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plots

pytorch_lattice.plots

Plotting functions for PyTorch Lattice calibrated models using matplotlib.

calibrator(model, feature_name)

Plots the calibrator for the given feature and calibrated model.

Parameters:

Name Type Description Default
model Union[CalibratedLinear, CalibratedLattice]

The calibrated model for which to plot calibrators.

 required
feature_name str

The name of the feature for which to plot the calibrator.

 required
Source code in pytorch_lattice/plots.py 
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def calibrator(
    model: Union[CalibratedLinear, CalibratedLattice],
    feature_name: str,
) -> None:
    """Plots the calibrator for the given feature and calibrated model.

    Args:
        model: The calibrated model for which to plot calibrators.
        feature_name: The name of the feature for which to plot the calibrator.
    """
    if feature_name not in model.calibrators:
        raise ValueError(f"Feature {feature_name} not found in model.")

    calibrator = model.calibrators[feature_name]
    input_keypoints = calibrator.keypoints_inputs().numpy()
    output_keypoints = calibrator.keypoints_outputs().numpy()

    if isinstance(calibrator, CategoricalCalibrator):
        model_feature = next(
            (x for x in model.features if x.feature_name == feature_name), None
        )
        if isinstance(model_feature, CategoricalFeature):
            input_keypoints = np.array(
                [
                    model_feature.categories[i]
                    if i < len(input_keypoints) - 1
                    else "<Missing>"
                    for i, ik in enumerate(input_keypoints)
                ]
            )
        plt.xticks(rotation=45)
        plt.bar(input_keypoints, output_keypoints)
    else:
        plt.plot(input_keypoints, output_keypoints)

    plt.title(f"Calibrator: {feature_name}")
    plt.xlabel("Input Keypoints")
    plt.ylabel("Output Keypoints")
    plt.show()

linear_coefficients(model)

Plots the coefficients for the linear layer of a calibrated linear model.

Source code in pytorch_lattice/plots.py 
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def linear_coefficients(model: CalibratedLinear) -> None:
    """Plots the coefficients for the linear layer of a calibrated linear model."""
    if not isinstance(model, CalibratedLinear):
        raise ValueError(
            "Model must be a `CalibratedLinear` model to plot linear coefficients."
        )
    linear_coefficients = dict(
        zip(
            [feature.feature_name for feature in model.features],
            model.linear.kernel.detach().numpy().flatten(),
        )
    )
    if model.use_bias:
        linear_coefficients["bias"] = model.linear.bias.detach().numpy()[0]

    plt.bar(list(linear_coefficients.keys()), list(linear_coefficients.values()))
    plt.title("Linear Coefficients")
    plt.xlabel("Feature Name")
    plt.xticks(rotation=45)
    plt.ylabel("Coefficient Value")
    plt.show()