formatting

scripts/model_builder/plot_data.py

@@ -17,12 +17,10 @@ def load_validation_data():
         "data/dataset/val.h5", label_name="modulation", data_key="validation_data"
     )
 
-
     x = np.stack([x.numpy() for x, _ in val_dataset])  # shape: (N, C, L)
     y = np.array([y.item() for _, y in val_dataset])  # shape: (N,)
     class_names = list(val_dataset.label_encoder.classes_)
 
-
     return x, y, class_names
 
 
@@ -46,27 +44,22 @@ def build_model_from_ckpt(
     return model
 
 
-
-
 def evaluate_checkpoint(ckpt_path: str):
     """
     Loads the model from checkpoint and evaluates it on a validation set.
     Prints classification metrics and plots a confusion matrix.
     """
 
-
     # Load validation data
     X_val, y_true, class_names = load_validation_data()
     num_classes = len(class_names)
     in_channels = X_val.shape[1]
 
-
     # Load model
     model = build_model_from_ckpt(
         ckpt_path, in_channels=in_channels, num_classes=num_classes
     )
 
-
     # Inference
     y_pred = []
     with torch.no_grad():
@@ -76,15 +69,12 @@ def evaluate_checkpoint(ckpt_path: str):
             pred = torch.argmax(logits, dim=1).item()
             y_pred.append(pred)
 
-
     # Print classification report
     print("\nClassification Report:")
     print(
         classification_report(y_true, y_pred, target_names=class_names, zero_division=0)
     )
 
-  
-  
     print_confusion_matrix(
         y_true=np.array(y_true),
         y_pred=np.array(y_pred),
@@ -94,8 +84,6 @@ def evaluate_checkpoint(ckpt_path: str):
     )
 
 
-
-
 def print_confusion_matrix(
     y_true: np.ndarray,
     y_pred: np.ndarray,
@@ -120,7 +108,6 @@ def print_confusion_matrix(
     for t, p in zip(y_true, y_pred):
         cm[t, p] += 1
 
-  
     # 2) normalize if requested
     if normalize:
         with np.errstate(divide="ignore", invalid="ignore"):
@@ -131,10 +118,9 @@ def print_confusion_matrix(
         print_confusion_matrix_helper(cm, classes)
 
 
-
-
 import numpy as np
 
+
 def print_confusion_matrix_helper(matrix, classes=None, normalize=False, digits=2):
     """
     Pretty prints a confusion matrix with x/y labels.
@@ -169,6 +155,6 @@ def print_confusion_matrix_helper(matrix, classes=None, normalize=False, digits=
 
 if __name__ == "__main__":
     settings = get_app_settings()
-   evaluate_checkpoint(os.path.join("checkpoint_files", "inference_recognition_model.ckpt"))
-
-
+    evaluate_checkpoint(
+        os.path.join("checkpoint_files", "inference_recognition_model.ckpt")
+    )