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27a36bfb40
* Bump lightgbm from 3.3.5 to 4.0.0 Bumps [lightgbm](https://github.com/microsoft/LightGBM) from 3.3.5 to 4.0.0. - [Release notes](https://github.com/microsoft/LightGBM/releases) - [Commits](https://github.com/microsoft/LightGBM/compare/v3.3.5...v4.0.0) --- updated-dependencies: - dependency-name: lightgbm dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> * fix: ensure freqai lightgbm variants conform to v4.0.0 * remove random file --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: robcaulk <rob.caulk@gmail.com>
49 lines
1.9 KiB
Python
49 lines
1.9 KiB
Python
import logging
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from typing import Any, Dict
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from lightgbm import LGBMClassifier
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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 LightGBMClassifier(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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if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
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eval_set = None
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test_weights = None
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else:
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eval_set = [(data_dictionary["test_features"].to_numpy(),
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data_dictionary["test_labels"].to_numpy()[:, 0])]
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test_weights = data_dictionary["test_weights"]
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X = data_dictionary["train_features"].to_numpy()
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y = data_dictionary["train_labels"].to_numpy()[:, 0]
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train_weights = data_dictionary["train_weights"]
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init_model = self.get_init_model(dk.pair)
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model = LGBMClassifier(**self.model_training_parameters)
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model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
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eval_sample_weight=[test_weights], init_model=init_model)
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return model
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