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Add test for logistic
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‎tests/testthat/_snaps/mlclassificationlogisticmultinomial/data-split-1.svg‎

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‎tests/testthat/_snaps/mlclassificationlogisticmultinomial/data-split.svg‎ renamed to ‎tests/testthat/_snaps/mlclassificationlogisticmultinomial/data-split-2.svg‎

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‎tests/testthat/test-mlclassificationlogisticmultinomial.R‎

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@@ -1,6 +1,105 @@
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context("Machine Learning Logistic / Multinomial Regression Classification")
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# Test fixed model #############################################################
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# Test logistic regression model ############################################
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options <- initMlOptions("mlClassificationLogisticMultinomial")
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options$addIndicator <- FALSE
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options$addPredictions <- FALSE
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options$coefTable <- TRUE
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options$coefTableConfInt <- TRUE
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options$classProportionsTable <- TRUE
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options$holdoutData <- "holdoutManual"
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options$modelOptimization <- "manual"
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options$modelValid <- "validationManual"
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options$predictionsColumn <- ""
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options$predictors <- c("x", "y")
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options$predictors.types <- rep("scale", 2)
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options$saveModel <- FALSE
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options$savePath <- ""
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options$setSeed <- TRUE
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options$target <- "color"
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options$target.types <- "nominal"
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options$testDataManual <- 0.2
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options$testIndicatorColumn <- ""
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options$testSetIndicatorVariable <- ""
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options$validationDataManual <- 0.2
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options$validationMeasures <- TRUE
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options$tableShap <- TRUE
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options$fromIndex <- 1
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options$toIndex <- 5
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options$featureImportanceTable <- TRUE
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options$seed <- 2
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set.seed(1)
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results <- jaspTools::runAnalysis("mlClassificationLogisticMultinomial", "spiral.csv", options)
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test_that("Class Proportions table results match", {
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table <- results[["results"]][["classProportionsTable"]][["data"]]
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jaspTools::expect_equal_tables(table,
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list(0.5, "Black", 0.575, 0.48125, 0.5, "Red", 0.425, 0.51875))
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})
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test_that("Model Summary: Logistic Regression Classification table results match", {
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table <- results[["results"]][["classificationTable"]][["data"]]
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jaspTools::expect_equal_tables(table,
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list("Binomial", "Logit", 40, 160, 0.675))
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})
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test_that("Regression Coefficients table results match", {
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table <- results[["results"]][["coefTable"]][["data"]]
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jaspTools::expect_equal_tables(table,
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list(0.0784909051640228, -0.241691087574101, 0.630847847956443, 0.163341057208965,
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0.400211265504598, "(Intercept)", 0.480533838247466, -0.0733280693358763,
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-0.389515022216725, 0.647229030486731, 0.160239637348256, 0.241355688318239,
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"x", -0.457615047995329, -0.520574613112014, -0.864252780481388,
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0.00221841580161717, 0.170160350681228, -0.194437228429846,
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"y", -3.0593179376272))
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})
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test_that("Confusion Matrix table results match", {
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table <- results[["results"]][["confusionTable"]][["data"]]
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jaspTools::expect_equal_tables(table,
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list("Observed", "Black", 14, 9, "", "Red", 4, 13))
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})
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test_that("Feature Importance Metrics table results match", {
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table <- results[["results"]][["featureImportanceTable"]][["data"]]
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jaspTools::expect_equal_tables(table,
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list(0.49553121577218, "y", 0.367228915662651, "x"))
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})
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test_that("Data Split plot matches", {
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plotName <- results[["results"]][["plotDataSplit"]][["data"]]
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testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
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jaspTools::expect_equal_plots(testPlot, "data-split-1")
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})
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test_that("Additive Explanations for Predictions of Test Set Cases table results match", {
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table <- results[["results"]][["tableShap"]][["data"]]
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jaspTools::expect_equal_tables(table,
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list(0.518749999999986, 1, "Red (0.511)", -0.0104280206091216, 0.00223442804446772,
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0.481250000000014, 2, "Black (0.505)", 0.0113934073181472, 0.0126826432048778,
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0.518749999999986, 3, "Red (0.694)", -0.0215827660760237, 0.196894941069013,
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0.518749999999986, 4, "Red (0.707)", -0.0148699072618508, 0.203086871619157,
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0.481250000000014, 5, "Black (0.584)", 0.00703631455756359,
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0.0957186107999987))
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})
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test_that("Model Performance Metrics table results match", {
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table <- results[["results"]][["validationMeasures"]][["data"]]
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jaspTools::expect_equal_tables(table,
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list(0.675, 0.686700767263427, 0.682926829268293, 0.222222222222222,
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0.391304347826087, 0.409090909090909, 0.235294117647059, "Black",
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0.371036713180216, 0.590909090909091, 0.777777777777778, 0.608695652173913,
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0.45, 23, 0.764705882352941, 0.823529411764706, 0.675, 0.686700767263427,
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0.666666666666667, 0.409090909090909, 0.235294117647059, 0.222222222222222,
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0.391304347826087, "Red", 0.371036713180216, 0.777777777777778,
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0.590909090909091, 0.764705882352941, 0.55, 17, 0.608695652173913,
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0.590909090909091, 0.675, 0.686700767263427, 0.676016260162602,
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0.315656565656566, 0.313299232736573, 0.315656565656566, 0.313299232736573,
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"Average / Total", 0.371036713180216, 0.684343434343434, 0.698358585858586,
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0.675, 1, 40, 0.686700767263427, 0.707219251336898))
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})
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# Test multinomial regression model ############################################
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options <- initMlOptions("mlClassificationLogisticMultinomial")
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options$addIndicator <- FALSE
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options$addPredictions <- FALSE
@@ -88,7 +187,7 @@ test_that("Feature Importance Metrics table results match", {
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test_that("Data Split plot matches", {
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plotName <- results[["results"]][["plotDataSplit"]][["data"]]
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testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
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jaspTools::expect_equal_plots(testPlot, "data-split")
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jaspTools::expect_equal_plots(testPlot, "data-split-2")
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})
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test_that("Additive Explanations for Predictions of Test Set Cases table results match", {

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