add test for CNNPredictionModel
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@ -43,7 +43,6 @@ class BaseTensorFlowModel(IFreqaiModel):
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start_time = time()
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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_df,
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dk.training_features_list,
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@ -14,9 +14,6 @@ from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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# tf.config.run_functions_eagerly(True)
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# tf.data.experimental.enable_debug_mode()
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class CNNPredictionModel(BaseTensorFlowModel):
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"""
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@ -49,6 +46,7 @@ class CNNPredictionModel(BaseTensorFlowModel):
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# we need to remove batch_size from the model_training_params because
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# we dont want fit() to get the incorrect assignment (we use the WindowGenerator)
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# to handle our batches.
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if 'batch_size' in self.model_training_parameters:
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self.model_training_parameters.pop('batch_size')
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input_dims = [BATCH_SIZE, self.CONV_WIDTH, n_features]
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@ -34,7 +34,8 @@ def is_mac() -> bool:
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('CatboostRegressor', False, False, False),
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('ReinforcementLearner', False, True, False),
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('ReinforcementLearner_multiproc', False, False, False),
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('ReinforcementLearner_test_4ac', False, False, False)
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('ReinforcementLearner_test_4ac', False, False, False),
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('CNNPredictionModel', False, False, False)
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])
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def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan, float32):
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if is_arm() and model == 'CatboostRegressor':
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@ -71,6 +72,10 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
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if 'test_4ac' in model:
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freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
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if 'CNNPredictionModel' in model:
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freqai_conf['freqai']['model_training_parameters'].pop('n_estimators')
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model_save_ext = 'h5'
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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