a7b46c627b
## What changes were proposed in this pull request? For randomForest classifier, if test data contains unseen labels, it will throw an error. The StringIndexer already has the handleInvalid logic. The patch add a new method to set the underlying StringIndexer handleInvalid logic. This patch should also apply to other classifiers. This PR focuses on the main logic and randomForest classifier. I will do follow-up PR for other classifiers. ## How was this patch tested? Add a new unit test based on the error case in the JIRA. Author: wangmiao1981 <wm624@hotmail.com> Closes #18496 from wangmiao1981/handle.
334 lines
13 KiB
R
334 lines
13 KiB
R
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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library(testthat)
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context("MLlib tree-based algorithms")
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# Tests for MLlib tree-based algorithms in SparkR
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sparkSession <- sparkR.session(master = sparkRTestMaster, enableHiveSupport = FALSE)
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absoluteSparkPath <- function(x) {
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sparkHome <- sparkR.conf("spark.home")
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file.path(sparkHome, x)
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}
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test_that("spark.gbt", {
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# regression
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data <- suppressWarnings(createDataFrame(longley))
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model <- spark.gbt(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16, seed = 123)
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predictions <- collect(predict(model, data))
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expect_equal(predictions$prediction, c(60.323, 61.122, 60.171, 61.187,
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63.221, 63.639, 64.989, 63.761,
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66.019, 67.857, 68.169, 66.513,
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68.655, 69.564, 69.331, 70.551),
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tolerance = 1e-4)
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stats <- summary(model)
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expect_equal(stats$numTrees, 20)
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expect_equal(stats$maxDepth, 5)
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expect_equal(stats$formula, "Employed ~ .")
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expect_equal(stats$numFeatures, 6)
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expect_equal(length(stats$treeWeights), 20)
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if (windows_with_hadoop()) {
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modelPath <- tempfile(pattern = "spark-gbtRegression", fileext = ".tmp")
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write.ml(model, modelPath)
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expect_error(write.ml(model, modelPath))
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write.ml(model, modelPath, overwrite = TRUE)
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model2 <- read.ml(modelPath)
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stats2 <- summary(model2)
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expect_equal(stats$formula, stats2$formula)
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expect_equal(stats$numFeatures, stats2$numFeatures)
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expect_equal(stats$features, stats2$features)
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expect_equal(stats$featureImportances, stats2$featureImportances)
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expect_equal(stats$maxDepth, stats2$maxDepth)
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expect_equal(stats$numTrees, stats2$numTrees)
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expect_equal(stats$treeWeights, stats2$treeWeights)
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unlink(modelPath)
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}
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# classification
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# label must be binary - GBTClassifier currently only supports binary classification.
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iris2 <- iris[iris$Species != "virginica", ]
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data <- suppressWarnings(createDataFrame(iris2))
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model <- spark.gbt(data, Species ~ Petal_Length + Petal_Width, "classification")
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stats <- summary(model)
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expect_equal(stats$numFeatures, 2)
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expect_equal(stats$numTrees, 20)
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expect_equal(stats$maxDepth, 5)
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expect_error(capture.output(stats), NA)
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expect_true(length(capture.output(stats)) > 6)
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predictions <- collect(predict(model, data))$prediction
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# test string prediction values
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expect_equal(length(grep("setosa", predictions)), 50)
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expect_equal(length(grep("versicolor", predictions)), 50)
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if (windows_with_hadoop()) {
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modelPath <- tempfile(pattern = "spark-gbtClassification", fileext = ".tmp")
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write.ml(model, modelPath)
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expect_error(write.ml(model, modelPath))
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write.ml(model, modelPath, overwrite = TRUE)
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model2 <- read.ml(modelPath)
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stats2 <- summary(model2)
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expect_equal(stats$depth, stats2$depth)
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expect_equal(stats$numNodes, stats2$numNodes)
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expect_equal(stats$numClasses, stats2$numClasses)
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unlink(modelPath)
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}
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iris2$NumericSpecies <- ifelse(iris2$Species == "setosa", 0, 1)
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df <- suppressWarnings(createDataFrame(iris2))
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m <- spark.gbt(df, NumericSpecies ~ ., type = "classification")
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s <- summary(m)
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# test numeric prediction values
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expect_equal(iris2$NumericSpecies, as.double(collect(predict(m, df))$prediction))
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expect_equal(s$numFeatures, 5)
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expect_equal(s$numTrees, 20)
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expect_equal(stats$maxDepth, 5)
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# spark.gbt classification can work on libsvm data
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if (windows_with_hadoop()) {
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data <- read.df(absoluteSparkPath("data/mllib/sample_binary_classification_data.txt"),
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source = "libsvm")
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model <- spark.gbt(data, label ~ features, "classification")
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expect_equal(summary(model)$numFeatures, 692)
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}
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})
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test_that("spark.randomForest", {
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# regression
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data <- suppressWarnings(createDataFrame(longley))
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model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16,
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numTrees = 1)
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predictions <- collect(predict(model, data))
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expect_equal(predictions$prediction, c(60.323, 61.122, 60.171, 61.187,
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63.221, 63.639, 64.989, 63.761,
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66.019, 67.857, 68.169, 66.513,
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68.655, 69.564, 69.331, 70.551),
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tolerance = 1e-4)
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stats <- summary(model)
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expect_equal(stats$numTrees, 1)
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expect_equal(stats$maxDepth, 5)
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expect_error(capture.output(stats), NA)
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expect_true(length(capture.output(stats)) > 6)
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model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16,
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numTrees = 20, seed = 123)
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predictions <- collect(predict(model, data))
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expect_equal(predictions$prediction, c(60.32820, 61.22315, 60.69025, 62.11070,
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63.53160, 64.05470, 65.12710, 64.30450,
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66.70910, 67.86125, 68.08700, 67.21865,
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68.89275, 69.53180, 69.39640, 69.68250),
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tolerance = 1e-4)
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stats <- summary(model)
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expect_equal(stats$numTrees, 20)
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expect_equal(stats$maxDepth, 5)
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if (windows_with_hadoop()) {
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modelPath <- tempfile(pattern = "spark-randomForestRegression", fileext = ".tmp")
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write.ml(model, modelPath)
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expect_error(write.ml(model, modelPath))
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write.ml(model, modelPath, overwrite = TRUE)
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model2 <- read.ml(modelPath)
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stats2 <- summary(model2)
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expect_equal(stats$formula, stats2$formula)
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expect_equal(stats$numFeatures, stats2$numFeatures)
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expect_equal(stats$features, stats2$features)
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expect_equal(stats$featureImportances, stats2$featureImportances)
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expect_equal(stats$numTrees, stats2$numTrees)
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expect_equal(stats$maxDepth, stats2$maxDepth)
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expect_equal(stats$treeWeights, stats2$treeWeights)
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unlink(modelPath)
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}
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# classification
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data <- suppressWarnings(createDataFrame(iris))
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model <- spark.randomForest(data, Species ~ Petal_Length + Petal_Width, "classification",
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maxDepth = 5, maxBins = 16)
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stats <- summary(model)
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expect_equal(stats$numFeatures, 2)
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expect_equal(stats$numTrees, 20)
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expect_equal(stats$maxDepth, 5)
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expect_error(capture.output(stats), NA)
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expect_true(length(capture.output(stats)) > 6)
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# Test string prediction values
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predictions <- collect(predict(model, data))$prediction
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expect_equal(length(grep("setosa", predictions)), 50)
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expect_equal(length(grep("versicolor", predictions)), 50)
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if (windows_with_hadoop()) {
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modelPath <- tempfile(pattern = "spark-randomForestClassification", fileext = ".tmp")
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write.ml(model, modelPath)
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expect_error(write.ml(model, modelPath))
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write.ml(model, modelPath, overwrite = TRUE)
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model2 <- read.ml(modelPath)
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stats2 <- summary(model2)
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expect_equal(stats$depth, stats2$depth)
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expect_equal(stats$numNodes, stats2$numNodes)
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expect_equal(stats$numClasses, stats2$numClasses)
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unlink(modelPath)
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}
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# Test numeric response variable
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labelToIndex <- function(species) {
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switch(as.character(species),
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setosa = 0.0,
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versicolor = 1.0,
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virginica = 2.0
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)
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}
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iris$NumericSpecies <- lapply(iris$Species, labelToIndex)
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data <- suppressWarnings(createDataFrame(iris[-5]))
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model <- spark.randomForest(data, NumericSpecies ~ Petal_Length + Petal_Width, "classification",
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maxDepth = 5, maxBins = 16)
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stats <- summary(model)
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expect_equal(stats$numFeatures, 2)
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expect_equal(stats$numTrees, 20)
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expect_equal(stats$maxDepth, 5)
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# Test numeric prediction values
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predictions <- collect(predict(model, data))$prediction
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expect_equal(length(grep("1.0", predictions)), 50)
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expect_equal(length(grep("2.0", predictions)), 50)
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# Test unseen labels
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data <- data.frame(clicked = base::sample(c(0, 1), 10, replace = TRUE),
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someString = base::sample(c("this", "that"), 10, replace = TRUE),
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stringsAsFactors = FALSE)
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trainidxs <- base::sample(nrow(data), nrow(data) * 0.7)
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traindf <- as.DataFrame(data[trainidxs, ])
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testdf <- as.DataFrame(rbind(data[-trainidxs, ], c(0, "the other")))
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model <- spark.randomForest(traindf, clicked ~ ., type = "classification",
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maxDepth = 10, maxBins = 10, numTrees = 10)
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predictions <- predict(model, testdf)
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expect_error(collect(predictions))
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model <- spark.randomForest(traindf, clicked ~ ., type = "classification",
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maxDepth = 10, maxBins = 10, numTrees = 10,
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handleInvalid = "skip")
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predictions <- predict(model, testdf)
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expect_equal(class(collect(predictions)$clicked[1]), "character")
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# spark.randomForest classification can work on libsvm data
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if (windows_with_hadoop()) {
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data <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
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source = "libsvm")
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model <- spark.randomForest(data, label ~ features, "classification")
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expect_equal(summary(model)$numFeatures, 4)
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}
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})
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test_that("spark.decisionTree", {
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# regression
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data <- suppressWarnings(createDataFrame(longley))
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model <- spark.decisionTree(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16)
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predictions <- collect(predict(model, data))
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expect_equal(predictions$prediction, c(60.323, 61.122, 60.171, 61.187,
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63.221, 63.639, 64.989, 63.761,
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66.019, 67.857, 68.169, 66.513,
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68.655, 69.564, 69.331, 70.551),
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tolerance = 1e-4)
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stats <- summary(model)
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expect_equal(stats$maxDepth, 5)
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expect_error(capture.output(stats), NA)
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expect_true(length(capture.output(stats)) > 6)
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if (windows_with_hadoop()) {
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modelPath <- tempfile(pattern = "spark-decisionTreeRegression", fileext = ".tmp")
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write.ml(model, modelPath)
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expect_error(write.ml(model, modelPath))
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write.ml(model, modelPath, overwrite = TRUE)
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model2 <- read.ml(modelPath)
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stats2 <- summary(model2)
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expect_equal(stats$formula, stats2$formula)
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expect_equal(stats$numFeatures, stats2$numFeatures)
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expect_equal(stats$features, stats2$features)
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expect_equal(stats$featureImportances, stats2$featureImportances)
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expect_equal(stats$maxDepth, stats2$maxDepth)
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unlink(modelPath)
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}
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# classification
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data <- suppressWarnings(createDataFrame(iris))
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model <- spark.decisionTree(data, Species ~ Petal_Length + Petal_Width, "classification",
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maxDepth = 5, maxBins = 16)
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stats <- summary(model)
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expect_equal(stats$numFeatures, 2)
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expect_equal(stats$maxDepth, 5)
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expect_error(capture.output(stats), NA)
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expect_true(length(capture.output(stats)) > 6)
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# Test string prediction values
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predictions <- collect(predict(model, data))$prediction
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expect_equal(length(grep("setosa", predictions)), 50)
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expect_equal(length(grep("versicolor", predictions)), 50)
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if (windows_with_hadoop()) {
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modelPath <- tempfile(pattern = "spark-decisionTreeClassification", fileext = ".tmp")
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write.ml(model, modelPath)
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expect_error(write.ml(model, modelPath))
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write.ml(model, modelPath, overwrite = TRUE)
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model2 <- read.ml(modelPath)
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stats2 <- summary(model2)
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expect_equal(stats$depth, stats2$depth)
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expect_equal(stats$numNodes, stats2$numNodes)
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expect_equal(stats$numClasses, stats2$numClasses)
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unlink(modelPath)
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}
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# Test numeric response variable
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labelToIndex <- function(species) {
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switch(as.character(species),
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setosa = 0.0,
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versicolor = 1.0,
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virginica = 2.0
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)
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}
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iris$NumericSpecies <- lapply(iris$Species, labelToIndex)
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data <- suppressWarnings(createDataFrame(iris[-5]))
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model <- spark.decisionTree(data, NumericSpecies ~ Petal_Length + Petal_Width, "classification",
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maxDepth = 5, maxBins = 16)
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stats <- summary(model)
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expect_equal(stats$numFeatures, 2)
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expect_equal(stats$maxDepth, 5)
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# Test numeric prediction values
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predictions <- collect(predict(model, data))$prediction
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expect_equal(length(grep("1.0", predictions)), 50)
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expect_equal(length(grep("2.0", predictions)), 50)
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# spark.decisionTree classification can work on libsvm data
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if (windows_with_hadoop()) {
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data <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
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source = "libsvm")
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model <- spark.decisionTree(data, label ~ features, "classification")
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expect_equal(summary(model)$numFeatures, 4)
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}
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})
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sparkR.session.stop()
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