SCVAE

This is the codebase for SCVAE.

Usage

This section of the README walks through how to train and evaluate the SCVAE model.

Preparing Data

The training code reads time series from a directory of .csv files. In the data folder, we have provided synthesized data including normal data and 6 types of anoamly data.

For creating your own data, simply prepare the time series data in the same format. The .csv files should at least contain colums of "instantaneous_output_power","instantaneous_global_radiation","clearsky_Tamb" for power yeild, irradiation intensity and environment temperature data. You should also scale the data at the time dimension using Z-Score standardization.

Training

To train your model, you should first decide some hyperparameters.Here are some reasonable defaults for a baseline:

"--h_dim 512 --z_dim 128 --mode 2 --learning_rate 1e-5 --batch_size 256 --seq_shape (-1, 12, 6)"

Once you have setup your hyper-parameters, you can run an experiment like so:

python scripts/SCVAE_train.py --h_dim 512 --z_dim 128

Evaluating

The above training script saves checkpoints to .pth files in the "saved_model" directory.

Once you have a path to your model, you can evaluate your model's reconstruction results using synthesized data like so:

python scripts/SCVAE_eval.py --model_path ./saved_model/SCVAE_savedmodel.pth --anomaly_type all

Getting latent variables & Clustering and classfication

Once you have a path to your model, you can also get the latent variable s of synthesized data as its representations like so. The latent variables are saved as a set of .npy files in the "re_z" folder.

python scripts/get_latent_var.py --model_path ./saved_model/SCVAE_savedmodel.pth --is_trainsites True

Once you have a set of latent variables files, you can do some clustering and classification experiments to analysis these latent variables like so:

python scripts/cluster_and_classify.py --latent_var_path ./re_z --task clustering --latent_mode diff --anomaly_type whole_anomaly
python scripts/cluster_and_classify.py --latent_var_path ./re_z --task classification --latent_mode diff --anomaly_type whole_anomaly --classifier XGB

urbanmobility/PV-system-anomaly-detection

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