add kwargs, reduce duplicated code

freqtrade/freqai/freqai_interface.py

@@ -661,11 +661,20 @@ class IFreqaiModel(ABC):
                 self.train_time = 0
         return
 
+    def get_init_model(self, pair: str) -> Any:
+        if pair not in self.dd.model_dictionary or not self.continual_learning:
+            init_model = None
+        else:
+            init_model = self.dd.model_dictionary[pair]
+
+        return init_model
+
     # Following methods which are overridden by user made prediction models.
     # See freqai/prediction_models/CatboostPredictionModel.py for an example.
 
     @abstractmethod
-    def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any:
+    def train(self, unfiltered_dataframe: DataFrame, pair: str,
+              dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         Filter the training data and train a model to it. Train makes heavy use of the datahandler
         for storing, saving, loading, and analyzing the data.
@@ -675,7 +684,7 @@ class IFreqaiModel(ABC):
         """
 
     @abstractmethod
-    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen) -> Any:
+    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         Most regressors use the same function names and arguments e.g. user
         can drop in LGBMRegressor in place of CatBoostRegressor and all data
@@ -688,7 +697,7 @@ class IFreqaiModel(ABC):
 
     @abstractmethod
     def predict(
-        self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True
+        self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True, **kwargs
     ) -> Tuple[DataFrame, NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -2,6 +2,7 @@ import logging
 from typing import Any, Dict
 
 from catboost import CatBoostClassifier, Pool
+
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
 
@@ -16,7 +17,7 @@ class CatboostClassifier(BaseClassifierModel):
     has its own DataHandler where data is held, saved, loaded, and managed.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen) -> Any:
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :params:
@@ -36,10 +37,7 @@ class CatboostClassifier(BaseClassifierModel):
             **self.model_training_parameters,
         )
 
-        if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
-            init_model = None
-        else:
-            init_model = self.dd.model_dictionary[dk.pair]
+        init_model = self.get_init_model(dk.pair)
 
         cbr.fit(train_data, init_model=init_model)
 

freqtrade/freqai/prediction_models/CatboostRegressor.py

@@ -1,10 +1,9 @@
-import gc
 import logging
 from typing import Any, Dict
 
 from catboost import CatBoostRegressor, Pool
-from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
 
 
@@ -18,7 +17,7 @@ class CatboostRegressor(BaseRegressionModel):
     has its own DataHandler where data is held, saved, loaded, and managed.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen) -> Any:
+    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 constructed by DataHandler to hold
@@ -39,10 +38,7 @@ class CatboostRegressor(BaseRegressionModel):
                 weight=data_dictionary["test_weights"],
             )
 
-        if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
-            init_model = None
-        else:
-            init_model = self.dd.model_dictionary[dk.pair]
+        init_model = self.get_init_model(dk.pair)
 
         model = CatBoostRegressor(
             allow_writing_files=False,

freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py

@@ -3,6 +3,7 @@ from typing import Any, Dict
 
 from catboost import CatBoostRegressor  # , Pool
 from sklearn.multioutput import MultiOutputRegressor
+
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
 
@@ -17,7 +18,7 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
     has its own DataHandler where data is held, saved, loaded, and managed.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen) -> Any:
+    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 constructed by DataHandler to hold

freqtrade/freqai/prediction_models/LightGBMClassifier.py

@@ -3,8 +3,9 @@ from typing import Any, Dict
 
 from lightgbm import LGBMClassifier
 
-from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
+
 
 logger = logging.getLogger(__name__)
 
@@ -16,7 +17,7 @@ class LightGBMClassifier(BaseClassifierModel):
     has its own DataHandler where data is held, saved, loaded, and managed.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen) -> Any:
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :params:
@@ -35,10 +36,7 @@ class LightGBMClassifier(BaseClassifierModel):
         y = data_dictionary["train_labels"].to_numpy()[:, 0]
         train_weights = data_dictionary["train_weights"]
 
-        if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
-            init_model = None
-        else:
-            init_model = self.dd.model_dictionary[dk.pair]
+        init_model = self.get_init_model(dk.pair)
 
         model = LGBMClassifier(**self.model_training_parameters)
 

freqtrade/freqai/prediction_models/LightGBMRegressor.py

@@ -3,8 +3,9 @@ from typing import Any, Dict
 
 from lightgbm import LGBMRegressor
 
-from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
+
 
 logger = logging.getLogger(__name__)
 
@@ -16,7 +17,7 @@ class LightGBMRegressor(BaseRegressionModel):
     has its own DataHandler where data is held, saved, loaded, and managed.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen) -> Any:
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         Most regressors use the same function names and arguments e.g. user
         can drop in LGBMRegressor in place of CatBoostRegressor and all data
@@ -35,10 +36,7 @@ class LightGBMRegressor(BaseRegressionModel):
         y = data_dictionary["train_labels"]
         train_weights = data_dictionary["train_weights"]
 
-        if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
-            init_model = None
-        else:
-            init_model = self.dd.model_dictionary[dk.pair]
+        init_model = self.get_init_model(dk.pair)
 
         model = LGBMRegressor(**self.model_training_parameters)
 

freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py

@@ -4,8 +4,9 @@ from typing import Any, Dict
 from lightgbm import LGBMRegressor
 from sklearn.multioutput import MultiOutputRegressor
 
-from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
+
 
 logger = logging.getLogger(__name__)
 
@@ -17,7 +18,7 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
     has its own DataHandler where data is held, saved, loaded, and managed.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen) -> Any:
+    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 constructed by DataHandler to hold