Function plot_partial_dependence

alphapy/plots.py:882–928

r"""Display a Partial Dependence Plot. Parameters ---------- est : estimator The scikit-learn estimator for calculating partial dependence. X : numpy array The data on which the estimator was trained. features : list of int Feature numbers of ``X``. f

(est, X, features, fnames, tag,
                            n_jobs=-1, verbosity=0, directory=None)

#

def plot_partial_dependence(est, X, features, fnames, tag,
                            n_jobs=-1, verbosity=0, directory=None):
    r"""Display a Partial Dependence Plot.

    Parameters
    ----------
    est : estimator
        The scikit-learn estimator for calculating partial dependence.
    X : numpy array
        The data on which the estimator was trained.
    features : list of int
        Feature numbers of ``X``.
    fnames : list of str
        The feature names to plot.
    tag : str
        Unique identifier for the plot
    n_jobs : int, optional
        The maximum number of parallel jobs.
    verbosity : int, optional
        The amount of logging from 0 (minimum) and higher.
    directory : str
        Directory where the plot will be stored.

    Returns
    -------
    None : None.

    References
    ----------

    http://scikit-learn.org/stable/auto_examples/ensemble/plot_partial_dependence.html#sphx-glr-auto-examples-ensemble-plot-partial-dependence-py

    """

    logger.info("Generating Partial Dependence Plot")

    # Plot partial dependence

    fig, axs = plot_partial_dependence(est, X, features, feature_names=fnames,
                                       grid_resolution=50, n_jobs=n_jobs,
                                       verbose=verbosity)
    title = "Partial Dependence Plot"
    fig.suptitle(title)
    plt.subplots_adjust(top=0.9)  # tight_layout causes overlap with suptitle

    # Save the plot
    write_plot(model, 'matplotlib', plt, 'partial_dependence', tag, directory)


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