Initial commit

# TraceVAE

This is the source code for "Unsupervised Anomaly Detection on Microservice Traces through Graph VAE".

## Usage

1. `pip3 install -r requirements.txt`.

2. Convert the dataset with `python3 -m tracegnn.cli.data_process preprocess -i [input_path] -o [dataset_path]`. The sample dataset is under `sample_dataset`. (Note: This sample dataset only shows data format and usage, and cannot be used to evaluate model performance. Please replace it with your dataset.)

sample:

```

python3 -m tracegnn.cli.data_process preprocess -i sample_dataset -o sample_dataset

```

3. Train the model with `bash train.sh [dataset_path]`:

```

bash train.sh sample_dataset

```

4. Evaluate the model with `bash teset.sh [model_path] [dataset_path]`. The default model path is under `results/train/models/final.pt`:

```

bash test.sh results/train/models/final.pt sample_dataset

```

git+https://gitee.com/haowen-xu/ml-essentials

git+https://gitee.com/haowen-xu/tensorkit

git+https://gitee.com/mirrors/ZhuSuan.git

click

jinja2

networkx

numpy

pandas

PyYAML

python-snappy

scikit-learn

seaborn

semver

natsort

imageio

fs

lazy_object_proxy

kafka-python

tqdm

loguru

tensorboard==1.15

numba

-f https://download.pytorch.org/whl/cu116/torch_stable.html

torch==1.12.0+cu116

-f https://data.dgl.ai/wheels/repo.html

dgl-cu116

-f https://data.pyg.org/whl/torch-1.12.0+cu116.html

torch-scatter

torch-sparse

torch-cluster

torch-spline-conv

torch-geometric