ruffed

Author: hzavadil98

CollaborativeCoding/metrics/accuracy.py

@@ -1,6 +1,6 @@
+import numpy as np
 import torch
 from torch import nn
-import numpy as np
 
 
 class Accuracy(nn.Module):
@@ -78,22 +78,20 @@ def _micro_acc(self):
             Micro-average accuracy score.
         """
         return (self.y_true == self.y_pred).float().mean().item()
-    
+
     def __returnmetric__(self):
         if self.y_true == [] or self.y_pred == []:
             return np.nan
-        if isinstance(self.y_true,list):
+        if isinstance(self.y_true, list):
             if len(self.y_true) == 1:
                 self.y_true = self.y_true[0]
                 self.y_pred = self.y_pred[0]
             else:
                 self.y_true = torch.cat(self.y_true)
                 self.y_pred = torch.cat(self.y_pred)
         return self._micro_acc() if not self.macro_averaging else self._macro_acc()
-    
+
     def __reset__(self):
         self.y_true = []
         self.y_pred = []
         return None
-
-

main.py

@@ -1,12 +1,11 @@
 import numpy as np
 import torch as th
 import torch.nn as nn
-import wandb
 from torch.utils.data import DataLoader
 from torchvision import transforms
 from tqdm import tqdm
-#from wandb_api import WANDB_API
 
+import wandb
 from CollaborativeCoding import (
     MetricWrapper,
     createfolders,
@@ -15,6 +14,9 @@
     load_model,
 )
 
+# from wandb_api import WANDB_API
+
+
 
 def main():
     """
@@ -126,7 +128,7 @@ def main():
         print("Dry run completed successfully.")
         exit()
 
-#    wandb.login(key=WANDB_API)
+    #    wandb.login(key=WANDB_API)
     wandb.init(
         entity="ColabCode",
         project=args.run_name,

tests/test_metrics.py

@@ -3,7 +3,13 @@
 import pytest
 
 from CollaborativeCoding.load_metric import MetricWrapper
-from CollaborativeCoding.metrics import Accuracy, F1Score, Precision, Recall, EntropyPrediction
+from CollaborativeCoding.metrics import (
+    Accuracy,
+    EntropyPrediction,
+    F1Score,
+    Precision,
+    Recall,
+)
 
 
 @pytest.mark.parametrize(
@@ -128,8 +134,8 @@ def test_precision():
 
 
 def test_accuracy():
-    import torch
     import numpy as np
+    import torch
 
     # Test the accuracy metric
     y_true = torch.tensor([0, 1, 2, 3, 4, 5])
@@ -141,20 +147,25 @@ def test_accuracy():
     assert accuracy.__returnmetric__() is np.nan, "Expected accuracy to be 0.0"
     y_pred = torch.tensor([0, 1, 2, 3, 4, 4])
     accuracy(y_true, y_pred)
-    assert np.abs(accuracy.__returnmetric__() - 0.8333333134651184) < 1e-5, "Expected accuracy to be 0.8333333134651184"
+    assert np.abs(accuracy.__returnmetric__() - 0.8333333134651184) < 1e-5, (
+        "Expected accuracy to be 0.8333333134651184"
+    )
     accuracy.__reset__()
     accuracy.macro_averaging = True
     accuracy(y_true, y_pred)
     y_true_1 = torch.tensor([0, 1, 2, 3, 4, 5])
     y_pred_1 = torch.tensor([0, 1, 2, 3, 4, 4])
     accuracy(y_true_1, y_pred_1)
-    assert np.abs(accuracy.__returnmetric__() - 0.8333333134651184) < 1e-5, "Expected accuracy to be 0.8333333134651186"
+    assert np.abs(accuracy.__returnmetric__() - 0.8333333134651184) < 1e-5, (
+        "Expected accuracy to be 0.8333333134651186"
+    )
     accuracy.macro_averaging = False
-    assert np.abs(accuracy.__returnmetric__() - 0.8333333134651184) < 1e-5, "Expected accuracy to be 0.8333333134651184"
+    assert np.abs(accuracy.__returnmetric__() - 0.8333333134651184) < 1e-5, (
+        "Expected accuracy to be 0.8333333134651184"
+    )
     accuracy.__reset__()
 
 
-
 def test_entropypred():
     import torch as th
 

tests/test_models.py

@@ -1,7 +1,12 @@
 import pytest
 import torch
 
-from CollaborativeCoding.models import ChristianModel, JanModel, MagnusModel, SolveigModel
+from CollaborativeCoding.models import (
+    ChristianModel,
+    JanModel,
+    MagnusModel,
+    SolveigModel,
+)
 
 
 @pytest.mark.parametrize(