plot_and_save module

calculate_metrics(predictions, actuals, threshold=0.8)

Calculate epsilon, beta and additional metrics (RMSE, RMSLE, MAPE, Bias, MAE).

Parameters:

Name	Type	Description	Default
predictions	array-like	Predicted values.	required
actuals	array-like	Actual values.	required
threshold	float	Relative error threshold. Defaults to 0.8.	0.8

Returns:

Type	Description
tuple	Tuple containing: - epsilon (float): Epsilon value. - beta (float): Beta value. - rmse (float): Root mean square error. - rmsle (float): Root mean square log error. - mape (float): Mean absolute percentage error. - bias (float): Bias value. - mae (float): Mean absolute error.

Source code in hypercoast/moe_vae/plot_and_save.py

def calculate_metrics(predictions, actuals, threshold=0.8):
    """Calculate epsilon, beta and additional metrics (RMSE, RMSLE, MAPE, Bias, MAE).

    Args:
        predictions (array-like): Predicted values.
        actuals (array-like): Actual values.
        threshold (float, optional): Relative error threshold. Defaults to 0.8.

    Returns:
        tuple: Tuple containing:
            - epsilon (float): Epsilon value.
            - beta (float): Beta value.
            - rmse (float): Root mean square error.
            - rmsle (float): Root mean square log error.
            - mape (float): Mean absolute percentage error.
            - bias (float): Bias value.
            - mae (float): Mean absolute error.
    """
    # Apply the threshold to filter out predictions with large relative error
    # mask = np.abs(predictions - actuals) / np.abs(actuals+1e-10) < threshold
    # filtered_predictions = predictions[mask]
    # filtered_actuals = actuals[mask]
    predictions = np.where(predictions <= 1e-10, 1e-10, predictions)
    actuals = np.where(actuals <= 1e-10, 1e-10, actuals)
    filtered_predictions = predictions
    filtered_actuals = actuals

    # Calculate epsilon and beta
    log_ratios = np.log10(filtered_predictions / filtered_actuals)
    Y = np.median(np.abs(log_ratios))
    Z = np.median(log_ratios)
    epsilon = 100 * (10**Y - 1)
    beta = 50 * np.sign(Z) * (10 ** np.abs(Z) - 1)

    # Calculate additional metrics
    rmse = np.sqrt(np.mean((filtered_predictions - filtered_actuals) ** 2))
    rmsle = np.sqrt(
        np.mean(
            (np.log10(filtered_predictions + 1) - np.log10(filtered_actuals + 1)) ** 2
        )
    )
    mape = 50 * np.median(
        np.abs((filtered_predictions - filtered_actuals) / filtered_actuals)
    )
    bias = 10 ** (np.mean(np.log10(filtered_predictions) - np.log10(filtered_actuals)))
    mae = 10 ** np.mean(
        np.abs(np.log10(filtered_predictions) - np.log10(filtered_actuals))
    )

    return epsilon, beta, rmse, rmsle, mape, bias, mae

plot_results(predictions_rescaled, actuals_rescaled, save_dir, threshold=10, mode='test')

Plot the results of the MoE-VAE model.

Parameters:

Name	Type	Description	Default
predictions_rescaled	array-like	Predicted values.	required
actuals_rescaled	array-like	Actual values.	required
save_dir	str	Directory to save the plot.	required
threshold	float	Relative error threshold. Defaults to 10.	10
mode	str	Mode of the plot. Defaults to "test".	'test'

Source code in hypercoast/moe_vae/plot_and_save.py

def plot_results(
    predictions_rescaled, actuals_rescaled, save_dir, threshold=10, mode="test"
):
    """Plot the results of the MoE-VAE model.

    Args:
        predictions_rescaled (array-like): Predicted values.
        actuals_rescaled (array-like): Actual values.
        save_dir (str): Directory to save the plot.
        threshold (float, optional): Relative error threshold. Defaults to 10.
        mode (str, optional): Mode of the plot. Defaults to "test".
    """

    actuals = actuals_rescaled.flatten()
    predictions = predictions_rescaled.flatten()

    log_actuals = np.log10(actuals)
    log_predictions = np.log10(predictions)

    # mask = np.abs(predictions - actuals) / np.abs(actuals+1e-10) < threshold
    mask = np.abs(log_predictions - log_actuals) < threshold
    filtered_predictions = predictions[mask]
    filtered_actuals = actuals[mask]

    log_actual = np.log10(np.where(actuals == 0, 1e-10, actuals))
    log_prediction = np.log10(np.where(predictions == 0, 1e-10, predictions))

    filtered_log_actual = np.log10(
        np.where(filtered_actuals == 0, 1e-10, filtered_actuals)
    )
    filtered_log_prediction = np.log10(
        np.where(filtered_predictions == 0, 1e-10, filtered_predictions)
    )

    epsilon, beta, rmse, rmsle, mape, bias, mae = calculate_metrics(
        filtered_predictions, filtered_actuals, threshold
    )

    valid_mask = np.isfinite(filtered_log_actual) & np.isfinite(filtered_log_prediction)
    slope, intercept = np.polyfit(
        filtered_log_actual[valid_mask], filtered_log_prediction[valid_mask], 1
    )
    x = np.array([-4, 4])
    y = slope * x + intercept

    plt.figure(figsize=(6, 6))

    plt.plot(x, y, linestyle="--", color="blue", linewidth=0.8)
    lims = [-4, 4]
    plt.plot(lims, lims, linestyle="-", color="black", linewidth=0.8)

    sns.scatterplot(x=log_actual, y=log_prediction, alpha=0.5)

    sns.kdeplot(
        x=filtered_log_actual,
        y=filtered_log_prediction,
        levels=3,
        color="black",
        fill=False,
        linewidths=0.8,
    )

    plt.xlabel("Actual Values", fontsize=16, fontname="Ubuntu")
    plt.ylabel("Predicted Values", fontsize=16, fontname="Ubuntu")
    plt.xlim(-4, 4)
    plt.ylim(-4, 4)
    plt.grid(True, which="both", ls="--")

    plt.legend(
        title=(
            f"MAE = {mae:.2f}, RMSE = {rmse:.2f}, RMSLE = {rmsle:.2f} \n"
            f"Bias = {bias:.2f}, Slope = {slope:.2f} \n"
            f"MAPE = {mape:.2f}%, ε = {epsilon:.2f}%, β = {beta:.2f}%"
        ),
        fontsize=16,
        title_fontsize=12,
        prop={"family": "Ubuntu"},
    )

    plt.xticks(fontsize=20, fontname="Ubuntu")
    plt.yticks(fontsize=20, fontname="Ubuntu")

    plt.savefig(os.path.join(save_dir, f"{mode}_plot.pdf"), bbox_inches="tight")
    plt.close()

save_and_plot_results_from_excel(predictions, actuals, sample_ids, dates, original_excel_path, save_dir)

Save and plot the results from an Excel file.

Parameters:

Name	Type	Description	Default
predictions	array-like	Predicted values.	required
actuals	array-like	Actual values.	required
sample_ids	array-like	IDs of the samples.	required
dates	array-like	Dates of the samples.	required
original_excel_path	str	Path to the original Excel file.	required
save_dir	str	Directory to save the results.	required

Source code in hypercoast/moe_vae/plot_and_save.py

def save_and_plot_results_from_excel(
    predictions, actuals, sample_ids, dates, original_excel_path, save_dir
):
    """Save and plot the results from an Excel file.

    Args:
        predictions (array-like): Predicted values.
        actuals (array-like): Actual values.
        sample_ids (array-like): IDs of the samples.
        dates (array-like): Dates of the samples.
        original_excel_path (str): Path to the original Excel file.
        save_dir (str): Directory to save the results.
    """
    os.makedirs(save_dir, exist_ok=True)

    filename = os.path.basename(original_excel_path)
    dataset_name = os.path.splitext(filename)[0]

    save_results_to_excel(
        sample_ids,
        actuals,
        predictions,
        os.path.join(save_dir, f"{dataset_name}.xlsx"),
        dates=dates,
    )
    plot_results(predictions, actuals, save_dir, mode=dataset_name)

save_model_structure_from_classes(save_path)

Save the model structure from classes to a file.

Parameters:

Name	Type	Description	Default
save_path	str	Path to save the model structure.	required

Source code in hypercoast/moe_vae/plot_and_save.py

def save_model_structure_from_classes(save_path):
    """Save the model structure from classes to a file.

    Args:
        save_path (str): Path to save the model structure.
    """
    classes = [SparseDispatcher, VAE, MoE_VAE]

    with open(save_path, "w") as f:
        for cls in classes:
            f.write(f"# === Source for {cls.__name__} ===\n")
            f.write(inspect.getsource(cls))
            f.write("\n\n")

save_results_to_excel(ids, actuals, predictions, file_path, dates=None)

Save prediction results to an Excel file. - If dates are included, output ID, Date, Actual, Predicted; - Otherwise, output ID, Actual, Predicted.

Parameters:

Name	Type	Description	Default
ids	array-like	IDs of the samples.	required
actuals	array-like	Actual values.	required
predictions	array-like	Predicted values.	required
file_path	str	Path to save the Excel file.	required
dates	array-like	Dates of the samples. Defaults to None.	None

Source code in hypercoast/moe_vae/plot_and_save.py

def save_results_to_excel(ids, actuals, predictions, file_path, dates=None):
    """
    Save prediction results to an Excel file.
    - If dates are included, output ID, Date, Actual, Predicted;
    - Otherwise, output ID, Actual, Predicted.

    Args:
        ids (array-like): IDs of the samples.
        actuals (array-like): Actual values.
        predictions (array-like): Predicted values.
        file_path (str): Path to save the Excel file.
        dates (array-like, optional): Dates of the samples. Defaults to None.
    """
    if dates is not None:
        df = pd.DataFrame(
            {"ID": ids, "Date": dates, "Actual": actuals, "Predicted": predictions}
        )
    else:
        df = pd.DataFrame({"ID": ids, "Actual": actuals, "Predicted": predictions})

    df.to_excel(file_path, index=False)