def test_zoom(self):
# Arrange
X = np.zeros((300, 1, 128, 128))
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
zoomed = ds.zoom(0.5)
# Assert
assert zoomed.X.shape == (300, 1, 64, 64)
def test_zoom(self):
# Arrange
X = np.zeros((300, 1, 128, 128))
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
zoomed = ds.zoom(0.5)
# Assert
assert zoomed.X.shape == (300, 1, 64, 64)
def test_unstandardize2(self):
# Arrange
X = np.random.randn(1e4, 1, 32, 32) * 10 + 12
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
ds.standardize(standardization_type='individual')
ds.unstandardize()
# Assert
np.testing.assert_allclose(X, ds.X, atol=1e-5)
def test_to_nxmxc(self):
# Arrange
X = np.zeros((100, 3, 4, 2))
y = np.arange(len(X))
ds = DataSet(X, y)
assert ds.X.shape == (100, 3, 4, 2)
# Apply
nxmxc = ds.to_nxmxc()
# Assert
assert nxmxc.X.shape == (100, 4, 2, 3)
for x in nxmxc.X:
assert x.shape == (4, 2, 3)
def test_swap_standardization(self):
# Arrange
X = np.random.randn(1e4, 1, 32, 32) * 10 + 12
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
ds.standardize(standardization_type='individual')
ds.standardize(standardization_type='global')
# Assert
assert ds.mean.shape == ()
np.testing.assert_allclose(ds.mean, 12, atol=1e-1)
np.testing.assert_allclose(ds.std, 10, atol=1e-1)
def test_to_nxmxc(self):
# Arrange
X = np.zeros((100, 3, 4, 2))
y = np.arange(len(X))
ds = DataSet(X, y)
assert ds.X.shape == (100, 3, 4, 2)
# Apply
nxmxc = ds.to_nxmxc()
# Assert
assert nxmxc.X.shape == (100, 4, 2, 3)
for x in nxmxc.X:
assert x.shape == (4, 2, 3)
def test_standardize_none(self):
# Arrange
x1 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]])
x2 = np.array([[1, 0, 1], [1, 0, 1], [1, 0, 1]])
X = np.array([x1, x2])
y = np.arange(len(X))
ds = DataSet(X, y)
Xorig = ds.X.copy()
# Apply
ds.standardize(standardization_type=None)
# Assert
np.testing.assert_allclose(Xorig, ds.X)
def test_standardize_global(self):
# Arrange
x1 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]])
x2 = np.array([[1, 0, 1], [1, 0, 1], [1, 0, 1]])
X = np.array([x1, x2])
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
ds.standardize(standardization_type='global')
# Assert
assert ds.mean.shape == ()
assert ds.mean == 0.5
assert ds.X.std() == 1
def test_standardize(self):
# Arrange
x1 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]])
x2 = np.array([[1, 0, 1], [1, 0, 1], [1, 0, 1]])
X = np.array([x1, x2])
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
ds.standardize()
# Assert
for x in ds.X:
np.testing.assert_allclose(x.mean(), 0, atol=1e-7)
np.testing.assert_allclose(x.std(), 1, atol=1e-7)
def test_standardize_none(self):
# Arrange
x1 = np.array([[0, 1, 0],
[0, 1, 0],
[0, 1, 0]])
x2 = np.array([[1, 0, 1],
[1, 0, 1],
[1, 0, 1]])
X = np.array([x1, x2])
y = np.arange(len(X))
ds = DataSet(X, y)
Xorig = ds.X.copy()
# Apply
ds.standardize(standardization_type=None)
# Assert
np.testing.assert_allclose(Xorig, ds.X)
def test_standardize(self):
# Arrange
x1 = np.array([[0, 1, 0],
[0, 1, 0],
[0, 1, 0]])
x2 = np.array([[1, 0, 1],
[1, 0, 1],
[1, 0, 1]])
X = np.array([x1, x2])
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
ds.standardize()
# Assert
for x in ds.X:
np.testing.assert_allclose(x.mean(), 0, atol=1e-7)
np.testing.assert_allclose(x.std(), 1, atol=1e-7)
def test_unstandardize(self):
# Arrange
x1 = np.array([[0, 1, 0],
[0, 1, 0],
[0, 1, 0]])
x2 = np.array([[1, 0, 1],
[1, 0, 1],
[1, 0, 1]])
X = np.array([x1, x2])
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
ds.standardize()
ds.unstandardize()
# Assert
np.testing.assert_allclose(ds.X[0].squeeze(), x1)
np.testing.assert_allclose(ds.X[1].squeeze(), x2)
def test_standardize_global(self):
# Arrange
x1 = np.array([[0, 1, 0],
[0, 1, 0],
[0, 1, 0]])
x2 = np.array([[1, 0, 1],
[1, 0, 1],
[1, 0, 1]])
X = np.array([x1, x2])
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
ds.standardize(standardization_type='global')
# Assert
assert ds.mean.shape == ()
assert ds.mean == 0.5
assert ds.X.std() == 1
def test_classes(self):
# Arrange
X = np.ones((10, 32, 32))
y = [0, 0, 1, 2, 2, 1, 3, 3, 4, 4, 0]
ds = DataSet(X, y)
# Apply
classes = ds.classes
# Assert
assert classes == [0, 1, 2, 3, 4]
def test_one_of_every_class(self):
# Arrange
a = np.array([[[0]]])
a2 = np.array([[[0]]])
b = np.array([[[1]]])
b2 = np.array([[[1]]])
c = np.array([[[2]]])
d = np.array([[[3]]])
d2 = np.array([[[3]]])
d3 = np.array([[[3]]])
X = np.array([a, a2, b, b2, c, d, d2, d3])
y = np.array([0, 0, 1, 1, 2, 3, 3, 3])
ds = DataSet(X, y).shuffle()
# Apply
sample = ds.one_of_class()
# Assert
np.testing.assert_equal(sample.X, np.array([a, b, c, d]))
np.testing.assert_equal(sample.y, np.array([0, 1, 2, 3]))
def test_one_of_every_class(self):
# Arrange
a = np.array([[[0]]])
a2 = np.array([[[0]]])
b = np.array([[[1]]])
b2 = np.array([[[1]]])
c = np.array([[[2]]])
d = np.array([[[3]]])
d2 = np.array([[[3]]])
d3 = np.array([[[3]]])
X = np.array([a, a2, b, b2, c, d, d2, d3])
y = np.array([0, 0, 1, 1, 2, 3, 3, 3])
ds = DataSet(X, y).shuffle()
# Apply
sample = ds.one_of_class()
# Assert
np.testing.assert_equal(sample.X, np.array([a, b, c, d]))
np.testing.assert_equal(sample.y, np.array([0, 1, 2, 3]))
def test_unstandardize2(self):
# Arrange
X = np.random.randn(1e4, 1, 32, 32) * 10 + 12
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
ds.standardize(standardization_type='individual')
ds.unstandardize()
# Assert
np.testing.assert_allclose(X, ds.X, atol=1e-5)
def test_unstandardize_global(self):
# Arrange
X = np.random.randn(1e4, 1, 32, 32) * 10 + 12
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
ds.standardize(standardization_type='global')
ds.unstandardize()
# Assert
assert ds.mean.shape == ()
np.testing.assert_allclose(ds.mean, 12, atol=1e-1)
np.testing.assert_allclose(ds.std, 10, atol=1e-1)
def test_unstandardize(self):
# Arrange
x1 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]])
x2 = np.array([[1, 0, 1], [1, 0, 1], [1, 0, 1]])
X = np.array([x1, x2])
y = np.arange(len(X))
ds = DataSet(X, y)
# Apply
ds.standardize()
ds.unstandardize()
# Assert
np.testing.assert_allclose(ds.X[0].squeeze(), x1)
np.testing.assert_allclose(ds.X[1].squeeze(), x2)