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43 lines
1.4 KiB
43 lines
1.4 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from typing import Dict
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def classifier_evaluation_dict(tp: int, tn: int, fp: int, fn: int) -> Dict:
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p = tp + fn
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n = fp + tn
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d: Dict = {
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"Predicted positive": fp + tp,
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"Predicted negative": fn + tn,
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"Condition positive": p,
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"Condition negative": n,
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"True positive": tp,
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"True negative": tn,
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"False positive": fp,
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"False negative": fn,
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"Accuracy": (tp + tn) / (p + n),
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"F1 score": (2 * tp) / (2 * tp + fp + fn),
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}
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if p > 0:
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d["Recall"] = tp / p
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else:
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d["Recall"] = 1.0
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if tp + fp > 0:
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d["Precision"] = tp / (tp + fp)
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else:
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d["Precision"] = 1.0
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t = (p + n) / 100.0
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d["Predicted positive (%)"] = d["Predicted positive"] / t
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d["Predicted negative (%)"] = d["Predicted negative"] / t
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d["Condition positive (%)"] = d["Condition positive"] / t
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d["Condition negative (%)"] = d["Condition negative"] / t
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d["True positive (%)"] = d["True positive"] / t
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d["True negative (%)"] = d["True negative"] / t
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d["False positive (%)"] = d["False positive"] / t
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d["False negative (%)"] = d["False negative"] / t
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return d
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