Create a SHAP monitoring plot.
    
    (Note this function is preliminary and subject to change!!)
    A SHAP monitoring plot is meant to display the behavior of a model
    over time. Often the shap_values given to this plot explain the loss
    of a model, so changes in a feature's impact on the model's loss over
    time can help in monitoring the model's performance.

    Parameters
    ----------
    ind : int
        Index of the feature to plot.

    shap_values : numpy.array
        Matrix of SHAP values (# samples x # features)

    features : numpy.array or pandas.DataFrame
        Matrix of feature values (# samples x # features)

    feature_names : list
        Names of the features (length # features)

def monitoring_plot(ind, shap_values, features, feature_names=None):
    """ Create a SHAP monitoring plot.
    
    (Note this function is preliminary and subject to change!!)
    A SHAP monitoring plot is meant to display the behavior of a model
    over time. Often the shap_values given to this plot explain the loss
    of a model, so changes in a feature's impact on the model's loss over
    time can help in monitoring the model's performance.

    Parameters
    ----------
    ind : int
        Index of the feature to plot.

    shap_values : numpy.array
        Matrix of SHAP values (# samples x # features)

    features : numpy.array or pandas.DataFrame
        Matrix of feature values (# samples x # features)

    feature_names : list
        Names of the features (length # features)
    """
    
    if str(type(features)).endswith("'pandas.core.frame.DataFrame'>"):
        if feature_names is None:
            feature_names = features.columns
        features = features.values
        
    pl.figure(figsize=(10,3))
    ys = shap_values[:,ind]
    xs = np.arange(len(ys))#np.linspace(0, 12*2, len(ys))
    
    pvals = []
    inc = 50
    for i in range(inc, len(ys)-inc, inc):
        #stat, pval = scipy.stats.mannwhitneyu(v[:i], v[i:], alternative="two-sided")
        stat, pval = scipy.stats.ttest_ind(ys[:i], ys[i:])
        pvals.append(pval)
    min_pval = np.min(pvals)
    min_pval_ind = np.argmin(pvals)*inc + inc
    
    if min_pval < 0.05 / shap_values.shape[1]:
        pl.axvline(min_pval_ind, linestyle="dashed", color="#666666", alpha=0.2)
        
    pl.scatter(xs, ys, s=10, c=features[:,ind], cmap=colors.red_blue)
    
    pl.xlabel("Sample index")
    pl.ylabel(truncate_text(feature_names[ind], 30) + "\nSHAP value", size=13)
    pl.gca().xaxis.set_ticks_position('bottom')
    pl.gca().yaxis.set_ticks_position('left')
    pl.gca().spines['right'].set_visible(False)
    pl.gca().spines['top'].set_visible(False)
    cb = pl.colorbar()
    cb.outline.set_visible(False)
    bbox = cb.ax.get_window_extent().transformed(pl.gcf().dpi_scale_trans.inverted())
    cb.ax.set_aspect((bbox.height - 0.7) * 20)
    cb.set_label(truncate_text(feature_names[ind], 30), size=13)
    pl.show()