Function plot_confusion_matrix

alphapy/plots.py:598–690

r"""Draw the confusion matrix. Parameters ---------- model : alphapy.Model The model object with plotting specifications. partition : alphapy.Partition Reference to the dataset. Returns ------- None : None References ---------- http://sciki

(model, partition)

#

def plot_confusion_matrix(model, partition):
    r"""Draw the confusion matrix.

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

    Returns
    -------
    None : None

    References
    ----------

    http://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix

    """

    logger.info("Generating Confusion Matrices")
    plot_dir = get_plot_directory(model)
    pstring = datasets[partition]

    # For classification only

    if model.specs['model_type'] != ModelType.classification:
        logger.info('Confusion Matrix is for classification only')
        return None

    # Get X, Y for correct partition.
    X, y = get_partition_data(model, partition)

    # Plot Parameters
    np.set_printoptions(precision=2)
    cmap = plt.cm.Blues
    fmt = '.2f'

    # Generate a Confusion Matrix for each algorithm

    for algo in model.algolist:
        logger.info("Confusion Matrix for Algorithm: %s", algo)

        # get predictions for this partition
        y_pred = model.preds[(algo, partition)]

        # compute confusion matrix
        cm = confusion_matrix(y, y_pred)
        logger.info('Confusion Matrix:')
        logger.info('%s', cm)

        # normalize confusion matrix
        cm_pct = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

        # initialize plot
        _, ax = plt.subplots()