176 lines
5.4 KiB
Python
176 lines
5.4 KiB
Python
"""ProtoTorch datasets test suite"""
|
|
|
|
import os
|
|
import shutil
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
import prototorch as pt
|
|
from prototorch.datasets.abstract import Dataset, ProtoDataset
|
|
|
|
|
|
class TestAbstract(unittest.TestCase):
|
|
def setUp(self):
|
|
self.ds = Dataset("./artifacts")
|
|
|
|
def test_getitem(self):
|
|
with self.assertRaises(NotImplementedError):
|
|
_ = self.ds[0]
|
|
|
|
def test_len(self):
|
|
with self.assertRaises(NotImplementedError):
|
|
_ = len(self.ds)
|
|
|
|
def tearDown(self):
|
|
del self.ds
|
|
|
|
|
|
class TestProtoDataset(unittest.TestCase):
|
|
def test_download(self):
|
|
with self.assertRaises(NotImplementedError):
|
|
_ = ProtoDataset("./artifacts", download=True)
|
|
|
|
def test_exists(self):
|
|
with self.assertRaises(RuntimeError):
|
|
_ = ProtoDataset("./artifacts", download=False)
|
|
|
|
|
|
class TestNumpyDataset(unittest.TestCase):
|
|
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)
|
|
|
|
|
|
class TestCSVDataset(unittest.TestCase):
|
|
def setUp(self):
|
|
data = np.random.rand(100, 4)
|
|
targets = np.random.randint(2, size=(100, 1))
|
|
arr = np.hstack([data, targets])
|
|
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")
|
|
|
|
|
|
class TestSpiral(unittest.TestCase):
|
|
def test_init(self):
|
|
ds = pt.datasets.Spiral(num_samples=10)
|
|
self.assertEqual(len(ds), 10)
|
|
|
|
|
|
class TestIris(unittest.TestCase):
|
|
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):
|
|
def test_size(self):
|
|
ds = pt.datasets.Blobs(num_samples=10)
|
|
self.assertEqual(len(ds), 10)
|
|
|
|
|
|
class TestRandom(unittest.TestCase):
|
|
def test_size(self):
|
|
ds = pt.datasets.Random(num_samples=10)
|
|
self.assertEqual(len(ds), 10)
|
|
|
|
|
|
class TestCircles(unittest.TestCase):
|
|
def test_size(self):
|
|
ds = pt.datasets.Circles(num_samples=10)
|
|
self.assertEqual(len(ds), 10)
|
|
|
|
|
|
class TestMoons(unittest.TestCase):
|
|
def test_size(self):
|
|
ds = pt.datasets.Moons(num_samples=10)
|
|
self.assertEqual(len(ds), 10)
|
|
|
|
|
|
# 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()
|