Add new auc function - remove sklearn dependancy (#102)

* new_auc function

* remove sklearn from dependancies

DESCRIPTION

@@ -51,7 +51,6 @@ Config/reticulate:
       list(package = "polars"),
       list(package = "tqdm"),
       list(package = "connectorx"),
-      list(package = "scikit-learn"),
       list(package = "pyarrow")
       )
   )

inst/python/Estimator.py

@@ -5,7 +5,6 @@
 from torch.utils.data import DataLoader, BatchSampler, RandomSampler, SequentialSampler
 import torch.nn.functional as F
 from tqdm import tqdm
-from sklearn.metrics import roc_auc_score
 
 
 class Estimator:
@@ -155,8 +154,7 @@ def score(self, dataloader):
             mean_loss = loss.mean().item()
             predictions = torch.concat(predictions)
             targets = torch.concat(targets)
-            auc = roc_auc_score(targets.cpu(), predictions.cpu())
-            # auc = compute_auc(predictions, targets)
+            auc = compute_auc(targets.cpu(), predictions.cpu())
             scores = dict()
             if self.metric:
                 if self.metric["name"] == "auc":
@@ -330,26 +328,30 @@ def batch_to_device(batch, device='cpu'):
     return batch
 
 
-def compute_auc(input, target, n_threshold=1000):
-    threshold = torch.linspace(0, 1.0, n_threshold).to(device=input.device)
-    pred_label = input >= threshold[:, None, None]
-    input_target = pred_label * target
+def compute_auc(y_true, y_pred):
+    """
+    Computes the AUC score for binary classification predictions with a fast algorithm.
+    Args:
+    y_true (torch.Tensor): True binary labels.
+    y_pred (torch.Tensor): Predicted scores.
+    Returns:
+    float: Computed AUC score.
+    """
+    # Ensure inputs are sorted by predicted score
+    y_pred_sorted, sorted_indices = torch.sort(y_pred, descending=True)
+    y_true_sorted = y_true[sorted_indices]
 
-    cum_tp = F.pad(input_target.sum(dim=-1).rot90(1, [1, 0]), (1, 0), value=0.0)
-    cum_fp = F.pad(
-        (pred_label.sum(dim=-1) - input_target.sum(dim=-1)).rot90(1, [1, 0]),
-        (1, 0),
-        value=0.0,
-    )
+    # Get the number of positive and negative examples
+    n_pos = y_true_sorted.sum()
+    n_neg = (1 - y_true_sorted).sum()
 
-    if len(cum_tp.shape) > 1:
-        factor = cum_tp[:, -1] * cum_fp[:, -1]
-    else:
-        factor = cum_tp[-1] * cum_fp[-1]
-    # Set AUROC to 0.5 when the target contains all ones or all zeros.
-    auroc = torch.where(
-        factor == 0,
-        0.5,
-        torch.trapz(cum_tp, cum_fp).double() / factor,
-    )
-    return auroc.item()
+    # for every negative label, count preceding positive labels in sorted labels
+    num_crossings = torch.cumsum(y_true_sorted, 0)[y_true_sorted == 0].sum()
+
+    # Compute AUC
+    auc = num_crossings / (n_pos * n_neg)
+    return auc
+
+
+
+