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update to new datasieve interface, add noise to pipeline

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This commit is contained in:
robcaulk 2023-06-07 17:58:27 +02:00
parent 4d4589becd
commit dc577d2a1a
4 changed files with 23 additions and 31 deletions
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11
freqtrade/freqai/data_kitchen.py
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@ -371,17 +371,6 @@ class FreqaiDataKitchen:
return df
def add_noise_to_training_features(self) -> None:
"""
Add noise to train features to reduce the risk of overfitting.
"""
mu = 0 # no shift
sigma = self.freqai_config["feature_parameters"]["noise_standard_deviation"]
compute_df = self.data_dictionary['train_features']
noise = np.random.normal(mu, sigma, [compute_df.shape[0], compute_df.shape[1]])
self.data_dictionary['train_features'] += noise
return
def find_features(self, dataframe: DataFrame) -> None:
"""
Find features in the strategy provided dataframe

13
freqtrade/freqai/freqai_interface.py
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@ -510,12 +510,12 @@ class IFreqaiModel(ABC):
def define_data_pipeline(self, dk: FreqaiDataKitchen) -> None:
ft_params = self.freqai_info["feature_parameters"]
dk.feature_pipeline = Pipeline([
('const', ds.DataSieveVarianceThreshold(threshold=0)),
('const', ds.VarianceThreshold(threshold=0)),
('scaler', SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1))))
])
if ft_params.get("principal_component_analysis", False):
dk.feature_pipeline.append(('pca', ds.DataSievePCA()))
dk.feature_pipeline.append(('pca', ds.PCA()))
dk.feature_pipeline.append(('post-pca-scaler',
SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1)))))
@ -529,14 +529,15 @@ class IFreqaiModel(ABC):
dk.feature_pipeline.append(('di', ds.DissimilarityIndex(di_threshold=di)))
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
dk.feature_pipeline.append(('dbscan', ds.DataSieveDBSCAN()))
dk.feature_pipeline.append(('dbscan', ds.DBSCAN()))
sigma = self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0)
if sigma:
dk.feature_pipeline.append(('noise', ds.Noise(sigma=sigma)))
dk.feature_pipeline.fitparams = dk.feature_pipeline._validate_fitparams(
{}, dk.feature_pipeline.steps)
# if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
# dk.pipeline.extend(('noise', ds.Noise()))
def define_label_pipeline(self, dk: FreqaiDataKitchen) -> None:
dk.label_pipeline = Pipeline([

2
requirements-freqai.txt
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@ -10,4 +10,4 @@ catboost==1.2; 'arm' not in platform_machine and (sys_platform != 'darwin' or py
lightgbm==3.3.5
xgboost==1.7.5
tensorboard==2.13.0
datasieve==0.1.1
datasieve==0.1.2

28
tests/freqai/test_freqai_interface.py
View File

@ -37,21 +37,22 @@ def can_run_model(model: str) -> None:
pytest.skip("Reinforcement learning / PyTorch module not available on intel based Mac OS.")
@pytest.mark.parametrize('model, pca, dbscan, float32, can_short, shuffle, buffer', [
('LightGBMRegressor', True, False, True, True, False, 0),
('XGBoostRegressor', False, True, False, True, False, 10),
('XGBoostRFRegressor', False, False, False, True, False, 0),
('CatboostRegressor', False, False, False, True, True, 0),
('PyTorchMLPRegressor', False, False, False, False, False, 0),
('PyTorchTransformerRegressor', False, False, False, False, False, 0),
('ReinforcementLearner', False, True, False, True, False, 0),
('ReinforcementLearner_multiproc', False, False, False, True, False, 0),
('ReinforcementLearner_test_3ac', False, False, False, False, False, 0),
('ReinforcementLearner_test_3ac', False, False, False, True, False, 0),
('ReinforcementLearner_test_4ac', False, False, False, True, False, 0),
@pytest.mark.parametrize('model, pca, dbscan, float32, can_short, shuffle, buffer, noise', [
('LightGBMRegressor', True, False, True, True, False, 0, 0),
('XGBoostRegressor', False, True, False, True, False, 10, 0.05),
('XGBoostRFRegressor', False, False, False, True, False, 0, 0),
('CatboostRegressor', False, False, False, True, True, 0, 0),
('PyTorchMLPRegressor', False, False, False, False, False, 0, 0),
('PyTorchTransformerRegressor', False, False, False, False, False, 0, 0),
('ReinforcementLearner', False, True, False, True, False, 0, 0),
('ReinforcementLearner_multiproc', False, False, False, True, False, 0, 0),
('ReinforcementLearner_test_3ac', False, False, False, False, False, 0, 0),
('ReinforcementLearner_test_3ac', False, False, False, True, False, 0, 0),
('ReinforcementLearner_test_4ac', False, False, False, True, False, 0, 0),
])
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
dbscan, float32, can_short, shuffle, buffer):
dbscan, float32, can_short, shuffle,
buffer, noise):
can_run_model(model)
@ -68,6 +69,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
freqai_conf.update({"reduce_df_footprint": float32})
freqai_conf['freqai']['feature_parameters'].update({"shuffle_after_split": shuffle})
freqai_conf['freqai']['feature_parameters'].update({"buffer_train_data_candles": buffer})
freqai_conf['freqai']['feature_parameters'].update({"noise_standard_deviation": noise})
if 'ReinforcementLearner' in model:
model_save_ext = 'zip'