File size: 1,443 Bytes
b5159c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | import pandas as pd
from Bio import SeqIO
def run_full_split_workflow(tsv_path, fasta_path, train_pct=0.6, val_pct=0.2, test_pct=0.2):
df = pd.read_csv(tsv_path, sep='\t', names=['rep', 'member'])
cluster_groups = df.groupby('rep')['member'].apply(list).to_dict()
sorted_reps = sorted(cluster_groups.keys(), key=lambda x: len(cluster_groups[x]), reverse=True)
total_seqs = len(df)
targets = {'train': total_seqs * train_pct, 'val': total_seqs * val_pct, 'test': total_seqs * test_pct}
split_ids = {'train': set(), 'val': set(), 'test': set()}
counts = {'train': 0, 'val': 0, 'test': 0}
for rep in sorted_reps:
members = cluster_groups[rep]
deficit = {k: targets[k] - counts[k] for k in split_ids.keys()}
best_fit = max(deficit, key=deficit.get)
split_ids[best_fit].update(members)
counts[best_fit] += len(members)
files = {k: open(f"{k}.fasta", "w") for k in split_ids.keys()}
written_counts = {k: 0 for k in split_ids.keys()}
for record in SeqIO.parse(fasta_path, "fasta"):
for split_name, id_set in split_ids.items():
if record.id in id_set:
SeqIO.write(record, files[split_name], "fasta")
written_counts[split_name] += 1
break
for f in files.values():
f.close()
mmseqs_tsv = "iiab_db_cluster.tsv"
fasta = "iiab_db.fasta"
run_full_split_workflow(mmseqs_tsv, fasta)
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