Quick Start

A minimal example of (unconditional) time series synthesis for sine waves.

import torch
from gents.model import VanillaDDPM
from gents.dataset import SineND
from gents.evaluation import qualitative_visual
from lightning import Trainer

# setup dataset and model
dm = SineND(seq_len=64, seq_dim=2, batch_size=64)
model = VanillaDDPM(seq_len=dm.seq_len, seq_dim=dm.seq_dim)

# training (on CPU for example)
trainer = Trainer(max_epochs=100, accelerator="cpu")
trainer.fit(model, dm)

# testing
dm.setup("test")
real_data = torch.cat([batch["seq"] for batch in dm.test_dataloader()])  # [N, 64, 2]
gen_data = model.sample(n_sample=len(real_data))  # [N, 64, 2]

# visualization with tsne
tsne_visual(real_data, gen_data, save_root="tsne.png")

The resulting “fake” time series versus real time series:

Throughout the test set, visualization with TSNE: