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"""
Anomaly Detection Model using LSTM Neural Network
"""
import torch
import torch.nn as nn
import numpy as np
from pathlib import Path
import pickle
class LSTMAnomalyDetector(nn.Module):
"""
LSTM-based anomaly detection model for time-series sensor data
"""
def __init__(self, input_size, hidden_size=64, num_layers=2, dropout=0.2):
super(LSTMAnomalyDetector, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
# LSTM layers
self.lstm = nn.LSTM(
input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0
)
# Fully connected layers
self.fc1 = nn.Linear(hidden_size, 32)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(dropout)
self.fc2 = nn.Linear(32, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
# LSTM forward pass
lstm_out, _ = self.lstm(x)
# Take the last output
last_output = lstm_out[:, -1, :]
# Fully connected layers
out = self.fc1(last_output)
out = self.relu(out)
out = self.dropout(out)
out = self.fc2(out)
out = self.sigmoid(out)
return out
class AnomalyDetectionModel:
"""
Wrapper class for anomaly detection model with training and inference
"""
def __init__(self, input_size, sequence_length=50, device=None):
self.input_size = input_size
self.sequence_length = sequence_length
self.device = device or torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model = LSTMAnomalyDetector(input_size).to(self.device)
self.criterion = nn.BCELoss()
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=0.001)
print(f"Initialized Anomaly Detection Model on {self.device}")
def create_sequences(self, data, labels=None):
"""
Create sequences for LSTM input
Args:
data: numpy array of shape (n_samples, n_features)
labels: optional numpy array of labels
Returns:
Sequences and labels (if provided)
"""
sequences = []
seq_labels = []
for i in range(len(data) - self.sequence_length + 1):
seq = data[i:i + self.sequence_length]
sequences.append(seq)
if labels is not None:
# Label is 1 if any point in sequence is anomalous
label = labels[i + self.sequence_length - 1]
seq_labels.append(label)
sequences = np.array(sequences)
if labels is not None:
seq_labels = np.array(seq_labels)
return sequences, seq_labels
return sequences
def train_epoch(self, train_loader):
"""Train for one epoch"""
self.model.train()
total_loss = 0
for batch_x, batch_y in train_loader:
batch_x = batch_x.to(self.device)
batch_y = batch_y.to(self.device)
# Forward pass
outputs = self.model(batch_x)
loss = self.criterion(outputs.squeeze(), batch_y.float())
# Backward pass
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
total_loss += loss.item()
return total_loss / len(train_loader)
def evaluate(self, val_loader):
"""Evaluate on validation set"""
self.model.eval()
total_loss = 0
all_preds = []
all_labels = []
with torch.no_grad():
for batch_x, batch_y in val_loader:
batch_x = batch_x.to(self.device)
batch_y = batch_y.to(self.device)
outputs = self.model(batch_x)
loss = self.criterion(outputs.squeeze(), batch_y.float())
total_loss += loss.item()
preds = (outputs.squeeze() > 0.5).cpu().numpy()
all_preds.extend(preds)
all_labels.extend(batch_y.cpu().numpy())
avg_loss = total_loss / len(val_loader)
# Calculate metrics
all_preds = np.array(all_preds)
all_labels = np.array(all_labels)
accuracy = (all_preds == all_labels).mean()
return avg_loss, accuracy
def predict(self, data):
"""
Predict anomalies for given data
Args:
data: numpy array of shape (n_samples, n_features)
Returns:
Anomaly scores and binary predictions
"""
self.model.eval()
# Create sequences
sequences = self.create_sequences(data)
# Convert to tensor
sequences_tensor = torch.FloatTensor(sequences).to(self.device)
# Predict
with torch.no_grad():
scores = self.model(sequences_tensor).squeeze().cpu().numpy()
# Binary predictions
predictions = (scores > 0.5).astype(int)
return scores, predictions
def save(self, path):
"""Save model"""
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
torch.save({
'model_state_dict': self.model.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'input_size': self.input_size,
'sequence_length': self.sequence_length,
}, path)
print(f"✓ Model saved to {path}")
def load(self, path):
"""Load model"""
checkpoint = torch.load(path, map_location=self.device)
self.model.load_state_dict(checkpoint['model_state_dict'])
self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
self.input_size = checkpoint['input_size']
self.sequence_length = checkpoint['sequence_length']
print(f"✓ Model loaded from {path}")
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