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121 lines
4.5 KiB
Python
121 lines
4.5 KiB
Python
import logging
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from time import time
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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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from pandas import DataFrame
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from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class BasePyTorchRegressor(BasePyTorchModel):
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"""
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A PyTorch implementation of a regressor.
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User must implement fit method
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"""
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def predict(
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self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
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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_df: 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_df)
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filtered_df, _ = dk.filter_features(
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unfiltered_df, dk.training_features_list, training_filter=False
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)
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dk.data_dictionary["prediction_features"] = filtered_df
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dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform(
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dk.data_dictionary["prediction_features"], outlier_check=True
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)
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x = self.data_convertor.convert_x(
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dk.data_dictionary["prediction_features"], device=self.device
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)
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self.model.model.eval()
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y = self.model.model(x)
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pred_df = DataFrame(y.detach().tolist(), columns=[dk.label_list[0]])
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pred_df, _, _ = dk.label_pipeline.inverse_transform(pred_df)
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if dk.feature_pipeline["di"]:
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dk.DI_values = dk.feature_pipeline["di"].di_values
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else:
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dk.DI_values = np.zeros(outliers.shape[0])
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dk.do_predict = outliers
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return (pred_df, dk.do_predict)
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def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> 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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dd = dk.make_train_test_datasets(features_filtered, labels_filtered)
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if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
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dk.fit_labels()
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dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
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dk.label_pipeline = self.define_label_pipeline(threads=dk.thread_count)
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dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
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dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])
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(dd["train_features"], dd["train_labels"], dd["train_weights"]) = (
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dk.feature_pipeline.fit_transform(
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dd["train_features"], dd["train_labels"], dd["train_weights"]
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)
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)
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dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
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if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
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(dd["test_features"], dd["test_labels"], dd["test_weights"]) = (
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dk.feature_pipeline.transform(
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dd["test_features"], dd["test_labels"], dd["test_weights"]
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)
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)
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dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])
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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(dd['train_features'])} data points")
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model = self.fit(dd, dk)
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end_time = time()
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logger.info(
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f"-------------------- Done training {pair} "
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f"({end_time - start_time:.2f} secs) --------------------"
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)
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return model
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