causalml/metrics/visualize.py [501:526]:
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                           treatment_col=treatment_col, p_col=p_col, n_segment=n_segment, cv=cv,
                           calibrate_propensity=calibrate_propensity, ci=ci)
    if ci:
        model_names = [x.replace(" LB", "") for x in plot_df.columns]
        model_names = list(set([x.replace(" UB", "") for x in model_names]))

        fig, ax = plt.subplots(figsize=figsize)
        cmap = plt.get_cmap("tab10")
        cindex = 0

        for col in model_names:
            lb_col = col + " LB"
            up_col = col + " UB"

            if col != 'Random':
                ax.plot(plot_df.index, plot_df[col], color=cmap(cindex))
                ax.fill_between(plot_df.index, plot_df[lb_col], plot_df[up_col], color=cmap(cindex), alpha=0.25)
            else:
                ax.plot(plot_df.index, plot_df[col], color=cmap(cindex))
            cindex += 1

        ax.legend()
    else:
        plot_df.plot(figsize=figsize)

    plt.xlabel('Population')
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causalml/metrics/visualize.py [549:574]:
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                           treatment_col=treatment_col, p_col=p_col, n_segment=n_segment, cv=cv,
                           calibrate_propensity=calibrate_propensity, ci=ci)
    if ci:
        model_names = [x.replace(" LB", "") for x in plot_df.columns]
        model_names = list(set([x.replace(" UB", "") for x in model_names]))

        fig, ax = plt.subplots(figsize=figsize)
        cmap = plt.get_cmap("tab10")
        cindex = 0

        for col in model_names:
            lb_col = col + " LB"
            up_col = col + " UB"

            if col != 'Random':
                ax.plot(plot_df.index, plot_df[col], color=cmap(cindex))
                ax.fill_between(plot_df.index, plot_df[lb_col], plot_df[up_col], color=cmap(cindex), alpha=0.25)
            else:
                ax.plot(plot_df.index, plot_df[col], color=cmap(cindex))
            cindex += 1

        ax.legend()
    else:
        plot_df.plot(figsize=figsize)

    plt.xlabel('Population')
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