Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

fix various bugs in RareLabelEncoder #665

Merged
merged 2 commits into from
Apr 30, 2023
Merged

fix various bugs in RareLabelEncoder #665

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
00a5933
fixed a few typos in rare categories
solegalli Apr 27, 2023
f9322a3
list when fit and nan
solegalli Apr 27, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
22 changes: 11 additions & 11 deletions feature_engine/encoding/rare_label.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -218,7 +218,7 @@ def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
"indicated in n_categories. Thus, all categories will be "
"considered frequent".format(var)
)
self.encoder_dict_[var] = X[var].unique()
self.encoder_dict_[var] = list(X[var].unique())

self.variables_ = variables_
self._get_feature_names_in(X)
Expand Down Expand Up @@ -247,19 +247,19 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame:
_check_optional_contains_na(X, self.variables_)

for feature in self.variables_:
X[feature] = np.where(
X[feature].isin(self.encoder_dict_[feature]),
X[feature],
self.replace_with,
)
if X[feature].dtype == "category":
X[feature] = X[feature].cat.add_categories(self.replace_with)
X.loc[
~X[feature].isin(self.encoder_dict_[feature]), feature
] = self.replace_with

else:
for feature in self.variables_:
X[feature] = np.where(
X[feature].isin(self.encoder_dict_[feature] + [np.nan]),
X[feature],
self.replace_with,
)
if X[feature].dtype == "category":
X[feature] = X[feature].cat.add_categories(self.replace_with)
X.loc[
~X[feature].isin(self.encoder_dict_[feature] + [np.nan]), feature
] = self.replace_with

return X

Expand Down
188 changes: 186 additions & 2 deletions tests/test_encoding/test_rare_label_encoder.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,34 @@ def test_defo_params_plus_automatically_find_variables(df_enc_big):
pd.testing.assert_frame_equal(X, df)


def test_when_varnames_are_numbers(df_enc_big):
input_df = df_enc_big.copy()
input_df.columns = [1, 2, 3]

encoder = RareLabelEncoder(
tol=0.06, n_categories=5, variables=None, replace_with="Rare"
)
X = encoder.fit_transform(input_df)

# expected output
df = {
1: ["A"] * 6 + ["B"] * 10 + ["C"] * 4 + ["D"] * 10 + ["Rare"] * 4 + ["G"] * 6,
2: ["A"] * 10 + ["B"] * 6 + ["C"] * 4 + ["D"] * 10 + ["Rare"] * 4 + ["G"] * 6,
3: ["A"] * 4 + ["B"] * 6 + ["C"] * 10 + ["D"] * 10 + ["Rare"] * 4 + ["G"] * 6,
}
df = pd.DataFrame(df)

frequenc_cat = {
1: ["B", "D", "A", "G", "C"],
2: ["A", "D", "B", "G", "C"],
3: ["C", "D", "B", "G", "A"],
}

assert encoder.variables_ == [1, 2, 3]
assert encoder.encoder_dict_ == frequenc_cat
pd.testing.assert_frame_equal(X, df)


def test_correctly_ignores_nan_in_transform(df_enc_big):
encoder = RareLabelEncoder(
tol=0.06,
Expand Down Expand Up @@ -102,7 +130,7 @@ def test_correctly_ignores_nan_in_fit(df_enc_big):
n_categories=3,
missing_values="ignore",
)
X = encoder.fit_transform(df)
encoder.fit(df)

# expected:
frequenc_cat = {
Expand Down Expand Up @@ -134,6 +162,90 @@ def test_correctly_ignores_nan_in_fit(df_enc_big):
pd.testing.assert_frame_equal(X, tt)


def test_correctly_ignores_nan_in_fit_when_var_is_numerical(df_enc_big):

df = df_enc_big.copy()
df["var_C"] = [
1,
1,
1,
1,
2,
2,
2,
2,
2,
2,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
5,
5,
6,
6,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
]

encoder = RareLabelEncoder(
tol=0.06,
n_categories=3,
missing_values="ignore",
ignore_format=True,
)
encoder.fit(df)

# expected:
frequenc_cat = {
"var_A": ["B", "D", "A", "G", "C"],
"var_B": ["A", "D", "B", "G", "C"],
"var_C": [3, 4, 2, 1],
}
assert encoder.encoder_dict_ == frequenc_cat

# input
t = pd.DataFrame(
{
"var_A": ["A", np.nan, "J", "G"],
"var_B": ["A", np.nan, "J", "G"],
"var_C": [3, np.nan, 9, 10],
}
)

# expected
tt = pd.DataFrame(
{
"var_A": ["A", np.nan, "Rare", "G"],
"var_B": ["A", np.nan, "Rare", "G"],
"var_C": [3.0, np.nan, "Rare", "Rare"],
}
)

X = encoder.transform(t)
pd.testing.assert_frame_equal(X, tt, check_dtype=False)


def test_user_provides_grouping_label_name_and_variable_list(df_enc_big):
# test case 2: user provides alternative grouping value and variable list
encoder = RareLabelEncoder(
Expand Down Expand Up @@ -316,12 +428,84 @@ def test_variables_cast_as_category(df_enc_big):
+ ["G"] * 6,
}
df = pd.DataFrame(df)
df["var_B"] = pd.Categorical(df["var_B"])

# test fit attr
assert encoder.variables_ == ["var_A", "var_B", "var_C"]
assert encoder.n_features_in_ == 3
# test transform output
pd.testing.assert_frame_equal(X, df)
pd.testing.assert_frame_equal(X, df, check_categorical=False)


def test_variables_cast_as_category_with_na_in_transform(df_enc_big):
encoder = RareLabelEncoder(
tol=0.06,
n_categories=5,
variables=None,
replace_with="Rare",
missing_values="ignore",
)

df_enc_big = df_enc_big.copy()
df_enc_big["var_B"] = df_enc_big["var_B"].astype("category")
encoder.fit(df_enc_big)

# input
t = pd.DataFrame(
{
"var_A": ["A", np.nan, "J", "G"],
"var_B": ["A", np.nan, "J", "G"],
"var_C": ["A", np.nan, "J", "G"],
}
)
t["var_B"] = pd.Categorical(t["var_B"])

# expected
tt = pd.DataFrame(
{
"var_A": ["A", np.nan, "Rare", "G"],
"var_B": ["A", np.nan, "Rare", "G"],
"var_C": ["A", np.nan, "Rare", "G"],
}
)
tt["var_B"] = pd.Categorical(tt["var_B"])
pd.testing.assert_frame_equal(encoder.transform(t), tt, check_categorical=False)


def test_variables_cast_as_category_with_na_in_fit(df_enc_big):

df = df_enc_big.copy()
df.loc[df["var_C"] == "G", "var_C"] = np.nan
df["var_C"] = df["var_C"].astype("category")

encoder = RareLabelEncoder(
tol=0.06,
n_categories=3,
missing_values="ignore",
)
encoder.fit(df)

# input
t = pd.DataFrame(
{
"var_A": ["A", np.nan, "J", "G"],
"var_B": ["A", np.nan, "J", "G"],
"var_C": ["C", np.nan, "J", "G"],
}
)
t["var_C"] = pd.Categorical(t["var_C"])

# expected
tt = pd.DataFrame(
{
"var_A": ["A", np.nan, "Rare", "G"],
"var_B": ["A", np.nan, "Rare", "G"],
"var_C": ["C", np.nan, "Rare", "Rare"],
}
)
tt["var_C"] = pd.Categorical(tt["var_C"])

pd.testing.assert_frame_equal(encoder.transform(t), tt, check_categorical=False)


def test_inverse_transform_raises_not_implemented_error(df_enc_big):
Expand Down