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rollback to the original add noise

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longyu 2023-01-31 19:50:39 +01:00
parent b42d396a01
commit dd0713e80f
1 changed files with 5 additions and 19 deletions
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24
freqtrade/freqai/data_kitchen.py
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@ -884,25 +884,11 @@ class FreqaiDataKitchen:
""" """
Add noise to train features to reduce the risk of overfitting. Add noise to train features to reduce the risk of overfitting.
""" """
da = self.freqai_config["feature_parameters"]["data_augment"] mu = 0 # no shift
X = self.data_dictionary['train_features'] sigma = self.freqai_config["feature_parameters"]["noise_standard_deviation"]
y = self.data_dictionary['train_labels'] compute_df = self.data_dictionary['train_features']
da_type = da.get("type", "std") noise = np.random.normal(mu, sigma, [compute_df.shape[0], compute_df.shape[1]])
if da_type == "std": self.data_dictionary['train_features'] += noise
# generate alpha values of 0-mean and 1-std
alpha = np.random.randn(*X.shape)
scale = da.get("vaue", 0.01)
Xaugmented = X + alpha * scale * X.std(0)[None, :]
X = np.vstack((X, Xaugmented))
y = y.append(y)
self.data_dictionary['train_features'] = X
self.data_dictionary['train_labels'] = y
elif da_type == "constant":
mu = 0 # no shift
sigma = self.freqai_config["feature_parameters"]["data_augment"]["value"]
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 return
def find_features(self, dataframe: DataFrame) -> None: def find_features(self, dataframe: DataFrame) -> None: