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Feat/freqai (#7105)

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Vectorize weight setting, log training dates

Co-authored-by: robcaulk <rob.caulk@gmail.com>
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lolong 2022-07-19 17:49:18 +02:00 committed by GitHub
parent 714d9534b6
commit 9c051958a6
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2 changed files with 8 additions and 7 deletions
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9
freqtrade/freqai/data_kitchen.py
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@ -680,12 +680,9 @@ class FreqaiDataKitchen:
Set weights so that recent data is more heavily weighted during Set weights so that recent data is more heavily weighted during
training than older data. training than older data.
""" """
wfactor = self.config["freqai"]["feature_parameters"]["weight_factor"]
weights = np.zeros(num_weights) weights = np.exp(
for i in range(1, len(weights)): - np.arange(num_weights) / (wfactor * num_weights))[::-1]
weights[len(weights) - i] = np.exp(
-i / (self.config["freqai"]["feature_parameters"]["weight_factor"] * num_weights)
)
return weights return weights
def append_predictions(self, predictions, do_predict, len_dataframe): def append_predictions(self, predictions, do_predict, len_dataframe):

6
freqtrade/freqai/prediction_models/BaseRegressionModel.py
View File

@ -39,7 +39,7 @@ class BaseRegressionModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict) :model: Trained model which can be used to inference (self.predict)
""" """
logger.info("--------------------Starting training " f"{pair} --------------------") logger.info("-------------------- Starting training " f"{pair} --------------------")
# filter the features requested by user in the configuration file and elegantly handle NaNs # filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features( features_filtered, labels_filtered = dk.filter_features(
@ -49,6 +49,10 @@ class BaseRegressionModel(IFreqaiModel):
training_filter=True, training_filter=True,
) )
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------")
# split data into train/test data. # split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0): if not self.freqai_info.get('fit_live_predictions', 0):