# bagel-tensorflow

**Repository Path**: liuzhaopk/bagel-tensorflow

## Basic Information

- **Project Name**: bagel-tensorflow
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-24
- **Last Updated**: 2024-07-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Bagel

![version-2.2.0](https://img.shields.io/badge/version-2.2.0-blue)
![python->=3.10](https://img.shields.io/badge/python->=3.10-blue?logo=python&logoColor=white)
![TensorFlow 2.13](https://img.shields.io/badge/TensorFlow-2.13-FF6F00?logo=tensorflow&logoColor=white)
![license-MIT](https://img.shields.io/badge/license-MIT-green)

<img width="140" alt="Bagel Logo" align="right" src="https://www.svgrepo.com/show/275681/bagel.svg"/>

Bagel is a robust and unsupervised KPI anomaly detection algorithm based on conditional variational autoencoder.

This is an implementation of Bagel in TensorFlow 2. The original PyTorch 0.4 implementation can be found at
[NetManAIOps/Bagel](https://github.com/NetManAIOps/Bagel).

## Install

`pip` will automatically install required PyPI dependencies when you install this package:

- For development use:

    ```
    git clone https://github.com/alumik/bagel-tensorflow.git
    cd bagel-tensorflow
    pip install -e .
    ```

- For production use:

    ```
    pip install git+https://github.com/alumik/bagel-tensorflow.git
    ```

An `environment.yml` is also provided if you prefer `conda` to manage dependencies:

```
conda env create -f environment.yml
```

## Run

### KPI Format

KPI data must be stored in csv files in the following format:

```
timestamp,   value,       label
1469376000,  0.847300274, 0
1469376300, -0.036137314, 0
1469376600,  0.074292384, 0
1469376900,  0.074292384, 0
1469377200, -0.036137314, 0
1469377500,  0.184722083, 0
1469377800, -0.036137314, 0
1469378100,  0.184722083, 0
```

- `timestamp`: timestamps in seconds (10-digit).
- `label` (optional): `0` for normal points, `1` for anomaly points.
- Labels are used only for evaluation and are not required in model training and inference. However, if labels are
  provided, the model can still take labeled data to improve the performance.

### Sample Script

A sample script can be found at `sample/main.py`:

## Usage

To prepare the data:

```python
import bagel

kpi = bagel.data.load_kpi('kpi.csv')
kpi.complete_timestamp()
train_kpi, valid_kpi, test_kpi = kpi.split((0.49, 0.21, 0.3))
train_kpi, mean, std = train_kpi.standardize()
valid_kpi, _, _ = valid_kpi.standardize(mean=mean, std=std)
test_kpi, _, _ = test_kpi.standardize(mean=mean, std=std)
dataset = bagel.data.KPIDataset(
    train_kpi.use_labels(0.),
    window_size=window_size,
    time_feature=time_feature,
    missing_injection_rate=missing_injection_rate,
)
valid_dataset = bagel.data.KPIDataset(valid_kpi, window_size=window_size, time_feature=time_feature)
test_dataset = bagel.data.KPIDataset(test_kpi.no_labels(), window_size=window_size, time_feature=time_feature)
```

To build and train a Bagel model:

```python
model = bagel.Bagel(
    window_size=window_size,
    hidden_dims=hidden_dims,
    latent_dim=latent_dim,
    dropout_rate=dropout_rate,
)
lr_scheduler = tf.keras.optimizers.schedules.ExponentialDecay(
    initial_learning_rate=learning_rate,
    decay_steps=10 * len(dataset) // batch_size,
    decay_rate=0.75,
    staircase=True,
)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_scheduler, clipnorm=clipnorm)
model.compile(optimizer=optimizer, jit_compile=True)
model.fit(
    x=[dataset.values, dataset.time_code, dataset.normal],
    batch_size=batch_size,
    epochs=epochs,
    validation_data=([valid_dataset.values, valid_dataset.time_code, valid_dataset.normal], None),
    validation_batch_size=batch_size,
)
```

To use the trained model for prediction:

```python
anomaly_scores = model.predict(
    x=[test_dataset.values, test_dataset.time_code, test_dataset.normal],
    batch_size=batch_size,
)
```

Use `tf.keras.Model.save` API to save the model.

## Citation

```bibtex
@inproceedings{conf/ipccc/LiCP18,
    author    = {Zeyan Li and
                 Wenxiao Chen and
                 Dan Pei},
    title     = {Robust and Unsupervised {KPI} Anomaly Detection Based on Conditional
                 Variational Autoencoder},
    booktitle = {37th {IEEE} International Performance Computing and Communications
                 Conference, {IPCCC} 2018, Orlando, FL, USA, November 17-19, 2018},
    pages     = {1--9},
    publisher = {{IEEE}},
    year      = {2018},
    url       = {https://doi.org/10.1109/PCCC.2018.8710885},
    doi       = {10.1109/PCCC.2018.8710885}
}
```