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import traceback
import hydra
from hydra.utils import instantiate
from omegaconf import DictConfig
from transformers import logging as transformer_log
from src.data_reader import load_train_test_df
from src.utils import (get_x_columns, pretty_cfg, report_to_telegram,
save_experiment, seed_everything)
seed_everything()
transformer_log.set_verbosity_error()
def run(cfg):
predictor = instantiate(cfg.predictor)
validator = instantiate(cfg.validator)
train_df, test_df = load_train_test_df()
x_columns = get_x_columns()
train_x, train_y = train_df[x_columns], train_df.drop(columns=['full_text'])
results = validator.fit(predictor, train_x, train_y)
cv_mean = results.iloc[len(results) - 1].mean()
print("CV results")
print(results)
print(f"CV mean: {cv_mean}")
submission_df = validator.predict(test_df)
save_experiment(cfg, submission_df, results, validator.saving_dir)
return cv_mean
@hydra.main(version_base=None, config_path="config/conf", config_name="config")
def main(cfg: DictConfig):
try:
metric = run(cfg)
message = f"✅ Successful run from {cfg.timestamp}!\n\n"
message += f"Metric: {metric:.5f} MCRMSE\n\n"
message += f"Configuration:\n{pretty_cfg(cfg)}"
except Exception:
message = f"🚫 Run from {cfg.timestamp} failed!\n\n"
message += traceback.format_exc()
report_to_telegram(message)
if __name__ == "__main__":
main()
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