def test_pred_single_prod():
# Load model
model = darkchem.utils.load_model(
'/Family/David/UW_ChemE/2019_Spring/ChemE_547/DarKnight_File/Final Trained DarkChem Network Weights/N7b_[M+H]/'
)
# Generate some data to use
smilescc = {
'Reactants': [
'CC(=O)C', 'O=C1CCCCC1', 'O=C1CCCC1C', 'O=C1CCCC1C', 'CCCC(=O)CCC',
'CCCCC(=O)CCCC', 'CC(=O)CC(=O)C', 'CC(=O)CC(=O)C', 'COCC(=O)C',
'COCC(=O)C', 'CC(=O)c1ccccc1'
],
'Products': [
'CCC(=O)C', 'CC1CCCCC1=O', 'CC1CCC(C1=O)C', 'O=C1CCCC1(C)C',
'CCCC(=O)C(CC)C', 'CCCCC(=O)C(CCC)C', 'CC(C(=O)C)C(=O)C',
'CC(=O)C(C(=O)C)(C)C', 'COCC(=O)CC', 'COC(C(=O)CC)C',
'CCC(=O)c1ccccc1'
]
}
smi = 'CCCc1ccc(CCC=O)cc1'
path_vec = functions.path_vec(smilescc, model)
# Operate the function wihch is being tested
pred_single = functions.pred_single_prod(smi, model, path_vec, k=1)
# Assert several arguments
assert len(pred_single) == 1
assert pred_single.shape == (1, 2)
return 0
def test_path_vec():
# Load model
model = darkchem.utils.load_model(
'/Family/David/UW_ChemE/2019_Spring/ChemE_547/DarKnight_File/Final Trained DarkChem Network Weights/N7b_[M+H]/'
)
# Generate some data to use
smilescc = {
'Reactants': [
'CC(=O)C', 'O=C1CCCCC1', 'O=C1CCCC1C', 'O=C1CCCC1C', 'CCCC(=O)CCC',
'CCCCC(=O)CCCC', 'CC(=O)CC(=O)C', 'CC(=O)CC(=O)C', 'COCC(=O)C',
'COCC(=O)C', 'CC(=O)c1ccccc1'
],
'Products': [
'CCC(=O)C', 'CC1CCCCC1=O', 'CC1CCC(C1=O)C', 'O=C1CCCC1(C)C',
'CCCC(=O)C(CC)C', 'CCCCC(=O)C(CCC)C', 'CC(C(=O)C)C(=O)C',
'CC(=O)C(C(=O)C)(C)C', 'COCC(=O)CC', 'COC(C(=O)CC)C',
'CCC(=O)c1ccccc1'
]
}
# Operate the function wihch is being tested
path_vec = functions.path_vec(smilescc, model)
# Assert several arguments
assert path_vec.shape == (128, )
assert len(path_vec) == 128
assert type(path_vec).__module__ == 'numpy'
return 0
def test_transform_r2p_str():
# Load model
model = darkchem.utils.load_model(
'/Family/David/UW_ChemE/2019_Spring/ChemE_547/DarKnight_File/Final Trained DarkChem Network Weights/N7b_[M+H]/'
)
# Generate some data to use
smilescc = {
'Reactants': [
'CC(=O)C', 'O=C1CCCCC1', 'O=C1CCCC1C', 'O=C1CCCC1C', 'CCCC(=O)CCC',
'CCCCC(=O)CCCC', 'CC(=O)CC(=O)C', 'CC(=O)CC(=O)C', 'COCC(=O)C',
'COCC(=O)C', 'CC(=O)c1ccccc1'
],
'Products': [
'CCC(=O)C', 'CC1CCCCC1=O', 'CC1CCC(C1=O)C', 'O=C1CCCC1(C)C',
'CCCC(=O)C(CC)C', 'CCCCC(=O)C(CCC)C', 'CC(C(=O)C)C(=O)C',
'CC(=O)C(C(=O)C)(C)C', 'COCC(=O)CC', 'COC(C(=O)CC)C',
'CCC(=O)c1ccccc1'
]
}
smi = 'CC(=O)C'
path_vec = functions.path_vec(smilescc, model)
# Operate the function wihch is being tested
transform_r2p_str = functions.transform_r2p_str(smi, model, path_vec, 5)
# Assert several arguments
assert len(transform_r2p_str) == 5
assert isinstance(transform_r2p_str, list)
return 0
def test_output_multiple_prod():
# Load model
model = darkchem.utils.load_model('../../../database/N7b_[M+H]/')
# Generate some data to use
smilescc = {
'Reactants': [
'CC(=O)C', 'O=C1CCCCC1', 'O=C1CCCC1C', 'O=C1CCCC1C', 'CCCC(=O)CCC',
'CCCCC(=O)CCCC', 'CC(=O)CC(=O)C', 'CC(=O)CC(=O)C', 'COCC(=O)C',
'COCC(=O)C', 'CC(=O)c1ccccc1'
],
'Products': [
'CCC(=O)C', 'CC1CCCCC1=O', 'CC1CCC(C1=O)C', 'O=C1CCCC1(C)C',
'CCCC(=O)C(CC)C', 'CCCCC(=O)C(CCC)C', 'CC(C(=O)C)C(=O)C',
'CC(=O)C(C(=O)C)(C)C', 'COCC(=O)CC', 'COC(C(=O)CC)C',
'CCC(=O)c1ccccc1'
]
}
testdf = {
'Reactants': [
'CC(=O)C', 'O=C1CCCCC1', 'O=C1CCCC1C', 'O=C1CCCC1C', 'CCCC(=O)CCC',
'CCCCC(=O)CCCC', 'CC(=O)CC(=O)C', 'CC(=O)CC(=O)C', 'COCC(=O)C',
'COCC(=O)C', 'CC(=O)c1ccccc1'
],
'Products': [
'CCC(=O)C', 'CC1CCCCC1=O', 'CC1CCC(C1=O)C', 'O=C1CCCC1(C)C',
'CCCC(=O)C(CC)C', 'CCCCC(=O)C(CCC)C', 'CC(C(=O)C)C(=O)C',
'CC(=O)C(C(=O)C)(C)C', 'COCC(=O)CC', 'COC(C(=O)CC)C',
'CCC(=O)c1ccccc1'
]
}
path_vec = functions.path_vec(smilescc, model)
testdf = pd.DataFrame(testdf)
# Operate the function wihch is being tested
output_multiple_prod = functions.output_multiple_prod(testdf,
model,
path_vec,
k=15)
# Assert several arguments
assert len(output_multiple_prod) == len(testdf)
assert output_multiple_prod.shape == (11, 2)
return 0
def test_pred_multiple_prod():
# Load model
model = darkchem.utils.load_model(
'/Family/David/UW_ChemE/2019_Spring/ChemE_547/DarKnight_File/Final Trained DarkChem Network Weights/N7b_[M+H]/'
)
# Generate some data to use
smilescc = {
'Reactants': [
'CC(=O)C', 'O=C1CCCCC1', 'O=C1CCCC1C', 'O=C1CCCC1C', 'CCCC(=O)CCC',
'CCCCC(=O)CCCC', 'CC(=O)CC(=O)C', 'CC(=O)CC(=O)C', 'COCC(=O)C',
'COCC(=O)C', 'CC(=O)c1ccccc1'
],
'Products': [
'CCC(=O)C', 'CC1CCCCC1=O', 'CC1CCC(C1=O)C', 'O=C1CCCC1(C)C',
'CCCC(=O)C(CC)C', 'CCCCC(=O)C(CCC)C', 'CC(C(=O)C)C(=O)C',
'CC(=O)C(C(=O)C)(C)C', 'COCC(=O)CC', 'COC(C(=O)CC)C',
'CCC(=O)c1ccccc1'
]
}
testdf = {
'Reactants': [
'CC(=O)C', 'O=C1CCCCC1', 'O=C1CCCC1C', 'O=C1CCCC1C', 'CCCC(=O)CCC',
'CCCCC(=O)CCCC', 'CC(=O)CC(=O)C', 'CC(=O)CC(=O)C', 'COCC(=O)C',
'COCC(=O)C', 'CC(=O)c1ccccc1'
],
'Products': [
'CCC(=O)C', 'CC1CCCCC1=O', 'CC1CCC(C1=O)C', 'O=C1CCCC1(C)C',
'CCCC(=O)C(CC)C', 'CCCCC(=O)C(CCC)C', 'CC(C(=O)C)C(=O)C',
'CC(=O)C(C(=O)C)(C)C', 'COCC(=O)CC', 'COC(C(=O)CC)C',
'CCC(=O)c1ccccc1'
]
}
path_vec = functions.path_vec(smilescc, model)
testdf = pd.DataFrame(testdf)
# Operate the function wihch is being tested
pred_multiple = functions.pred_multiple_prod(testdf, model, path_vec, k=1)
# Assert several arguments
assert len(pred_multiple) == len(testdf)
assert type(pred_multiple) == type(testdf)
return 0