XGen-Time-Series 0.1.1

An eXplainable framework for Generative Time Series in Python

Documentation

This library offers a comprehensive implementation of various generative models, all unified under a single framework. It enables benchmark experiments and facilitates model comparisons by training the models using the same autoencoding neural network architecture. With the "make your own generative time series" feature, you can train any of these models using your own data and customize the Encoder and Decoder neural networks as per your requirements.

Additionally, the library integrates popular experiment monitoring tools such as wandb, mlflow, and comet-ml 🧪. It also allows for easy model sharing and loading from the HuggingFace Hub 🤗 with just a few lines of code.

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Note

Your XGen Time Series now supports distributed training using PyTorch's DDP (Distributed Data Parallel). With this new feature, you can now train your preferred Generative Time Series models faster and on custom datasets, all with just a few lines of code. This allows for improved scalability and accelerated training across multiple GPUs or even distributed systems. To showcase the enhanced performance, we have conducted a comprehensive benchmarking analysis. You can find the detailed results in the benchmark section of our documentation. This benchmark highlights the significant speed-up achieved by leveraging the distributed training capabilities of XGen Time Series. Take advantage of XGen 0.2's distributed training support and experience accelerated training for your Generative Time Series models. Visit our documentation and explore the benchmark section to learn more about the performance improvements and how to make the most out of this latest release.

Quick access:

• Installation
• Implemented models / Implemented samplers
• Reproducibility statement / Results flavor
• Model training / Data generation / Custom network architectures / Distributed training
• Model sharing with 🤗 Hub / Experiment tracking with wandb / Experiment tracking with mlflow / Experiment tracking with comet_ml
• Tutorials / Documentation
• Contributing 🚀 / Issues 🛠️
• Citing this repository

Installation

To install the latest stable release of this library run the following using pip

$ pip install XGen

To install the latest github version of this library run the following using pip

$ pip install git+https://github.com/XgenTimeSeries/xgen-timeseries

or alternatively you can clone the github repo to access to tests, tutorials and scripts.

$ git clone https://github.com/XgenTimeSeries/xgen-timeseries

and install the library

$ cd xgen-timeseries
$ pip install -e .

Available Models

Below is the list of the models currently implemented in the library.

Models	Training example	Paper	Official Implementation
PSA-GAN		link
WaveGAN		link
TimeGAN		link
GT-GAN		link
RCGAN		link
Professor Forcing		link
RGAN		link

See reconstruction and generation results for all models

Reproducibility

We validate the implementations by reproducing some results presented in the original publications when the official code has been released or when enough details about the experimental section of the papers were available.

Training Step

To launch a model training, you only need to call a TrainingPipeline instance.

       from XGen.pipelines import TrainingPipeline
       from XGen.models import VAE, VAEConfig
       from XGen.trainers import BaseTrainerConfig

       # Set up the training configuration
       my_training_config = BaseTrainerConfig(
   	output_dir='my_model',
   	num_epochs=50,
   	learning_rate=1e-3,
   	per_device_train_batch_size=200,
   	per_device_eval_batch_size=200,
   	train_dataloader_num_workers=2,
   	eval_dataloader_num_workers=2,
   	steps_saving=20,
   	optimizer_cls="AdamW",
   	optimizer_params={"weight_decay": 0.05, "betas": (0.91, 0.995)},
   	scheduler_cls="ReduceLROnPlateau",
   	scheduler_params={"patience": 5, "factor": 0.5}
    )
       # Set up the model configuration 
       my_xgen_config = XGenConfig(
   	input_dim=(28, 3600), # (features_time, time_sequence_szie)
   	latent_dim=10
    )
       # Build the model
       my_xgen_model = XGenModel(
   	model_config=my_xgen_config
    )
       # Build the Pipeline
       pipeline = TrainingPipeline(
    	training_config=my_training_config,
    	model=my_vae_model
    )
       # Launch the Pipeline
       pipeline(
   	train_data=your_train_data, # must be torch.Tensor, np.array or torch datasets
   	eval_data=your_eval_data # must be torch.Tensor, np.array or torch datasets
    )

At the end of training, the best model weights, model configuration and training configuration are stored in a final_model folder available in my_model/MODEL_NAME_training_YYYY-MM-DD_hh-mm-ss (with my_model being the output_dir argument of the BaseTrainerConfig). If you further set the steps_saving argument to a certain value, folders named checkpoint_epoch_k containing the best model weights, optimizer, scheduler, configuration and training configuration at epoch k will also appear in my_model/MODEL_NAME_training_YYYY-MM-DD_hh-mm-ss.

Training on XGen Time Series datasets

We also provide a training script example here that can be used to train the models on benchmarks datasets (Ukdale, Refit, Redd ).

python training.py --dataset ukdale --model_name TimeGAN --model_config 'configs/ae_config.json' --training_config 'configs/base_training_config.json'

See README.md for further details on this script

Generate new Time Series

Using the GenerationPipeline

The easiest way to launch a data generation from a trained model consists in using the built-in GenerationPipeline provided in XGen. Say you want to generate 100 samples using a MAFSampler all you have to do is 1) relaod the trained model, 2) define the sampler's configuration and 3) create and launch the GenerationPipeline.

Samplers Modules

You can launch the data generation process from a trained model directly with the sampler. For instance, to generate new data with your sampler, run the following.

       from XGen.models import AutoModel
       from XGen.samplers import NormalSampler
       # Retrieve the trained model
       my_trained_vae = AutoModel.load_from_folder(
   	'path/to/your/trained/model'
    )
       # Define your sampler
       my_samper = NormalSampler(
   	model=my_trained_xgen
    )
       # Generate samples
       gen_data = my_samper.sample(
   	num_samples=50,
   	batch_size=10,
   	output_dir=None,
   	return_gen=True
    )

If you set output_dir to a specific path, the generated time series will be saved as .csv.

Your own Model architecture for forecasting or Energy Dissagregation

XGen provides you the possibility to define your own neural networks within the VAE models. For instance, say you want to train a Wassertstein AE with a specific encoder and decoder, you can do the following:

       from XGen.models.nn import BaseEncoder, BaseDecoder
       from XGen.models.base.base_utils import ModelOutput
       class My_Encoder(BaseEncoder):
   	def __init__(self, args=None): # Args is a ModelConfig instance
   		BaseEncoder.__init__(self)
   		self.layers = my_nn_layers()
   		
   	def forward(self, x:torch.Tensor) -> ModelOutput:
   		out = self.layers(x)
   		output = ModelOutput(
   			embedding=out # Set the output from the encoder in a ModelOutput instance 
   		)
   		return output
   
    class My_Decoder(BaseDecoder):
   	def __init__(self, args=None):
   		BaseDecoder.__init__(self)
   		self.layers = my_nn_layers()
   		
   	def forward(self, x:torch.Tensor) -> ModelOutput:
   		out = self.layers(x)
   		output = ModelOutput(
   			reconstruction=out # Set the output from the decoder in a ModelOutput instance
   		)
   		return output
   
       my_encoder = My_Encoder()
       my_decoder = My_Decoder()

And now build the model

       from XGen.models import WAE_MMD, WAE_MMD_Config
       # Set up the model configuration 
       my_wae_config = model_config = WAE_MMD_Config(
   	input_dim=(1, 28, 28),
   	latent_dim=10
    )
   
       # Build the model
       my_wae_model = WAE_MMD(
   	model_config=my_wae_config,
   	encoder=my_encoder, # pass your encoder as argument when building the model
   	decoder=my_decoder # pass your decoder as argument when building the model
    )

important note 1: For all AE-based models (AE, WAE, RAE_L2, RAE_GP), both the encoder and decoder must return a ModelOutput instance. For the encoder, the ModelOutput instance must contain the embbeddings under the key embedding. For the decoder, the ModelOutput instance must contain the reconstructions under the key reconstruction.

important note 2: For all VAE-based models (VAE, BetaVAE, IWAE, HVAE, VAMP, RHVAE), both the encoder and decoder must return a ModelOutput instance. For the encoder, the ModelOutput instance must contain the embbeddings and log-covariance matrices (of shape batch_size x latent_space_dim) respectively under the key embedding and log_covariance key. For the decoder, the ModelOutput instance must contain the reconstructions under the key reconstruction.

Using benchmark neural nets

You can also find predefined neural network architectures for the most common data sets (i.e. MNIST, CIFAR, CELEBA ) that can be loaded as follows

       from XGen.models.nn.benchmark.mnist import (
   	Encoder_Conv_AE_MNIST, # For AE based model (only return embeddings)
   	Encoder_Conv_VAE_MNIST, # For VAE based model (return embeddings and log_covariances)
   	Decoder_Conv_AE_MNIST
    )

Replace mnist by cifar or celeba to access to other neural nets.

Distributed Training with XGen

As of v0.1.0, XGen now supports distributed training using PyTorch's DDP. It allows you to train your favorite VAE faster and on larger dataset using multi-gpu and/or multi-node training.

To do so, you can build a python script that will then be launched by a launcher (such as srun on a cluster). The only thing that is needed in the script is to specify some elements relative to the distributed environment (such as the number of nodes/gpus) directly in the training configuration as follows

       training_config = BaseTrainerConfig(
        num_epochs=10,
        learning_rate=1e-3,
        per_device_train_batch_size=64,
        per_device_eval_batch_size=64,
        train_dataloader_num_workers=8,
        eval_dataloader_num_workers=8,
        dist_backend="nccl", # distributed backend
        world_size=8 # number of gpus to use (n_nodes x n_gpus_per_node),
        rank=5 # process/gpu id,
        local_rank=1 # node id,
        master_addr="localhost" # master address,
        master_port="12345" # master port,
    )

See this example script that defines a multi-gpu VQVAE training on ImageNet dataset. Please note that the way the distributed environnement variables (world_size, rank ) are recovered may be specific to the cluster and launcher you use.

Benchmark

Below are indicated the training times for a Vector Quantized VAE (VQ-VAE) with XGen for 100 epochs on MNIST on V100 16GB GPU(s), for 50 epochs on FFHQ (1024x1024 images) and for 20 epochs on ImageNet-1k on V100 32GB GPU(s).

	Train Data	1 GPU	4 GPUs	2x4 GPUs
UK DALE	Energy data from UK households	7h 30min	3h 12min	1h 58min
REDD	Energy data from US households	9h 14min	4h 26min	2h 53min
REFIT	Energy data from UK households	6h 51min	3h 02min	1h 47min

For each dataset, we provide the benchmarking scripts here

Sharing your models with the HuggingFace Hub 🤗

XGen also allows you to share your models on the HuggingFace Hub. To do so you need:

• a valid HuggingFace account
• the package huggingface_hub installed in your virtual env. If not you can install it with

$ python -m pip install huggingface_hub

• to be logged in to your HuggingFace account using

$ huggingface-cli login

Uploading a model to the Hub

Any XGen model can be easily uploaded using the method push_to_hf_hub

       my_vae_model.push_to_hf_hub(hf_hub_path="your_hf_username/your_hf_hub_repo")

Note: If your_hf_hub_repo already exists and is not empty, files will be overridden. In case, the repo your_hf_hub_repo does not exist, a folder having the same name will be created.

Downloading models from the Hub

Equivalently, you can download or reload any XGen's model directly from the Hub using the method load_from_hf_hub

       from XGen.models import AutoModel
       my_downloaded_vae = AutoModel.load_from_hf_hub(hf_hub_path="path_to_hf_repo")

Monitoring your experiments with wandb 🧪

XGen also integrates the experiment tracking tool wandb allowing users to store their configs, monitor their trainings and compare runs through a graphic interface. To be able use this feature you will need:

• a valid wandb account
• the package wandb installed in your virtual env. If not you can install it with

$ pip install wandb

• to be logged in to your wandb account using

$ wandb login

Use WandbCallback for logs

Launching an experiment with time-real logs with wandb in XGen Time Series is pretty simple. The only thing a user needs to do is create a WandbCallback instance:

	# Create your callback
	from XGen.trainers.training_callbacks import WandbCallback
	callbacks = [] # the TrainingPipeline expects a list of callbacks
	wandb_callback = WandbCallback() # Build the callback
	wandb_callback.setup(
	training_config=your_training_config, # training config
	model_config=your_model_config, # model config
	project_name="wandb_project", # your and project
	entity_name="wandb_entity", # your wandb entity
    )
       callbacks.append(wandb_callback) # Add it to the callbacks list

       pipeline = TrainingPipeline(
   	training_config=config,
   	model=model
    )
       pipeline(
   	train_data=train_dataset,
   	eval_data=eval_dataset,
   	callbacks=callbacks 
    )
       # You can log to https://wandb.ai/your_wandb_entity/your_wandb_project to monitor your training

See the detailed tutorial

Monitoring your experiments with mlflow 🧪

XGen also integrates the experiment tracking tool mlflow allowing users to store their configs, monitor their trainings and compare runs through a graphic interface. To be able use this feature you will need:

• the package mlfow installed in your virtual env. If not you can install it with

$ pip install mlflow

Creating a MLFlowCallback

Launching an experiment monitoring with mlfow in XGen is pretty simple. The only thing a user needs to do is create a MLFlowCallback instance

       # Create you callback
       from XGen.trainers.training_callbacks import MLFlowCallback
       callbacks = [] # the TrainingPipeline expects a list of callbacks
       mlflow_cb = MLFlowCallback() # Build the callback 
       # SetUp the callback 
       mlflow_cb.setup(
   	training_config=your_training_config, # training config
   	model_config=your_model_config, # model config
   	run_name="mlflow_cb_example", # specify your mlflow run
    )
       callbacks.append(mlflow_cb) # Add it to the callbacks list

and then pass it to the TrainingPipeline.

       pipeline = TrainingPipeline(
   	training_config=config,
   	model=model
    )
       pipeline(
   	train_data=train_dataset,
   	eval_data=eval_dataset,
   	callbacks=callbacks # pass the callbacks to the TrainingPipeline and you are done!
    )

you can visualize your metric by running the following in the directory where the ./mlruns

$ mlflow ui 

See the detailed tutorial

Monitoring your experiments with comet_ml 🧪

XGen also integrates the experiment tracking tool comet_ml allowing users to store their configs, monitor their trainings and compare runs through a graphic interface. To be able use this feature you will need:

• the package comet_ml installed in your virtual env. If not you can install it with

$ pip install comet_ml

Creating a CometCallback

Launching an experiment monitoring with comet_ml in XGen is pretty simple. The only thing a user needs to do is create a CometCallback instance

       # Create you callback
       from XGen.trainers.training_callbacks import CometCallback
       callbacks = [] # the TrainingPipeline expects a list of callbacks
       comet_cb = CometCallback() # Build the callback 
       # SetUp the callback 
       comet_cb.setup(
   	training_config=training_config, # training config
   	model_config=model_config, # model config
   	api_key="your_comet_api_key", # specify your comet api-key
   	project_name="your_comet_project", # specify your wandb project
   	#offline_run=True, # run in offline mode
   	#offline_directory='my_offline_runs' # set the directory to store the offline runs
    )
       callbacks.append(comet_cb) # Add it to the callbacks list

and then pass it to the TrainingPipeline.

       pipeline = TrainingPipeline(
   	training_config=config,
   	model=model
    )
       pipeline(
   	train_data=train_dataset,
   	eval_data=eval_dataset,
   	callbacks=callbacks # pass the callbacks to the TrainingPipeline and you are done!
    )
       # You can log to https://comet.com/your_comet_username/your_comet_project to monitor your training

See the detailed tutorial

Dealing with issues 🛠️

If you are experiencing any issues while running the code or request new features/models to be implemented please open an issue on github.

Contributing 🚀

You want to contribute to this library by adding a model, a sampler or simply fix a bug ? That's awesome! Thank you! Please see CONTRIBUTING.md to follow the main contributing guidelines.

Citation

If you find this work useful or use it in your research, please consider citing us

@inproceedings{Koublal23XGenTS,
 author = {khalid Oublal, Ladjal, Benhaiem, le-borgne and Roueff},
 booktitle = {Advances in Neural Information Processing Systems},
 pages = {21575--21589},
 publisher = {Curran Associates, Inc.},
 title = {XGen: A Comprehensive Archive and an eXplainable Time Series Generation Framework for Energy},
 volume = {35},
 year = {2023}
}