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https://github.com/freqtrade/freqtrade.git
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feat: remove catboost models and dependency
This commit is contained in:
Matthias
@@ -1,61 +0,0 @@
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import logging
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from pathlib import Path
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from typing import Any
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from catboost import CatBoostClassifier, Pool
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from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class CatboostClassifier(BaseClassifierModel):
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"""
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User created prediction model. The class inherits IFreqaiModel, which
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means it has full access to all Frequency AI functionality. Typically,
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users would use this to override the common `fit()`, `train()`, or
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`predict()` methods to add their custom data handling tools or change
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various aspects of the training that cannot be configured via the
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top level config.json file.
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"""
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def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param data_dictionary: the dictionary holding all data for train, test,
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labels, weights
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:param dk: The datakitchen object for the current coin/model
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"""
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train_data = Pool(
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data=data_dictionary["train_features"],
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label=data_dictionary["train_labels"],
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weight=data_dictionary["train_weights"],
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)
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if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
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test_data = None
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else:
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test_data = Pool(
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data=data_dictionary["test_features"],
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label=data_dictionary["test_labels"],
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weight=data_dictionary["test_weights"],
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)
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cbr = CatBoostClassifier(
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allow_writing_files=True,
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loss_function="MultiClass",
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train_dir=Path(dk.data_path),
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**self.model_training_parameters,
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)
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init_model = self.get_init_model(dk.pair)
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cbr.fit(
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X=train_data,
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eval_set=test_data,
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init_model=init_model,
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)
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return cbr
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@@ -1,79 +0,0 @@
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import logging
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from pathlib import Path
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from typing import Any
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from catboost import CatBoostClassifier, Pool
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from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
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from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class CatboostClassifierMultiTarget(BaseClassifierModel):
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"""
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User created prediction model. The class inherits IFreqaiModel, which
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means it has full access to all Frequency AI functionality. Typically,
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users would use this to override the common `fit()`, `train()`, or
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`predict()` methods to add their custom data handling tools or change
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various aspects of the training that cannot be configured via the
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top level config.json file.
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"""
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def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param data_dictionary: the dictionary holding all data for train, test,
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labels, weights
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:param dk: The datakitchen object for the current coin/model
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"""
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cbc = CatBoostClassifier(
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allow_writing_files=True,
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loss_function="MultiClass",
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train_dir=Path(dk.data_path),
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**self.model_training_parameters,
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)
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X = data_dictionary["train_features"]
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y = data_dictionary["train_labels"]
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sample_weight = data_dictionary["train_weights"]
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eval_sets = [None] * y.shape[1]
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if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
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eval_sets = [None] * data_dictionary["test_labels"].shape[1]
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for i in range(data_dictionary["test_labels"].shape[1]):
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eval_sets[i] = Pool(
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data=data_dictionary["test_features"],
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label=data_dictionary["test_labels"].iloc[:, i],
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weight=data_dictionary["test_weights"],
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)
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init_model = self.get_init_model(dk.pair)
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if init_model:
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init_models = init_model.estimators_
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else:
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init_models = [None] * y.shape[1]
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fit_params = []
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for i in range(len(eval_sets)):
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fit_params.append(
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{
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"eval_set": eval_sets[i],
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"init_model": init_models[i],
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}
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)
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model = FreqaiMultiOutputClassifier(estimator=cbc)
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thread_training = self.freqai_info.get("multitarget_parallel_training", False)
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if thread_training:
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model.n_jobs = y.shape[1]
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model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
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return model
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@@ -1,60 +0,0 @@
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import logging
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from pathlib import Path
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from typing import Any
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from catboost import CatBoostRegressor, Pool
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from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class CatboostRegressor(BaseRegressionModel):
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"""
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User created prediction model. The class inherits IFreqaiModel, which
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means it has full access to all Frequency AI functionality. Typically,
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users would use this to override the common `fit()`, `train()`, or
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`predict()` methods to add their custom data handling tools or change
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various aspects of the training that cannot be configured via the
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top level config.json file.
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"""
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def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param data_dictionary: the dictionary holding all data for train, test,
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labels, weights
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:param dk: The datakitchen object for the current coin/model
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"""
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train_data = Pool(
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data=data_dictionary["train_features"],
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label=data_dictionary["train_labels"],
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weight=data_dictionary["train_weights"],
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)
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if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
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test_data = None
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else:
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test_data = Pool(
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data=data_dictionary["test_features"],
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label=data_dictionary["test_labels"],
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weight=data_dictionary["test_weights"],
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)
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init_model = self.get_init_model(dk.pair)
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model = CatBoostRegressor(
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allow_writing_files=True,
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train_dir=Path(dk.data_path),
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**self.model_training_parameters,
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)
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model.fit(
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X=train_data,
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eval_set=test_data,
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init_model=init_model,
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)
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return model
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@@ -1,78 +0,0 @@
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import logging
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from pathlib import Path
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from typing import Any
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from catboost import CatBoostRegressor, Pool
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from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
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from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class CatboostRegressorMultiTarget(BaseRegressionModel):
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"""
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User created prediction model. The class inherits IFreqaiModel, which
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means it has full access to all Frequency AI functionality. Typically,
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users would use this to override the common `fit()`, `train()`, or
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`predict()` methods to add their custom data handling tools or change
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various aspects of the training that cannot be configured via the
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top level config.json file.
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"""
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def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param data_dictionary: the dictionary holding all data for train, test,
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labels, weights
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:param dk: The datakitchen object for the current coin/model
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"""
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cbr = CatBoostRegressor(
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allow_writing_files=True,
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train_dir=Path(dk.data_path),
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**self.model_training_parameters,
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)
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X = data_dictionary["train_features"]
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y = data_dictionary["train_labels"]
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sample_weight = data_dictionary["train_weights"]
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eval_sets = [None] * y.shape[1]
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if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
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eval_sets = [None] * data_dictionary["test_labels"].shape[1]
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for i in range(data_dictionary["test_labels"].shape[1]):
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eval_sets[i] = Pool(
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data=data_dictionary["test_features"],
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label=data_dictionary["test_labels"].iloc[:, i],
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weight=data_dictionary["test_weights"],
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)
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init_model = self.get_init_model(dk.pair)
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if init_model:
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init_models = init_model.estimators_
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else:
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init_models = [None] * y.shape[1]
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fit_params = []
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for i in range(len(eval_sets)):
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fit_params.append(
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{
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"eval_set": eval_sets[i],
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"init_model": init_models[i],
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}
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)
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model = FreqaiMultiOutputRegressor(estimator=cbr)
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thread_training = self.freqai_info.get("multitarget_parallel_training", False)
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if thread_training:
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model.n_jobs = y.shape[1]
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model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
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return model
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@@ -85,7 +85,6 @@ hyperopt = [
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freqai = [
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"scikit-learn",
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"joblib",
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"catboost; platform_machine != 'arm'",
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"lightgbm",
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"xgboost",
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"tensorboard",
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@@ -5,7 +5,6 @@
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# Required for freqai
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scikit-learn==1.7.2
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joblib==1.5.2
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catboost==1.2.8; 'arm' not in platform_machine
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lightgbm==4.6.0
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xgboost==3.1.2
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tensorboard==2.20.0
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