prototorch/tests/test_datasets.py

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"""ProtoTorch datasets test suite"""
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import os
import unittest
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import numpy as np
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import torch
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import prototorch as pt
from prototorch.datasets.abstract import Dataset, ProtoDataset
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class TestAbstract(unittest.TestCase):
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def setUp(self):
self.ds = Dataset("./artifacts")
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def test_getitem(self):
with self.assertRaises(NotImplementedError):
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_ = self.ds[0]
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def test_len(self):
with self.assertRaises(NotImplementedError):
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_ = len(self.ds)
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def tearDown(self):
del self.ds
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class TestProtoDataset(unittest.TestCase):
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def test_download(self):
with self.assertRaises(NotImplementedError):
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_ = ProtoDataset("./artifacts", download=True)
def test_exists(self):
with self.assertRaises(RuntimeError):
_ = ProtoDataset("./artifacts", download=False)
class TestNumpyDataset(unittest.TestCase):
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def test_list_init(self):
ds = pt.datasets.NumpyDataset([1], [1])
self.assertEqual(len(ds), 1)
def test_numpy_init(self):
data = np.random.randn(3, 2)
targets = np.array([0, 1, 2])
ds = pt.datasets.NumpyDataset(data, targets)
self.assertEqual(len(ds), 3)
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class TestCSVDataset(unittest.TestCase):
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def setUp(self):
data = np.random.rand(100, 4)
targets = np.random.randint(2, size=(100, 1))
arr = np.hstack([data, targets])
if not os.path.exists("./artifacts"):
os.mkdir("./artifacts")
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np.savetxt("./artifacts/test.csv", arr, delimiter=",")
def test_len(self):
ds = pt.datasets.CSVDataset("./artifacts/test.csv")
self.assertEqual(len(ds), 100)
def tearDown(self):
os.remove("./artifacts/test.csv")
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class TestSpiral(unittest.TestCase):
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def test_init(self):
ds = pt.datasets.Spiral(num_samples=10)
self.assertEqual(len(ds), 10)
class TestIris(unittest.TestCase):
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def setUp(self):
self.ds = pt.datasets.Iris()
def test_size(self):
self.assertEqual(len(self.ds), 150)
def test_dims(self):
self.assertEqual(self.ds.data.shape[1], 4)
def test_dims_selection(self):
ds = pt.datasets.Iris(dims=[0, 1])
self.assertEqual(ds.data.shape[1], 2)
class TestBlobs(unittest.TestCase):
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def test_size(self):
ds = pt.datasets.Blobs(num_samples=10)
self.assertEqual(len(ds), 10)
class TestRandom(unittest.TestCase):
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def test_size(self):
ds = pt.datasets.Random(num_samples=10)
self.assertEqual(len(ds), 10)
class TestCircles(unittest.TestCase):
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def test_size(self):
ds = pt.datasets.Circles(num_samples=10)
self.assertEqual(len(ds), 10)
class TestMoons(unittest.TestCase):
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def test_size(self):
ds = pt.datasets.Moons(num_samples=10)
self.assertEqual(len(ds), 10)
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# class TestTecator(unittest.TestCase):
# def setUp(self):
# self.artifacts_dir = "./artifacts/Tecator"
# self._remove_artifacts()
# def _remove_artifacts(self):
# if os.path.exists(self.artifacts_dir):
# shutil.rmtree(self.artifacts_dir)
# def test_download_false(self):
# rootdir = self.artifacts_dir.rpartition("/")[0]
# self._remove_artifacts()
# with self.assertRaises(RuntimeError):
# _ = pt.datasets.Tecator(rootdir, download=False)
# def test_download_caching(self):
# rootdir = self.artifacts_dir.rpartition("/")[0]
# _ = pt.datasets.Tecator(rootdir, download=True, verbose=False)
# _ = pt.datasets.Tecator(rootdir, download=False, verbose=False)
# def test_repr(self):
# rootdir = self.artifacts_dir.rpartition("/")[0]
# train = pt.datasets.Tecator(rootdir, download=True, verbose=True)
# self.assertTrue("Split: Train" in train.__repr__())
# def test_download_train(self):
# rootdir = self.artifacts_dir.rpartition("/")[0]
# train = pt.datasets.Tecator(root=rootdir,
# train=True,
# download=True,
# verbose=False)
# train = pt.datasets.Tecator(root=rootdir, download=True, verbose=False)
# x_train, y_train = train.data, train.targets
# self.assertEqual(x_train.shape[0], 144)
# self.assertEqual(y_train.shape[0], 144)
# self.assertEqual(x_train.shape[1], 100)
# def test_download_test(self):
# rootdir = self.artifacts_dir.rpartition("/")[0]
# test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
# x_test, y_test = test.data, test.targets
# self.assertEqual(x_test.shape[0], 71)
# self.assertEqual(y_test.shape[0], 71)
# self.assertEqual(x_test.shape[1], 100)
# def test_class_to_idx(self):
# rootdir = self.artifacts_dir.rpartition("/")[0]
# test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
# _ = test.class_to_idx
# def test_getitem(self):
# rootdir = self.artifacts_dir.rpartition("/")[0]
# test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
# x, y = test[0]
# self.assertEqual(x.shape[0], 100)
# self.assertIsInstance(y, int)
# def test_loadable_with_dataloader(self):
# rootdir = self.artifacts_dir.rpartition("/")[0]
# test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
# _ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)
# def tearDown(self):
# self._remove_artifacts()