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Enhance logging in run_evaluation.py for better traceability #13

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Enhance logging in run_evaluation.py for better traceability #13

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17 changes: 11 additions & 6 deletions run_evaluation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,24 +17,25 @@
def get_dataset(dataset: str, tokenizer: PreTrainedTokenizerBase) -> (pd.DataFrame, pd.DataFrame, List):
da = DATASET_NAMES2LOADERS[dataset]()
# Filter extremely long samples from both train and test samples:
_logger.info("filtering test set:")
_logger.info(f"Filtering test set for dataset: {dataset}")
test_df = filter_extremely_long_samples(da.test_df, tokenizer)
_logger.info("filtering train set:")
_logger.info(f"Filtering train set for dataset: {dataset}")
train_df = filter_extremely_long_samples(da.train_df, tokenizer)
return test_df, train_df, da.labels


def run_pcw_experiment(dataset: str, model: str, cache_dir: str, subsample_test_set: int, output_dir: str,
n_windows: List[int], n_shots_per_window: Optional[int], n_runs: int,
random_seed: int, right_indentation: bool) -> None:
random_seed: int, right_indentation: bool) -> None {
_logger.info(f'Starting experiment with dataset: {dataset} and model: {model}')
pcw_model = load_pcw_wrapper(model, cache_dir, right_indentation, max(n_windows))

test_df, train_df, labels = get_dataset(dataset, pcw_model.tokenizer)

if n_shots_per_window is None:
# default behaviour: we take the maximum number of samples per window
if n_shots_per_window is None:
# Default behaviour: we take the maximum number of samples per window
n_shots_per_window = get_max_n_shots(train_df, test_df, pcw_model.tokenizer, pcw_model.context_window_size)
_logger.info(f"Found max n shot per window = {n_shots_per_window}")
_logger.info(f"Found max n shot per window = {n_shots_per_window} for dataset: {dataset}")

n_shots = [i * n_shots_per_window for i in n_windows]

Expand All @@ -43,6 +44,8 @@ def run_pcw_experiment(dataset: str, model: str, cache_dir: str, subsample_test_

accuracies = em.run_experiment_across_shots(n_shots, n_runs)
save_results(dataset, n_shots, accuracies, output_dir, model)
_logger.info(f'Experiment completed for dataset: {dataset} and model: {model}')
}


if __name__ == '__main__':
Expand All @@ -69,3 +72,5 @@ def run_pcw_experiment(dataset: str, model: str, cache_dir: str, subsample_test_
action='store_true', default=False)
args = parser.parse_args()
run_pcw_experiment(**vars(args))
}