Function plot_boundary

alphapy/plots.py:786–875

r"""Display a comparison of classifiers Parameters ---------- model : alphapy.Model The model object with plotting specifications. partition : alphapy.Partition Reference to the dataset. f1 : int Number of the first feature to compare. f2 : int

(model, partition, f1=0, f2=1)

#

def plot_boundary(model, partition, f1=0, f2=1):
    r"""Display a comparison of classifiers

    Parameters
    ----------
    model : alphapy.Model
        The model object with plotting specifications.
    partition : alphapy.Partition
        Reference to the dataset.
    f1 : int
        Number of the first feature to compare.
    f2 : int
        Number of the second feature to compare.

    Returns
    -------
    None : None

    References
    ----------
    Code excerpts from authors:

    * Gael Varoquaux
    * Andreas Muller

    http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html

    """

    logger.info("Generating Boundary Plots")
    pstring = datasets[partition]

    # For classification only

    if model.specs['model_type'] != ModelType.classification:
        logger.info('Boundary Plots are for classification only')
        return None

    # Get X, Y for correct partition

    X, y = get_partition_data(model, partition)

    # Subset for the two boundary features

    X = X[[f1, f2]]

    # Initialize plot

    n_classifiers = len(model.algolist)
    plt.figure(figsize=(3 * 2, n_classifiers * 2))
    plt.subplots_adjust(bottom=.2, top=.95)

    xx = np.linspace(3, 9, 100)
    yy = np.linspace(1, 5, 100).T
    xx, yy = np.meshgrid(xx, yy)
    Xfull = np.c_[xx.ravel(), yy.ravel()]

    # Plot each classification probability