Model Evaluation:

  1. Model Loading:

    • The method begins by constructing the path to the trained model file based on the roof type, quantiles, past time steps, forecast steps, and city name.
  2. Data Loading and Preprocessing:

    • Data for features and labels are loaded using the load_data method.
    • The features and labels are converted to NumPy arrays and then transformed into a dataset suitable for prediction using the create_dataset method.
    • The data is then reshaped for scaling, and the scaler objects for features and labels are loaded using the joblib library.
    • All the data is transformed using the respective scalers to match the distribution the model was trained on.
  3. Model Prediction:

    • The features are reshaped again to fit the model input requirements.
    • A random index is chosen to select a data window equivalent to 7 days for testing.
    • Predictions are made using the model's predict method on the selected test features.
  4. Data Post-processing:

    • Predictions and labels are reshaped to a 2D format for inverse scaling.
    • The scaler is used to reverse the normalization of predictions and labels to obtain their original scale.
    • The inverse-transformed data is reshaped back to its original dimensions.
  5. Metrics Calculation:

    • The calculate_metrics method is called with the inverse-transformed labels and predictions to evaluate the model's performance.
    • The calculated metrics are printed out, displaying the model's performance in percentage terms.
  6. Visualization:

    • Finally, the visualize_results method is called to create visual representations of the model's predictions compared to the actual values.
def evaluate_prediction(self):
    # Define the path to the saved model
    model_pth = f"data/pth/{self.roof_type}_{self.quantiles}_{self.past_time_steps}_{self.forecast_steps}_{self.city_name}.h5"
    print(f"Loading model from : {model_pth} ...")
    # Load your data
    features, labels = self.load_data()
    print(f"evaluate_prediction features shape: {features.shape}")
    print(f"evaluate_prediction labels shape: {labels.shape}")
    # Convert the features and labels to numpy arrays
    features = features.to_numpy()
    labels = labels.to_numpy()
    # Create the dataset
    features, labels = self.create_dataset(features, labels)
    print(f"evaluate_prediction features shape1: {features.shape}")
    print(f"evaluate_prediction labels shape1: {labels.shape}")
    # Reshape the features and labels to fit the scaler
    features = features.reshape(-1, features.shape[-1])
    labels = labels.reshape(-1, labels.shape[-1])
    print(f"evaluate_prediction features shape2: {features.shape}")
    print(f"evaluate_prediction labels shape2: {labels.shape}")
    # Load the scalers
    scaler_labels = load("data/pth/scaler_labels.joblib")
    scaler_features = load("data/pth/scaler_features.joblib")
    # Transform all the data
    features = scaler_features.transform(features)
    labels = scaler_labels.transform(labels)
    # Reshape the features and labels to fit the model input
    features = features.reshape(-1, self.past_time_steps, features.shape[-1])
    labels = labels.reshape(-1, self.forecast_steps, labels.shape[-1])
    # Load the model
    model = models.load_model(f"{model_pth}", custom_objects={"quantiles_loss": self.quantiles_loss})
    # Randomly select a window of 7 days of data for prediction
    idx = random.randint(0, len(features) - int(self.prediction_days * 12 * 24))
    idx = 34648
    print(f"idx: {idx}")
    features_test = [features[idx: idx + int(self.prediction_days * 12 * 24), :, i: i + 1] for i in range(features.shape[-1])]
    labels_test = labels[idx: idx + int(self.prediction_days * 12 * 24)]
    # Make predictions
    predictions = model.predict(features_test)
    # Reshape the data for inverse normalization
    predictions_2D = predictions.reshape(-1, predictions.shape[-1])
    labels_test_2D = labels_test.reshape(-1, labels_test.shape[-1])
    # Inverse normalize the predictions and actual values
    predictions_inverse_2D = scaler_labels.inverse_transform(predictions_2D)
    labels_inverse_2D = scaler_labels.inverse_transform(labels_test_2D)
    # Reshape the inverse normalized data to its original shape
    predictions_inverse = predictions_inverse_2D.reshape(predictions.shape)
    labels_inverse = labels_inverse_2D.reshape(labels_test.shape)
    # Calculate the metrics
    metrics = self.calculate_metrics(labels_inverse, predictions_inverse)
    for name, value in metrics.items():
        print(f"{name}: {value * 100:.2f}%")
    # Visualize the results
    self.visualize_results(predictions_inverse, labels_inverse)