minor code cleanup

pytorch_widedeep/metrics.py

@@ -1,7 +1,6 @@
 import numpy as np
 import torch
 from torchmetrics import Metric as TorchMetric
-from torchmetrics import AUC
 
 from .wdtypes import *  # noqa: F403
 
@@ -394,62 +393,3 @@ def __call__(self, y_pred: Tensor, y_true: Tensor) -> np.ndarray:
         y_true_avg = self.y_true_sum / self.num_examples
         self.denominator += ((y_true - y_true_avg) ** 2).sum().item()
         return np.array((1 - (self.numerator / self.denominator)))
-
-
-class Accuracy(Metric):
-    r"""Class to calculate the accuracy for both binary and categorical problems
-
-    Parameters
-    ----------
-    top_k: int, default = 1
-        Accuracy will be computed using the top k most likely classes in
-        multiclass problems
-
-    Examples
-    --------
-    >>> import torch
-    >>>
-    >>> from pytorch_widedeep.metrics import Accuracy
-    >>>
-    >>> acc = Accuracy()
-    >>> y_true = torch.tensor([0, 1, 0, 1]).view(-1, 1)
-    >>> y_pred = torch.tensor([[0.3, 0.2, 0.6, 0.7]]).view(-1, 1)
-    >>> acc(y_pred, y_true)
-    array(0.5)
-    >>>
-    >>> acc = Accuracy(top_k=2)
-    >>> y_true = torch.tensor([0, 1, 2])
-    >>> y_pred = torch.tensor([[0.3, 0.5, 0.2], [0.1, 0.1, 0.8], [0.1, 0.5, 0.4]])
-    >>> acc(y_pred, y_true)
-    array(0.66666667)
-    """
-
-    def __init__(self, top_k: int = 1):
-        super(Accuracy, self).__init__()
-
-        self.top_k = top_k
-        self.correct_count = 0
-        self.total_count = 0
-        self._name = "acc"
-
-    def reset(self):
-        """
-        resets counters to 0
-        """
-        self.correct_count = 0
-        self.total_count = 0
-
-    def __call__(self, y_pred: Tensor, y_true: Tensor) -> np.ndarray:
-        num_classes = y_pred.size(1)
-
-        if num_classes == 1:
-            y_pred = y_pred.round()
-            y_true = y_true
-        elif num_classes > 1:
-            y_pred = y_pred.topk(self.top_k, 1)[1]
-            y_true = y_true.view(-1, 1).expand_as(y_pred)
-
-        self.correct_count += y_pred.eq(y_true).sum().item()  # type: ignore[assignment]
-        self.total_count += len(y_pred)
-        accuracy = float(self.correct_count) / float(self.total_count)
-        return np.array(accuracy)