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02_train_xgboost.R
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02_train_xgboost.R
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# Data downloaded from kaggle at https://www.kaggle.com/stefanoleone992/fifa-20-complete-player-dataset
library(data.table)
library(dplyr)
library(tidyr)
library(stringr)
library(forcats)
library(tidygraph)
library(ggraph)
library(caret)
library(xgboost)
library(plotly)
library(viridis)
library(htmlwidgets)
library(matrixStats)
# Load data
# Use R/load_data.R to create these datasets
all_train_df <- readRDS('data/all_train_df.rds')
inference_df <- readRDS('data/inference_df.rds')
# ---- Train baseline model ----
# Create train test split by time
train <- all_train_df %>% filter(season <= 18) %>% select(improvement, overall:ncol(.), -nationality, -player_positions, -pos, -team_position, -work_rate_att, -work_rate_def)
test <- all_train_df %>% filter(season > 18) %>% bind_rows(inference_df)
# Train baseline linear model
base_model <- lm(improvement ~ ., data = train[complete.cases(train),])
# In-sample results
summary(base_model)
# Predictive accuracy
preds <- predict(base_model, newdata = test, na.action = "na.pass")
# Metrics df
eval_df <- tibble(obs = test$improvement, pred = preds)
# Display metrics
lm_res <- eval_df %>%
mutate(pred_category = ifelse(pred > 0, "improve", "decline/same"),
obs_category = ifelse(obs > 0, "improve", "decline/same")) %>%
filter(!is.na(pred), !is.na(obs)) %>%
summarise(rmse = RMSE(pred, obs),
r_square = R2(pred, obs),
sens = sensitivity(factor(pred_category), reference = factor(obs_category)),
spec = specificity(factor(pred_category), reference = factor(obs_category)),
pos_pred_rate = mean(obs_category[pred_category == "improve"] == "improve"),
neg_pred_rate = mean(obs_category[pred_category != "improve"] != "improve"),
num = n()) %>%
mutate(model = "lm") %>%
select(model, everything())
# ---- Xgboost ----
# Prepare train object
train_xgb <- train %>%
select(-improvement) %>%
as.matrix(na.action = na.pass) %>%
xgb.DMatrix(label = train$improvement)
# Prepare test object
test_xgb <- test %>%
select(names(train)) %>%
select(-improvement) %>%
as.matrix(na.action = na.pass) %>%
xgb.DMatrix(label = test$improvement)
# Set parameters
xgb_params <- list(
eta = 0.1,
max_depth = 4,
min_child_weight = 5,
colsample = 0.7
)
# Cross validate with default parameters
xgb_cv_res <- xgb.cv(
objective = 'reg:linear',
params = xgb_params,
data = train_xgb,
nrounds = 500,
nfold = 5,
prediction = TRUE,
early_stopping_rounds = 50,
print_every_n = 20
)
# Plot training history
xgb_cv_res$evaluation_log %>%
select(iter, rmse_mean=train_rmse_mean, rmse_std=train_rmse_std) %>%
mutate(train_test = "train") %>%
bind_rows(select(mutate(xgb_cv_res$evaluation_log, train_test = "test"),
iter, rmse_mean=test_rmse_mean, rmse_std=test_rmse_std, train_test)) %>%
ggplot(aes(x=iter, y=rmse_mean, colour = train_test)) +
geom_line() +
geom_point() +
geom_errorbar(aes(ymin=rmse_mean - rmse_std, ymax = rmse_mean + rmse_std)) +
expand_limits(y = 0) +
labs(title = "Training history")
# Train final model
xgb_model <- xgb.train(
data = train_xgb,
params = xgb_params,
nrounds = xgb_cv_res$best_iteration
)
# Save
saveRDS(xgb_cv_res, 'models/xgb_cv_results.rds')
xgb.save(xgb_model, 'models/xgb.model')
# ---- CV evaluation ----
# Variable importance
var_imp <- xgb.importance(feature_names = names(select(train, -improvement)), model = xgb_model)
# Plot
xgb.ggplot.importance(var_imp, top_n = 20)
# Get cv predictions
xgb_eval_df <- tibble(obs = train$improvement, pred = xgb_cv_res$pred)
# Summarise
xgb_res <- xgb_eval_df %>%
mutate(pred_category = ifelse(pred > 0, "improve", "decline/same"),
obs_category = ifelse(obs > 0, "improve", "decline/same")) %>%
filter(!is.na(pred), !is.na(obs)) %>%
summarise(rmse = RMSE(pred, obs),
r_square = R2(pred, obs),
sens = sensitivity(factor(pred_category), reference = factor(obs_category)),
spec = specificity(factor(pred_category), reference = factor(obs_category)),
pos_pred_rate = mean(obs_category[pred_category == "improve"] == "improve"),
neg_pred_rate = mean(obs_category[pred_category != "improve"] != "improve"),
num = n()) %>%
mutate(model = "xgboost") %>%
select(model, everything())
# Compare with lm
bind_rows(lm_res, xgb_res)
# Add predictions to data
cv_df <- all_train_df %>%
filter(season <= 18) %>%
mutate(pred_improvement = xgb_cv_res$pred) %>%
select(1:improvement, pred_improvement, everything())
# Create a csv file with some examples
examples <- cv_df %>%
group_by(sofifa_id) %>%
arrange(season) %>%
mutate(last_club = lag(club)) %>%
ungroup %>%
select(season, short_name, last_club, club, age, player_positions, potential, overall, overall_next, improvement, pred_improvement) %>%
arrange(desc(overall)) %>%
group_by(improvement > 0) %>%
filter((short_name == "A. Schürrle" & season == 17) |
(short_name == "M. Salah" & season == 18) |
row_number() %in% sample(1:nrow(.), 9)) %>%
ungroup %>%
arrange(desc(improvement))
# Save
write.csv(examples, 'data/example_cv_predictions.csv', row.names = FALSE)
# Are the top improvers predicted to get better?
cv_df %>% select(season, short_name, age, pos, overall, overall_next, improvement, pred_improvement) %>% arrange(desc(improvement))
# Do the top predicted improvers actually improve?
cv_df %>% select(season, short_name, age, pos, overall, overall_next, improvement, pred_improvement) %>% arrange(desc(pred_improvement))
# Do the players predicted to become top-class live up to their predictions?
cv_df %>%
filter(overall + pred_improvement > 80,
overall < 80) %>%
select(season, short_name, age, pos, overall, overall_next, improvement, pred_improvement) %>% arrange(desc(pred_improvement))
# Summarise this
cv_df %>%
filter(overall + pred_improvement > 80,
overall < 80) %>%
select(season, short_name, age, pos, overall, overall_next, improvement, pred_improvement) %>% arrange(desc(pred_improvement)) %>%
summarise(improved = mean(improvement > 0),
became_top_class = mean(overall_next > 80))
# Plot it
cv_df %>%
mutate(pred_top = overall + pred_improvement > 80) %>%
filter(overall + pred_improvement > 80 | overall_next > 80,
overall <= 80) %>%
ggplot(aes(x = pred_improvement, y = improvement, colour = pred_top)) +
geom_jitter(alpha = 0.5) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0)
# ---- Evaluate ----
# Predict on test set
preds <- predict(xgb_model, newdata = test_xgb)
# Add to data
test <- test %>%
mutate(pred_improvement = preds,
residual = improvement - pred_improvement) %>%
select(1:improvement, pred_improvement, residual, everything())
# Metrics
test %>%
filter(season == 19) %>%
mutate(pred = pred_improvement, obs = improvement) %>%
mutate(pred_category = ifelse(pred > 0, "improve", "decline/same"),
obs_category = ifelse(obs > 0, "improve", "decline/same")) %>%
filter(!is.na(pred), !is.na(obs)) %>%
summarise(rmse = RMSE(pred, obs),
r_square = R2(pred, obs),
mae = MAE(pred, obs),
sens = sensitivity(factor(pred_category), reference = factor(obs_category)),
spec = specificity(factor(pred_category), reference = factor(obs_category)),
pos_pred_rate = mean(obs_category[pred_category == "improve"] == "improve"),
neg_pred_rate = mean(obs_category[pred_category != "improve"] != "improve"),
num = n()) %>%
mutate(model = "xgboost") %>%
select(model, everything())
# Histogram of residuals
test %>%
ggplot(aes(x=residual)) +
geom_histogram(fill = "grey60", colour = "grey40") +
geom_vline(xintercept = mean(test$residual, na.rm = TRUE))
# Histogram of predictions vs actuals
test %>%
filter(season == 19) %>%
select(improvement, pred_improvement) %>%
gather(variable, value) %>%
ggplot(aes(x=value, fill=variable)) +
geom_density(colour = "grey40", alpha = 0.5) +
geom_vline(xintercept = mean(test$residual, na.rm = TRUE)) +
labs(title = "All players",
subtitle = "Distributions of predictions and actuals")
# Players that changed club?
test %>%
group_by(sofifa_id) %>%
arrange(season) %>%
filter(club != lead(club)) %>%
ungroup %>%
filter(season == 19) %>%
summarise(rmse = RMSE(pred_improvement, improvement),
mae = MAE(pred_improvement, improvement))
# Predictions vs actual distributions
test %>%
group_by(sofifa_id) %>%
arrange(season) %>%
filter(club != lead(club)) %>%
ungroup %>%
filter(season == 19) %>%
select(improvement, pred_improvement) %>%
gather(variable, value) %>%
ggplot(aes(x=value, fill=variable)) +
geom_density(colour = "grey40", alpha = 0.5) +
geom_vline(xintercept = mean(test$residual, na.rm = TRUE)) +
labs(title = "Players who changed clubs",
subtitle = "Distributions of predictions and actuals")
# Error is higher on these players
# Preds vs obs plot
# Colour by simpler positions
test %>%
filter(season == 19) %>%
ggplot(aes(x=pred_improvement, y=improvement, colour = pos)) +
geom_jitter(alpha = 0.5) +
geom_abline()
# Case studies
# Over estimates
test %>%
filter(residual < -5) %>%
select(short_name, age, player_positions, club, pred_improvement, improvement, overall, overall_next, potential)
# Under estimates
test %>%
filter(residual > 5) %>%
select(short_name, age, player_positions, club, pred_improvement, improvement, overall, overall_next, potential)
# Confusion matrix
conf_mat <- test %>%
filter(season == 19) %>%
mutate(pred_change = ifelse(pred_improvement > 0, "Up", "Down/Same"),
actual_change = ifelse(improvement > 0, "Up", "Down/Same")) %>%
select(pred_change, actual_change) %>%
group_by(pred_change, actual_change) %>%
summarise(num = n())
# Display
conf_mat %>% spread(key = actual_change, value = num)
# Plot
conf_mat %>%
ggplot(aes(x = pred_change, y = actual_change)) +
geom_tile(aes(fill = num, alpha = num), colour = "white") +
geom_text(aes(label = sprintf("%1.0f", num)), vjust = 1) +
#scale_fill_gradient(low = "blue", high = "red") +
theme_bw() +
coord_equal() +
theme(legend.position = "none")
# Sens and spec
conf_mat %>%
ungroup %>%
filter(actual_change == "Up") %>%
summarise(sens = .$num[2]/sum(num))
conf_mat %>%
ungroup %>%
filter(actual_change == "Down/Same") %>%
summarise(spec = .$num[1]/sum(num))
# Positive predictive value (precision)
# Number of correct positive predictions over all positive predictions
# "If we predict that you'll get better, how likely is it that you actually will?"
conf_mat %>%
ungroup %>%
filter(pred_change == "Up") %>%
summarise(precision = num[2]/sum(num))