def test_MathematicalCombinator_only_one_mathematical_operation(
dataframe_vartypes):
# case 2: selected only one operation:
transformer = MathematicalCombinator(math_operations=['sum'])
X = transformer.fit_transform(dataframe_vartypes)
ref = pd.DataFrame.from_dict({
'Name': ['tom', 'nick', 'krish', 'jack'],
'City': ['London', 'Manchester', 'Liverpool', 'Bristol'],
'Age': [20, 21, 19, 18],
'Marks': [0.9, 0.8, 0.7, 0.6],
'dob':
pd.date_range('2020-02-24', periods=4, freq='T'),
'sum(Age-Marks)': [20.9, 21.8, 19.7, 18.6]
})
# init params
assert transformer.variables == ['Age', 'Marks']
assert transformer.operations == ['sum']
# fit params
assert transformer.input_shape_ == (4, 5)
assert transformer.combination_dict_ == {'sum(Age-Marks)': 'sum'}
# transform params
pd.testing.assert_frame_equal(X, ref)
def test_MathematicalCombinator__user_gives_new_variable_names(
dataframe_vartypes):
transformer = MathematicalCombinator(
math_operations=['sum', 'mean'],
new_variables_names=['sum_of_two_vars', 'mean_of_two_vars'])
X = transformer.fit_transform(dataframe_vartypes)
ref = pd.DataFrame.from_dict({
'Name': ['tom', 'nick', 'krish', 'jack'],
'City': ['London', 'Manchester', 'Liverpool', 'Bristol'],
'Age': [20, 21, 19, 18],
'Marks': [0.9, 0.8, 0.7, 0.6],
'dob':
pd.date_range('2020-02-24', periods=4, freq='T'),
'sum_of_two_vars': [20.9, 21.8, 19.7, 18.6],
'mean_of_two_vars': [10.45, 10.9, 9.85, 9.3],
})
# init params
assert transformer.variables == ['Age', 'Marks']
assert transformer.operations == ['sum', 'mean']
assert transformer.new_variables_names == [
'sum_of_two_vars', 'mean_of_two_vars'
]
# fit params
assert transformer.input_shape_ == (4, 5)
assert transformer.combination_dict_ == {
'sum_of_two_vars': 'sum',
'mean_of_two_vars': 'mean'
}
# transform params
pd.testing.assert_frame_equal(X, ref)
def test_MathematicalCombinator_error_when_variable_names_and_operations_LengthNotEqual(
dataframe_vartypes):
with pytest.raises(KeyError):
transformer = MathematicalCombinator(math_operations=['sum', 'mean'],
new_variables_names=[
'sum_of_two_vars',
'mean_of_two_vars',
'another_alias',
'not_permitted'
])
with pytest.raises(KeyError):
transformer = MathematicalCombinator(
math_operations=['sum', 'mean'],
new_variables_names=['sum_of_two_vars'])
def test_MathematicalCombinator_default_parameters(dataframe_vartypes):
transformer = MathematicalCombinator()
X = transformer.fit_transform(dataframe_vartypes)
ref = pd.DataFrame.from_dict({
'Name': ['tom', 'nick', 'krish', 'jack'],
'City': ['London', 'Manchester', 'Liverpool', 'Bristol'],
'Age': [20, 21, 19, 18],
'Marks': [0.9, 0.8, 0.7, 0.6],
'dob':
pd.date_range('2020-02-24', periods=4, freq='T'),
'sum(Age-Marks)': [20.9, 21.8, 19.7, 18.6],
'prod(Age-Marks)':
[18.0, 16.8, 13.299999999999999, 10.799999999999999],
'mean(Age-Marks)': [10.45, 10.9, 9.85, 9.3],
'std(Age-Marks)': [
13.505739520663058, 14.28355697996826, 12.94005409571382,
12.303657992645928
],
'max(Age-Marks)': [20.0, 21.0, 19.0, 18.0],
'min(Age-Marks)': [0.9, 0.8, 0.7, 0.6]
})
# init params
assert transformer.variables == ['Age', 'Marks']
assert transformer.operations == [
'sum', 'prod', 'mean', 'std', 'max', 'min'
]
# fit params
assert transformer.input_shape_ == (4, 5)
assert transformer.combination_dict_ == {
'sum(Age-Marks)': 'sum',
'prod(Age-Marks)': 'prod',
'mean(Age-Marks)': 'mean',
'std(Age-Marks)': 'std',
'max(Age-Marks)': 'max',
'min(Age-Marks)': 'min'
}
# transform params
pd.testing.assert_frame_equal(X, ref)
def test_MathematicalCombinator_error_when_selected__variable_not_numeric(
dataframe_vartypes):
transformer = MathematicalCombinator(variables=['Name', 'Age', 'Marks'])
with pytest.raises(TypeError):
X = transformer.fit_transform(dataframe_vartypes)
def test_MathematicalCombinator_error_when_select_one_feature(
dataframe_vartypes):
with pytest.raises(KeyError):
transformer = MathematicalCombinator(variables=['Age'])
def test_MathematicalCombinator_SelectedoperationsWrongType(
dataframe_vartypes):
with pytest.raises(KeyError):
transformer = MathematicalCombinator(math_operations=[sum])
def test_MathematicalCombinator_SelectedoperationsOutsidePermittedList(
dataframe_vartypes):
with pytest.raises(KeyError):
transformer = MathematicalCombinator(
math_operations=['OperationOutsidePermittedList'])
def test_MathematicalCombinator_Serror_when_selected__variable_not_in_df(
dataframe_vartypes):
transformer = MathematicalCombinator(
variables=['FeatOutsideDataset', 'Age'])
with pytest.raises(KeyError):
X = transformer.fit_transform(dataframe_vartypes)