def dependence_plot(ind, shap_values, features, feature_names=None, display_features=None,
                    interaction_index="auto",
                    color="#1E88E5", axis_color="#333333", cmap=colors.red_blue,
                    dot_size=16, x_jitter=0, alpha=1, title=None, xmin=None, xmax=None, show=True):
    """ Create a SHAP dependence plot, colored by an interaction feature.

    Plots the value of the feature on the x-axis and the SHAP value of the same feature
    on the y-axis. This shows how the model depends on the given feature, and is like a
    richer extenstion of the classical parital dependence plots. Vertical dispersion of the
    data points represents interaction effects. Grey ticks along the y-axis are data
    points where the feature's value was NaN.


    Parameters
    ----------
    ind : int or string
        If this is an int it is the index of the feature to plot. If this is a string it is
        either the name of the feature to plot, or it can have the form "rank(int)" to specify
        the feature with that rank (ordered by mean absolute SHAP value over all the samples).

    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).

    display_features : numpy.array or pandas.DataFrame
        Matrix of feature values for visual display (such as strings instead of coded values).

    interaction_index : "auto", None, int, or string
        The index of the feature used to color the plot. The name of a feature can also be passed
        as a string. If "auto" then shap.common.approximate_interactions is used to pick what
        seems to be the strongest interaction (note that to find to true stongest interaction you
        need to compute the SHAP interaction values).
        
    x_jitter : float (0 - 1)
        Adds random jitter to feature values. May increase plot readability when feature
        is discrete.

    alpha : float
        The transparency of the data points (between 0 and 1). This can be useful to the
        show density of the data points when using a large dataset.

    xmin : float or string
        Represents the lower bound of the plot's x-axis. It can be a string of the format
        "percentile(float)" to denote that percentile of the feature's value used on the x-axis.

    xmax : float or string
        Represents the upper bound of the plot's x-axis. It can be a string of the format
        "percentile(float)" to denote that percentile of the feature's value used on the x-axis.
    """

    # convert from DataFrames if we got any
    if str(type(features)).endswith("'pandas.core.frame.DataFrame'>"):
        if feature_names is None:
            feature_names = features.columns
        features = features.values
    if str(type(display_features)).endswith("'pandas.core.frame.DataFrame'>"):
        if feature_names is None:
            feature_names = display_features.columns
        display_features = display_features.values
    elif display_features is None:
        display_features = features

    if feature_names is None:
        feature_names = [labels['FEATURE'] % str(i) for i in range(shap_values.shape[1])]

    # allow vectors to be passed
    if len(shap_values.shape) == 1:
        shap_values = np.reshape(shap_values, len(shap_values), 1)
    if len(features.shape) == 1:
        features = np.reshape(features, len(features), 1)

    ind = convert_name(ind, shap_values, feature_names)
    
    # plotting SHAP interaction values
    if len(shap_values.shape) == 3 and len(ind) == 2:
        ind1 = convert_name(ind[0], shap_values, feature_names)
        ind2 = convert_name(ind[1], shap_values, feature_names)
        if ind1 == ind2:
            proj_shap_values = shap_values[:, ind2, :]
        else:
            proj_shap_values = shap_values[:, ind2, :] * 2  # off-diag values are split in half

        # TODO: remove recursion; generally the functions should be shorter for more maintainable code
        dependence_plot(
            ind1, proj_shap_values, features, feature_names=feature_names,
            interaction_index=ind2, display_features=display_features, show=False,
            xmin=xmin, xmax=xmax
        )
        if ind1 == ind2:
            pl.ylabel(labels['MAIN_EFFECT'] % feature_names[ind1])
        else:
            pl.ylabel(labels['INTERACTION_EFFECT'] % (feature_names[ind1], feature_names[ind2]))

        if show:
            pl.show()
        return

    assert shap_values.shape[0] == features.shape[0], \
        "'shap_values' and 'features' values must have the same number of rows!"
    assert shap_values.shape[1] == features.shape[1], \
        "'shap_values' must have the same number of columns as 'features'!"

    # get both the raw and display feature values
    oinds = np.arange(shap_values.shape[0]) # we randomize the ordering so plotting overlaps are not related to data ordering
    np.random.shuffle(oinds)
    xv = features[oinds, ind].astype(np.float64)
    xd = display_features[oinds, ind]
    s = shap_values[oinds, ind]
    if type(xd[0]) == str:
        name_map = {}
        for i in range(len(xv)):
            name_map[xd[i]] = xv[i]
        xnames = list(name_map.keys())

    # allow a single feature name to be passed alone
    if type(feature_names) == str:
        feature_names = [feature_names]
    name = feature_names[ind]

    # guess what other feature as the stongest interaction with the plotted feature
    if interaction_index == "auto":
        interaction_index = approximate_interactions(ind, shap_values, features)[0]
    interaction_index = convert_name(interaction_index, shap_values, feature_names)
    categorical_interaction = False

    # get both the raw and display color values
    color_norm = None
    if interaction_index is not None:
        cv = features[:, interaction_index]
        cd = display_features[:, interaction_index]
        clow = np.nanpercentile(cv.astype(np.float), 5)
        chigh = np.nanpercentile(cv.astype(np.float), 95)
        if type(cd[0]) == str:
            cname_map = {}
            for i in range(len(cv)):
                cname_map[cd[i]] = cv[i]
            cnames = list(cname_map.keys())
            categorical_interaction = True
        elif clow % 1 == 0 and chigh % 1 == 0 and chigh - clow < 10:
            categorical_interaction = True

        # discritize colors for categorical features
        if categorical_interaction and clow != chigh:
            clow = np.nanmin(cv.astype(np.float))
            chigh = np.nanmax(cv.astype(np.float))
            bounds = np.linspace(clow, chigh, int(chigh - clow + 2))
            color_norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N-1)

    # optionally add jitter to feature values
    if x_jitter > 0:
        if x_jitter > 1: x_jitter = 1
        xvals = xv.copy()
        if isinstance(xvals[0], float):
            xvals = xvals.astype(np.float)
            xvals = xvals[~np.isnan(xvals)]
        xvals = np.unique(xvals)
        if len(xvals) >= 2:
            smallest_diff = np.min(np.diff(np.sort(xvals)))
            jitter_amount = x_jitter * smallest_diff
            xv += (np.random.ranf(size = len(xv))*jitter_amount) - (jitter_amount/2)

    # the actual scatter plot, TODO: adapt the dot_size to the number of data points?
    xv_nan = np.isnan(xv)
    xv_notnan = np.invert(xv_nan)
    if interaction_index is not None:

        # plot the nan values in the interaction feature as grey
        cvals = features[oinds, interaction_index].astype(np.float64)
        cvals_imp = cvals.copy()
        cvals_imp[np.isnan(cvals)] = (clow + chigh) / 2.0
        cvals[cvals_imp > chigh] = chigh
        cvals[cvals_imp < clow] = clow
        p = pl.scatter(
            xv[xv_notnan], s[xv_notnan], s=dot_size, linewidth=0, c=cvals[xv_notnan],
            cmap=cmap, alpha=alpha, vmin=clow, vmax=chigh,
            norm=color_norm, rasterized=len(xv) > 500
        )
        p.set_array(cvals[xv_notnan])
    else:
        pl.scatter(xv, s, s=dot_size, linewidth=0, color=color,
                   alpha=alpha, rasterized=len(xv) > 500)

    if interaction_index != ind and interaction_index is not None:
        # draw the color bar
        if type(cd[0]) == str:
            tick_positions = [cname_map[n] for n in cnames]
            if len(tick_positions) == 2:
                tick_positions[0] -= 0.25
                tick_positions[1] += 0.25
            cb = pl.colorbar(ticks=tick_positions)
            cb.set_ticklabels(cnames)
        else:
            cb = pl.colorbar()

        cb.set_label(feature_names[interaction_index], size=13)
        cb.ax.tick_params(labelsize=11)
        if categorical_interaction:
            cb.ax.tick_params(length=0)
        cb.set_alpha(1)
        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)

    # handles any setting of xmax and xmin
    # note that we handle None,float, or "percentile(float)" formats
    if xmin is not None or xmax is not None:
        if type(xmin) == str and xmin.startswith("percentile"):
            xmin = np.nanpercentile(xv, float(xmin[11:-1]))
        if type(xmax) == str and xmax.startswith("percentile"):
            xmax = np.nanpercentile(xv, float(xmax[11:-1]))

        if xmin is None or xmin == np.nanmin(xv):
            xmin = np.nanmin(xv) - (xmax - np.nanmin(xv))/20
        if xmax is None or xmax == np.nanmax(xv):
            xmax = np.nanmax(xv) + (np.nanmax(xv) - xmin)/20
        
        pl.xlim(xmin, xmax)

    # plot any nan feature values as tick marks along the y-axis
    xlim = pl.xlim()
    if interaction_index is not None:
        p = pl.scatter(
            xlim[0] * np.ones(xv_nan.sum()), s[xv_nan], marker=1,
            linewidth=2, c=cvals_imp[xv_nan], cmap=cmap, alpha=alpha,
            vmin=clow, vmax=chigh
        )
        p.set_array(cvals[xv_nan])
    else:
        pl.scatter(
            xlim[0] * np.ones(xv_nan.sum()), s[xv_nan], marker=1,
            linewidth=2, color=color, alpha=alpha
        )
    pl.xlim(*xlim)

    # make the plot more readable
    if interaction_index != ind:
        pl.gcf().set_size_inches(7.5, 5)
    else:
        pl.gcf().set_size_inches(6, 5)
    pl.xlabel(name, color=axis_color, fontsize=13)
    pl.ylabel(labels['VALUE_FOR'] % name, color=axis_color, fontsize=13)
    if title is not None:
        pl.title(title, color=axis_color, fontsize=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)
    pl.gca().tick_params(color=axis_color, labelcolor=axis_color, labelsize=11)
    for spine in pl.gca().spines.values():
        spine.set_edgecolor(axis_color)
    if type(xd[0]) == str:
        pl.xticks([name_map[n] for n in xnames], xnames, rotation='vertical', fontsize=11)
    if show:
        with warnings.catch_warnings(): # ignore expected matplotlib warnings
            warnings.simplefilter("ignore", RuntimeWarning)
            pl.show()
Create a SHAP dependence plot, colored by an interaction feature.

    Plots the value of the feature on the x-axis and the SHAP value of the same feature
    on the y-axis. This shows how the model depends on the given feature, and is like a
    richer extenstion of the classical parital dependence plots. Vertical dispersion of the
    data points represents interaction effects. Grey ticks along the y-axis are data
    points where the feature's value was NaN.


    Parameters
    ----------
    ind : int or string
        If this is an int it is the index of the feature to plot. If this is a string it is
        either the name of the feature to plot, or it can have the form "rank(int)" to specify
        the feature with that rank (ordered by mean absolute SHAP value over all the samples).

    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).

    display_features : numpy.array or pandas.DataFrame
        Matrix of feature values for visual display (such as strings instead of coded values).

    interaction_index : "auto", None, int, or string
        The index of the feature used to color the plot. The name of a feature can also be passed
        as a string. If "auto" then shap.common.approximate_interactions is used to pick what
        seems to be the strongest interaction (note that to find to true stongest interaction you
        need to compute the SHAP interaction values).
        
    x_jitter : float (0 - 1)
        Adds random jitter to feature values. May increase plot readability when feature
        is discrete.

    alpha : float
        The transparency of the data points (between 0 and 1). This can be useful to the
        show density of the data points when using a large dataset.

    xmin : float or string
        Represents the lower bound of the plot's x-axis. It can be a string of the format
        "percentile(float)" to denote that percentile of the feature's value used on the x-axis.

    xmax : float or string
        Represents the upper bound of the plot's x-axis. It can be a string of the format
        "percentile(float)" to denote that percentile of the feature's value used on the x-axis.