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maciejkula
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Nov 13, 2017
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Thanks a lot!
In addition to my inline comments, would you mind adding a test for the functionality as well?
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| prec = [] | ||
| ranking = np.sort(st.rankdata(predictions)[row.indices]) | ||
| for index, value in enumerate(ranking): |
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Could probably do prec = np.arange(1, len(ranking) + 1) / ranking or something similar to make use of vectorized numpy operations?
| prec.append((index + 1) / value) | ||
| ap.append(sum(prec) / len(ranking)) | ||
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| return np.mean(ap) |
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The other metrics return raw arrays, leaving the averaging (or getting other descriptive statistics) down to the user. Could we do it this way here as well?
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I'll try to create a small test for the metric. I have a controlled test with a set scoring instead of model.predict for the metric so I'll devise something. As for the inline comments, sure, no problem. |
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From #58
Wanted to add the MAP metric to the evaluation.py script.
Same interface as mrr_score. If the training dataset is provided, scores of known interactions are set to very low values to discard them.
Next step: Tweak it to calculate MAP@K