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ctree_teamplayer.R
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library(dplyr)
library(partykit)
df = read.csv("ctree_teamplayer_diff_df.csv")
df$X = NULL
df$season = as.factor(df$season)
df$league_id = as.factor(df$league_id)
#df$fold = NULL
#df$home = NULL
columns = colnames(df)
top3_col = c("result", columns[grepl( "^top3", columns) | grepl( "max", columns)], columns[84:101])
avg_col = c("result", columns[grepl( "^avg", columns) | grepl( "max", columns)], columns[84:101])
top3_col = top3_col[!grepl("avg_potential", top3_col)]
avg_col = avg_col[!grepl("avg_potential", avg_col)]
#--------------------------------------------------------------------------------------------------------------
ctree_fit_list = list()
ctree_losses = rep(0,17)
for (i in 1:17){
fold_i_train_df = df[df[,columns[83+i]]=="train", top3_col]
fold_i_train_df[,columns[84:100]] = NULL
fold_i_train_df$result = as.factor(fold_i_train_df$result)
fold_i_test_df = df[df[,columns[83+i]]=="test", top3_col]
fold_i_test_df[,columns[84:100]] = NULL
fold_i_test_df$result = as.factor(fold_i_test_df$result)
ctree_fit_fold_i_train <- ctree(result ~ ., data = fold_i_train_df,
control=ctree_control(testtype = "Bonferroni",
mincriterion = 0.99,
#maxdepth=6,
mtry=12))
ctree_fit_list[[i]] = ctree_fit_fold_i_train
pred = predict(ctree_fit_fold_i_train, newdata=fold_i_test_df, type = "prob")
pred_df = data.frame(result=fold_i_test_df$result)
pred_df= cbind(pred_df, pred)
pred_df$loss = 0
pred_df[pred_df$result==-1,"loss"] = -1*log(pred_df[pred_df$result== -1,2])
pred_df[pred_df$result==0,"loss"] = -1*log(pred_df[pred_df$result==- 0,3])
pred_df[pred_df$result==1,"loss"] = -1*log(pred_df[pred_df$result== 1,4])
ctree_losses[i] = mean(pred_df$loss)
}
#--------------------------------------------------------------------------------------------------------------
ctree_fit_2_list = list()
ctree_losses_2 = rep(0,17)
for (i in 1:17){
fold_i_train_df = df[df[,columns[83+i]]=="train", avg_col]
fold_i_train_df[,columns[84:100]] = NULL
fold_i_train_df$result = as.factor(fold_i_train_df$result)
fold_i_test_df = df[df[,columns[83+i]]=="test", avg_col]
fold_i_test_df[,columns[84:100]] = NULL
fold_i_test_df$result = as.factor(fold_i_test_df$result)
ctree_fit_2_fold_i_train <- ctree(result ~ ., data = fold_i_train_df,
control=ctree_control(testtype = "Bonferroni",
mincriterion = 0.99,
#maxdepth=6,
mtry=6))
ctree_fit_2_list[[i]] = ctree_fit_2_fold_i_train
pred = predict(ctree_fit_2_fold_i_train, newdata=fold_i_test_df, type = "prob")
pred_df = data.frame(result=fold_i_test_df$result)
pred_df= cbind(pred_df, pred)
pred_df$loss = 0
pred_df[pred_df$result==-1,"loss"] = -1*log(pred_df[pred_df$result== -1,2])
pred_df[pred_df$result==0,"loss"] = -1*log(pred_df[pred_df$result==- 0,3])
pred_df[pred_df$result==1,"loss"] = -1*log(pred_df[pred_df$result== 1,4])
ctree_losses_2[i] = mean(pred_df$loss)
}
#--------------------------------------------------------------------------------------------------------------
ctree_fit_3_list = list()
ctree_losses_3 = rep(0,17)
for (i in 1:17){
fold_i_train_df = df[df[,columns[83+i]]=="train", avg_col]
fold_i_train_df[,columns[84:100]] = NULL
fold_i_train_df$result = as.factor(fold_i_train_df$result)
fold_i_test_df = df[df[,columns[83+i]]=="test", avg_col]
fold_i_test_df[,columns[84:100]] = NULL
fold_i_test_df$result = as.factor(fold_i_test_df$result)
ctree_fit_3_fold_i_train <- ctree(result ~ ., data = fold_i_train_df,
control=ctree_control(testtype = "Bonferroni",
mincriterion = 0.99,
#maxdepth=6,
mtry=12))
ctree_fit_3_list[[i]] = ctree_fit_3_fold_i_train
pred = predict(ctree_fit_3_fold_i_train, newdata=fold_i_test_df, type = "prob")
pred_df = data.frame(result=fold_i_test_df$result)
pred_df= cbind(pred_df, pred)
pred_df$loss = 0
pred_df[pred_df$result==-1,"loss"] = -1*log(pred_df[pred_df$result== -1,2])
pred_df[pred_df$result==0,"loss"] = -1*log(pred_df[pred_df$result==- 0,3])
pred_df[pred_df$result==1,"loss"] = -1*log(pred_df[pred_df$result== 1,4])
ctree_losses_3[i] = mean(pred_df$loss)
}
plot(ctree_fit_3_list[[10]])
#"avg_overall_rating", "avg_volleys_imp", "avg_long_passing", "avg_finishing, "avg_ball_control
#"avg_dribbling", "avg_short_passing", "avg_reactions", "avg_dribbling", "avg_vision_imp", "avg_penalties"
#"avg_agility_imp"