| # |
| # 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 tree-based algorithms") |
| |
| # Tests for MLlib tree-based algorithms in SparkR |
| sparkSession <- sparkR.session(master = sparkRTestMaster, enableHiveSupport = FALSE) |
| |
| absoluteSparkPath <- function(x) { |
| sparkHome <- sparkR.conf("spark.home") |
| file.path(sparkHome, x) |
| } |
| |
| 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$maxDepth, 5) |
| expect_equal(stats$formula, "Employed ~ .") |
| expect_equal(stats$numFeatures, 6) |
| expect_equal(length(stats$treeWeights), 20) |
| |
| if (windows_with_hadoop()) { |
| 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$maxDepth, stats2$maxDepth) |
| 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$Species != "virginica", ] |
| data <- suppressWarnings(createDataFrame(iris2)) |
| model <- spark.gbt(data, Species ~ Petal_Length + Petal_Width, "classification", seed = 12) |
| stats <- summary(model) |
| expect_equal(stats$numFeatures, 2) |
| expect_equal(stats$numTrees, 20) |
| expect_equal(stats$maxDepth, 5) |
| expect_error(capture.output(stats), NA) |
| expect_true(length(capture.output(stats)) > 6) |
| predictions <- collect(predict(model, data))$prediction |
| # test string prediction values |
| expect_equal(length(grep("setosa", predictions)), 50) |
| expect_equal(length(grep("versicolor", predictions)), 50) |
| |
| if (windows_with_hadoop()) { |
| modelPath <- tempfile(pattern = "spark-gbtClassification", fileext = ".tmp") |
| write.ml(model, modelPath) |
| expect_error(write.ml(model, modelPath)) |
| write.ml(model, modelPath, overwrite = TRUE) |
| model2 <- read.ml(modelPath) |
| stats2 <- summary(model2) |
| expect_equal(stats$depth, stats2$depth) |
| expect_equal(stats$numNodes, stats2$numNodes) |
| expect_equal(stats$numClasses, stats2$numClasses) |
| |
| unlink(modelPath) |
| } |
| |
| iris2$NumericSpecies <- ifelse(iris2$Species == "setosa", 0, 1) |
| df <- suppressWarnings(createDataFrame(iris2)) |
| m <- spark.gbt(df, NumericSpecies ~ ., type = "classification", seed = 12) |
| s <- summary(m) |
| # test numeric prediction values |
| expect_equal(iris2$NumericSpecies, as.double(collect(predict(m, df))$prediction)) |
| expect_equal(s$numFeatures, 5) |
| expect_equal(s$numTrees, 20) |
| expect_equal(stats$maxDepth, 5) |
| |
| # spark.gbt classification can work on libsvm data |
| if (windows_with_hadoop()) { |
| data <- read.df(absoluteSparkPath("data/mllib/sample_binary_classification_data.txt"), |
| source = "libsvm") |
| model <- spark.gbt(data, label ~ features, "classification", seed = 12) |
| expect_equal(summary(model)$numFeatures, 692) |
| } |
| |
| # Test unseen labels |
| data <- data.frame(clicked = base::sample(c(0, 1), 10, replace = TRUE), |
| someString = base::sample(c("this", "that"), 10, replace = TRUE), |
| stringsAsFactors = FALSE) |
| trainidxs <- base::sample(nrow(data), nrow(data) * 0.7) |
| traindf <- as.DataFrame(data[trainidxs, ]) |
| testdf <- as.DataFrame(rbind(data[-trainidxs, ], c(0, "the other"))) |
| model <- spark.gbt(traindf, clicked ~ ., type = "classification", seed = 23) |
| predictions <- predict(model, testdf) |
| expect_error(collect(predictions)) |
| model <- spark.gbt(traindf, clicked ~ ., type = "classification", handleInvalid = "keep", |
| seed = 23) |
| predictions <- predict(model, testdf) |
| expect_equal(class(collect(predictions)$clicked[1]), "character") |
| }) |
| |
| test_that("spark.randomForest", { |
| # regression |
| data <- suppressWarnings(createDataFrame(longley)) |
| model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16, |
| numTrees = 1, seed = 1, bootstrap = FALSE) |
| |
| 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_equal(stats$maxDepth, 5) |
| expect_error(capture.output(stats), NA) |
| expect_true(length(capture.output(stats)) > 6) |
| |
| model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16, |
| numTrees = 20, seed = 123) |
| predictions <- collect(predict(model, data)) |
| expect_equal(predictions$prediction, c(60.32495, 61.06495, 60.52120, 61.98500, |
| 63.64450, 64.21910, 65.00810, 64.30450, |
| 66.70910, 67.96875, 68.22140, 67.21865, |
| 68.89275, 69.55900, 69.30160, 69.93050), |
| tolerance = 1e-4) |
| stats <- summary(model) |
| expect_equal(stats$numTrees, 20) |
| expect_equal(stats$maxDepth, 5) |
| |
| if (windows_with_hadoop()) { |
| 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$maxDepth, stats2$maxDepth) |
| 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, seed = 123) |
| |
| stats <- summary(model) |
| expect_equal(stats$numFeatures, 2) |
| expect_equal(stats$numTrees, 20) |
| expect_equal(stats$maxDepth, 5) |
| expect_error(capture.output(stats), NA) |
| expect_true(length(capture.output(stats)) > 6) |
| # Test string prediction values |
| predictions <- collect(predict(model, data))$prediction |
| expect_equal(length(grep("setosa", predictions)), 50) |
| expect_equal(length(grep("versicolor", predictions)), 50) |
| |
| if (windows_with_hadoop()) { |
| 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, seed = 123) |
| stats <- summary(model) |
| expect_equal(stats$numFeatures, 2) |
| expect_equal(stats$numTrees, 20) |
| expect_equal(stats$maxDepth, 5) |
| |
| # Test numeric prediction values |
| predictions <- collect(predict(model, data))$prediction |
| expect_equal(length(grep("1.0", predictions)), 50) |
| expect_equal(length(grep("2.0", predictions)), 50) |
| |
| # Test unseen labels |
| data <- data.frame(clicked = base::sample(c(0, 1), 10, replace = TRUE), |
| someString = base::sample(c("this", "that"), 10, replace = TRUE), |
| stringsAsFactors = FALSE) |
| trainidxs <- base::sample(nrow(data), nrow(data) * 0.7) |
| traindf <- as.DataFrame(data[trainidxs, ]) |
| testdf <- as.DataFrame(rbind(data[-trainidxs, ], c(0, "the other"))) |
| model <- spark.randomForest(traindf, clicked ~ ., type = "classification", |
| maxDepth = 10, maxBins = 10, numTrees = 10, seed = 123) |
| predictions <- predict(model, testdf) |
| expect_error(collect(predictions)) |
| model <- spark.randomForest(traindf, clicked ~ ., type = "classification", |
| maxDepth = 10, maxBins = 10, numTrees = 10, |
| handleInvalid = "keep", seed = 123) |
| predictions <- predict(model, testdf) |
| expect_equal(class(collect(predictions)$clicked[1]), "character") |
| |
| # spark.randomForest classification can work on libsvm data |
| if (windows_with_hadoop()) { |
| data <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"), |
| source = "libsvm") |
| model <- spark.randomForest(data, label ~ features, "classification", seed = 123) |
| expect_equal(summary(model)$numFeatures, 4) |
| } |
| }) |
| |
| test_that("spark.decisionTree", { |
| # regression |
| data <- suppressWarnings(createDataFrame(longley)) |
| model <- spark.decisionTree(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16, |
| seed = 42) |
| |
| predictions <- collect(predict(model, data)) |
| expect_equal(predictions$prediction, c(60.323, 61.122, 60.171, 61.187, |
| 63.221, 63.639, 64.989, 63.761, |
| 66.019, 67.857, 68.169, 66.513, |
| 68.655, 69.564, 69.331, 70.551), |
| tolerance = 1e-4) |
| |
| stats <- summary(model) |
| expect_equal(stats$maxDepth, 5) |
| expect_error(capture.output(stats), NA) |
| expect_true(length(capture.output(stats)) > 6) |
| |
| if (windows_with_hadoop()) { |
| modelPath <- tempfile(pattern = "spark-decisionTreeRegression", 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$maxDepth, stats2$maxDepth) |
| |
| unlink(modelPath) |
| } |
| |
| # classification |
| data <- suppressWarnings(createDataFrame(iris)) |
| model <- spark.decisionTree(data, Species ~ Petal_Length + Petal_Width, "classification", |
| maxDepth = 5, maxBins = 16, seed = 43) |
| |
| stats <- summary(model) |
| expect_equal(stats$numFeatures, 2) |
| expect_equal(stats$maxDepth, 5) |
| expect_error(capture.output(stats), NA) |
| expect_true(length(capture.output(stats)) > 6) |
| # Test string prediction values |
| predictions <- collect(predict(model, data))$prediction |
| expect_equal(length(grep("setosa", predictions)), 50) |
| expect_equal(length(grep("versicolor", predictions)), 50) |
| |
| if (windows_with_hadoop()) { |
| modelPath <- tempfile(pattern = "spark-decisionTreeClassification", fileext = ".tmp") |
| write.ml(model, modelPath) |
| expect_error(write.ml(model, modelPath)) |
| write.ml(model, modelPath, overwrite = TRUE) |
| model2 <- read.ml(modelPath) |
| stats2 <- summary(model2) |
| expect_equal(stats$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$Species, labelToIndex) |
| data <- suppressWarnings(createDataFrame(iris[-5])) |
| model <- spark.decisionTree(data, NumericSpecies ~ Petal_Length + Petal_Width, "classification", |
| maxDepth = 5, maxBins = 16, seed = 44) |
| stats <- summary(model) |
| expect_equal(stats$numFeatures, 2) |
| expect_equal(stats$maxDepth, 5) |
| |
| # Test numeric prediction values |
| predictions <- collect(predict(model, data))$prediction |
| expect_equal(length(grep("1.0", predictions)), 50) |
| expect_equal(length(grep("2.0", predictions)), 50) |
| |
| # spark.decisionTree classification can work on libsvm data |
| if (windows_with_hadoop()) { |
| data <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"), |
| source = "libsvm") |
| model <- spark.decisionTree(data, label ~ features, "classification", seed = 45) |
| expect_equal(summary(model)$numFeatures, 4) |
| } |
| |
| # Test unseen labels |
| data <- data.frame(clicked = base::sample(c(0, 1), 10, replace = TRUE), |
| someString = base::sample(c("this", "that"), 10, replace = TRUE), |
| stringsAsFactors = FALSE) |
| trainidxs <- base::sample(nrow(data), nrow(data) * 0.7) |
| traindf <- as.DataFrame(data[trainidxs, ]) |
| testdf <- as.DataFrame(rbind(data[-trainidxs, ], c(0, "the other"))) |
| model <- spark.decisionTree(traindf, clicked ~ ., type = "classification", |
| maxDepth = 5, maxBins = 16, seed = 46) |
| predictions <- predict(model, testdf) |
| expect_error(collect(predictions)) |
| model <- spark.decisionTree(traindf, clicked ~ ., type = "classification", |
| maxDepth = 5, maxBins = 16, handleInvalid = "keep", seed = 46) |
| predictions <- predict(model, testdf) |
| expect_equal(class(collect(predictions)$clicked[1]), "character") |
| }) |
| |
| sparkR.session.stop() |
