def get_weights(self, weights_options):
effective_csm_estimator = {"function": "power2_inverse_decreasing", "options": {"max_csm": 8.0}}
self_weight_estimator = {
"function": "power2_decreasing_exp",
"options": {"max_csm": 5.4230949041608305, "alpha": 1.0},
}
self_csm_weight = SelfCSMNbSetWeight(
effective_csm_estimator=effective_csm_estimator, weight_estimator=self_weight_estimator
)
surface_definition = {
"type": "standard_elliptic",
"distance_bounds": {"lower": 1.05, "upper": 2.0},
"angle_bounds": {"lower": 0.05, "upper": 0.95},
}
da_area_weight = DistanceAngleAreaNbSetWeight(
weight_type="has_intersection",
surface_definition=surface_definition,
nb_sets_from_hints="fallback_to_source",
other_nb_sets="0_weight",
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB,
)
weight_estimator = {"function": "smootherstep", "options": {"delta_csm_min": 0.5, "delta_csm_max": 3.0}}
symmetry_measure_type = "csm_wcs_ctwcc"
delta_csm_weight = DeltaCSMNbSetWeight(
effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator,
symmetry_measure_type=symmetry_measure_type,
)
bias_weight = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=4.0)
angle_weight = AngleNbSetWeight()
nad_weight = NormalizedAngleDistanceNbSetWeight(average_type="geometric", aa=1, bb=1)
weights = {
"DistAngArea": da_area_weight,
"SelfCSM": self_csm_weight,
"DeltaCSM": delta_csm_weight,
"CNBias": bias_weight,
"Angle": angle_weight,
"NormalizedAngDist": nad_weight,
}
return weights
def test_normalized_angle_distance_weight(self):
fake_nb_set = FakeNbSet()
dummy_se = DummyStructureEnvironments()
nadw1 = NormalizedAngleDistanceNbSetWeight(average_type='geometric', aa=1, bb=1)
nadw2 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=1, bb=1)
nadw3 = NormalizedAngleDistanceNbSetWeight(average_type='geometric', aa=0, bb=1)
nadw4 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=1, bb=0)
nadw5 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=0.1, bb=0.1)
nadw6 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=0, bb=0.1)
nadw7 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=0.1, bb=0)
nadw8 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=2, bb=0)
nadw9 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=0, bb=2)
nadw10 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=2, bb=2)
nadw11 = NormalizedAngleDistanceNbSetWeight(average_type='geometric', aa=1, bb=2)
nadw12 = NormalizedAngleDistanceNbSetWeight(average_type='geometric', aa=2, bb=1)
self.assertNotEqual(nadw11, nadw12)
with self.assertRaisesRegex(ValueError, 'Both exponents are 0.'):
NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=0, bb=0)
with self.assertRaisesRegex(ValueError, 'Average type is "arithmetix" '
'while it should be "geometric" or "arithmetic"'):
NormalizedAngleDistanceNbSetWeight(average_type='arithmetix', aa=1, bb=1)
fake_nb_set.normalized_distances = [1.2632574171572457, 1.1231971151388764, 1.0,
1.1887986376446249, 1.188805134890625]
fake_nb_set.normalized_angles = [0.6026448601336767, 0.8498933334305273, 1.0,
0.7355039801931018, 0.7354996568248028]
w1 = nadw1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 0.67310887189488189, delta=1e-8)
w2 = nadw2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 0.69422258996523023, delta=1e-8)
w3 = nadw3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 0.8700949310182079, delta=1e-8)
w4 = nadw4.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w4, 0.7847083661164217, delta=1e-8)
w5 = nadw5.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w5, 0.96148050989126843, delta=1e-8)
w6 = nadw6.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w6, 0.98621181678741754, delta=1e-8)
w7 = nadw7.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w7, 0.97479580875402994, delta=1e-8)
w8 = nadw8.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w8, 0.63348507114489783, delta=1e-8)
w9 = nadw9.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w9, 0.7668954450583646, delta=1e-8)
w10 = nadw10.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w10, 0.51313920014833292, delta=1e-8)
w11 = nadw11.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w11, 0.585668617459, delta=1e-8)
w12 = nadw12.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w12, 0.520719679281, delta=1e-8)
def test_strategies(self):
simplest_strategy_1 = SimplestChemenvStrategy()
simplest_strategy_2 = SimplestChemenvStrategy(distance_cutoff=1.5,
angle_cutoff=0.5)
self.assertFalse(simplest_strategy_1 == simplest_strategy_2)
simplest_strategy_1_from_dict = SimplestChemenvStrategy.from_dict(
simplest_strategy_1.as_dict())
self.assertTrue(simplest_strategy_1, simplest_strategy_1_from_dict)
effective_csm_estimator = {
"function": "power2_inverse_decreasing",
"options": {
"max_csm": 8.0
},
}
self_csm_weight = SelfCSMNbSetWeight()
surface_definition = {
"type": "standard_elliptic",
"distance_bounds": {
"lower": 1.1,
"upper": 1.9
},
"angle_bounds": {
"lower": 0.1,
"upper": 0.9
},
}
surface_definition_2 = {
"type": "standard_elliptic",
"distance_bounds": {
"lower": 1.1,
"upper": 1.9
},
"angle_bounds": {
"lower": 0.1,
"upper": 0.95
},
}
da_area_weight = DistanceAngleAreaNbSetWeight(
weight_type="has_intersection",
surface_definition=surface_definition,
nb_sets_from_hints="fallback_to_source",
other_nb_sets="0_weight",
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB,
)
da_area_weight_2 = DistanceAngleAreaNbSetWeight(
weight_type="has_intersection",
surface_definition=surface_definition_2,
nb_sets_from_hints="fallback_to_source",
other_nb_sets="0_weight",
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB,
)
weight_estimator = {
"function": "smootherstep",
"options": {
"delta_csm_min": 0.5,
"delta_csm_max": 3.0
},
}
symmetry_measure_type = "csm_wcs_ctwcc"
delta_weight = DeltaCSMNbSetWeight(
effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator,
symmetry_measure_type=symmetry_measure_type,
)
bias_weight = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0,
weight_cn13=4.0)
bias_weight_2 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0,
weight_cn13=5.0)
angle_weight = AngleNbSetWeight()
nad_weight = NormalizedAngleDistanceNbSetWeight(
average_type="geometric", aa=1, bb=1)
multi_weights_strategy_1 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type,
)
multi_weights_strategy_2 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight_2,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type,
)
multi_weights_strategy_3 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight_2,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type,
)
multi_weights_strategy_1_from_dict = MultiWeightsChemenvStrategy.from_dict(
multi_weights_strategy_1.as_dict())
self.assertTrue(
multi_weights_strategy_1 == multi_weights_strategy_1_from_dict)
self.assertFalse(simplest_strategy_1 == multi_weights_strategy_1)
self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_2)
self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_3)
self.assertFalse(multi_weights_strategy_2 == multi_weights_strategy_3)
def test_normalized_angle_distance_weight(self):
fake_nb_set = FakeNbSet()
dummy_se = DummyStructureEnvironments()
nadw1 = NormalizedAngleDistanceNbSetWeight(average_type='geometric', aa=1, bb=1)
nadw2 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=1, bb=1)
nadw3 = NormalizedAngleDistanceNbSetWeight(average_type='geometric', aa=0, bb=1)
nadw4 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=1, bb=0)
nadw5 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=0.1, bb=0.1)
nadw6 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=0, bb=0.1)
nadw7 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=0.1, bb=0)
nadw8 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=2, bb=0)
nadw9 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=0, bb=2)
nadw10 = NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=2, bb=2)
nadw11 = NormalizedAngleDistanceNbSetWeight(average_type='geometric', aa=1, bb=2)
nadw12 = NormalizedAngleDistanceNbSetWeight(average_type='geometric', aa=2, bb=1)
self.assertNotEqual(nadw11, nadw12)
with self.assertRaisesRegex(ValueError, 'Both exponents are 0.'):
NormalizedAngleDistanceNbSetWeight(average_type='arithmetic', aa=0, bb=0)
with self.assertRaisesRegex(ValueError, 'Average type is "arithmetix" '
'while it should be "geometric" or "arithmetic"'):
NormalizedAngleDistanceNbSetWeight(average_type='arithmetix', aa=1, bb=1)
fake_nb_set.normalized_distances = [1.2632574171572457, 1.1231971151388764, 1.0,
1.1887986376446249, 1.188805134890625]
fake_nb_set.normalized_angles = [0.6026448601336767, 0.8498933334305273, 1.0,
0.7355039801931018, 0.7354996568248028]
w1 = nadw1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 0.67310887189488189, delta=1e-8)
w2 = nadw2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 0.69422258996523023, delta=1e-8)
w3 = nadw3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 0.8700949310182079, delta=1e-8)
w4 = nadw4.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w4, 0.7847083661164217, delta=1e-8)
w5 = nadw5.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w5, 0.96148050989126843, delta=1e-8)
w6 = nadw6.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w6, 0.98621181678741754, delta=1e-8)
w7 = nadw7.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w7, 0.97479580875402994, delta=1e-8)
w8 = nadw8.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w8, 0.63348507114489783, delta=1e-8)
w9 = nadw9.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w9, 0.7668954450583646, delta=1e-8)
w10 = nadw10.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w10, 0.51313920014833292, delta=1e-8)
w11 = nadw11.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w11, 0.585668617459, delta=1e-8)
w12 = nadw12.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w12, 0.520719679281, delta=1e-8)
def get_weights(self, weights_options):
effective_csm_estimator = {
'function': 'power2_inverse_decreasing',
'options': {
'max_csm': 8.0
}
}
self_weight_estimator = {
'function': 'power2_decreasing_exp',
'options': {
'max_csm': 5.4230949041608305,
'alpha': 1.0
}
}
self_csm_weight = SelfCSMNbSetWeight(
effective_csm_estimator=effective_csm_estimator,
weight_estimator=self_weight_estimator)
surface_definition = {
'type': 'standard_elliptic',
'distance_bounds': {
'lower': 1.05,
'upper': 2.0
},
'angle_bounds': {
'lower': 0.05,
'upper': 0.95
}
}
da_area_weight = DistanceAngleAreaNbSetWeight(
weight_type='has_intersection',
surface_definition=surface_definition,
nb_sets_from_hints='fallback_to_source',
other_nb_sets='0_weight',
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB)
weight_estimator = {
'function': 'smootherstep',
'options': {
'delta_csm_min': 0.5,
'delta_csm_max': 3.0
}
}
symmetry_measure_type = 'csm_wcs_ctwcc'
delta_csm_weight = DeltaCSMNbSetWeight(
effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator,
symmetry_measure_type=symmetry_measure_type)
bias_weight = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0,
weight_cn13=4.0)
angle_weight = AngleNbSetWeight()
nad_weight = NormalizedAngleDistanceNbSetWeight(
average_type='geometric', aa=1, bb=1)
weights = {
'DistAngArea': da_area_weight,
'SelfCSM': self_csm_weight,
'DeltaCSM': delta_csm_weight,
'CNBias': bias_weight,
'Angle': angle_weight,
'NormalizedAngDist': nad_weight
}
return weights
def test_strategies(self):
simplest_strategy_1 = SimplestChemenvStrategy()
simplest_strategy_2 = SimplestChemenvStrategy(distance_cutoff=1.5,
angle_cutoff=0.5)
self.assertFalse(simplest_strategy_1 == simplest_strategy_2)
simplest_strategy_1_from_dict = SimplestChemenvStrategy.from_dict(
simplest_strategy_1.as_dict())
self.assertTrue(simplest_strategy_1, simplest_strategy_1_from_dict)
effective_csm_estimator = {
'function': 'power2_inverse_decreasing',
'options': {
'max_csm': 8.0
}
}
self_csm_weight = SelfCSMNbSetWeight()
surface_definition = {
'type': 'standard_elliptic',
'distance_bounds': {
'lower': 1.1,
'upper': 1.9
},
'angle_bounds': {
'lower': 0.1,
'upper': 0.9
}
}
surface_definition_2 = {
'type': 'standard_elliptic',
'distance_bounds': {
'lower': 1.1,
'upper': 1.9
},
'angle_bounds': {
'lower': 0.1,
'upper': 0.95
}
}
da_area_weight = DistanceAngleAreaNbSetWeight(
weight_type='has_intersection',
surface_definition=surface_definition,
nb_sets_from_hints='fallback_to_source',
other_nb_sets='0_weight',
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB)
da_area_weight_2 = DistanceAngleAreaNbSetWeight(
weight_type='has_intersection',
surface_definition=surface_definition_2,
nb_sets_from_hints='fallback_to_source',
other_nb_sets='0_weight',
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB)
weight_estimator = {
'function': 'smootherstep',
'options': {
'delta_csm_min': 0.5,
'delta_csm_max': 3.0
}
}
symmetry_measure_type = 'csm_wcs_ctwcc'
delta_weight = DeltaCSMNbSetWeight(
effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator,
symmetry_measure_type=symmetry_measure_type)
bias_weight = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0,
weight_cn13=4.0)
bias_weight_2 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0,
weight_cn13=5.0)
angle_weight = AngleNbSetWeight()
nad_weight = NormalizedAngleDistanceNbSetWeight(
average_type='geometric', aa=1, bb=1)
multi_weights_strategy_1 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type)
multi_weights_strategy_2 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight_2,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type)
multi_weights_strategy_3 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight_2,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type)
multi_weights_strategy_1_from_dict = MultiWeightsChemenvStrategy.from_dict(
multi_weights_strategy_1.as_dict())
self.assertTrue(
multi_weights_strategy_1 == multi_weights_strategy_1_from_dict)
self.assertFalse(simplest_strategy_1 == multi_weights_strategy_1)
self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_2)
self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_3)
self.assertFalse(multi_weights_strategy_2 == multi_weights_strategy_3)