add test for CNNPredictionModel

freqtrade/freqai/base_models/BaseTensorFlowModel.py

@@ -43,7 +43,6 @@ class BaseTensorFlowModel(IFreqaiModel):
 
         start_time = time()
 
-        # filter the features requested by user in the configuration file and elegantly handle NaNs
         features_filtered, labels_filtered = dk.filter_features(
             unfiltered_df,
             dk.training_features_list,

freqtrade/freqai/prediction_models/CNNPredictionModel.py

@@ -14,9 +14,6 @@ from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 
 logger = logging.getLogger(__name__)
 
-# tf.config.run_functions_eagerly(True)
-# tf.data.experimental.enable_debug_mode()
-
 
 class CNNPredictionModel(BaseTensorFlowModel):
     """
@@ -49,7 +46,8 @@ class CNNPredictionModel(BaseTensorFlowModel):
         # we need to remove batch_size from the model_training_params because
         # we dont want fit() to get the incorrect assignment (we use the WindowGenerator)
         # to handle our batches.
-        self.model_training_parameters.pop('batch_size')
+        if 'batch_size' in self.model_training_parameters:
+            self.model_training_parameters.pop('batch_size')
         input_dims = [BATCH_SIZE, self.CONV_WIDTH, n_features]
 
         w1 = WindowGenerator(

tests/freqai/test_freqai_interface.py

@@ -34,7 +34,8 @@ def is_mac() -> bool:
     ('CatboostRegressor', False, False, False),
     ('ReinforcementLearner', False, True, False),
     ('ReinforcementLearner_multiproc', False, False, False),
-    ('ReinforcementLearner_test_4ac', False, False, False)
+    ('ReinforcementLearner_test_4ac', False, False, False),
+    ('CNNPredictionModel', False, False, False)
     ])
 def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan, float32):
     if is_arm() and model == 'CatboostRegressor':
@@ -71,6 +72,10 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
     if 'test_4ac' in model:
         freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
 
+    if 'CNNPredictionModel' in model:
+        freqai_conf['freqai']['model_training_parameters'].pop('n_estimators')
+        model_save_ext = 'h5'
+
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)
     exchange = get_patched_exchange(mocker, freqai_conf)
     strategy.dp = DataProvider(freqai_conf, exchange)