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]))