feat: remove catboost models and dependency

2025-12-22 07:11:11 +01:00
parent c6f3b00819
commit 5c9f140c5d
6 changed files with 0 additions and 280 deletions
@@ -1,61 +0,0 @@
 import logging
 from pathlib import Path
 from typing import Any
 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,
        )
        return cbr
@@ -1,79 +0,0 @@
 import logging
 from pathlib import Path
 from typing import Any
 from catboost import CatBoostClassifier, Pool
 from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
 from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 logger = logging.getLogger(__name__)
 class CatboostClassifierMultiTarget(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
        """
        cbc = CatBoostClassifier(
            allow_writing_files=True,
            loss_function="MultiClass",
            train_dir=Path(dk.data_path),
            **self.model_training_parameters,
        )
        X = data_dictionary["train_features"]
        y = data_dictionary["train_labels"]
        sample_weight = data_dictionary["train_weights"]
        eval_sets = [None] * y.shape[1]
        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
            eval_sets = [None] * data_dictionary["test_labels"].shape[1]
            for i in range(data_dictionary["test_labels"].shape[1]):
                eval_sets[i] = Pool(
                    data=data_dictionary["test_features"],
                    label=data_dictionary["test_labels"].iloc[:, i],
                    weight=data_dictionary["test_weights"],
                )
        init_model = self.get_init_model(dk.pair)
        if init_model:
            init_models = init_model.estimators_
        else:
            init_models = [None] * y.shape[1]
        fit_params = []
        for i in range(len(eval_sets)):
            fit_params.append(
                {
                    "eval_set": eval_sets[i],
                    "init_model": init_models[i],
                }
            )
        model = FreqaiMultiOutputClassifier(estimator=cbc)
        thread_training = self.freqai_info.get("multitarget_parallel_training", False)
        if thread_training:
            model.n_jobs = y.shape[1]
        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
        return model
@@ -1,60 +0,0 @@
 import logging
 from pathlib import Path
 from typing import Any
 from catboost import CatBoostRegressor, Pool
 from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 logger = logging.getLogger(__name__)
 class CatboostRegressor(BaseRegressionModel):
    """
    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"],
            )
        init_model = self.get_init_model(dk.pair)
        model = CatBoostRegressor(
            allow_writing_files=True,
            train_dir=Path(dk.data_path),
            **self.model_training_parameters,
        )
        model.fit(
            X=train_data,
            eval_set=test_data,
            init_model=init_model,
        )
        return model
@@ -1,78 +0,0 @@
 import logging
 from pathlib import Path
 from typing import Any
 from catboost import CatBoostRegressor, Pool
 from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
 from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 logger = logging.getLogger(__name__)
 class CatboostRegressorMultiTarget(BaseRegressionModel):
    """
    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
        """
        cbr = CatBoostRegressor(
            allow_writing_files=True,
            train_dir=Path(dk.data_path),
            **self.model_training_parameters,
        )
        X = data_dictionary["train_features"]
        y = data_dictionary["train_labels"]
        sample_weight = data_dictionary["train_weights"]
        eval_sets = [None] * y.shape[1]
        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
            eval_sets = [None] * data_dictionary["test_labels"].shape[1]
            for i in range(data_dictionary["test_labels"].shape[1]):
                eval_sets[i] = Pool(
                    data=data_dictionary["test_features"],
                    label=data_dictionary["test_labels"].iloc[:, i],
                    weight=data_dictionary["test_weights"],
                )
        init_model = self.get_init_model(dk.pair)
        if init_model:
            init_models = init_model.estimators_
        else:
            init_models = [None] * y.shape[1]
        fit_params = []
        for i in range(len(eval_sets)):
            fit_params.append(
                {
                    "eval_set": eval_sets[i],
                    "init_model": init_models[i],
                }
            )
        model = FreqaiMultiOutputRegressor(estimator=cbr)
        thread_training = self.freqai_info.get("multitarget_parallel_training", False)
        if thread_training:
            model.n_jobs = y.shape[1]
        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
        return model
@@ -85,7 +85,6 @@ hyperopt = [
 freqai = [
  "scikit-learn",
  "joblib",
  "catboost; platform_machine != 'arm'",
  "lightgbm",
  "xgboost",
  "tensorboard",
@@ -5,7 +5,6 @@
 # Required for freqai
 scikit-learn==1.7.2
 joblib==1.5.2
 catboost==1.2.8; 'arm' not in platform_machine
 lightgbm==4.6.0
 xgboost==3.1.2
 tensorboard==2.20.0