stable/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py

71 lines
2.4 KiB
Python
Raw Normal View History

2022-07-12 17:10:09 +00:00
import logging
2022-07-26 14:01:54 +00:00
from typing import Any
2022-07-12 17:10:09 +00:00
from pandas import DataFrame
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.freqai_interface import IFreqaiModel
logger = logging.getLogger(__name__)
class BaseTensorFlowModel(IFreqaiModel):
"""
Base class for TensorFlow type models.
User *must* inherit from this class and set fit() and predict().
"""
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
2022-07-26 14:01:54 +00:00
) -> Any:
2022-07-12 17:10:09 +00:00
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
2022-07-24 14:54:39 +00:00
:param unfiltered_dataframe: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
2022-07-12 17:10:09 +00:00
:returns:
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("--------------------Starting training " f"{pair} --------------------")
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
unfiltered_dataframe,
dk.training_features_list,
dk.label_list,
training_filter=True,
)
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
2022-07-12 17:10:09 +00:00
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
# optional additional data cleaning/analysis
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary)
if pair not in self.dd.historic_predictions:
self.set_initial_historic_predictions(
data_dictionary['train_features'], model, dk, pair)
if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
self.fit_live_predictions(dk)
else:
dk.fit_labels()
2022-07-12 17:10:09 +00:00
self.dd.save_historic_predictions_to_disk()
logger.info(f"--------------------done training {pair}--------------------")
return model