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Creates some plots from the model fits created by this script: https://gist.github.com/rasmusab/b29bb53cfc3fe25f3f80
library(ggplot2)
library(plyr)
n_games <- readRDS("n_games.Rdata")
n_positions <- readRDS("n_positions.Rdata")
fullmoves_white <- readRDS("fullmoves_white.Rdata")
fits <- readRDS("chess_fits.Rdata")
fit <- fits$fit
fit <- fit
fit_bishop <- fits$fit_bishops
fit_over_moves <- fits$fit_over_moves
fit_df <- data.frame(name = c("White's Advantage", "Pawn", "Knight", "Bishop", "Rook", "Queen"),
value = fit,
image = c("","", "", "","",""))
fit_df$name <- factor(fit_df$name, levels = as.character(fit_df$name[order(fit_df$value)]))
fit_bishop_df <- data.frame(
name = c("White's Advantage", "Pawn", "Knight", "Bishop", "Bishop pair","Rook", "Queen"),
value = fit_bishop,
image = c("","", "", "", "♝♥♝","",""))
fit_bishop_df$name <- factor(fit_bishop_df$name,
levels = as.character(fit_bishop_df$name[order(fit_bishop_df$value)]))
fit_over_moves_df <- ldply(seq_len(length(fit_over_moves)), function(i) {
data.frame(
name = c("White's Advantage", "Pawn", "Knight", "Bishop", "Bishop pair","Rook", "Queen"),
value = fit_over_moves[[i]],
image = c("","", "", "", "♝♥♝","",""),
moves = paste((i - 1) * 10, "-", i * 10))
})
fit_over_moves_df$name <- factor(fit_over_moves_df$name,
levels = c("White's Advantage", "Pawn", "Knight", "Bishop", "Bishop pair","Rook", "Queen"))
fit_over_moves_df_rel_pawn <- ldply(seq_len(length(fit_over_moves)), function(i) {
data.frame(
name = c("White's Advantage", "Pawn", "Knight", "Bishop", "Bishop pair","Rook", "Queen"),
value = fit_over_moves[[i]] / fit_over_moves[[i]][2],
image = c("","", "", "", "♝♥♝","",""),
moves = paste((i - 1) * 10, "-", i * 10))
})
fit_over_moves_df_rel_pawn$name <- factor(fit_over_moves_df_rel_pawn$name,
levels = c("White's Advantage", "Pawn", "Knight", "Bishop", "Bishop pair","Rook", "Queen"))
qplot(name, value, data = fit_df, geom=c("text"), label = image,
main = "Predictive piece value (in log-odds)",
size = I(10), ylim=c(0, NA), xlab="", ylab='Piece "value"') + coord_flip()
qplot(name, value / fit_df$value[fit_df$name == "Pawn"], data = fit_df, geom=c("text"), label = image,
main = "Predictive piece value with pawn fixed to 1.0",
size = I(10), ylim=c(0, NA), xlab="", ylab='Piece "value"') + coord_flip()
qplot(name, value / fit_bishop_df$value[fit_bishop_df$name == "Pawn"], data = fit_bishop_df, geom=c("text"), label = image,
main = "Predictive piece value including bishop pair",
size = I(10), ylim=c(0, NA), xlab="", ylab='Piece "value"') + coord_flip()
qplot(name, value / fit_bishop_df$value[fit_bishop_df$name == "Knight"] * 3,
data = fit_bishop_df, geom=c("text"), label = image,
main = "Predictive piece value with knight fixed to 3.0",
size = I(10), xlab="", ylab='Piece "value"') +
coord_flip() + scale_y_continuous(breaks = c(0, 1, 3, 5, 9), limits = c(0, 9))
old_par <- par(mar = c(4.2, 3.5, 2.5, 0.5))
hist(fullmoves_white, col = "darkgreen", border = "darkgreen",
breaks = 0:101, yaxt = "n", ylab = "", main = "% of games playing after x full moves", xlab = "No. full moves")
axis(2, seq(0, n_games, length.out = 6), labels = paste0(seq(0, 100, 20), "%"), las = 1)
par(old_par)
qplot(moves, value, size = I(6),geom=c("text"), label=image, group = name, color = name, ylab = "Piece value (in log-odds)",
main = "Predictive piece value as the game progresses", xlab = "Full moves",data=fit_over_moves_df) +
geom_line(linewidth = 1) + theme(legend.position="none")
qplot(name, value , color = name,data = fit_over_moves_df[ fit_over_moves_df$moves == "0 - 10",], geom=c("text"), label = image,
main = "Predictive piece value moves 1-10 (in log-odds)",
size = I(10), ylim=c(-0.28, 0.28), xlab="", ylab='Piece "value"') + coord_flip() + theme(legend.position="none")
qplot(moves, value,size = I(6),geom=c("text"), label=image, group = name, color = name, ylab = "Piece value relative to pawn", xlab = "Full moves", main = "Predictive piece value as the game progresses",
data=fit_over_moves_df_rel_pawn[ fit_over_moves_df_rel_pawn$moves != "0 - 10",]) + geom_line(linewidth = 1) + theme(legend.position="none")
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