Added results with FP normalization removed

logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_best_model_n0/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_best_model_n0/metrics.csv

@@ -1,39 +1,39 @@
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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_best_model_n1/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_best_model_n1/metrics.csv

@@ -1,43 +1,39 @@
 val_loss,val_acc,val_f1_score,val_precision,val_recall,val_roc_auc,epoch,step,train_loss_epoch,train_acc_epoch,train_f1_score_epoch,train_precision_epoch,train_recall_epoch,train_roc_auc_epoch,lr-Adam,train_loss_step
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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold0/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold0/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold1/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold1/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold2/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold2/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold4/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random_random_cv_model_fold4/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_best_model_n0/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_best_model_n0/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_best_model_n1/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_best_model_n1/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold1/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold1/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold2/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold2/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold3/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold3/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold4/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto_tanimoto_cv_model_fold4/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_best_model_n1/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_best_model_n1/metrics.csv

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logs/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_best_model_n2/logs_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot_best_model_n2/metrics.csv

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models/{best_model_n0_random-epoch=6-test_acc=0.78-test_roc_auc=0.827.ckpt → best_model_n0_random-epoch=13-test_acc=0.83-test_roc_auc=0.841.ckpt}

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+size 5127967

models/{cv_model_random_fold2-epoch=13-val_acc=0.88-val_roc_auc=0.908.ckpt → best_model_n1_tanimoto-epoch=14-test_acc=0.79-test_roc_auc=0.847.ckpt}

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+size 2565343

models/best_model_n1_tanimoto-epoch=5-test_acc=0.75-test_roc_auc=0.820.ckpt

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models/{best_model_n2_random-epoch=9-test_acc=0.78-test_roc_auc=0.840.ckpt → best_model_n2_random-epoch=9-test_acc=0.80-test_roc_auc=0.841.ckpt}

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+size 5127967

models/{best_model_n2_uniprot-epoch=2-test_acc=0.56-test_roc_auc=0.655.ckpt → best_model_n2_uniprot-epoch=5-test_acc=0.61-test_roc_auc=0.635.ckpt}

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models/cv_model_random_fold2-epoch=16-val_acc=0.86-val_roc_auc=0.908.ckpt

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models/cv_model_tanimoto_fold1-epoch=6-val_acc=0.85-val_roc_auc=0.891.ckpt

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models/cv_model_tanimoto_fold1-epoch=7-val_acc=0.85-val_roc_auc=0.913.ckpt

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models/{cv_model_uniprot_fold1-epoch=8-val_acc=0.86-val_roc_auc=0.923.ckpt → cv_model_uniprot_fold1-epoch=12-val_acc=0.86-val_roc_auc=0.899.ckpt}

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models/{cv_model_uniprot_fold2-epoch=3-val_acc=0.51-val_roc_auc=0.550.ckpt → cv_model_uniprot_fold2-epoch=0-val_acc=0.52-val_roc_auc=0.665.ckpt}

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models/{cv_model_uniprot_fold3-epoch=4-val_acc=0.50-val_roc_auc=0.483.ckpt → cv_model_uniprot_fold3-epoch=3-val_acc=0.46-val_roc_auc=0.579.ckpt}

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protac_degradation_predictor/__init__.py

@@ -4,6 +4,9 @@ from .data_utils import (
     get_fingerprint,
     is_active,
 )
+from .protac_dataset import (
+    PROTAC_Dataset,
+)
 from .pytorch_models import (
     PROTAC_Predictor,
     PROTAC_Model,

protac_degradation_predictor/protac_dataset.py

@@ -167,15 +167,6 @@ class PROTAC_Dataset(Dataset):
             scalers (dict): The scalers for each feature.
             use_single_scaler (bool): Whether to use a single scaler for all features.
         """
-        # TODO: The following check is WRONG: for val and test sets I must NOT
-        # use run the fit_scaling method, but I must use the scalers from the
-        # training set.
-        # if self.use_single_scaler is None:
-        #     raise ValueError(
-        #         "The fit_scaling method must be called before apply_scaling.")
-        # if use_single_scaler != self.use_single_scaler:
-        #     raise ValueError(
-        #         f"The use_single_scaler parameter must be the same as the one used in the fit_scaling method. Got {use_single_scaler}, previously {self.use_single_scaler}.")
         if use_single_scaler:
             embeddings = np.hstack([
                 np.array(self.data['Smiles'].tolist()),
@@ -192,18 +183,30 @@ class PROTAC_Dataset(Dataset):
                 self.active_label: self.data[self.active_label]
             })
         else:
-            self.data['Smiles'] = self.data['Smiles'].apply(lambda x: scalers['Smiles'].transform(x[np.newaxis, :])[0])
+            # NOTE: The fingerprints are already in [0, 1], no need to scale them
+            # self.data['Smiles'] = self.data['Smiles'].apply(lambda x: scalers['Smiles'].transform(x[np.newaxis, :])[0])
             self.data['Uniprot'] = self.data['Uniprot'].apply(lambda x: scalers['Uniprot'].transform(x[np.newaxis, :])[0])
             self.data['E3 Ligase Uniprot'] = self.data['E3 Ligase Uniprot'].apply(lambda x: scalers['E3 Ligase Uniprot'].transform(x[np.newaxis, :])[0])
             self.data['Cell Line Identifier'] = self.data['Cell Line Identifier'].apply(lambda x: scalers['Cell Line Identifier'].transform(x[np.newaxis, :])[0])
 
-    def get_numpy_arrays(self):
-        X = np.hstack([
-            np.array(self.data['Smiles'].tolist()),
-            np.array(self.data['Uniprot'].tolist()),
-            np.array(self.data['E3 Ligase Uniprot'].tolist()),
-            np.array(self.data['Cell Line Identifier'].tolist()),
-        ]).copy()
+    def get_numpy_arrays(self, component: Optional[str] = None) -> Tuple[np.ndarray, np.ndarray]:
+        """ Get the numpy arrays for the dataset.
+
+        Args:
+            component (str): The component to get the numpy arrays for. Defaults to None, i.e., get a single stacked array.
+        
+        Returns:
+            tuple: The numpy arrays for the dataset. The first element is the input array, and the second element is the output array.
+        """
+        if component is not None:
+            X = np.array(self.data[component].tolist()).copy()
+        else:
+            X = np.hstack([
+                np.array(self.data['Smiles'].tolist()),
+                np.array(self.data['Uniprot'].tolist()),
+                np.array(self.data['E3 Ligase Uniprot'].tolist()),
+                np.array(self.data['Cell Line Identifier'].tolist()),
+            ]).copy()
         y = self.data[self.active_label].values.copy()
         return X, y
 

reports/ablation_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random.csv

@@ -1,29 +1,29 @@
 test_loss,test_acc,test_f1_score,test_precision,test_recall,test_roc_auc,train_len,train_active_perc,train_inactive_perc,train_avg_tanimoto_dist,test_len,test_active_perc,test_inactive_perc,test_avg_tanimoto_dist,num_leaking_uniprot_train_test,num_leaking_smiles_train_test,perc_leaking_uniprot_train_test,perc_leaking_smiles_train_test,majority_vote,model_type,disabled_embeddings,test_f1,split_type
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-,0.5232558250427246,,0.0,0.0,0.655978262424469,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled e3,0.0,random
-0.8378005027770996,0.5232558250427246,0.3492063581943512,0.47826087474823,0.2750000059604645,0.5456521511077881,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi,,random
-1.5188343524932861,0.4883720874786377,0.52173912525177,0.4615384638309479,0.6000000238418579,0.510869562625885,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi,,random
-1.0929217338562012,0.5581395626068115,0.17391304671764374,0.6666666865348816,0.10000000149011612,0.6092391014099121,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi,,random
-,0.5348837375640869,,0.0,0.0,0.614673912525177,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled poi,0.0,random
-1.2380424737930298,0.5348837375640869,0.0,0.0,0.0,0.729076087474823,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled cell,,random
-1.3619405031204224,0.5116279125213623,0.625,0.4861111044883728,0.875,0.69701087474823,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled cell,,random
-1.4850413799285889,0.45348837971687317,0.6178861856460571,0.45783132314682007,0.949999988079071,0.7122282981872559,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled cell,,random
-,0.5348837375640869,,0.0,0.0,0.7127717733383179,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled cell,0.0,random
-0.7713937163352966,0.5348837375640869,0.2857142984867096,0.5,0.20000000298023224,0.572282612323761,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled smiles,,random
-0.8858662247657776,0.5116279125213623,0.4166666567325592,0.46875,0.375,0.52173912525177,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled smiles,,random
-1.1161818504333496,0.4883720874786377,0.043478261679410934,0.1666666716337204,0.02500000037252903,0.4581522047519684,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled smiles,,random
-,0.5348837375640869,,0.0,0.0,0.43967390060424805,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled smiles,0.0,random
-1.6560124158859253,0.5348837375640869,0.0,0.0,0.0,0.6184782385826111,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled e3 cell,,random
-1.5063010454177856,0.4651162922382355,0.5892857313156128,0.4583333432674408,0.824999988079071,0.520652174949646,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled e3 cell,,random
-1.5758358240127563,0.5,0.626086950302124,0.47999998927116394,0.8999999761581421,0.5043478608131409,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled e3 cell,,random
-,0.5348837375640869,,0.0,0.0,0.49673908948898315,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled e3 cell,0.0,random
-1.3611284494400024,0.5348837375640869,0.23076923191547394,0.5,0.15000000596046448,0.519565224647522,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3,,random
-1.4174710512161255,0.569767415523529,0.593406617641449,0.529411792755127,0.675000011920929,0.616847813129425,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3,,random
-1.0514087677001953,0.5813953280448914,0.4000000059604645,0.6000000238418579,0.30000001192092896,0.6277173757553101,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3,,random
-,0.5465116500854492,,0.6666666865348816,0.05000000074505806,0.5744565725326538,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled poi e3,0.09302325546741486,random
-1.843766689300537,0.5348837375640869,0.0,0.0,0.0,0.5684782266616821,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3 cell,,random
-1.8325330018997192,0.5116279125213623,0.5799999833106995,0.4833333194255829,0.7250000238418579,0.5888587236404419,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3 cell,,random
-1.0352420806884766,0.4883720874786377,0.5769230723381042,0.46875,0.75,0.5793477892875671,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3 cell,,random
-,0.5348837375640869,,0.0,0.0,0.532065212726593,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled poi e3 cell,0.0,random
+0.8996752500534058,0.5813953280448914,0.5135135054588318,0.5588235259056091,0.4749999940395355,0.6592391729354858,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled e3,,random
+0.7169563174247742,0.5930232405662537,0.5783132314682007,0.5581395626068115,0.6000000238418579,0.676630437374115,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled e3,,random
+0.8599091172218323,0.6279069781303406,0.5555555820465088,0.625,0.5,0.6347826719284058,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled e3,,random
+,0.604651153087616,,1.0,0.15000000596046448,0.676086962223053,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled e3,0.260869562625885,random
+0.7291305065155029,0.6744186282157898,0.6216216087341309,0.6764705777168274,0.574999988079071,0.695652186870575,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi,,random
+0.892604410648346,0.5,0.4819277226924896,0.4651162922382355,0.5,0.564130425453186,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi,,random
+1.633378267288208,0.5,0.64462810754776,0.48148149251937866,0.9750000238418579,0.7048913240432739,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi,,random
+,0.604651153087616,,0.6153846383094788,0.4000000059604645,0.658152163028717,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled poi,0.4848484992980957,random
+0.9581289887428284,0.5348837375640869,0.6610169410705566,0.5,0.9750000238418579,0.6850544214248657,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled cell,,random
+0.7156464457511902,0.6511628031730652,0.699999988079071,0.5833333134651184,0.875,0.6839674115180969,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled cell,,random
+0.7450031042098999,0.5813953280448914,0.47058823704719543,0.5714285969734192,0.4000000059604645,0.604891300201416,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled cell,,random
+,0.5232558250427246,,0.48275861144065857,0.3499999940395355,0.572554349899292,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled cell,0.4057970941066742,random
+0.7880722880363464,0.4651162922382355,0.6290322542190552,0.4642857015132904,0.9750000238418579,0.5239130258560181,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled smiles,,random
+0.701041579246521,0.5348837375640869,0.0,0.0,0.0,0.5652173757553101,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled smiles,,random
+0.7424529194831848,0.4883720874786377,0.6333333253860474,0.4749999940395355,0.949999988079071,0.5125000476837158,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled smiles,,random
+,0.5348837375640869,,0.0,0.0,0.539130449295044,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled smiles,0.0,random
+1.3007844686508179,0.5,0.44155845046043396,0.45945945382118225,0.42500001192092896,0.521195650100708,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled e3 cell,,random
+0.9464340209960938,0.5348837375640869,0.5833333134651184,0.5,0.699999988079071,0.624456524848938,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled e3 cell,,random
+1.0031085014343262,0.5348837375640869,0.45945945382118225,0.5,0.42500001192092896,0.582065224647522,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled e3 cell,,random
+,0.5581395626068115,,0.5714285969734192,0.20000000298023224,0.5951087474822998,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled e3 cell,0.29629629850387573,random
+1.0007070302963257,0.5813953280448914,0.4193548262119293,0.5909090638160706,0.32499998807907104,0.6706522107124329,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3,,random
+0.9461693167686462,0.5465116500854492,0.41791045665740967,0.5185185074806213,0.3499999940395355,0.6217391490936279,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3,,random
+1.8655686378479004,0.4767441749572754,0.6399999856948853,0.47058823704719543,1.0,0.5652173757553101,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3,,random
+,0.5581395626068115,,0.625,0.125,0.689130425453186,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled poi e3,0.2083333283662796,random
+1.8578402996063232,0.4651162922382355,0.34285715222358704,0.4000000059604645,0.30000001192092896,0.499456524848938,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3 cell,,random
+0.9636232256889343,0.5581395626068115,0.5476190447807312,0.5227272510528564,0.574999988079071,0.6076086759567261,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3 cell,,random
+2.152951717376709,0.4651162922382355,0.6349206566810608,0.4651162922382355,1.0,0.6603261232376099,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,False,Pytorch,disabled poi e3 cell,,random
+,0.5232558250427246,,0.4761904776096344,0.25,0.5342391133308411,771,0.5149156939040207,0.48508430609597925,0.3768059369269877,86,0.46511627906976744,0.5348837209302325,0.38114673659326254,34,44,0.8326848249027238,0.10246433203631647,True,Pytorch,disabled poi e3 cell,0.32786884903907776,random

reports/ablation_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv

@@ -1,29 +1,29 @@
 test_loss,test_acc,test_f1_score,test_precision,test_recall,test_roc_auc,train_len,train_active_perc,train_inactive_perc,train_avg_tanimoto_dist,test_len,test_active_perc,test_inactive_perc,test_avg_tanimoto_dist,num_leaking_uniprot_train_test,num_leaking_smiles_train_test,perc_leaking_uniprot_train_test,perc_leaking_smiles_train_test,majority_vote,model_type,disabled_embeddings,test_f1,split_type
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+1.002088189125061,0.5411764979362488,0.09302325546741486,0.3333333432674408,0.054054055362939835,0.5805180072784424,772,0.5181347150259067,0.48186528497409326,0.37254018872057115,85,0.43529411764705883,0.5647058823529412,0.4199408355934975,22,0,0.5699481865284974,0.0,False,Pytorch,disabled poi e3 cell,,tanimoto
+1.469865322113037,0.5176470875740051,0.3692307770252228,0.4285714328289032,0.3243243098258972,0.4650900959968567,772,0.5181347150259067,0.48186528497409326,0.37254018872057115,85,0.43529411764705883,0.5647058823529412,0.4199408355934975,22,0,0.5699481865284974,0.0,False,Pytorch,disabled poi e3 cell,,tanimoto
+1.170265793800354,0.43529412150382996,0.5714285969734192,0.4266666769981384,0.8648648858070374,0.5399774312973022,772,0.5181347150259067,0.48186528497409326,0.37254018872057115,85,0.43529411764705883,0.5647058823529412,0.4199408355934975,22,0,0.5699481865284974,0.0,False,Pytorch,disabled poi e3 cell,,tanimoto
+,0.5647059082984924,,0.5,0.027027027681469917,0.6317567229270935,772,0.5181347150259067,0.48186528497409326,0.37254018872057115,85,0.43529411764705883,0.5647058823529412,0.4199408355934975,22,0,0.5699481865284974,0.0,True,Pytorch,disabled poi e3 cell,0.05128205195069313,tanimoto

reports/ablation_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot.csv

@@ -1,29 +1,29 @@
 test_loss,test_acc,test_f1_score,test_precision,test_recall,test_roc_auc,train_len,train_active_perc,train_inactive_perc,train_avg_tanimoto_dist,test_len,test_active_perc,test_inactive_perc,test_avg_tanimoto_dist,num_leaking_uniprot_train_test,num_leaking_smiles_train_test,perc_leaking_uniprot_train_test,perc_leaking_smiles_train_test,majority_vote,model_type,disabled_embeddings,test_f1,split_type
-0.6918061971664429,0.4588235318660736,0.41025641560554504,0.5,0.3478260934352875,0.541248619556427,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3,,uniprot
-0.692865252494812,0.48235294222831726,0.6451612710952759,0.5128205418586731,0.8695651888847351,0.48885172605514526,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3,,uniprot
-0.6930828094482422,0.47058823704719543,0.6017699241638184,0.5074626803398132,0.739130437374115,0.4303233027458191,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3,,uniprot
-,0.4117647111415863,,0.4285714328289032,0.260869562625885,0.45986616611480713,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled e3,0.3243243098258972,uniprot
-0.7012832164764404,0.5176470875740051,0.6371681690216064,0.5373134613037109,0.782608687877655,0.47881829738616943,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi,,uniprot
-0.6897164583206177,0.5529412031173706,0.6779661178588867,0.5555555820465088,0.8695651888847351,0.5774804949760437,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi,,uniprot
-0.6919873356819153,0.4588235318660736,0.0,0.0,0.0,0.4007803797721863,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi,,uniprot
-,0.5058823823928833,,0.5454545617103577,0.52173912525177,0.4960980713367462,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled poi,0.5333333611488342,uniprot
-0.7027028203010559,0.4588235318660736,0.0,0.0,0.0,0.5175585150718689,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled cell,,uniprot
-0.6922561526298523,0.4588235318660736,0.0,0.0,0.0,0.4216833710670471,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled cell,,uniprot
-0.6935735940933228,0.4470588266849518,0.0,0.0,0.0,0.5119843482971191,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled cell,,uniprot
-,0.4588235318660736,,0.0,0.0,0.5136566162109375,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled cell,0.0,uniprot
-0.7248849868774414,0.4588235318660736,0.0,0.0,0.0,0.4654403328895569,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled smiles,,uniprot
-0.6911444664001465,0.5647059082984924,0.6991869807243347,0.5584415793418884,0.9347826242446899,0.6577480435371399,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled smiles,,uniprot
-0.6910174489021301,0.4588235318660736,0.0,0.0,0.0,0.4816053509712219,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled smiles,,uniprot
-,0.4588235318660736,,0.0,0.0,0.4420289695262909,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled smiles,0.0,uniprot
-0.7061877846717834,0.48235294222831726,0.2142857164144516,0.6000000238418579,0.1304347813129425,0.5128204822540283,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3 cell,,uniprot
-0.6927449703216553,0.5411764979362488,0.6929134130477905,0.5432098507881165,0.95652174949646,0.48773688077926636,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3 cell,,uniprot
-0.6949978470802307,0.4588235318660736,0.2068965584039688,0.5,0.1304347813129425,0.4431437849998474,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3 cell,,uniprot
-,0.4588235318660736,,0.0,0.0,0.4481605291366577,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled e3 cell,0.0,uniprot
-0.6848310232162476,0.529411792755127,0.6296296119689941,0.5483871102333069,0.739130437374115,0.5579710006713867,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3,,uniprot
-0.6916128993034363,0.5058823823928833,0.6612903475761414,0.5256410241127014,0.8913043737411499,0.5167223811149597,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3,,uniprot
-0.6955404281616211,0.4470588266849518,0.3188405930995941,0.47826087474823,0.239130437374115,0.4331103563308716,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3,,uniprot
-,0.47058823704719543,,0.5333333611488342,0.17391304671764374,0.4576365351676941,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled poi e3,0.26229506731033325,uniprot
-0.7010320425033569,0.5058823823928833,0.5333333611488342,0.5454545617103577,0.52173912525177,0.5,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3 cell,,uniprot
-0.6908637881278992,0.5176470875740051,0.6495726704597473,0.5352112650871277,0.8260869383811951,0.5317726135253906,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3 cell,,uniprot
-0.6938910484313965,0.4588235318660736,0.0416666679084301,0.5,0.021739130839705467,0.5362318754196167,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3 cell,,uniprot
-,0.4470588266849518,,0.3333333432674408,0.021739130839705467,0.47212928533554077,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled poi e3 cell,0.040816325694322586,uniprot
+0.710076630115509,0.4588235318660736,0.0416666679084301,0.5,0.021739130839705467,0.5401337742805481,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3,,uniprot
+0.6931973695755005,0.47058823704719543,0.6218487620353699,0.5068492889404297,0.804347813129425,0.5139353275299072,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3,,uniprot
+0.7137736678123474,0.4470588266849518,0.040816325694322586,0.3333333432674408,0.021739130839705467,0.4069119095802307,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3,,uniprot
+,0.4588235318660736,,0.0,0.0,0.5156075954437256,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled e3,0.0,uniprot
+0.6939821243286133,0.4941176474094391,0.60550457239151,0.523809552192688,0.717391312122345,0.5016722679138184,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi,,uniprot
+0.688806414604187,0.5764706134796143,0.694915235042572,0.5694444179534912,0.8913043737411499,0.5780379176139832,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi,,uniprot
+0.7017605900764465,0.48235294222831726,0.0833333358168602,1.0,0.043478261679410934,0.5674470663070679,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi,,uniprot
+,0.4588235318660736,,0.0,0.0,0.38573020696640015,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled poi,0.0,uniprot
+0.7191236615180969,0.4588235318660736,0.0,0.0,0.0,0.535953164100647,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled cell,,uniprot
+0.6930621266365051,0.529411792755127,0.692307710647583,0.5357142686843872,0.97826087474823,0.505574107170105,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled cell,,uniprot
+0.7100733518600464,0.47058823704719543,0.3076923191547394,0.5263158082962036,0.21739129722118378,0.47101449966430664,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled cell,,uniprot
+,0.4588235318660736,,0.0,0.0,0.5370680093765259,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled cell,0.0,uniprot
+0.7005730271339417,0.4588235318660736,0.0,0.0,0.0,0.4816053509712219,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled smiles,,uniprot
+0.6925779581069946,0.529411792755127,0.6875,0.5365853905677795,0.95652174949646,0.457079142332077,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled smiles,,uniprot
+0.6919492483139038,0.4941176474094391,0.6504064798355103,0.5194805264472961,0.8695651888847351,0.49052396416664124,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled smiles,,uniprot
+,0.4588235318660736,,0.0,0.0,0.43979930877685547,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled smiles,0.0,uniprot
+0.725774347782135,0.47058823704719543,0.042553190141916275,1.0,0.021739130839705467,0.527313232421875,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3 cell,,uniprot
+0.6943013668060303,0.4941176474094391,0.6126126050949097,0.5230769515037537,0.739130437374115,0.45930880308151245,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3 cell,,uniprot
+0.7366461753845215,0.47058823704719543,0.042553190141916275,1.0,0.021739130839705467,0.4361761212348938,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled e3 cell,,uniprot
+,0.4588235318660736,,0.0,0.0,0.4626532793045044,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled e3 cell,0.0,uniprot
+0.6917157769203186,0.5411764979362488,0.41791045665740967,0.6666666865348816,0.30434781312942505,0.5551839470863342,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3,,uniprot
+0.6937269568443298,0.48235294222831726,0.6333333253860474,0.5135135054588318,0.8260869383811951,0.38795986771583557,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3,,uniprot
+0.7266149520874023,0.4588235318660736,0.0,0.0,0.0,0.48104795813560486,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3,,uniprot
+,0.4588235318660736,,0.0,0.0,0.4442586302757263,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled poi e3,0.0,uniprot
+0.7062892913818359,0.4941176474094391,0.1568627506494522,0.800000011920929,0.08695652335882187,0.5139353275299072,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3 cell,,uniprot
+0.6901537179946899,0.5529412031173706,0.6607142686843872,0.560606062412262,0.804347813129425,0.5367892980575562,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3 cell,,uniprot
+0.7415007948875427,0.4588235318660736,0.0,0.0,0.0,0.4715718924999237,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,False,Pytorch,disabled poi e3 cell,,uniprot
+,0.4588235318660736,,0.0,0.0,0.5852842926979065,772,0.5064766839378239,0.49352331606217614,0.3753049487934892,85,0.5411764705882353,0.4588235294117647,0.39483030881358294,0,6,0.0,0.011658031088082901,True,Pytorch,disabled poi e3 cell,0.0,uniprot

reports/cv_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random.csv

@@ -1,6 +1,6 @@
 model_type,fold,train_len,val_len,train_perc,val_perc,train_active_perc,train_inactive_perc,train_avg_tanimoto_dist,val_active_perc,val_inactive_perc,val_avg_tanimoto_dist,test_len,test_active_perc,test_inactive_perc,test_avg_tanimoto_dist,num_leaking_uniprot_train_val,num_leaking_smiles_train_val,perc_leaking_uniprot_train_val,perc_leaking_smiles_train_val,num_leaking_uniprot_train_test,num_leaking_smiles_train_test,perc_leaking_uniprot_train_test,perc_leaking_smiles_train_test,train_loss,train_loss_step,train_loss_epoch,train_acc,train_acc_epoch,train_f1_score,train_f1_score_epoch,train_precision,train_precision_epoch,train_recall,train_recall_epoch,train_roc_auc,train_roc_auc_epoch,val_loss,val_acc,val_f1_score,val_precision,val_recall,val_roc_auc,test_loss,test_acc,test_f1_score,test_precision,test_recall,test_roc_auc,split_type
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reports/cv_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv

@@ -1,6 +1,6 @@
 model_type,fold,train_len,val_len,train_perc,val_perc,train_active_perc,train_inactive_perc,train_avg_tanimoto_dist,val_active_perc,val_inactive_perc,val_avg_tanimoto_dist,test_len,test_active_perc,test_inactive_perc,test_avg_tanimoto_dist,num_leaking_uniprot_train_val,num_leaking_smiles_train_val,perc_leaking_uniprot_train_val,perc_leaking_smiles_train_val,num_leaking_uniprot_train_test,num_leaking_smiles_train_test,perc_leaking_uniprot_train_test,perc_leaking_smiles_train_test,train_unique_groups,val_unique_groups,train_loss,train_loss_step,train_loss_epoch,train_acc,train_acc_epoch,train_f1_score,train_f1_score_epoch,train_precision,train_precision_epoch,train_recall,train_recall_epoch,train_roc_auc,train_roc_auc_epoch,val_loss,val_acc,val_f1_score,val_precision,val_recall,val_roc_auc,test_loss,test_acc,test_f1_score,test_precision,test_recall,test_roc_auc,split_type
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reports/cv_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot.csv

@@ -1,6 +1,6 @@
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reports/hparam_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random.csv

@@ -1,2 +1,2 @@
 hidden_dim,learning_rate,smote_k_neighbors,use_smote,dropout,use_batch_norm,split_type
-64,0.0009067092168400103,5,True,0.3693189141810386,True,random
+128,0.0009252997695638103,10,True,0.2163504523983239,True,random

reports/hparam_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv

@@ -1,2 +1,2 @@
 hidden_dim,learning_rate,smote_k_neighbors,use_smote,dropout,use_batch_norm,split_type
-256,0.0009609725342470751,8,True,0.2640487682023353,True,tanimoto
+64,0.0007711844442771359,13,False,0.3045354410518719,True,tanimoto

reports/hparam_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot.csv

@@ -1,2 +1,2 @@
 hidden_dim,learning_rate,smote_k_neighbors,use_smote,dropout,use_batch_norm,split_type
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+32,0.0006485744798313806,4,True,0.09263762731228274,False,uniprot

reports/majority_vote_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_random.csv

@@ -1,3 +1,3 @@
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reports/majority_vote_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv

@@ -1,3 +1,3 @@
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reports/majority_vote_report_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_uniprot.csv

@@ -1,3 +1,3 @@
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