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@@ -256,12 +256,19 @@ input data. Autmomatically reducing to smaller number.")
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{
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if(is.null(modellingParams@trainParams@getFeatures))
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selectedFeatures <- originalFeatures[selectedFeaturesIndices]
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- else selectedFeatures <- colnames(measurementsTrain)[rankedFeaturesIndices]
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+ else selectedFeatures <- colnames(measurementsTrain)[selectedFeaturesIndices]
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} else {
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featureColumns <- na.omit(match(c("assay", "feature"), colnames(S4Vectors::mcols(measurementsTrain))))
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- if(is.null(modellingParams@trainParams@getFeatures))
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- selectedFeatures <- originalFeatures[selectedFeaturesIndices, ]
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- else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]
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+ if(length(featureColumns) == 1)
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+ {
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+ if(is.null(modellingParams@trainParams@getFeatures))
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+ selectedFeatures <- originalFeatures[selectedFeaturesIndices]
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+ else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]
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+ } else {
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+ if(is.null(modellingParams@trainParams@getFeatures))
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+ selectedFeatures <- originalFeatures[selectedFeaturesIndices, ]
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+ else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]
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+ }
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}
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importanceTable <- S4Vectors::DataFrame(selectedFeatures, performanceChanges)
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if(ncol(importanceTable) == 2) colnames(importanceTable)[1] <- "feature"
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@@ -272,6 +279,7 @@ input data. Autmomatically reducing to smaller number.")
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if(!is.null(tuneDetailsSelect)) tuneDetails <- tuneDetailsSelect else tuneDetails <- tuneDetailsTrain
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# Convert back into original, potentially unsafe feature identifiers unless it is a nested cross-validation.
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+
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if(is.null(.iteration) || .iteration != "internal")
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{
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if(!is.null(rankedFeaturesIndices))
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@@ -283,9 +291,16 @@ input data. Autmomatically reducing to smaller number.")
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else rankedFeatures <- colnames(measurementsTrain)[rankedFeaturesIndices]
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} else {
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featureColumns <- na.omit(match(c("assay", "feature"), colnames(S4Vectors::mcols(measurementsTrain))))
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- if(is.null(modellingParams@trainParams@getFeatures))
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- rankedFeatures <- originalFeatures[rankedFeaturesIndices, ]
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- else rankedFeatures <- S4Vectors::mcols(measurementsTrain)[rankedFeaturesIndices, featureColumns]
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+ if(length(featureColumns) == 1)
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+ {
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+ if(is.null(modellingParams@trainParams@getFeatures))
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+ rankedFeatures <- originalFeatures[rankedFeaturesIndices]
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+ else rankedFeatures <- S4Vectors::mcols(measurementsTrain)[rankedFeaturesIndices, featureColumns]
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+ } else {
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+ if(is.null(modellingParams@trainParams@getFeatures))
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+ rankedFeatures <- originalFeatures[rankedFeaturesIndices, ]
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+ else rankedFeatures <- S4Vectors::mcols(measurementsTrain)[rankedFeaturesIndices, featureColumns]
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+ }
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}
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} else { rankedFeatures <- NULL}
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if(!is.null(selectedFeaturesIndices))
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@@ -297,9 +312,16 @@ input data. Autmomatically reducing to smaller number.")
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else selectedFeatures <- colnames(measurementsTrain)[selectedFeaturesIndices]
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} else {
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featureColumns <- na.omit(match(c("assay", "feature"), colnames(S4Vectors::mcols(measurementsTrain))))
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- if(is.null(modellingParams@trainParams@getFeatures))
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- selectedFeatures <- originalFeatures[selectedFeaturesIndices, ]
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- else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]
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+ if(length(featureColumns) == 1)
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+ {
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+ if(is.null(modellingParams@trainParams@getFeatures))
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+ selectedFeatures <- originalFeatures[selectedFeaturesIndices]
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+ else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]
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+ } else {
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+ if(is.null(modellingParams@trainParams@getFeatures))
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+ selectedFeatures <- originalFeatures[selectedFeaturesIndices, ]
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+ else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]
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+ }
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}
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} else { selectedFeatures <- NULL}
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} else { # Nested use in feature selection. No feature selection in inner execution, so ignore features.
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