Bug: metric_fn in HIST and IGMTF always raises ValueError for default metric
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Description
Description
metric_fn() in pytorch_hist.py (line 173) and pytorch_igmtf.py (line 166) uses == to compare self.metric against the tuple ("", "loss"):
if self.metric == ("", "loss"):
return -self.loss_fn(pred[mask], label[mask])
Since self.metric is always a string (default is ""), this comparison is never true. The function falls through to raise ValueError("unknown metric").
All other models correctly use in:
# pytorch_alstm.py, pytorch_gru.py, pytorch_lstm.py, etc.
if self.metric in ("", "loss"):
return -self.loss_fn(pred[mask], label[mask])
Impact
Any user training HIST or IGMTF with the default metric (or metric="loss") will get a crash:
ValueError: unknown metric ``
Fix
- if self.metric == ("", "loss"):
+ if self.metric in ("", "loss"):
I will submit a fix shortly.
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Research direction
Start with metric_fn in pytorch_hist.py around line 173 and pytorch_igmtf.py around line 166, then compare the corresponding implementations in pytorch_alstm.py, pytorch_gru.py, and pytorch_lstm.py. Verify that the default metric and metric="loss" paths complete without the unknown-metric error while other metric handling remains unchanged.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Quiet
- Clarity
- Clearly specified
- Newbie friendliness
- 82/100