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gender_knn.R
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gender_knn.R
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library(class)
library(ggplot2)
library(reshape2)
library(devtools)
library(ggbiplot)
source('./load_data.R')
source('./load_user_pc.R')
n_user_pc_df.pca <- prcomp(n_user_pc_df, center = TRUE)
user_cat <- sapply(as.list(rownames(n_user_pc_df)), function(id){purchase_sum[purchase_sum['User_ID'] == id,'Gender']})
pca.plot <- ggplot(as.data.frame(n_user_pc_df.pca$x), aes(x=PC1, y=PC2, color=user_cat)) +
geom_point()
ggsave('figures/pca_categories.pdf', plot = pca.plot, device='pdf', width = 4, height = 2.5)
pca.plot.axis <- ggbiplot(n_user_pc_df.pca, alpha=1, groups=user_cat) + coord_equal(ratio = 0.70)
pca.plot.axis$layers <- c(pca.plot.axis$layers[[2]], pca.plot.axis$layers[[3]], pca.plot.axis$layers[[1]])
ggsave('figures/pca_categories_axis.pdf', plot = pca.plot.axis, device='pdf', width = 5, height = 3.5)
scatter3D(n_user_pc_df.pca$x[,1], n_user_pc_df.pca$x[,2], n_user_pc_df.pca$x[,3],
colvar = NULL, col = c("#1B9E77", "#D95F02"), col.var = as.integer(user_cat),
theta = 275, phi = 10)
user_gender <- sapply(as.list(rownames(n_user_pc_df)), function(id){purchase_sum[purchase_sum['User_ID'] == id,'Gender']})
set.seed(10)
ran <- sample(1:nrow(n_user_pc_df), 0.70 * nrow(n_user_pc_df))
user_train <- as.data.frame(n_user_pc_df[ran,])
user_test <- n_user_pc_df[-ran,]
user_train_cat <- user_gender[ran]
user_test_cat <- user_gender[-ran]
eval_best_k_knn <- function(user_df, user_z, N=10, Ks=seq(1, 10, 1)){
res_error <- rep(0, length(Ks))
res_prod <- rep(0, length(Ks))
res_MM <- rep(0, length(Ks))
res_FF <- rep(0, length(Ks))
i <- 1
for (k in Ks){
avg_error <- 0
avg_MM <- 0
avg_FF <- 0
for (n in 1:N){
ran <- sample(1:nrow(user_df), 0.70 * nrow(user_df))
user_train <- as.data.frame(user_df[ran,])
user_val <- user_df[-ran,]
user_train_cat <- user_z[ran]
user_val_cat <- user_z[-ran]
user_train_F <- user_train[user_train_cat == 'F',]
user_train <- rbind(user_train, user_train_F)
user_train <- rbind(user_train, user_train_F)
user_train_cat <- factor(c(as.character(user_train_cat), as.character(rep('F', 2*sum(user_train_cat == 'F')))), levels=levels(user_train_cat))
pr <- knn(user_train,user_val,cl=user_train_cat,k=k)
avg_error <- avg_error + (mean(pr == user_val_cat)) / N
confusion_matrix_knn <- table(pr,user_val_cat)
confusion_matrix_knn_normalised <- t(apply(confusion_matrix_knn, 2, function(line){
line / sum(line)
}))
avg_MM <- avg_MM + confusion_matrix_knn_normalised[2,2] / N
avg_FF <- avg_FF + confusion_matrix_knn_normalised[1,1] / N
}
res_error[i] <- avg_error
res_MM[i] <- avg_MM
res_FF[i] <- avg_FF
i<-i+1
}
res <- NULL
res$error <- res_error
res$MM <- res_MM
res$FF <- res_FF
res
}
Ks <- seq(1, 50, 1)
k_errors <- eval_best_k_knn(user_train, user_train_cat, Ks=Ks, N=1)
k_errors.plot <- ggplot(mapping = aes(x = Ks[1:length(k_errors$MM)])) +
geom_line(aes(y = k_errors$MM, colour = "MM")) +
geom_line(aes(y = k_errors$FF, colour = "FF")) +
geom_line(aes(y = (k_errors$error), colour = "Good")) +
xlab('k') +
ylab('')
print(k_errors.plot)
ggsave('figures/knn_error_clone.pdf', plot = k_errors.plot, device='pdf', width = 4, height = 3)
Ks <- seq(1, 100, 2)
k_errors <- eval_best_k_knn(user_train, user_train_cat, Ks=Ks, N=10)
k_errors.plot <- ggplot(mapping = aes(x = Ks[1:length(k_errors$prod)])) +
geom_line(aes(y = k_errors$prod, colour = "Product")) +
geom_line(aes(y = k_errors$MM, colour = "MM")) +
geom_line(aes(y = k_errors$FF, colour = "FF")) +
geom_line(aes(y = k_errors$error, colour = "Error")) +
xlab('k') +
ylab('')
print(k_errors.plot)
ggsave('figures/knn_error.pdf', plot = k_errors.plot, device='pdf', width = 4, height = 3)
ggplot(mapping = aes(x = Ks[1:length(k_errors$prod)])) +
geom_line(aes(y = k_errors$error, colour = "Error")) +
xlab('k') +
ylab('')
user_train_F <- user_train[user_train_cat == 'F',]
user_train_clone <- rbind(user_train, user_train_F)
user_train_clone <- rbind(user_train_clone, user_train_F)
user_train_cat_clone <- factor(c(as.character(user_train_cat), as.character(rep('F', 2*sum(user_train_cat == 'F')))), levels=levels(user_train_cat))
pr <- knn(user_train_clone,user_test,cl=user_train_cat_clone,k=25)
confusion_matrix_knn <- table(pr,user_test_cat)
print(confusion_matrix_knn)
confusion_matrix_knn_normalised <- apply(confusion_matrix_knn, 2, function(line){
line / sum(line)
})
confusion_matrix_knn_normalised
melted_confusion_matrix <- melt(confusion_matrix_knn_normalised)
colnames(melted_confusion_matrix) <- c('Prédiction', 'Réalité', 'values')
confusion_matrix.plot <- ggplot(data = melted_confusion_matrix, aes(x=Réalité, y=Prédiction, fill=values)) +
geom_tile(aes(fill = values)) +
geom_text(aes(label = round(values, 2)))
print(confusion_matrix.plot)
ggsave('figures/confusion_matrix_categories_clone.pdf', plot = confusion_matrix.plot, device='pdf', width = 4, height = 3)
inertia <- function (df){
mu <- apply(df, 2, mean)
mean(apply(df, 1, function(x){
dist(rbind(mu, x))
}))
}
inertia(n_user_pc_df[user_gender == 'M',])
inertia(n_user_pc_df[user_gender == 'F',])
### SMOTE
eval_best_k_knn_SMOTE <- function(user_df, user_z,N=10, Ks=seq(1, 10, 1), replication_factor=1){
res_error <- rep(0, length(Ks))
res_prod <- rep(0, length(Ks))
res_MM <- rep(0, length(Ks))
res_FF <- rep(0, length(Ks))
i <- 1
for (n in 1:N){
ran <- sample(1:nrow(user_df), 0.70 * nrow(user_df))
user_train <- as.data.frame(user_df[ran,])
user_train_M <- user_train[user_z[ran] == 'M',]
user_train_F <- user_train[user_z[ran] == 'F',]
user_train_F_SMOTE <- SMOTE(user_train_F, replication_factor, 5)
user_train_SMOTE <- rbind(user_train_M, user_train_F_SMOTE)
user_train_cat <- factor(c(as.character(rep('M', dim(user_train_M)[1])), as.character(rep('F', dim(user_train_F_SMOTE)[1]))), levels=levels(user_train_cat))
user_val <- user_df[-ran,]
user_val_cat <- user_z[-ran]
i <- 1
for (k in Ks){
avg_error <- 0
avg_MM <- 0
avg_FF <- 0
pr <- knn(user_train_SMOTE ,user_val,cl=user_train_cat,k=k)
confusion_matrix_knn <- table(pr,user_val_cat)
res_error[i] <- res_error[i] + (mean(pr == user_val_cat)) / N
confusion_matrix_knn_normalised <- t(apply(confusion_matrix_knn, 2, function(line){
line / sum(line)
}))
res_MM[i] <- res_MM[i] + confusion_matrix_knn_normalised[2,2] / N
res_FF[i] <- res_FF[i] + confusion_matrix_knn_normalised[1,1] / N
i<-i+1
}
}
res <- NULL
res$error <- res_error
res$MM <- res_MM
res$FF <- res_FF
res
}
replication_factor <- 2.25
#user_M <- n_user_pc_df[user_gender == 'M',]
#user_F <- n_user_pc_df[user_gender == 'F',]
#user_F_SMOTE <- SMOTE(user_F, replication_factor, 10)
#user_SMOTE <- rbind(user_M, user_F_SMOTE)
#user_gender_SMOTE <- factor(c(as.character(rep('M', dim(user_M)[1])), as.character(rep('F', dim(user_F_SMOTE)[1]))), levels=levels(user_train_cat))
user_train_M_SMOTE <- user_train[user_gender[ran] == 'M',]
user_train_F <- user_train[user_gender[ran] == 'F',]
user_train_F_SMOTE <- SMOTE(user_train_F, replication_factor, 5)
user_train_SMOTE <- rbind(user_train_M, user_train_F_SMOTE)
user_train_cat_SMOTE <- factor(c(as.character(rep('M', dim(user_train_M)[1])), as.character(rep('F', dim(user_train_F_SMOTE)[1]))), levels=levels(user_train_cat))
pr <- knn(user_train_SMOTE ,user_test,cl=user_train_cat_SMOTE,k=25)
confusion_matrix_knn <- table(pr,user_test_cat)
print(confusion_matrix_knn)
confusion_matrix_knn_normalised <- apply(confusion_matrix_knn, 2, function(line){
line / sum(line)
})
confusion_matrix_knn_normalised
melted_confusion_matrix <- melt(confusion_matrix_knn_normalised)
colnames(melted_confusion_matrix) <- c('Réalité', 'Prédiction', 'values')
confusion_matrix.plot <- ggplot(data = melted_confusion_matrix, aes(x=Prédiction, y=Réalité, fill=values)) +
geom_tile(aes(fill = values)) +
geom_text(aes(label = round(values, 2)))
print(confusion_matrix.plot)
replication_factor <- 2
Ks <- seq(1, 25, 5)
k_errors <- eval_best_k_knn_SMOTE(user_train, user_train_cat ,Ks=Ks, N=3, replication_factor=replication_factor)
k_errors.plot <- ggplot(mapping = aes(x = Ks[1:length(k_errors$MM)])) +
geom_line(aes(y = k_errors$MM, colour = "MM")) +
geom_line(aes(y = k_errors$FF, colour = "FF")) +
geom_line(aes(y = k_errors$error, colour = "Good")) +
xlab('k') +
ylab('')
print(k_errors.plot)
ggsave('figures/k_SMOTE_3.pdf', plot = k_errors.plot, device='pdf', width = 4, height = 3)
### 2D
Ks <- seq(1, 25, 1)
rfs <- seq(0.6, 4, 0.2)
res.error <- matrix(ncol=length(Ks), nrow=length(rfs))
res.MM <- matrix(ncol=length(Ks), nrow=length(rfs))
res.FF <- matrix(ncol=length(Ks), nrow=length(rfs))
index <- 1
for (rf in rfs){
print(index)
k_errors <- eval_best_k_knn_SMOTE(user_SMOTE, user_gender_SMOTE,Ks=Ks, N=5, replication_factor=rf)
res.error[index, ] <- k_errors$error
res.FF[index, ] <- k_errors$FF
res.MM[index, ] <- k_errors$MM
index <- index + 1
}
image(rfs, Ks,res.FF)
image(rfs, Ks,res.MM)
image(rfs, Ks,res.error)
contour(rfs, Ks,res.error, levels=seq(0.0, 1, 0.025), add=T)
Ks <- seq(1, 25, 1)
rfs <- seq(0.6, 4, 0.2)
zmin <- min(res.error, res.MM, res.FF)
zmax <- max(res.error, res.MM, res.FF)
pdf('./figures/rf_k.pdf', height=2, width=6)
par(mfrow=c(1,3))
par(oma=c( 0,0,0,4))
#
image(rfs, Ks,res.error, xlab='Facteur de replication', ylab='k', zlim=c(zmin,zmax), col=tim.colors(), main='Score général')
contour(rfs, Ks,res.error, levels=seq(0, 1, 0.05), add=T)
image(rfs, Ks,res.MM, xlab='Facteur de replication', ylab='k', zlim=c(zmin,zmax), col=tim.colors(), main='Score MM')
contour(rfs, Ks,res.MM, levels=c(0.5, 0.6, 0.7, 0.8, 0.9), add=T)
image(rfs, Ks,res.FF, xlab='Facteur de replication', ylab='k', zlim=c(zmin,zmax), col=tim.colors(), main='Score FF')
contour(rfs, Ks,res.FF, levels=c(0.5, 0.6, 0.7, 0.8), add=T)
par(oma=c( 0,0,0,1))
image.plot(legend.only=TRUE, zlim=c(zmin,zmax))
dev.off()
persp(rfs, Ks,res.FF, xlab='Replication factor', ylab='k',zlab='Good', zlim=c(0,1), col='blue', theta=-30)
persp(rfs, Ks,res.MM)
persp(rfs, Ks,res.error)
par(mfrow=c(1,1))
image(rfs, Ks, abs(res.MM - res.FF) < 0.03, xlab='Facteur de replication', ylab='k', col=tim.colors(), main='Score')
contour()