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Optimal transport domain adaptation methods. — SKADA : Scikit Adaptation

This example illustrates the Optimal Transport deep DA method from on a simple image classification task.

# Author: Théo Gnassounou
#
# License: BSD 3-Clause
# sphinx_gallery_thumbnail_number = 4

dataset = load_mnist_usps(n_classes=2, n_samples=0.5, return_dataset=True)
X, y, sample_domain = dataset.pack_train(as_sources=["mnist"], as_targets=["usps"])
X_test, y_test, sample_domain_test = dataset.pack_test(as_targets=["usps"])

/home/circleci/project/skada/datasets/_mnist_usps.py:72: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  mnist_target = torch.tensor(mnist_dataset.targets)

  epoch    train_loss     dur
-------  ------------  ------
      1        1.6460  6.1998
      2        0.4327  6.4993
      3        0.1498  6.6010
      4        0.0746  12.6988
      5        0.0511  13.5012

0.8938906752411575

model = DeepJDOT(
    MNISTtoUSPSNet(),
    layer_name="fc1",
    batch_size=128,
    max_epochs=5,
    train_split=False,
    reg_dist=0.1,
    reg_cl=0.01,
    lr=1e-2,
)
model.fit(X, y, sample_domain=sample_domain)
model.score(X_test, y_test, sample_domain=sample_domain_test)

  epoch    train_loss      dur
-------  ------------  -------
      1        2.1794  38.5859
      2        1.3292  13.6001
      3        0.8222  10.7061
      4        0.6452  10.4921
      5        0.5480  10.5015

0.9389067524115756

Total running time of the script: (2 minutes 14.978 seconds)

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