enable continual learning and evaluation sets on multioutput models.

freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py

@@ -1,11 +1,11 @@
 import logging
 from typing import Any, Dict
 
-from sklearn.multioutput import MultiOutputRegressor
 from xgboost import XGBRegressor
 
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
+from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
 
 
 logger = logging.getLogger(__name__)
@@ -29,15 +29,32 @@ class XGBoostRegressorMultiTarget(BaseRegressionModel):
 
         X = data_dictionary["train_features"]
         y = data_dictionary["train_labels"]
-        eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
         sample_weight = data_dictionary["train_weights"]
 
-        if self.continual_learning:
-            logger.warning('Continual learning not supported for MultiTarget models')
+        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"]]
+            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], 'sample_weight_eval_set': eval_weights,
+                 'xgb_model': init_models[i]})
+
+        model = FreqaiMultiOutputRegressor(estimator=xgb)
+        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
 
-        model = MultiOutputRegressor(estimator=xgb)
-        model.fit(X=X, y=y, sample_weight=sample_weight)  # , eval_set=eval_set)
-        train_score = model.score(X, y)
-        test_score = model.score(*eval_set)
-        logger.info(f"Train score {train_score}, Test score {test_score}")
         return model