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svgp_predplot

compare_distributions_svgpr(trainer, x_instance, y_actual=None, num_samples=10000, ax=None, dtype=np.float32)

Compare the actual and predicted outcome value/distributions for the SVGPR model.

Parameters:

Name Type Description Default
trainer SVGPRTrainer

The trained SVGPRTrainer instance.

 required
x_instance np.ndarray

The instance for which to predict the outcome distribution.

 required
y_actual float or np.ndarray

The actual outcome. If a single value, plot as a vertical line. If an array or list, plot as a KDE. If None, don't plot actual outcome.

 None
num_samples int

The number of samples to generate from the predicted distribution. Default is 10000.

 10000
ax matplotlib.axes.Axes

The axes on which to plot. If None, create a new figure.

 None
dtype np.dtype

Data type to use for plotting. Default is np.float32.

 np.float32

Returns:

Type Description

None
Source code in uncertaintyplayground/predplot/svgp_predplot.py 
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def compare_distributions_svgpr(trainer, x_instance, y_actual=None, num_samples=10000, ax=None, dtype=np.float32):
    """
    Compare the actual and predicted outcome value/distributions for the SVGPR model.

    Args:
        trainer (SVGPRTrainer): The trained SVGPRTrainer instance.
        x_instance (np.ndarray): The instance for which to predict the outcome distribution.
        y_actual (float or np.ndarray, optional): The actual outcome. If a single value, plot as a vertical line.
                                                  If an array or list, plot as a KDE. If None, don't plot actual outcome.
        num_samples (int, optional): The number of samples to generate from the predicted distribution.
                                     Default is 10000.
        ax (matplotlib.axes.Axes, optional): The axes on which to plot. If None, create a new figure.
        dtype (np.dtype, optional): Data type to use for plotting. Default is np.float32.

    Returns:
        None
    """
    # Ensure x_instance is a 2D array
    if x_instance.ndim == 1:
        x_instance = np.expand_dims(x_instance, axis=0)

    # Change the prediction instance to the desired data type
    x_instance = x_instance.astype(dtype)

    # Get the predicted mean and standard deviation
    mu, sigma = trainer.predict_with_uncertainty(x_instance)

    # Generate samples from the predicted distribution
    predicted_samples = np.random.normal(mu, sigma, num_samples)

    # Plot KDE of predicted samples
    if ax is None:
        sns.kdeplot(predicted_samples, fill=True, color="r", label="Predicted distribution")
        plt.axvline(mu, color="r", linestyle="--", label="Predicted value")
    else:
        sns.kdeplot(predicted_samples, fill=True, color="r", label="Predicted distribution", ax=ax)
        ax.axvline(mu, color="r", linestyle="--", label="Predicted value")

    # Plot the actual value
    if y_actual is not None:
        if ax is None:
            plt.axvline(y_actual, color="b", linestyle="--", label="Actual value")
        else:
            ax.axvline(y_actual, color="b", linestyle="--", label="Actual value")

    # Set an option when doing multiple plots
    if ax is None:
        plt.xlabel('Value')
        plt.ylabel('Density')
        plt.legend()
        plt.grid(axis='y', alpha=0.75)
        plt.show()