In 8080labs/ppscoreR: An R implementation of the Predictive Power Score (PPS)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.ext = "png",
  fig.align = "center"
)

library(ggplot2)
library(corrplot)

library(ppscore)

pps_heatmap <- function(pps_mat) {
  melted_pps_mat <- melt(pps_mat)
  pps_heatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value)) +
    geom_tile(color = "white") +
    scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                         midpoint = 0, limit = c(-1,1), space = "Lab", 
                         name="PPS Score") +
    geom_text(aes(Var2, Var1, label = value), color = "black", size = 4) +
    theme_minimal() + 
    theme(axis.text.x = element_text(angle = 45, vjust = 1, 
    size = 12, hjust = 1)) +
    theme(axis.title.x = "feature", axis.text.x = axis.ticks.x = element_blank(),
          axis.title.y = "target", axis.text.y = axis.ticks.y = element_blank(),
          panel.grid.major = panel.border = panel.background = axis.ticks = element_blank()) +
    coord_fixed() + 
  return(pps_heatmap)
}

x <- runif(1000000, -2, 2)
error <- runif(1000000, -0.5, 0.5)
y <- x * x + error
df <- data.frame(x, error, y)

ggplot(df, aes(x = x, y = y)) +
  geom_point()

matrix = ppscore::matrix(df)
matrix

pps_heatmap(matrix)

ppscore::score(df, "x", "y")

8080labs/ppscoreR documentation built on June 13, 2020, 1:26 a.m.