from explainerdashboard.custom import *
from dash import html
import dash_bootstrap_components as dbc


class CustomModelTab(ExplainerComponent):
    def __init__(self, explainer, name=None):
        super().__init__(explainer, title="Model Summary", name=name)
        self.precision = PrecisionComponent(explainer,
                                title='Precision',
                                hide_subtitle=True, hide_footer=True,
                                hide_selector=True,
                                cutoff=None)
        self.shap_summary = ShapSummaryComponent(explainer,
                                title='Impact',
                                hide_subtitle=True, hide_selector=True,
                                hide_depth=True, depth=8,
                                hide_cats=True, cats=True)
        self.shap_dependence = ShapDependenceComponent(explainer,
                                title='Dependence',
                                hide_subtitle=True, hide_selector=True,
                                hide_cats=True, cats=True,
                                hide_index=True,
                                col='Fare', color_col="PassengerClass")
        self.connector = ShapSummaryDependenceConnector(
                self.shap_summary, self.shap_dependence)

        self.register_components()

    def layout(self):
        return dbc.Container([
            dbc.Row([
                dbc.Col([
                     html.H3("Model Performance"),
                    html.Div("As you can see on the right, the model performs quite well."),
                    html.Div("The higher the predicted probability of survival predicted by "
                            "the model on the basis of learning from examples in the training set"
                            ", the higher is the actual percentage of passengers surviving in "
                            "the test set"),
                ], width=4, style=dict(margin=30)),
                dbc.Col([
                    self.precision.layout()
                ], style=dict(margin=30))
            ]),
            dbc.Row([
                dbc.Col([
                    self.shap_summary.layout()
                ], style=dict(margin=30)),
                dbc.Col([
                    html.H3("Feature Importances"),
                    html.Div("On the left you can check out for yourself which parameters were the most important."),
                    html.Div(f"Clearly {self.explainer.columns_ranked_by_shap()[0]} was the most important"
                            f", followed by {self.explainer.columns_ranked_by_shap()[1]}"
                            f" and {self.explainer.columns_ranked_by_shap()[2]}."),
                    html.Div("If you select 'detailed' you can see the impact of that variable on "
                            "each individual prediction. With 'aggregate' you see the average impact size "
                            "of that variable on the final prediction."),
                    html.Div("With the detailed view you can clearly see that the the large impact from Sex "
                            "stems both from males having a much lower chance of survival and females a much "
                            "higher chance.")
                ], width=4, style=dict(margin=30)),
            ]),
            dbc.Row([
                dbc.Col([
                    html.H3("Feature dependence"),
                    html.Div("In the plot to the right you can see that the higher the cost "
                            "of the fare that passengers paid, the higher the chance of survival. "
                            "Probably the people with more expensive tickets were in higher up cabins, "
                            "and were more likely to make it to a lifeboat."),
                    html.Div("When you color the impacts by PassengerClass, you can clearly see that "
                            "the more expensive tickets were mostly 1st class, and the cheaper tickets "
                            "mostly 3rd class."),
                    html.Div("On the right you can check out for yourself how different features impacted "
                            "the model output."),
                ], width=4, style=dict(margin=30)),
                dbc.Col([
                    self.shap_dependence.layout()
                ], style=dict(margin=30)),
            ])
        ])

class CustomPredictionsTab(ExplainerComponent):
    def __init__(self, explainer, name=None):
        super().__init__(explainer, title="Predictions", name=name)

        self.index = ClassifierRandomIndexComponent(explainer,
                                                    hide_title=True, hide_index=False,
                                                    hide_slider=True, hide_labels=True,
                                                    hide_pred_or_perc=True,
                                                    hide_selector=True, hide_button=False)

        self.contributions = ShapContributionsGraphComponent(explainer,
                                                            hide_title=True, hide_index=True,
                                                            hide_depth=True, hide_sort=True,
                                                            hide_orientation=True, hide_cats=True,
                                                            hide_selector=True,
                                                            sort='importance')

        self.trees = DecisionTreesComponent(explainer,
                                            hide_title=True, hide_index=True,
                                            hide_highlight=True, hide_selector=True)


        self.connector = IndexConnector(self.index, [self.contributions, self.trees])

        self.register_components()

    def layout(self):
        return dbc.Container([
            dbc.Row([
                dbc.Col([
                    html.H3("Enter name:"),
                    self.index.layout()
                ])
            ]),
            dbc.Row([
                dbc.Col([
                    html.H3("Contributions to prediction:"),
                    self.contributions.layout()
                ]),

            ]),
            dbc.Row([

                dbc.Col([
                    html.H3("Every tree in the Random Forest:"),
                    self.trees.layout()
                ]),
            ])
        ])