1a93323c5b
Added tests for function that are reported as masked, to make sure the base:: or stats:: function can be called.
For those we can't call, added them to SparkR programming guide.
It would seem to me `table, sample, subset, filter, cov` not working are not actually expected - I investigated/experimented with them but couldn't get them to work. It looks like as they are defined in base or stats they are missing the S3 generic, eg.
```
> methods("transform")
[1] transform,ANY-method transform.data.frame
[3] transform,DataFrame-method transform.default
see '?methods' for accessing help and source code
> methods("subset")
[1] subset.data.frame subset,DataFrame-method subset.default
[4] subset.matrix
see '?methods' for accessing help and source code
Warning message:
In .S3methods(generic.function, class, parent.frame()) :
function 'subset' appears not to be S3 generic; found functions that look like S3 methods
```
Any idea?
More information on masking:
http://www.ats.ucla.edu/stat/r/faq/referencing_objects.htm
http://www.sfu.ca/~sweldon/howTo/guide4.pdf
This is what the output doc looks like (minus css):
Author: felixcheung <felixcheung_m@hotmail.com>
Closes #9785 from felixcheung/rmasked.
116 lines
4.6 KiB
R
116 lines
4.6 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("glm and predict", {
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training <- createDataFrame(sqlContext, iris)
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test <- select(training, "Sepal_Length")
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model <- glm(Sepal_Width ~ Sepal_Length, training, family = "gaussian")
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prediction <- predict(model, test)
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expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
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# Test stats::predict is working
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x <- rnorm(15)
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y <- x + rnorm(15)
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expect_equal(length(predict(lm(y ~ x))), 15)
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})
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test_that("glm should work with long formula", {
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training <- createDataFrame(sqlContext, iris)
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training$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("predictions match with native glm", {
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training <- createDataFrame(sqlContext, iris)
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model <- glm(Sepal_Width ~ Sepal_Length + Species, 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("dot minus and intercept vs native glm", {
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training <- createDataFrame(sqlContext, iris)
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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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})
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test_that("feature interaction vs native glm", {
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training <- createDataFrame(sqlContext, iris)
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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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})
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test_that("summary coefficients match with native glm", {
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training <- createDataFrame(sqlContext, iris)
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stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training, solver = "normal"))
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coefs <- unlist(stats$coefficients)
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devianceResiduals <- unlist(stats$devianceResiduals)
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rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
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rCoefs <- unlist(rStats$coefficients)
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rDevianceResiduals <- c(-0.95096, 0.72918)
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expect_true(all(abs(rCoefs - coefs) < 1e-5))
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expect_true(all(abs(rDevianceResiduals - devianceResiduals) < 1e-5))
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expect_true(all(
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rownames(stats$coefficients) ==
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c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
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})
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test_that("summary coefficients match with native glm of family 'binomial'", {
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df <- createDataFrame(sqlContext, iris)
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training <- filter(df, df$Species != "setosa")
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stats <- summary(glm(Species ~ Sepal_Length + Sepal_Width, data = training,
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family = "binomial"))
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coefs <- as.vector(stats$coefficients[,1])
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rTraining <- iris[iris$Species %in% c("versicolor","virginica"),]
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rCoefs <- as.vector(coef(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
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family = binomial(link = "logit"))))
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expect_true(all(abs(rCoefs - coefs) < 1e-4))
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expect_true(all(
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rownames(stats$coefficients) ==
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c("(Intercept)", "Sepal_Length", "Sepal_Width")))
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})
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test_that("summary works on base GLM models", {
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baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris)
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baseSummary <- summary(baseModel)
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expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4)
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})
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