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Bump lightgbm from 3.3.5 to 4.0.0 (#8923)

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* 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>
This commit is contained in:
dependabot[bot] 2023-07-22 15:30:58 +02:00 committed by GitHub
parent 14d2e3e88e
commit 27a36bfb40
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 6 additions and 7 deletions
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5
freqtrade/freqai/prediction_models/LightGBMClassifier.py
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@ -32,8 +32,8 @@ class LightGBMClassifier(BaseClassifierModel):
eval_set = None eval_set = None
test_weights = None test_weights = None
else: else:
eval_set = (data_dictionary["test_features"].to_numpy(), eval_set = [(data_dictionary["test_features"].to_numpy(),
data_dictionary["test_labels"].to_numpy()[:, 0]) data_dictionary["test_labels"].to_numpy()[:, 0])]
test_weights = data_dictionary["test_weights"] test_weights = data_dictionary["test_weights"]
X = data_dictionary["train_features"].to_numpy() X = data_dictionary["train_features"].to_numpy()
y = data_dictionary["train_labels"].to_numpy()[:, 0] y = data_dictionary["train_labels"].to_numpy()[:, 0]
@ -42,7 +42,6 @@ class LightGBMClassifier(BaseClassifierModel):
init_model = self.get_init_model(dk.pair) init_model = self.get_init_model(dk.pair)
model = LGBMClassifier(**self.model_training_parameters) model = LGBMClassifier(**self.model_training_parameters)
model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights, model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
eval_sample_weight=[test_weights], init_model=init_model) eval_sample_weight=[test_weights], init_model=init_model)

2
freqtrade/freqai/prediction_models/LightGBMRegressor.py
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@ -32,7 +32,7 @@ class LightGBMRegressor(BaseRegressionModel):
eval_set = None eval_set = None
eval_weights = None eval_weights = None
else: else:
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"]) eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
eval_weights = data_dictionary["test_weights"] eval_weights = data_dictionary["test_weights"]
X = data_dictionary["train_features"] X = data_dictionary["train_features"]
y = data_dictionary["train_labels"] y = data_dictionary["train_labels"]

4
freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
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@ -42,10 +42,10 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
eval_weights = [data_dictionary["test_weights"]] eval_weights = [data_dictionary["test_weights"]]
eval_sets = [(None, None)] * data_dictionary['test_labels'].shape[1] # type: ignore eval_sets = [(None, None)] * data_dictionary['test_labels'].shape[1] # type: ignore
for i in range(data_dictionary['test_labels'].shape[1]): for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = ( # type: ignore eval_sets[i] = [( # type: ignore
data_dictionary["test_features"], data_dictionary["test_features"],
data_dictionary["test_labels"].iloc[:, i] data_dictionary["test_labels"].iloc[:, i]
) )]
init_model = self.get_init_model(dk.pair) init_model = self.get_init_model(dk.pair)
if init_model: if init_model:

2
requirements-freqai.txt
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@ -6,7 +6,7 @@
scikit-learn==1.1.3 scikit-learn==1.1.3
joblib==1.3.1 joblib==1.3.1
catboost==1.2; 'arm' not in platform_machine catboost==1.2; 'arm' not in platform_machine
lightgbm==3.3.5 lightgbm==4.0.0
xgboost==1.7.6 xgboost==1.7.6
tensorboard==2.13.0 tensorboard==2.13.0
datasieve==0.1.7 datasieve==0.1.7