Download preprocess_data/generate_inputs/make_features.py from latticetower/nested-tkf-repro-workspace: direct link, hf CLI and curl.
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- Download file 1.68 kB
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https://huggingface.co/datasets/latticetower/nested-tkf-repro-workspace/resolve/main/preprocess_data/generate_inputs/make_features.py
- Command line
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hf download hf://datasets/latticetower/nested-tkf-repro-workspace/preprocess_data/generate_inputs/make_features.py
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curl -L -o make_features.py https://huggingface.co/datasets/latticetower/nested-tkf-repro-workspace/resolve/main/preprocess_data/generate_inputs/make_features.py
1.68 kB
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| from prepare_for_featurization.prepare_for_featurization import main as split_n_pick | |
| from generate_inputs.generate_inputs import samples_from_file | |
| from utils.utils import make_sub_folder | |
| def main(num_splits, | |
| max_len, | |
| seed_folder, | |
| trees_folder, | |
| cherries_folder): | |
| split_file_lst = ['pfams_in_OOD_valid.tsv'] + [f'pfams_in_split{i}.tsv' for i in range(num_splits)] | |
| for pfam_filename in split_file_lst: | |
| splitname = pfam_filename.replace('.tsv','').split('_')[-1] | |
| cherries_dset_prefix = f'CHERRIES_{splitname}' | |
| with open(f'{pfam_filename}','r') as f: | |
| pfams_in_split = [line.strip() for line in f] | |
| file_lst = [f'{cherries_folder}/{pf}_cherries.tsv' | |
| for pf in pfams_in_split] | |
| for suffix in ['full_length', 'summarized_counts', 'all_metadata']: | |
| this_split_cherries_folder = f'{cherries_dset_prefix}_{suffix}' | |
| make_sub_folder(in_dir = '.', sub_folder=this_split_cherries_folder) | |
| for i in range(len(pfams_in_split)): | |
| if i % 10 == 0: | |
| print(f'{i}/{len(pfams_in_split)}') | |
| pfam = pfams_in_split[i] | |
| file_of_cherries = file_lst[i] | |
| samples_from_file(pfam = pfam, | |
| seed_folder = seed_folder, | |
| trees_folder = trees_folder, | |
| filename = file_of_cherries, | |
| dset_prefix = cherries_dset_prefix, | |
| max_len = max_len) | |