def test_orbital_field_matrix(self):
ofm_maker = OrbitalFieldMatrix()
ofm = ofm_maker.featurize(self.diamond)[0]
mtarget = np.zeros((32, 32))
mtarget[1][1] = 1.4789015 # 1.3675444
mtarget[1][3] = 1.4789015 # 1.3675444
mtarget[3][1] = 1.4789015 # 1.3675444
mtarget[3][3] = 1.4789015 # 1.3675444 if for a coord# of exactly 4
for i in range(32):
for j in range(32):
if not i in [1, 3] and not j in [1, 3]:
self.assertEquals(ofm[i, j], 0.0)
mtarget = np.matrix(mtarget)
self.assertAlmostEqual(np.linalg.norm(ofm - mtarget), 0.0, places=4)
def test_orbital_field_matrix(self):
ofm_maker = OrbitalFieldMatrix()
ofm = ofm_maker.featurize(self.diamond)[0]
mtarget = np.zeros((32, 32))
mtarget[1][1] = 1.4789015 # 1.3675444
mtarget[1][3] = 1.4789015 # 1.3675444
mtarget[3][1] = 1.4789015 # 1.3675444
mtarget[3][3] = 1.4789015 # 1.3675444 if for a coord# of exactly 4
for i in range(32):
for j in range(32):
if not i in [1, 3] and not j in [1, 3]:
self.assertEqual(ofm[i, j], 0.0)
mtarget = np.matrix(mtarget)
self.assertAlmostEqual(
np.linalg.norm(ofm - mtarget), 0.0, places=4)
ofm_maker = OrbitalFieldMatrix(True)
ofm = ofm_maker.featurize(self.diamond)[0]
mtarget = np.zeros((39, 39))
mtarget[1][1] = 1.4789015
mtarget[1][3] = 1.4789015
mtarget[3][1] = 1.4789015
mtarget[3][3] = 1.4789015
mtarget[1][33] = 1.4789015
mtarget[3][33] = 1.4789015
mtarget[33][1] = 1.4789015
mtarget[33][3] = 1.4789015
mtarget[33][33] = 1.4789015
mtarget = np.matrix(mtarget)
self.assertAlmostEqual(
np.linalg.norm(ofm - mtarget), 0.0, places=4)
ofm_flat = OrbitalFieldMatrix(period_tag=False, flatten=True)
self.assertEqual(len(ofm_flat.feature_labels()), 1024)
ofm_flat = OrbitalFieldMatrix(period_tag=True, flatten=True)
self.assertEqual(len(ofm_flat.feature_labels()), 1521)
ofm_vector = ofm_flat.featurize(self.diamond)
for ix in [40, 42, 72, 118, 120, 150, 1288, 1320]:
self.assertAlmostEqual(ofm_vector[ix], 1.4789015345821415)
def test_orbital_field_matrix(self):
ofm_maker = OrbitalFieldMatrix(flatten=False)
ofm = ofm_maker.featurize(self.diamond)[0]
mtarget = np.zeros((32, 32))
mtarget[1][1] = 1.4789015 # 1.3675444
mtarget[1][3] = 1.4789015 # 1.3675444
mtarget[3][1] = 1.4789015 # 1.3675444
mtarget[3][3] = 1.4789015 # 1.3675444 if for a coord# of exactly 4
for i in range(32):
for j in range(32):
if not i in [1, 3] and not j in [1, 3]:
self.assertEqual(ofm[i, j], 0.0)
mtarget = np.matrix(mtarget)
self.assertAlmostEqual(np.linalg.norm(ofm - mtarget), 0.0, places=4)
ofm_maker = OrbitalFieldMatrix(True, flatten=False)
ofm = ofm_maker.featurize(self.diamond)[0]
mtarget = np.zeros((39, 39))
mtarget[1][1] = 1.4789015
mtarget[1][3] = 1.4789015
mtarget[3][1] = 1.4789015
mtarget[3][3] = 1.4789015
mtarget[1][33] = 1.4789015
mtarget[3][33] = 1.4789015
mtarget[33][1] = 1.4789015
mtarget[33][3] = 1.4789015
mtarget[33][33] = 1.4789015
mtarget = np.matrix(mtarget)
self.assertAlmostEqual(np.linalg.norm(ofm - mtarget), 0.0, places=4)
ofm_flat = OrbitalFieldMatrix(period_tag=False, flatten=True)
self.assertEqual(len(ofm_flat.feature_labels()), 1024)
ofm_flat = OrbitalFieldMatrix(period_tag=True, flatten=True)
self.assertEqual(len(ofm_flat.feature_labels()), 1521)
ofm_vector = ofm_flat.featurize(self.diamond)
for ix in [40, 42, 72, 118, 120, 150, 1288, 1320]:
self.assertAlmostEqual(ofm_vector[ix], 1.4789015345821415)
rmse += np.mean(((Y_pred - Y_test) / N_test)**2)**0.5 / NUM_SPLITS
r2 += sklearn.metrics.r2_score(Y_test / N_test,
Y_pred / N_test) / NUM_SPLITS
print("SCM RESULTS MAE = %f, RMSE = %f, R-SQUARED = %f" % (mae, rmse, r2))
finish = time.monotonic()
print("TIME TO TEST SCM %f SECONDS" % (finish - start))
print()
# OFM evaluation
for ROW in [False, True]:
print("ROW ELEMS", ROW)
# Featurize dataframe with OFM and time it
start = time.monotonic()
ofm = OrbitalFieldMatrix(ROW)
ofm.set_n_jobs(NJOBS)
df = ofm.featurize_dataframe(df, 'structure')
df['orbital field matrix'] = pd.Series([s.flatten() \
for s in df['orbital field matrix']], df.index)
finish = time.monotonic()
print("TIME TO FEATURIZE OFM %f SECONDS" % (finish - start))
print()
# Get OFM descriptor and set up KRR model
krr = KernelRidge()
hpsel = GridSearchCV(krr,
params['orbital field matrix'],
cv=inner_cv,
refit=True)
X = df['orbital field matrix'].as_matrix()
mae += np.mean(np.abs(Y_pred - Y_test) / N_test) / NUM_SPLITS
rmse += np.mean(((Y_pred - Y_test) / N_test)**2)**0.5 / NUM_SPLITS
r2 += sklearn.metrics.r2_score(Y_test / N_test, Y_pred / N_test) / NUM_SPLITS
print ("SCM RESULTS MAE = %f, RMSE = %f, R-SQUARED = %f" % (mae, rmse, r2))
finish = time.monotonic()
print ("TIME TO TEST SCM %f SECONDS" % (finish-start))
print()
# OFM evaluation
for ROW in [False, True]:
print ("ROW ELEMS", ROW)
# Featurize dataframe with OFM and time it
start = time.monotonic()
ofm = OrbitalFieldMatrix(ROW)
ofm.set_n_jobs(NJOBS)
df = ofm.featurize_dataframe(df, 'structure')
df['orbital field matrix'] = pd.Series([s.flatten() \
for s in df['orbital field matrix']], df.index)
finish = time.monotonic()
print ("TIME TO FEATURIZE OFM %f SECONDS" % (finish-start))
print()
# Get OFM descriptor and set up KRR model
krr = KernelRidge()
hpsel = GridSearchCV(krr, params['orbital field matrix'],
cv=inner_cv, refit=True)
X = df['orbital field matrix'].as_matrix()
# Flatten each OFM to form a vector descriptor
XLIST = []
rmse += np.mean(((Y_pred - Y_test) / N_test)**2)**0.5 / NUM_SPLITS
r2 += sklearn.metrics.r2_score(Y_test / N_test,
Y_pred / N_test) / NUM_SPLITS
print("SCM RESULTS MAE = %f, RMSE = %f, R-SQUARED = %f" % (mae, rmse, r2))
finish = time.monotonic()
print("TIME TO TEST SCM %f SECONDS" % (finish - start))
print()
# OFM evaluation
for ROW in [False, True]:
print("ROW ELEMS", ROW)
# Featurize dataframe with OFM and time it
start = time.monotonic()
ofm = OrbitalFieldMatrix(period_tag=ROW)
ofm.set_n_jobs(NJOBS)
df = ofm.fit_featurize_dataframe(df, 'structure')
finish = time.monotonic()
print("TIME TO FEATURIZE OFM %f SECONDS" % (finish - start))
print()
# Get OFM descriptor and set up KRR model
krr = KernelRidge()
hpsel = GridSearchCV(krr,
params['orbital field matrix'],
cv=inner_cv,
refit=True)
X = df[ofm.feature_labels()].to_numpy()
# Flatten each OFM to form a vector descriptor
XLIST = []
import numpy as np
from matminer.featurizers.structure import OrbitalFieldMatrix
cen_structures = np.load(
'/home/dennis/Dropbox/mp_share_dt_ag/structs/centrosymmetric_insulators.npy',
allow_pickle=True)
print(len(cen_structures))
#create ofm representations
ofm = OrbitalFieldMatrix()
cen_ofm = []
for item in cen_structures:
cen_ofm.append(ofm.featurize(item))
np.save('centrosymmetric_ofm_representation.npy', cen_ofm)
non_cen_structures = np.load(
'/home/dennis/Dropbox/mp_share_dt_ag/structs/non_centrosymmetric_insulators.npy',
allow_pickle=True)
print(len(non_cen_structures))
non_cen_ofm = []
for item in non_cen_structures:
non_cen_ofm.append(ofm.featurize(item))
np.save('non_centrosymmetric_ofm_representation.npy', non_cen_ofm)
