MIPLearn v0.3

miplearn/components/__init__.py

@@ -1,47 +1,3 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+#  Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
-from typing import Dict
-
-
-def classifier_evaluation_dict(
-    tp: int,
-    tn: int,
-    fp: int,
-    fn: int,
-) -> Dict[str, float]:
-    p = tp + fn
-    n = fp + tn
-    d: Dict = {
-        "Predicted positive": fp + tp,
-        "Predicted negative": fn + tn,
-        "Condition positive": p,
-        "Condition negative": n,
-        "True positive": tp,
-        "True negative": tn,
-        "False positive": fp,
-        "False negative": fn,
-        "Accuracy": (tp + tn) / (p + n),
-        "F1 score": (2 * tp) / (2 * tp + fp + fn),
-    }
-
-    if p > 0:
-        d["Recall"] = tp / p
-    else:
-        d["Recall"] = 1.0
-
-    if tp + fp > 0:
-        d["Precision"] = tp / (tp + fp)
-    else:
-        d["Precision"] = 1.0
-
-    t = (p + n) / 100.0
-    d["Predicted positive (%)"] = d["Predicted positive"] / t
-    d["Predicted negative (%)"] = d["Predicted negative"] / t
-    d["Condition positive (%)"] = d["Condition positive"] / t
-    d["Condition negative (%)"] = d["Condition negative"] / t
-    d["True positive (%)"] = d["True positive"] / t
-    d["True negative (%)"] = d["True negative"] / t
-    d["False positive (%)"] = d["False positive"] / t
-    d["False negative (%)"] = d["False negative"] / t
-    return d