80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
import logging
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from abc import ABC, abstractmethod
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from time import time
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from typing import Any
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import torch
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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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from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor
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logger = logging.getLogger(__name__)
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class BasePyTorchModel(IFreqaiModel, ABC):
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"""
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Base class for PyTorch type models.
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User *must* inherit from this class and set fit() and predict() and
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data_convertor property.
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"""
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def __init__(self, **kwargs):
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super().__init__(config=kwargs["config"])
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self.dd.model_type = "pytorch"
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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test_size = self.freqai_info.get('data_split_parameters', {}).get('test_size')
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self.splits = ["train", "test"] if test_size != 0 else ["train"]
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def train(
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self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
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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_df: Full dataframe for the current training period
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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(f"-------------------- Starting training {pair} --------------------")
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start_time = time()
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_df,
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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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# 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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end_time = time()
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logger.info(f"-------------------- Done training {pair} "
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f"({end_time - start_time:.2f} secs) --------------------")
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return model
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@property
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@abstractmethod
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def data_convertor(self) -> PyTorchDataConvertor:
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raise NotImplementedError("Abstract property")
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