prototorch/tests/test_datasets.py
Alexander Engelsberger a28601751e
Use github actions for CI (#10)
* 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original fork no longer maintained, move config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
2022-01-10 20:23:18 +01:00

188 lines
5.5 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])
if not os.path.exists("./artifacts"):
os.mkdir("./artifacts")
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()