def test_train(self):
"""Tests train."""
# with test set
train_size = 0.75
opt = Optimizer((self.A, self.y), train_size=train_size)
self.assertIsNone(opt._rmse_train)
self.assertIsNone(opt._rmse_test)
self.assertIsNone(opt.train_scatter_data)
self.assertIsNone(opt.test_scatter_data)
opt.train()
self.assertIsNotNone(opt._rmse_train)
self.assertIsNotNone(opt._rmse_test)
self.assertIsNotNone(opt.train_scatter_data)
self.assertIsNotNone(opt.test_scatter_data)
# without testing
train_size = 1.0
opt = Optimizer((self.A, self.y), train_size=train_size)
self.assertIsNone(opt._rmse_train)
self.assertIsNone(opt._rmse_test)
self.assertIsNone(opt.train_scatter_data)
self.assertIsNone(opt.test_scatter_data)
opt.train()
self.assertIsNotNone(opt._rmse_train)
self.assertIsNone(opt._rmse_test)
self.assertIsNotNone(opt.train_scatter_data)
self.assertIsNone(opt.test_scatter_data)
def test_get_rows_from_indices(self):
"""Tests _get_rows_from_indices."""
opt = Optimizer((self.A, self.y))
all_rows = np.arange(self.n_rows)
train_size = int(0.8 * self.n_rows)
train_set_target = np.random.choice(all_rows,
train_size,
replace=False)
test_set_target = sorted(np.setdiff1d(all_rows, train_set_target))
# specify only train_set, test set should default to remaining rows
train_set, test_set = opt._get_rows_from_indices(
train_set_target, None)
self.assertSequenceEqual(sorted(train_set_target), sorted(train_set))
self.assertSequenceEqual(sorted(test_set_target), sorted(test_set))
# specify only test_set, train set should default to remaining rows
train_set, test_set = opt._get_rows_from_indices(None, test_set_target)
self.assertSequenceEqual(sorted(train_set_target), sorted(train_set))
self.assertSequenceEqual(sorted(test_set_target), sorted(test_set))
# specify partial sets meaning not all rows are used
train_set_target = np.delete(train_set_target, [0, 1, 2])
test_set_target = np.delete(test_set_target, [0, 1, 2])
train_set, test_set = opt._get_rows_from_indices(
train_set_target, test_set_target)
self.assertSequenceEqual(sorted(train_set_target), sorted(train_set))
self.assertSequenceEqual(sorted(test_set_target), sorted(test_set))
# test invalid input
with self.assertRaises(ValueError):
opt._get_rows_from_indices(None, None)
def test_size_properties(self):
"""Tests the properties in regards to training/test sets and sizes."""
# test without test_set
train_set = np.arange(0, self.n_rows)
opt = Optimizer((self.A, self.y), train_set=train_set)
self.assertSequenceEqual(opt.train_set.tolist(), train_set.tolist())
self.assertEqual(len(train_set), opt.train_size)
self.assertEqual(1.0, opt.train_fraction)
self.assertIsNone(opt.test_set)
self.assertEqual(opt.test_size, 0)
self.assertEqual(opt.test_fraction, 0)
# test with test set
test_set = np.arange(int(0.7 * self.n_rows), int(0.8 * self.n_rows))
opt = Optimizer((self.A, self.y), test_set=test_set)
self.assertSequenceEqual(test_set.tolist(), opt.test_set.tolist())
self.assertEqual(opt.test_size, len(test_set))
self.assertAlmostEqual(opt.test_fraction, len(test_set) / self.n_rows)
def test_summary_property(self):
"""Tests summary property."""
# without having trained
opt = Optimizer((self.A, self.y))
self.assertIsInstance(opt.summary, dict)
# with having trained
opt.train()
self.assertIsInstance(opt.summary, dict)
self.assertIn('rmse_train', opt.summary.keys())
self.assertIn('rmse_test', opt.summary.keys())
def test_zero_error_with_least_square_fit(self):
""" Test that the error is zero if training without noise and with
least-squares. """
# set up dummy linear problem data
for standardize in [True, False]:
y = np.dot(self.A, self.x)
opt = Optimizer((self.A, y),
fit_method='least-squares',
standardize=standardize)
opt.train()
self.assertAlmostEqual(opt.rmse_train, 0.0)
self.assertAlmostEqual(opt.rmse_test, 0.0)
self.assertAlmostEqual(np.abs(self.x - opt.parameters).max(), 0)
def test_get_rows_via_sizes(self):
"""Tests _get_rows_via_sizes functionality."""
opt = Optimizer((self.A, self.y))
# test with only train_size defined
train_size, test_size = int(0.8 * self.n_rows), None
train_set, test_set = opt._get_rows_via_sizes(train_size, test_size)
self.assertEqual(train_size, len(train_set))
self.assertEqual(self.n_rows - train_size, len(test_set))
# test with only test_size defined
train_size, test_size = None, int(0.8 * self.n_rows)
train_set, test_set = opt._get_rows_via_sizes(train_size, test_size)
self.assertEqual(test_size, len(test_set))
self.assertEqual(self.n_rows - test_size, len(train_set))
# test with both defined
train_size, test_size = int(0.8 * self.n_rows), int(0.15 * self.n_rows)
train_set, test_set = opt._get_rows_via_sizes(train_size, test_size)
self.assertEqual(train_size, len(train_set))
self.assertEqual(test_size, len(test_set))
# test with fractions
train_size, test_size = 0.7, 0.2
train_set, test_set = opt._get_rows_via_sizes(train_size, test_size)
self.assertLess(abs(train_size * self.n_rows - len(train_set)),
self.tol)
self.assertLess(abs(test_size * self.n_rows - len(test_set)), self.tol)
# test edge case with full training set
test_size = None
for train_size in [1.0, self.n_rows]:
train_set, test_set = opt._get_rows_via_sizes(
train_size, test_size)
self.assertEqual(len(train_set), self.n_rows)
self.assertIsNone(test_set)
# test invalid sizes
with self.assertRaises(ValueError):
train_size, test_size = None, 1.0
opt._get_rows_via_sizes(train_size, test_size)
with self.assertRaises(ValueError):
train_size, test_size = None, None
opt._get_rows_via_sizes(train_size, test_size)
def test_setup_rows(self):
"""
Tests _setup_rows.
Simply test that function raise when no training data available
"""
opt = Optimizer((self.A, self.y))
# no training data from train_size
with self.assertRaises(ValueError):
train_size, test_size = 0, 0.5
opt._setup_rows(train_size, test_size, None, None)
# no training data from train_set
with self.assertRaises(ValueError):
train_set, test_set = [], np.arange(0, int(0.5 * self.n_rows))
opt._setup_rows(None, None, train_set, test_set)
# overlapping indices in train_set and test_set
with self.assertRaises(ValueError):
train_set, test_set = [1, 2, 3, 4, 5], [5, 6, 7, 8, 9, 10]
opt._setup_rows(None, None, train_set, test_set)
A = np.random.random((n, m))
y = np.random.random(n)
# constraint sum eci[inds] = 0
inds1 = [1, 3, 4, 5]
inds2 = [2, 6, 7, 8]
M = np.zeros((2, m))
M[0, inds1] = 1
M[1, inds2] = 1
c = Constraints(m)
c.add_constraint(M)
Ac = c.transform(A)
opt = Optimizer((Ac, y), fit_method='ridge')
opt.train()
parameters = c.inverse_transform(opt.parameters)
sum_1 = parameters[inds1].sum()
sum_2 = parameters[inds2].sum()
print('constraints 1, ', sum_1)
print('constraints 2, ', sum_2)
assert abs(sum_1) < 1e-12
assert abs(sum_2) < 1e-12
# Test get_mixing_energy_constraints function
a = 4.0
prim = bulk('Au', a=a)
prim.append(Atom('H', position=(a / 2, a / 2, a / 2)))
def test_repr(self):
"""Tests repr dunder."""
opt = Optimizer((self.A, self.y))
self.assertIsInstance(repr(opt), str)