stable/freqtrade/freqai/prediction_models/LightGBMClassifier.py

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import logging
from typing import Any, Dict
from lightgbm import LGBMClassifier
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
logger = logging.getLogger(__name__)
class LightGBMClassifier(BaseClassifierModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
eval_set = None
test_weights = None
else:
eval_set = (data_dictionary["test_features"].to_numpy(),
data_dictionary["test_labels"].to_numpy()[:, 0])
test_weights = data_dictionary["test_weights"]
X = data_dictionary["train_features"].to_numpy()
y = data_dictionary["train_labels"].to_numpy()[:, 0]
train_weights = data_dictionary["train_weights"]
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init_model = self.get_init_model(dk.pair)
model = LGBMClassifier(**self.model_training_parameters)
model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
eval_sample_weight=[test_weights], init_model=init_model)
return model