98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
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import logging
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from typing import Dict
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from typing import Any, Dict, Tuple
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import numpy.typing as npt
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import numpy as np
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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.freqai.base_models.BasePytorchModel import BasePytorchModel
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from freqtrade.freqai.base_models.PytorchModelTrainer import PytorchModelTrainer
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.prediction_models.PytorchMLPModel import MLP
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logger = logging.getLogger(__name__)
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class PytorchClassifierMultiTarget(BasePytorchModel):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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# todo move to config
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self.n_hidden = 1024
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self.labels = ['0.0', '1.0', '2.0']
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self.max_iters = 100
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self.batch_size = 64
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self.learning_rate = 3e-4
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param tensor_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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n_features = data_dictionary['train_features'].shape[-1]
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tensor_dictionary = self.convert_data_to_tensors(data_dictionary)
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model = MLP(
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input_dim=n_features,
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hidden_dim=self.n_hidden,
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output_dim=len(self.labels)
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)
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model.to(self.device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
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init_model = self.get_init_model(dk.pair)
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trainer = PytorchModelTrainer(model, optimizer, init_model=init_model)
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trainer.fit(tensor_dictionary, self.max_iters, self.batch_size)
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return trainer
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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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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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dk.data_dictionary["prediction_features"] = torch.tensor(
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dk.data_dictionary["prediction_features"].values
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).to(self.device)
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logits, _ = self.model.model(dk.data_dictionary["prediction_features"])
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probs = F.softmax(logits, dim=-1)
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label_ints = torch.argmax(probs, dim=-1)
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pred_df_prob = DataFrame(probs.detach().numpy(), columns=self.labels)
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pred_df = DataFrame(label_ints, columns=dk.label_list).astype(float).astype(str)
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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 convert_data_to_tensors(self, data_dictionary: Dict) -> Dict:
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tensor_dictionary = {}
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for split in ['train', 'test']:
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tensor_dictionary[f'{split}_features'] = torch.tensor(
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data_dictionary[f'{split}_features'].values
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).to(self.device)
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tensor_dictionary[f'{split}_labels'] = torch.tensor(
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data_dictionary[f'{split}_labels'].astype(float).values
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).long().to(self.device)
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return tensor_dictionary
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