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Make classifier evaluation metrics work when p=0

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Alinson S. Xavier
2020-04-15 10:36:47 -05:00
parent 8039ac7845
commit 5b02c6ea0e
1 changed files with 6 additions and 2 deletions
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8
src/python/miplearn/components/__init__.py
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@@ -17,9 +17,13 @@ def classifier_evaluation_dict(tp, tn, fp, fn):
"False negative": fn, "False negative": fn,
"Accuracy": (tp + tn) / (p + n), "Accuracy": (tp + tn) / (p + n),
"F1 score": (2 * tp) / (2 * tp + fp + fn), "F1 score": (2 * tp) / (2 * tp + fp + fn),
"Recall": tp / p,
"Precision": tp / (tp + fp),
} }
if p > 0:
d["Recall"] = tp / p
d["Precision"] = tp / (tp + fp)
else:
d["Recall"] = 1.0
d["Precision"] = 1.0
t = (p + n) / 100.0 t = (p + n) / 100.0
d["Predicted positive (%)"] = d["Predicted positive"] / t d["Predicted positive (%)"] = d["Predicted positive"] / t
d["Predicted negative (%)"] = d["Predicted negative"] / t d["Predicted negative (%)"] = d["Predicted negative"] / t