as.character(predict(m, t1[1, ])), "Yes")
|
|
}
|
|
})
|
|
|
|
test_that("spark.survreg", {
|
|
# R code to reproduce the result.
|
|
#
|
|
#' rData <- list(time = c(4, 3, 1, 1, 2, 2, 3), status = c(1, 1, 1, 0, 1, 1, 0),
|
|
#' x = c(0, 2, 1, 1, 1, 0, 0), sex = c(0, 0, 0, 0, 1, 1, 1))
|
|
#' library(survival)
|
|
#' model <- survreg(Surv(time, status) ~ x + sex, rData)
|
|
#' summary(model)
|
|
#' predict(model, data)
|
|
#
|
|
# -- output of 'summary(model)'
|
|
#
|
|
# Value Std. Error z p
|
|
# (Intercept) 1.315 0.270 4.88 1.07e-06
|
|
# x -0.190 0.173 -1.10 2.72e-01
|
|
# sex -0.253 0.329 -0.77 4.42e-01
|
|
# Log(scale) -1.160 0.396 -2.93 3.41e-03
|
|
#
|
|
# -- output of 'predict(model, data)'
|
|
#
|
|
# 1 2 3 4 5 6 7
|
|
# 3.724591 2.545368 3.079035 3.079035 2.390146 2.891269 2.891269
|
|
#
|
|
data <- list(list(4, 1, 0, 0), list(3, 1, 2, 0), list(1, 1, 1, 0),
|
|
list(1, 0, 1, 0), list(2, 1, 1, 1), list(2, 1, 0, 1), list(3, 0, 0, 1))
|
|
df <- createDataFrame(data, c("time", "status", "x", "sex"))
|
|
model <- spark.survreg(df, Surv(time, status) ~ x + sex)
|
|
stats <- summary(model)
|
|
coefs <- as.vector(stats$coefficients[, 1])
|
|
rCoefs <- c(1.3149571, -0.1903409, -0.2532618, -1.1599800)
|
|
expect_equal(coefs, rCoefs, tolerance = 1e-4)
|
|
expect_true(all(
|
|
rownames(stats$coefficients) ==
|
|
c("(Intercept)", "x", "sex", "Log(scale)")))
|
|
p <- collect(select(predict(model, df), "prediction"))
|
|
expect_equal(p$prediction, c(3.724591, 2.545368, 3.079035, 3.079035,
|
|
2.390146, 2.891269, 2.891269), tolerance = 1e-4)
|
|
|
|
# Test model save/load
|
|
modelPath <- tempfile(pattern = "spark-survreg", 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)
|
|
coefs2 <- as.vector(stats2$coefficients[, 1])
|
|
expect_equal(coefs, coefs2)
|
|
expect_equal(rownames(stats$coefficients), rownames(stats2$coefficients))
|
|
|
|
unlink(modelPath)
|
|
|
|
# Test survival::survreg
|
|
if (requireNamespace("survival", quietly = TRUE)) {
|
|
rData <- list(time = c(4, 3, 1, 1, 2, 2, 3), status = c(1, 1, 1, 0, 1, 1, 0),
|
|
x = c(0, 2, 1, 1, 1, 0, 0), sex = c(0, 0, 0, 0, 1, 1, 1))
|
|
expect_error(
|
|
model <- survival::survreg(formula = survival::Surv(time, status) ~ x + sex, data = rData),
|
|
NA)
|
|
expect_equal(predict(model, rData)[[1]], 3.724591, tolerance = 1e-4)
|
|
}
|
|
})
|
|
|
|
test_that("spark.isotonicRegression", {
|
|
label <- c(7.0, 5.0, 3.0, 5.0, 1.0)
|
|
feature <- c(0.0, 1.0, 2.0, 3.0, 4.0)
|
|
weight <- c(1.0, 1.0, 1.0, 1.0, 1.0)
|
|
data <- as.data.frame(cbind(label, feature, weight))
|
|
df <- suppressWarnings(createDataFrame(data))
|
|
|
|
model <- spark.isoreg(df, label ~ feature, isotonic = FALSE,
|
|
weightCol = "weight")
|
|
# only allow one variable on the right hand side of the formula
|
|
expect_error(model2 <- spark.isoreg(df, ~., isotonic = FALSE))
|
|
result <- summary(model)
|
|
expect_equal(result$predictions, list(7, 5, 4, 4, 1))
|
|
|
|
# Test model prediction
|
|
predict_data <- list(list(-2.0), list(-1.0), list(0.5),
|
|
list(0.75), list(1.0), list(2.0), list(9.0))
|
|
predict_df <- createDataFrame(predict_data, c("feature"))
|
|
predict_result <- collect(select(predict(model, predict_df), "prediction"))
|
|
expect_equal(predict_result$prediction, c(7.0, 7.0, 6.0, 5.5, 5.0, 4.0, 1.0))
|
|
|
|
# Test model save/load
|
|
modelPath <- tempfile(pattern = "spark-isotonicRegression", fileext = ".tmp")
|
|
write.ml(model, modelPath)
|
|
expect_error(write.ml(model, modelPath))
|
|
write.ml(model, modelPath, overwrite = TRUE)
|
|
model2 <- read.ml(modelPath)
|
|
expect_equal(result, summary(model2))
|
|
|
|
unlink(modelPath)
|
|
})
|
|
|
|
test_that("spark.gaussianMixture", {
|
|
# R code to reproduce the result.
|
|
# nolint start
|
|
#' library(mvtnorm)
|
|
#' set.seed(1)
|
|
#' a <- rmvnorm(7, c(0, 0))
|
|
#' b <- rmvnorm(8, c(10, 10))
|
|
#' data <- rbind(a, b)
|
|
#' model <- mvnormalmixEM(data, k = 2)
|
|
#' model$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.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$vocabulary
|
|
|
|
expect_false(isDistributed)
|
|
expect_true(logLikelihood <= 0 & is.finite(logLikelihood))
|
|
expect_true(logPerplexity >= 0 & is.finite(logPerplexity))
|
|
expect_equal(vocabSize, 11)
|
|
expect_true(is.null(vocabulary))
|
|
|
|
# Test model save/load
|
|
modelPath <- tempfile(pattern = "spark-lda", fileext = ".tmp")
|
|
write.ml(model, modelPath)
|
|
expect_error(write.ml(model, modelPath))
|
|
write.ml(model, modelPath, overwrite = TRUE)
|
|
model2 <- read.ml(modelPath)
|
|
stats2 <- summary(model2)
|
|
|
|
expect_false(stats2$isDistributed)
|
|
expect_equal(logLikelihood, stats2$logLikelihood)
|
|
expect_equal(logPerplexity, stats2$logPerplexity)
|
|
expect_equal(vocabSize, stats2$vocabSize)
|
|
expect_equal(vocabulary, stats2$vocabulary)
|
|
|
|
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$vocabulary
|
|
|
|
expect_false(isDistributed)
|
|
expect_true(logLikelihood <= 0 & is.finite(logLikelihood))
|
|
expect_true(logPerplexity >= 0 & is.finite(logPerplexity))
|
|
expect_equal(vocabSize, 10)
|
|
expect_true(setequal(stats$vocabulary, c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9")))
|
|
|
|
# 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$vocabulary))
|
|
|
|
unlink(modelPath)
|
|
})
|
|
|
|
test_that("spark.posterior and spark.perplexity", {
|
|
text <- read.text(absoluteSparkPath("data/mllib/sample_lda_data.txt"))
|
|
model <- spark.lda(text, features = "value", k = 3)
|
|
|
|
# Assert perplexities are equal
|
|
stats <- summary(model)
|
|
logPerplexity <- spark.perplexity(model, text)
|
|
expect_equal(logPerplexity, stats$logPerplexity)
|
|
|
|
# Assert the sum of every topic distribution is equal to 1
|
|
posterior <- spark.posterior(model, text)
|
|
local.posterior <- collect(posterior)$topicDistribution
|
|
expect_equal(length(local.posterior), sum(unlist(local.posterior)))
|
|
})
|
|
|
|
test_that("spark.als", {
|
|
data <- list(list(0, 0, 4.0), list(0, 1, 2.0), list(1, 1, 3.0), list(1, 2, 4.0),
|
|
list(2, 1, 1.0), list(2, 2, 5.0))
|
|
df <- createDataFrame(data, c("user", "item", "score"))
|
|
model <- spark.als(df, ratingCol = "score", userCol = "user", itemCol = "item",
|
|
rank = 10, maxIter = 5, seed = 0, reg = 0.1)
|
|
stats <- summary(model)
|
|
expect_equal(stats$rank, 10)
|
|
test <- createDataFrame(list(list(0, 2), list(1, 0), list(2, 0)), c("user", "item"))
|
|
predictions <- collect(predict(model, test))
|
|
|
|
expect_equal(predictions$prediction, c(-0.1380762, 2.6258414, -1.5018409),
|
|
tolerance = 1e-4)
|
|
|
|
# Test model save/load
|
|
modelPath <- tempfile(pattern = "spark-als", fileext = ".tmp")
|
|
write.ml(model, modelPath)
|
|
expect_error(write.ml(model, modelPath))
|
|
write.ml(model, modelPath, overwrite = TRUE)
|
|
model2 <- read.ml(modelPath)
|
|
stats2 <- summary(model2)
|
|
expect_equal(stats2$rating, "score")
|
|
userFactors <- collect(stats$userFactors)
|
|
itemFactors <- collect(stats$itemFactors)
|
|
userFactors2 <- collect(stats2$userFactors)
|
|
itemFactors2 <- collect(stats2$itemFactors)
|
|
|
|
orderUser <- order(userFactors$id)
|
|
orderUser2 <- order(userFactors2$id)
|
|
expect_equal(userFactors$id[orderUser], userFactors2$id[orderUser2])
|
|
expect_equal(userFactors$features[orderUser], userFactors2$features[orderUser2])
|
|
|
|
orderItem <- order(itemFactors$id)
|
|
orderItem2 <- order(itemFactors2$id)
|
|
expect_equal(itemFactors$id[orderItem], itemFactors2$id[orderItem2])
|
|
expect_equal(itemFactors$features[orderItem], itemFactors2$features[orderItem2])
|
|
|
|
unlink(modelPath)
|
|
})
|
|
|
|
test_that("spark.kstest", {
|
|
data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25, -1, -0.5))
|
|
df <- createDataFrame(data)
|
|
testResult <- spark.kstest(df, "test", "norm")
|
|
stats <- summary(testResult)
|
|
|
|
rStats <- ks.test(data$test, "pnorm", alternative = "two.sided")
|
|
|
|
expect_equal(stats$p.value, rStats$p.value, tolerance = 1e-4)
|
|
expect_equal(stats$statistic, unname(rStats$statistic), tolerance = 1e-4)
|
|
expect_match(capture.output(stats)[1], "Kolmogorov-Smirnov test summary:")
|
|
|
|
testResult <- spark.kstest(df, "test", "norm", -0.5)
|
|
stats <- summary(testResult)
|
|
|
|
rStats <- ks.test(data$test, "pnorm", -0.5, 1, alternative = "two.sided")
|
|
|
|
expect_equal(stats$p.value, rStats$p.value, tolerance = 1e-4)
|
|
expect_equal(stats$statistic, unname(rStats$statistic), tolerance = 1e-4)
|
|
expect_match(capture.output(stats)[1], "Kolmogorov-Smirnov test summary:")
|
|
})
|
|
|
|
sparkR.session.stop()
|