def test_error_if_input_df_contains_na_in_transform(df_vartypes, df_na):
# test case 1: when dataset contains na, transform method
age_dict = {"Age": [0, 10, 20, 30, np.Inf]}
with pytest.raises(ValueError):
transformer = ArbitraryDiscretiser(binning_dict=age_dict)
transformer.fit(df_vartypes)
transformer.transform(df_na[["Name", "City", "Age", "Marks", "dob"]])
def test_error_when_nan_introduced_during_transform():
# test error when NA are introduced during the discretisation.
rng = default_rng()
# create dataframe with 2 variables, 1 normal and 1 skewed
random = skewnorm.rvs(a=-50, loc=4, size=100)
random = random - min(
random) # Shift so the minimum value is equal to zero.
train = pd.concat(
[
pd.Series(rng.standard_normal(100)),
pd.Series(random),
],
axis=1,
)
train.columns = ["var_a", "var_b"]
# create a dataframe with 2 variables normally distributed
test = pd.concat(
[
pd.Series(rng.standard_normal(100)),
pd.Series(rng.standard_normal(100)),
],
axis=1,
)
test.columns = ["var_a", "var_b"]
msg = ("During the discretisation, NaN values were introduced "
"in the feature(s) var_b.")
limits_dict = {"var_a": [-5, -2, 0, 2, 5], "var_b": [0, 2, 5]}
# check for warning when errors equals 'ignore'
with pytest.warns(UserWarning) as record:
transformer = ArbitraryDiscretiser(binning_dict=limits_dict,
errors="ignore")
transformer.fit(train)
transformer.transform(test)
# check that only one warning was returned
assert len(record) == 1
# check that message matches
assert record[0].message.args[0] == msg
# check for error when errors equals 'raise'
with pytest.raises(ValueError) as record:
transformer = ArbitraryDiscretiser(binning_dict=limits_dict,
errors="raise")
transformer.fit(train)
transformer.transform(test)
# check that error message matches
assert str(record.value) == msg