stable/freqtrade/freqai/prediction_models/XGBoostClassifier.py

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 inherits IFreqaiModel, which
    means it has full access to all Frequency AI functionality. Typically,
    users would use this to override the common `fit()`, `train()`, or
    `predict()` methods to add their custom data handling tools or change
    various aspects of the training that cannot be configured via the
    top level config.json file.
    """

    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 holding all data for train, test,
            labels, weights
        :param dk: The datakitchen object for the current coin/model
        """

        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)