formatting

scripts/model_builder/plot_data.py

@@ -13,128 +13,114 @@ from helpers.app_settings import get_app_settings
 
 
 def load_validation_data():
-   val_dataset = ModulationH5Dataset(
-       "data/dataset/val.h5", label_name="modulation", data_key="validation_data"
-   )
+    val_dataset = ModulationH5Dataset(
+        "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_)
 
-   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
+    return x, y, class_names
 
 
 def build_model_from_ckpt(
-   ckpt_path: str, in_channels: int, num_classes: int
+    ckpt_path: str, in_channels: int, num_classes: int
 ) -> torch.nn.Module:
-   """
-   Build and return a PyTorch model loaded from a checkpoint.
-   """
-   model = RFClassifier(
-       model=mobilenetv3(
-           model_size="mobilenetv3_small_050",
-           num_classes=num_classes,
-           in_chans=in_channels,
-       )
-   )
-   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-   checkpoint = torch.load(ckpt_path, weights_only=True, map_location=device)
-   model.load_state_dict(checkpoint["state_dict"])
-   model.eval()
-   return model
-
-
+    """
+    Build and return a PyTorch model loaded from a checkpoint.
+    """
+    model = RFClassifier(
+        model=mobilenetv3(
+            model_size="mobilenetv3_small_050",
+            num_classes=num_classes,
+            in_chans=in_channels,
+        )
+    )
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    checkpoint = torch.load(ckpt_path, weights_only=True, map_location=device)
+    model.load_state_dict(checkpoint["state_dict"])
+    model.eval()
+    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.
-   """
+    """
+    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 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():
+        for x in X_val:
+            x_tensor = torch.tensor(x[np.newaxis, ...], dtype=torch.float32)
+            logits = model(x_tensor)
+            pred = torch.argmax(logits, dim=1).item()
+            y_pred.append(pred)
 
-   # Load model
-   model = build_model_from_ckpt(
-       ckpt_path, in_channels=in_channels, num_classes=num_classes
-   )
-
-
-   # Inference
-   y_pred = []
-   with torch.no_grad():
-       for x in X_val:
-           x_tensor = torch.tensor(x[np.newaxis, ...], dtype=torch.float32)
-           logits = model(x_tensor)
-           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),
-       classes=class_names,
-       normalize=True,
-       title="Normalized Confusion Matrix",
-   )
-
+    # 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),
+        classes=class_names,
+        normalize=True,
+        title="Normalized Confusion Matrix",
+    )
 
 
 def print_confusion_matrix(
-   y_true: np.ndarray,
-   y_pred: np.ndarray,
-   classes: list[str],
-   normalize: bool = True,
-   title: str = "Confusion Matrix (counts and normalized)",
+    y_true: np.ndarray,
+    y_pred: np.ndarray,
+    classes: list[str],
+    normalize: bool = True,
+    title: str = "Confusion Matrix (counts and normalized)",
 ) -> None:
-   """
-   Plot a confusion matrix showing both raw counts and (optionally) normalized values.
+    """
+    Plot a confusion matrix showing both raw counts and (optionally) normalized values.
 
 
-   Args:
-       y_true: true labels (integers 0..C-1)
-       y_pred: predicted labels (same shape as y_true)
-       classes: list of class‐name strings in index order
-       normalize: if True, each row is normalized to sum=1
-       title: title for the plot
-   """
-   # 1) build raw CM
-   c = len(classes)
-   cm = np.zeros((c, c), dtype=int)
-   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"):
-           cm_norm = cm.astype(float) / cm.sum(axis=1)[:, None]
-           cm_norm = np.nan_to_num(cm_norm)
-       print_confusion_matrix_helper(cm_norm, classes)
-   else:
-       print_confusion_matrix_helper(cm, classes)
-
+    Args:
+        y_true: true labels (integers 0..C-1)
+        y_pred: predicted labels (same shape as y_true)
+        classes: list of class‐name strings in index order
+        normalize: if True, each row is normalized to sum=1
+        title: title for the plot
+    """
+    # 1) build raw CM
+    c = len(classes)
+    cm = np.zeros((c, c), dtype=int)
+    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"):
+            cm_norm = cm.astype(float) / cm.sum(axis=1)[:, None]
+            cm_norm = np.nan_to_num(cm_norm)
+        print_confusion_matrix_helper(cm_norm, classes)
+    else:
+        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.
@@ -168,7 +154,7 @@ 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"))
-
-
+    settings = get_app_settings()
+    evaluate_checkpoint(
+        os.path.join("checkpoint_files", "inference_recognition_model.ckpt")
+    )