def panel_ml_forecast_summary()

in afa/app/app.py [0:0]

• 14 lines of code
• 5 McCabe index (conditional complexity)

def panel_ml_forecast_summary():
    """
    """

    df = state.report["data"].get("df", None)
    df_preds = state.report["afc"].get("df_preds", None)
    df_results = state.report["afc"].get("df_results", None)
    df_backtests = state.report["afc"].get("df_backtests", None)

    if df is None or df_results is None or df_backtests is None or \
        df_preds is None:
        return

    with st.beta_expander("🎯 Forecast Summary", expanded=True):
        df_accuracies = calc_afc_ml_accuracies(METRIC)
        ml_acc = df_accuracies["acc"].mean()

        _cols = st.beta_columns([3,1])

        with _cols[0]:
            st.write(dedent(f"""