import logging from typing import Any, Dict, Tuple import numpy as np import numpy.typing as npt from pandas import DataFrame from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import LabelEncoder from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel from freqtrade.freqai.data_kitchen import FreqaiDataKitchen logger = logging.getLogger(__name__) class SKLearnRandomForestClassifier(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] 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] eval_set = (test_features, test_labels) if self.freqai_info.get("continual_learning", False): logger.warning("Continual learning is not supported for " "SKLearnRandomForestClassifier, ignoring.") train_weights = data_dictionary["train_weights"] model = RandomForestClassifier(**self.model_training_parameters) model.fit(X=X, y=y, sample_weight=train_weights) if eval_set: logger.info("Score: %s", model.score(eval_set[0], eval_set[1])) 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)