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add test for CNNPredictionModel

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This commit is contained in:
robcaulk 2022-12-06 23:50:34 +01:00
parent 665eed3906
commit 389ab7e44b
3 changed files with 8 additions and 6 deletions
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1
freqtrade/freqai/base_models/BaseTensorFlowModel.py
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@ -43,7 +43,6 @@ class BaseTensorFlowModel(IFreqaiModel):
start_time = time() start_time = time()
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features( features_filtered, labels_filtered = dk.filter_features(
unfiltered_df, unfiltered_df,
dk.training_features_list, dk.training_features_list,

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

7
tests/freqai/test_freqai_interface.py
View File

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