mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 18:23:55 +00:00
69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
import logging
|
|
from typing import Any, Dict
|
|
|
|
from lightgbm import LGBMClassifier
|
|
|
|
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 LightGBMClassifierMultiTarget(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
|
|
"""
|
|
|
|
lgb = LGBMClassifier(**self.model_training_parameters)
|
|
|
|
X = data_dictionary["train_features"]
|
|
y = data_dictionary["train_labels"]
|
|
sample_weight = data_dictionary["train_weights"]
|
|
|
|
eval_weights = None
|
|
eval_sets = [None] * y.shape[1]
|
|
|
|
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
|
|
eval_weights = [data_dictionary["test_weights"]]
|
|
eval_sets = [(None, None)] * data_dictionary['test_labels'].shape[1] # type: ignore
|
|
for i in range(data_dictionary['test_labels'].shape[1]):
|
|
eval_sets[i] = ( # type: ignore
|
|
data_dictionary["test_features"],
|
|
data_dictionary["test_labels"].iloc[:, i]
|
|
)
|
|
|
|
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], 'eval_sample_weight': eval_weights,
|
|
'init_model': init_models[i]})
|
|
|
|
model = FreqaiMultiOutputClassifier(estimator=lgb)
|
|
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
|