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 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)