57 lines
2.0 KiB
R
57 lines
2.0 KiB
R
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# # Generate Sample Data.
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# n <- 250
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# # see: simulation_binary.R
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# data <- simulateData.binary(n / 2, n / 2, (p <- 10), (t <- 5), 0.3, 0.3)
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# X <- data$X
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# colnames(X) <- paste('X[', outer(1:p, 1:t, paste, sep = ','), ']', sep = '')
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# Y <- 2 * data$Y
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# write.csv(data.frame(X, Y), file = 'example_data.csv', row.names = FALSE)
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suppressPackageStartupMessages({
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library(pROC)
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})
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source('../tensor_predictors/tensor_predictors.R')
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# Read sample data from file and split into predictors and responces.
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data <- read.csv('example_data.csv')
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X <- as.matrix(data[, names(data) != 'Y'])
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Y <- as.matrix(data[, 'Y'])
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# Set parameters (and check)
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n <- nrow(X)
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p <- 10
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t <- 5
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stopifnot(p * t == ncol(X))
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# Setup folds (folds contains indices of the test set).
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nr.folds <- n # leave-one-out when number of folds equals the sample size `n`.
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folds <- split(sample.int(n), (seq(0, n - 1) * nr.folds) %/% n)
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labels <- vector('list', nr.folds) # True test values (per fold)
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predictions <- vector('list', nr.folds) # Predictions on test set.
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for (i in seq_along(folds)) {
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fold <- folds[[i]]
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# Split data into train and test sets.
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X.train <- X[-fold, ]
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Y.train <- Y[-fold, , drop = FALSE]
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X.test <- X[fold, ]
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Y.test <- Y[fold, , drop = FALSE]
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# Compute reduction (method = c('KPIR_LS' ,'KPIR_MLE', 'KPFC1', 'KPFC2', 'KPFC3'))
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# or LSIR(X.train, Y.train, p, t) in 'lsir.R'.
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dr <- tensor_predictor(X.train, Y.train, p, t, method = 'KPIR_LS')
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B <- kronecker(dr$alpha, dr$beta) # Also available: Gamma_1, Gamma_2, Gamma, B.
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# Predict via a logit model building on the reduced data.
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model <- glm(y ~ x, family = binomial(link = "logit"),
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data = data.frame(x = X.train %*% B, y = as.integer(Y.train > 0)))
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labels[[i]] <- as.integer(Y.test > 0)
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predictions[[i]] <- predict(model, data.frame(x = X.test %*% B), type = "response")
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}
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# Compute classic ROC for predicted samples (mean AUC makes no sense for leave-one-out)
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y.true <- unlist(labels)
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y.pred <- unlist(predictions)
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roc(y.true, y.pred)
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