started fixing my stuff

Author: Johanmkr

CollaborativeCoding/metrics/precision.py

@@ -40,6 +40,12 @@ def forward(self, y_true: torch.tensor, logits: torch.tensor) -> torch.tensor:
             if self.macro_averaging
             else self._micro_avg_precision(y_true, y_pred)
         )
+        
+    def accumulate(self):
+        pass # TODO fill
+    
+    def reset(self):
+        pass # TODO fill
 
     def _micro_avg_precision(
         self, y_true: torch.tensor, y_pred: torch.tensor

tests/test_metrics.py

@@ -79,8 +79,6 @@ def test_f1score():
 
 
 def test_precision():
-    from random import randint
-
     import numpy as np
     import torch
     from sklearn.metrics import precision_score
@@ -107,12 +105,12 @@ def test_precision():
     assert macro_precision_score.item() >= 0, "Expected non-negative value"
 
     # find predictions
-    y_pred = logits.argmax(dim=-1, keepdims=True)
+    y_pred = logits.argmax(dim=-1)
 
     # check dimension
-    assert y_true.shape == torch.Size([N, 1]) or torch.Size([N])
+    assert y_true.shape == torch.Size([N])
     assert logits.shape == torch.Size([N, C])
-    assert y_pred.shape == torch.Size([N, 1]) or torch.Size([N])
+    assert y_pred.shape == torch.Size([N])
 
     # find true values with scikit learn
     scikit_macro_precision = precision_score(y_true, y_pred, average="macro")