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Added one more unit test which fails with current get_columns_to_drop…
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…_3b algorithm
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@monk-04
monk-04 committed Sep 26, 2023
1 parent 8322c4a commit 0a22792
Showing 1 changed file with 118 additions and 30 deletions.
148 changes: 118 additions & 30 deletions tests/test_optimize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,24 +12,59 @@ def Nb_Sn_chemistry():
yield chemistry_config

@pytest.fixture()
def bspline_config_larger_cutoff(Nb_Sn_chemistry):
def config_1():
min_max_ks = {}
min_max_ks["rmin_2b"] = 0.01
min_max_ks["rmax_2b"] = 6.01
min_max_ks["rmin_3b"] = 0.8
min_max_ks["rmax_3b"] = 4
min_max_ks["knot_spacing_2b"] = 0.4
min_max_ks["knot_spacing_3b"] = 0.8
yield min_max_ks


@pytest.fixture()
def bspline_config_larger_cutoff(Nb_Sn_chemistry,config_1):
element_list = ['Nb', 'Sn']
chemistry_config = composition.ChemicalSystem(element_list,degree=3)
bspline_config = optimize.get_bspline_config(Nb_Sn_chemistry, rmin=0,
rmax_2b=6, rmax_3b=4,
knot_spacing=0.4)
bspline_config = optimize.get_bspline_config(Nb_Sn_chemistry,
rmin_2b=config_1["rmin_2b"],
rmin_3b=config_1["rmin_3b"],
rmax_2b=config_1["rmax_2b"],
rmax_3b=config_1["rmax_3b"],
knot_spacing_2b=config_1["knot_spacing_2b"],
knot_spacing_3b=config_1["knot_spacing_3b"],
leading_trim=0,
trailing_trim=3)
yield bspline_config

@pytest.fixture()
def config_2():
min_max_ks = {}
min_max_ks["rmin_2b"] = 0.1
min_max_ks["rmax_2b"] = 9.1
min_max_ks["rmin_3b"] = 0.9
min_max_ks["rmax_3b"] = 7.2
min_max_ks["knot_spacing_2b"] = 0.3
min_max_ks["knot_spacing_3b"] = 0.9
yield min_max_ks

@pytest.fixture()
def bspline_config_smaller_cutoff(Nb_Sn_chemistry):
def bspline_config_larger_cutoff_2(Nb_Sn_chemistry,config_2):
element_list = ['Nb', 'Sn']
chemistry_config = composition.ChemicalSystem(element_list,degree=3)
bspline_config = optimize.get_bspline_config(Nb_Sn_chemistry, rmin=0,
rmax_2b=4.4, rmax_3b=3.2,
knot_spacing=0.4)
bspline_config = optimize.get_bspline_config(Nb_Sn_chemistry,
rmin_2b=config_2["rmin_2b"],
rmin_3b=config_2["rmin_3b"],
rmax_2b=config_2["rmax_2b"],
rmax_3b=config_2["rmax_3b"],
knot_spacing_2b=config_2["knot_spacing_2b"],
knot_spacing_3b=config_2["knot_spacing_3b"],
leading_trim=0,
trailing_trim=3)
yield bspline_config


@pytest.fixture()
def Nb3Sn_geom():
geom = ase.Atoms('Nb6Sn2',
Expand All @@ -50,7 +85,7 @@ def Nb3Sn_geom():


class TestOptimize:

"""
def test_get_bspline_config(self,bspline_config_larger_cutoff):
for i in bspline_config_larger_cutoff.interactions_map[2]:
Expand All @@ -63,41 +98,94 @@ def test_get_bspline_config(self,bspline_config_larger_cutoff):
assert bspline_config_larger_cutoff.r_max_map[i] == [4, 4, 8]
assert bspline_config_larger_cutoff.resolution_map[i] == [10,10,20]

def test_get_possible_lower_cutoffs(self,bspline_config_larger_cutoff):
cutoff_dict = optimize.get_possible_lower_cutoffs(bspline_config_larger_cutoff)

print(cutoff_dict)
assert np.allclose(cutoff_dict['rmax_2b_poss'],np.array([0.4, 0.8, 1.2,\
1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. , 4.4, 4.8, 5.2, 5.6, 6. ]))
assert np.allclose(cutoff_dict['rmax_3b_poss'],np.array([0.8, 1.6, 2.4, 3.2, 4. ]))
"""

def test_drop_columns(self,bspline_config_larger_cutoff,Nb_Sn_chemistry, Nb3Sn_geom):
def test_drop_columns(self,bspline_config_larger_cutoff,Nb_Sn_chemistry, Nb3Sn_geom, config_1):

cutoff_dict = optimize.get_possible_lower_cutoffs(bspline_config_larger_cutoff)

bspline_handler_larger_cutoff = BasisFeaturizer(bspline_config_larger_cutoff)
for i in range(len(cutoff_dict['rmax_2b_poss'])):
for j in range(len(cutoff_dict['rmax_3b_poss'])):
bspline_config_smaller_cutoff = optimize.get_bspline_config(Nb_Sn_chemistry,
rmin_2b=config_1["rmin_2b"],
rmin_3b=config_1["rmin_3b"],
rmax_2b=cutoff_dict['rmax_2b_poss'][i],
rmax_3b=cutoff_dict['rmax_3b_poss'][j],
knot_spacing_2b=config_1["knot_spacing_2b"],
knot_spacing_3b=config_1["knot_spacing_3b"],
leading_trim=0,
trailing_trim=3)

bspline_handler_larger_cutoff = BasisFeaturizer(bspline_config_larger_cutoff)
bspline_handler_smaller_cutoff = BasisFeaturizer(bspline_config_smaller_cutoff)

columns_to_drop_2b = optimize.get_columns_to_drop_2b(original_bspline_config=bspline_config_larger_cutoff,
modify_2b_cutoff=4.4,
knot_spacing=0.4)
columns_to_drop_2b = optimize.get_columns_to_drop_2b(original_bspline_config=bspline_config_larger_cutoff,
modify_2b_cutoff=cutoff_dict['rmax_2b_poss'][i],
knot_spacing=config_1["knot_spacing_2b"])

columns_to_drop_3b = optimize.get_columns_to_drop_3b(original_bspline_config=bspline_config_larger_cutoff,
modify_3b_cutoff=3.2,
knot_spacing=0.4)
columns_to_drop_3b = optimize.get_columns_to_drop_3b(original_bspline_config=bspline_config_larger_cutoff,
modify_3b_cutoff=cutoff_dict['rmax_3b_poss'][j],
knot_spacing=config_1["knot_spacing_3b"])

columns_to_drop = columns_to_drop_2b + columns_to_drop_3b

assert len(bspline_handler_larger_cutoff.columns) - len(columns_to_drop) == len(bspline_handler_smaller_cutoff.columns)

energy_feature_larger_cutoff = bspline_handler_larger_cutoff.evaluate_configuration(geom=Nb3Sn_geom,energy=-69.44185169)
energy_feature_smaller_cutoff = bspline_handler_smaller_cutoff.evaluate_configuration(geom=Nb3Sn_geom,energy=-69.44185169)

index_to_drop = np.where(np.isin(bspline_handler_larger_cutoff.columns,columns_to_drop))[0]
energy_feature_from_larger_cutoff = np.delete(energy_feature_larger_cutoff['energy'],index_to_drop)

assert np.allclose(energy_feature_smaller_cutoff['energy'],energy_feature_from_larger_cutoff)


def test_drop_columns_2(self,bspline_config_larger_cutoff_2,Nb_Sn_chemistry, Nb3Sn_geom, config_2):

cutoff_dict = optimize.get_possible_lower_cutoffs(bspline_config_larger_cutoff_2)
print(bspline_config_larger_cutoff_2.knots_map[('Nb','Nb','Nb')])
for i in range(len(cutoff_dict['rmax_2b_poss'])):
for j in range(len(cutoff_dict['rmax_3b_poss'])):
print(cutoff_dict['rmax_2b_poss'][i],cutoff_dict['rmax_3b_poss'][j])
bspline_config_smaller_cutoff = optimize.get_bspline_config(Nb_Sn_chemistry,
rmin_2b=config_2["rmin_2b"],
rmin_3b=config_2["rmin_3b"],
rmax_2b=cutoff_dict['rmax_2b_poss'][i],
rmax_3b=cutoff_dict['rmax_3b_poss'][j],
knot_spacing_2b=config_2["knot_spacing_2b"],
knot_spacing_3b=config_2["knot_spacing_3b"],
leading_trim=0,
trailing_trim=3)

print(bspline_config_smaller_cutoff.knots_map[('Nb','Nb','Nb')])

bspline_handler_larger_cutoff = BasisFeaturizer(bspline_config_larger_cutoff_2)
bspline_handler_smaller_cutoff = BasisFeaturizer(bspline_config_smaller_cutoff)

columns_to_drop_2b = optimize.get_columns_to_drop_2b(original_bspline_config=bspline_config_larger_cutoff_2,
modify_2b_cutoff=cutoff_dict['rmax_2b_poss'][i],
knot_spacing=config_2["knot_spacing_2b"])

columns_to_drop = columns_to_drop_2b + columns_to_drop_3b
columns_to_drop_3b = optimize.get_columns_to_drop_3b(original_bspline_config=bspline_config_larger_cutoff_2,
modify_3b_cutoff=cutoff_dict['rmax_3b_poss'][j],
knot_spacing=config_2["knot_spacing_3b"])

bspline_config_smaller_cutoff = optimize.get_bspline_config(Nb_Sn_chemistry, rmin=0,
rmax_2b=4.4, rmax_3b=3.2,
knot_spacing=0.4)
bspline_handler_smaller_cutoff = BasisFeaturizer(bspline_config_smaller_cutoff)
columns_to_drop = columns_to_drop_2b + columns_to_drop_3b

assert len(bspline_handler_larger_cutoff.columns) - len(columns_to_drop) == len(bspline_handler_smaller_cutoff.columns)
assert len(bspline_handler_larger_cutoff.columns) - len(columns_to_drop) == len(bspline_handler_smaller_cutoff.columns)

energy_feature_larger_cutoff = bspline_handler_larger_cutoff.evaluate_configuration(geom=Nb3Sn_geom,energy=-69.44185169)
energy_feature_smaller_cutoff = bspline_handler_smaller_cutoff.evaluate_configuration(geom=Nb3Sn_geom,energy=-69.44185169)
energy_feature_larger_cutoff = bspline_handler_larger_cutoff.evaluate_configuration(geom=Nb3Sn_geom,energy=-69.44185169)
energy_feature_smaller_cutoff = bspline_handler_smaller_cutoff.evaluate_configuration(geom=Nb3Sn_geom,energy=-69.44185169)

index_to_drop = np.where(np.isin(bspline_handler_larger_cutoff.columns,columns_to_drop))[0]
energy_feature_from_larger_cutoff = np.delete(energy_feature_larger_cutoff['energy'],index_to_drop)
index_to_drop = np.where(np.isin(bspline_handler_larger_cutoff.columns,columns_to_drop))[0]
energy_feature_from_larger_cutoff = np.delete(energy_feature_larger_cutoff['energy'],index_to_drop)

assert np.allclose(energy_feature_smaller_cutoff['energy'],energy_feature_from_larger_cutoff)
assert np.allclose(energy_feature_smaller_cutoff['energy'],energy_feature_from_larger_cutoff)

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