fixes

2025-05-19 13:59:16 +02:00
parent 426f4d6963
commit c6defa2065
196 changed files with 18625 additions and 1 deletions
@@ -0,0 +1,61 @@
+import numpy as np
+import torch
+from torch import nn
+
+from utils.dataset_creation import inverse_scale_feature
+
+
+def plot_prediction_windows(index: int,
+                            training_configuration: dict,
+                            item_x: np.ndarray,
+                            item_y: np.ndarray,
+                            preds: np.ndarray,
+                            scalers: dict,
+                            features_to_plot: list,
+                            output_feature_names: list,
+                            fig_widget):
+    window_features = item_x[index]
+    indices = np.arange(window_features.shape[0])
+    actual = item_y[index].ravel()
+    predicted = preds[index]
+
+    # clear previous traces
+    fig_widget.data = []
+
+    for feature in features_to_plot:
+        feature_index = feature["index"]
+        feature_name = feature["name"]
+        fig_widget.add_scatter(
+            x=indices,
+            y=window_features[:, feature_index],
+            mode="lines",
+            name=feature_name,
+        )
+
+    num_outputs = predicted.shape[-1]
+    scaled_preds = list()
+    for i in range(num_outputs):
+        if isinstance(training_configuration["loss_functions"][i], nn.BCEWithLogitsLoss):
+            output = torch.sigmoid(torch.tensor(predicted[i]))
+        else:
+            output = predicted[i]
+
+        output = float(output)
+
+        scaled_output = inverse_scale_feature(output,
+                                              output_feature_names[i],
+                                              scalers)
+
+        scaled_preds.append(scaled_output)
+
+    scaled_actuals = list()
+    for i in range(num_outputs):
+        output = float(actual[i].numpy())
+        scaled_output = inverse_scale_feature(output,
+                                              output_feature_names[i],
+                                              scalers)
+
+        scaled_actuals.append(scaled_output)
+
+    # add actual and predicted values
+    print(f"actual: {scaled_actuals}, predicted: {scaled_preds}")