import logging
from typing import Any, Dict, Tuple

import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from pandas.api.types import is_integer_dtype
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBClassifier

from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen


logger = logging.getLogger(__name__)


class XGBoostClassifier(BaseClassifierModel):
    """
    User created prediction model. The class needs to override three necessary
    functions, predict(), train(), fit(). The class inherits ModelHandler which
    has its own DataHandler where data is held, saved, loaded, and managed.
    """

    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
        """
        User sets up the training and test data to fit their desired model here
        :param data_dictionary: the dictionary constructed by DataHandler to hold
                                all the training and test data/labels.
        """

        X = data_dictionary["train_features"].to_numpy()
        y = data_dictionary["train_labels"].to_numpy()[:, 0]

        le = LabelEncoder()
        if not is_integer_dtype(y):
            y = pd.Series(le.fit_transform(y), dtype="int64")

        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
            eval_set = None
        else:
            test_features = data_dictionary["test_features"].to_numpy()
            test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]

            if not is_integer_dtype(test_labels):
                test_labels = pd.Series(le.transform(test_labels), dtype="int64")

            eval_set = [(test_features, test_labels)]

        train_weights = data_dictionary["train_weights"]

        init_model = self.get_init_model(dk.pair)

        model = XGBClassifier(**self.model_training_parameters)

        model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
                  xgb_model=init_model)

        return model

    def predict(
        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
        """
        Filter the prediction features data and predict with it.
        :param unfiltered_df: Full dataframe for the current backtest period.
        :return:
        :pred_df: dataframe containing the predictions
        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
        data (NaNs) or felt uncertain about data (PCA and DI index)
        """

        (pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)

        le = LabelEncoder()
        label = dk.label_list[0]
        labels_before = list(dk.data['labels_std'].keys())
        labels_after = le.fit_transform(labels_before).tolist()
        pred_df[label] = le.inverse_transform(pred_df[label])
        pred_df = pred_df.rename(
            columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))})

        return (pred_df, dk.do_predict)