Bug fixes for release 0.95 (#461)

Author: koenderks

R/commonMachineLearningClassification.R

@@ -455,6 +455,9 @@
   plot$dependOn(options = c(.mlClassificationDependencies(options), "decisionBoundary", "pointsShown", "legendShown"))
   jaspResults[["decisionBoundary"]] <- plot
   if (!ready || length(options[["predictors"]]) < 2) {
+    if (length(options[["predictors"]]) == 1) {
+      plot$setError(gettext("Cannot create plot: You need at least two (numeric) features to create the decision boundary matrix. You have currently included only one feature."))
+    }
     return()
   }
   .classificationFillDecisionBoundary(dataset, options, jaspResults, plot, type)
@@ -520,9 +523,9 @@
   x_max <- xBreaks[length(xBreaks)]
   y_min <- yBreaks[1]
   y_max <- yBreaks[length(yBreaks)]
-  # Adjust the graining
-  hs <- min(c(diff(range(xBreaks)), diff(range(yBreaks)))) / 50
-  grid <- as.data.frame(expand.grid(seq(x_min, x_max, by = hs), seq(y_min, y_max, by = hs)))
+  xseq <- seq(x_min, x_max, length.out = 100)
+  yseq <- seq(y_min, y_max, length.out = 100)
+  grid <- as.data.frame(expand.grid(xseq, yseq))
   colnames(grid) <- colnames(predictors)
   classificationResult <- jaspResults[["classificationResult"]]$object
   if (type == "lda") {

R/commonMachineLearningClustering.R

@@ -476,7 +476,7 @@
   table$setData(clusterMeans)
 }
 
-.mlClusteringPlotDensities <- function(dataset, options, jaspResults, ready, position) {
+.mlClusteringPlotDensities <- function(dataset, options, jaspResults, ready, position, type) {
   if (!is.null(jaspResults[["clusterDensities"]]) || !options[["clusterDensityPlot"]]) {
     return()
   }
@@ -488,9 +488,15 @@
     return()
   }
   clusterResult <- jaspResults[["clusterResult"]]$object
+  predictions <- clusterResult[["pred.values"]]
+  ncolors <- clusterResult[["clusters"]]
+  if (type == "densitybased") {
+    ncolors <- ncolors + 1
+    predictions[predictions == 0] <- gettext("Noisepoint")
+  }
+  clusters <- as.factor(predictions)
   if (!options[["clusterDensityPlotSingleFigure"]]) {
     for (variable in unlist(options[["predictors"]])) {
-      clusters <- as.factor(clusterResult[["pred.values"]])
       xBreaks <- jaspGraphs::getPrettyAxisBreaks(dataset[[variable]], min.n = 4)
       plotData <- data.frame(
         cluster = clusters,
@@ -500,7 +506,7 @@
         ggplot2::geom_density(mapping = ggplot2::aes(fill = cluster), color = "black", alpha = 0.6) +
         ggplot2::scale_x_continuous(name = variable, breaks = xBreaks, limits = range(xBreaks)) +
         ggplot2::scale_y_continuous(name = gettext("Density")) +
-        ggplot2::scale_fill_manual(name = gettext("Cluster"), values = .mlColorScheme(length(levels(clusters)))) +
+        ggplot2::scale_fill_manual(name = gettext("Cluster"), values = .mlColorScheme(ncolors)) +
         jaspGraphs::geom_rangeframe() +
         jaspGraphs::themeJaspRaw(legend.position = "right") +
         ggplot2::theme(axis.ticks.y = ggplot2::element_blank(), axis.text.y = ggplot2::element_blank())
@@ -509,11 +515,11 @@
     }
   } else {
     dataList <- c(dataset[, options[["predictors"]]])
-    plotData <- data.frame(value = unlist(dataList), variable = rep(options[["predictors"]], lengths(dataList)), cluster = rep(clusterResult[["pred.values"]], length(options[["predictors"]])))
+    plotData <- data.frame(value = unlist(dataList), variable = rep(options[["predictors"]], lengths(dataList)), cluster = rep(predictions, length(options[["predictors"]])))
     xBreaks <- jaspGraphs::getPrettyAxisBreaks(plotData[["value"]])
     p <- ggplot2::ggplot(data = plotData, mapping = ggplot2::aes(x = value, y = factor(variable), height = ..density.., fill = factor(cluster))) +
       ggridges::geom_density_ridges(stat = "density", alpha = .6) +
-      ggplot2::scale_fill_manual(name = gettext("Cluster"), values = .mlColorScheme(length(unique(clusterResult[["pred.values"]])))) +
+      ggplot2::scale_fill_manual(name = gettext("Cluster"), values = .mlColorScheme(ncolors)) +
       ggplot2::scale_x_continuous(name = gettext("Value"), breaks = xBreaks, limits = range(xBreaks)) +
       ggplot2::scale_y_discrete(name = gettext("Feature")) +
       jaspGraphs::geom_rangeframe(sides = "b") +
@@ -523,7 +529,7 @@
   }
 }
 
-.mlClusteringPlotMeans <- function(dataset, options, jaspResults, ready, position) {
+.mlClusteringPlotMeans <- function(dataset, options, jaspResults, ready, position, type) {
   if (!is.null(jaspResults[["clusterMeans"]]) || !options[["clusterMeanPlot"]]) {
     return()
   }
@@ -536,8 +542,14 @@
   }
   clusterDataset <- data.frame(dataset[, options[["predictors"]], drop = FALSE])
   clusterResult <- jaspResults[["clusterResult"]]$object
+  predictions <- clusterResult[["pred.values"]]
+  ncolors <- clusterResult[["clusters"]]
+  if (type == "densitybased") {
+    ncolors <- ncolors + 1
+    predictions[predictions == 0] <- gettext("Noisepoint")
+  }
+  clusters <- as.factor(predictions)
   if (options[["clusterMeanPlotSingleFigure"]]) {
-    clusters <- as.factor(clusterResult[["pred.values"]])
     xBreaks <- c(1, (as.numeric(levels(clusters)) + 1) * length(options[["predictors"]]))
     clusterMeansData <- aggregate(clusterDataset, list(clusters), mean)
     clusterSdData <- aggregate(clusterDataset, list(clusters), sd)
@@ -578,14 +590,13 @@
     }
     p <- p + ggplot2::scale_x_continuous(name = NULL, breaks = xBreaks, labels = xLabels) +
       ggplot2::scale_y_continuous(name = gettext("Cluster Mean"), breaks = yBreaks, limits = range(yBreaks)) +
-      ggplot2::scale_fill_manual(name = gettext("Cluster"), values = .mlColorScheme(length(unique(clusterResult[["pred.values"]])))) +
+      ggplot2::scale_fill_manual(name = gettext("Cluster"), values = .mlColorScheme(ncolors)) +
       jaspGraphs::geom_rangeframe(sides = "l") +
       jaspGraphs::themeJaspRaw(legend.position = "right") +
       ggplot2::theme(axis.ticks.x = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(angle = 20))
     plot[["oneFigure"]] <- createJaspPlot(plot = p, title = gettext("All Features"), height = 400, width = 200 * length(options[["predictors"]]))
   } else {
     for (variable in unlist(options[["predictors"]])) {
-      clusters <- as.factor(clusterResult[["pred.values"]])
       xBreaks <- as.numeric(levels(clusters))
       clusterMeansData <- aggregate(clusterDataset[[variable]], list(clusters), mean)
       clusterSdData <- aggregate(clusterDataset[[variable]], list(clusters), sd)
@@ -608,7 +619,7 @@
       }
       p <- p + ggplot2::scale_x_discrete(name = gettext("Cluster"), breaks = xBreaks) +
         ggplot2::scale_y_continuous(name = variable, breaks = yBreaks, limits = range(yBreaks)) +
-        ggplot2::scale_fill_manual(name = gettext("Cluster"), values = .mlColorScheme(length(unique(clusterResult[["pred.values"]])))) +
+        ggplot2::scale_fill_manual(name = gettext("Cluster"), values = .mlColorScheme(ncolors)) +
         jaspGraphs::geom_rangeframe(sides = "l") +
         jaspGraphs::themeJaspRaw() +
         ggplot2::theme(axis.ticks.x = ggplot2::element_blank())
@@ -634,7 +645,7 @@
   unlist(regmatches(p[[1]], gregexpr("[[:digit:]]+\\.*[[:digit:]]*", p[[1]])))
 }
 
-.mlClusteringMatrixPlot <- function(dataset, options, jaspResults, ready, position) {
+.mlClusteringMatrixPlot <- function(dataset, options, jaspResults, ready, position, type) {
   if (!is.null(jaspResults[["matrixPlot"]]) || !options[["matrixPlot"]]) {
     return()
   }
@@ -667,17 +678,23 @@
   oldFontSize <- jaspGraphs::getGraphOption("fontsize")
   jaspGraphs::setGraphOption("fontsize", .85 * oldFontSize)
   startProgressbar(length(plotMat) + 1)
+  ncolors <- clusterResult[["clusters"]]
+  predictions <- clusterResult[["pred.values"]]
+  if (type == "densitybased") {
+    ncolors <- ncolors + 1
+    predictions[predictions == 0] <- gettext("Noisepoint")
+  }
   for (row in 2:l) {
     for (col in 1:(l - 1)) {
       if (col < row) {
         predictors <- dataset[, variables]
         predictors <- predictors[, c(col, row)]
-        plotData <- data.frame(x = predictors[, 1], y = predictors[, 2], cluster = as.factor(clusterResult[["pred.values"]]))
+        plotData <- data.frame(x = predictors[, 1], y = predictors[, 2], cluster = as.factor(predictions))
         xBreaks <- jaspGraphs::getPrettyAxisBreaks(plotData$x, min.n = 4)
         yBreaks <- jaspGraphs::getPrettyAxisBreaks(plotData$y, min.n = 4)
         p <- ggplot2::ggplot(data = plotData, mapping = ggplot2::aes(x = x, y = y, fill = cluster)) +
           jaspGraphs::geom_point() +
-          ggplot2::scale_fill_manual(name = NULL, values = .mlColorScheme(clusterResult[["clusters"]])) +
+          ggplot2::scale_fill_manual(name = NULL, values = .mlColorScheme(ncolors)) +
           ggplot2::scale_x_continuous(name = NULL, breaks = xBreaks, limits = range(xBreaks)) +
           ggplot2::scale_y_continuous(name = NULL, breaks = yBreaks, limits = range(yBreaks)) +
           jaspGraphs::geom_rangeframe() +
@@ -704,21 +721,21 @@
             y <- sqrt(lambda1) * sin(theta) * cos(t) + sqrt(lambda2) * cos(theta) * sin(t) + mu_y
             ellips <- data.frame(x = x, y = y)
             p <- p + ggplot2::geom_path(data = ellips, mapping = ggplot2::aes(x = x, y = y), color = "black", inherit.aes = FALSE, linewidth = 1.5) +
-                       ggplot2::geom_path(data = ellips, mapping = ggplot2::aes(x = x, y = y), color = .mlColorScheme(clusterResult[["clusters"]])[i], inherit.aes = FALSE, linewidth = 0.75)
+                       ggplot2::geom_path(data = ellips, mapping = ggplot2::aes(x = x, y = y), color = .mlColorScheme(ncolors)[i], inherit.aes = FALSE, linewidth = 0.75)
           }
         }
 
         plotMat[[row - 1, col]] <- p
       }
       if (l > 2) {
         predictors <- dataset[, options[["predictors"]]]
-        plotData <- data.frame(cluster = as.factor(clusterResult[["pred.values"]]), predictor = predictors[, 1])
+        plotData <- data.frame(cluster = as.factor(predictions), predictor = predictors[, 1])
         p <- ggplot2::ggplot(plotData, ggplot2::aes(y = cluster, x = cluster, show.legend = TRUE)) +
           jaspGraphs::geom_point(ggplot2::aes(fill = cluster), alpha = 0) +
           ggplot2::xlab(NULL) +
           ggplot2::ylab(NULL) +
           ggplot2::theme(legend.key = ggplot2::element_blank()) +
-          ggplot2::scale_fill_manual(name = gettext("Cluster"), values = .mlColorScheme(clusterResult[["clusters"]])) +
+          ggplot2::scale_fill_manual(name = gettext("Cluster"), values = .mlColorScheme(ncolors)) +
           jaspGraphs::geom_rangeframe(sides = "") +
           jaspGraphs::themeJaspRaw(legend.position = "left") +
           ggplot2::theme(axis.ticks = ggplot2::element_blank(), axis.text.x = ggplot2::element_blank(), axis.text.y = ggplot2::element_blank()) +

R/mlClassificationLda.R

@@ -509,7 +509,13 @@ mlClassificationLda <- function(jaspResults, dataset, options, ...) {
   if (!ready) {
     return()
   }
-  result <- mvnormalTest::mardia(dataset[, options[["predictors"]]])
+  p <- try({
+    result <- mvnormalTest::mardia(dataset[, options[["predictors"]]])
+  })
+  if (isTryError(p)) { # Fail gracefully
+    table$setError(gettextf("An error occurred when creating this table: %s", jaspBase:::.extractErrorMessage(p)))
+    return()
+  }
   table[["statistic"]] <- as.numeric(as.character(result[["mv.test"]][1:2, "Statistic"]))
   table[["p"]] <- as.numeric(as.character(result[["mv.test"]][1:2, "p-value"]))
 }

R/mlClusteringDensityBased.R

@@ -46,13 +46,13 @@ mlClusteringDensityBased <- function(jaspResults, dataset, options, ...) {
   .mlClusteringPlotTsne(dataset, options, jaspResults, ready, position = 6, type = "densitybased")
 
   # Create the matrix plot
-  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 7)
+  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 7, type = "densitybased")
 
   # Create the cluster means plot
-  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 8)
+  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 8, type = "densitybased")
 
   # Create the cluster densities plot
-  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 9)
+  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 9, type = "densitybased")
 }
 
 .densityBasedClustering <- function(dataset, options, jaspResults) {

R/mlClusteringFuzzyCMeans.R

@@ -46,13 +46,13 @@ mlClusteringFuzzyCMeans <- function(jaspResults, dataset, options, ...) {
   .mlClusteringPlotTsne(dataset, options, jaspResults, ready, position = 6, type = "cmeans")
 
   # Create the matrix plot
-  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 7)
+  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 7, type = "cmeans")
 
   # Create the cluster means plot
-  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 8)
+  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 8, type = "cmeans")
 
   # Create the cluster densities plot
-  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 9)
+  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 9, type = "cmeans")
 }
 
 .cMeansClustering <- function(dataset, options, jaspResults, ready) {

R/mlClusteringHierarchical.R

@@ -46,13 +46,13 @@ mlClusteringHierarchical <- function(jaspResults, dataset, options, ...) {
   .mlClusteringPlotTsne(dataset, options, jaspResults, ready, position = 6, type = "hierarchical")
 
   # Create the matrix plot
-  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 7)
+  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 7, type = "hierarchical")
 
   # Create the cluster means plot
-  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 8)
+  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 8, type = "hierarchical")
 
   # Create the cluster densities plot
-  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 9)
+  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 9, type = "hierarchical")
 
   # Create dendrogram
   .mlClusteringHierarchicalDendogram(dataset, options, jaspResults, ready, position = 10)

R/mlClusteringKMeans.R

@@ -46,13 +46,13 @@ mlClusteringKMeans <- function(jaspResults, dataset, options, ...) {
   .mlClusteringPlotTsne(dataset, options, jaspResults, ready, position = 6, type = "kmeans")
 
   # Create the matrix plot
-  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 7)
+  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 7, type = "kmeans")
 
   # Create the cluster means plot
-  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 8)
+  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 8, type = "kmeans")
 
   # Create the cluster densities plot
-  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 9)
+  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 9, type = "kmeans")
 }
 
 .kMeansClustering <- function(dataset, options, jaspResults, ready) {

R/mlClusteringModelBased.R

@@ -49,13 +49,13 @@ mlClusteringModelBased <- function(jaspResults, dataset, options, ...) {
   .mlClusteringPlotTsne(dataset, options, jaspResults, ready, position = 7, type = "modelbased")
 
   # Create the matrix plot
-  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 8)
+  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 8, type = "modelbased")
 
   # Create the cluster means plot
-  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 9)
+  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 9, type = "modelbased")
 
   # Create the cluster densities plot
-  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 10)
+  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 10, type = "modelbased")
 }
 
 emControl <- mclust::emControl

R/mlClusteringRandomForest.R

@@ -49,13 +49,13 @@ mlClusteringRandomForest <- function(jaspResults, dataset, options, ...) {
   .mlClusteringPlotTsne(dataset, options, jaspResults, ready, position = 7, type = "randomForest")
 
   # Create the matrix plot
-  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 8)
+  .mlClusteringMatrixPlot(dataset, options, jaspResults, ready, position = 8, type = "randomForest")
 
   # Create the cluster means plot
-  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 9)
+  .mlClusteringPlotMeans(dataset, options, jaspResults, ready, position = 9, type = "randomForest")
 
   # Create the cluster densities plot
-  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 10)
+  .mlClusteringPlotDensities(dataset, options, jaspResults, ready, position = 10, type = "randomForest")
 }
 
 .randomForestClustering <- function(dataset, options, jaspResults) {

inst/qml/common/tables/ModelPerformance.qml

@@ -24,5 +24,5 @@ CheckBox
 {
 	name:	"validationMeasures"
 	text:	qsTr("Model performance")
-	info:	qsTr("Displays available model performance metrics. For regression, these metrics include mean squared error (MSE), root mean squared error (RMSE), R<sup>2</sup> and more. For classification, these metrics include precision, recall, the F1-score, support, AUC (area under the ROC curve) and more.")
+	info:	qsTr("Displays available model performance metrics. For regression, these metrics include mean squared error (MSE), root mean squared error (RMSE), R<sup>2</sup> and more. For classification, these metrics include precision, recall, the F1-score, support, AUC (area under the ROC curve) and more. For clustering, these metrics include entropy, Dunn index and more.")
 }