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JF_H20_GBM_script_v1.3.R
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JF_H20_GBM_script_v1.3.R
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# H2O GBM script version 1
library(caret)
library(plyr)
library(dplyr)
library(data.table)
library(h2o)
cat("reading the train and test data (with data.table) \n")
train <- fread("../data/train3.csv",stringsAsFactors = T)
test <- fread("../data/test3.csv",stringsAsFactors = T)
store <- fread("./input/store.csv",stringsAsFactors = T)
train <- train[Sales > 0,] ## We are not judged on 0 sales records in test set
## create stratified folds for cross-validation
# folds <- createFolds(factor(store$Store), k = 10, list = FALSE)
# store$fold <- folds
# ddply(store, 'fold', summarise, prop=mean(store$fold)/10)
train <- merge(train,store,by="Store")
test <- merge(test,store,by="Store")
# cat("train data column names and details\n")
# summary(train)
# cat("test data column names and details\n")
# summary(test)
## more care should be taken to ensure the dates of test can be projected from train
## decision trees do not project well, so you will want to have some strategy here, if using the dates
train[,Date:=as.Date(Date)]
test[,Date:=as.Date(Date)]
# competition feature
train$Competition <- (sqrt(max(train$CompetitionDistance, na.rm = TRUE)-train$CompetitionDistance))*
(((train$year - train$CompetitionOpenSinceYear) * 12) - (train$CompetitionOpenSinceMonth-train$month))
test$Competition <- (sqrt(max(test$CompetitionDistance, na.rm = TRUE)-test$CompetitionDistance))*
(((test$year - test$CompetitionOpenSinceYear) * 12) - (test$CompetitionOpenSinceMonth-test$month))
str(train)
str(test)
# Set appropriate variables to factors
for (j in c("Store", "DayOfWeek", "Promo",
"year", "month",
# "day", "day_of_year", "week_of_year", "PromoFirstDate",
# "State", "PromoSecondDate", "DayBeforeClosed", "DayAfterClosed",
"CompetitionOpenSinceMonth", "CompetitionOpenSinceYear",
"Promo2", "Promo2SinceWeek", "Promo2SinceYear")) {
train[[j]] <- as.factor(train[[j]])
test[[j]] <- as.factor(test[[j]])
}
## log transformation to not be as sensitive to high sales
## decent rule of thumb:
## if the data spans an order of magnitude, consider a log transform
train[,logSales:=log1p(Sales)]
str(train)
str(test)
## Useful functions:
rmse = function(predictions, targets) {
return(((predictions - targets)/targets) ** 2)
}
sumup = function(model, trainHex, train) {
train_pred = as.data.frame(h2o.predict(model,trainHex))
train_pred <- expm1(train_pred[,1])
train$pred = train_pred
train$rmse = rmse(train_pred, train$Sales)
train2 = filter(train, month %in% c(8,9))
total_rmse = sqrt(sum(train$rmse)/nrow(train))
print("Total RMSE:")
print(total_rmse)
partial_rmse = sqrt(sum(train2$rmse)/nrow(train2))
print("RMSE on Aug/Sep:")
print(partial_rmse)
temp = as.data.frame(rbind(summary(train_pred), summary(train$Sales), summary(train2$pred), summary(train2$Sales)))
temp$sd = c(round(sd(train_pred)), round(sd(train$Sales)), round(sd(train2$pred)), round(sd(train2$Sales)))
print("Stats of predictions vs. actual:")
print(temp)
}
## Use H2O's random forest
## Start cluster with all available threads
h2o.init(nthreads=-1,max_mem_size='5G', assertion = FALSE)
# create validation and training set
# trainHex<-as.h2o(train[year <15 | month <6,],destination_frame = "trainHex")
# validHex<-as.h2o(train[year == 15 & month >= 6,],destination_frame = "validHex")
# dim(trainHex); dim(validHex)
## Load data into cluster from R
trainHex<-as.h2o(train)
features<- c('Sales_day_avg', 'day_of_year', 'day', 'Sales_month_avg', 'weeknum', 'State',
'Open1', 'is_month_end', 'DayOfWeek', 'ConsConf', 'DAX', 'Open4', 'Sales_all_avg',
'Open-1', 'Open-3', 'is_quarter_end', 'month', 'Assortment', 'CompetitionDistance',
'Promo', 'CompetitionOpenSinceYear', 'StoreType', 'Promo2SinceYear', 'CompetitionOpenSinceMonth',
'Events6', 'Store', 'PromoInterval', 'MSCI', 'CLeadIndic', 'Competition', 'Events13',
'SchoolHoliday', 'Mean_Sea_Level_PressurehPa', 'Max_TemperatureC', 'Open8', 'Merck',
'Events-1', 'Events', 'Events2', 'Open-2', 'StateHoliday', 'Events10', 'Open11', 'Events1',
'Mean_TemperatureC14', 'Open-6', 'Events4', 'Events-2', 'Events3', 'quarter',
'Min_Sea_Level_PressurehPa', 'Events9', 'Precipitationmm', 'Events8', 'Events7',
'Max_Sea_Level_PressurehPa', 'Events14', 'BusConf', 'Events11', 'Promo2SinceWeek',
'Events12', 'Open20', 'Events-3', 'Events5', 'Min_Humidity', 'Open-15', 'Mean_TemperatureC-3',
'Mean_TemperatureC3', 'Mean_TemperatureC1', 'Mean_TemperatureC8', 'Mean_TemperatureC2',
'PromoFirstDate', 'Mean_TemperatureC13', 'Mean_TemperatureC', 'Max_Wind_SpeedKm_h1',
'Mean_Wind_SpeedKm_h', 'Mean_TemperatureC4', 'Open-10', 'Mean_TemperatureC9', 'MeanDew_PointC',
'Open15', 'Mean_TemperatureC-2', 'Open12', 'Open7', 'Max_Wind_SpeedKm_h2', 'Dew_PointC',
'Mean_TemperatureC7', 'Mean_TemperatureC12', 'Mean_Humidity', 'WindDirDegrees',
'Mean_TemperatureC-1', 'Min_TemperatureC', 'Open5', 'CloudCover', 'Mean_TemperatureC5',
'Mean_TemperatureC11', 'Mean_TemperatureC6', 'Mean_TemperatureC10', 'Mean_VisibilityKm',
'Max_Wind_SpeedKm_h8', "Store", "DayOfWeek", "Promo", "year", "month", "CompetitionOpenSinceMonth",
"CompetitionOpenSinceYear", "Promo2", "Promo2SinceWeek", "Promo2SinceYear")
features = unique(features)
####################################################################################
gbmHex <- h2o.gbm(x=features,
y="logSales",
training_frame=trainHex,
model_id="introGBM",
nbins_cats=1115, #5, 12, 223, 1115,
sample_rate = 0.5,
col_sample_rate = 0.5,
max_depth = 20,
learn_rate=0.05,
seed = 12345678, #Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded
ntrees = 300
)
summary(gbmHex)
(varimps = data.frame(h2o.varimp(gbmHex)))
sumup(model = gbmHex, trainHex = trainHex, train = train)
gbm_20_05_300 = gbmHex
h2o.saveModel(gbm_20_05_300, path = '/Users/jfdarre/Documents/NYCDS/Project4/H2O_models', force = FALSE)
####################################################################################
features2 = varimps$variable[1:100]
gbmHex2 <- h2o.gbm( x=features2,
y="logSales",
training_frame=trainHex,
model_id="introGBM",
nbins_cats=1115,
sample_rate = 0.5,
col_sample_rate = 0.5,
max_depth = 15,
learn_rate=0.05,
seed = 12345678, #Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded
ntrees = 50)
summary(gbmHex2)
(varimps2 = data.frame(h2o.varimp(gbmHex2)))
####################################################################################
temp = c(varimps2$variable[1:80], "Store", "DayOfWeek", "Promo", "year", "month", "CompetitionOpenSinceMonth",
"CompetitionOpenSinceYear", "Promo2", "Promo2SinceWeek", "Promo2SinceYear")
features3 = unique(temp)
gbmHex3 <- h2o.gbm( x=features3,
y="logSales",
training_frame=trainHex,
model_id="introGBM",
nbins_cats=1115,
sample_rate = 0.5,
col_sample_rate = 0.5,
max_depth = 20,
learn_rate=0.1,
seed = 12345678, #Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded
ntrees = 50)
summary(gbmHex3)
(varimps3 = data.frame(h2o.varimp(gbmHex3)))
####################################################################################
train_pred = as.data.frame(h2o.predict(gbmHex,trainHex))
train_pred <- expm1(train_pred[,1])
train_pred = train_pred
train$pred = train_pred
train$rmse = rmse(train_pred, train$Sales)
train2 = filter(train, month %in% c(8,9))
(total_rmse = sqrt(sum(train$rmse)/nrow(train)))
(partial_rmse = sqrt(sum(train2$rmse)/nrow(train2)))
sumup = as.data.frame(rbind(summary(train_pred), summary(train$Sales), summary(train2$pred), summary(train2$Sales)))
sumup$sd = c(round(sd(train_pred)), round(sd(train$Sales)), round(sd(train2$pred)), round(sd(train2$Sales)))
sumup
####################################################################################
cat("Predicting Sales\n")
## Load test data into cluster from R
testHex<-as.h2o(test)
## Get predictions out; predicts in H2O, as.data.frame gets them into R
predictions<-as.data.frame(h2o.predict(gbmHex,testHex))
## Return the predictions to the original scale of the Sales data
pred <- expm1(predictions[,1])
summary(pred)
submission <- data.frame(Id=test$Id, Sales=pred)
cat("saving the submission file\n")
write.csv(submission, "./H2O_submits/h2o_GBM_20_05_300.csv",row.names=F)