568 lines
20 KiB
Python
568 lines
20 KiB
Python
#!/usr/bin/env python3
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"""
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CNN Model - PyTorch Implementation
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This module implements a CNN model using PyTorch for time series analysis.
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The model consists of multiple convolutional pathways and LSTM layers.
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"""
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import os
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import logging
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import numpy as np
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import matplotlib.pyplot as plt
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from datetime import datetime
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader, TensorDataset
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from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
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# Configure logging
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logger = logging.getLogger(__name__)
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class CNNPyTorch(nn.Module):
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"""PyTorch CNN model for time series analysis"""
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def __init__(self, input_shape, output_size=5):
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"""
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Initialize the enhanced CNN model.
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Args:
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input_shape (tuple): Shape of input data (window_size, features)
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output_size (int): Always 5 for our trading signals
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"""
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super(CNNPyTorch, self).__init__()
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window_size, num_features = input_shape
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kernel_size = 5
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dropout_rate = 0.3
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# Enhanced CNN Architecture
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self.conv_layers = nn.Sequential(
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# Block 1
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nn.Conv1d(num_features, 64, kernel_size, padding='same'),
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nn.BatchNorm1d(64),
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nn.ReLU(),
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# Block 2
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nn.Conv1d(64, 128, kernel_size, padding='same'),
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nn.BatchNorm1d(128),
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nn.ReLU(),
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nn.MaxPool1d(2),
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# Block 3
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nn.Conv1d(128, 256, kernel_size, padding='same'),
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nn.BatchNorm1d(256),
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nn.ReLU(),
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# Block 4
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nn.Conv1d(256, 512, kernel_size, padding='same'),
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nn.BatchNorm1d(512),
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nn.ReLU(),
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nn.MaxPool1d(2)
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)
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# Calculate flattened size after conv and pooling
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conv_output_size = 512 * (window_size // 4)
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# Enhanced dense layers
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self.dense_block = nn.Sequential(
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nn.Flatten(),
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nn.Linear(conv_output_size, 512),
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nn.BatchNorm1d(512),
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nn.ReLU(),
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nn.Dropout(dropout_rate),
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nn.Linear(512, 256),
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nn.BatchNorm1d(256),
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nn.ReLU(),
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nn.Dropout(dropout_rate),
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nn.Linear(256, 128),
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nn.BatchNorm1d(128),
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nn.ReLU(),
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nn.Linear(128, output_size)
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)
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# Activation based on output size
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if output_size == 1:
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self.activation = nn.Sigmoid() # Binary classification or regression
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elif output_size > 1:
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self.activation = nn.Softmax(dim=1) # Multi-class classification
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else:
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self.activation = nn.Identity() # No activation
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def forward(self, x):
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"""
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Forward pass through enhanced network.
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Args:
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x: Input tensor of shape [batch_size, window_size, features]
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Returns:
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Output tensor of shape [batch_size, output_size]
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"""
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# Transpose for conv1d: [batch, features, window]
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x_t = x.transpose(1, 2)
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# Process through all CNN layers
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conv_out = self.conv_layers(x_t)
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# Process through dense layers
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output = self.dense_block(conv_out)
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return self.activation(output)
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class CNNModelPyTorch:
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"""
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CNN model wrapper class for time series analysis using PyTorch.
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This class provides methods for building, training, evaluating, and making
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predictions with the CNN model.
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"""
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def __init__(self, window_size, num_features, output_size=5, timeframes=None):
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"""
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Initialize the CNN model.
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Args:
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window_size (int): Size of the input window
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num_features (int): Number of features in the input data
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output_size (int): Size of the output (1 for regression, 3 for classification)
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timeframes (list): List of timeframes used (for logging)
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"""
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self.window_size = window_size
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self.num_features = num_features
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self.output_size = output_size
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self.timeframes = timeframes or []
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# Determine device (GPU or CPU)
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self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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logger.info(f"Using device: {self.device}")
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# Initialize model
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self.model = None
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self.build_model()
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# Initialize training history
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self.history = {
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'loss': [],
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'val_loss': [],
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'accuracy': [],
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'val_accuracy': []
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}
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def build_model(self):
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"""Build the CNN model architecture"""
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logger.info(f"Building PyTorch CNN model with window_size={self.window_size}, "
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f"num_features={self.num_features}, output_size={self.output_size}")
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self.model = CNNPyTorch(
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input_shape=(self.window_size, self.num_features),
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output_size=self.output_size
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).to(self.device)
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# Initialize optimizer
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self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)
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# Initialize loss function based on output size
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if self.output_size == 1:
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self.criterion = nn.BCELoss() # Binary classification
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elif self.output_size > 1:
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self.criterion = nn.CrossEntropyLoss() # Multi-class classification
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else:
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self.criterion = nn.MSELoss() # Regression
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logger.info(f"Model built successfully with {sum(p.numel() for p in self.model.parameters())} parameters")
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def train_epoch(self, X_train, y_train, batch_size=32):
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"""Train for one epoch and return loss and accuracy"""
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# Convert to PyTorch tensors
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X_train_tensor = torch.tensor(X_train, dtype=torch.float32).to(self.device)
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if self.output_size == 1:
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y_train_tensor = torch.tensor(y_train, dtype=torch.float32).to(self.device)
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else:
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y_train_tensor = torch.tensor(y_train, dtype=torch.long).to(self.device)
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# Create DataLoader
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train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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self.model.train()
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running_loss = 0.0
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correct = 0
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total = 0
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for inputs, targets in train_loader:
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# Zero gradients
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self.optimizer.zero_grad()
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# Forward pass
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outputs = self.model(inputs)
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# Calculate loss
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if self.output_size == 1:
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loss = self.criterion(outputs, targets.unsqueeze(1))
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else:
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loss = self.criterion(outputs, targets)
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# Backward pass and optimize
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loss.backward()
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self.optimizer.step()
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# Statistics
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running_loss += loss.item()
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if self.output_size > 1:
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_, predicted = torch.max(outputs, 1)
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total += targets.size(0)
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correct += (predicted == targets).sum().item()
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epoch_loss = running_loss / len(train_loader)
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epoch_acc = correct / total if total > 0 else 0
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return epoch_loss, epoch_acc
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def evaluate(self, X_val, y_val):
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"""Evaluate on validation data and return loss and accuracy"""
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X_val_tensor = torch.tensor(X_val, dtype=torch.float32).to(self.device)
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if self.output_size == 1:
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y_val_tensor = torch.tensor(y_val, dtype=torch.float32).to(self.device)
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else:
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y_val_tensor = torch.tensor(y_val, dtype=torch.long).to(self.device)
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val_dataset = TensorDataset(X_val_tensor, y_val_tensor)
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val_loader = DataLoader(val_dataset, batch_size=32)
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self.model.eval()
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val_loss = 0.0
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correct = 0
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total = 0
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with torch.no_grad():
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for inputs, targets in val_loader:
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# Forward pass
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outputs = self.model(inputs)
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# Calculate loss
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if self.output_size == 1:
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loss = self.criterion(outputs, targets.unsqueeze(1))
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else:
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loss = self.criterion(outputs, targets)
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val_loss += loss.item()
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# Calculate accuracy
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if self.output_size > 1:
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_, predicted = torch.max(outputs, 1)
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total += targets.size(0)
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correct += (predicted == targets).sum().item()
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return val_loss / len(val_loader), correct / total if total > 0 else 0
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def predict(self, X):
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"""Make predictions on input data"""
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self.model.eval()
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X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
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with torch.no_grad():
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outputs = self.model(X_tensor)
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if self.output_size > 1:
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_, predicted = torch.max(outputs, 1)
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return predicted.cpu().numpy()
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else:
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return outputs.cpu().numpy()
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def predict_next_candles(self, X, n_candles=3):
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"""
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Predict the next n candles for each timeframe.
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Args:
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X: Input data of shape [batch_size, window_size, features]
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n_candles: Number of future candles to predict
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Returns:
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Dictionary of predictions for each timeframe
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"""
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self.model.eval()
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X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
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with torch.no_grad():
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# Get the last window of data
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last_window = X_tensor[-1:] # [1, window_size, features]
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# Initialize predictions
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predictions = {}
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# For each timeframe, predict next n candles
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for i, tf in enumerate(self.timeframes):
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# Extract features for this timeframe
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tf_features = last_window[:, :, i*5:(i+1)*5] # [1, window_size, 5]
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# Predict next n candles
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tf_predictions = []
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current_window = tf_features
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for _ in range(n_candles):
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# Get prediction for next candle
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output = self.model(current_window)
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tf_predictions.append(output.cpu().numpy())
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# Update window for next prediction
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current_window = torch.cat([
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current_window[:, 1:, :],
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output.unsqueeze(1)
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], dim=1)
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predictions[tf] = np.concatenate(tf_predictions, axis=0)
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return predictions
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def train(self, X_train, y_train, X_val=None, y_val=None, batch_size=32, epochs=100):
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"""
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Train the CNN model.
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Args:
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X_train: Training input data
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y_train: Training target data
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X_val: Validation input data
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y_val: Validation target data
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batch_size: Batch size for training
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epochs: Number of training epochs
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Returns:
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Training history
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"""
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logger.info(f"Training PyTorch CNN model with {len(X_train)} samples, "
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f"batch_size={batch_size}, epochs={epochs}")
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# Convert numpy arrays to PyTorch tensors
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X_train_tensor = torch.tensor(X_train, dtype=torch.float32).to(self.device)
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# Handle different output sizes for y_train
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if self.output_size == 1:
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y_train_tensor = torch.tensor(y_train, dtype=torch.float32).to(self.device)
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else:
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y_train_tensor = torch.tensor(y_train, dtype=torch.long).to(self.device)
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# Create DataLoader for training data
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train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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# Create DataLoader for validation data if provided
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if X_val is not None and y_val is not None:
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X_val_tensor = torch.tensor(X_val, dtype=torch.float32).to(self.device)
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if self.output_size == 1:
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y_val_tensor = torch.tensor(y_val, dtype=torch.float32).to(self.device)
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else:
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y_val_tensor = torch.tensor(y_val, dtype=torch.long).to(self.device)
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val_dataset = TensorDataset(X_val_tensor, y_val_tensor)
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val_loader = DataLoader(val_dataset, batch_size=batch_size)
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else:
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val_loader = None
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# Training loop
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for epoch in range(epochs):
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# Training phase
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self.model.train()
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running_loss = 0.0
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correct = 0
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total = 0
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for inputs, targets in train_loader:
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# Zero the parameter gradients
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self.optimizer.zero_grad()
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# Forward pass
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outputs = self.model(inputs)
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# Calculate loss
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if self.output_size == 1:
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loss = self.criterion(outputs, targets.unsqueeze(1))
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else:
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loss = self.criterion(outputs, targets)
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# Backward pass and optimize
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loss.backward()
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self.optimizer.step()
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# Statistics
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running_loss += loss.item()
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if self.output_size > 1:
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_, predicted = torch.max(outputs, 1)
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total += targets.size(0)
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correct += (predicted == targets).sum().item()
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epoch_loss = running_loss / len(train_loader)
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epoch_acc = correct / total if total > 0 else 0
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# Validation phase
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if val_loader is not None:
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val_loss, val_acc = self.evaluate(X_val, y_val)
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logger.info(f"Epoch {epoch+1}/{epochs} - "
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f"loss: {epoch_loss:.4f} - acc: {epoch_acc:.4f} - "
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f"val_loss: {val_loss:.4f} - val_acc: {val_acc:.4f}")
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# Update history
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self.history['loss'].append(epoch_loss)
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self.history['accuracy'].append(epoch_acc)
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self.history['val_loss'].append(val_loss)
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self.history['val_accuracy'].append(val_acc)
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else:
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logger.info(f"Epoch {epoch+1}/{epochs} - "
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f"loss: {epoch_loss:.4f} - acc: {epoch_acc:.4f}")
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# Update history without validation
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self.history['loss'].append(epoch_loss)
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self.history['accuracy'].append(epoch_acc)
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logger.info("Training completed")
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return self.history
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def evaluate_metrics(self, X_test, y_test):
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"""
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Calculate and return comprehensive evaluation metrics as dict
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"""
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X_test_tensor = torch.tensor(X_test, dtype=torch.float32).to(self.device)
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self.model.eval()
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with torch.no_grad():
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y_pred = self.model(X_test_tensor)
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if self.output_size > 1:
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_, y_pred_class = torch.max(y_pred, 1)
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y_pred_class = y_pred_class.cpu().numpy()
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else:
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y_pred_class = (y_pred.cpu().numpy() > 0.5).astype(int).flatten()
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metrics = {
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'accuracy': accuracy_score(y_test, y_pred_class),
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'precision': precision_score(y_test, y_pred_class, average='weighted', zero_division=0),
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'recall': recall_score(y_test, y_pred_class, average='weighted', zero_division=0),
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'f1_score': f1_score(y_test, y_pred_class, average='weighted', zero_division=0)
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}
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return metrics
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def save(self, filepath):
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"""
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Save the model to a file.
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Args:
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filepath: Path to save the model
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"""
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# Create directory if it doesn't exist
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os.makedirs(os.path.dirname(filepath), exist_ok=True)
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# Save the model state
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model_state = {
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'model_state_dict': self.model.state_dict(),
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'optimizer_state_dict': self.optimizer.state_dict(),
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'history': self.history,
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'window_size': self.window_size,
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'num_features': self.num_features,
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'output_size': self.output_size,
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'timeframes': self.timeframes
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}
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torch.save(model_state, f"{filepath}.pt")
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logger.info(f"Model saved to {filepath}.pt")
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def load(self, filepath):
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"""
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Load the model from a file.
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Args:
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filepath: Path to load the model from
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"""
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# Check if file exists
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if not os.path.exists(f"{filepath}.pt"):
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logger.error(f"Model file {filepath}.pt not found")
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return False
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# Load the model state
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model_state = torch.load(f"{filepath}.pt", map_location=self.device)
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# Update model parameters
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self.window_size = model_state['window_size']
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self.num_features = model_state['num_features']
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self.output_size = model_state['output_size']
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self.timeframes = model_state['timeframes']
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# Rebuild the model
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self.build_model()
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# Load the model state
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self.model.load_state_dict(model_state['model_state_dict'])
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self.optimizer.load_state_dict(model_state['optimizer_state_dict'])
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self.history = model_state['history']
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logger.info(f"Model loaded from {filepath}.pt")
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return True
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def plot_training_history(self):
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"""Plot the training history"""
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if not self.history['loss']:
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logger.warning("No training history to plot")
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return
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plt.figure(figsize=(12, 4))
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# Plot loss
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plt.subplot(1, 2, 1)
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plt.plot(self.history['loss'], label='Training Loss')
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if 'val_loss' in self.history and self.history['val_loss']:
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plt.plot(self.history['val_loss'], label='Validation Loss')
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plt.title('Model Loss')
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plt.ylabel('Loss')
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plt.xlabel('Epoch')
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plt.legend()
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# Plot accuracy
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plt.subplot(1, 2, 2)
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plt.plot(self.history['accuracy'], label='Training Accuracy')
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if 'val_accuracy' in self.history and self.history['val_accuracy']:
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plt.plot(self.history['val_accuracy'], label='Validation Accuracy')
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plt.title('Model Accuracy')
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|
plt.ylabel('Accuracy')
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|
plt.xlabel('Epoch')
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|
plt.legend()
|
|
|
|
# Save the plot
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|
os.makedirs('plots', exist_ok=True)
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|
plt.savefig(os.path.join('plots', f"cnn_history_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png"))
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|
plt.close()
|
|
|
|
logger.info("Training history plots saved to plots directory")
|
|
|
|
def extract_hidden_features(self, X):
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|
"""
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|
Extract hidden features from the model - outputs from last dense layer before output.
|
|
|
|
Args:
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|
X: Input data
|
|
|
|
Returns:
|
|
Hidden features (output from penultimate dense layer)
|
|
"""
|
|
# Convert to PyTorch tensor
|
|
X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
|
|
|
|
# Forward pass through the model
|
|
self.model.eval()
|
|
with torch.no_grad():
|
|
# Get features through CNN layers
|
|
x_t = X_tensor.transpose(1, 2)
|
|
conv_out = self.model.conv_layers(x_t)
|
|
|
|
# Process through all dense layers except the output layer
|
|
features = conv_out
|
|
for layer in self.model.dense_block[:-2]: # Exclude last linear layer and dropout
|
|
features = layer(features)
|
|
|
|
return features.cpu().numpy()
|