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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
library(testthat)
context("MLlib 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$Species
model <- glm(LongLongLongLongLongName ~ VeryLongLongLongLonLongName + AnotherLongLongLongLongName,
data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})
test_that("predictions match with native glm", {
training <- createDataFrame(sqlContext, iris)
model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})
test_that("dot minus and intercept vs native glm", {
training <- createDataFrame(sqlContext, iris)
model <- glm(Sepal_Width ~ . - Species + 0, data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})
test_that("feature interaction vs native glm", {
training <- createDataFrame(sqlContext, iris)
model <- glm(Sepal_Width ~ Species:Sepal_Length, data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})
test_that("summary coefficients match with native glm", {
training <- createDataFrame(sqlContext, iris)
stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training, solver = "normal"))
coefs <- unlist(stats$Coefficients)
devianceResiduals <- unlist(stats$DevianceResiduals)
rCoefs <- as.vector(coef(glm(Sepal.Width ~ Sepal.Length + Species, data = iris)))
rStdError <- c(0.23536, 0.04630, 0.07207, 0.09331)
rTValue <- c(7.123, 7.557, -13.644, -10.798)
rPValue <- c(0.0, 0.0, 0.0, 0.0)
rDevianceResiduals <- c(-0.95096, 0.72918)
expect_true(all(abs(rCoefs - coefs[1:4]) < 1e-6))
expect_true(all(abs(rStdError - coefs[5:8]) < 1e-5))
expect_true(all(abs(rTValue - coefs[9:12]) < 1e-3))
expect_true(all(abs(rPValue - coefs[13:16]) < 1e-6))
expect_true(all(abs(rDevianceResiduals - devianceResiduals) < 1e-5))
expect_true(all(
rownames(stats$Coefficients) ==
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$Species %in% c("versicolor","virginica"),]
rCoefs <- as.vector(coef(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
family = binomial(link = "logit"))))
rStdError <- c(3.0974, 0.5169, 0.8628)
rTValue <- c(-4.212, 3.680, 0.469)
rPValue <- c(0.000, 0.000, 0.639)
expect_true(all(abs(rCoefs - coefs[1:3]) < 1e-4))
expect_true(all(abs(rStdError - coefs[4:6]) < 1e-4))
expect_true(all(abs(rTValue - coefs[7:9]) < 1e-3))
expect_true(all(abs(rPValue - coefs[10:12]) < 1e-3))
expect_true(all(
rownames(stats$Coefficients) ==
c("(Intercept)", "Sepal_Length", "Sepal_Width")))
})
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