enable continual learning and evaluation sets on multioutput models.

freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py

@@ -0,0 +1,75 @@
+
+from joblib import Parallel
+from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
+from sklearn.utils.fixes import delayed
+from sklearn.utils.validation import has_fit_parameter
+
+
+class FreqaiMultiOutputRegressor(MultiOutputRegressor):
+
+    def fit(self, X, y, sample_weight=None, fit_params=None):
+        """Fit the model to data, separately for each output variable.
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
+            Multi-output targets. An indicator matrix turns on multilabel
+            estimation.
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If `None`, then samples are equally weighted.
+            Only supported if the underlying regressor supports sample
+            weights.
+        fit_params : A list of dicts for the fit_params
+            Parameters passed to the ``estimator.fit`` method of each step.
+            Each dict may contain same or different values (e.g. different
+            eval_sets or init_models)
+            .. versionadded:: 0.23
+        Returns
+        -------
+        self : object
+            Returns a fitted instance.
+        """
+
+        if not hasattr(self.estimator, "fit"):
+            raise ValueError("The base estimator should implement a fit method")
+
+        y = self._validate_data(X="no_validation", y=y, multi_output=True)
+
+        # if is_classifier(self):
+        #     check_classification_targets(y)
+
+        if y.ndim == 1:
+            raise ValueError(
+                "y must have at least two dimensions for "
+                "multi-output regression but has only one."
+            )
+
+        if sample_weight is not None and not has_fit_parameter(
+            self.estimator, "sample_weight"
+        ):
+            raise ValueError("Underlying estimator does not support sample weights.")
+
+        # fit_params_validated = _check_fit_params(X, fit_params)
+
+        if not fit_params:
+            fit_params = [None] * y.shape[1]
+
+        # if not init_models:
+        #     init_models = [None] * y.shape[1]
+
+        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+            delayed(_fit_estimator)(
+                self.estimator, X, y[:, i], sample_weight, **fit_params[i]
+                # init_model=init_models[i], eval_set=eval_sets[i],
+                # **fit_params_validated
+            )
+            for i in range(y.shape[1])
+        )
+
+        if hasattr(self.estimators_[0], "n_features_in_"):
+            self.n_features_in_ = self.estimators_[0].n_features_in_
+        if hasattr(self.estimators_[0], "feature_names_in_"):
+            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+
+        return

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -3,8 +3,8 @@ from typing import Any, Dict
 
 from catboost import CatBoostClassifier, Pool
 
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
 
 
 logger = logging.getLogger(__name__)

freqtrade/freqai/prediction_models/CatboostRegressor.py

@@ -3,8 +3,8 @@ from typing import Any, Dict
 
 from catboost import CatBoostRegressor, Pool
 
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
 
 
 logger = logging.getLogger(__name__)

freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py

@@ -1,11 +1,11 @@
 import logging
 from typing import Any, Dict
 
-from catboost import CatBoostRegressor  # , Pool
+from catboost import CatBoostRegressor, Pool
-from sklearn.multioutput import MultiOutputRegressor
 
+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__)
@@ -32,17 +32,34 @@ class CatboostRegressorMultiTarget(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:
+        eval_sets = [None] * y.shape[1]
-            logger.warning('Continual learning not supported for MultiTarget models')
-
-        model = MultiOutputRegressor(estimator=cbr)
-        model.fit(X=X, y=y, sample_weight=sample_weight)  # , eval_set=eval_set)
 
         if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
-            train_score = model.score(X, y)
+            eval_sets = [None] * data_dictionary['test_labels'].shape[1]
-            test_score = model.score(*eval_set)
+
-            logger.info(f"Train score {train_score}, Test score {test_score}")
+            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)
+
+        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],  'init_model': init_models[i]})
+
+        model = FreqaiMultiOutputRegressor(estimator=cbr)
+        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
+
         return model

freqtrade/freqai/prediction_models/LightGBMClassifier.py

@@ -3,8 +3,8 @@ from typing import Any, Dict
 
 from lightgbm import LGBMClassifier
 
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
 
 
 logger = logging.getLogger(__name__)

freqtrade/freqai/prediction_models/LightGBMRegressor.py

@@ -3,8 +3,8 @@ from typing import Any, Dict
 
 from lightgbm import LGBMRegressor
 
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
 
 
 logger = logging.getLogger(__name__)

freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py

@@ -2,10 +2,10 @@ import logging
 from typing import Any, Dict
 
 from lightgbm import LGBMRegressor
-from sklearn.multioutput import MultiOutputRegressor
 
+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 +29,36 @@ class LightGBMRegressorMultiTarget(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:
+        eval_weights = None
-            logger.warning('Continual learning not supported for MultiTarget models')
+        eval_sets = [None] * y.shape[1]
 
-        model = MultiOutputRegressor(estimator=lgb)
+        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
-        model.fit(X=X, y=y, sample_weight=sample_weight)  # , eval_set=eval_set)
+            eval_weights = [data_dictionary["test_weights"]]
-        train_score = model.score(X, y)
+            eval_sets = [(None, None)] * data_dictionary['test_labels'].shape[1]  # type: ignore
-        test_score = model.score(*eval_set)
+            for i in range(data_dictionary['test_labels'].shape[1]):
-        logger.info(f"Train score {train_score}, Test score {test_score}")
+                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], 'eval_sample_weight': eval_weights,
+                 'init_model': init_models[i]})
+
+        model = FreqaiMultiOutputRegressor(estimator=lgb)
+        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
+
+        # model = FreqaiMultiOutputRegressor(estimator=lgb)
+        # model.fit(X=X, y=y, sample_weight=sample_weight, init_models=init_models,
+        #           eval_sets=eval_sets, eval_sample_weight=eval_weights)
         return model

freqtrade/freqai/prediction_models/XGBoostRegressor.py

@@ -3,8 +3,8 @@ from typing import Any, Dict
 
 from xgboost import XGBRegressor
 
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
 
 
 logger = logging.getLogger(__name__)
@@ -31,6 +31,7 @@ class XGBoostRegressor(BaseRegressionModel):
             eval_set = None
         else:
             eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
+            eval_weights = [data_dictionary['test_weights']]
 
         sample_weight = data_dictionary["train_weights"]
 
@@ -38,6 +39,7 @@ class XGBoostRegressor(BaseRegressionModel):
 
         model = XGBRegressor(**self.model_training_parameters)
 
-        model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set, xgb_model=xgb_model)
+        model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
+                  sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
 
         return model

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:
+        eval_weights = None
-            logger.warning('Continual learning not supported for MultiTarget models')
+        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