922338812c
This integrates the Interaction feature transformer with SparkR R formula support (i.e. support `:`). To generate reasonable ML attribute names for feature interactions, it was necessary to add the ability to read attribute the original attribute names back from `StructField`, and also to specify custom group prefixes in `VectorAssembler`. This also has the side-benefit of cleaning up the double-underscores in the attributes generated for non-interaction terms. mengxr Author: Eric Liang <ekl@databricks.com> Closes #8830 from ericl/interaction-2.
70 lines
2.8 KiB
R
70 lines
2.8 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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})
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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))
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coefs <- as.vector(stats$coefficients)
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rCoefs <- as.vector(coef(glm(Sepal.Width ~ Sepal.Length + Species, data = iris)))
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expect_true(all(abs(rCoefs - coefs) < 1e-6))
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expect_true(all(
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as.character(stats$features) ==
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c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
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})
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