From d4b0212a929b19c56718b23425e7f5f62c51da62 Mon Sep 17 00:00:00 2001
From: Lorenzo Volpi <lorenzo.volpi@outlook.com>
Date: Thu, 4 Apr 2024 17:06:21 +0200
Subject: [PATCH] quacc errors added to adapt refactoring

---
 quacc/error.py | 82 +++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 81 insertions(+), 1 deletion(-)

diff --git a/quacc/error.py b/quacc/error.py
index fa16318..4cbb0af 100644
--- a/quacc/error.py
+++ b/quacc/error.py
@@ -3,8 +3,9 @@ from typing import List
 
 import numpy as np
 import quapy as qp
+from sklearn.metrics import accuracy_score, f1_score
 
-from quacc.data import ExtendedPrev
+from quacc.legacy.data import ExtendedPrev
 
 
 def from_name(err_name):
@@ -78,6 +79,85 @@ def maccd(
     return accd(true_prevs, estim_prevs).mean()
 
 
+def from_contingency_table(param1, param2):
+    if (
+        param2 is None
+        and isinstance(param1, np.ndarray)
+        and param1.ndim == 2
+        and (param1.shape[0] == param1.shape[1])
+    ):
+        return True
+    elif (
+        isinstance(param1, np.ndarray)
+        and isinstance(param2, np.ndarray)
+        and param1.shape == param2.shape
+    ):
+        return False
+    else:
+        raise ValueError("parameters for evaluation function not understood")
+
+
+def vanilla_acc_fn(param1, param2=None):
+    if from_contingency_table(param1, param2):
+        return _vanilla_acc_from_ct(param1)
+    else:
+        return accuracy_score(param1, param2)
+
+
+def macrof1_fn(param1, param2=None):
+    if from_contingency_table(param1, param2):
+        return macro_f1_from_ct(param1)
+    else:
+        return f1_score(param1, param2, average="macro")
+
+
+def _vanilla_acc_from_ct(cont_table):
+    return np.diag(cont_table).sum() / cont_table.sum()
+
+
+def _f1_bin(tp, fp, fn):
+    if tp + fp + fn == 0:
+        return 1
+    else:
+        return (2 * tp) / (2 * tp + fp + fn)
+
+
+def macro_f1_from_ct(cont_table):
+    n = cont_table.shape[0]
+
+    if n == 2:
+        tp = cont_table[1, 1]
+        fp = cont_table[0, 1]
+        fn = cont_table[1, 0]
+        return _f1_bin(tp, fp, fn)
+
+    f1_per_class = []
+    for i in range(n):
+        tp = cont_table[i, i]
+        fp = cont_table[:, i].sum() - tp
+        fn = cont_table[i, :].sum() - tp
+        f1_per_class.append(_f1_bin(tp, fp, fn))
+
+    return np.mean(f1_per_class)
+
+
+def microf1(cont_table):
+    n = cont_table.shape[0]
+
+    if n == 2:
+        tp = cont_table[1, 1]
+        fp = cont_table[0, 1]
+        fn = cont_table[1, 0]
+        return _f1_bin(tp, fp, fn)
+
+    tp, fp, fn = 0, 0, 0
+    for i in range(n):
+        tp += cont_table[i, i]
+        fp += cont_table[:, i] - tp
+        fn += cont_table[i, :] - tp
+    return _f1_bin(tp, fp, fn)
+
+
 ACCURACY_ERROR = {maccd}
 ACCURACY_ERROR_SINGLE = {accd}
 ACCURACY_ERROR_NAMES = {func.__name__ for func in ACCURACY_ERROR}