freqtrade_origin/freqtrade/freqai/prediction_models/LightGBMClassifier.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

49 lines
1.9 KiB
Python

import logging
from typing import Any, Dict
from lightgbm import LGBMClassifier
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class LightGBMClassifier(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
"""
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"]
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