def test_all_interstice_and_packing_mro(self):
all_df = self.cluster_packing_df.join(self.atomic_packing_df)
all_df = all_df.join(self.sro_volume_area_df)
mro = MRO(save=False)
result_df = mro.fit_transform(X=all_df, dependent_df=self.nn_df)
self.assertEqual(len(result_df.columns), 72)
self.assertEqual(result_df.iloc[3, -1], -0.026077608080648407)
def test_voro_index_mro(self):
mro = MRO(save=False)
result_df = mro.fit_transform(X=self.voro_idx_df,
dependent_df=self.nn_df)
self.assertTrue('MRO_mean_Voronoi_idx_5_voro' in result_df.columns)
self.assertEqual(len(result_df), len(self.voro_idx_df))
self.assertAlmostEqual(
result_df.iloc[4]['MRO_mean_Voronoi_idx_3_voro'], 0.777778, 6)
self.assertAlmostEqual(result_df.iloc[4]['MRO_std_Voronoi_idx_3_voro'],
1.227262, 6)
self.assertAlmostEqual(result_df.iloc[4]['MRO_min_Voronoi_idx_3_voro'],
0.000000, 6)
self.assertAlmostEqual(result_df.iloc[4]['MRO_max_Voronoi_idx_3_voro'],
4.000000, 6)
self.assertAlmostEqual(result_df.iloc[4]['MRO_Avg_5_fold_symm_idx'],
0.375000, 6)
self.assertAlmostEqual(result_df.iloc[4]['MRO_Avg_6_fold_symm_idx'],
0.250000, 6)
def test_interstice_pipeline(self):
featurizers = [
VoroNN(bds=self.sc_bds,
cutoff=5,
allow_neighbor_limit=300,
n_neighbor_limit=80,
pbc=[1, 1, 1],
save=False),
DistanceNN(bds=self.sc_bds,
cutoff=4,
allow_neighbor_limit=300,
n_neighbor_limit=80,
pbc=[1, 1, 1],
save=False),
VolumeAreaInterstice(atomic_number_list=[29, 40],
save=False,
radii=None,
radius_type="miracle_radius",
verbose=1),
ClusterPackingEfficiency(atomic_number_list=[29, 40],
save=False,
radii=None,
radius_type="miracle_radius",
verbose=1),
AtomicPackingEfficiency(atomic_number_list=[29, 40],
save=False,
radii=None,
radius_type="miracle_radius",
verbose=1),
MRO(output_file_prefix='pipeline_mro', save=False)
]
multi_featurizer = FeaturizePipeline(featurizers=featurizers,
save=False)
result_df = multi_featurizer.fit_transform(X=self.sc_df,
bds=self.sc_bds,
lammps_df=self.sc_df)
self.assertAlmostEqual(
result_df.iloc[4]['Volume_interstice_mean_voro'], 0.144229, 6)
self.assertAlmostEqual(result_df.iloc[4]['Volume_interstice_std_voro'],
0.196815, 6)
self.assertAlmostEqual(result_df.iloc[4]['Volume_interstice_min_voro'],
0.000000, 6)
self.assertAlmostEqual(result_df.iloc[4]['Volume_interstice_max_voro'],
0.539218, 6)
self.assertAlmostEqual(
result_df.iloc[4]['MRO_mean_Atomic_packing_efficiency_'
'miracle_voro'], 0.158738, 6)
self.assertAlmostEqual(
result_df.iloc[4]['MRO_std_Atomic_packing_efficiency_'
'miracle_voro'], 0.164291, 6)
self.assertAlmostEqual(
result_df.iloc[4]['MRO_min_Atomic_packing_efficiency_'
'miracle_voro'], 0.038186, 6)
self.assertAlmostEqual(
result_df.iloc[4]['MRO_max_Atomic_packing_efficiency_'
'miracle_voro'], 0.509640, 6)
def test_atomic_packing_mro(self):
mro = MRO(save=False, stats_types=[0, 1, 1, 1, 1, 0])
result_df = mro.fit_transform(X=self.atomic_packing_df,
dependent_df=self.nn_df)
self.assertEqual(len(result_df.columns), 4)
self.assertEqual(result_df.iloc[5, 0], 0.18774192756735897)
def test_cluster_packing_mro(self):
mro = MRO(save=False, stat_ops=['sum', 'mean', 'std', 'min', 'max'])
result_df = mro.fit_transform(X=self.cluster_packing_df,
dependent_df=self.nn_df)
self.assertEqual(len(result_df.columns), 5)
self.assertEqual(result_df.iloc[7, 0], 11.287025655788824)
def test_volume_area_interstice_mro(self):
mro = MRO(save=False, stats_types="all")
result_df = mro.fit_transform(X=self.sro_volume_area_df,
dependent_df=self.nn_df)
self.assertEqual(len(result_df.columns), 60)
self.assertEqual(result_df.iloc[3, -1], -0.026077608080648407)
VoroNN(bds=bds, cutoff=5.0, output_path=output_path),
DistanceNN(bds=bds, cutoff=4.0, output_path=output_path),
# distance interstice
DistanceInterstice(atomic_number_list=atomic_number_list,
dependent_class='voro',
stat_ops=stat_ops,
output_path=output_path),
DistanceInterstice(atomic_number_list=atomic_number_list,
dependent_class='dist',
stat_ops=stat_ops,
output_path=output_path),
# area and volume interstice
VolumeAreaInterstice(atomic_number_list=atomic_number_list,
stat_ops=stat_ops,
output_path=output_path),
# from SRO to MRO
MRO(stats_types=[0, 1, 1, 1, 1, 0], output_path=output_path)
]
# defining a featurize_pipeline
featurize_pipeline = FeaturizePipeline(featurizers=featurizers,
output_path=output_path)
# featurization
feature_df = featurize_pipeline.fit_transform(X=structure,
bds=bds,
lammps_df=structure)