144 lines
5.4 KiB
Python
144 lines
5.4 KiB
Python
import logging
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from typing import 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 PyTorchClassifier(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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"""
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def __init__(self, **kwargs):
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"""
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int: The number of nodes in the hidden layer of the neural network.
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int: The maximum number of iterations to run during training.
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int: The batch size to use during training.
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float: The learning rate to use during training.
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int: The number of training iterations between each evaluation.
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dict: A dictionary mapping class names to their corresponding indices.
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dict: A dictionary mapping indices to their corresponding class names.
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"""
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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 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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filtered_df = dk.normalize_data_from_metadata(filtered_df)
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dk.data_dictionary["prediction_features"] = filtered_df
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self.data_cleaning_predict(dk)
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x = torch.from_numpy(dk.data_dictionary["prediction_features"].values)\
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.float()\
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.to(self.device)
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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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pred_df_prob = DataFrame(probs.detach().numpy(), 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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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 ["train", "test"]:
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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 hasattr(self, "class_names"):
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raise ValueError(
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"Missing attribute: self.class_names "
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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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