Model frequency exhibit generator

modelcomp.py

def modelcomp(df, outpath, titlestr=None):
    """
    Generate partial residual model comparison exhibits.
    """
    try:
        pdf = PdfPages(outpath)
        df = df.rename({"INCIDENCE_MNTH":"actual_count"}, axis=1)
        keepfields = [i for i in df.columns if i.endswith("count")] + ["EXPOSURE"]
        varcols = [i for i in df.columns if i not in keepfields]
        cfields = [i for i in df.columns if i.endswith("count")]
        rfields = [i.replace("count", "rate") for i in cfields]
        rcapped = [i + "_capped" for i in rfields]
        lfields = [i.replace("_count", "").strip() for i in cfields]
        linesty = ["--" if i.startswith("actual") else "-" for i in cfields]
        colorlist = [
            "#2c475c", "#E02C70", "#6EA1D5", "#8e3e95", "#ff9325", "#ff88b8",
            "#ffce83", "#33f008", "#623381", "#eee646" ,"#50758b", "#389365", 
            "#6EA1D5",
            ]
            
        # Create dict to hold parameters for each model to include with exhibits.
        modelslist = []
        for indx, mdldesc in enumerate(lfields):
            dmodel = {"count":cfields[indx], "rate":rfields[indx], "legend":mdldesc,
                      "color":"#888888" if mdldesc=="actual" else colorlist[indx], 
                      "capped":rcapped[indx],
                      "linewidth":1, "linestyle":linesty[indx],}
            modelslist.append(dmodel)
                

        for var in varcols:
            print(" + Summarizing modeled output via `{}`.".format(var))
            dfvar = df[keepfields + [var]].groupby(var, as_index=False).sum()
            for cstr, rstr in zip(cfields, rfields):
                dfvar[rstr] = dfvar[cstr] / dfvar["EXPOSURE"]
                
            if var=="ATTAINED_AGE":
                la, ua = 65, 105
                dfvar = dfvar[(dfvar["ATTAINED_AGE"]>=la) & (dfvar["ATTAINED_AGE"]<=ua)].reset_index(drop=True)
                rotation, legendloc, labelsize, font = 90, "upper left", 6, 6
                y_axis_min, y_axis_incr, y_axis_max = 0, .02, .32
                dfvar["ATTAINED_AGE"] = dfvar["ATTAINED_AGE"].astype(np.int)
            
            else:
                legendloc, rotation, labelsize, font = "lower left", 0, 7, 7
                actual_ratis_maximus = dfvar[rfields].values.max()
                if actual_ratis_maximus<=.05:
                    y_axis_min, y_axis_max, y_axis_incr = 0, .06, .01
                elif actual_ratis_maximus>.05 and actual_ratis_maximus<=.10:
                    y_axis_min, y_axis_max, y_axis_incr = 0, .11, .01
                elif actual_ratis_maximus>.10 and actual_ratis_maximus<=.12:
                    y_axis_min, y_axis_max, y_axis_incr = 0, .13, .02
                elif actual_ratis_maximus>.12 and actual_ratis_maximus<=.15:
                    y_axis_min, y_axis_max, y_axis_incr = 0, .17, .02
                elif actual_ratis_maximus>.15:
                    y_axis_min, y_axis_max, y_axis_incr = 0, .22, .02

                    
            # Compute rate capped fields to keep plotted rates within frame.
            for rf, rfc in zip(rfields, rcapped):
                dfvar[rfc] = dfvar[rf].map(
                    lambda v: np.min([v, y_axis_max - y_axis_incr])
                    )
            
            if titlestr is None:
                titlestr_ = "Modeled Rates vs. {}".format(var)
            else:
                titlestr_ = titlestr + " (vs. {})".format(var) 
            y_expos = dfvar["EXPOSURE"].values.tolist()
            x_vals = dfvar[var].values.tolist()
            x_indx = [i for i in range(len(x_vals))]
            
            # Initialize plot. 
            fig, ax1 = plt.subplots(1, 1, tight_layout=True); ax2 = ax1.twinx()
            ax1.set_title(titlestr_, color="red", loc="left", fontsize=7)
            ax1.bar(x_indx, y_expos, color="#FFFFFF", edgecolor="#000000", linewidth=.70, alpha=.875)
            add_value_labels(ax1, spacing=3, annotate_font=font, rotation=rotation)

            # Add scatter points to axis.
            splist = [
                ax2.scatter(
                    x_indx, dfvar[dmdl["capped"]], marker="s", label=dmdl["legend"],
                    edgecolor=dmdl["color"], color=dmdl["color"], s=9
                    ) for dmdl in modelslist
                ]

            # Add point plot to axis.
            pplist = [
                ax2.plot(
                    x_indx, dfvar[dmdl["capped"]], color=dmdl["color"],
                    linewidth=dmdl["linewidth"], linestyle=dmdl["linestyle"]
                    ) for dmdl in modelslist
                ]
            
            yrange = np.arange(y_axis_min, y_axis_max, y_axis_incr)
            yticklabels = ["{:.2f}".format(i) for i in yrange]
            ax1.set_xticks(x_indx); ax1.set_xticklabels(x_vals, rotation=0)
            ax2.yaxis.set_ticks(yrange); ax2.yaxis.set_ticklabels(yticklabels)
            ax1.tick_params(
                axis="x", which="both", labelsize=labelsize, top=False,
                bottom=True, labeltop=False, labelbottom=True, direction="out",
                )
            ax1.tick_params(
                axis="y", which="both", left=False, right=False, labelleft=False,
                labelright=False, length=0,
                )
            ax2.tick_params(
                axis="x", which="both", top=False, bottom=False, labeltop=False,
                labelbottom=False, length=0,
                )
            ax2.tick_params(
                axis="y", which="both", left=False, right=True, color="red",
                labelleft=False, labelright=True, labelsize=labelsize,
                length=0,
                )

            ax1.grid(False); ax2.grid(False)
            ax1.yaxis.set_major_formatter(plt.NullFormatter())
            ax2.set_ylim(bottom=0)
            
            
            # Overlay table if number of distinct x_vals is less than 10.
            if len(x_vals)<=10:
                actual_rates = dfvar["actual_rate"].values
                modeled_rates_hdrs = [i for i in rfields if not i.startswith("actual")]
                max_len = np.max([dfvar[var].map(lambda v: len(str(v))).max() / 215, .05])
                row_labels, col_labels = [dmdl["legend"] for dmdl in modelslist], x_vals
                row_labels_ae = [i + "_a/e" for i in row_labels if not i.lower().startswith("act")]
                row_labels = row_labels + row_labels_ae
                table_data = [
                    ["{:.5f}".format(i) for i in dfvar[dmdl["rate"]]] for dmdl in modelslist
                        #for dmdl in modelslist if not dmdl["rate"].startswith("actual")
                    ]
                table_data_ae = [
                    (actual_rates / dfvar[mrate].values).tolist() for mrate in modeled_rates_hdrs
                    ]
                table_data_ae = [
                    ["{:.5f}".format(i) for i in nested] for nested in table_data_ae
                    ]
                table_data = table_data + table_data_ae
                expos_fmt = ["{:,.0f}".format(i) for i in y_expos]
                tblloc = "upper right"
                rowloc, widths = "left", [max_len] * len(x_vals)
                plt.table(cellText=table_data, rowLabels=row_labels, colLabels=col_labels,
                          loc=tblloc, colWidths=widths, rowLoc=rowloc, colLoc="center",
                          bbox=None)

            legend = ax2.legend(
                handles=splist, loc=legendloc, frameon=True, fontsize="medium", 
                fancybox=True, framealpha=.60
                )
            frame = legend.get_frame(); frame.set_facecolor("#FFFFFF",)
            plt.savefig(pdf, format="pdf"); plt.close(fig=fig)
    finally:
        pdf.close()