def test_split_by_enzyme():
'''Tests split_by_enzyme function in mol_sim.py'''
input_df = pd.read_csv('mol_sim_test_df.csv')
test_df = mol_sim.fingerprint_products(mol_sim.input_data(input_df))
assert isinstance(mol_sim.split_by_enzyme(test_df), list), """TypeError,
function should return a pandas dataframe"""
return '1/1 tests successful'
def test_sim_i_j():
'''Tests sim_i_j function in mol_sim.py'''
input_df = pd.read_csv('mol_sim_test_df.csv')
test_df = mol_sim.fingerprint_products(mol_sim.input_data(input_df))
A = test_df.iloc[0]
assert mol_sim.sim_i_j(A, A) == 1, "Self correlation is broken"
return '1/1 tests successful'
def test_fingerprint_products():
'''Tests fingerprint_products function in mol_sim.py'''
input_df = pd.read_csv('mol_sim_test_df.csv')
test_df = mol_sim.input_data(input_df)
assert isinstance(mol_sim.fingerprint_products(test_df),
pd.DataFrame), """TypeError,
function should return a pandas dataframe"""
return '1/1 tests successful'
def test_sim_metric():
'''Test sim_i_all functionin mol_sim.py'''
input_df = pd.read_csv('mol_sim_test_df.csv')
test_df = mol_sim.fingerprint_products(mol_sim.input_data(input_df))
assert isinstance(mol_sim.sim_metric(test_df), pd.DataFrame), """TypeError,
function should return a dataframe"""
assert mol_sim.sim_metric(
test_df).isnull().values.any() == False, """ValueError,
function-generated dataframe should not contain null values"""
return "2/2 Tests successful"
def test_split_by_enzyme():
'''Tests split_by_enzyme function in mol_sim.py'''
input_df = pd.read_csv('playground_df_cleaned_kegg_with_smiles.csv')
test_df = mol_sim.fingerprint_products(mol_sim.input_data(input_df))
assert isinstance(mol_sim.split_by_enzyme(test_df),
list) == True, """TypeError,
function should return a pandas dataframe"""
#assert
return '1/1 tests successful'
def test_sim_metric():
'''Test sim_i_all functionin mol_sim.py'''
input_df = pd.read_csv('playground_df_cleaned_kegg_with_smiles.csv')
test_df = mol_sim.fingerprint_products(mol_sim.input_data(input_df))
assert isinstance(mol_sim.sim_metric(test_df),
pd.DataFrame) == True, """TypeError,
function should return a dataframe"""
assert mol_sim.sim_metric(
test_df).isnull().values.any() == False, """ValueError,
function-generated dataframe should not contain null values"""
#assert test_df.size == mol_sim.sim_metric(test_df).size, """ShapeError,
#function-generated dataframe should be the same size as input dataframe"""
return "2/2 Tests successful"
def test_sim_i_j():
'''Tests sim_i_j function in mol_sim.py'''
input_df = pd.read_csv('playground_df_cleaned_kegg_with_smiles.csv')
test_df = mol_sim.fingerprint_products(mol_sim.input_data(input_df))
A = test_df.iloc[0]
#B = test_df.iloc[1]
#C = test_df.iloc[2]
assert mol_sim.sim_i_j(A, A) == 1, "Self correlation is broken"
#assert mol_sim.sim_i_j(A, B) == -1, "Standard correlation is broken"
#assert mol_sim.sim_i_j(A, C) == 0, "Standard correlation is broken"
return '1/1 tests successful'
def test_sim_i_all():
'''Test sim_i_all functionin mol_sim.py'''
input_df = pd.read_csv('mol_sim_test_df.csv')
test_df = mol_sim.fingerprint_products(mol_sim.input_data(input_df))
metric = pd.DataFrame()
assert metric.empty, """ShapeError, input metric dataframe
should be initialized as empty"""
for index, row in test_df.iterrows():
assert mol_sim.sim_i_all(test_df, index, row,
metric) is None, """OutputError, function
shouldn't return anything"""
assert metric[index].all() >= 0 and metric[index].all(
) <= 1.0, """ValueError,
metric should be between 0 and 1"""
return "3/3 Tests successful"