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res_train_31800000_180428.R
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if (!require("pacman")) install.packages("pacman")
pacman::p_load(knitr, tidyverse, highcharter, data.table, lubridate, pROC, tictoc, DescTools, lightgbm)
set.seed(71)
options(scipen = 9999, warn = -1, digits= 4)
train <- fread("train_red3.csv",
showProgress = FALSE)
X <- copy(train)[, -c("click_time", "attributed_time", "nextClick", "is_attributed"), with=F]
fea <- c("ip", "app", "device", "os", "channel")
for (f in fea) {
levels <- sort(names(which(table(X[[f]]) > 300)))
X[[f]] <- factor(X[[f]], levels=levels)
}
colnames(X) <- c("ip_F", "app_F", "device_F", "os_F", "channel_F")
train <- data.frame(train, X[, 2:5])
rm(X)
gc()
gc()
train2 <- fread("train_reduce_2.csv",
col.names =c("ip", "app", "device", "os", "channel", "click_time",
"is_attributed" , "nextClick"),
showProgress = FALSE)
most_freq_hours_in_test_data <- c("4","5","9","10","13","14")
least_freq_hours_in_test_data <- c("6","11","15")
train <- train %>% mutate(wday = Weekday(click_time),
hour = hour(click_time),
in_test_hh = ifelse(hour %in% most_freq_hours_in_test_data, 1,
ifelse(hour %in% least_freq_hours_in_test_data, 3, 2))) %>%
select(-c(click_time)) %>%
add_count(ip, wday, in_test_hh) %>% rename("nip_day_test_hh" = n) %>%
select(-c(in_test_hh)) %>%
add_count(os, device) %>% rename("n_os_dev" = n) %>%
add_count(os, app, channel) %>% rename("n_os_app_chan" = n) %>%
add_count(app, channel) %>% rename("n_app_chan" = n) %>%
add_count(ip, wday, hour) %>% rename("n_ip" = n) %>%
add_count(ip, wday, hour, app, os) %>% rename("n_ip_app_os" = n)
invisible(gc())
invisible(gc())
temp <- train %>%
group_by(ip, app, os) %>%
summarise(
mean_h_ip_app_os = mean(hour),
sd_h_ip_app_os = sd(hour))
train <- train %>%
left_join(temp, by = c("ip", "app", "os")) %>%
select(-c(ip))
rm(temp)
invisible(gc())
categorical_features = c("app", "device", "os", "channel", "hour",
"app_F", "device_F", "os_F", "channel_F",
"app_SD", "device_SD", "os_SD", "channel_SD")
train1 <- filter(train, wday < 4)
train2 <- train1 %>%
select(-c(is_attributed, wday))
dtrain = lgb.Dataset(data.matrix(train2),
label = as.numeric(train1$is_attributed),
categorical_feature = categorical_features)
rm(train1, train2)
invisible(gc())
train3 <- filter(train, wday == 4)
train4 <- train3 %>%
select(-c(is_attributed, wday))
dvalid = lgb.Dataset(data.matrix(train4),
label = as.numeric(train3$is_attributed),
categorical_feature = categorical_features)
rm(train, train4, train3)
invisible(gc())
params = list(objective = "binary",
metric = "auc",
learning_rate= 0.1,
num_leaves= 7,
max_depth= 3,
min_child_samples= 100,
max_bin= 255, # RAM dependent as per LightGBM documentation
subsample= 0.7,
subsample_freq= 1,
colsample_bytree= 0.7,
min_child_weight= 0,
min_split_gain= 0,
scale_pos_weight=99.75) # calculated for this dataset
set.seed(71)
model <- lgb.train(params, dtrain, valids = list(validation = dvalid), nthread = 7,
nrounds = 2000, verbose= 1, early_stopping_rounds = 30, eval_freq = 25)
kable(lgb.importance(model, percentage = TRUE))
#Predict
test <- fread("test.csv", colClasses = list(numeric=2:6), showProgress = FALSE)
sub <- data.table(click_id = test$click_id, is_attributed = NA)
test$click_id <- NULL
invisible(gc())
X <- copy(test)[, -c("click_time", "attributed_time", "nextClick", "is_attributed"), with=F]
fea <- c("ip", "app", "device", "os", "channel")
for (f in fea) {
levels <- sort(names(which(table(X[[f]]) > 300)))
X[[f]] <- factor(X[[f]], levels=levels)
}
colnames(X) <- c("ip_F", "app_F", "device_F", "os_F", "channel_F")
test <- data.frame(test, X[, 2:5])
rm(X)
gc()
gc()
test2 <- fread("test.csv",
col.names =c("ip", "app", "device", "os", "channel", "click_time",
"is_attributed" , "nextClick"),
showProgress = FALSE)
test2[, (fea) := lapply(.SD, as.factor), .SDcols = fea
][, (fea) := lapply(.SD, fct_lump, prop=0.002), .SDcols = fea
][, c("click_time", "is_attributed") := NULL]
colnames(test2) <- c("ip_SD", "app_SD", "device_SD", "os_SD", "channel_SD" , "nextClick")
test <- data.frame(test, test2[, 2:5])
rm(test2)
gc()
gc()
test <- test %>% mutate(wday = Weekday(click_time),
hour = hour(click_time),
in_test_hh = ifelse(hour %in% most_freq_hours_in_test_data, 1,
ifelse(hour %in% least_freq_hours_in_test_data, 3, 2))) %>%
select(-c(click_time)) %>%
add_count(ip, wday, in_test_hh) %>% rename("nip_day_test_hh" = n) %>%
select(-c(in_test_hh)) %>%
add_count(os, device) %>% rename("n_os_dev" = n) %>%
add_count(os, app, channel) %>% rename("n_os_app_chan" = n) %>%
add_count(app, channel) %>% rename("n_app_chan" = n) %>%
add_count(ip, wday, hour) %>% rename("n_ip" = n) %>%
add_count(ip, wday, hour, app, os) %>% rename("n_ip_app_os" = n)
invisible(gc())
invisible(gc())
temp <- test %>%
group_by(ip, app, os) %>%
summarise(
mean_h_ip_app_os = mean(hour),
sd_h_ip_app_os = sd(hour))
test <- test %>%
left_join(temp, by = c("ip", "app", "os")) %>%
select(-c(ip))
rm(temp)
invisible(gc())
preds <- predict(model, data = data.matrix(test[, colnames(test)]), n = model$best_iter)
preds <- as.data.frame(preds)
sub$is_attributed <- NULL
sub$is_attributed <- preds
rm(test)
invisible(gc())
fwrite(sub, "res_train_31800000_180426.csv")