mlp_model.py
import copy
from collections import defaultdict
from typing import Literal, cast
import numpy as np
import pandas as pd
import polars as pl
from sklearn.metrics import mean_squared_error # type: ignore
import torch
import torch.nn as nn
import torch.optim as optim
from vnpy.alpha import (
AlphaDataset,
AlphaModel,
Segment,
logger
)
class MlpModel(AlphaModel):
"""
Multi-Layer Perceptron Model
Alpha factor prediction model implemented using multi-layer perceptron, with main features including:
1. Building and training multi-layer perceptron neural networks
2. Predicting Alpha factor values
3. Model evaluation and feature importance analysis
4. Support for early stopping and overfitting prevention
5. Support for MSE loss function
6. Optional Adam or SGD optimizer
"""
def __init__(
self,
input_size: int,
hidden_sizes: tuple[int] = (256,),
lr: float = 0.001,
n_epochs: int = 300,
batch_size: int = 2000,
early_stop_rounds: int = 50,
eval_steps: int = 20,
optimizer: Literal["sgd", "adam"] = "adam",
weight_decay: float = 0.0,
device: str = "cpu",
seed: int | None = None
) -> None:
"""
Initialize MLP model
Parameters
----------
input_size : int, default 360
Input feature dimension
hidden_sizes : tuple[int], default (256,)
Number of neurons in hidden layers
lr : float, default 0.001
Learning rate
n_epochs : int, default 300
Maximum training steps
batch_size : int, default 2000
Number of samples per batch
early_stop_rounds : int, default 50
Early stopping rounds, training stops if validation loss doesn't improve within these rounds
eval_steps : int, default 20
Evaluate model every this many steps
optimizer : Literal["sgd", "adam"], default "adam"
Optimizer type, options are "sgd" or "adam"
weight_decay : float, default 0.0
L2 regularization coefficient
seed : Optional[int], optional
Random seed for reproducibility
device : str, default "cpu"
Training device
"""
# Save model hyperparameters
self.input_size: int = input_size
self.hidden_sizes: tuple[int] = hidden_sizes
self.lr: float = lr
self.n_epochs: int = n_epochs
self.batch_size: int = batch_size
self.early_stop_rounds: int = early_stop_rounds
self.eval_steps: int = eval_steps
self.device: str = device
self.fitted: bool = False
self.feature_names: list[str] = []
self.best_step: int | None = None
# Set random seed for reproducibility
if seed is not None:
np.random.seed(seed)
torch.manual_seed(seed)
# Set loss function type
self._scorer = mean_squared_error
# Initialize model
self.model: nn.Module = MlpNetwork(
input_size=input_size,
hidden_sizes=hidden_sizes,
)
# Move model to specified device
self.model = self.model.to(device)
# Set optimizer
optimizer_name = optimizer.lower()
if optimizer_name == "adam":
self.optimizer: optim.Optimizer = optim.Adam(
self.model.parameters(),
lr=lr,
weight_decay=weight_decay
)
elif optimizer_name == "sgd":
self.optimizer = optim.SGD(
self.model.parameters(),
lr=lr,
weight_decay=weight_decay
)
else:
raise NotImplementedError(f"optimizer {optimizer} is not supported!")
# Set learning rate scheduler
self.scheduler: optim.lr_scheduler.ReduceLROnPlateau = optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer,
mode="min",
factor=0.5,
patience=10,
threshold=0.0001,
threshold_mode="rel",
cooldown=0,
min_lr=0.00001,
eps=1e-08,
)
def fit(
self,
dataset: AlphaDataset,
evaluation_results: dict | None = None,
) -> None:
"""
Train the multi-layer perceptron model
Trains the MLP model using the given dataset, with main steps including:
1. Preparing training and validation data
2. Iteratively training for multiple steps
3. Evaluating model performance at fixed intervals
4. Implementing early stopping to prevent overfitting
Parameters
----------
dataset : AlphaDataset
Dataset object containing training data
evaluation_results : dict
Dictionary for storing evaluation metrics during training
"""
# Initialize a new dictionary if evaluation_results is None
if evaluation_results is None:
evaluation_results = {}
# Dictionary to store training and validation data
train_valid_data: dict[str, dict] = defaultdict(dict)
# Process training and validation sets separately
for segment in [Segment.TRAIN, Segment.VALID]:
# Get learning data and sort by time and trading code
df: pl.DataFrame = dataset.fetch_learn(segment)
df = df.sort(["datetime", "vt_symbol"])
# Extract features and labels
features = df.select(df.columns[2: -1]).to_numpy()
labels = np.array(df["label"])
# Store feature and label data
train_valid_data["x"][segment] = torch.from_numpy(features).float().to(self.device)
train_valid_data["y"][segment] = torch.from_numpy(labels).float().to(self.device)
# Initialize evaluation results list
evaluation_results[segment] = []
# Get feature names
df = dataset.fetch_learn(Segment.TRAIN)
self.feature_names = df.columns[2:-1]
# Initialize training state
early_stop_count: int = 0 # Number of steps without performance improvement
train_loss: float = 0 # Current training loss
best_valid_score: float = np.inf # Best validation loss
best_params = None # Best model parameters
train_samples: int = train_valid_data["y"][Segment.TRAIN].shape[0]
# Iterate through training steps
for step in range(1, self.n_epochs + 1):
# Check if early stopping condition is met
if early_stop_count >= self.early_stop_rounds:
logger.info("达到早停条件,训练结束")
break
# Train one batch
batch_loss = self._train_step(train_valid_data, train_samples)
train_loss += batch_loss
# Periodically evaluate the model
if step % self.eval_steps == 0 or step == self.n_epochs:
early_stop_count, best_valid_score, best_params = self._evaluate_step(
train_valid_data,
evaluation_results,
step,
train_loss,
early_stop_count,
best_valid_score
)
train_loss = 0
# Mark model as trained
self.fitted = True
# Load best model parameters
if best_params:
self.model.load_state_dict(best_params)
def _train_step(
self,
train_valid_data: dict[str, dict[Segment, torch.Tensor]],
train_samples: int
) -> float:
"""
Execute one training step
Parameters
----------
train_valid_data : dict
Training and validation data
train_samples : int
Number of training samples
Returns
-------
float
Current batch loss value
"""
batch_loss = AverageMeter()
self.model.train()
self.optimizer.zero_grad()
# Randomly select batch data
batch_indices = np.random.choice(train_samples, self.batch_size)
batch_features = train_valid_data["x"][Segment.TRAIN][batch_indices]
batch_labels = train_valid_data["y"][Segment.TRAIN][batch_indices]
# Forward and backward propagation
predictions = self.model(batch_features)
cur_loss = self._loss_fn(predictions, batch_labels)
cur_loss.backward()
# Update model parameters
self.optimizer.step()
batch_loss.update(cur_loss.item())
return batch_loss.val
def _evaluate_step(
self,
train_valid_data: dict[str, dict[Segment, torch.Tensor]],
evaluation_results: dict[Segment, list[float]],
step: int,
train_loss: float,
early_stop_count: int,
best_valid_score: float
) -> tuple[int, float, dict[str, torch.Tensor] | None]:
"""
Evaluate current model performance
Parameters
----------
train_valid_data : dict
Training and validation data
evaluation_results : dict
Evaluation results record
step : int
Current training step
train_loss : float
Current training loss
early_stop_count : int
Count of steps without improvement
best_valid_score : float
Best validation loss
Returns
-------
tuple[int, float, dict] | None
Returns updated early stop count, best validation loss, and best model parameters
"""
early_stop_count += 1
train_loss /= self.eval_steps
# Evaluate model on validation set
with torch.no_grad():
self.model.eval()
data: torch.Tensor = train_valid_data["x"][Segment.VALID]
pred: torch.Tensor = cast(torch.Tensor, self._predict_batch(data, return_cpu=False))
valid_loss = self._loss_fn(pred, train_valid_data["y"][Segment.VALID])
loss_val = valid_loss.item()
# Record evaluation results
logger.info(f"[Step {step}]: train_loss {train_loss:.6f}, valid_loss {loss_val:.6f}")
evaluation_results[Segment.TRAIN].append(train_loss)
evaluation_results[Segment.VALID].append(loss_val)
# Update best model if validation performance improves
best_params = None
if loss_val < best_valid_score:
logger.info(f"\t验证集损失从 {best_valid_score:.6f} 降低到 {loss_val:.6f}")
best_valid_score = loss_val
self.best_step = step
early_stop_count = 0
best_params = copy.deepcopy(self.model.state_dict())
# Update learning rate
if self.scheduler is not None:
self.scheduler.step(metrics=valid_loss, epoch=step)
return early_stop_count, best_valid_score, best_params
def _loss_fn(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Calculate loss value
Parameters
----------
pred : torch.Tensor
Model predictions
target : torch.Tensor
Target true values
Returns
-------
torch.Tensor
Calculated loss value
"""
pred, target = pred.reshape(-1), target.reshape(-1)
loss: torch.Tensor = nn.MSELoss()(pred, target)
return loss
def _predict_batch(self, data: torch.Tensor, return_cpu: bool = True) -> np.ndarray | torch.Tensor:
"""
Neural network prediction function
Parameters
----------
data : torch.Tensor
Input data
return_cpu : bool, default True
Whether to return CPU tensor
step : Optional[int], optional
Current training step
Returns
-------
np.ndarray | torch.Tensor
Model prediction results
"""
data = data.to(self.device)
predictions: list[torch.Tensor] = []
self.model.eval()
with torch.no_grad():
batch_size: int = 8096
for i in range(0, len(data), batch_size):
x: torch.Tensor = data[i: i + batch_size]
predictions.append(self.model(x.to(self.device)).detach().reshape(-1))
if return_cpu:
return cast(np.ndarray, np.concatenate([pr.cpu().numpy() for pr in predictions]))
else:
return torch.cat(predictions, dim=0)
def predict(self, dataset: AlphaDataset, segment: Segment) -> np.ndarray:
"""
Model prediction interface
Parameters
----------
dataset : AlphaDataset
Prediction dataset
segment : Segment
Dataset segment
Returns
-------
np.ndarray
Prediction result array
"""
if not self.fitted:
raise ValueError("Model has not been trained yet!")
df: pl.DataFrame = dataset.fetch_infer(segment)
df = df.sort(["datetime", "vt_symbol"])
data: np.ndarray = df.select(df.columns[2: -1]).to_numpy()
return cast(np.ndarray, self._predict_batch(torch.Tensor(data)))
def _check_tensor_nan(self, tensor: torch.Tensor, name: str) -> None:
"""
Check if tensor contains NaN values
Parameters
----------
tensor : torch.Tensor
Tensor to check
name : str
Tensor name
Returns
-------
None
"""
if torch.isnan(tensor).any():
print(f"NaN values detected: {name}")
def detail(self) -> pd.DataFrame | None:
"""
Output MLP model detail information
Returns
-------
pd.DataFrame
Feature importance dataframe
"""
if not self.fitted:
logger.info("模型尚未训练，无法显示详细信息")
return None
# 显示模型基本信息
logger.info(f"输入特征维度: {self.input_size}")
logger.info(f"隐藏层大小: {self.hidden_sizes}")
# 计算模型总参数量
total_params = sum(p.numel() for p in self.model.parameters())
logger.info(f"模型总参数量: {total_params:,}")
# 显示训练状态信息
logger.info(f"训练设备: {self.device}")
logger.info(f"当前学习率: {self.lr}")
logger.info(f"批次大小: {self.batch_size}")
# Calculate feature importance
importance_df = self._calculate_feature_importance()
return importance_df
def _calculate_feature_importance(self) -> pd.DataFrame:
"""
Calculate feature importance
Returns
-------
pd.DataFrame
Feature importance dataframe
"""
self.model.eval()
importance_dict = {}
test_data = torch.randn(1000, self.input_size).to(self.device)
base_pred = self.model(test_data).detach()
noise_level = 0.1
for i, feature_name in enumerate(self.feature_names):
perturbed_data = test_data.clone()
perturbed_data[:, i] += torch.randn(1000).to(self.device) * noise_level
with torch.no_grad():
new_pred = self.model(perturbed_data)
importance = torch.std(torch.abs(new_pred - base_pred)).item()
importance_dict[feature_name] = importance
df = pd.DataFrame({
'Feature': list(importance_dict.keys()),
'Importance': list(importance_dict.values())
})
df = df.sort_values('Importance', ascending=False)
df = df.set_index('Feature')
return df
class AverageMeter:
"""
Class for calculating and storing average and current values
Attributes
----------
val : float
Current value
avg : float
Average value
sum : float
Sum
count : int
Count
"""
def __init__(self) -> None:
"""
Initialize AverageMeter
Returns
-------
None
"""
self.reset()
def reset(self) -> None:
"""
Reset all statistics
Returns
-------
None
"""
self.val: float = 0
self.avg: float = 0
self.sum: float = 0
self.count: int = 0
def update(self, val: float, n: int = 1) -> None:
"""
Update statistics
Parameters
----------
val : float
Current value
n : int, default 1
Current batch size
Returns
-------
None
"""
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class MlpNetwork(nn.Module):
"""
Deep Neural Network Model Structure
Used to build multi-layer perceptron network structure, supporting multiple hidden layers
and different activation functions.
Attributes
----------
network : nn.ModuleList
List of neural network layers
"""
def __init__(
self,
input_size: int,
output_size: int = 1,
hidden_sizes: tuple[int] = (256,),
activation: str = "LeakyReLU"
) -> None:
"""
Constructor
Parameters
----------
input_size : int
Input feature dimension, i.e., number of features per sample
output_size : int, default 1
Output dimension, used for predicting target values
hidden_sizes : tuple[int], default (256,)
Tuple of hidden layer neuron counts, e.g., (256, 128) represents two hidden layers
with 256 and 128 neurons respectively
activation : str, default "LeakyReLU"
Activation function type, options:
- "LeakyReLU": Leaky ReLU function
- "SiLU": Sigmoid Linear Unit function
"""
super().__init__()
# Build network layers
layers: list[nn.Module] = []
layer_sizes = [input_size] + list(hidden_sizes)
# Input layer Dropout
layers.append(nn.Dropout(0.05))
# Build hidden layers
for in_size, out_size in zip(layer_sizes[:-1], layer_sizes[1:], strict=False):
# Add a neural network block: linear layer + batch normalization + activation function
layers.extend([
nn.Linear(in_size, out_size),
nn.BatchNorm1d(out_size),
self._get_activation(activation)
])
# Output layer
layers.extend([
nn.Dropout(0.05),
nn.Linear(hidden_sizes[-1], output_size)
])
# Combine all layers into a sequence
self.network = nn.ModuleList(layers)
# Initialize network weights
self._initialize_weights()
def _get_activation(self, name: str) -> nn.Module:
"""
Get specified activation function layer
Parameters
----------
name : str
Activation function name
Returns
-------
nn.Module
Activation function layer instance
Raises
------
ValueError
When an unsupported activation function type is specified
"""
if name == "LeakyReLU":
return nn.LeakyReLU(negative_slope=0.1)
elif name == "SiLU":
return nn.SiLU()
else:
raise ValueError(f"Unsupported activation function type: {name}")
def _initialize_weights(self) -> None:
"""
Initialize network weight parameters
Uses Kaiming initialization method for all linear layers, which is particularly
suitable for deep networks using LeakyReLU activation functions.
Returns
-------
None
"""
for module in self.modules():
if isinstance(module, nn.Linear):
nn.init.kaiming_normal_(
module.weight,
a=0.1, # LeakyReLU negative slope
mode="fan_in", # Scale using input node count
nonlinearity="leaky_relu"
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward propagation calculation
Parameters
----------
x : torch.Tensor
Input feature tensor, shape (batch_size, input_size)
Returns
-------
torch.Tensor
Model output tensor, shape (batch_size, output_size)
"""
# Pass through all layers in the network sequentially
for layer in self.network:
x = layer(x)
return x