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This commit is contained in:
Jamie Hardt
2025-08-26 17:14:56 -07:00
parent 3d67623d77
commit 5ea64d089f
3 changed files with 57 additions and 54 deletions
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54
ucsinfer/__main__.py
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@@ -22,7 +22,7 @@ def recommend():
"""
Infer a UCS category for a text description
"""
pass
pass
@ucsinfer.command('gather')
@@ -36,7 +36,7 @@ def gather(paths, outfile):
types = ['.wav', '.flac']
table = csv.writer(outfile)
print(f"Loading category list...")
catid_list = [cat.catid for cat in load_ucs()]
catid_list = [cat.catid for cat in load_ucs()]
scan_list = []
for path in paths:
@@ -47,12 +47,12 @@ def gather(paths, outfile):
if ext in types and \
(ucs_components := parse_ucs(root, catid_list)) and \
not filename.startswith("._"):
scan_list.append((ucs_components.cat_id,
scan_list.append((ucs_components.cat_id,
os.path.join(dirpath, filename)))
print(f"Found {len(scan_list)} files to process.")
for pair in tqdm.tqdm(scan_list, unit='files',file=sys.stderr):
for pair in tqdm.tqdm(scan_list, unit='files', file=sys.stderr):
if desc := ffmpeg_description(pair[1]):
table.writerow([pair[0], desc])
@@ -62,13 +62,13 @@ def finetune():
"""
Fine-tune a model with training data
"""
pass
pass
@ucsinfer.command('evaluate')
@click.option('--offset', type=int, default=0)
@click.option('--limit', type=int, default=-1)
@click.argument('dataset', type=click.File('r', encoding='utf8'),
@click.argument('dataset', type=click.File('r', encoding='utf8'),
default='dataset.csv')
def evaluate(dataset, offset, limit):
"""
@@ -82,7 +82,7 @@ def evaluate(dataset, offset, limit):
for i, row in enumerate(tqdm.tqdm(reader)):
if i < offset:
continue
if limit > 0 and i >= limit + offset:
break
@@ -107,33 +107,33 @@ def evaluate(dataset, offset, limit):
miss_counts = []
for cat in cats:
miss_counts.append((cat, len([x for x in results \
if x['catid'] == cat and x['result'] == 'MISS'])))
miss_counts.append((cat, len([x for x in results
if x['catid'] == cat and x['result'] == 'MISS'])))
miss_counts = sorted(miss_counts, key=lambda x: x[1])
print(f" === RESULTS === ")
table = [
["Total records in sample:", f"{total}"],
["Top Result:", f"{total_top}",
f"{float(total_top)/float(total):.2%}"],
["Top 5 Result:", f"{total_top_5}",
f"{float(total_top_5)/float(total):.2%}"],
["Top 10 Result:", f"{total_top_10}",
f"{float(total_top_10)/float(total):.2%}"],
SEPARATING_LINE,
["UCS category count:", f"{len(ctx.catlist)}"],
["Total categories in sample:", f"{total_cats}",
f"{float(total_cats)/float(len(ctx.catlist)):.2%}"],
[f"Most missed category ({miss_counts[-1][0]}):",
f"{miss_counts[-1][1]}",
f"{float(miss_counts[-1][1])/float(total):.2%}"]
]
["Total records in sample:", f"{total}"],
["Top Result:", f"{total_top}",
f"{float(total_top)/float(total):.2%}"],
["Top 5 Result:", f"{total_top_5}",
f"{float(total_top_5)/float(total):.2%}"],
["Top 10 Result:", f"{total_top_10}",
f"{float(total_top_10)/float(total):.2%}"],
SEPARATING_LINE,
["UCS category count:", f"{len(ctx.catlist)}"],
["Total categories in sample:", f"{total_cats}",
f"{float(total_cats)/float(len(ctx.catlist)):.2%}"],
[f"Most missed category ({miss_counts[-1][0]}):",
f"{miss_counts[-1][1]}",
f"{float(miss_counts[-1][1])/float(total):.2%}"]
]
print(tabulate(table, headers=['', 'n', 'pct']))
print(tabulate(table, headers=['','n','pct']))
if __name__ == '__main__':
os.environ['TOKENIZERS_PARALLELISM'] = 'false'