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Update datasets test suite

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This commit is contained in:
Jensun Ravichandran 2021-06-11 23:43:18 +02:00
parent 44e4709387
commit 4a99bcbf0d
1 changed files with 88 additions and 23 deletions
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111
tests/test_datasets.py
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@ -1,32 +1,97 @@
"""ProtoTorch datasets test suite."""
"""ProtoTorch datasets test suite"""
import os
import shutil
import unittest
import numpy as np
import torch
from prototorch.datasets import abstract, tecator
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):
abstract.Dataset("./artifacts")[0]
_ = self.ds[0]
def test_len(self):
with self.assertRaises(NotImplementedError):
len(abstract.Dataset("./artifacts"))
_ = len(self.ds)
def tearDown(self):
del self.ds
class TestProtoDataset(unittest.TestCase):
def test_getitem(self):
with self.assertRaises(NotImplementedError):
abstract.ProtoDataset("./artifacts")[0]
def test_download(self):
with self.assertRaises(NotImplementedError):
abstract.ProtoDataset("./artifacts").download()
_ = 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 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):
@ -42,25 +107,25 @@ class TestTecator(unittest.TestCase):
rootdir = self.artifacts_dir.rpartition("/")[0]
self._remove_artifacts()
with self.assertRaises(RuntimeError):
_ = tecator.Tecator(rootdir, download=False)
_ = pt.datasets.Tecator(rootdir, download=False)
def test_download_caching(self):
rootdir = self.artifacts_dir.rpartition("/")[0]
_ = tecator.Tecator(rootdir, download=True, verbose=False)
_ = tecator.Tecator(rootdir, download=False, verbose=False)
_ = 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 = tecator.Tecator(rootdir, download=True, verbose=True)
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 = tecator.Tecator(root=rootdir,
train=True,
download=True,
verbose=False)
train = tecator.Tecator(root=rootdir, download=True, verbose=False)
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)
@ -68,7 +133,7 @@ class TestTecator(unittest.TestCase):
def test_download_test(self):
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
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)
@ -76,20 +141,20 @@ class TestTecator(unittest.TestCase):
def test_class_to_idx(self):
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
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 = tecator.Tecator(root=rootdir, train=False, verbose=False)
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 = tecator.Tecator(root=rootdir, train=False, verbose=False)
test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
_ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)
def tearDown(self):
pass
self._remove_artifacts()