def test_load_json_nested(self):
# We will hack on connectivity a sub transformer on its `depth` param.
data = {
'parameters': {
'n_jobs': 2,
'depth': {
'parameters': {
'n_jobs': 3,
'input_type': 'list'},
'attributes': {'_base_chains': [['H']]},
'transformer': 'molml.molecule.Connectivity'},
'input_type': 'list'},
'attributes': {'_base_chains': [['H']]},
'transformer': 'molml.molecule.Connectivity'
}
path = '/tmp/somefile.json'
with open(path, 'w') as f:
json.dump(data, f)
m = load_json(path)
self.assertEqual(m.__class__.__name__,
data["transformer"].split('.')[-1])
self.assertEqual(m.n_jobs, data["parameters"]["n_jobs"])
self.assertEqual(m._base_chains, data["attributes"]["_base_chains"])
in_data = data["parameters"]["depth"]
in_m = m.depth
self.assertEqual(in_m.__class__.__name__,
in_data["transformer"].split('.')[-1])
self.assertEqual(in_m.n_jobs, in_data["parameters"]["n_jobs"])
self.assertEqual(in_m._base_chains,
in_data["attributes"]["_base_chains"])
def test_load_json_nested(self):
# We will hack on connectivity a sub transformer on its `depth` param.
data = {
'parameters': {
'n_jobs': 2,
'depth': {
'parameters': {
'n_jobs': 3,
'input_type': 'list'},
'attributes': {'_base_chains': [['H']]},
'transformer': 'molml.molecule.Connectivity'},
'input_type': 'list'},
'attributes': {'_base_chains': [['H']]},
'transformer': 'molml.molecule.Connectivity'
}
path = '/tmp/somefile.json'
with open(path, 'w') as f:
json.dump(data, f)
m = load_json(path)
self.assertEqual(m.__class__.__name__,
data["transformer"].split('.')[-1])
self.assertEqual(m.n_jobs, data["parameters"]["n_jobs"])
self.assertEqual(m._base_chains, data["attributes"]["_base_chains"])
in_data = data["parameters"]["depth"]
in_m = m.depth
self.assertEqual(in_m.__class__.__name__,
in_data["transformer"].split('.')[-1])
self.assertEqual(in_m.n_jobs, in_data["parameters"]["n_jobs"])
self.assertEqual(in_m._base_chains,
in_data["attributes"]["_base_chains"])
def test_load_json(self):
data = {'parameters': {'n_jobs': 2,
'input_type': 'list'},
'attributes': {'_base_chains': [['H']]},
'transformer': 'molml.molecule.Connectivity'}
path = '/tmp/somefile.json'
with open(path, 'w') as f:
json.dump(data, f)
with open(path, 'r') as f:
for x in (path, f):
m = load_json(path)
self.assertEqual(m.__class__.__name__,
data["transformer"].split('.')[-1])
self.assertEqual(m.n_jobs, data["parameters"]["n_jobs"])
self.assertEqual(m._base_chains,
data["attributes"]["_base_chains"])
def test_load_json(self):
data = {'parameters': {'n_jobs': 2,
'input_type': 'list'},
'attributes': {'_base_chains': [['H']]},
'transformer': 'molml.molecule.Connectivity'}
path = '/tmp/somefile.json'
with open(path, 'w') as f:
json.dump(data, f)
with open(path, 'r') as f:
for x in (path, f):
m = load_json(path)
self.assertEqual(m.__class__.__name__,
data["transformer"].split('.')[-1])
self.assertEqual(m.n_jobs, data["parameters"]["n_jobs"])
self.assertEqual(m._base_chains,
data["attributes"]["_base_chains"])
[-1.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
]
HCN = (HCN_ELES, HCN_COORDS)
if __name__ == "__main__":
# Example of fitting the Coulomb matrix and then saving it
feat = CoulombMatrix()
feat.fit([H2, HCN])
print("Saving Model")
feat.save_json("coulomb_model.json")
print("Loading Model")
feat2 = load_json("coulomb_model.json")
print(feat2.transform([H2, HCN]))
# Example of fitting a generallized crystal with the Coulomb matrix and
# then saving it
input_type = ("elements", "coords", "unit_cell")
radius = 4.1
feat = CoulombMatrix(input_type=input_type)
crystal = GenerallizedCrystal(transformer=feat, radius=radius)
feat.fit([H2_FULL])
print("Saving Model")
feat.save_json("coulomb_crystal_model.json")
print("Loading Model")
feat2 = load_json("coulomb_crystal_model.json")
print(feat2.transform([H2_FULL]))
HCN_COORDS = [
[-1.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
]
HCN = (HCN_ELES, HCN_COORDS)
if __name__ == "__main__":
# Example of fitting the Coulomb matrix and then saving it
feat = CoulombMatrix()
feat.fit([H2, HCN])
print("Saving Model")
feat.save_json("coulomb_model.json")
print("Loading Model")
feat2 = load_json("coulomb_model.json")
print(feat2.transform([H2, HCN]))
# Example of fitting a generallized crystal with the Coulomb matrix and
# then saving it
input_type = ("elements", "coords", "unit_cell")
radius = 4.1
feat = CoulombMatrix(input_type=input_type)
crystal = GenerallizedCrystal(transformer=feat, radius=radius)
feat.fit([H2_FULL])
print("Saving Model")
feat.save_json("coulomb_crystal_model.json")
print("Loading Model")
feat2 = load_json("coulomb_crystal_model.json")
print(feat2.transform([H2_FULL]))