def test_automatically_find_variables_and_return_as_numeric(df_normal_dist):
# test case 1: automatically select variables, return_object=False
transformer = EqualFrequencyDiscretiser(q=10,
variables=None,
return_object=False)
X = transformer.fit_transform(df_normal_dist)
# output expected for fit attr
_, bins = pd.qcut(x=df_normal_dist["var"],
q=10,
retbins=True,
duplicates="drop")
bins[0] = float("-inf")
bins[len(bins) - 1] = float("inf")
# expected transform output
X_t = [x for x in range(0, 10)]
# test init params
assert transformer.q == 10
assert transformer.variables == ["var"]
assert transformer.return_object is False
# test fit attr
assert transformer.input_shape_ == (100, 1)
# test transform output
assert (transformer.binner_dict_["var"] == bins).all()
assert len([x for x in X["var"].unique() if x not in X_t]) == 0
# in equal frequency discretisation, all intervals get same proportion of values
assert len((X["var"].value_counts()).unique()) == 1
def test_automatically_find_variables_and_return_as_object(df_normal_dist):
# test case 2: return variables cast as object
transformer = EqualFrequencyDiscretiser(q=10,
variables=None,
return_object=True)
X = transformer.fit_transform(df_normal_dist)
assert X["var"].dtypes == "O"
