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# 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.
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#
library(testthat)
context("MLlib functions")
# Tests for MLlib functions in SparkR
sc <- sparkR.init()
sqlContext <- sparkRSQL.init(sc)
test_that("glm and predict", {
training <- createDataFrame(sqlContext, iris)
test <- select(training, "Sepal_Length")
model <- glm(Sepal_Width ~ Sepal_Length, training, family = "gaussian")
prediction <- predict(model, test)
expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
})
test_that("glm should work with long formula", {
training <- createDataFrame(sqlContext, iris)
training$LongLongLongLongLongName <- training$Sepal_Width
training$VeryLongLongLongLonLongName <- training$Sepal_Length
training$AnotherLongLongLongLongName <- training$Species
model <- glm(LongLongLongLongLongName ~ VeryLongLongLongLonLongName + AnotherLongLongLongLongName,
data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})
test_that("predictions match with native glm", {
training <- createDataFrame(sqlContext, iris)
model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})
test_that("dot minus and intercept vs native glm", {
training <- createDataFrame(sqlContext, iris)
model <- glm(Sepal_Width ~ . - Species + 0, data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})
test_that("feature interaction vs native glm", {
training <- createDataFrame(sqlContext, iris)
model <- glm(Sepal_Width ~ Species:Sepal_Length, data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})
test_that("summary coefficients match with native glm", {
training <- createDataFrame(sqlContext, iris)
stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training, solver = "l-bfgs"))
coefs <- as.vector(stats$coefficients)
rCoefs <- as.vector(coef(glm(Sepal.Width ~ Sepal.Length + Species, data = iris)))
expect_true(all(abs(rCoefs - coefs) < 1e-6))
expect_true(all(
as.character(stats$features) ==
c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
})
test_that("summary coefficients match with native glm of family 'binomial'", {
df <- createDataFrame(sqlContext, iris)
training <- filter(df, df$Species != "setosa")
stats <- summary(glm(Species ~ Sepal_Length + Sepal_Width, data = training,
family = "binomial"))
coefs <- as.vector(stats$coefficients)
rTraining <- iris[iris$Species %in% c("versicolor","virginica"),]
rCoefs <- as.vector(coef(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
family = binomial(link = "logit"))))
expect_true(all(abs(rCoefs - coefs) < 1e-4))
expect_true(all(
as.character(stats$features) ==
c("(Intercept)", "Sepal_Length", "Sepal_Width")))
})
