freqtrade_origin/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py

import logging
from typing import Any, Dict, List, Optional, Tuple

import numpy as np
import numpy.typing as npt
import pandas as pd
import torch
from pandas import DataFrame
from torch.nn import functional as F

from freqtrade.exceptions import OperationalException
from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
from freqtrade.freqai.base_models.PyTorchModelTrainer import PyTorchModelTrainer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.PyTorchMLPModel import PyTorchMLPModel


logger = logging.getLogger(__name__)


class PyTorchClassifierMultiTarget(BasePyTorchModel):
    """
    A PyTorch implementation of a multi-target classifier.
    """
    def __init__(self, **kwargs):
        """
        int: The number of nodes in the hidden layer of the neural network.
        int: The maximum number of iterations to run during training.
        int: The batch size to use during training.
        float: The learning rate to use during training.
        int: The number of training iterations between each evaluation.
        dict: A dictionary mapping class names to their corresponding indices.
        dict: A dictionary mapping indices to their corresponding class names.
        """

        super().__init__(**kwargs)
        trainer_kwargs = self.freqai_info.get("trainer_kwargs", {})
        self.n_hidden: int = trainer_kwargs.get("n_hidden", 1024)
        self.max_iters: int = trainer_kwargs.get("max_iters", 100)
        self.batch_size: int = trainer_kwargs.get("batch_size", 64)
        self.learning_rate: float = trainer_kwargs.get("learning_rate", 3e-4)
        self.max_n_eval_batches: Optional[int] = trainer_kwargs.get("max_n_eval_batches", None)
        self.model_kwargs: Dict = trainer_kwargs.get("model_kwargs", {})
        self.class_name_to_index = None
        self.index_to_class_name = None

    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
        """
        User sets up the training and test data to fit their desired model here
        :param data_dictionary: the dictionary constructed by DataHandler to hold
                                all the training and test data/labels.
        :raises ValueError: If self.class_names is not defined in the parent class.

        """

        if not hasattr(self, "class_names"):
            raise ValueError(
                "Missing attribute: self.class_names "
                "set self.freqai.class_names = [\"class a\", \"class b\", \"class c\"] "
                "inside IStrategy.set_freqai_targets method."
            )

        self.init_class_names_to_index_mapping(self.class_names)
        self.encode_classes_name(data_dictionary, dk)
        n_features = data_dictionary["train_features"].shape[-1]
        model = PyTorchMLPModel(
            input_dim=n_features,
            output_dim=len(self.class_names),
            **self.model_kwargs
        )
        model.to(self.device)
        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
        criterion = torch.nn.CrossEntropyLoss()
        init_model = self.get_init_model(dk.pair)
        trainer = PyTorchModelTrainer(
            model=model,
            optimizer=optimizer,
            criterion=criterion,
            model_meta_data={"class_names": self.class_names},
            device=self.device,
            batch_size=self.batch_size,
            max_iters=self.max_iters,
            max_n_eval_batches=self.max_n_eval_batches,
            init_model=init_model
        )
        trainer.fit(data_dictionary)
        return trainer

    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)
        :raises ValueError: if 'class_name' doesn't exist in model meta_data.
        """

        class_names = self.model.model_meta_data.get("class_names", None)
        if not class_names:
            raise ValueError(
                "Missing class names. "
                "self.model.model_meta_data[\"class_names\"] is None."
            )
        self.init_class_names_to_index_mapping(class_names)

        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)
        dk.data_dictionary["prediction_features"] = torch.tensor(
            dk.data_dictionary["prediction_features"].values
        ).float().to(self.device)

        logits = self.model.model(dk.data_dictionary["prediction_features"])
        probs = F.softmax(logits, dim=-1)
        predicted_classes = torch.argmax(probs, dim=-1)
        predicted_classes_str = self.decode_classes_name(predicted_classes)
        pred_df_prob = DataFrame(probs.detach().numpy(), columns=class_names)
        pred_df = DataFrame(predicted_classes_str, columns=[dk.label_list[0]])
        pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
        return (pred_df, dk.do_predict)

    def encode_classes_name(self, data_dictionary: Dict[str, pd.DataFrame], dk: FreqaiDataKitchen):
        """
        encode class name str -> int
        assuming first column of *_labels data frame to contain class names
        """

        target_column_name = dk.label_list[0]
        for split in ["train", "test"]:
            label_df = data_dictionary[f"{split}_labels"]
            self.assert_valid_class_names(label_df[target_column_name])
            label_df[target_column_name] = list(
                map(lambda x: self.class_name_to_index[x], label_df[target_column_name])
            )

    def assert_valid_class_names(self, labels: pd.Series):
        non_defined_labels = set(labels) - set(self.class_names)
        if len(non_defined_labels) != 0:
            raise OperationalException(
                f"Found non defined labels: {non_defined_labels}, ",
                f"expecting labels: {self.class_names}"
            )

    def decode_classes_name(self, classes: torch.Tensor) -> List[str]:
        """
        decode class name int -> str
        """

        return list(map(lambda x: self.index_to_class_name[x.item()], classes))

    def init_class_names_to_index_mapping(self, class_names):
        self.class_name_to_index = {s: i for i, s in enumerate(class_names)}
        self.index_to_class_name = {i: s for i, s in enumerate(class_names)}
        logger.info(f"class_name_to_index: {self.class_name_to_index}")