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author | Yanbo Liang <ybliang8@gmail.com> | 2016-12-07 00:31:11 -0800 |
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committer | Yanbo Liang <ybliang8@gmail.com> | 2016-12-07 00:31:11 -0800 |
commit | 90b59d1bf262b41c3a5f780697f504030f9d079c (patch) | |
tree | b52ec6f3b7a43040557ec882225f98aeccd336a6 /R/pkg/inst/tests/testthat/test_mllib.R | |
parent | 5c6bcdbda4dd23bbd112a7395cd9d1cfd04cf4bb (diff) | |
download | spark-90b59d1bf262b41c3a5f780697f504030f9d079c.tar.gz spark-90b59d1bf262b41c3a5f780697f504030f9d079c.tar.bz2 spark-90b59d1bf262b41c3a5f780697f504030f9d079c.zip |
[SPARK-18686][SPARKR][ML] Several cleanup and improvements for spark.logit.
## What changes were proposed in this pull request?
Several cleanup and improvements for ```spark.logit```:
* ```summary``` should return coefficients matrix, and should output labels for each class if the model is multinomial logistic regression model.
* ```summary``` should not return ```areaUnderROC, roc, pr, ...```, since most of them are DataFrame which are less important for R users. Meanwhile, these metrics ignore instance weights (setting all to 1.0) which will be changed in later Spark version. In case it will introduce breaking changes, we do not expose them currently.
* SparkR test improvement: comparing the training result with native R glmnet.
* Remove argument ```aggregationDepth``` from ```spark.logit```, since it's an expert Param(related with Spark architecture and job execution) that would be used rarely by R users.
## How was this patch tested?
Unit tests.
The ```summary``` output after this change:
multinomial logistic regression:
```
> df <- suppressWarnings(createDataFrame(iris))
> model <- spark.logit(df, Species ~ ., regParam = 0.5)
> summary(model)
$coefficients
versicolor virginica setosa
(Intercept) 1.514031 -2.609108 1.095077
Sepal_Length 0.02511006 0.2649821 -0.2900921
Sepal_Width -0.5291215 -0.02016446 0.549286
Petal_Length 0.03647411 0.1544119 -0.190886
Petal_Width 0.000236092 0.4195804 -0.4198165
```
binomial logistic regression:
```
> df <- suppressWarnings(createDataFrame(iris))
> training <- df[df$Species %in% c("versicolor", "virginica"), ]
> model <- spark.logit(training, Species ~ ., regParam = 0.5)
> summary(model)
$coefficients
Estimate
(Intercept) -6.053815
Sepal_Length 0.2449379
Sepal_Width 0.1648321
Petal_Length 0.4730718
Petal_Width 1.031947
```
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #16117 from yanboliang/spark-18686.
Diffstat (limited to 'R/pkg/inst/tests/testthat/test_mllib.R')
-rw-r--r-- | R/pkg/inst/tests/testthat/test_mllib.R | 183 |
1 files changed, 128 insertions, 55 deletions
diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R index dcfeeb4cd2..0802a2ae48 100644 --- a/R/pkg/inst/tests/testthat/test_mllib.R +++ b/R/pkg/inst/tests/testthat/test_mllib.R @@ -635,68 +635,141 @@ test_that("spark.isotonicRegression", { }) test_that("spark.logit", { - # test binary logistic regression - label <- c(0.0, 0.0, 0.0, 1.0, 1.0) - feature <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776) - binary_data <- as.data.frame(cbind(label, feature)) - binary_df <- createDataFrame(binary_data) - - blr_model <- spark.logit(binary_df, label ~ feature, thresholds = 1.0) - blr_predict <- collect(select(predict(blr_model, binary_df), "prediction")) - expect_equal(blr_predict$prediction, c("0.0", "0.0", "0.0", "0.0", "0.0")) - blr_model1 <- spark.logit(binary_df, label ~ feature, thresholds = 0.0) - blr_predict1 <- collect(select(predict(blr_model1, binary_df), "prediction")) - expect_equal(blr_predict1$prediction, c("1.0", "1.0", "1.0", "1.0", "1.0")) - - # test summary of binary logistic regression - blr_summary <- summary(blr_model) - blr_fmeasure <- collect(select(blr_summary$fMeasureByThreshold, "threshold", "F-Measure")) - expect_equal(blr_fmeasure$threshold, c(0.6565513, 0.6214563, 0.3325291, 0.2115995, 0.1778653), - tolerance = 1e-4) - expect_equal(blr_fmeasure$"F-Measure", c(0.6666667, 0.5000000, 0.8000000, 0.6666667, 0.5714286), - tolerance = 1e-4) - blr_precision <- collect(select(blr_summary$precisionByThreshold, "threshold", "precision")) - expect_equal(blr_precision$precision, c(1.0000000, 0.5000000, 0.6666667, 0.5000000, 0.4000000), - tolerance = 1e-4) - blr_recall <- collect(select(blr_summary$recallByThreshold, "threshold", "recall")) - expect_equal(blr_recall$recall, c(0.5000000, 0.5000000, 1.0000000, 1.0000000, 1.0000000), - tolerance = 1e-4) + # R code to reproduce the result. + # nolint start + #' library(glmnet) + #' iris.x = as.matrix(iris[, 1:4]) + #' iris.y = as.factor(as.character(iris[, 5])) + #' logit = glmnet(iris.x, iris.y, family="multinomial", alpha=0, lambda=0.5) + #' coef(logit) + # + # $setosa + # 5 x 1 sparse Matrix of class "dgCMatrix" + # s0 + # 1.0981324 + # Sepal.Length -0.2909860 + # Sepal.Width 0.5510907 + # Petal.Length -0.1915217 + # Petal.Width -0.4211946 + # + # $versicolor + # 5 x 1 sparse Matrix of class "dgCMatrix" + # s0 + # 1.520061e+00 + # Sepal.Length 2.524501e-02 + # Sepal.Width -5.310313e-01 + # Petal.Length 3.656543e-02 + # Petal.Width -3.144464e-05 + # + # $virginica + # 5 x 1 sparse Matrix of class "dgCMatrix" + # s0 + # -2.61819385 + # Sepal.Length 0.26574097 + # Sepal.Width -0.02005932 + # Petal.Length 0.15495629 + # Petal.Width 0.42122607 + # nolint end - # test model save and read - modelPath <- tempfile(pattern = "spark-logisticRegression", fileext = ".tmp") - write.ml(blr_model, modelPath) - expect_error(write.ml(blr_model, modelPath)) - write.ml(blr_model, modelPath, overwrite = TRUE) - blr_model2 <- read.ml(modelPath) - blr_predict2 <- collect(select(predict(blr_model2, binary_df), "prediction")) - expect_equal(blr_predict$prediction, blr_predict2$prediction) - expect_error(summary(blr_model2)) + # Test multinomial logistic regression againt three classes + df <- suppressWarnings(createDataFrame(iris)) + model <- spark.logit(df, Species ~ ., regParam = 0.5) + summary <- summary(model) + versicolorCoefsR <- c(1.52, 0.03, -0.53, 0.04, 0.00) + virginicaCoefsR <- c(-2.62, 0.27, -0.02, 0.16, 0.42) + setosaCoefsR <- c(1.10, -0.29, 0.55, -0.19, -0.42) + versicolorCoefs <- unlist(summary$coefficients[, "versicolor"]) + virginicaCoefs <- unlist(summary$coefficients[, "virginica"]) + setosaCoefs <- unlist(summary$coefficients[, "setosa"]) + expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1)) + expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1)) + expect_true(all(abs(setosaCoefs - setosaCoefs) < 0.1)) + + # Test model save and load + modelPath <- tempfile(pattern = "spark-logit", fileext = ".tmp") + write.ml(model, modelPath) + expect_error(write.ml(model, modelPath)) + write.ml(model, modelPath, overwrite = TRUE) + model2 <- read.ml(modelPath) + coefs <- summary(model)$coefficients + coefs2 <- summary(model2)$coefficients + expect_equal(coefs, coefs2) unlink(modelPath) - # test prediction label as text - training <- suppressWarnings(createDataFrame(iris)) - binomial_training <- training[training$Species %in% c("versicolor", "virginica"), ] - binomial_model <- spark.logit(binomial_training, Species ~ Sepal_Length + Sepal_Width) - prediction <- predict(binomial_model, binomial_training) + # R code to reproduce the result. + # nolint start + #' library(glmnet) + #' iris2 <- iris[iris$Species %in% c("versicolor", "virginica"), ] + #' iris.x = as.matrix(iris2[, 1:4]) + #' iris.y = as.factor(as.character(iris2[, 5])) + #' logit = glmnet(iris.x, iris.y, family="multinomial", alpha=0, lambda=0.5) + #' coef(logit) + # + # $versicolor + # 5 x 1 sparse Matrix of class "dgCMatrix" + # s0 + # 3.93844796 + # Sepal.Length -0.13538675 + # Sepal.Width -0.02386443 + # Petal.Length -0.35076451 + # Petal.Width -0.77971954 + # + # $virginica + # 5 x 1 sparse Matrix of class "dgCMatrix" + # s0 + # -3.93844796 + # Sepal.Length 0.13538675 + # Sepal.Width 0.02386443 + # Petal.Length 0.35076451 + # Petal.Width 0.77971954 + # + #' logit = glmnet(iris.x, iris.y, family="binomial", alpha=0, lambda=0.5) + #' coef(logit) + # + # 5 x 1 sparse Matrix of class "dgCMatrix" + # s0 + # (Intercept) -6.0824412 + # Sepal.Length 0.2458260 + # Sepal.Width 0.1642093 + # Petal.Length 0.4759487 + # Petal.Width 1.0383948 + # + # nolint end + + # Test multinomial logistic regression againt two classes + df <- suppressWarnings(createDataFrame(iris)) + training <- df[df$Species %in% c("versicolor", "virginica"), ] + model <- spark.logit(training, Species ~ ., regParam = 0.5, family = "multinomial") + summary <- summary(model) + versicolorCoefsR <- c(3.94, -0.16, -0.02, -0.35, -0.78) + virginicaCoefsR <- c(-3.94, 0.16, -0.02, 0.35, 0.78) + versicolorCoefs <- unlist(summary$coefficients[, "versicolor"]) + virginicaCoefs <- unlist(summary$coefficients[, "virginica"]) + expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1)) + expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1)) + + # Test binomial logistic regression againt two classes + model <- spark.logit(training, Species ~ ., regParam = 0.5) + summary <- summary(model) + coefsR <- c(-6.08, 0.25, 0.16, 0.48, 1.04) + coefs <- unlist(summary$coefficients[, "Estimate"]) + expect_true(all(abs(coefsR - coefs) < 0.1)) + + # Test prediction with string label + prediction <- predict(model, training) expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "character") - expected <- c("virginica", "virginica", "virginica", "versicolor", "virginica", - "versicolor", "virginica", "versicolor", "virginica", "versicolor") + expected <- c("versicolor", "versicolor", "virginica", "versicolor", "versicolor", + "versicolor", "versicolor", "versicolor", "versicolor", "versicolor") expect_equal(as.list(take(select(prediction, "prediction"), 10))[[1]], expected) - # test multinomial logistic regression - label <- c(0.0, 1.0, 2.0, 0.0, 0.0) - feature1 <- c(4.845940, 5.64480, 7.430381, 6.464263, 5.555667) - feature2 <- c(2.941319, 2.614812, 2.162451, 3.339474, 2.970987) - feature3 <- c(1.322733, 1.348044, 3.861237, 9.686976, 3.447130) - feature4 <- c(1.3246388, 0.5510444, 0.9225810, 1.2147881, 1.6020842) - data <- as.data.frame(cbind(label, feature1, feature2, feature3, feature4)) + # Test prediction with numeric label + label <- c(0.0, 0.0, 0.0, 1.0, 1.0) + feature <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776) + data <- as.data.frame(cbind(label, feature)) df <- createDataFrame(data) - - model <- spark.logit(df, label ~., family = "multinomial", thresholds = c(0, 1, 1)) - predict1 <- collect(select(predict(model, df), "prediction")) - expect_equal(predict1$prediction, c("0.0", "0.0", "0.0", "0.0", "0.0")) - # Summary of multinomial logistic regression is not implemented yet - expect_error(summary(model)) + model <- spark.logit(df, label ~ feature) + prediction <- collect(select(predict(model, df), "prediction")) + expect_equal(prediction$prediction, c("0.0", "0.0", "1.0", "1.0", "0.0")) }) test_that("spark.gaussianMixture", { |