def test_weighting_invariance():
"""Test that weights do not affect model selection using perfect L0 and L1 cases."""
phase_models = {
"components": ["AL", "B"],
"phases": {
"ALPHA": {
"sublattice_model": [["AL", "B"]],
"sublattice_site_ratios": [1]
}
}
}
L0_data = {
"components": ["AL", "B"],
"phases": ["ALPHA"],
"solver": {
"sublattice_site_ratios": [1],
"sublattice_occupancies": [[[0.5, 0.5]]],
"sublattice_configurations": [[["AL", "B"]]],
"mode": "manual"
},
"conditions": {
"P": 101325,
"T": 298.15
},
"output": "HM_MIX",
"values": [[[-1000]]]
}
L1_data = {
"components": ["AL", "B"],
"phases": ["ALPHA"],
"solver": {
"sublattice_site_ratios": [1],
"sublattice_occupancies": [[[0.25, 0.75]], [[0.5, 0.5]],
[[0.75, 0.25]]],
"sublattice_configurations": [[["AL", "B"]], [["AL", "B"]],
[["AL", "B"]]],
"mode":
"manual"
},
"conditions": {
"P": 101325,
"T": 298.15
},
"output": "HM_MIX",
"values": [[[-1000.0, 0, 1000.0]]]
}
# Perfect L0, no weight
datasets_db = PickleableTinyDB(storage=MemoryStorage)
datasets_db.insert(L0_data)
dbf = generate_parameters(phase_models, datasets_db, 'SGTE91', 'linear')
datasets_db.close()
params = dbf._parameters.search(where('parameter_type') == 'L')
print([f"L{p['parameter_order']}: {p['parameter']}" for p in params])
print({
str(p['parameter']): dbf.symbols[str(p['parameter'])]
for p in params
})
assert len(params) == 1
assert dbf.symbols['VV0000'] == -4000
# Perfect L0, with weight
datasets_db = PickleableTinyDB(storage=MemoryStorage)
L0_data['weight'] = 0.1 # lower weight
datasets_db.insert(L0_data)
dbf = generate_parameters(phase_models, datasets_db, 'SGTE91', 'linear')
datasets_db.close()
params = dbf._parameters.search(where('parameter_type') == 'L')
print([f"L{p['parameter_order']}: {p['parameter']}" for p in params])
print({
str(p['parameter']): dbf.symbols[str(p['parameter'])]
for p in params
})
assert len(params) == 1
assert dbf.symbols['VV0000'] == -4000
# Perfect L1, no weight
datasets_db = PickleableTinyDB(storage=MemoryStorage)
datasets_db.insert(L1_data)
dbf = generate_parameters(phase_models, datasets_db, 'SGTE91', 'linear')
datasets_db.close()
params = dbf._parameters.search(where('parameter_type') == 'L')
print([f"L{p['parameter_order']}: {p['parameter']}" for p in params])
print({
str(p['parameter']): dbf.symbols[str(p['parameter'])]
for p in params
})
assert len(params) == 2
assert np.isclose(dbf.symbols['VV0000'], 1000 * 32 / 3) # L1
assert np.isclose(dbf.symbols['VV0001'], 0) # L0
# Perfect L1, with weight
datasets_db = PickleableTinyDB(storage=MemoryStorage)
L1_data['weight'] = 0.1 # lower weight
datasets_db.insert(L1_data)
dbf = generate_parameters(phase_models, datasets_db, 'SGTE91', 'linear')
datasets_db.close()
params = dbf._parameters.search(where('parameter_type') == 'L')
print([f"L{p['parameter_order']}: {p['parameter']}" for p in params])
print({
str(p['parameter']): dbf.symbols[str(p['parameter'])]
for p in params
})
# TODO: sometimes the presence of L0 terms can be flaky
# assert len(params) == 2
assert np.isclose(dbf.symbols['VV0000'], 1000 * 32 / 3) # L1
def datasets_db():
"""Returns a clean instance of a PickleableTinyDB for datasets"""
db = PickleableTinyDB(storage=MemoryStorage)
yield db
db.close()
