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library(testthat)
context("MLlib functions")
# Tests for MLlib functions in SparkR
sparkSession <- sparkR.session(enableHiveSupport = FALSE)
absoluteSparkPath <- function(x) {
sparkHome <- sparkR.conf("spark.home")
file.path(sparkHome, x)
}
test_that("formula of spark.glm", {
training <- suppressWarnings(createDataFrame(iris))
# directly calling the spark API
# dot minus and intercept vs native glm
model <- spark.glm(training, Sepal_Width ~ . - Species + 0)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
# feature interaction vs native glm
model <- spark.glm(training, Sepal_Width ~ Species:Sepal_Length)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
# glm should work with long formula
training <- suppressWarnings(createDataFrame(iris))
training$LongLongLongLongLongName <- training$Sepal_Width
training$VeryLongLongLongLonLongName <- training$Sepal_Length
training$AnotherLongLongLongLongName <- training$Species
model <- spark.glm(training, LongLongLongLongLongName ~ VeryLongLongLongLonLongName +
AnotherLongLongLongLongName)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})
test_that("spark.glm and predict", {
training <- suppressWarnings(createDataFrame(iris))
# gaussian family
model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species)
prediction <- predict(model, training)
expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
vals <- collect(select(prediction, "prediction"))
rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
# poisson family
model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species,
family = poisson(link = identity))
prediction <- predict(model, training)
expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
vals <- collect(select(prediction, "prediction"))
rVals <- suppressWarnings(predict(glm(Sepal.Width ~ Sepal.Length + Species,
data = iris, family = poisson(link = identity)), iris))
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
# Test stats::predict is working
x <- rnorm(15)
y <- x + rnorm(15)
expect_equal(length(predict(lm(y ~ x))), 15)
})
test_that("spark.glm summary", {
# gaussian family
training <- suppressWarnings(createDataFrame(iris))
stats <- summary(spark.glm(training, Sepal_Width ~ Sepal_Length + Species))
rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
coefs <- unlist(stats$coefficients)
rCoefs <- unlist(rStats$coefficients)
expect_true(all(abs(rCoefs - coefs) < 1e-4))
expect_true(all(
rownames(stats$coefficients) ==
c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
expect_equal(stats$dispersion, rStats$dispersion)
expect_equal(stats$null.deviance, rStats$null.deviance)
expect_equal(stats$deviance, rStats$deviance)
expect_equal(stats$df.null, rStats$df.null)
expect_equal(stats$df.residual, rStats$df.residual)
expect_equal(stats$aic, rStats$aic)
out <- capture.output(print(stats))
expect_match(out[2], "Deviance Residuals:")
expect_true(any(grepl("AIC: 59.22", out)))
# binomial family
df <- suppressWarnings(createDataFrame(iris))
training <- df[df$Species %in% c("versicolor", "virginica"), ]
stats <- summary(spark.glm(training, Species ~ Sepal_Length + Sepal_Width,
family = binomial(link = "logit")))
rTraining <- iris[iris$Species %in% c("versicolor", "virginica"), ]
rStats <- summary(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
family = binomial(link = "logit")))
coefs <- unlist(stats$coefficients)
rCoefs <- unlist(rStats$coefficients)
expect_true(all(abs(rCoefs - coefs) < 1e-4))
expect_true(all(
rownames(stats$coefficients) ==
c("(Intercept)", "Sepal_Length", "Sepal_Width")))
expect_equal(stats$dispersion, rStats$dispersion)
expect_equal(stats$null.deviance, rStats$null.deviance)
expect_equal(stats$deviance, rStats$deviance)
expect_equal(stats$df.null, rStats$df.null)
expect_equal(stats$df.residual, rStats$df.residual)
expect_equal(stats$aic, rStats$aic)
# Test spark.glm works with weighted dataset
a1 <- c(0, 1, 2, 3)
a2 <- c(5, 2, 1, 3)
w <- c(1, 2, 3, 4)
b <- c(1, 0, 1, 0)
data <- as.data.frame(cbind(a1, a2, w, b))
df <- createDataFrame(data)
stats <- summary(spark.glm(df, b ~ a1 + a2, family = "binomial", weightCol = "w"))
rStats <- summary(glm(b ~ a1 + a2, family = "binomial", data = data, weights = w))
coefs <- unlist(stats$coefficients)
rCoefs <- unlist(rStats$coefficients)
expect_true(all(abs(rCoefs - coefs) < 1e-3))
expect_true(all(rownames(stats$coefficients) == c("(Intercept)", "a1", "a2")))
expect_equal(stats$dispersion, rStats$dispersion)
expect_equal(stats$null.deviance, rStats$null.deviance)
expect_equal(stats$deviance, rStats$deviance)
expect_equal(stats$df.null, rStats$df.null)
expect_equal(stats$df.residual, rStats$df.residual)
expect_equal(stats$aic, rStats$aic)
# Test summary works on base GLM models
baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris)
baseSummary <- summary(baseModel)
expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4)
# Test spark.glm works with regularization parameter
data <- as.data.frame(cbind(a1, a2, b))
df <- suppressWarnings(createDataFrame(data))
regStats <- summary(spark.glm(df, b ~ a1 + a2, regParam = 1.0))
expect_equal(regStats$aic, 13.32836, tolerance = 1e-4) # 13.32836 is from summary() result
# Test spark.glm works on collinear data
A <- matrix(c(1, 2, 3, 4, 2, 4, 6, 8), 4, 2)
b <- c(1, 2, 3, 4)
data <- as.data.frame(cbind(A, b))
df <- createDataFrame(data)
stats <- summary(spark.glm(df, b ~ . - 1))
coefs <- unlist(stats$coefficients)
expect_true(all(abs(c(0.5, 0.25) - coefs) < 1e-4))
})
test_that("spark.glm save/load", {
training <- suppressWarnings(createDataFrame(iris))
m <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species)
s <- summary(m)
modelPath <- tempfile(pattern = "spark-glm", 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$coefficients, s2$coefficients)
expect_equal(rownames(s$coefficients), rownames(s2$coefficients))
expect_equal(s$dispersion, s2$dispersion)
expect_equal(s$null.deviance, s2$null.deviance)
expect_equal(s$deviance, s2$deviance)
expect_equal(s$df.null, s2$df.null)
expect_equal(s$df.residual, s2$df.residual)
expect_equal(s$aic, s2$aic)
expect_equal(s$iter, s2$iter)
expect_true(!s$is.loaded)
expect_true(s2$is.loaded)
unlink(modelPath)
})
test_that("formula of glm", {
training <- suppressWarnings(createDataFrame(iris))
# dot minus and intercept vs native glm
model <- glm(Sepal_Width ~ . - Species + 0, data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
# feature interaction vs native glm
model <- glm(Sepal_Width ~ Species:Sepal_Length, data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
# glm should work with long formula
training <- suppressWarnings(createDataFrame(iris))
training$LongLongLongLongLongName <- training$Sepal_Width
training$VeryLongLongLongLonLongName <- training$Sepal_Length
training$AnotherLongLongLongLongName <- training$Species
model <- glm(LongLongLongLongLongName ~ VeryLongLongLongLonLongName + AnotherLongLongLongLongName,
data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})
test_that("glm and predict", {
training <- suppressWarnings(createDataFrame(iris))
# gaussian family
model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
prediction <- predict(model, training)
expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
vals <- collect(select(prediction, "prediction"))
rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
# poisson family
model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training,
family = poisson(link = identity))
prediction <- predict(model, training)
expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
vals <- collect(select(prediction, "prediction"))
rVals <- suppressWarnings(predict(glm(Sepal.Width ~ Sepal.Length + Species,
data = iris, family = poisson(link = identity)), iris))
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
# Test stats::predict is working
x <- rnorm(15)
y <- x + rnorm(15)
expect_equal(length(predict(lm(y ~ x))), 15)
})
test_that("glm summary", {
# gaussian family
training <- suppressWarnings(createDataFrame(iris))
stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training))
rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
coefs <- unlist(stats$coefficients)
rCoefs <- unlist(rStats$coefficients)
expect_true(all(abs(rCoefs - coefs) < 1e-4))
expect_true(all(
rownames(stats$coefficients) ==
c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
expect_equal(stats$dispersion, rStats$dispersion)
expect_equal(stats$null.deviance, rStats$null.deviance)
expect_equal(stats$deviance, rStats$deviance)
expect_equal(stats$df.null, rStats$df.null)
expect_equal(stats$df.residual, rStats$df.residual)
expect_equal(stats$aic, rStats$aic)
# binomial family
df <- suppressWarnings(createDataFrame(iris))
training <- df[df$Species %in% c("versicolor", "virginica"), ]
stats <- summary(glm(Species ~ Sepal_Length + Sepal_Width, data = training,
family = binomial(link = "logit")))
rTraining <- iris[iris$Species %in% c("versicolor", "virginica"), ]
rStats <- summary(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
family = binomial(link = "logit")))
coefs <- unlist(stats$coefficients)
rCoefs <- unlist(rStats$coefficients)
expect_true(all(abs(rCoefs - coefs) < 1e-4))
expect_true(all(
rownames(stats$coefficients) ==
c("(Intercept)", "Sepal_Length", "Sepal_Width")))
expect_equal(stats$dispersion, rStats$dispersion)
expect_equal(stats$null.deviance, rStats$null.deviance)
expect_equal(stats$deviance, rStats$deviance)
expect_equal(stats$df.null, rStats$df.null)
expect_equal(stats$df.residual, rStats$df.residual)
expect_equal(stats$aic, rStats$aic)
# Test summary works on base GLM models
baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris)
baseSummary <- summary(baseModel)
expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4)
})
test_that("glm save/load", {
training <- suppressWarnings(createDataFrame(iris))
m <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
s <- summary(m)
modelPath <- tempfile(pattern = "glm", fileext = ".tmp")
write.ml(m, modelPath)
expect_error(write.ml(m, modelPath))
write.ml(m, modelPath, overwrite = TRUE)
m2 <- read.ml(modelPath)
s2 <- summary(m2)
expect_equal(s$coefficients, s2$coefficients)
expect_equal(rownames(s$coefficients), rownames(s2$coefficients))
expect_equal(s$dispersion, s2$dispersion)
expect_equal(s$null.deviance, s2$null.deviance)
expect_equal(s$deviance, s2$deviance)
expect_equal(s$df.null, s2$df.null)
expect_equal(s$df.residual, s2$df.residual)
expect_equal(s$aic, s2$aic)
expect_equal(s$iter, s2$iter)
expect_true(!s$is.loaded)
expect_true(s2$is.loaded)
unlink(modelPath)
})
test_that("spark.kmeans", {
newIris <- iris
newIris$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$is.loaded)
unlink(modelPath)
})
test_that("spark.mlp", {
df <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
source = "libsvm")
model <- spark.mlp(df, label ~ features, blockSize = 128, layers = c(4, 5, 4, 3),
solver = "l-bfgs", maxIter = 100, tol = 0.5, stepSize = 1, seed = 1)
# Test summary method
summary <- summary(model)
expect_equal(summary$numOfInputs, 4)
expect_equal(summary$numOfOutputs, 3)
expect_equal(summary$layers, c(4, 5, 4, 3))
expect_equal(length(summary$weights), 64)
expect_equal(head(summary$weights, 5), list(-0.878743, 0.2154151, -1.16304, -0.6583214, 1.009825),
tolerance = 1e-6)
# Test predict method
mlpTestDF <- df
mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
expect_equal(head(mlpPredictions$prediction, 6), c("1.0", "0.0", "0.0", "0.0", "0.0", "0.0"))
# Test model save/load
modelPath <- tempfile(pattern = "spark-mlp", fileext = ".tmp")
write.ml(model, modelPath)
expect_error(write.ml(model, modelPath))
write.ml(model, modelPath, overwrite = TRUE)
model2 <- read.ml(modelPath)
summary2 <- summary(model2)
expect_equal(summary2$numOfInputs, 4)
expect_equal(summary2$numOfOutputs, 3)
expect_equal(summary2$layers, c(4, 5, 4, 3))
expect_equal(length(summary2$weights), 64)
unlink(modelPath)
# Test default parameter
model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3))
mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
expect_equal(head(mlpPredictions$prediction, 10),
c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
# Test illegal parameter
expect_error(spark.mlp(df, label ~ features, layers = NULL),
"layers must be a integer vector with length > 1.")
expect_error(spark.mlp(df, label ~ features, layers = c()),
"layers must be a integer vector with length > 1.")
expect_error(spark.mlp(df, label ~ features, layers = c(3)),
"layers must be a integer vector with length > 1.")
# Test random seed
# default seed
model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3), maxIter = 10)
mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
expect_equal(head(mlpPredictions$prediction, 10),
c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
# seed equals 10
model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3), maxIter = 10, seed = 10)
mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
expect_equal(head(mlpPredictions$prediction, 10),
c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
# test initialWeights
model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2, initialWeights =
c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9))
mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
expect_equal(head(mlpPredictions$prediction, 10),
c("1.0", "1.0", "1.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2, initialWeights =
c(0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 5.0, 5.0, 9.0, 9.0, 9.0, 9.0, 9.0))
mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
expect_equal(head(mlpPredictions$prediction, 10),
c("1.0", "1.0", "1.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2)
mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
expect_equal(head(mlpPredictions$prediction, 10),
c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "0.0", "2.0", "1.0", "0.0"))
# Test formula works well
df <- suppressWarnings(createDataFrame(iris))
model <- spark.mlp(df, Species ~ Sepal_Length + Sepal_Width + Petal_Length + Petal_Width,
layers = c(4, 3))
summary <- summary(model)
expect_equal(summary$numOfInputs, 4)
expect_equal(summary$numOfOutputs, 3)
expect_equal(summary$layers, c(4, 3))
expect_equal(length(summary$weights), 15)
expect_equal(head(summary$weights, 5), list(-1.1957257, -5.2693685, 7.4489734, -6.3751413,
-10.2376130), tolerance = 1e-6)
})
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 numeric response variable
t1$NumericSurvived <- ifelse(t1$Survived == "No", 0, 1)
t2 <- t1[-4]
df <- suppressWarnings(createDataFrame(t2))
m <- spark.naiveBayes(df, NumericSurvived ~ ., smoothing = 0.0)
s <- summary(m)
expect_equal(as.double(s$apriori[1, 1]), 0.5833333, tolerance = 1e-6)
expect_equal(sum(s$apriori), 1)
expect_equal(as.double(s$tables[1, "Age_Adult"]), 0.5714286, tolerance = 1e-6)
})
test_that("spark.survreg", {
# R code to reproduce the result.
#
#' rData <- list(time = c(4, 3, 1, 1, 2, 2, 3), status = c(1, 1, 1, 0, 1, 1, 0),
#' x = c(0, 2, 1, 1, 1, 0, 0), sex = c(0, 0, 0, 0, 1, 1, 1))
#' library(survival)
#' model <- survreg(Surv(time, status) ~ x + sex, rData)
#' summary(model)
#' predict(model, data)
#
# -- output of 'summary(model)'
#
# Value Std. Error z p
# (Intercept) 1.315 0.270 4.88 1.07e-06
# x -0.190 0.173 -1.10 2.72e-01
# sex -0.253 0.329 -0.77 4.42e-01
# Log(scale) -1.160 0.396 -2.93 3.41e-03
#
# -- output of 'predict(model, data)'
#
# 1 2 3 4 5 6 7
# 3.724591 2.545368 3.079035 3.079035 2.390146 2.891269 2.891269
#
data <- list(list(4, 1, 0, 0), list(3, 1, 2, 0), list(1, 1, 1, 0),
list(1, 0, 1, 0), list(2, 1, 1, 1), list(2, 1, 0, 1), list(3, 0, 0, 1))
df <- createDataFrame(data, c("time", "status", "x", "sex"))
model <- spark.survreg(df, Surv(time, status) ~ x + sex)
stats <- summary(model)
coefs <- as.vector(stats$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$coefficients))
unlink(modelPath)
# Test survival::survreg
if (requireNamespace("survival", quietly = TRUE)) {
rData <- list(time = c(4, 3, 1, 1, 2, 2, 3), status = c(1, 1, 1, 0, 1, 1, 0),
x = c(0, 2, 1, 1, 1, 0, 0), sex = c(0, 0, 0, 0, 1, 1, 1))
expect_error(
model <- survival::survreg(formula = survival::Surv(time, status) ~ x + sex, data = rData),
NA)
expect_equal(predict(model, rData)[[1]], 3.724591, tolerance = 1e-4)
}
})
test_that("spark.isotonicRegression", {
label <- c(7.0, 5.0, 3.0, 5.0, 1.0)
feature <- c(0.0, 1.0, 2.0, 3.0, 4.0)
weight <- c(1.0, 1.0, 1.0, 1.0, 1.0)
data <- as.data.frame(cbind(label, feature, weight))
df <- createDataFrame(data)
model <- spark.isoreg(df, label ~ feature, isotonic = FALSE,
weightCol = "weight")
# only allow one variable on the right hand side of the formula
expect_error(model2 <- spark.isoreg(df, ~., isotonic = FALSE))
result <- summary(model)
expect_equal(result$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.logit", {
# R code to reproduce the result.
# nolint start
#' library(glmnet)
#' iris.x = as.matrix(iris[, 1:4])
#' iris.y = as.factor(as.character(iris[, 5]))
#' logit = glmnet(iris.x, iris.y, family="multinomial", alpha=0, lambda=0.5)
#' coef(logit)
#
# $setosa
# 5 x 1 sparse Matrix of class "dgCMatrix"
# s0
# 1.0981324
# Sepal.Length -0.2909860
# Sepal.Width 0.5510907
# Petal.Length -0.1915217
# Petal.Width -0.4211946
#
# $versicolor
# 5 x 1 sparse Matrix of class "dgCMatrix"
# s0
# 1.520061e+00
# Sepal.Length 2.524501e-02
# Sepal.Width -5.310313e-01
# Petal.Length 3.656543e-02
# Petal.Width -3.144464e-05
#
# $virginica
# 5 x 1 sparse Matrix of class "dgCMatrix"
# s0
# -2.61819385
# Sepal.Length 0.26574097
# Sepal.Width -0.02005932
# Petal.Length 0.15495629
# Petal.Width 0.42122607
# nolint end
# Test multinomial logistic regression againt three classes
df <- suppressWarnings(createDataFrame(iris))
model <- spark.logit(df, Species ~ ., regParam = 0.5)
summary <- summary(model)
versicolorCoefsR <- c(1.52, 0.03, -0.53, 0.04, 0.00)
virginicaCoefsR <- c(-2.62, 0.27, -0.02, 0.16, 0.42)
setosaCoefsR <- c(1.10, -0.29, 0.55, -0.19, -0.42)
versicolorCoefs <- unlist(summary$coefficients[, "versicolor"])
virginicaCoefs <- unlist(summary$coefficients[, "virginica"])
setosaCoefs <- unlist(summary$coefficients[, "setosa"])
expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
expect_true(all(abs(setosaCoefs - setosaCoefs) < 0.1))
# Test model save and load
modelPath <- tempfile(pattern = "spark-logit", fileext = ".tmp")
write.ml(model, modelPath)
expect_error(write.ml(model, modelPath))
write.ml(model, modelPath, overwrite = TRUE)
model2 <- read.ml(modelPath)
coefs <- summary(model)$coefficients
coefs2 <- summary(model2)$coefficients
expect_equal(coefs, coefs2)
unlink(modelPath)
# R code to reproduce the result.
# nolint start
#' library(glmnet)
#' iris2 <- iris[iris$Species %in% c("versicolor", "virginica"), ]
#' iris.x = as.matrix(iris2[, 1:4])
#' iris.y = as.factor(as.character(iris2[, 5]))
#' logit = glmnet(iris.x, iris.y, family="multinomial", alpha=0, lambda=0.5)
#' coef(logit)
#
# $versicolor
# 5 x 1 sparse Matrix of class "dgCMatrix"
# s0
# 3.93844796
# Sepal.Length -0.13538675
# Sepal.Width -0.02386443
# Petal.Length -0.35076451
# Petal.Width -0.77971954
#
# $virginica
# 5 x 1 sparse Matrix of class "dgCMatrix"
# s0
# -3.93844796
# Sepal.Length 0.13538675
# Sepal.Width 0.02386443
# Petal.Length 0.35076451
# Petal.Width 0.77971954
#
#' logit = glmnet(iris.x, iris.y, family="binomial", alpha=0, lambda=0.5)
#' coef(logit)
#
# 5 x 1 sparse Matrix of class "dgCMatrix"
# s0
# (Intercept) -6.0824412
# Sepal.Length 0.2458260
# Sepal.Width 0.1642093
# Petal.Length 0.4759487
# Petal.Width 1.0383948
#
# nolint end
# Test multinomial logistic regression againt two classes
df <- suppressWarnings(createDataFrame(iris))
training <- df[df$Species %in% c("versicolor", "virginica"), ]
model <- spark.logit(training, Species ~ ., regParam = 0.5, family = "multinomial")
summary <- summary(model)
versicolorCoefsR <- c(3.94, -0.16, -0.02, -0.35, -0.78)
virginicaCoefsR <- c(-3.94, 0.16, -0.02, 0.35, 0.78)
versicolorCoefs <- unlist(summary$coefficients[, "versicolor"])
virginicaCoefs <- unlist(summary$coefficients[, "virginica"])
expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
# Test binomial logistic regression againt two classes
model <- spark.logit(training, Species ~ ., regParam = 0.5)
summary <- summary(model)
coefsR <- c(-6.08, 0.25, 0.16, 0.48, 1.04)
coefs <- unlist(summary$coefficients[, "Estimate"])
expect_true(all(abs(coefsR - coefs) < 0.1))
# Test prediction with string label
prediction <- predict(model, training)
expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "character")
expected <- c("versicolor", "versicolor", "virginica", "versicolor", "versicolor",
"versicolor", "versicolor", "versicolor", "versicolor", "versicolor")
expect_equal(as.list(take(select(prediction, "prediction"), 10))[[1]], expected)
# Test prediction with numeric label
label <- c(0.0, 0.0, 0.0, 1.0, 1.0)
feature <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776)
data <- as.data.frame(cbind(label, feature))
df <- createDataFrame(data)
model <- spark.logit(df, label ~ feature)
prediction <- collect(select(predict(model, df), "prediction"))
expect_equal(prediction$prediction, c("0.0", "0.0", "1.0", "1.0", "0.0"))
})
test_that("spark.gaussianMixture", {
# R code to reproduce the result.
# nolint start
#' library(mvtnorm)
#' set.seed(1)
#' a <- rmvnorm(7, c(0, 0))
#' b <- rmvnorm(8, c(10, 10))
#' data <- rbind(a, b)
#' model <- mvnormalmixEM(data, k = 2)
#' model$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)$topicDistribution
expect_equal(length(local.posterior), sum(unlist(local.posterior)))
})
test_that("spark.als", {
data <- list(list(0, 0, 4.0), list(0, 1, 2.0), list(1, 1, 3.0), list(1, 2, 4.0),
list(2, 1, 1.0), list(2, 2, 5.0))
df <- createDataFrame(data, c("user", "item", "score"))
model <- spark.als(df, ratingCol = "score", userCol = "user", itemCol = "item",
rank = 10, maxIter = 5, seed = 0, regParam = 0.1)
stats <- summary(model)
expect_equal(stats$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:")
# Test print.summary.KSTest
printStats <- capture.output(print.summary.KSTest(stats))
expect_match(printStats[1], "Kolmogorov-Smirnov test summary:")
expect_match(printStats[5],
"Low presumption against null hypothesis: Sample follows theoretical distribution. ")
})
test_that("spark.randomForest", {
# regression
data <- suppressWarnings(createDataFrame(longley))
model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16,
numTrees = 1)
predictions <- collect(predict(model, data))
expect_equal(predictions$prediction, c(60.323, 61.122, 60.171, 61.187,
63.221, 63.639, 64.989, 63.761,
66.019, 67.857, 68.169, 66.513,
68.655, 69.564, 69.331, 70.551),
tolerance = 1e-4)
stats <- summary(model)
expect_equal(stats$numTrees, 1)
expect_error(capture.output(stats), NA)
expect_true(length(capture.output(stats)) > 6)
model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16,
numTrees = 20, seed = 123)
predictions <- collect(predict(model, data))
expect_equal(predictions$prediction, c(60.32820, 61.22315, 60.69025, 62.11070,
63.53160, 64.05470, 65.12710, 64.30450,
66.70910, 67.86125, 68.08700, 67.21865,
68.89275, 69.53180, 69.39640, 69.68250),
tolerance = 1e-4)
stats <- summary(model)
expect_equal(stats$numTrees, 20)
modelPath <- tempfile(pattern = "spark-randomForestRegression", 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$formula, stats2$formula)
expect_equal(stats$numFeatures, stats2$numFeatures)
expect_equal(stats$features, stats2$features)
expect_equal(stats$featureImportances, stats2$featureImportances)
expect_equal(stats$numTrees, stats2$numTrees)
expect_equal(stats$treeWeights, stats2$treeWeights)
unlink(modelPath)
# classification
data <- suppressWarnings(createDataFrame(iris))
model <- spark.randomForest(data, Species ~ Petal_Length + Petal_Width, "classification",
maxDepth = 5, maxBins = 16)
stats <- summary(model)
expect_equal(stats$numFeatures, 2)
expect_equal(stats$numTrees, 20)
expect_error(capture.output(stats), NA)
expect_true(length(capture.output(stats)) > 6)
# Test string prediction values
predictions <- collect(predict(model, data))$prediction
expect_equal(length(grep("setosa", predictions)), 50)
expect_equal(length(grep("versicolor", predictions)), 50)
modelPath <- tempfile(pattern = "spark-randomForestClassification", fileext = ".tmp")
write.ml(model, modelPath)
expect_error(write.ml(model, modelPath))
write.ml(model, modelPath, overwrite = TRUE)
model2 <- read.ml(modelPath)
stats2 <- summary(model2)
expect_equal(stats$depth, stats2$depth)
expect_equal(stats$numNodes, stats2$numNodes)
expect_equal(stats$numClasses, stats2$numClasses)
unlink(modelPath)
# Test numeric response variable
labelToIndex <- function(species) {
switch(as.character(species),
setosa = 0.0,
versicolor = 1.0,
virginica = 2.0
)
}
iris$NumericSpecies <- lapply(iris$Species, labelToIndex)
data <- suppressWarnings(createDataFrame(iris[-5]))
model <- spark.randomForest(data, NumericSpecies ~ Petal_Length + Petal_Width, "classification",
maxDepth = 5, maxBins = 16)
stats <- summary(model)
expect_equal(stats$numFeatures, 2)
expect_equal(stats$numTrees, 20)
# Test numeric prediction values
predictions <- collect(predict(model, data))$prediction
expect_equal(length(grep("1.0", predictions)), 50)
expect_equal(length(grep("2.0", predictions)), 50)
# spark.randomForest classification can work on libsvm data
data <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
source = "libsvm")
model <- spark.randomForest(data, label ~ features, "classification")
expect_equal(summary(model)$numFeatures, 4)
})
test_that("spark.gbt", {
# regression
data <- suppressWarnings(createDataFrame(longley))
model <- spark.gbt(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16, seed = 123)
predictions <- collect(predict(model, data))
expect_equal(predictions$prediction, c(60.323, 61.122, 60.171, 61.187,
63.221, 63.639, 64.989, 63.761,
66.019, 67.857, 68.169, 66.513,
68.655, 69.564, 69.331, 70.551),
tolerance = 1e-4)
stats <- summary(model)
expect_equal(stats$numTrees, 20)
expect_equal(stats$formula, "Employed ~ .")
expect_equal(stats$numFeatures, 6)
expect_equal(length(stats$treeWeights), 20)
modelPath <- tempfile(pattern = "spark-gbtRegression", 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$formula, stats2$formula)
expect_equal(stats$numFeatures, stats2$numFeatures)
expect_equal(stats$features, stats2$features)
expect_equal(stats$featureImportances, stats2$featureImportances)
expect_equal(stats$numTrees, stats2$numTrees)
expect_equal(stats$treeWeights, stats2$treeWeights)
unlink(modelPath)
# classification
# label must be binary - GBTClassifier currently only supports binary classification.
iris2 <- iris[iris$Species != "virginica", ]
data <- suppressWarnings(createDataFrame(iris2))
model <- spark.gbt(data, Species ~ Petal_Length + Petal_Width, "classification")
stats <- summary(model)
expect_equal(stats$numFeatures, 2)
expect_equal(stats$numTrees, 20)
expect_error(capture.output(stats), NA)
expect_true(length(capture.output(stats)) > 6)
predictions <- collect(predict(model, data))$prediction
# test string prediction values
expect_equal(length(grep("setosa", predictions)), 50)
expect_equal(length(grep("versicolor", predictions)), 50)
modelPath <- tempfile(pattern = "spark-gbtClassification", 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$depth, stats2$depth)
expect_equal(stats$numNodes, stats2$numNodes)
expect_equal(stats$numClasses, stats2$numClasses)
unlink(modelPath)
iris2$NumericSpecies <- ifelse(iris2$Species == "setosa", 0, 1)
df <- suppressWarnings(createDataFrame(iris2))
m <- spark.gbt(df, NumericSpecies ~ ., type = "classification")
s <- summary(m)
# test numeric prediction values
expect_equal(iris2$NumericSpecies, as.double(collect(predict(m, df))$prediction))
expect_equal(s$numFeatures, 5)
expect_equal(s$numTrees, 20)
# spark.gbt classification can work on libsvm data
data <- read.df(absoluteSparkPath("data/mllib/sample_binary_classification_data.txt"),
source = "libsvm")
model <- spark.gbt(data, label ~ features, "classification")
expect_equal(summary(model)$numFeatures, 692)
})
sparkR.session.stop()
