allow user to pass test_size = 0 and avoid using eval sets in prediction models

freqtrade/freqai/data_kitchen.py

@@ -243,6 +243,7 @@ class FreqaiDataKitchen:
         else:
             stratification = None
 
+        if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
             (
                 train_features,
                 test_features,
@@ -257,6 +258,13 @@ class FreqaiDataKitchen:
                 stratify=stratification,
                 **self.config["freqai"]["data_split_parameters"],
             )
+        else:
+            test_labels = np.zeros(2)
+            test_features = pd.DataFrame()
+            test_weights = np.zeros(2)
+            train_features = filtered_dataframe
+            train_labels = labels
+            train_weights = weights
 
         return self.build_data_dictionary(
             train_features, test_features, train_labels, test_labels, train_weights, test_weights
@@ -392,6 +400,7 @@ class FreqaiDataKitchen:
                 / (train_labels_max - train_labels_min)
                 - 1
             )
+            if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
                 data_dictionary["test_labels"][item] = (
                     2
                     * (data_dictionary["test_labels"][item] - train_labels_min)
@@ -555,6 +564,7 @@ class FreqaiDataKitchen:
         self.data["training_features_list_raw"] = copy.deepcopy(self.training_features_list)
         self.training_features_list = self.data_dictionary["train_features"].columns
 
+        if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
             self.data_dictionary["test_features"] = pd.DataFrame(
                 data=test_components,
                 columns=["PC" + str(i) for i in range(0, n_keep_components)],
@@ -652,12 +662,14 @@ class FreqaiDataKitchen:
             )
 
             # same for test data
+            if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
                 y_pred = self.svm_model.predict(self.data_dictionary["test_features"])
                 dropped_points = np.where(y_pred == -1, 0, y_pred)
                 self.data_dictionary["test_features"] = self.data_dictionary["test_features"][
                     (y_pred == 1)
                 ]
-            self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][(y_pred == 1)]
+                self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][(
+                    y_pred == 1)]
                 self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][
                     (y_pred == 1)
                 ]

freqtrade/freqai/prediction_models/CatboostPredictionModel.py

@@ -28,7 +28,9 @@ class CatboostPredictionModel(BaseRegressionModel):
             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"],
@@ -39,6 +41,9 @@ class CatboostPredictionModel(BaseRegressionModel):
             allow_writing_files=False,
             **self.model_training_parameters,
         )
+
+        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
+            test_data = None
         model.fit(X=train_data, eval_set=test_data)
 
         return model

freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py

@@ -36,6 +36,8 @@ class CatboostPredictionMultiModel(BaseRegressionModel):
 
         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)
             test_score = model.score(*eval_set)
             logger.info(f"Train score {train_score}, Test score {test_score}")

freqtrade/freqai/prediction_models/LightGBMPredictionModel.py

@@ -25,11 +25,15 @@ class LightGBMPredictionModel(BaseRegressionModel):
                                 all the training and test data/labels.
         """
 
+        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
+            eval_set = None
+        else:
             eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
         X = data_dictionary["train_features"]
         y = data_dictionary["train_labels"]
 
         model = LGBMRegressor(**self.model_training_parameters)
+
         model.fit(X=X, y=y, eval_set=eval_set)
 
         return model