import torch
import torch.nn as nn
from einops import rearrange
from finol.data_layer.scaler_selector import ScalerSelector
from finol.utils import load_config
[文件]class RNN(nn.Module):
"""
Class to generate predicted scores for the input assets based on the Recurrent Neural Network (RNN) model.
The RNN model takes an input tensor ``x`` of shape ``(batch_size, num_assets, num_features_augmented)``,
where ``num_features_augmented`` represents the number of features (including any preprocessed or augmented
features) for each asset.
The final output of the model is a tensor of shape ``(batch_size, num_assets)``, where each element
represents the predicted score for the corresponding asset.
:param model_args: Dictionary containing model arguments, such as the number of features.
:param model_params: Dictionary containing model hyperparameters, such as the number of layers, the hidden size, and the dropout rate.
Example:
.. code:: python
>>> from finol.data_layer.dataset_loader import DatasetLoader
>>> from finol.model_layer.model_instantiator import ModelInstantiator
>>> from finol.utils import load_config, update_config, portfolio_selection
>>>
>>> # Configuration
>>> config = load_config()
>>> config["MODEL_NAME"] = "RNN"
>>> config["MODEL_PARAMS"]["RNN"]["NUM_LAYERS"] = 1
>>> config["MODEL_PARAMS"]["RNN"]["HIDDEN_SIZE"] = 64
>>> config["MODEL_PARAMS"]["RNN"]["DROPOUT"] = 0.1
>>> update_config(config)
>>>
>>> # Data Layer
>>> load_dataset_output = DatasetLoader().load_dataset()
>>>
>>> # Model Layer & Optimization Layer
>>> ...
>>> model = ModelInstantiator(load_dataset_output).instantiate_model()
>>> print(f"model: {model}")
>>> ...
>>> train_loader = load_dataset_output["train_loader"]
>>> for i, data in enumerate(train_loader, 1):
... x_data, label = data
... final_scores = model(x_data.float())
... portfolio = portfolio_selection(final_scores)
... print(f"batch {i} input shape: {x_data.shape}")
... print(f"batch {i} label shape: {label.shape}")
... print(f"batch {i} output shape: {portfolio.shape}")
... print("-"*50)
.. note::
Users can refer to this implementation and use it as a starting point for developing their own advanced RNN-based models.
\\
"""
def __init__(self, model_args, model_params):
super().__init__()
self.config = load_config()
self.model_args = model_args
self.model_params = model_params
self.rnn = nn.RNN(
input_size=model_args["num_features_original"],
hidden_size=model_params["HIDDEN_SIZE"],
num_layers=model_params["NUM_LAYERS"],
batch_first=True
)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(model_params["DROPOUT"])
self.fc = nn.Linear(model_params["HIDDEN_SIZE"], 1)
[文件] def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass of the model.
:param x: Input tensor of shape ``(batch_size, num_assets, num_features_augmented)``.
:return: Output tensor of shape ``(batch_size, num_assets)`` containing the predicted scores for each asset.
"""
batch_size, num_assets, num_features_augmented = x.shape
"""Input Transformation"""
x = x.view(batch_size, num_assets, self.model_args["window_size"], self.model_args["num_features_original"])
x = rearrange(x, "b m n d -> (b m) n d")
if self.config["SCALER"].startswith("Window"):
x = ScalerSelector().window_normalize(x)
"""Temporal Representation Extraction"""
out, _ = self.rnn(x)
out = self.dropout(out)
out = out[:, -1, :]
"""Final Scores for Assets"""
out = out.view(batch_size, num_assets, self.model_params["HIDDEN_SIZE"])
final_scores = self.fc(out).squeeze(-1)
return final_scores
