🚧 Creation of the pyproject.toml file. Remove the setup.py file and yml files for different envs.

Author: adriencrtrcap

environment.dev.yml

@@ -0,0 +1,57 @@
+rm(list=ls())
+library(PKLMtest)
+
+set.seed(42)
+
+# Generate uniform random variables
+unif_features <- replicate(100, runif(500, min = 0, max = 1))
+
+empirical_cdf <- function(data) {
+  sorted_data <- sort(data)
+  y_values <- seq(1, length(data)) / length(data)
+  return(list(x = sorted_data, y = y_values))
+}
+
+# Generate MCAR data
+
+num_sim = 500
+num_variables <- 5
+num_samples <- 100
+r <- 0.65
+prob_nan <- 1 - r^(1/num_variables)
+
+results_p_values <- numeric(num_sim)
+
+for (i in 1:num_sim) {
+  data <- matrix(runif(num_variables * num_samples, min = 0, max = 1), ncol = num_variables)
+  mask <- matrix(runif(num_variables * num_samples) < prob_nan, ncol = num_variables)
+  data[mask] <- NaN
+  results_p_values[[i]] <- PKLMtest(
+    data,
+    num.proj = 100,
+    nrep = 30,
+    num.trees.per.proj = 200,
+    size.resp.set = 2
+  )
+}
+
+cdf_empirique_p_values <- ecdf(results_p_values)
+
+# Plot
+plot(
+  cdf_empirique_p_values,
+  main="Cumulative distribution function value of the p-values under H0",
+  xlab="x: p_values under H0",
+  ylab="F(x)",
+  col="black",
+  lwd=2,
+  xlim = c(0, 1),
+  ylim = c(0, 1)
+)
+
+for(i in 1:100) {
+  cdf <- empirical_cdf(unif_features[, i])
+  lines(cdf$x, cdf$y, col = rgb(0, 0, 1, alpha = 0.2))
+}
+
+abline(a = 0, b = 1, col = "red", lwd = 2)