add integrated tests for PCA and DBSCAN
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@ -27,13 +27,13 @@ def is_mac() -> bool:
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return "Darwin" in machine
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return "Darwin" in machine
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@pytest.mark.parametrize('model', [
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@pytest.mark.parametrize('model, pca, dbscan', [
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'LightGBMRegressor',
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('LightGBMRegressor', True, False),
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'XGBoostRegressor',
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('XGBoostRegressor', False, True),
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'XGBoostRFRegressor',
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('XGBoostRFRegressor', False, False),
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'CatboostRegressor',
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('CatboostRegressor', False, False),
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])
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])
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def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
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def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan):
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if is_arm() and model == 'CatboostRegressor':
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if is_arm() and model == 'CatboostRegressor':
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pytest.skip("CatBoost is not supported on ARM")
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pytest.skip("CatBoost is not supported on ARM")
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@ -41,6 +41,8 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
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freqai_conf.update({"freqaimodel": model})
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freqai_conf.update({"freqaimodel": model})
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"strategy": "freqai_test_strat"})
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freqai_conf.update({"strategy": "freqai_test_strat"})
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freqai_conf['freqai']['feature_parameters'].update({"principal_component_analysis": pca})
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freqai_conf['freqai']['feature_parameters'].update({"use_DBSCAN_to_remove_outliers": dbscan})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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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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exchange = get_patched_exchange(mocker, freqai_conf)
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