From 9b51886d702b22018db08ba8a30ab47d36f64aac Mon Sep 17 00:00:00 2001
From: Samuel Fadel <samuelfadel@gmail.com>
Date: Thu, 18 Aug 2016 23:14:43 -0300
Subject: Renamed main script file.

---
 tests.R | 250 ----------------------------------------------------------------
 1 file changed, 250 deletions(-)
 delete mode 100644 tests.R

(limited to 'tests.R')

diff --git a/tests.R b/tests.R
deleted file mode 100644
index 4c073a0..0000000
--- a/tests.R
+++ /dev/null
@@ -1,250 +0,0 @@
-require(ggplot2)
-require(gridExtra)
-require(mp)
-
-source("measures.R")
-
-#automated.m <- function(D, labels) {
-#  D.m <- D
-#  for (label in unique(labels)) {
-#    same.label <- labels == label
-#    D.m[same.label, same.label] <- D[same.label, same.label] * 0.1
-#  }
-#
-#  D.m
-#}
-automated.m <- function(Xs, labels) {
-  n <- nrow(Xs)
-  p <- ncol(Xs)
-  Xs <- cbind(Xs, matrix(data=0, nrow=n, ncol=p))
-  for (label in unique(labels)) {
-    for (j in 1:p) {
-      Xs[labels == label, j + p] <- mean(Xs[labels == label, j])
-    }
-  }
-
-  dist(Xs)
-}
-
-color_scale.blue_orange <- function(name) {
-  scale_colour_gradient(name = name, high = "#376092", low = "#e46c0a", space = "Lab")
-}
-
-color_scale.gradient2 <- function(name) {
-  scale_colour_gradient2(name = name, mid = "#dddddd", space = "Lab")
-}
-
-test <- function(file, suffix, output.dir) {
-  message("Testing dataset: ", file)
-  dataset <- read.table(file)
-
-  # Extract labels
-  labels <- dataset[, ncol(dataset)]
-  classes <- as.factor(labels)
-  X <- dataset[, -ncol(dataset)]
-
-  n <- nrow(X)
-
-  # Calculate distances (X) and normalize
-  message("\tCalculating dist(X)")
-  Dx <- dist(X)
-  Dx <- Dx / mean(Dx)
-  Dx <- as.matrix(Dx)
-
-  # Sample dataset
-  sample.indices <- sample(n, 3*sqrt(n))
-  classes.s <- as.factor(labels[sample.indices])
-
-  # Automatic sample positioning
-  message("\tCalculating Ys")
-  Dx.s <- Dx[sample.indices, sample.indices]
-  Ys   <- forceScheme(Dx.s)
-
-  # LAMP
-  message("\tCalculating Y")
-  Y <- lamp(X, sample.indices, Ys)
-
-  # Calculate distances (Y) and normalize
-  message("\tCalculating dist(Y)")
-  Dy <- dist(Y)
-  Dy <- Dy / mean(Dy)
-  Dy <- as.matrix(Dy)
-
-  message("\tCalculating P and Q")
-  prob <- d2p(Dx^2)
-  P <- prob$P
-  Q <- d2p.beta(Dy^2, prob$beta)
-
-  # Calculate measures
-  message("\tCalculating measures")
-  np <- NP(Dx, Dy)
-  silh <- silhouette(Dy, classes)
-  precision <- klDivergence(Q, P)
-  recall <- klDivergence(P, Q)
-
-  measures <- rbind(data.frame(mean=mean(np), median=median(np), sd=sd(np)),
-                    data.frame(mean=mean(silh), median=median(silh), sd=sd(silh)),
-                    data.frame(mean=mean(precision), median=median(precision), sd=sd(precision)),
-                    data.frame(mean=mean(recall), median=median(recall), sd=sd(recall)))
-  write.table(measures, paste(output.dir, suffix, "-measures.csv", sep=""), row.names=F)
-
-  if (!(all(is.finite(np)) &&
-      all(is.finite(silh)) &&
-      all(is.finite(precision)) &&
-      all(is.finite(recall)))) {
-    stop("Non-finite measures found")
-  }
-
-  # Plot results
-  message("\tPlotting results")
-  shape_scale <- scale_shape_manual(name = "Classe", values = 1:nlevels(classes))
-  Ys <- cbind(as.data.frame(Ys), classes.s)
-  Y  <- cbind(as.data.frame(Y), classes, np, silh, precision, recall)
-  p.s <- ggplot(Ys) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes.s, colour = classes.s)) +
-      shape_scale + scale_color_manual(name = "Classe", values = 1:nlevels(classes))
-  ggsave(paste(output.dir, "subsample-", suffix, ".pdf", sep=""), p.s, width=5, height=5)
-
-  p <- ggplot(Y) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes, colour = classes)) +
-      shape_scale + scale_color_manual(name = "Classe", values = 1:nlevels(classes))
-  ggsave(paste(output.dir, suffix, ".pdf", sep=""), p, width=5, height=5)
-
-  p.np <- ggplot(Y) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes, colour = np)) +
-      shape_scale + color_scale.blue_orange("NP")
-  ggsave(paste(output.dir, "np-", suffix, ".pdf", sep=""), p.np, width=5, height=5)
-
-  p.silh <- ggplot(Y) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes, colour = silh)) +
-      shape_scale + color_scale.blue_orange("Silhueta")
-  ggsave(paste(output.dir, "silh-", suffix, ".pdf", sep=""), p.silh, width=5, height=5)
-
-  p.precision <- ggplot(Y) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes, colour = precision)) +
-      shape_scale + color_scale.blue_orange("Precisão")
-  ggsave(paste(output.dir, "precision-", suffix, ".pdf", sep=""), p.precision, width=5, height=5)
-
-  p.recall <- ggplot(Y) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes, colour = recall)) +
-      shape_scale + color_scale.blue_orange("Revocação")
-  ggsave(paste(output.dir, "recall-", suffix, ".pdf", sep=""), p.recall, width=5, height=5)
-
-  
-  pdf(paste(output.dir, "all-", suffix, ".pdf", sep=""), width = 12, height = 16)
-  grid.arrange(p.s, p, p.np, p.silh, p.precision, p.recall, ncol = 2)
-  dev.off()
-
-  # Perform manipulation
-  message("\tCalculating Ys.m")
-  Dx.m <- automated.m(X[sample.indices, ], labels[sample.indices])
-  Ys.m <- forceScheme(Dx.m, Ys[, 1:2])
-
-  # LAMP
-  message("\tCalculating Y.m")
-  Y.m <- lamp(X, sample.indices, Ys.m)
-
-  # Calculate distances (Y.m) and normalize
-  message("\tCalculating dist(Y.m)")
-  Dy <- dist(Y.m)
-  Dy <- Dy / mean(Dy)
-  Dy <- as.matrix(Dy)
-
-  message("\tCalculating Q")
-  Q <- d2p.beta(Dy^2, prob$beta)
-
-  # Calculate measures
-  message("\tCalculating measures")
-  np <- NP(Dx, Dy) - np
-  silh <- silhouette(Dy, classes) - silh
-  precision <- klDivergence(Q, P) - precision
-  recall <- klDivergence(P, Q) - recall
-
-  measures <- rbind(data.frame(mean=mean(np), median=median(np), sd=sd(np)),
-                    data.frame(mean=mean(silh), median=median(silh), sd=sd(silh)),
-                    data.frame(mean=mean(precision), median=median(precision), sd=sd(precision)),
-                    data.frame(mean=mean(recall), median=median(recall), sd=sd(recall)))
-  write.table(measures, paste(output.dir, suffix, "-manip-measures.csv", sep=""), row.names=F)
-
-  if (!(all(is.finite(np)) &&
-      all(is.finite(silh)) &&
-      all(is.finite(precision)) &&
-      all(is.finite(recall)))) {
-    stop("Non-finite measures found")
-  }
-
-  # Plot results
-  message("\tPlotting results")
-  Ys.m <- cbind(as.data.frame(Ys.m), classes.s)
-  Y.m  <- cbind(as.data.frame(Y.m), classes, np, silh, precision, recall)
-  pm.s <- ggplot(Ys.m) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes.s, colour = classes.s)) +
-      shape_scale + scale_color_manual(name = "Classe", values = 1:nlevels(classes))
-  ggsave(paste(output.dir, "manip-subsample-", suffix, ".pdf", sep=""), pm.s, width=5, height=5)
-
-  pm <- ggplot(Y.m) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes, colour = classes)) +
-      shape_scale + scale_color_manual(name = "Classe", values = 1:nlevels(classes))
-  ggsave(paste(output.dir, "manip-", suffix, ".pdf", sep=""), pm, width=5, height=5)
-
-  p.np <- ggplot(Y.m) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes, colour = np)) +
-      shape_scale + color_scale.gradient2("NP")
-  ggsave(paste(output.dir, "manip-np-", suffix, ".pdf", sep=""), p.np, width=5, height=5)
-
-  p.silh <- ggplot(Y.m) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes, colour = silh)) +
-      shape_scale + color_scale.gradient2("Silhueta")
-  ggsave(paste(output.dir, "manip-silh-", suffix, ".pdf", sep=""), p.silh, width=5, height=5)
-
-  p.precision <- ggplot(Y.m) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes, colour = precision)) +
-      shape_scale + color_scale.gradient2("Precisão")
-  ggsave(paste(output.dir, "manip-precision-", suffix, ".pdf", sep=""), p.precision, width=5, height=5)
-
-  p.recall <- ggplot(Y.m) +
-      theme_bw() +
-      labs(x = "", y = "") +
-      geom_point(aes(x = V1, y = V2, shape = classes, colour = recall)) +
-      shape_scale + color_scale.gradient2("Revocação")
-  ggsave(paste(output.dir, "manip-recall-", suffix, ".pdf", sep=""), p.recall, width=5, height=5)
-
-  pdf(paste(output.dir, "original-manip-", suffix, ".pdf", sep=""), width = 10, height = 8)
-  grid.arrange(p.s, p, pm.s, pm, ncol = 2)
-  dev.off()
-
-  pdf(paste(output.dir, "manip-measures-", suffix, ".pdf", sep=""), width = 10, height = 8)
-  grid.arrange(p.np, p.silh, p.precision, p.recall, ncol = 2)
-  dev.off()
-  
-  pdf(paste(output.dir, "manip-all-", suffix, ".pdf", sep=""), width = 12, height = 16)
-  grid.arrange(pm.s, pm, p.np, p.silh, p.precision, p.recall, ncol = 2)
-  dev.off()
-}
-
-#test(file = "datasets/iris.tbl", suffix = "iris", "tests/")
-test(file = "datasets/wdbc.tbl", suffix = "wdbc", "tests/")
-test(file = "datasets/segmentation.tbl", suffix = "segmentation", "tests/")
-test(file = "datasets/glass.tbl", suffix = "glass", "tests/")
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