def test_crystal_model(self):
callbacks = [
ModelCheckpointMAE(
filepath='./val_mae_{epoch:05d}_{val_mae:.6f}.hdf5',
save_best_only=True,
val_gen=self.train_gen_crystal,
steps_per_val=1,
is_pa=False),
GeneratorLog(self.train_gen_crystal,
1,
self.train_gen_crystal,
1,
val_names=['Ef'],
val_units=['eV/atom']),
ManualStop()
]
self.model.fit_generator(generator=self.train_gen_crystal,
steps_per_epoch=1,
epochs=2,
verbose=1,
callbacks=callbacks)
model_files = glob('val_mae*.hdf5')
self.assertGreater(len(model_files), 0)
for i in model_files:
os.remove(i)
def test_crystal_model(self):
model = set2set_with_embedding_mp(self.n_bond_features,
self.n_global_features,
n_blocks=1,
lr=1e-2,
n1=4,
n2=4,
n3=4,
n_pass=1,
n_target=1)
callbacks = [
ModelCheckpointMAE(
filepath='./val_mae_{epoch:05d}_{val_mae:.6f}.hdf5',
save_best_only=True,
val_gen=self.train_gen_crystal,
steps_per_val=1,
is_pa=False),
GeneratorLog(self.train_gen_crystal,
1,
self.train_gen_crystal,
1,
val_names=['Ef'],
val_units=['eV/atom']),
ManualStop()
]
model.fit_generator(generator=self.train_gen_crystal,
steps_per_epoch=1,
epochs=2,
verbose=1,
callbacks=callbacks)
model_files = glob('val_mae*.hdf5')
self.assertGreater(len(model_files), 0)
for i in model_files:
os.remove(i)
def test_callback(self):
callbacks = [
GeneratorLog(self.train_gen,
steps_per_train=1,
val_gen=self.train_gen,
steps_per_val=1,
n_every=1,
val_names=['conductivity'],
val_units=['S/cm']),
ModelCheckpointMAE(
filepath='./val_mae_{epoch:05d}_{val_mae:.6f}.hdf5',
val_gen=self.train_gen,
steps_per_val=1),
]
captured_output = StringIO()
sys.stdout = captured_output
before_fit_file = glob.glob("./val_mae*.hdf5")
self.model.fit_generator(self.train_gen,
steps_per_epoch=1,
epochs=1,
callbacks=callbacks,
verbose=0)
after_fit_file = glob.glob("./val_mae*.hdf5")
sys.stdout = sys.__stdout__
result = captured_output.getvalue()
self.assertRegex(result,
"Train MAE:\nconductivity: [-+]?\d*\.\d+|\d+ S/cm")
self.assertRegex(result,
"Test MAE:\nconductivity: [-+]?\d*\.\d+|\d+ S/cm")
self.assertEqual(len(before_fit_file), 0)
self.assertEqual(len(after_fit_file), 1)
os.remove(after_fit_file[0])
def test_callback(self):
callbacks = [GeneratorLog(self.train_gen, steps_per_train=1, val_gen=self.train_gen, steps_per_val=1,
n_every=1, val_names=['conductivity'], val_units=['S/cm']),
ModelCheckpointMAE(filepath='./val_mae_{epoch:05d}_{val_mae:.6f}.hdf5', val_gen=self.train_gen,
steps_per_val=1),
]
before_fit_file = glob.glob("./val_mae*.hdf5")
self.model.fit_generator(self.train_gen, steps_per_epoch=1, epochs=1, callbacks=callbacks, verbose=0)
after_fit_file = glob.glob("./val_mae*.hdf5")
self.assertEqual(len(before_fit_file), 0)
self.assertEqual(len(after_fit_file), 1)
os.remove(after_fit_file[0])
callback_mae = ModelCheckpointMAE(filepath='./val_mae_{epoch:05d}_{val_mae:.6f}.hdf5', val_gen=self.train_gen,
steps_per_val=1, target_scaler=StandardScaler(1, 1, is_intensive=True))
dummy_target = np.array([[1, 1], [2, 2]])
dummy_nb_atoms = np.array([[2], [3]])
transformed = callback_mae.target_scaler.inverse_transform(dummy_target, dummy_nb_atoms)
self.assertTrue(np.allclose(transformed, np.array([[2, 2], [3, 3]])))
callback_mae = ModelCheckpointMAE(filepath='./val_mae_{epoch:05d}_{val_mae:.6f}.hdf5', val_gen=self.train_gen,
steps_per_val=1, target_scaler=StandardScaler(1, 1, is_intensive=False))
dummy_target = np.array([[1, 1], [2, 2]])
dummy_nb_atoms = np.array([[2], [3]])
transformed = callback_mae.target_scaler.inverse_transform(dummy_target, dummy_nb_atoms)
self.assertTrue(np.allclose(transformed, np.array([[4, 4], [9, 9]])))