a28601751e
* chore: Absolute imports * feat: Add new mesh util * chore: replace bumpversion original fork no longer maintained, move config * ci: remove old configuration files * ci: update github action * ci: add python 3.10 test * chore: update pre-commit hooks * ci: update supported python versions supported are 3.7, 3.8 and 3.9. 3.6 had EOL in december 2021. 3.10 has no pytorch distribution yet. * ci: add windows test * ci: update action less windows tests, pre commit * ci: fix typo * chore: run precommit for all files * ci: two step tests * ci: compatibility waits for style * fix: init file had missing imports * ci: add deployment script * ci: skip complete publish step * ci: cleanup readme
188 lines
5.5 KiB
Python
188 lines
5.5 KiB
Python
"""ProtoTorch datasets test suite"""
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import os
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import shutil
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import unittest
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import numpy as np
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import torch
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import prototorch as pt
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from prototorch.datasets.abstract import Dataset, ProtoDataset
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class TestAbstract(unittest.TestCase):
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def setUp(self):
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self.ds = Dataset("./artifacts")
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def test_getitem(self):
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with self.assertRaises(NotImplementedError):
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_ = self.ds[0]
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def test_len(self):
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with self.assertRaises(NotImplementedError):
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_ = len(self.ds)
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def tearDown(self):
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del self.ds
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class TestProtoDataset(unittest.TestCase):
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def test_download(self):
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with self.assertRaises(NotImplementedError):
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_ = ProtoDataset("./artifacts", download=True)
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def test_exists(self):
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with self.assertRaises(RuntimeError):
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_ = ProtoDataset("./artifacts", download=False)
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class TestNumpyDataset(unittest.TestCase):
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def test_list_init(self):
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ds = pt.datasets.NumpyDataset([1], [1])
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self.assertEqual(len(ds), 1)
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def test_numpy_init(self):
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data = np.random.randn(3, 2)
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targets = np.array([0, 1, 2])
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ds = pt.datasets.NumpyDataset(data, targets)
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self.assertEqual(len(ds), 3)
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class TestCSVDataset(unittest.TestCase):
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def setUp(self):
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data = np.random.rand(100, 4)
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targets = np.random.randint(2, size=(100, 1))
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arr = np.hstack([data, targets])
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if not os.path.exists("./artifacts"):
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os.mkdir("./artifacts")
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np.savetxt("./artifacts/test.csv", arr, delimiter=",")
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def test_len(self):
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ds = pt.datasets.CSVDataset("./artifacts/test.csv")
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self.assertEqual(len(ds), 100)
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def tearDown(self):
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os.remove("./artifacts/test.csv")
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class TestSpiral(unittest.TestCase):
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def test_init(self):
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ds = pt.datasets.Spiral(num_samples=10)
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self.assertEqual(len(ds), 10)
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class TestIris(unittest.TestCase):
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def setUp(self):
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self.ds = pt.datasets.Iris()
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def test_size(self):
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self.assertEqual(len(self.ds), 150)
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def test_dims(self):
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self.assertEqual(self.ds.data.shape[1], 4)
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def test_dims_selection(self):
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ds = pt.datasets.Iris(dims=[0, 1])
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self.assertEqual(ds.data.shape[1], 2)
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class TestBlobs(unittest.TestCase):
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def test_size(self):
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ds = pt.datasets.Blobs(num_samples=10)
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self.assertEqual(len(ds), 10)
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class TestRandom(unittest.TestCase):
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def test_size(self):
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ds = pt.datasets.Random(num_samples=10)
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self.assertEqual(len(ds), 10)
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class TestCircles(unittest.TestCase):
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def test_size(self):
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ds = pt.datasets.Circles(num_samples=10)
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self.assertEqual(len(ds), 10)
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class TestMoons(unittest.TestCase):
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def test_size(self):
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ds = pt.datasets.Moons(num_samples=10)
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self.assertEqual(len(ds), 10)
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# class TestTecator(unittest.TestCase):
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# def setUp(self):
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# self.artifacts_dir = "./artifacts/Tecator"
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# self._remove_artifacts()
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# def _remove_artifacts(self):
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# if os.path.exists(self.artifacts_dir):
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# shutil.rmtree(self.artifacts_dir)
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# def test_download_false(self):
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# rootdir = self.artifacts_dir.rpartition("/")[0]
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# self._remove_artifacts()
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# with self.assertRaises(RuntimeError):
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# _ = pt.datasets.Tecator(rootdir, download=False)
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# def test_download_caching(self):
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# rootdir = self.artifacts_dir.rpartition("/")[0]
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# _ = pt.datasets.Tecator(rootdir, download=True, verbose=False)
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# _ = pt.datasets.Tecator(rootdir, download=False, verbose=False)
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# def test_repr(self):
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# rootdir = self.artifacts_dir.rpartition("/")[0]
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# train = pt.datasets.Tecator(rootdir, download=True, verbose=True)
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# self.assertTrue("Split: Train" in train.__repr__())
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# def test_download_train(self):
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# rootdir = self.artifacts_dir.rpartition("/")[0]
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# train = pt.datasets.Tecator(root=rootdir,
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# train=True,
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# download=True,
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# verbose=False)
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# train = pt.datasets.Tecator(root=rootdir, download=True, verbose=False)
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# x_train, y_train = train.data, train.targets
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# self.assertEqual(x_train.shape[0], 144)
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# self.assertEqual(y_train.shape[0], 144)
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# self.assertEqual(x_train.shape[1], 100)
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# def test_download_test(self):
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# rootdir = self.artifacts_dir.rpartition("/")[0]
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# test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
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# x_test, y_test = test.data, test.targets
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# self.assertEqual(x_test.shape[0], 71)
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# self.assertEqual(y_test.shape[0], 71)
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# self.assertEqual(x_test.shape[1], 100)
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# def test_class_to_idx(self):
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# rootdir = self.artifacts_dir.rpartition("/")[0]
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# test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
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# _ = test.class_to_idx
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# def test_getitem(self):
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# rootdir = self.artifacts_dir.rpartition("/")[0]
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# test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
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# x, y = test[0]
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# self.assertEqual(x.shape[0], 100)
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# self.assertIsInstance(y, int)
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# def test_loadable_with_dataloader(self):
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# rootdir = self.artifacts_dir.rpartition("/")[0]
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# test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
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# _ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)
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# def tearDown(self):
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# self._remove_artifacts()
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