LycorisAD is an elegant outlier detection algorithm framework based on AutoEncoder.
The neural network is built with LycorisNet, and the threshold of reconstruction error is calculated by genetic algorithm (based on deap).
Features
- Lightweight and elegant.
- Need not to manually design the structure of the autoencoder.
- Provide minimalist APIs to make development more efficient.
Installation
The project is based on LycorisNet, and the installation of LycorisNet can be found here.
pip install LycorisAD
Documents
The APIs provided by AnomalyDetection (from LycorisAD import AnomalyDetection):
| Function | Description | Inputs | Returns |
|---|---|---|---|
| AnomalyDetection(config) | Constructor. | config: The configuration information, including 12 configuration fields. | An object of the class AnomalyDetection. |
| encode(data, normals, anomalies) | Self-encode the samples and calculate the threshold. |
data: Normal samples for self-encoding. normals: Normal samples used to calculate the threshold. anomalies: Anomaly samples used to calculate the threshold. |
|
| detect(data) | Detect samples. | data: Samples to be detected. | The results after detecting the samples are returned as a list. There are two fields, the first is a Boolean value, and the second is the reconstruction error. Where 'True' indicates normal and 'False' indicates anomaly. |
| save(path1, path2) | Save the model and related configurations. |
path1: The path to store the model. path2: The path to store the configurations. |
|
@staticmethodload(path1, path2) |
Import pre-trained models and related configurations. |
path1: The path to import the model. path2: The path to import the configurations. |
|
| set_config(config) | Set the configuration information of AnomalyDetection. | config: The configuration information, including 12 configuration fields. | |
| set_lr(learning_rate) | Set the learning rate of the AutoEncoder. | learning_rate: The learning rate of the AutoEncoder. | |
| set_workers(workers) | Set the number of worker threads to train the model. | workers: The number of worker threads. | |
| get_threshold() | Get the threshold. | ||
@staticmethodversion() |
Returns the version information of AnomalyDetection. |
Configurable fields:
| Field | Description | Default |
|---|---|---|
| capacity | Capacity of LycorisNet. | |
| dimension | Dimension of samples. | |
| nodes | The number of hidden nodes added for each neural network. | |
| connections | The number of connections added for each neural network. | |
| depths | Total layers of each neural network. | |
| batch_size | Batch size. | |
| epoch | Epoch. | |
| evolution | Number of LycorisNet evolutions. | 0 |
| population | Population in the genetic algorithm. | 256 |
| generation | Generation in the genetic algorithm. | 16 |
| weight | The weights used to calculate the threshold. The first parameter determines the prediction accuracy of normal samples, while the second parameter determines that of anomaly samples. |
[1, 1] |
| verbose | Whether to output intermediate information. | False |
Examples
- MNIST
For a description of the data and its source, see here.
Import the dependent modules:
from LycorisAD import AnomalyDetection
import scipy.io as sio
import random
from sklearn.metrics import roc_auc_scoreRead the data and divide it:
X = sio.loadmat('mnist.mat')["X"]
X = (X + 138) / 393.0
normal = X[:6903]
random.shuffle(normal)
normal = normal[:6900]
anomaly = X[6903:]
random.shuffle(anomaly)
data1 = normal[:5560]
data2 = normal[5560:6210]
data3 = anomaly[:630]
test_normal = normal[6210:]
test_anomaly = anomaly[630:]Prepare the configuration information and instantiate the AnomalyDetection object:
conf = {"capacity": 64, "dimension": 100, "nodes": 300, "connections": 6000, "depths": 4, "batch_size": 8,
"epoch": 1, "evolution": 1, "verbose": True}
lad = AnomalyDetection(conf)Self-encoding:
lad.encode(data1, data2, data3)Calculate the AUC on the test set:
result1 = lad.detect(test_normal)
result2 = lad.detect(test_anomaly)
auc1 = [0] * len(result1) + [1] * len(result2)
auc2 = []
for item in (result1 + result2):
if item[0]:
auc2.append(0)
else:
auc2.append(1)
print("AUC:", roc_auc_score(auc1, auc2))- More examples will be released in the future.
License
LycorisAD is released under the LGPL-3.0 license. By using, distributing, or contributing to this project, you agree to the terms and conditions of this license.
