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>

freqtrade/freqai/prediction_models/LightGBMClassifier.py

@@ -32,8 +32,8 @@ class LightGBMClassifier(BaseClassifierModel):
             eval_set = None
             test_weights = None
         else:
-            eval_set = (data_dictionary["test_features"].to_numpy(),
-                        data_dictionary["test_labels"].to_numpy()[:, 0])
+            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]
@@ -42,7 +42,6 @@ class LightGBMClassifier(BaseClassifierModel):
         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)
 

freqtrade/freqai/prediction_models/LightGBMRegressor.py

@@ -32,7 +32,7 @@ class LightGBMRegressor(BaseRegressionModel):
             eval_set = None
             eval_weights = None
         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"]
         X = data_dictionary["train_features"]
         y = data_dictionary["train_labels"]

freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py

@@ -42,10 +42,10 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
             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
+                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:

requirements-freqai.txt

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