add docs

pytorch_lightning/metrics/functional/classification.py

@@ -144,14 +144,19 @@ def accuracy(pred: torch.Tensor, target: torch.Tensor,
 
     Args:
         pred: predicted labels
+
         target: ground truth labels
+
         num_classes: number of classes
+
         reduction: method for reducing accuracies over labels (default: takes the mean)
 
            Available reduction methods:
 
            - elementwise_mean: takes the mean
+
            - none: pass array
+
            - sum: add elements
 
     Output:
@@ -170,6 +175,20 @@ def accuracy(pred: torch.Tensor, target: torch.Tensor,
 
 def confusion_matrix(pred: torch.Tensor, target: torch.Tensor,
                      normalize: bool = False) -> torch.Tensor:
+    '''
+    Computes the confusion matrix C where each entry C_{i,j} is the number of observations
+    in group i that were predicted in group j.
+
+    Args:
+        pred: estimated targets
+
+        target: groud truth labels
+
+        normalize: normalizes confusion matrix
+
+    Output:
+        Tensor, confusion matrix C [num_classes, num_classes ]
+    '''
     num_classes = get_num_classes(pred, target, None)
 
     d = target.size(-1)
@@ -190,6 +209,30 @@ def precision_recall(pred: torch.Tensor, target: torch.Tensor,
                      num_classes: Optional[int] = None,
                      reduction: str = 'elementwise_mean'
                      ) -> Tuple[torch.Tensor, torch.Tensor]:
+    '''
+    Computes precision and recall for different thresholds
+
+    Args:
+
+        pred: estimated probabilities
+
+        target: ground-truth labels
+
+        num_classes: number of classes
+
+        reduction: method for reducing precision-recall values (default: takes the mean)
+
+           Available reduction methods:
+
+           - elementwise_mean: takes the mean
+
+           - none: pass array
+
+           - sum: add elements
+
+    Output:
+        Tensor with precision and recall
+    '''
     tps, fps, tns, fns = stat_scores_multiple_classes(pred=pred,
                                                       target=target,
                                                       num_classes=num_classes)
@@ -209,13 +252,61 @@ def precision_recall(pred: torch.Tensor, target: torch.Tensor,
 def precision(pred: torch.Tensor, target: torch.Tensor,
               num_classes: Optional[int] = None,
               reduction: str = 'elementwise_mean') -> torch.Tensor:
+    '''
+    Computes precision score.
+
+    Args:
+
+        pred: estimated probabilities
+
+        target: ground-truth labels
+
+        num_classes: number of classes
+
+        reduction: method for reducing precision values (default: takes the mean)
+
+           Available reduction methods:
+
+           - elementwise_mean: takes the mean
+
+           - none: pass array
+
+           - sum: add elements
+
+    Output:
+        Tensor with precision.
+    '''
     return precision_recall(pred=pred, target=target,
                             num_classes=num_classes, reduction=reduction)[0]
 
 
 def recall(pred: torch.Tensor, target: torch.Tensor,
            num_classes: Optional[int] = None,
            reduction: str = 'elementwise_mean') -> torch.Tensor:
+    '''
+    Computes recall score.
+
+    Args:
+
+        pred: estimated probabilities
+
+        target: ground-truth labels
+
+        num_classes: number of classes
+
+        reduction: method for reducing recall values (default: takes the mean)
+
+           Available reduction methods:
+
+           - elementwise_mean: takes the mean
+
+           - none: pass array
+
+           - sum: add elements
+
+    Output:
+        Tensor with recall.
+    '''
     return precision_recall(pred=pred, target=target,
                             num_classes=num_classes, reduction=reduction)[1]