spark-instrumented-optimizer/R/pkg/inst/tests/test_mllib.R
Eric Liang 8d5bb5283c [SPARK-9391] [ML] Support minus, dot, and intercept operators in SparkR RFormula
Adds '.', '-', and intercept parsing to RFormula. Also splits RFormulaParser into a separate file.

Umbrella design doc here: https://docs.google.com/document/d/10NZNSEurN2EdWM31uFYsgayIPfCFHiuIu3pCWrUmP_c/edit?usp=sharing

mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #7707 from ericl/string-features-2 and squashes the following commits:

8588625 [Eric Liang] exclude complex types for .
8106ffe [Eric Liang] comments
a9350bb [Eric Liang] s/var/val
9c50d4d [Eric Liang] Merge branch 'string-features' into string-features-2
581afb2 [Eric Liang] Merge branch 'master' into string-features
08ae539 [Eric Liang] Merge branch 'string-features' into string-features-2
f99131a [Eric Liang] comments
cecec43 [Eric Liang] Merge branch 'string-features' into string-features-2
0bf3c26 [Eric Liang] update docs
4592df2 [Eric Liang] intercept supports
7412a2e [Eric Liang] Fri Jul 24 14:56:51 PDT 2015
3cf848e [Eric Liang] fix the parser
0556c2b [Eric Liang] Merge branch 'string-features' into string-features-2
c302a2c [Eric Liang] fix tests
9d1ac82 [Eric Liang] Merge remote-tracking branch 'upstream/master' into string-features
e713da3 [Eric Liang] comments
cd231a9 [Eric Liang] Wed Jul 22 17:18:44 PDT 2015
4d79193 [Eric Liang] revert to seq + distinct
169a085 [Eric Liang] tweak functional test
a230a47 [Eric Liang] Merge branch 'master' into string-features
72bd6f3 [Eric Liang] fix merge
d841cec [Eric Liang] Merge branch 'master' into string-features
5b2c4a2 [Eric Liang] Mon Jul 20 18:45:33 PDT 2015
b01c7c5 [Eric Liang] add test
8a637db [Eric Liang] encoder wip
a1d03f4 [Eric Liang] refactor into estimator
2015-07-28 14:16:57 -07:00

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R

#
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# The ASF licenses this file to You under the Apache License, Version 2.0
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#
library(testthat)
context("MLlib functions")
# Tests for MLlib functions in SparkR
sc <- sparkR.init()
sqlContext <- sparkRSQL.init(sc)
test_that("glm and predict", {
training <- createDataFrame(sqlContext, iris)
test <- select(training, "Sepal_Length")
model <- glm(Sepal_Width ~ Sepal_Length, training, family = "gaussian")
prediction <- predict(model, test)
expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
})
test_that("predictions match with native glm", {
training <- createDataFrame(sqlContext, iris)
model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})
test_that("dot minus and intercept vs native glm", {
training <- createDataFrame(sqlContext, iris)
model <- glm(Sepal_Width ~ . - Species + 0, data = training)
vals <- collect(select(predict(model, training), "prediction"))
rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
})