library(deeptriangle)
library(tidyverse)
library(h2o)
source("analysis/automl-utils.R")
automl_data <- automl_data_prep(insurance::schedule_p, dt_group_codes)
h2o.init()
automl_results <- map_df(automl_data, function(d) {
df_with_lags <- d %>%
group_by(group_code, accident_year) %>%
arrange(group_code, accident_year, development_lag) %>%
mutate(!!!lags(incremental_paid, 9))
c(automl_training, automl_validation, automl_full_training) %<-%
automl_train_validation_split(df_with_lags)
automl_full_training_h2o <- as.h2o(automl_full_training)
response <- "incremental_paid"
predictors <- c("group_code", paste0("lag_incremental_paid_0", 1:9))
automl_model <- h2o.automl(x = predictors, y = response,
training_frame = automl_full_training_h2o,
max_runtime_secs = 5*60, seed = 2018)
predictions_df <- automl_compute_predictions(automl_model, d)
automl_compute_metrics(predictions_df)
}) %>%
bind_rows()
write_csv(automl_results, "analysis/automl_results.csv")
h2o.shutdown(FALSE)
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