andrea.pedrotti
/
gfun_multimodal
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

metrics for binary classification scoring

This commit is contained in:
Andrea Pedrotti 2023-03-07 10:49:24 +01:00
parent f274ec7615
commit 48127a2027
2 changed files with 12 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
evaluation/evaluate.py
View File

@ -13,6 +13,9 @@ def evaluation_metrics(y, y_):
macroK(y, y_), macroK(y, y_),
microK(y, y_), microK(y, y_),
# macroAcc(y, y_), # macroAcc(y, y_),
microAcc(
y, y_
), # TODO: we're using micro-averaging for accuracy, it is == to accuracy_score on binary classification
) )
@ -32,10 +35,12 @@ def log_eval(l_eval, phase="training", verbose=True):
print(f"\n[Results {phase}]") print(f"\n[Results {phase}]")
metrics = [] metrics = []
for lang in l_eval.keys(): for lang in l_eval.keys():
macrof1, microf1, macrok, microk = l_eval[lang] macrof1, microf1, macrok, microk, microAcc = l_eval[lang]
metrics.append([macrof1, microf1, macrok, microk]) metrics.append([macrof1, microf1, macrok, microk, microAcc])
if phase != "validation": if phase != "validation":
print(f"Lang {lang}: macro-F1 = {macrof1:.3f} micro-F1 = {microf1:.3f}") print(
f"Lang {lang}: macro-F1 = {macrof1:.3f} micro-F1 = {microf1:.3f} acc = {microAcc:.3f}"
)
averages = np.mean(np.array(metrics), axis=0) averages = np.mean(np.array(metrics), axis=0)
if verbose: if verbose:
print( print(

4
evaluation/metrics.py
View File

@ -239,3 +239,7 @@ def microK(true_labels, predicted_labels):
def macroAcc(true_labels, predicted_labels): def macroAcc(true_labels, predicted_labels):
return macro_average(true_labels, predicted_labels, accuracy) return macro_average(true_labels, predicted_labels, accuracy)
def microAcc(true_labels, predicted_labels):
return micro_average(true_labels, predicted_labels, accuracy)