def get_data_explorer_page()

in src/responsibleai/rai_analyse/_score_card/regression_components.py [0:0]

• 51 lines of code
• 3 McCabe index (conditional complexity)

def get_data_explorer_page(data):
    de_heading_left_elms = p(
        "Evaluate your dataset to assess representation of identified cohorts:"
    )

    de_heading_left_elms.append(get_bar_plot_explanation_image())

    de_heading_left_container = div(de_heading_left_elms, _class="left")

    # heading_container = div(de_heading_left_container, _class="container")

    containers = []

    for c in data:
        left_elems = []
        main_elems = []
        main_elems.append(h3(c["feature_name"]))
        left_elems = [h3(c["feature_name"])]
        feature_list = ul()
        for i in c["data"]:
            feature_list.append(li("{}: {}".format(i["short_label"], i["label"])))
            main_elems.append(
                p(
                    "For cohort: {} {}, {} is the {} between the actual and predicted values.".format(
                        c["feature_name"],
                        i["label"],
                        round(i[c["primary_metric"]], 1),
                        c["primary_metric"],
                    )
                )
            )
        main_elems.append(
            div(
                p(
                    "Histogram of your model errors (difference between the actual and predicted values)"
                ),
                cc.get_de_box_plot_image(c),
                _class="nobreak_div",
            )
        )

        left_elems.append(feature_list)
        containers.append(
            str(
                div(
                    div(left_elems, _class="left"),
                    div(main_elems, _class="main"),
                    _class="container",
                )
            )
        )

    return str(
        div(
            cc.get_page_divider("Data Explorer"),
            de_heading_left_container,
            _class="container",
        )
    ) + "".join(containers)