plots.py 文件源码

def generate_plots(model, partition):
    r"""Generate plots while running the pipeline.

    Parameters
    ----------
    model : alphapy.Model
        The model object with plotting specifications.
    partition : alphapy.Partition
        Reference to the dataset.

    Returns
    -------
    None : None

    """

    logger.info('='*80)
    logger.info("Generating Plots for partition: %s", datasets[partition])

    # Extract model parameters

    calibration_plot = model.specs['calibration_plot']
    confusion_matrix = model.specs['confusion_matrix']
    importances = model.specs['importances']
    learning_curve = model.specs['learning_curve']
    roc_curve = model.specs['roc_curve']

    # Generate plots

    if calibration_plot:
        plot_calibration(model, partition)
    if confusion_matrix:
        plot_confusion_matrix(model, partition)
    if roc_curve:
        plot_roc_curve(model, partition)
    if partition == Partition.train:
        if learning_curve:
            plot_learning_curve(model, partition)
        if importances:
            plot_importance(model, partition)


#
# Function get_plot_directory
#