dbb06c689c
## What changes were proposed in this pull request? Some PySpark & SparkR tests run with tiny dataset and tiny ```maxIter```, which means they are not converged. I don’t think checking intermediate result during iteration make sense, and these intermediate result may vulnerable and not stable, so we should switch to check the converged result. We hit this issue at #17746 when we upgrade breeze to 0.13.1. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #17757 from yanboliang/flaky-test.
386 lines
14 KiB
R
386 lines
14 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 classification algorithms, except for tree-based algorithms")
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# Tests for MLlib classification algorithms in SparkR
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sparkSession <- sparkR.session(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.svmLinear", {
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df <- suppressWarnings(createDataFrame(iris))
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training <- df[df$Species %in% c("versicolor", "virginica"), ]
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model <- spark.svmLinear(training, Species ~ ., regParam = 0.01, maxIter = 10)
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summary <- summary(model)
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# test summary coefficients return matrix type
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expect_true(class(summary$coefficients) == "matrix")
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expect_true(class(summary$coefficients[, 1]) == "numeric")
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coefs <- summary$coefficients[, "Estimate"]
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expected_coefs <- c(-0.1563083, -0.460648, 0.2276626, 1.055085)
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expect_true(all(abs(coefs - expected_coefs) < 0.1))
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expect_equal(summary$intercept, -0.06004978, tolerance = 1e-2)
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# Test prediction with string label
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prediction <- predict(model, training)
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expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "character")
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expected <- c("versicolor", "versicolor", "versicolor", "virginica", "virginica",
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"virginica", "virginica", "virginica", "virginica", "virginica")
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expect_equal(sort(as.list(take(select(prediction, "prediction"), 10))[[1]]), expected)
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# Test model save and load
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modelPath <- tempfile(pattern = "spark-svm-linear", 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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coefs <- summary(model)$coefficients
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coefs2 <- summary(model2)$coefficients
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expect_equal(coefs, coefs2)
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unlink(modelPath)
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# Test prediction with numeric label
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label <- c(0.0, 0.0, 0.0, 1.0, 1.0)
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feature <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776)
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data <- as.data.frame(cbind(label, feature))
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df <- createDataFrame(data)
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model <- spark.svmLinear(df, label ~ feature, regParam = 0.1)
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prediction <- collect(select(predict(model, df), "prediction"))
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expect_equal(sort(prediction$prediction), c("0.0", "0.0", "0.0", "1.0", "1.0"))
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})
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test_that("spark.logit", {
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# R code to reproduce the result.
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# nolint start
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#' library(glmnet)
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#' iris.x = as.matrix(iris[, 1:4])
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#' iris.y = as.factor(as.character(iris[, 5]))
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#' logit = glmnet(iris.x, iris.y, family="multinomial", alpha=0, lambda=0.5)
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#' coef(logit)
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#
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# $setosa
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# 5 x 1 sparse Matrix of class "dgCMatrix"
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# s0
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# 1.0981324
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# Sepal.Length -0.2909860
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# Sepal.Width 0.5510907
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# Petal.Length -0.1915217
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# Petal.Width -0.4211946
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#
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# $versicolor
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# 5 x 1 sparse Matrix of class "dgCMatrix"
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# s0
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# 1.520061e+00
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# Sepal.Length 2.524501e-02
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# Sepal.Width -5.310313e-01
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# Petal.Length 3.656543e-02
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# Petal.Width -3.144464e-05
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#
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# $virginica
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# 5 x 1 sparse Matrix of class "dgCMatrix"
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# s0
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# -2.61819385
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# Sepal.Length 0.26574097
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# Sepal.Width -0.02005932
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# Petal.Length 0.15495629
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# Petal.Width 0.42122607
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# nolint end
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# Test multinomial logistic regression againt three classes
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df <- suppressWarnings(createDataFrame(iris))
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model <- spark.logit(df, Species ~ ., regParam = 0.5)
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summary <- summary(model)
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# test summary coefficients return matrix type
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expect_true(class(summary$coefficients) == "matrix")
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expect_true(class(summary$coefficients[, 1]) == "numeric")
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versicolorCoefsR <- c(1.52, 0.03, -0.53, 0.04, 0.00)
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virginicaCoefsR <- c(-2.62, 0.27, -0.02, 0.16, 0.42)
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setosaCoefsR <- c(1.10, -0.29, 0.55, -0.19, -0.42)
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versicolorCoefs <- summary$coefficients[, "versicolor"]
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virginicaCoefs <- summary$coefficients[, "virginica"]
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setosaCoefs <- summary$coefficients[, "setosa"]
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expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
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expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
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expect_true(all(abs(setosaCoefs - setosaCoefs) < 0.1))
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# Test model save and load
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modelPath <- tempfile(pattern = "spark-logit", 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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coefs <- summary(model)$coefficients
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coefs2 <- summary(model2)$coefficients
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expect_equal(coefs, coefs2)
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unlink(modelPath)
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# R code to reproduce the result.
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# nolint start
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#' library(glmnet)
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#' iris2 <- iris[iris$Species %in% c("versicolor", "virginica"), ]
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#' iris.x = as.matrix(iris2[, 1:4])
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#' iris.y = as.factor(as.character(iris2[, 5]))
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#' logit = glmnet(iris.x, iris.y, family="multinomial", alpha=0, lambda=0.5)
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#' coef(logit)
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#
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# $versicolor
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# 5 x 1 sparse Matrix of class "dgCMatrix"
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# s0
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# 3.93844796
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# Sepal.Length -0.13538675
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# Sepal.Width -0.02386443
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# Petal.Length -0.35076451
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# Petal.Width -0.77971954
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#
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# $virginica
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# 5 x 1 sparse Matrix of class "dgCMatrix"
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# s0
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# -3.93844796
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# Sepal.Length 0.13538675
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# Sepal.Width 0.02386443
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# Petal.Length 0.35076451
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# Petal.Width 0.77971954
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#
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#' logit = glmnet(iris.x, iris.y, family="binomial", alpha=0, lambda=0.5)
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#' coef(logit)
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#
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# 5 x 1 sparse Matrix of class "dgCMatrix"
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# s0
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# (Intercept) -6.0824412
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# Sepal.Length 0.2458260
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# Sepal.Width 0.1642093
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# Petal.Length 0.4759487
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# Petal.Width 1.0383948
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#
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# nolint end
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# Test multinomial logistic regression againt two classes
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df <- suppressWarnings(createDataFrame(iris))
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training <- df[df$Species %in% c("versicolor", "virginica"), ]
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model <- spark.logit(training, Species ~ ., regParam = 0.5, family = "multinomial")
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summary <- summary(model)
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versicolorCoefsR <- c(3.94, -0.16, -0.02, -0.35, -0.78)
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virginicaCoefsR <- c(-3.94, 0.16, -0.02, 0.35, 0.78)
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versicolorCoefs <- summary$coefficients[, "versicolor"]
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virginicaCoefs <- summary$coefficients[, "virginica"]
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expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
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expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
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# Test binomial logistic regression againt two classes
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model <- spark.logit(training, Species ~ ., regParam = 0.5)
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summary <- summary(model)
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coefsR <- c(-6.08, 0.25, 0.16, 0.48, 1.04)
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coefs <- summary$coefficients[, "Estimate"]
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expect_true(all(abs(coefsR - coefs) < 0.1))
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# Test prediction with string label
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prediction <- predict(model, training)
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expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "character")
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expected <- c("versicolor", "versicolor", "virginica", "versicolor", "versicolor",
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"versicolor", "versicolor", "versicolor", "versicolor", "versicolor")
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expect_equal(as.list(take(select(prediction, "prediction"), 10))[[1]], expected)
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# Test prediction with numeric label
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label <- c(0.0, 0.0, 0.0, 1.0, 1.0)
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feature <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776)
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data <- as.data.frame(cbind(label, feature))
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df <- createDataFrame(data)
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model <- spark.logit(df, label ~ feature)
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prediction <- collect(select(predict(model, df), "prediction"))
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expect_equal(sort(prediction$prediction), c("0.0", "0.0", "0.0", "1.0", "1.0"))
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# Test prediction with weightCol
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weight <- c(2.0, 2.0, 2.0, 1.0, 1.0)
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data2 <- as.data.frame(cbind(label, feature, weight))
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df2 <- createDataFrame(data2)
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model2 <- spark.logit(df2, label ~ feature, weightCol = "weight")
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prediction2 <- collect(select(predict(model2, df2), "prediction"))
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expect_equal(sort(prediction2$prediction), c("0.0", "0.0", "0.0", "0.0", "0.0"))
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})
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test_that("spark.mlp", {
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df <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
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source = "libsvm")
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model <- spark.mlp(df, label ~ features, blockSize = 128, layers = c(4, 5, 4, 3),
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solver = "l-bfgs", maxIter = 100, tol = 0.5, stepSize = 1, seed = 1)
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# Test summary method
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summary <- summary(model)
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expect_equal(summary$numOfInputs, 4)
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expect_equal(summary$numOfOutputs, 3)
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expect_equal(summary$layers, c(4, 5, 4, 3))
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expect_equal(length(summary$weights), 64)
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expect_equal(head(summary$weights, 5), list(-0.878743, 0.2154151, -1.16304, -0.6583214, 1.009825),
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tolerance = 1e-6)
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# Test predict method
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mlpTestDF <- df
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mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
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expect_equal(head(mlpPredictions$prediction, 6), c("1.0", "0.0", "0.0", "0.0", "0.0", "0.0"))
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# Test model save/load
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modelPath <- tempfile(pattern = "spark-mlp", 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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summary2 <- summary(model2)
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expect_equal(summary2$numOfInputs, 4)
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expect_equal(summary2$numOfOutputs, 3)
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expect_equal(summary2$layers, c(4, 5, 4, 3))
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expect_equal(length(summary2$weights), 64)
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unlink(modelPath)
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# Test default parameter
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model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3))
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mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
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expect_equal(head(mlpPredictions$prediction, 10),
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c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
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# Test illegal parameter
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expect_error(spark.mlp(df, label ~ features, layers = NULL),
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"layers must be a integer vector with length > 1.")
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expect_error(spark.mlp(df, label ~ features, layers = c()),
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"layers must be a integer vector with length > 1.")
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expect_error(spark.mlp(df, label ~ features, layers = c(3)),
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"layers must be a integer vector with length > 1.")
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# Test random seed
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# default seed
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model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3), maxIter = 10)
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mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
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expect_equal(head(mlpPredictions$prediction, 10),
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c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
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# seed equals 10
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model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3), maxIter = 10, seed = 10)
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mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
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expect_equal(head(mlpPredictions$prediction, 10),
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c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
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# test initialWeights
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model <- spark.mlp(df, label ~ features, layers = c(4, 3), initialWeights =
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c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9))
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mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
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expect_equal(head(mlpPredictions$prediction, 10),
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c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
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# Test formula works well
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df <- suppressWarnings(createDataFrame(iris))
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model <- spark.mlp(df, Species ~ Sepal_Length + Sepal_Width + Petal_Length + Petal_Width,
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layers = c(4, 3))
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summary <- summary(model)
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expect_equal(summary$numOfInputs, 4)
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expect_equal(summary$numOfOutputs, 3)
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expect_equal(summary$layers, c(4, 3))
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expect_equal(length(summary$weights), 15)
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})
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test_that("spark.naiveBayes", {
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# R code to reproduce the result.
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# We do not support instance weights yet. So we ignore the frequencies.
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#
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#' library(e1071)
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#' t <- as.data.frame(Titanic)
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#' t1 <- t[t$Freq > 0, -5]
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#' m <- naiveBayes(Survived ~ ., data = t1)
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#' m
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#' predict(m, t1)
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#
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# -- output of 'm'
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#
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# A-priori probabilities:
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# Y
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# No Yes
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# 0.4166667 0.5833333
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#
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# Conditional probabilities:
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# Class
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# Y 1st 2nd 3rd Crew
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# No 0.2000000 0.2000000 0.4000000 0.2000000
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# Yes 0.2857143 0.2857143 0.2857143 0.1428571
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#
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# Sex
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# Y Male Female
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# No 0.5 0.5
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# Yes 0.5 0.5
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#
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# Age
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# Y Child Adult
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# No 0.2000000 0.8000000
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# Yes 0.4285714 0.5714286
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#
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# -- output of 'predict(m, t1)'
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#
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# Yes Yes Yes Yes No No Yes Yes No No Yes Yes Yes Yes Yes Yes Yes Yes No No Yes Yes No No
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#
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t <- as.data.frame(Titanic)
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t1 <- t[t$Freq > 0, -5]
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df <- suppressWarnings(createDataFrame(t1))
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m <- spark.naiveBayes(df, Survived ~ ., smoothing = 0.0)
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s <- summary(m)
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expect_equal(as.double(s$apriori[1, "Yes"]), 0.5833333, tolerance = 1e-6)
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expect_equal(sum(s$apriori), 1)
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expect_equal(as.double(s$tables["Yes", "Age_Adult"]), 0.5714286, tolerance = 1e-6)
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p <- collect(select(predict(m, df), "prediction"))
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expect_equal(p$prediction, c("Yes", "Yes", "Yes", "Yes", "No", "No", "Yes", "Yes", "No", "No",
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"Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "No",
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"Yes", "Yes", "No", "No"))
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# Test model save/load
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modelPath <- tempfile(pattern = "spark-naiveBayes", fileext = ".tmp")
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write.ml(m, modelPath)
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expect_error(write.ml(m, modelPath))
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write.ml(m, modelPath, overwrite = TRUE)
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m2 <- read.ml(modelPath)
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s2 <- summary(m2)
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expect_equal(s$apriori, s2$apriori)
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expect_equal(s$tables, s2$tables)
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unlink(modelPath)
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# Test e1071::naiveBayes
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if (requireNamespace("e1071", quietly = TRUE)) {
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expect_error(m <- e1071::naiveBayes(Survived ~ ., data = t1), NA)
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expect_equal(as.character(predict(m, t1[1, ])), "Yes")
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}
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# Test numeric response variable
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t1$NumericSurvived <- ifelse(t1$Survived == "No", 0, 1)
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t2 <- t1[-4]
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df <- suppressWarnings(createDataFrame(t2))
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m <- spark.naiveBayes(df, NumericSurvived ~ ., smoothing = 0.0)
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s <- summary(m)
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expect_equal(as.double(s$apriori[1, 1]), 0.5833333, tolerance = 1e-6)
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expect_equal(sum(s$apriori), 1)
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expect_equal(as.double(s$tables[1, "Age_Adult"]), 0.5714286, tolerance = 1e-6)
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})
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sparkR.session.stop()
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