add strat and config for testing on PR

freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py

@@ -6,13 +6,14 @@ from typing import Any, Dict
 from catboost import CatBoostClassifier, Pool
 
 from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 
 
 logger = logging.getLogger(__name__)
 
 
-class CatboostClassifier(BaseClassifierModel):
+class CatboostClassifierMultiTarget(BaseClassifierModel):
     """
     User created prediction model. The class needs to override three necessary
     functions, predict(), train(), fit(). The class inherits ModelHandler which
@@ -26,30 +27,48 @@ class CatboostClassifier(BaseClassifierModel):
                                 all the training and test data/labels.
         """
 
-        train_data = Pool(
-            data=data_dictionary["train_features"],
-            label=data_dictionary["train_labels"],
-            weight=data_dictionary["train_weights"],
-        )
-        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
-            test_data = None
-        else:
-            test_data = Pool(
-                data=data_dictionary["test_features"],
-                label=data_dictionary["test_labels"],
-                weight=data_dictionary["test_weights"],
-            )
-
-        cbr = CatBoostClassifier(
+        cbc = CatBoostClassifier(
             allow_writing_files=True,
             loss_function='MultiClass',
             train_dir=Path(dk.data_path),
             **self.model_training_parameters,
         )
 
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+
+        sample_weight = data_dictionary["train_weights"]
+
+        eval_sets = [None] * y.shape[1]
+
+        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
+            eval_sets = [None] * data_dictionary['test_labels'].shape[1]
+
+            for i in range(data_dictionary['test_labels'].shape[1]):
+                eval_sets[i] = Pool(
+                    data=data_dictionary["test_features"],
+                    label=data_dictionary["test_labels"].iloc[:, i],
+                    weight=data_dictionary["test_weights"],
+                )
+
         init_model = self.get_init_model(dk.pair)
 
-        cbr.fit(X=train_data, eval_set=test_data, init_model=init_model,
-                log_cout=sys.stdout, log_cerr=sys.stderr)
+        if init_model:
+            init_models = init_model.estimators_
+        else:
+            init_models = [None] * y.shape[1]
 
-        return cbr
+        fit_params = []
+        for i in range(len(eval_sets)):
+            fit_params.append({
+                'eval_set': eval_sets[i], 'init_model': init_models[i],
+                'log_cout': sys.stdout, 'log_cerr': sys.stderr,
+            })
+
+        model = FreqaiMultiOutputClassifier(estimator=cbc)
+        thread_training = self.freqai_info.get('multitarget_parallel_training', False)
+        if thread_training:
+            model.n_jobs = y.shape[1]
+        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
+
+        return model