edsteva.viz.plots.normalized_probe.normalized_probe

normalized_probe_plot

normalized_probe_plot(
    probe: BaseProbe,
    fitted_model: BaseModel,
    care_site_level: List[str] = None,
    start_date: Union[datetime, str] = None,
    end_date: Union[datetime, str] = None,
    t_min: int = None,
    t_max: int = None,
    save_path: str = None,
    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,
    error_line_config: Dict[str, str] = None,
    estimates_selections: Dict[str, str] = None,
    estimates_filters: Dict[str, str] = None,
    chart_style: Dict[str, float] = None,
    indexes_to_remove: List[str] = ["care_site_id"],
    **kwargs
)

Displays a chart with the aggregated normalized completeness predictor \(\frac{c(\Delta t)}{c_0}\) over normalized time \(\Delta t = t - t_0\). It represents the overall deviation from the Model.

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
care_site_level	EXAMPLE: ["Hospital"], ["Hôpital", "UF"] or ["UF", "UH"] TYPE: List[str] DEFAULT: None
start_date	EXAMPLE: "2019-05-01" TYPE: datetime DEFAULT: None
end_date	EXAMPLE: "2021-07-01" TYPE: datetime DEFAULT: None
t_min	Minimal difference with \(t_0\) in month \(\Delta t_{min}\). EXAMPLE: -24 TYPE: int DEFAULT: None
t_max	Maximal difference with \(t_0\) in month \(\Delta t_{max}\). EXAMPLE: 24 TYPE: int DEFAULT: None
save_path	Folder path where to save the chart in HTML format. TYPE: str DEFAULT: None
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
error_line_config	If not None, configuration used to construct the error 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
estimates_selections	If not None, configuration used to construct the estimates selections. TYPE: Dict[str, str] DEFAULT: None
estimates_filters	If not None, configuration used to construct the estimates filters. 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
indexes_to_remove	indexes to remove from the groupby selection. TYPE: List[str] DEFAULT: ['care_site_id']

Source code in edsteva/viz/plots/normalized_probe/normalized_probe.py

def normalized_probe_plot(
    probe: BaseProbe,
    fitted_model: BaseModel,
    care_site_level: List[str] = None,
    start_date: Union[datetime, str] = None,
    end_date: Union[datetime, str] = None,
    t_min: int = None,
    t_max: int = None,
    save_path: str = None,
    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,
    error_line_config: Dict[str, str] = None,
    estimates_selections: Dict[str, str] = None,
    estimates_filters: Dict[str, str] = None,
    chart_style: Dict[str, float] = None,
    indexes_to_remove: List[str] = ["care_site_id"],
    **kwargs,
):
    r"""Displays a chart with the aggregated normalized completeness predictor $\frac{c(\Delta t)}{c_0}$ over normalized time $\Delta t = t - t_0$. It represents the overall deviation from the Model.

    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
        Model fitted to the probe
    care_site_level : List[str], optional
        **EXAMPLE**: `["Hospital"]`, `["Hôpital", "UF"]` or `["UF", "UH"]`
    start_date : datetime, optional
        **EXAMPLE**: `"2019-05-01"`
    end_date : datetime, optional
        **EXAMPLE**: `"2021-07-01"`
    t_min : int, optional
        Minimal difference with $t_0$ in month $\Delta t_{min}$.

        **EXAMPLE**: `-24`
    t_max : int, optional
        Maximal difference with $t_0$ in month $\Delta t_{max}$.

        **EXAMPLE**: `24`
    save_path : str, optional
        Folder path where to save the chart in HTML format.
    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.
    error_line_config: Dict[str, str], optional
        If not None, configuration used to construct the error line.
    probe_line_config: Dict[str, str], optional
        If not None, configuration used to construct the probe line.
    estimates_selections: Dict[str, str], optional
        If not None, configuration used to construct the estimates selections.
    estimates_filters: Dict[str, str], optional
        If not None, configuration used to construct the estimates filters.
    chart_style: Dict[str, float], optional
        If not None, configuration used to configure the chart style.

        **EXAMPLE**: `{"labelFontSize": 13, "titleFontSize": 14}`
    indexes_to_remove: List[str], optional
        indexes to remove from the groupby selection.
    """

    alt.data_transformers.disable_max_rows()

    # Pre-processing
    predictor = probe.predictor.copy()
    predictor_metrics = probe._metrics.copy()
    estimates = fitted_model.estimates.copy()
    indexes = get_indexes_to_groupby(
        predictor_columns=predictor.columns,
        predictor_metrics=predictor_metrics,
        indexes_to_remove=indexes_to_remove,
    )
    predictor = predictor.merge(estimates, on=probe._index)
    predictor["normalized_date"] = month_diff(
        predictor["date"], predictor["t_0"]
    ).astype(int)
    for estimate in fitted_model._coefs + fitted_model._metrics:
        if pd.api.types.is_datetime64_any_dtype(predictor[estimate]):
            predictor[estimate] = predictor[estimate].dt.strftime("%Y-%m")
    predictor["normalized_c"] = predictor["c"].where(
        (predictor["normalized_date"] < 0) | (predictor["c_0"] == 0),
        predictor["c"] / predictor["c_0"],
    )
    predictor["model"] = 1
    predictor["model"] = predictor["model"].where(predictor["normalized_date"] >= 0, 0)
    predictor = filter_data(
        data=predictor,
        care_site_level=care_site_level,
        start_date=start_date,
        end_date=end_date,
        **kwargs,
    )
    if t_min:
        predictor = predictor[predictor.normalized_date >= t_min]
    if t_max:
        predictor = predictor[predictor.normalized_date <= t_max]

    # Get viz config
    probe_config = deepcopy(probe.get_viz_config("normalized_probe_plot"))
    model_config = deepcopy(
        fitted_model.get_viz_config("normalized_probe_plot", predictor=predictor)
    )
    if probe_line_config is None:
        probe_line_config = model_config["probe_line"]
    if model_line_config is None:
        model_line_config = model_config["model_line"]
    if error_line_config is None:
        error_line_config = model_config["error_line"]
    if estimates_selections is None:
        estimates_selections = model_config["estimates_selections"]
    if estimates_filters is None:
        estimates_filters = model_config["estimates_filters"]
    if main_chart_config is None:
        main_chart_config = probe_config["main_chart"]
    if chart_style is None:
        chart_style = probe_config["chart_style"]

    # Viz
    predictor["legend_model"] = (
        model_line_config.get("legend_title")
        if model_line_config.get("legend_title")
        else type(fitted_model).__name__
    )
    predictor["legend_predictor"] = probe_line_config["legend_title"]
    predictor["legend_error_band"] = error_line_config["legend_title"]
    index_selection, index_fields = create_groupby_selection(
        indexes=indexes,
        predictor=predictor,
    )
    base = alt.Chart(predictor)
    base = add_estimates_filters(
        base=base,
        estimates_filters=estimates_filters,
    )
    main_chart = generate_main_chart(
        base=base,
        main_chart_config=main_chart_config,
        index_selection=index_selection,
        index_fields=index_fields,
        x_axis_title=x_axis_title,
        y_axis_title=y_axis_title,
    )
    probe_line = generate_probe_line(
        main_chart=main_chart, probe_line_config=probe_line_config
    )
    error_line = generate_error_line(
        main_chart=main_chart, error_line_config=error_line_config
    )
    model_line = generate_model_line(
        main_chart=main_chart, model_line_config=model_line_config
    )
    main_chart = probe_line + error_line + model_line
    if index_selection:
        main_chart = main_chart.add_params(index_selection)

    for estimate_selection in estimates_selections:
        main_chart = main_chart.add_params(estimate_selection)

    main_chart = configure_style(chart=main_chart, chart_style=chart_style)

    if save_path:
        save_html(
            obj=main_chart,
            filename=save_path,
        )

    return main_chart