mirror of
https://github.com/freqtrade/freqtrade.git
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219 lines
8.1 KiB
Python
219 lines
8.1 KiB
Python
import logging
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from time import time
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from typing import Any, Dict, List, Tuple
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import numpy as np
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import numpy.typing as npt
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import pandas as pd
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import torch
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from pandas import DataFrame
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from torch.nn import functional as F
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from freqtrade.exceptions import OperationalException
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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 BasePyTorchClassifier(BasePyTorchModel):
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"""
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A PyTorch implementation of a classifier.
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User must implement fit method
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Important!
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- User must declare the target class names in the strategy,
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under IStrategy.set_freqai_targets method.
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for example, in your strategy:
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```
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def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
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self.freqai.class_names = ["down", "up"]
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dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) >
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dataframe["close"], 'up', 'down')
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return dataframe
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"""
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.class_name_to_index = None
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self.index_to_class_name = None
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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 dk: dk: The datakitchen object
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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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:raises ValueError: if 'class_names' doesn't exist in model meta_data.
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"""
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class_names = self.model.model_meta_data.get("class_names", None)
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if not class_names:
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raise ValueError(
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"Missing class names. "
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"self.model.model_meta_data['class_names'] is None."
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)
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if not self.class_name_to_index:
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self.init_class_names_to_index_mapping(class_names)
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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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x = self.data_convertor.convert_x(
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dk.data_dictionary["prediction_features"],
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device=self.device
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)
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self.model.model.eval()
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logits = self.model.model(x)
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probs = F.softmax(logits, dim=-1)
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predicted_classes = torch.argmax(probs, dim=-1)
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predicted_classes_str = self.decode_class_names(predicted_classes)
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# used .tolist to convert probs into an iterable, in this way Tensors
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# are automatically moved to the CPU first if necessary.
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pred_df_prob = DataFrame(probs.detach().tolist(), columns=class_names)
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pred_df = DataFrame(predicted_classes_str, columns=[dk.label_list[0]])
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pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
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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 encode_class_names(
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self,
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data_dictionary: Dict[str, pd.DataFrame],
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dk: FreqaiDataKitchen,
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class_names: List[str],
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):
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"""
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encode class name, str -> int
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assuming first column of *_labels data frame to be the target column
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containing the class names
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"""
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target_column_name = dk.label_list[0]
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for split in self.splits:
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label_df = data_dictionary[f"{split}_labels"]
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self.assert_valid_class_names(label_df[target_column_name], class_names)
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label_df[target_column_name] = list(
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map(lambda x: self.class_name_to_index[x], label_df[target_column_name])
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)
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@staticmethod
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def assert_valid_class_names(
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target_column: pd.Series,
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class_names: List[str]
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):
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non_defined_labels = set(target_column) - set(class_names)
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if len(non_defined_labels) != 0:
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raise OperationalException(
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f"Found non defined labels: {non_defined_labels}, ",
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f"expecting labels: {class_names}"
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)
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def decode_class_names(self, class_ints: torch.Tensor) -> List[str]:
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"""
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decode class name, int -> str
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"""
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return list(map(lambda x: self.index_to_class_name[x.item()], class_ints))
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def init_class_names_to_index_mapping(self, class_names):
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self.class_name_to_index = {s: i for i, s in enumerate(class_names)}
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self.index_to_class_name = {i: s for i, s in enumerate(class_names)}
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logger.info(f"encoded class name to index: {self.class_name_to_index}")
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def convert_label_column_to_int(
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self,
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data_dictionary: Dict[str, pd.DataFrame],
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dk: FreqaiDataKitchen,
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class_names: List[str]
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):
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self.init_class_names_to_index_mapping(class_names)
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self.encode_class_names(data_dictionary, dk, class_names)
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def get_class_names(self) -> List[str]:
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if not self.class_names:
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raise ValueError(
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"self.class_names is empty, "
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"set self.freqai.class_names = ['class a', 'class b', 'class c'] "
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"inside IStrategy.set_freqai_targets method."
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)
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return self.class_names
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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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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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(dd["train_features"],
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dd["train_labels"],
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dd["train_weights"]) = dk.feature_pipeline.fit_transform(dd["train_features"],
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dd["train_labels"],
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dd["train_weights"])
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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"],
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dd["test_labels"],
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dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],
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dd["test_labels"],
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dd["test_weights"])
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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(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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