add shuffle_after_split option
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@ -1,5 +1,6 @@
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import copy
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import logging
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import random
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import shutil
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from datetime import datetime, timezone
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from math import cos, sin
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@ -168,6 +169,19 @@ class FreqaiDataKitchen:
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train_labels = labels
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train_weights = weights
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if feat_dict.get("shuffle_after_split", False):
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rint1 = random.randint(0, 100)
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rint2 = random.randint(0, 100)
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train_features = train_features.sample(
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frac=1, random_state=rint1).reset_index(drop=True)
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train_labels = train_labels.sample(frac=1, random_state=rint1).reset_index(drop=True)
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train_weights = pd.DataFrame(train_weights).sample(
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frac=1, random_state=rint1).reset_index(drop=True).to_numpy()[:, 0]
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test_features = test_features.sample(frac=1, random_state=rint2).reset_index(drop=True)
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test_labels = test_labels.sample(frac=1, random_state=rint2).reset_index(drop=True)
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test_weights = pd.DataFrame(test_weights).sample(
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frac=1, random_state=rint2).reset_index(drop=True).to_numpy()[:, 0]
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# Simplest way to reverse the order of training and test data:
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if self.freqai_config['feature_parameters'].get('reverse_train_test_order', False):
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return self.build_data_dictionary(
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@ -27,16 +27,17 @@ def is_mac() -> bool:
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return "Darwin" in machine
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@pytest.mark.parametrize('model, pca, dbscan, float32', [
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('LightGBMRegressor', True, False, True),
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('XGBoostRegressor', False, True, False),
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('XGBoostRFRegressor', False, False, False),
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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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@pytest.mark.parametrize('model, pca, dbscan, float32, shuffle', [
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('LightGBMRegressor', True, False, True, False),
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('XGBoostRegressor', False, True, False, False),
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('XGBoostRFRegressor', False, False, False, False),
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('CatboostRegressor', False, False, False, True),
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('ReinforcementLearner', False, True, False, False),
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('ReinforcementLearner_multiproc', False, False, False, False),
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('ReinforcementLearner_test_4ac', False, 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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def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
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dbscan, float32, shuffle):
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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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@ -50,6 +51,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
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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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freqai_conf.update({"reduce_df_footprint": float32})
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freqai_conf['freqai']['feature_parameters'].update({"shuffle_after_split": shuffle})
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if 'ReinforcementLearner' in model:
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model_save_ext = 'zip'
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