# # 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)$prediction), "double") }) test_that("predictions match with native glm", { training <- createDataFrame(sqlContext, iris) model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training) vals <- collect(select(predict(model, training), "prediction")) rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris) expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals) }) test_that("dot minus and intercept vs native glm", { training <- createDataFrame(sqlContext, iris) model <- glm(Sepal_Width ~ . - Species + 0, data = training) vals <- collect(select(predict(model, training), "prediction")) rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris) expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals) }) test_that("feature interaction vs native glm", { training <- createDataFrame(sqlContext, iris) model <- glm(Sepal_Width ~ Species:Sepal_Length, data = training) vals <- collect(select(predict(model, training), "prediction")) rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris) expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals) }) test_that("summary coefficients match with native glm", { training <- createDataFrame(sqlContext, iris) stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training, solver = "l-bfgs")) coefs <- as.vector(stats$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"))) })