def test_get_labels_reduced(self):
a = LocalCoulombMatrix(max_occupancy=1, use_reduced=True)
X = a.fit_transform([METHANE])
labels = a.get_labels()
self.assertEqual(X.shape[2], len(labels))
expected = ('local-coul_0-0', 'local-coul_0-1', 'local-coul_1-1')
self.assertEqual(labels, expected)
def test_transform_alpha(self):
a = LocalCoulombMatrix(max_occupancy=1, alpha=2., use_reduced=True)
a.fit([METHANE, MID])
m = a.transform([METHANE, MID])
expected_results = numpy.array([
[
[36.8581052, 5.03182436, 0.5],
[0.5, 5.0317536, 36.8581052],
[0.5, 5.03182436, 36.8581052],
[0.5, 5.03158837, 36.8581052],
[0.5, 5.02392255, 36.8581052],
],
[
[36.8581052, 18.18762711, 36.8581052],
[36.8581052, 24.62755378, 73.51669472],
[73.51669472, 32.84431333, 73.51669472],
[73.51669472, 32.84431333, 73.51669472],
[73.51669472, 35.25529211, 73.51669472],
[73.51669472, 35.25529211, 73.51669472],
[0.5, 0.35524858, 0.5],
[0.5, 1., 0.5],
[0.5, 1., 0.5]
]
])
try:
mm = [numpy.linalg.norm(x) for x in (expected_results - m)]
numpy.testing.assert_array_almost_equal(
mm,
[0.0, 0.0])
except AssertionError as e:
self.fail(e)
def test_get_labels_reduced(self):
a = LocalCoulombMatrix(max_occupancy=1, use_reduced=True)
X = a.fit_transform([METHANE])
labels = a.get_labels()
self.assertEqual(X.shape[2], len(labels))
expected = ('local-coul_0-0', 'local-coul_0-1', 'local-coul_1-1')
self.assertEqual(labels, expected)
def test_transform_reduced(self):
a = LocalCoulombMatrix(max_occupancy=1, use_reduced=True)
a.fit([METHANE, MID])
m = a.transform([METHANE, MID])
expected_results = numpy.array([
[
[36.8581052, 3.53894587, 0.5],
[0.5, 3.53879657, 36.8581052],
[0.5, 3.53894587, 36.8581052],
[0.5, 3.53844797, 36.8581052],
[0.5, 3.52229971, 36.8581052],
],
[
[36.85810519942594, 4.642192257970912, 36.85810519942594],
[36.85810519942594, 6.483079598556282, 73.51669471981023],
[73.51669471981023, 8.650089711338763, 73.51669471981023],
[73.51669471981023, 8.650089711338763, 73.51669471981023],
[73.51669471981023, 10.698256448660478, 73.51669471981023],
[73.51669471981023, 10.698256448660478, 73.51669471981023],
[0.5, 0.04483292, 0.5],
[0.5, 1., 0.5],
[0.5, 1., 0.5]
]
])
try:
mm = [numpy.linalg.norm(x) for x in (expected_results - m)]
numpy.testing.assert_array_almost_equal(
mm,
[0.0, 0.0])
except AssertionError as e:
self.fail(e)
def test_transform_alpha(self):
a = LocalCoulombMatrix(max_occupancy=1, alpha=2., use_reduced=True)
a.fit([METHANE, MID])
m = a.transform([METHANE, MID])
expected_results = numpy.array([
[
[37.3581052, 5.53182436],
[37.3581052, 41.8898588],
[37.3581052, 41.88992956],
[37.3581052, 41.88969357],
[37.3581052, 41.88202775]
],
[
[73.7162104, 55.04573231],
[110.37479992, 98.1442485],
[147.03338944, 106.36100805],
[147.03338944, 106.36100805],
[147.03338944, 108.77198683],
[147.03338944, 108.77198683],
[1., 0.85524858],
[1., 1.5],
[1., 1.5]
]])
try:
mm = [numpy.linalg.norm(x) for x in (expected_results - m)]
numpy.testing.assert_array_almost_equal(
mm,
[0.0, 0.0])
except AssertionError as e:
self.fail(e)
def test_transform_reduced(self):
a = LocalCoulombMatrix(max_occupancy=1, use_reduced=True)
a.fit([METHANE, MID])
m = a.transform([METHANE, MID])
expected_results = numpy.array([
[
[37.3581052, 4.03894587],
[37.3581052, 40.39690177],
[37.3581052, 40.39705107],
[37.3581052, 40.39655317],
[37.3581052, 40.38040491]
],
[
[73.7162104, 41.50029746],
[110.37479992, 79.99977432],
[147.03338944, 82.16678443],
[147.03338944, 82.16678443],
[147.03338944, 84.21495117],
[147.03338944, 84.21495117],
[1., 0.54483292],
[1., 1.5],
[1., 1.5]
]])
try:
mm = [numpy.linalg.norm(x) for x in (expected_results - m)]
numpy.testing.assert_array_almost_equal(
mm,
[0.0, 0.0])
except AssertionError as e:
self.fail(e)
def test_fit_transform(self):
a = LocalCoulombMatrix(max_occupancy=1)
m = a.fit_transform([METHANE, MID])
try:
mm = [numpy.linalg.norm(x) for x in (BASE_LOCAL_COULOMB - m)]
numpy.testing.assert_array_almost_equal(mm, [0.0, 0.0])
except AssertionError as e:
self.fail(e)
def test_fit_transform(self):
a = LocalCoulombMatrix(max_occupancy=1)
m = a.fit_transform([METHANE, MID])
try:
mm = [numpy.linalg.norm(x) for x in (BASE_LOCAL_COULOMB - m)]
numpy.testing.assert_array_almost_equal(
mm,
[0.0, 0.0])
except AssertionError as e:
self.fail(e)
def test_transform_max_occupancy(self):
a = LocalCoulombMatrix(max_occupancy=5)
a.fit([METHANE, MID])
m = a.transform([METHANE, MID])
# Reduce to a sum to save space
expected_results = [337.53938456166259, 3019.413939202841]
try:
numpy.testing.assert_array_almost_equal([x.sum() for x in m],
expected_results)
except AssertionError as e:
self.fail(e)
def test_transform_max_occupancy(self):
a = LocalCoulombMatrix(max_occupancy=5)
a.fit([METHANE, MID])
m = a.transform([METHANE, MID])
# Reduce to a sum to save space
expected_results = [337.53938456166259, 3019.413939202841]
try:
numpy.testing.assert_array_almost_equal(
[x.sum() for x in m],
expected_results)
except AssertionError as e:
self.fail(e)
def test_transform_alpha(self):
a = LocalCoulombMatrix(max_occupancy=1, alpha=2., use_reduced=True)
a.fit([METHANE, MID])
m = a.transform([METHANE, MID])
expected_results = numpy.array(
[[
[36.8581052, 5.03182436, 0.5],
[0.5, 5.0317536, 36.8581052],
[0.5, 5.03182436, 36.8581052],
[0.5, 5.03158837, 36.8581052],
[0.5, 5.02392255, 36.8581052],
],
[[36.8581052, 18.18762711, 36.8581052],
[36.8581052, 24.62755378, 73.51669472],
[73.51669472, 32.84431333, 73.51669472],
[73.51669472, 32.84431333, 73.51669472],
[73.51669472, 35.25529211, 73.51669472],
[73.51669472, 35.25529211, 73.51669472], [0.5, 0.35524858, 0.5],
[0.5, 1., 0.5], [0.5, 1., 0.5]]])
try:
mm = [numpy.linalg.norm(x) for x in (expected_results - m)]
numpy.testing.assert_array_almost_equal(mm, [0.0, 0.0])
except AssertionError as e:
self.fail(e)
def test_transform_reduced(self):
a = LocalCoulombMatrix(max_occupancy=1, use_reduced=True)
a.fit([METHANE, MID])
m = a.transform([METHANE, MID])
expected_results = numpy.array(
[[
[36.8581052, 3.53894587, 0.5],
[0.5, 3.53879657, 36.8581052],
[0.5, 3.53894587, 36.8581052],
[0.5, 3.53844797, 36.8581052],
[0.5, 3.52229971, 36.8581052],
],
[[36.85810519942594, 4.642192257970912, 36.85810519942594],
[36.85810519942594, 6.483079598556282, 73.51669471981023],
[73.51669471981023, 8.650089711338763, 73.51669471981023],
[73.51669471981023, 8.650089711338763, 73.51669471981023],
[73.51669471981023, 10.698256448660478, 73.51669471981023],
[73.51669471981023, 10.698256448660478, 73.51669471981023],
[0.5, 0.04483292, 0.5], [0.5, 1., 0.5], [0.5, 1., 0.5]]])
try:
mm = [numpy.linalg.norm(x) for x in (expected_results - m)]
numpy.testing.assert_array_almost_equal(mm, [0.0, 0.0])
except AssertionError as e:
self.fail(e)
def test_transform_before_fit(self):
a = LocalCoulombMatrix(max_occupancy=1)
# This should not raise an error, becaues no fitting is needed
a.transform(ALL_DATA)
def test_fit(self):
a = LocalCoulombMatrix()
# This should not do anything
a.fit(ALL_DATA)
def test_transform_before_fit(self):
a = LocalCoulombMatrix(max_occupancy=1)
# This should not raise an error, becaues no fitting is needed
a.transform(ALL_DATA)
def test_fit(self):
a = LocalCoulombMatrix()
# This should not do anything
a.fit(ALL_DATA)