def test_spirals_replicable(self):
"""Test if the data generation is replicable with a specified `seed`
Tests:
- return the same value if raised with the same seed
- return different values if noise or seed is different
"""
seed = 42
noise = 0.1
modes = ('archimedes', 'bernoulli', 'fermat')
for mode in modes:
spir0 = synthetic.spirals(n_samples=1000, noise=noise, seed=seed)
spir1 = synthetic.spirals(n_samples=1000, noise=noise, seed=seed)
np.testing.assert_array_equal(spir0.data, spir1.data)
np.testing.assert_array_equal(spir0.target, spir1.target)
spir1 = synthetic.spirals(n_samples=1000, noise=noise, seed=seed + 1)
self.assertRaises(AssertionError, np.testing.assert_array_equal,
spir0.data, spir1.data)
self.assertRaises(AssertionError, np.testing.assert_array_equal,
spir0.target, spir1.target)
spir1 = synthetic.spirals(n_samples=1000, noise=noise / 2., seed=seed)
self.assertRaises(AssertionError, np.testing.assert_array_equal,
spir0.data, spir1.data)
def test_spirals_replicable(self):
"""Test if the data generation is replicable with a specified `seed`
Tests:
- return the same value if raised with the same seed
- return different values if noise or seed is different
"""
seed = 42
noise = 0.1
modes = ('archimedes', 'bernoulli', 'fermat')
for mode in modes:
spir0 = synthetic.spirals(n_samples=1000, noise=noise, seed=seed)
spir1 = synthetic.spirals(n_samples=1000, noise=noise, seed=seed)
np.testing.assert_array_equal(spir0.data, spir1.data)
np.testing.assert_array_equal(spir0.target, spir1.target)
spir1 = synthetic.spirals(n_samples=1000,
noise=noise,
seed=seed + 1)
self.assertRaises(AssertionError, np.testing.assert_array_equal,
spir0.data, spir1.data)
self.assertRaises(AssertionError, np.testing.assert_array_equal,
spir0.target, spir1.target)
spir1 = synthetic.spirals(n_samples=1000,
noise=noise / 2.,
seed=seed)
self.assertRaises(AssertionError, np.testing.assert_array_equal,
spir0.data, spir1.data)
def test_spirals(self):
"""Test if the circles are generated correctly
Tests:
- if mode is unknown, ValueError is raised
- return type is `Dataset`
- returned `data` shape is (n_samples, n_features)
- returned `target` shape is (n_samples,)
- set of unique classes range is [0, n_classes)
"""
self.assertRaises(ValueError, synthetic.spirals, mode='_unknown_mode_spiral_')
n_samples = 100
modes = ('archimedes', 'bernoulli', 'fermat')
for mode in modes:
spir = synthetic.spirals(n_samples = n_samples, noise = None, mode = mode)
self.assertIsInstance(spir, datasets.base.Dataset)
self.assertTupleEqual(spir.data.shape, (n_samples,2))
self.assertTupleEqual(spir.target.shape, (n_samples,))
self.assertSetEqual(set(spir.target), set(range(2)))
def test_spirals(self):
"""Test if the circles are generated correctly
Tests:
- if mode is unknown, ValueError is raised
- return type is `Dataset`
- returned `data` shape is (n_samples, n_features)
- returned `target` shape is (n_samples,)
- set of unique classes range is [0, n_classes)
"""
self.assertRaises(ValueError,
synthetic.spirals,
mode='_unknown_mode_spiral_')
n_samples = 100
modes = ('archimedes', 'bernoulli', 'fermat')
for mode in modes:
spir = synthetic.spirals(n_samples=n_samples,
noise=None,
mode=mode)
self.assertIsInstance(spir, datasets.base.Dataset)
self.assertTupleEqual(spir.data.shape, (n_samples, 2))
self.assertTupleEqual(spir.target.shape, (n_samples, ))
self.assertSetEqual(set(spir.target), set(range(2)))
def test_spirals(self): synthetic.spirals(3)
def test_spirals(self):
synthetic.spirals(3)
