freqtrade_origin/freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
dependabot[bot] 27a36bfb40
Bump lightgbm from 3.3.5 to 4.0.0 (#8923)
* 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>
2023-07-22 15:30:58 +02:00

69 lines
2.6 KiB
Python

import logging
from typing import Any, Dict
from lightgbm import LGBMRegressor
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 LightGBMRegressorMultiTarget(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
"""
lgb = LGBMRegressor(**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 = FreqaiMultiOutputRegressor(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