stable/freqtrade/freqai/base_models/BasePyTorchModel.py

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import logging
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from abc import ABC, abstractmethod
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from time import time
from typing import Any
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import torch
from pandas import DataFrame
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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
data_convertor property.
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"""
def __init__(self, **kwargs):
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super().__init__(config=kwargs["config"])
self.dd.model_type = "pytorch"
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')
self.splits = ["train", "test"] if test_size != 0 else ["train"]
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def train(
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
"""
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.
:param unfiltered_df: Full dataframe for the current training period
:return:
:model: Trained model which can be used to inference (self.predict)
"""
logger.info(f"-------------------- Starting training {pair} --------------------")
start_time = time()
features_filtered, labels_filtered = dk.filter_features(
unfiltered_df,
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)
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
dk.fit_labels()
# 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, dk)
end_time = time()
logger.info(f"-------------------- Done training {pair} "
f"({end_time - start_time:.2f} secs) --------------------")
return model
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@property
@abstractmethod
def data_convertor(self) -> PyTorchDataConvertor:
raise NotImplementedError("Abstract property")