Train on custom dataset

[khabalghoul]

Hi! How are you?

I found that tsdiff could be a great tool for generating eeg data. I have a dataset containing the channels measurements from an eeg obtained in an experiment and I would like to train your model with this data. How should I do in order to train your model with a custom dataset?

Thanks!

[abdulfatir]

Hi @tomyjara!

You can use something like this to build a custom dataset.

1. Create a JSON lines file with your time series data. Basically every line has one time series in JSON format with two keys, start (the start time stamp) and target (the actual time series). I have attached an example file. Note that the time series are not required to have the same start or length.

2. Use this function to load the file as a GluonTS dataset.

from pathlib import Path

from gluonts.dataset.split import split
from gluonts.dataset.common import (
    MetaData,
    TrainDatasets,
    FileDataset,
)


def get_custom_dataset(
    jsonl_path: Path,
    freq: str,
    prediction_length: int,
    split_offset: int = None,
):
    """Creates a custom GluonTS dataset from a JSONLines file and
    give parameters.

    Parameters
    ----------
    jsonl_path
        Path to a JSONLines file with time series
    freq
        Frequency in pandas format
        (e.g., `H` for hourly, `D` for daily)
    prediction_length
        Prediction length
    split_offset, optional
        Offset to split data into train and test sets, by default None

    Returns
    -------
        A gluonts dataset
    """
    if split_offset is None:
        split_offset = -prediction_length

    metadata = MetaData(freq=freq, prediction_length=prediction_length)
    test_ts = FileDataset(jsonl_path, freq)
    train_ts, _ = split(test_ts, offset=split_offset)
    dataset = TrainDatasets(metadata=metadata, train=train_ts, test=test_ts)
    return dataset

3. This get_custom_dataset can be used as a replacement for

   unconditional-time-series-diffusion/bin/train_model.py

   Line 135 in 50f52da

   dataset = get_gts_dataset(dataset_name)

4. Modify the default config appropriately, especially the context length, lags, etc.

Thanks @marcelkollovieh for helping with the response!

[gulugulu888]

tsdiff) rrr@rr:~/unconditional-time-series-diffusion$ python bin/train_model.py -c configs/train_fdr.yaml
DEBUG:root:Before importing pykeops...
DEBUG:root:After importing pykeops!
INFO:uncond_ts_diff.arch.s4:Pykeops installation found.
WARNING: Skipping key sampler_params!
WARNING:root:Cannot infer loader for /home/h/unconditional-time-series-diffusion/data/fdr/CAS/dummy_custom_data.json:Zone.Identifier.
WARNING:root:Cannot infer loader for /home/h/unconditional-time-series-diffusion/data/fdr/CAS/train - 副本.json:Zone.Identifier.
WARNING:root:Cannot infer loader for /home/h/unconditional-time-series-diffusion/data/fdr/CAS/dummy_custom_data.json:Zone.Identifier.
WARNING:root:Cannot infer loader for /home/h/unconditional-time-series-diffusion/data/fdr/CAS/train - 副本.json:Zone.Identifier.
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
INFO:bin/train_model.py:Logging to ./lightning_logs/version_44
/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/trainer/configuration_validator.py:108: PossibleUserWarning: You defined a validation_step but have no val_dataloader. Skipping val loop.
rank_zero_warn(
You are using a CUDA device ('NVIDIA GeForce RTX 4060 Laptop GPU') that has Tensor Cores. To properly utilize them, you should set torch.set_float32_matmul_precision('medium' | 'high') which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
┏━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ ┃ Name ┃ Type ┃ Params ┃
┡━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ 0 │ scaler │ MeanScaler │ 0 │
│ 1 │ embedder │ FeatureEmbedder │ 1 │
│ 2 │ backbone │ BackboneModel │ 193 K │
└───┴──────────┴─────────────────┴────────┘
Trainable params: 193 K
Non-trainable params: 0
Total params: 193 K
Total estimated model params size (MB): 0
DEBUG:fsspec.local:open file: /home/h/unconditional-time-series-diffusion/lightning_logs/version_44/hparams.yaml
DEBUG:fsspec.local:open file: /home/h/unconditional-time-series-diffusion/lightning_logs/version_44/hparams.yaml
Epoch 0/99 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/-- 0:00:00 • -:--:-- 0.00it/s
Traceback (most recent call last):
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/dataset/jsonl.py", line 127, in iter
yield json.loads(line)
orjson.JSONDecodeError: unexpected end of data: line 2 column 1 (char 3)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "bin/train_model.py", line 286, in
main(config=config, log_dir=args.out_dir)
File "bin/train_model.py", line 224, in main
trainer.fit(model, train_dataloaders=data_loader)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 608, in fit
call._call_and_handle_interrupt(
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 38, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 650, in _fit_impl
self._run(model, ckpt_path=self.ckpt_path)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1112, in _run
results = self._run_stage()
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1191, in _run_stage
self._run_train()
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1214, in _run_train
self.fit_loop.run()
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 267, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 194, in run
self.on_run_start(*args, **kwargs)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 160, in on_run_start
_ = iter(data_fetcher) # creates the iterator inside the fetcher
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 180, in iter
self.prefetching()
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 243, in prefetching
self._fetch_next_batch(iterator)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 280, in _fetch_next_batch
batch = next(iterator)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/trainer/supporters.py", line 571, in next
return self.request_next_batch(self.loader_iters)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/pytorch_lightning/trainer/supporters.py", line 583, in request_next_batch
return apply_to_collection(loader_iters, Iterator, next)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/lightning_utilities/core/apply_func.py", line 64, in apply_to_collection
return function(data, *args, **kwargs)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/itertools.py", line 181, in iter
yield from itertools.islice(self.iterable, self.length)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/transform/_base.py", line 103, in iter
yield from self.transformation(
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/transform/_base.py", line 124, in call
for data_entry in data_it:
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/dataset/loader.py", line 37, in call
yield from batcher(data, self.batch_size)
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/itertools.py", line 100, in get_batch
return list(itertools.islice(it, batch_size))
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/transform/_base.py", line 178, in call
for data_entry in data_it:
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/itertools.py", line 77, in iter
for el in self.iterable:
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/itertools.py", line 125, in iter
for element in self.iterable:
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/transform/_base.py", line 103, in iter
yield from self.transformation(
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/transform/_base.py", line 124, in call
for data_entry in data_it:
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/transform/_base.py", line 124, in call
for data_entry in data_it:
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/transform/_base.py", line 124, in call
for data_entry in data_it:
[Previous line repeated 8 more times]
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/dataset/init.py", line 56, in iter
yield from self.iter_sequential()
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/dataset/init.py", line 47, in iter_sequential
yield from dataset
File "/home/h/anaconda3/envs/tsdiff/lib/python3.8/site-packages/gluonts/dataset/jsonl.py", line 129, in iter
raise GluonTSDataError(
gluonts.exceptions.GluonTSDataError: Could not read json line 0, b'{\r\n'

I've used dummy json as train.json, but the error comes out as above