Metadataset

A dataset of datasets

This functionality will allow you to create a dataset from data stores in multiple, smaller datasets.

I’d like to thank both Thomas Capelle (https://github.com/tcapelle) and Xander Dunn (https://github.com/xanderdunn) for their contributions to make this code possible.

This functionality allows you to use multiple numpy arrays instead of a single one, which may be very useful in many practical settings. It’s been tested it with 10k+ datasets and it works well.

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TSMetaDatasets

 TSMetaDatasets (metadataset, splits)

Base class for lists with subsets

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TSMetaDataset

 TSMetaDataset (dataset_list, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Let’s create 3 datasets. In this case they will have different sizes.

vocab = alphabet[:10]
dsets = []
for i in range(3):
    size = np.random.randint(50, 150)
    X = torch.rand(size, 5, 50)
    y = vocab[torch.randint(0, 10, (size,))]
    tfms = [None, TSClassification(vocab=vocab)]
    dset = TSDatasets(X, y, tfms=tfms)
    dsets.append(dset)



metadataset = TSMetaDataset(dsets)
splits = TimeSplitter(show_plot=False)(metadataset)
metadatasets = TSMetaDatasets(metadataset, splits=splits)
dls = TSDataLoaders.from_dsets(metadatasets.train, metadatasets.valid)
xb, yb = dls.train.one_batch()
xb, yb

(TSTensor(samples:64, vars:5, len:50, device=cpu, dtype=torch.float32),
 TensorCategory([1, 0, 3, 9, 7, 2, 8, 6, 1, 1, 1, 8, 1, 1, 9, 2, 6, 6, 1, 5, 5,
                 6, 9, 2, 7, 1, 6, 4, 9, 2, 5, 0, 4, 9, 1, 4, 4, 6, 0, 8, 8, 5,
                 8, 6, 9, 0, 8, 8, 6, 4, 8, 9, 7, 3, 4, 7, 7, 8, 6, 2, 3, 0, 7,
                 4]))

You can train metadatasets as you would train any other time series model in tsai:

learn = ts_learner(dls, arch="TSTPlus")
learn.fit_one_cycle(1)
learn.export("test.pkl")

For inference, you should create the new metadatasets using the same method you used when you trained it. The you use fastai’s learn.get_preds method to generate predictions:

vocab = alphabet[:10]
dsets = []
for i in range(3):
    size = np.random.randint(50, 150)
    X = torch.rand(size, 5, 50)
    y = vocab[torch.randint(0, 10, (size,))]
    tfms = [None, TSClassification(vocab=vocab)]
    dset = TSDatasets(X, y, tfms=tfms)
    dsets.append(dset)
metadataset = TSMetaDataset(dsets)
dl = TSDataLoader(metadataset)


learn = load_learner("test.pkl")
learn.get_preds(dl=dl)

There also en easy way to map any particular sample in a batch to the original dataset and id:

dls = TSDataLoaders.from_dsets(metadatasets.train, metadatasets.valid)
xb, yb = first(dls.train)
mappings = dls.train.dataset.mapping_idxs
for i, (xbi, ybi) in enumerate(zip(xb, yb)):
    ds, idx = mappings[i]
    test_close(dsets[ds][idx][0].data.cpu(), xbi.cpu())
    test_close(dsets[ds][idx][1].data.cpu(), ybi.cpu())

For example the 3rd sample in this batch would be:

dls.train.dataset.mapping_idxs[2]

array([  0, 112], dtype=int32)