50 lines
1.6 KiB
Python
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)
|