75e05a5a96
* SparkR glm supports families and link functions which match R's signature for family. * SparkR glm API refactor. The comparative standard of the new API is R glm, so I only expose the arguments that R glm supports: ```formula, family, data, epsilon and maxit```. * This PR is focus on glm() and predict(), summary statistics will be done in a separate PR after this get in. * This PR depends on #12287 which make GLMs support link prediction at Scala side. After that merged, I will add more tests for predict() to this PR. Unit tests. cc mengxr jkbradley hhbyyh Author: Yanbo Liang <ybliang8@gmail.com> Closes #12294 from yanboliang/spark-12566.
215 lines
8.1 KiB
R
215 lines
8.1 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 functions")
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# Tests for MLlib functions in SparkR
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sc <- sparkR.init()
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sqlContext <- sparkRSQL.init(sc)
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test_that("formula of glm", {
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training <- suppressWarnings(createDataFrame(sqlContext, iris))
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# dot minus and intercept vs native glm
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model <- glm(Sepal_Width ~ . - Species + 0, data = training)
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vals <- collect(select(predict(model, training), "prediction"))
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rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# feature interaction vs native glm
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model <- glm(Sepal_Width ~ Species:Sepal_Length, data = training)
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vals <- collect(select(predict(model, training), "prediction"))
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rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# glm should work with long formula
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training <- suppressWarnings(createDataFrame(sqlContext, iris))
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training$LongLongLongLongLongName <- training$Sepal_Width
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training$VeryLongLongLongLonLongName <- training$Sepal_Length
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training$AnotherLongLongLongLongName <- training$Species
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model <- glm(LongLongLongLongLongName ~ VeryLongLongLongLonLongName + AnotherLongLongLongLongName,
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data = training)
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vals <- collect(select(predict(model, training), "prediction"))
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rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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})
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test_that("glm and predict", {
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training <- suppressWarnings(createDataFrame(sqlContext, iris))
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# gaussian family
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model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
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prediction <- predict(model, training)
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expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
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vals <- collect(select(prediction, "prediction"))
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rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# poisson family
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model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training,
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family = poisson(link = identity))
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prediction <- predict(model, training)
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expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
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vals <- collect(select(prediction, "prediction"))
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rVals <- suppressWarnings(predict(glm(Sepal.Width ~ Sepal.Length + Species,
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data = iris, family = poisson(link = identity)), iris))
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expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
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# Test stats::predict is working
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x <- rnorm(15)
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y <- x + rnorm(15)
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expect_equal(length(predict(lm(y ~ x))), 15)
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})
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test_that("kmeans", {
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newIris <- iris
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newIris$Species <- NULL
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training <- suppressWarnings(createDataFrame(sqlContext, newIris))
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# Cache the DataFrame here to work around the bug SPARK-13178.
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cache(training)
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take(training, 1)
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model <- kmeans(x = training, centers = 2)
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sample <- take(select(predict(model, training), "prediction"), 1)
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expect_equal(typeof(sample$prediction), "integer")
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expect_equal(sample$prediction, 1)
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# Test stats::kmeans is working
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statsModel <- kmeans(x = newIris, centers = 2)
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expect_equal(sort(unique(statsModel$cluster)), c(1, 2))
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# Test fitted works on KMeans
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fitted.model <- fitted(model)
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expect_equal(sort(collect(distinct(select(fitted.model, "prediction")))$prediction), c(0, 1))
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# Test summary works on KMeans
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summary.model <- summary(model)
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cluster <- summary.model$cluster
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expect_equal(sort(collect(distinct(select(cluster, "prediction")))$prediction), c(0, 1))
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})
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test_that("naiveBayes", {
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# R code to reproduce the result.
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# We do not support instance weights yet. So we ignore the frequencies.
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#
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#' library(e1071)
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#' t <- as.data.frame(Titanic)
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#' t1 <- t[t$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$Freq > 0, -5]
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df <- suppressWarnings(createDataFrame(sqlContext, t1))
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m <- naiveBayes(Survived ~ ., data = df)
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s <- summary(m)
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expect_equal(as.double(s$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 e1071::naiveBayes
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if (requireNamespace("e1071", quietly = TRUE)) {
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expect_that(m <- e1071::naiveBayes(Survived ~ ., data = t1), not(throws_error()))
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expect_equal(as.character(predict(m, t1[1, ])), "Yes")
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}
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})
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test_that("survreg", {
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# R code to reproduce the result.
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#
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#' rData <- list(time = c(4, 3, 1, 1, 2, 2, 3), status = c(1, 1, 1, 0, 1, 1, 0),
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#' x = c(0, 2, 1, 1, 1, 0, 0), sex = c(0, 0, 0, 0, 1, 1, 1))
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#' library(survival)
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#' model <- survreg(Surv(time, status) ~ x + sex, rData)
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#' summary(model)
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#' predict(model, data)
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#
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# -- output of 'summary(model)'
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#
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# Value Std. Error z p
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# (Intercept) 1.315 0.270 4.88 1.07e-06
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# x -0.190 0.173 -1.10 2.72e-01
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# sex -0.253 0.329 -0.77 4.42e-01
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# Log(scale) -1.160 0.396 -2.93 3.41e-03
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#
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# -- output of 'predict(model, data)'
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#
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# 1 2 3 4 5 6 7
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# 3.724591 2.545368 3.079035 3.079035 2.390146 2.891269 2.891269
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#
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data <- list(list(4, 1, 0, 0), list(3, 1, 2, 0), list(1, 1, 1, 0),
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list(1, 0, 1, 0), list(2, 1, 1, 1), list(2, 1, 0, 1), list(3, 0, 0, 1))
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df <- createDataFrame(sqlContext, data, c("time", "status", "x", "sex"))
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model <- survreg(Surv(time, status) ~ x + sex, df)
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stats <- summary(model)
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coefs <- as.vector(stats$coefficients[, 1])
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rCoefs <- c(1.3149571, -0.1903409, -0.2532618, -1.1599800)
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expect_equal(coefs, rCoefs, tolerance = 1e-4)
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expect_true(all(
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rownames(stats$coefficients) ==
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c("(Intercept)", "x", "sex", "Log(scale)")))
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p <- collect(select(predict(model, df), "prediction"))
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expect_equal(p$prediction, c(3.724591, 2.545368, 3.079035, 3.079035,
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2.390146, 2.891269, 2.891269), tolerance = 1e-4)
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# Test survival::survreg
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if (requireNamespace("survival", quietly = TRUE)) {
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rData <- list(time = c(4, 3, 1, 1, 2, 2, 3), status = c(1, 1, 1, 0, 1, 1, 0),
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x = c(0, 2, 1, 1, 1, 0, 0), sex = c(0, 0, 0, 0, 1, 1, 1))
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expect_that(
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model <- survival::survreg(formula = survival::Surv(time, status) ~ x + sex, data = rData),
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not(throws_error()))
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expect_equal(predict(model, rData)[[1]], 3.724591, tolerance = 1e-4)
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}
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})
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