100 lines
4.0 KiB
Python
100 lines
4.0 KiB
Python
import logging
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from typing import Any, Tuple
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import numpy as np
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import numpy.typing as npt
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import pandas as pd
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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 BaseClassifierModel(IFreqaiModel):
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"""
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Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.).
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User *must* inherit from this class and set fit() and predict(). See example scripts
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such as prediction_models/CatboostPredictionModel.py for guidance.
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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, dk)
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logger.info(f"--------------------done training {pair}--------------------")
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return model
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def predict(
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self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
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) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
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"""
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Filter the prediction features data and predict with it.
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:param: unfiltered_dataframe: Full dataframe for the current backtest period.
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:return:
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:pred_df: dataframe containing the predictions
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:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
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data (NaNs) or felt uncertain about data (PCA and DI index)
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"""
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dk.find_features(unfiltered_dataframe)
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filtered_dataframe, _ = dk.filter_features(
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unfiltered_dataframe, dk.training_features_list, training_filter=False
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)
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filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
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dk.data_dictionary["prediction_features"] = filtered_dataframe
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self.data_cleaning_predict(dk, filtered_dataframe)
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predictions = self.model.predict(dk.data_dictionary["prediction_features"])
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pred_df = DataFrame(predictions, columns=dk.label_list)
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predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"])
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pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_)
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pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
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return (pred_df, dk.do_predict)
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