65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
import logging
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from typing import Any
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from pandas import DataFrame
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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logger = logging.getLogger(__name__)
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class BaseTensorFlowModel(IFreqaiModel):
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"""
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Base class for TensorFlow type models.
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User *must* inherit from this class and set fit() and predict().
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"""
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def train(
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self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
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) -> Any:
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datakitchen
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for storing, saving, loading, and analyzing the data.
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:param unfiltered_dataframe: Full dataframe for the current training period
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:param metadata: pair metadata from strategy.
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:return:
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:model: Trained model which can be used to inference (self.predict)
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"""
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logger.info("-------------------- Starting training " f"{pair} --------------------")
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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_dataframe,
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dk.training_features_list,
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dk.label_list,
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training_filter=True,
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)
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start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
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end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
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logger.info(f"-------------------- Training on data from {start_date} to "
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f"{end_date}--------------------")
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# split data into train/test data.
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data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
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if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
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dk.fit_labels()
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# normalize all data based on train_dataset only
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data_dictionary = dk.normalize_data(data_dictionary)
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# optional additional data cleaning/analysis
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self.data_cleaning_train(dk)
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logger.info(
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f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
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)
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logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
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model = self.fit(data_dictionary)
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logger.info(f"--------------------done training {pair}--------------------")
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return model
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