Function plot_learning_curve

alphapy/plots.py:436–522

r"""Generate learning curves for a given partition. Parameters ---------- model : alphapy.Model The model object with plotting specifications. partition : alphapy.Partition Reference to the dataset. Returns ------- None : None References -------

(model, partition)

#

def plot_learning_curve(model, partition):
    r"""Generate learning curves for a given partition.

    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/auto_examples/ensemble/plot_forest_importances.html

    """

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

    # Extract model parameters.

    cv_folds = model.specs['cv_folds']
    n_jobs = model.specs['n_jobs']
    seed = model.specs['seed']
    shuffle = model.specs['shuffle']
    verbosity = model.specs['verbosity']

    # Get original estimators

    estimators = get_estimators(model)

    # Get X, Y for correct partition.

    X, y = get_partition_data(model, partition)

    # Set cross-validation parameters to get mean train and test curves.

    cv = StratifiedKFold(n_splits=cv_folds, shuffle=shuffle, random_state=seed)

    # Plot a learning curve for each algorithm.

    ylim = (0.4, 1.01)

    for algo in model.algolist:
        logger.info("Learning Curve for Algorithm: %s", algo)
        # get estimator
        est = estimators[algo].estimator
        # plot learning curve
        title = BSEP.join([algo, "Learning Curve [", pstring, "]"])
        # set up plot
        plt.style.use('classic')
        plt.figure()