stable/freqtrade/freqai/prediction_models/PyTorchRegressor.py
2023-03-20 18:41:04 +02:00

50 lines
1.6 KiB
Python

import logging
from typing import Tuple
import numpy as np
import numpy.typing as npt
import torch
from pandas import DataFrame
from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class PyTorchRegressor(BasePyTorchModel):
"""
A PyTorch implementation of a regressor.
User must implement fit method
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
dk.find_features(unfiltered_df)
filtered_df, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_df
self.data_cleaning_predict(dk)
x = torch.from_numpy(dk.data_dictionary["prediction_features"].values)\
.float()\
.to(self.device)
y = self.model.model(x)
pred_df = DataFrame(y.detach().numpy(), columns=[dk.label_list[0]])
return (pred_df, dk.do_predict)