Skip to content

Make explain predictions work for new data again #393

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 5 commits into from
Nov 25, 2024
Merged

Make explain predictions work for new data again #393

Show file tree
Hide file tree
Changes from 4 commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
d4ec9fc
Make explain predictions work for new data again
koenderks Nov 22, 2024
364f2a9
Use a function
koenderks Nov 22, 2024
22248be
No need to decode jaspML object anymore!
koenderks Nov 24, 2024
47ffb9b
Ensure correct column names in predictions tabel
koenderks Nov 25, 2024
569ba92
Don's comment
koenderks Nov 25, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 0 additions & 1 deletion R/commonMachineLearningClassification.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -342,7 +342,6 @@
model[["jaspVars"]]$encoded = list(target = options[["target"]], predictors = options[["predictors"]])
model[["jaspVersion"]] <- .baseCitation
model[["explainer"]] <- classificationResult[["explainer"]]
model <- .decodeJaspMLobject(model)
class(model) <- c(class(classificationResult[["model"]]), "jaspClassification", "jaspMachineLearning")
path <- options[["savePath"]]
if (!endsWith(path, ".jaspML")) {
Expand Down
5 changes: 2 additions & 3 deletions R/commonMachineLearningRegression.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -473,7 +473,6 @@
model[["jaspVars"]]$encoded = list(target = options[["target"]], predictors = options[["predictors"]])
model[["jaspVersion"]] <- .baseCitation
model[["explainer"]] <- regressionResult[["explainer"]]
model <- .decodeJaspMLobject(model)
class(model) <- c(class(regressionResult[["model"]]), "jaspRegression", "jaspMachineLearning")
path <- options[["savePath"]]
if (!endsWith(path, ".jaspML")) {
Expand Down Expand Up @@ -713,7 +712,7 @@
} else {
purpose <- "classification"
}
predictors <- options[["predictors"]][which(decodeColNames(options[["predictors"]]) %in% model[["jaspVars"]][["decoded"]]$predictors)]
predictors <- model[["jaspVars"]][["encoded"]]$predictors
} else {
predictors <- options[["predictors"]]
}
Expand All @@ -724,7 +723,7 @@
} else {
.mlClassificationDependencies(options)
},
"tableShap", "fromIndex", "toIndex"
"tableShap", "fromIndex", "toIndex", "trainedModelFilePath"
))
table$addColumnInfo(name = "id", title = gettext("Case"), type = "integer")
if (purpose == "regression") {
Expand Down
126 changes: 36 additions & 90 deletions R/mlPrediction.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@ mlPrediction <- function(jaspResults, dataset, options, ...) {

# Preparatory work
model <- .mlPredictionReadModel(options)
dataset <- .mlPredictionReadData(dataset, options)
dataset <- .mlPredictionReadData(dataset, options, model)

# Check if analysis is ready
ready <- .mlPredictionReady(model, dataset, options)
Expand Down Expand Up @@ -145,7 +145,6 @@ is.jaspMachineLearning <- function(x) {
as.character(levels(as.factor(model$model[[model[["jaspVars"]][["encoded"]]$target]]))[round(predict(model, newdata = dataset, type = "response"), 0) + 1])
}
.mlPredictionGetPredictions.vglm <- function(model, dataset) {
model[["original"]]@terms$terms <- model[["terms"]]
logodds <- predict(model[["original"]], newdata = dataset)
ncategories <- ncol(logodds) + 1
probabilities <- matrix(0, nrow = nrow(logodds), ncol = ncategories)
Expand Down Expand Up @@ -200,76 +199,6 @@ is.jaspMachineLearning <- function(x) {
nrow(model[["x"]])
}

# S3 method to decode the model variables in the result object
# so that they can be matched to variables in the prediction analysis
.decodeJaspMLobject <- function(model) {
UseMethod(".decodeJaspMLobject", model)
}
.decodeJaspMLobject.kknn <- function(model) {
formula <- formula(paste(decodeColNames(as.character(model$terms)[2]), "~", paste0(decodeColNames(strsplit(as.character(model$terms)[3], split = " + ", fixed = TRUE)[[1]]), collapse = " + ")))
model[["predictive"]]$terms <- stats::terms(formula)
colnames(model[["predictive"]]$data) <- decodeColNames(colnames(model[["predictive"]]$data))
return(model)
}
.decodeJaspMLobject.lda <- function(model) {
formula <- formula(paste(decodeColNames(as.character(model$terms)[2]), "~", paste0(decodeColNames(strsplit(as.character(model$terms)[3], split = " + ", fixed = TRUE)[[1]]), collapse = " + ")))
model$terms <- stats::terms(formula)
return(model)
}
.decodeJaspMLobject.lm <- function(model) {
formula <- formula(paste(decodeColNames(as.character(model$terms)[2]), "~", paste0(decodeColNames(strsplit(as.character(model$terms)[3], split = " + ", fixed = TRUE)[[1]]), collapse = " + ")))
model$terms <- stats::terms(formula)
return(model)
}
.decodeJaspMLobject.gbm <- function(model) {
model[["var.names"]] <- decodeColNames(model[["var.names"]])
return(model)
}
.decodeJaspMLobject.randomForest <- function(model) {
rownames(model$importance) <- decodeColNames(rownames(model$importance))
names(model$forest$xlevels) <- decodeColNames(names(model$forest$xlevels))
formula <- formula(paste("DOESNOTMATTER", "~", paste0(decodeColNames(names(model$forest$xlevels)), collapse = " + ")))
model$terms <- stats::terms(formula)
class(model) <- c(class(model), "randomForest.formula")
return(model)
}
.decodeJaspMLobject.cv.glmnet <- function(model) {
rownames(model[["glmnet.fit"]][["beta"]]) <- decodeColNames(rownames(model[["glmnet.fit"]][["beta"]]))
return(model)
}
.decodeJaspMLobject.nn <- function(model) {
model[["model.list"]]$variables <- decodeColNames(model[["model.list"]]$variables)
return(model)
}
.decodeJaspMLobject.rpart <- function(model) {
formula <- formula(paste(decodeColNames(as.character(model$terms)[2]), "~", paste0(decodeColNames(strsplit(as.character(model$terms)[3], split = " + ", fixed = TRUE)[[1]]), collapse = " + ")))
model$terms <- stats::terms(formula)
model$frame$var <- decodeColNames(model$frame$var)
rownames(model$splits) <- decodeColNames(rownames(model$splits))
return(model)
}
.decodeJaspMLobject.svm <- function(model) {
formula <- formula(paste(decodeColNames(as.character(model$terms)[2]), "~ 0 +", paste0(decodeColNames(strsplit(as.character(model$terms)[3], split = " + ", fixed = TRUE)[[1]]), collapse = " + ")))
model$terms <- stats::terms(formula)
return(model)
}
.decodeJaspMLobject.naiveBayes <- function(model) {
names(model[["isnumeric"]]) <- decodeColNames(names(model[["isnumeric"]]))
names(model[["tables"]]) <- decodeColNames(names(model[["tables"]]))
return(model)
}
.decodeJaspMLobject.glm <- function(model) {
vars <- all.vars(stats::terms(model))
formula <- formula(paste(decodeColNames(vars[1]), "~", paste0(decodeColNames(vars[-1]), collapse = " + ")))
model$terms <- stats::terms(formula)
return(model)
}
.decodeJaspMLobject.vglm <- function(model) {
formula <- formula(paste(decodeColNames(strsplit(as.character(model$terms), " ")[[1]][1]), "~", paste0(decodeColNames(strsplit(strsplit(as.character(model$terms), split = " ~ ")[[1]][2], split = " + ", fixed = TRUE)[[1]]), collapse = " + ")))
model$terms <- stats::terms(formula)
return(model)
}

.mlPredictionReadModel <- function(options) {
if (options[["trainedModelFilePath"]] != "") {
model <- try({
Expand All @@ -293,20 +222,31 @@ is.jaspMachineLearning <- function(x) {
# also define methods for other objects
.mlPredictionReady <- function(model, dataset, options) {
if (!is.null(model)) {
modelVars <- model[["jaspVars"]][["decoded"]]$predictors
presentVars <- decodeColNames(colnames(dataset))
modelVars <- model[["jaspVars"]][["encoded"]]$predictors
presentVars <- colnames(dataset)
ready <- all(modelVars %in% presentVars)
} else {
ready <- FALSE
}
return(ready)
}

.mlPredictionReadData <- function(dataset, options) {
# Ensure names in prediction data match names in training data
.matchDecodedNames <- function(names, model) {
decoded <- model[["jaspVars"]][["decoded"]]$predictors
encoded <- model[["jaspVars"]][["encoded"]]$predictors
matched_indices <- match(decodeColNames(names), decoded)
names[!is.na(matched_indices)] <- encoded[matched_indices[!is.na(matched_indices)]]
return(names)
}

.mlPredictionReadData <- function(dataset, options, model) {
dataset <- jaspBase::excludeNaListwise(dataset, options[["predictors"]])
if (options[["scaleVariables"]] && length(unlist(options[["predictors"]])) > 0) {
dataset <- .scaleNumericData(dataset)
}
dataset <- dataset[, which(decodeColNames(colnames(dataset)) %in% model[["jaspVars"]][["decoded"]]$predictors)] # Filter only predictors to prevent accidental double column names
colnames(dataset) <- .matchDecodedNames(colnames(dataset), model)
return(dataset)
}

Expand All @@ -315,7 +255,7 @@ is.jaspMachineLearning <- function(x) {
return(jaspResults[["predictions"]]$object)
} else {
if (ready) {
colnames(dataset) <- decodeColNames(colnames(dataset))
dataset <- dataset[, which(colnames(dataset) %in% model[["jaspVars"]][["encoded"]]$predictors)]
jaspResults[["predictions"]] <- createJaspState(.mlPredictionGetPredictions(model, dataset))
jaspResults[["predictions"]]$dependOn(options = c("loadPath", "predictors", "scaleVariables"))
return(jaspResults[["predictions"]]$object)
Expand Down Expand Up @@ -344,14 +284,16 @@ is.jaspMachineLearning <- function(x) {
if (is.null(model)) {
return()
}
modelVars <- model[["jaspVars"]][["decoded"]]$predictors
presentVars <- decodeColNames(colnames(dataset))
if (!all(modelVars %in% presentVars)) {
missingVars <- modelVars[which(!(modelVars %in% presentVars))]
modelVars_encoded <- model[["jaspVars"]][["encoded"]]$predictors
modelVars_decoded <- model[["jaspVars"]][["decoded"]]$predictors
presentVars_encoded <- colnames(dataset)
presentVars_decoded <- decodeColNames(options[["predictors"]])
if (!all(modelVars_decoded %in% presentVars_decoded)) {
missingVars <- modelVars_decoded[which(!(modelVars_decoded %in% presentVars_decoded))]
table$addFootnote(gettextf("The trained model is not applied because the the following features are missing: <i>%1$s</i>.", paste0(missingVars, collapse = ", ")))
}
if (!all(presentVars %in% modelVars)) {
unusedVars <- presentVars[which(!(presentVars %in% modelVars))]
if (!all(presentVars_decoded %in% modelVars_decoded)) {
unusedVars <- presentVars_decoded[which(!(presentVars_decoded %in% modelVars_decoded))]
table$addFootnote(gettextf("The following features are unused because they are not a feature variable in the trained model: <i>%1$s</i>.", paste0(unusedVars, collapse = ", ")))
}
if (inherits(model, "kknn")) {
Expand Down Expand Up @@ -394,7 +336,7 @@ is.jaspMachineLearning <- function(x) {
row[["family"]] <- gettext("Multinomial")
row[["link"]] <- gettext("Logit")
}
if (length(presentVars) > 0) {
if (length(presentVars_encoded) > 0) {
row[["nnew"]] <- nrow(dataset)
}
table$addRows(row)
Expand All @@ -405,7 +347,7 @@ is.jaspMachineLearning <- function(x) {
return()
}
table <- createJaspTable(gettext("Predictions for New Data"))
table$dependOn(options = c("predictors", "loadPath", "predictionsTable", "predictionsTableFeatures", "scaleVariables", "fromIndex", "toIndex"))
table$dependOn(options = c("predictors", "trainedModelFilePath", "predictionsTable", "predictionsTableFeatures", "scaleVariables", "fromIndex", "toIndex"))
table$position <- position
table$addColumnInfo(name = "row", title = gettext("Case"), type = "integer")
if (!is.null(model) && inherits(model, "jaspClassification")) {
Expand All @@ -422,16 +364,20 @@ is.jaspMachineLearning <- function(x) {
selection <- predictions[indexes]
cols <- list(row = indexes, pred = selection)
if (options[["predictionsTableFeatures"]]) {
for (i in colnames(dataset)) {
if (.columnIsNominal(i)) {
table$addColumnInfo(name = i, title = i, type = "string")
modelVars_encoded <- model[["jaspVars"]][["encoded"]]$predictors
modelVars_decoded <- model[["jaspVars"]][["decoded"]]$predictors
matched_names <- match(colnames(dataset), modelVars_encoded)
for (i in seq_len(ncol(dataset))) {
colName <- modelVars_decoded[matched_names[i]]
if (is.factor(dataset[[i]])) {
table$addColumnInfo(name = colName, title = colName, type = "string")
var <- levels(dataset[[i]])[dataset[[i]]]
} else {
table$addColumnInfo(name = i, title = i, type = "number")
table$addColumnInfo(name = colName, title = colName, type = "number")
var <- dataset[[i]]
}
var <- var[indexes]
cols[[i]] <- var
cols[[colName]] <- var
}
}
table$setData(cols)
Expand All @@ -442,7 +388,7 @@ is.jaspMachineLearning <- function(x) {
predictionsColumn <- rep(NA, max(as.numeric(rownames(dataset))))
predictionsColumn[as.numeric(rownames(dataset))] <- .mlPredictionsState(model, dataset, options, jaspResults, ready)
jaspResults[["predictionsColumn"]] <- createJaspColumn(columnName = options[["predictionsColumn"]])
jaspResults[["predictionsColumn"]]$dependOn(options = c("predictionsColumn", "predictors", "loadPath", "scaleVariables", "addPredictions"))
jaspResults[["predictionsColumn"]]$dependOn(options = c("predictionsColumn", "predictors", "trainedModelFilePath", "scaleVariables", "addPredictions"))
if (inherits(model, "jaspClassification")) jaspResults[["predictionsColumn"]]$setNominal(predictionsColumn)
if (inherits(model, "jaspRegression")) jaspResults[["predictionsColumn"]]$setScale(predictionsColumn)
}
Expand Down
Loading