adjustment fot CCS, notebook folder

metrics/multi_class_classification.py

@@ -2,7 +2,6 @@ from itertools import cycle
 
 import numpy as np
 import torch
-from pytorch_lightning.metrics import Recall
 from sklearn.metrics import f1_score, roc_curve, auc, roc_auc_score, ConfusionMatrixDisplay, confusion_matrix, \
     recall_score
 

modules/util.py

@@ -35,6 +35,7 @@ try:
             # self.precision_recall_curve = pl.metrics.PrecisionRecallCurve(self.n_classes, compute_on_step=False)
             # self.average_prec = pl.metrics.AveragePrecision(self.n_classes, compute_on_step=True)
             # self.roc = pl.metrics.ROC(self.n_classes, compute_on_step=False)
+            if self.n_classes > 2:
                 self.fbeta = pl.metrics.FBeta(self.n_classes, average='macro', compute_on_step=False)
                 self.f1 = pl.metrics.F1(self.n_classes, average='macro', compute_on_step=False)
 

utils/config.py

@@ -71,10 +71,6 @@ def parse_comandline_args_add_defaults(filepath, overrides=None):
                 auto_lr_find=not args['debug'],
                 weights_summary='top',
                 check_val_every_n_epoch=1 if args['debug'] else args.get('check_val_every_n_epoch', 1),
-                limit_train_batches = 2.0,
-                limit_val_batches = 2.0,
-                limit_test_batches = 2.0,
-                limit_predict_batches = 2.0,
                 )
 
     if overrides is not None and isinstance(overrides, (Mapping, Dict)):