Merge pull request #26 from vinhdc10998/Vinhdev

Vinhdev

utils/imputation.py

@@ -27,10 +27,8 @@ def evaluation(dataloader, model, device, loss_fn):
     predictions = torch.cat(predictions, dim=0)
     labels = torch.cat(labels, dim=0)
     test_loss /= size
-    _r2_score = r2_score(
-                labels.cpu().detach().numpy(),
-                predictions.cpu().detach().numpy()
-            )
+    n_samples = len(labels)
+    _r2_score = sum([r2_score(labels[i].cpu().detach().numpy(), predictions[i].cpu().detach().numpy()) for i in range(n_samples)])/n_samples
     return test_loss, _r2_score, (predictions, labels)
 
 def train(dataloader, model, device, loss_fn, optimizer, scheduler):
@@ -63,10 +61,9 @@ def train(dataloader, model, device, loss_fn, optimizer, scheduler):
 
     predictions = torch.cat(predictions, dim=0)
     labels = torch.cat(labels, dim=0)
-    _r2_score = r2_score(
-            labels.cpu().detach().numpy(),
-            predictions.cpu().detach().numpy()
-        )
+    n_samples = len(labels)
+    _r2_score = sum([r2_score(labels[i].cpu().detach().numpy(), predictions[i].cpu().detach().numpy()) for i in range(n_samples)])/n_samples
+
     return train_loss, _r2_score
 
 def save_model(model, region, type_model, path, best=False):

utils/plot_chart.py

@@ -35,10 +35,11 @@ def draw_MAF_R2(pred, label, a1_freq_list, type_model, region, bins=30, output_p
     for index in range(bins):
         y = torch.stack(label_bins[index]).detach().numpy().T
         y_pred = torch.stack(pred_bins[index]).detach().numpy().T
-        _r2_score = r2_score(y, y_pred)
+        n_samples = len(y)
+        _r2_score = sum([r2_score(y[i], y_pred[i]) for i in range(n_samples)])/n_samples
         r2_score_list.append(_r2_score)
-
     x_axis = np.unique(pd.cut(a1_freq_list, bins, labels=np.linspace(start=0, stop=0.5, num=bins)))
+    print(np.unique(bins_list))
     plt.plot(x_axis, r2_score_list)
     plt.grid(linestyle='--')
     plt.xlabel("Minor Allele Frequency")