import logging import sys from pathlib import Path from typing import Any, Dict from catboost import CatBoostClassifier, Pool from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel from freqtrade.freqai.data_kitchen import FreqaiDataKitchen logger = logging.getLogger(__name__) class CatboostClassifier(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 """ train_data = Pool( data=data_dictionary["train_features"], label=data_dictionary["train_labels"], weight=data_dictionary["train_weights"], ) if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0: test_data = None else: test_data = Pool( data=data_dictionary["test_features"], label=data_dictionary["test_labels"], weight=data_dictionary["test_weights"], ) cbr = CatBoostClassifier( allow_writing_files=True, loss_function='MultiClass', train_dir=Path(dk.data_path), **self.model_training_parameters, ) init_model = self.get_init_model(dk.pair) cbr.fit(X=train_data, eval_set=test_data, init_model=init_model, log_cout=sys.stdout, log_cerr=sys.stderr) return cbr