def test_transform_transformer(self):
trans = Shell(input_type="filename")
a = AtomKernel(transformer=trans)
a.fit(ALL)
res = a.transform(ALL)
try:
numpy.testing.assert_array_almost_equal(RBF_KERNEL, res)
except AssertionError as e:
self.fail(e)
def test_laplace_kernel(self):
# Set depth=2 so the comparison is not trivial
trans = Shell(input_type="filename", depth=2)
a = AtomKernel(transformer=trans, kernel="laplace", gamma=1.)
a.fit(ALL)
res = a.transform(ALL)
try:
numpy.testing.assert_array_almost_equal(LAPLACE_KERNEL, res)
except AssertionError as e:
self.fail(e)
def test_transform_features(self):
trans = Shell(input_type="filename")
feats = trans.fit_transform(ALL)
a = AtomKernel()
values = list(zip(feats, ALL_NUMS))
a.fit(values)
res = a.transform(values)
try:
numpy.testing.assert_array_almost_equal(RBF_KERNEL, res)
except AssertionError as e:
self.fail(e)
def test_custom_kernel(self):
# Set depth=2 so the comparison is not trivial
trans = Shell(input_type="filename", depth=2)
# Simple linear kernel
a = AtomKernel(transformer=trans,
kernel=lambda x, y: numpy.dot(x, numpy.transpose(y)))
a.fit(ALL)
res = a.transform(ALL)
try:
numpy.testing.assert_array_almost_equal(LINEAR_KERNEL, res)
except AssertionError as e:
self.fail(e)
def test_transform_before_fit(self):
a = AtomKernel()
with self.assertRaises(ValueError):
a.transform(ALL)
from molml.kernel import AtomKernel
from utils import load_qm7
if __name__ == "__main__":
# This is just boiler plate code to load the data
Xin_train, Xin_test, y_train, y_test = load_qm7()
# Look at just a few examples to be quick
n_train = 200
n_test = 200
Xin_train = Xin_train[:n_train]
y_train = y_train[:n_train]
Xin_test = Xin_test[:n_test]
y_test = y_test[:n_test]
gamma = 1e-7
alpha = 1e-7
kern = AtomKernel(gamma=gamma,
transformer=LocalEncodedBond(n_jobs=-1),
n_jobs=-1)
K_train = kern.fit_transform(Xin_train)
K_test = kern.transform(Xin_test)
clf = KernelRidge(alpha=alpha, kernel="precomputed")
clf.fit(K_train, y_train)
train_error = MAE(clf.predict(K_train), y_train)
test_error = MAE(clf.predict(K_test), y_test)
print("Train MAE: %.4f Test MAE: %.4f" % (train_error, test_error))
print()
from molml.kernel import AtomKernel
from utils import load_qm7
if __name__ == "__main__":
# This is just boiler plate code to load the data
Xin_train, Xin_test, y_train, y_test = load_qm7()
# Look at just a few examples to be quick
n_train = 200
n_test = 200
Xin_train = Xin_train[:n_train]
y_train = y_train[:n_train]
Xin_test = Xin_test[:n_test]
y_test = y_test[:n_test]
gamma = 1e-7
alpha = 1e-7
kern = AtomKernel(gamma=gamma, transformer=LocalEncodedBond(n_jobs=-1),
n_jobs=-1)
K_train = kern.fit_transform(Xin_train)
K_test = kern.transform(Xin_test)
clf = KernelRidge(alpha=alpha, kernel="precomputed")
clf.fit(K_train, y_train)
train_error = MAE(clf.predict(K_train), y_train)
test_error = MAE(clf.predict(K_test), y_test)
print("Train MAE: %.4f Test MAE: %.4f" % (train_error, test_error))
print()