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models.R
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executable file
·264 lines (241 loc) · 12.3 KB
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# Multitreatment uplift modeling approaches
# Based on single treatment uplift modeling by Floris Devriendt
model_mtum <- function(df_train,
treatment_1,
treatment_2,
outcome,
outcome_positive,
outcome_negative,
features,
model){
seed <- 100
if (model == "SMALR"){
# Factorize outcome variable
df_train[,outcome] <- factor(df_train[,outcome])
levels(df_train[,outcome]) <- c(outcome_negative, outcome_positive)
# DF treatment_1
df_treatment_1 <- subset(df_train, df_train[,treatment_1] == 1)
# DF treatment_2
df_treatment_2 <- subset(df_train, df_train[,treatment_2] == 1)
# DF control
df_control <- subset(df_train, df_train[,treatment_1] == 0 & df_train[,treatment_2] == 0)
# Training
set.seed(seed)
ctrl <- trainControl(method = "none", summaryFunction = twoClassSummary, classProbs = TRUE, savePredictions = TRUE)
# Model treatment 1
set.seed(seed)
model_treatment_1 <- train(df_treatment_1[,features], df_treatment_1[,outcome], method="glmStepAIC", family=binomial(), metric="ROC", trControl=ctrl)
# Model treatment 2
set.seed(seed)
model_treatment_2 <- train(df_treatment_2[,features], df_treatment_2[,outcome], method="glmStepAIC", family=binomial(), metric="ROC", trControl=ctrl)
# Model control
set.seed(seed)
model_control <- train(df_control[,features], df_control[,outcome], method="glmStepAIC", family=binomial(), metric="ROC", trControl=ctrl)
ans <- list(model_t1 = model_treatment_1,
model_t2 = model_treatment_2,
model_control = model_control)
return(ans)
} else if (model == "SMARF"){
# Factorize outcome variable
df_train[,outcome] <- factor(df_train[,outcome])
levels(df_train[,outcome]) <- c(outcome_negative, outcome_positive)
# DF treatment_1
df_treatment_1 <- subset(df_train, df_train[,treatment_1] == 1)
# DF treatment_2
df_treatment_2 <- subset(df_train, df_train[,treatment_2] == 1)
# DF control
df_control <- subset(df_train, df_train[,treatment_1] == 0 & df_train[,treatment_2] == 0)
# Training
set.seed(seed)
ctrl <- trainControl(method = "none", summaryFunction = twoClassSummary, classProbs = TRUE, savePredictions = TRUE)
# Model treatment 1
set.seed(seed)
model_treatment_1 <- train(df_treatment_1[,features], df_treatment_1[,outcome], method="rf", ntree = 500, metric="ROC", trControl=ctrl)
# Model treatment 2
set.seed(seed)
model_treatment_2 <- train(df_treatment_2[,features], df_treatment_2[,outcome], method="rf", ntree = 500, metric="ROC", trControl=ctrl)
# Model control
set.seed(seed)
model_control <- train(df_control[,features], df_control[,outcome], method="rf", ntree = 500, metric="ROC", trControl=ctrl)
ans <- list(model_t1 = model_treatment_1,
model_t2 = model_treatment_2,
model_control = model_control)
return(ans)
} else if (model == "DIALR"){
# Factorize outcome variable
df_train[,outcome] <- factor(df_train[,outcome])
levels(df_train[,outcome]) <- c(outcome_negative, outcome_positive)
# Feature selection
if(length(features) <= 5){
features <- features
} else if(length(features) > 5){
# DF treatment_1
df_treatment_1 <- subset(df_train, df_train[,treatment_1] == 1)
# DF treatment_2
df_treatment_2 <- subset(df_train, df_train[,treatment_2] == 1)
# DF control
df_control <- subset(df_train, df_train[,treatment_1] == 0 & df_train[,treatment_2] == 0)
set.seed(seed)
ctrl <- trainControl(method = "none", summaryFunction = twoClassSummary, classProbs = TRUE, savePredictions = TRUE)
set.seed(seed)
model_treatment_1 <- train(df_treatment_1[,features], df_treatment_1[,outcome], method="glmStepAIC", family=binomial(), metric="ROC", trControl=ctrl)
final_model_treatment_1 <- model_treatment_1[["finalModel"]][["formula"]][[3]]
features_model_treatment_1 <- all.vars(final_model_treatment_1)
set.seed(seed)
model_treatment_2 <- train(df_treatment_2[,features], df_treatment_2[,outcome], method="glmStepAIC", family=binomial(), metric="ROC", trControl=ctrl)
final_model_treatment_2 <- model_treatment_2[["finalModel"]][["formula"]][[3]]
features_model_treatment_2 <- all.vars(final_model_treatment_2)
features <- union(features_model_treatment_1, features_model_treatment_2)
set.seed(seed)
model_control <- train(df_control[,features], df_control[,outcome], method="glmStepAIC", family=binomial(), metric="ROC", trControl=ctrl)
final_model_control <- model_control[["finalModel"]][["formula"]][[3]]
features_model_treatment_control <- all.vars(final_model_control)
features <- union(features, features_model_treatment_control)
}
# Interactions
# Treatment 1
xt1 <- df_train[,features] * df_train[,treatment_1]
colnames(xt1) <- paste("Inter1", colnames(xt1), sep = "_")
# Treatment 2
xt2 <- df_train[,features] * df_train[,treatment_2]
colnames(xt2) <- paste("Inter2", colnames(xt2), sep = "_")
df_interactions <- cbind(df_train[,c(features,treatment_1,treatment_2, outcome)], xt1, xt2)
# Features
features <- c(features, colnames(xt1), colnames(xt2), treatment_1, treatment_2)
# Training
set.seed(seed)
ctrl <- trainControl(method = "none", summaryFunction = twoClassSummary, classProbs = TRUE, savePredictions = TRUE)
set.seed(seed)
model <- train(df_interactions[,features], df_interactions[,outcome], method="glm", family=binomial(), metric="ROC", trControl=ctrl)
ans <- list(model)
return(ans)
} else if (model == "DIARF"){
# Factorize outcome variable
df_train[,outcome] <- factor(df_train[,outcome])
levels(df_train[,outcome]) <- c(outcome_negative, outcome_positive)
# Feature selection
if(length(features) <= 5){
features <- features
} else if(length(features) > 5){
# DF treatment_1
df_treatment_1 <- subset(df_train, df_train[,treatment_1] == 1)
# DF treatment_2
df_treatment_2 <- subset(df_train, df_train[,treatment_2] == 1)
# DF control
df_control <- subset(df_train, df_train[,treatment_1] == 0 & df_train[,treatment_2] == 0)
set.seed(seed)
ctrl <- trainControl(method = "none", summaryFunction = twoClassSummary, classProbs = TRUE, savePredictions = TRUE)
set.seed(seed)
model_treatment_1 <- train(df_treatment_1[,features], df_treatment_1[,outcome], method="glmStepAIC", family=binomial(), metric="ROC", trControl=ctrl)
final_model_treatment_1 <- model_treatment_1[["finalModel"]][["formula"]][[3]]
features_model_treatment_1 <- all.vars(final_model_treatment_1)
set.seed(seed)
model_treatment_2 <- train(df_treatment_2[,features], df_treatment_2[,outcome], method="glmStepAIC", family=binomial(), metric="ROC", trControl=ctrl)
final_model_treatment_2 <- model_treatment_2[["finalModel"]][["formula"]][[3]]
features_model_treatment_2 <- all.vars(final_model_treatment_2)
features <- union(features_model_treatment_1, features_model_treatment_2)
set.seed(seed)
model_control <- train(df_control[,features], df_control[,outcome], method="glmStepAIC", family=binomial(), metric="ROC", trControl=ctrl)
final_model_control <- model_control[["finalModel"]][["formula"]][[3]]
features_model_treatment_control <- all.vars(final_model_control)
features <- union(features, features_model_treatment_control)
}
# Interactions
# Treatment 1
xt1 <- df_train[,features] * df_train[,treatment_1]
colnames(xt1) <- paste("Inter1", colnames(xt1), sep = "_")
# Treatment 2
xt2 <- df_train[,features] * df_train[,treatment_2]
colnames(xt2) <- paste("Inter2", colnames(xt2), sep = "_")
df_interactions <- cbind(df_train[,c(features,treatment_1,treatment_2, outcome)], xt1, xt2)
# Features
features <- c(features, colnames(xt1), colnames(xt2), treatment_1, treatment_2)
# Training
set.seed(seed)
ctrl <- trainControl(method = "none", summaryFunction = twoClassSummary, classProbs = TRUE, savePredictions = TRUE)
set.seed(seed)
model <- train(df_interactions[,features], df_interactions[,outcome], method="rf", ntree = 500, metric="ROC", trControl=ctrl)
ans <- model
return(ans)
} else if (model == "CKNN"){
# Factorize outcome variable
df_train[,outcome] <- factor(df_train[,outcome])
levels(df_train[,outcome]) <- c(outcome_negative, outcome_positive)
# Treatment indicator in training set
df_train$indicator_treatment <- ifelse(df_train[,treatment_1] == 1, 1, ifelse(df_train[,treatment_2] == 1, 2, 0))
df_train$indicator_treatment <- as.factor(df_train$indicator_treatment)
# Treatment indicator in test set
df_test$indicator_treatment <- ifelse(df_test[,treatment_1] == 1, 1, ifelse(df_test[,treatment_2] == 1, 2, 0))
df_test$indicator_treatment <- as.factor(df_test$indicator_treatment)
# Training
set.seed(seed)
model <- upliftKNN(df_train[,features], df_test[,features], df_train[,"outcome"], df_train[,"indicator_treatment"], k = 10)
ans <- model
return(ans)
} else if (model == "NUARF"){
# Treatment 1 vs. control
df_t1 <- df_train
df_t1 <- subset(df_t1, df_t1[,treatment_1] == 1 | df_t1[,treatment_2] == 0)
variables_t1 <- c(features, treatment_1, outcome)
df_t1 <- df_t1[,variables_t1]
t1 <- df_t1[,treatment_1]
y1 <- df_t1[,outcome]
# Treatment 2 vs. control
df_t2 <- df_train
df_t2 <- subset(df_t2, df_t2[,treatment_1] == 0 | df_t2[,treatment_2] == 1)
variables_t2 <- c(features, treatment_2, outcome)
df_t2 <- df_t2[,variables_t2]
t2 <- df_t2[,treatment_2]
y2 <- df_t2[,outcome]
# Training
# Formula Treatment 1 vs. control
formula_t1 <- as.formula(paste("y1 ~", "trt(t1) +", paste(features, sep = "", collapse = "+")))
formula_t2 <- as.formula(paste("y2 ~", "trt(t2) +", paste(features, sep = "", collapse = "+")))
set.seed(seed)
model_t1 <- upliftRF(formula_t1, data = df_t1, ntree = 500, split_method = "KL")
set.seed(seed)
model_t2 <- upliftRF(formula_t2, data = df_t2, ntree = 500, split_method = "KL")
ans <- list(model_t1 = model_t1,
model_t2 = model_t2)
return(ans)
} else if (model == "NUACCIF"){
# Treatment 1 vs. control
df_t1 <- df_train
df_t1 <- subset(df_t1, df_t1[,treatment_1] == 1 | df_t1[,treatment_2] == 0)
variables_t1 <- c(features, treatment_1, outcome)
df_t1 <- df_t1[,variables_t1]
t1 <- df_t1[,treatment_1]
y1 <- df_t1[,outcome]
# Treatment 2 vs. control
df_t2 <- df_train
df_t2 <- subset(df_t2, df_t2[,treatment_1] == 0 | df_t2[,treatment_2] == 1)
variables_t2 <- c(features, treatment_2, outcome)
df_t2 <- df_t2[,variables_t2]
t2 <- df_t2[,treatment_2]
y2 <- df_t2[,outcome]
# Training
# Formula Treatment 1 vs. control
formula_t1 <- as.formula(paste("y1 ~", "trt(t1) +", paste(features, sep = "", collapse = "+")))
formula_t2 <- as.formula(paste("y2 ~", "trt(t2) +", paste(features, sep = "", collapse = "+")))
set.seed(seed)
model_t1 <- ccif(formula_t1, data = df_t1, ntree = 500, split_method = "ED", pvalue = 0.01)
set.seed(seed)
model_t2 <- ccif(formula_t2, data = df_t2, ntree = 500, split_method = "ED", pvalue = 0.01)
ans <- list(model_t1 = model_t1,
model_t2 = model_t2)
return(ans)
} else if (model == "MMOALR"){
formula <- as.formula(paste("multi_outcome ~", paste(features, sep = "", collapse = "+")))
set.seed(seed)
model <- nnet::multinom(formula, data = df_train)
ans <- model
return(ans)
} else if (model == "MMOARF"){
set.seed(seed)
formula <- as.formula(paste("multi_outcome ~", paste(features, sep = "", collapse = "+")))
df_train$multi_outcome <- as.factor(df_train$multi_outcome)
model <- randomForest::randomForest(formula, data = df_train,ntree = 500)
ans <- model
return(ans)
}
}