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score_automl.R
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score_automl.R
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library(Mcomp)
library(anytime)
library(jsonlite)
library(Metrics)
library(tidyverse)
library(purrr)
results15 <- read_csv("15feature_automl_prediction.csv")
df15 <-
results15 %>%
filter(DATA_SPLIT == "TEST") %>%
mutate(start_date = anytime(START_DATE)) %>%
select(start_date, IDX, Y, predicted_Y)
mapes15 <-
results15 %>%
filter(DATA_SPLIT == "TEST") %>%
mutate(start_date = anytime(START_DATE)) %>%
select(start_date, IDX, Y, predicted_Y) %>%
group_by(IDX) %>%
top_n(-1, start_date) %>%
mutate(ts_smape = smape(Y, predicted_Y))
print(paste0("Mean sMAPE for 3 hour 15 feature test set: ", round(mean(mapes15$ts_smape)*100, 2)))
#############################################################################
results27 <- read_csv("27feature_automl_prediction.csv")
results27 %>%
filter(IDX=="IDX1") ->
idx27_1
results27 %>%
filter(IDX=="IDX10") ->
idx27_10
df27 <-
results27 %>%
filter(DATA_SPLIT == "TEST") %>%
mutate(start_date = anytime(START_DATE)) %>%
select(start_date, IDX, Y, predicted_Y)
mapes27 <-
results27 %>%
filter(DATA_SPLIT == "TEST") %>%
mutate(start_date = anytime(START_DATE)) %>%
select(start_date, IDX, Y, predicted_Y) %>%
group_by(IDX) %>%
top_n(-1, start_date) %>%
mutate(ts_smape = smape(Y, predicted_Y))
print(paste0("Mean sMAPE for 3 hour 27 feature test set: ", mean(mapes27$ts_smape)*100, 2))
#############################################################################
results5_27 <- read_csv("5hour_27feature_automl_prediction.csv")
results5_27 %>%
filter(IDX=="IDX1") ->
idx5_27_1
results5_27 %>%
filter(IDX=="IDX10") ->
idx5_27_10
df5_27<-
results5_27 %>%
filter(DATA_SPLIT == "TEST") %>%
mutate(start_date = anytime(START_DATE)) %>%
select(start_date, IDX, Y, predicted_Y)
mapes5_27 <-
results5_27 %>%
filter(DATA_SPLIT == "TEST") %>%
mutate(start_date = anytime(START_DATE)) %>%
select(start_date, IDX, Y, predicted_Y) %>%
group_by(IDX) %>%
top_n(-1, start_date) %>%
mutate(ts_smape = smape(Y, predicted_Y))
print(paste0("Mean sMAPE for 3.7 hour 27 feature test set: ", mean(mapes5_27$ts_smape)*100, 2))
#############################################################################
results1_40 <- read_csv("1hour_40feature_automl_prediction.csv")
results1_40 %>%
filter(IDX=="IDX1") ->
idx1_40_1
results1_40 %>%
filter(IDX=="IDX10") ->
idx1_40_10
df1_40 <-
results1_40 %>%
filter(DATA_SPLIT == "TEST") %>%
mutate(start_date = anytime(START_DATE)) %>%
select(start_date, IDX, Y, predicted_Y)
mapes1_40 <-
results1_40 %>%
filter(DATA_SPLIT == "TEST") %>%
mutate(start_date = anytime(START_DATE)) %>%
select(start_date, IDX, Y, predicted_Y) %>%
group_by(IDX) %>%
top_n(-1, start_date) %>%
mutate(ts_smape = smape(Y, predicted_Y))
print(paste0("Mean sMAPE for 1 hour 40 feature test set: ", mean(mapes1_40$ts_smape)*100, 2))
#############################################################################
# results_txt <- readLines("3hours_27features.json")
# results_list <- transpose(lapply(results_txt, fromJSON))
# results27 <- as.data.frame(lapply(results_list, unlist))
#
# df27 <-
# results27 %>%
# filter(DATA_SPLIT == "TEST") %>%
# mutate(start_date = anytime(START_DATE)) %>%
# select(start_date, IDX, Y, predicted_Y)
#
# mapes27 <-
# results27 %>%
# filter(DATA_SPLIT == "TEST") %>%
# mutate(start_date = anytime(START_DATE)) %>%
# select(start_date, IDX, Y, predicted_Y) %>%
# group_by(IDX) %>%
# top_n(-1, start_date) %>%
# mutate(ts_smape = smape(Y, predicted_Y))
#
# print(paste0("Mean sMAPE for 27 feature test set: ", round(mean(mapes27$ts_smape)*100, 2)))