edsteva.viz.dashboards.probe.wrapper

probe_dashboard

probe_dashboard(
    probe: BaseProbe,
    fitted_model: BaseModel = None,
    care_site_level: List[str] = None,
    save_path: str = None,
    legend_predictor: str = "Predictor c(t)",
    legend_model: str = "Model f(t)",
    x_axis_title: str = None,
    y_axis_title: str = None,
    main_chart_config: Dict[str, float] = None,
    model_line_config: Dict[str, str] = None,
    probe_line_config: Dict[str, str] = None,
    vertical_bar_charts_config: Dict[str, str] = None,
    horizontal_bar_charts_config: Dict[str, str] = None,
    time_line_config: Dict[str, str] = None,
    chart_style: Dict[str, float] = None,
    **kwargs
)

Displays an interactive chart with:

• On the top, the aggregated average completeness predictor \(c(t)\) over time \(t\) with the fitted model \(\hat{c}(t)\) if specified.
• On the bottom, interactive filters including all the concepts in the Probe (such as time, care site, number of visits...etc.)

Is is possible to save the chart in HTML with the "save_path" optional input.

PARAMETER	DESCRIPTION
probe	Class describing the completeness predictor \(c(t)\). TYPE: BaseProbe
fitted_model	Model fitted to the probe. TYPE: BaseModel DEFAULT: None
care_site_level	EXAMPLE: ["Hospital"], ["Hôpital", "UF"] or ["UF", "UH"] TYPE: List[str] DEFAULT: None
save_path	Folder path where to save the chart in HTML format. EXAMPLE: "my_folder/my_file.html" TYPE: str DEFAULT: None
legend_predictor	Label name for the predictor legend. TYPE: str DEFAULT: 'Predictor c(t)'
legend_model	Label name for the model legend. TYPE: str DEFAULT: 'Model f(t)'
x_axis_title	Label name for the x axis. TYPE: str DEFAULT: None
y_axis_title	Label name for the y axis. TYPE: str DEFAULT: None
main_chart_config	If not None, configuration used to construct the top main chart. TYPE: Dict[str, float] DEFAULT: None
model_line_config	If not None, configuration used to construct the model line. TYPE: Dict[str, str] DEFAULT: None
probe_line_config	If not None, configuration used to construct the probe line. TYPE: Dict[str, str] DEFAULT: None
vertical_bar_charts_config	If not None, configuration used to construct the vertical bar charts. TYPE: Dict[str, str] DEFAULT: None
horizontal_bar_charts_config	If not None, configuration used to construct the horizontal bar charts. TYPE: Dict[str, str] DEFAULT: None
time_line_config	If not None, configuration used to construct the time line. TYPE: Dict[str, str] DEFAULT: None
chart_style	If not None, configuration used to configure the chart style. EXAMPLE: {"labelFontSize": 13, "titleFontSize": 14} TYPE: Dict[str, float] DEFAULT: None

Source code in edsteva/viz/dashboards/probe/wrapper.py

def probe_dashboard(
    probe: BaseProbe,
    fitted_model: BaseModel = None,
    care_site_level: List[str] = None,
    save_path: str = None,
    legend_predictor: str = "Predictor c(t)",
    legend_model: str = "Model f(t)",
    x_axis_title: str = None,
    y_axis_title: str = None,
    main_chart_config: Dict[str, float] = None,
    model_line_config: Dict[str, str] = None,
    probe_line_config: Dict[str, str] = None,
    vertical_bar_charts_config: Dict[str, str] = None,
    horizontal_bar_charts_config: Dict[str, str] = None,
    time_line_config: Dict[str, str] = None,
    chart_style: Dict[str, float] = None,
    **kwargs,
):
    r"""Displays an interactive chart with:

    - On the top, the aggregated average completeness predictor $c(t)$ over time $t$ with the fitted model $\hat{c}(t)$ if specified.
    - On the bottom, interactive filters including all the concepts in the [Probe][probe] (such as time, care site, number of visits...etc.)

    Is is possible to save the chart in HTML with the "save_path" optional input.

    Parameters
    ----------
    probe : BaseProbe
        Class describing the completeness predictor $c(t)$.
    fitted_model : BaseModel, optional
        Model fitted to the probe.
    care_site_level : List[str], optional
        **EXAMPLE**: `["Hospital"]`, `["Hôpital", "UF"]` or `["UF", "UH"]`
    save_path : str, optional
        Folder path where to save the chart in HTML format.

        **EXAMPLE**: `"my_folder/my_file.html"`
    legend_predictor: str, optional,
        Label name for the predictor legend.
    legend_model: str, optional,
        Label name for the model legend.
    x_axis_title: str, optional,
        Label name for the x axis.
    y_axis_title: str, optional,
        Label name for the y axis.
    main_chart_config: Dict[str, str], optional
        If not None, configuration used to construct the top main chart.
    model_line_config: Dict[str, str], optional
        If not None, configuration used to construct the model line.
    probe_line_config: Dict[str, str], optional
        If not None, configuration used to construct the probe line.
    vertical_bar_charts_config: Dict[str, str], optional
        If not None, configuration used to construct the vertical bar charts.
    horizontal_bar_charts_config: Dict[str, str], optional
        If not None, configuration used to construct the horizontal bar charts.
    time_line_config: Dict[str, str], optional
        If not None, configuration used to construct the time line.
    chart_style: Dict[str, float], optional
        If not None, configuration used to configure the chart style.

        **EXAMPLE**: `{"labelFontSize": 13, "titleFontSize": 14}`
    """

    alt.data_transformers.enable("default")
    alt.data_transformers.disable_max_rows()

    probe_config = deepcopy(probe.get_viz_config("probe_dashboard"))
    if fitted_model:
        model_config = deepcopy(fitted_model.get_viz_config("probe_dashboard"))
        if model_line_config is None:
            model_line_config = model_config["model_line"]
        if probe_line_config is None:
            probe_line_config = model_config["probe_line"]
    if main_chart_config is None:
        main_chart_config = probe_config["main_chart"]
    if time_line_config is None:
        time_line_config = probe_config["time_line"]
    if vertical_bar_charts_config is None:
        vertical_bar_charts_config = probe_config["vertical_bar_charts"]
        if fitted_model:
            vertical_bar_charts_config["y"] = (
                vertical_bar_charts_config["y"]
                + model_config["extra_vertical_bar_charts"]
            )
    if horizontal_bar_charts_config is None:
        horizontal_bar_charts_config = probe_config["horizontal_bar_charts"]
        if fitted_model:
            horizontal_bar_charts_config["x"] = (
                horizontal_bar_charts_config["x"]
                + model_config["extra_horizontal_bar_charts"]
            )
    if chart_style is None:
        chart_style = probe_config["chart_style"]

    predictor = fitted_model.predict(probe) if fitted_model else probe.predictor.copy()
    predictor = filter_data(
        data=predictor,
        care_site_level=care_site_level,
        **kwargs,
    )

    if fitted_model:
        chart = fitted_probe_dashboard(
            predictor=predictor,
            legend_predictor=legend_predictor,
            legend_model=legend_model,
            x_axis_title=x_axis_title,
            y_axis_title=y_axis_title,
            main_chart_config=main_chart_config,
            model_line_config=model_line_config,
            probe_line_config=probe_line_config,
            vertical_bar_charts_config=vertical_bar_charts_config,
            horizontal_bar_charts_config=horizontal_bar_charts_config,
            time_line_config=time_line_config,
            chart_style=chart_style,
        )
    else:
        chart = probe_only_dashboard(
            predictor=predictor,
            x_axis_title=x_axis_title,
            y_axis_title=y_axis_title,
            main_chart_config=main_chart_config,
            vertical_bar_charts_config=vertical_bar_charts_config,
            horizontal_bar_charts_config=horizontal_bar_charts_config,
            time_line_config=time_line_config,
            chart_style=chart_style,
        )

    vis_probe = "id" + uuid.uuid4().hex
    new_index_probe_id = "id" + uuid.uuid4().hex
    old_index_probe_id = "id" + uuid.uuid4().hex
    left_shift = "145px" if fitted_model else "45px"
    html_chart = f"""
        <!DOCTYPE html>
        <html>
        <head>
          <script src="https://cdn.jsdelivr.net/npm/vega@{alt.VEGA_VERSION}"></script>
          <script src="https://cdn.jsdelivr.net/npm/vega-lite@{alt.VEGALITE_VERSION}"></script>
          <script src="https://cdn.jsdelivr.net/npm/vega-embed@{alt.VEGAEMBED_VERSION}"></script>
        </head>
        <body>

        <div class="container">
          <div class="row">
            <div>
            <div id={vis_probe}></div>
            </div>
            <div style="position:absolute;left:{left_shift};top:380px;width: -webkit-fill-available;">
            <div id={new_index_probe_id}>
              <div id={old_index_probe_id}></div>
            </div>
            <hr/>
            <h1 style="text-align:center"> Interactive filters </h1>
            </div>
          </div>
        </div>

        <script type="text/javascript">
        vegaEmbed('#{vis_probe}', {chart.to_json(indent=None)}).then(function(result) {{
            const sliders = document.getElementsByClassName('vega-bindings');
            const newparent = document.getElementById('{new_index_probe_id}');
            const oldchild = document.getElementById('{old_index_probe_id}');
            for (var i = 0; i < sliders.length; i++) {{
                if (sliders[i].parentElement.parentElement.id == '{vis_probe}') {{
                    var index_slider = sliders[i]
                    }}
                }}
            newparent.replaceChild(index_slider, oldchild);
            }}).catch(console.error);
        </script>
        </body>
        </html>
        """
    if save_path:
        save_html(
            obj=html_chart,
            filename=save_path,
        )
    else:
        display(HTML(html_chart))