def test_pca(self):
ct = make_column_transformer((PCA(2), column_number_exclude_timedelta))
X, y = get_df()
x_new = ct.fit_transform(X, y)
assert x_new.shape == (3, 2)
dfm = DataFrameMapper(
[
(column_number_exclude_timedelta, PCA(2)),
# (column_object_category_bool, [SimpleImputer(strategy='constant'), OneHotEncoder()]),
(column_object_category_bool, [
skex.SafeSimpleImputer(strategy='constant'),
OneHotEncoder()
]),
(column_number_exclude_timedelta, PolynomialFeatures(2)),
],
input_df=True,
df_out=True)
x_new = dfm.fit_transform(X, y)
# assert x_new.columns.to_list() == ['b_c_d_l_0', 'b_c_d_l_1', 'a_a', 'a_b', 'a_missing_value', 'e_False',
# 'e_True', 'f_c', 'f_d', 'f_missing_value', '1', 'b', 'c', 'd', 'l',
# 'b^2', 'b c', 'b d', 'b l', 'c^2', 'c d', 'c l', 'd^2', 'd l', 'l^2']
assert x_new.columns.to_list() == [
'b_c_d_l_0', 'b_c_d_l_1', 'a_a', 'a_b', 'a_missing_value',
'e_False', 'e_True', 'f_c', 'f_d', 'f_missing_value'
]
def test_no_feature(self):
df = get_df()[0]
dfm = DataFrameMapper(
[([], preprocessing.LabelEncoder())],
input_df=True,
df_out=True)
with pytest.raises(ValueError): # ValueError: No data output, maybe it's because your input feature is empty.
dfm.fit_transform(df, None)
def as_local(self):
target = DataFrameMapper(
[],
default=None,
df_out=self.df_out,
input_df=self.input_df,
df_out_dtype_transforms=self.df_out_dtype_transforms)
target.fitted_features_ = [(cols, t.as_local(), opts)
for cols, t, opts in self.fitted_features_]
return target
def test_no_categorical_feature(self):
df = get_df()[0][['b', 'd']]
dfm = DataFrameMapper(
[(column_object_category_bool, preprocessing.LabelEncoder())],
input_df=True,
df_out=True, default=None)
x_new = dfm.fit_transform(df, None)
assert 'b' in x_new
assert 'd' in x_new
def test_in_dataframe_mapper(self):
df = dsutils.load_bank()
df.drop(['id'], axis=1, inplace=True)
X_train, X_test = train_test_split(df.head(100),
test_size=0.2,
random_state=42)
ftt = FeatureGenerationTransformer(
task='binary',
trans_primitives=['cross_categorical'],
categories_cols=column_object_category_bool(X_train))
dfm = DataFrameMapper(features=[(X_train.columns.to_list(), ftt)],
input_df=True,
df_out=True)
X_t = dfm.fit_transform(X_train)
assert X_t.shape == (80, 62)
def test_func_transformer(self):
dfm = DataFrameMapper(
[(column_object_category_bool, [
SimpleImputer(strategy='constant'),
skex.MultiLabelEncoder(),
]
),
],
input_df=True,
df_out=True,
df_out_dtype_transforms=[
(column_object, 'category')
]
)
X, y = get_df()
x_new = dfm.fit_transform(X, y)
assert x_new.dtypes.to_list() == [pd.CategoricalDtype(categories=[0, 1, 2], ordered=False),
pd.CategoricalDtype(categories=[0, 1], ordered=False),
pd.CategoricalDtype(categories=[0, 1, 2], ordered=False)]
def general_preprocessor():
cat_transformer = Pipeline(
steps=[('imputer_cat', SimpleImputer(
strategy='constant')), ('encoder', OrdinalEncoder())])
num_transformer = Pipeline(
steps=[('imputer_num',
SimpleImputer(strategy='mean')), ('scaler', StandardScaler())])
preprocessor = DataFrameMapper(features=[
(column_object_category_bool, cat_transformer),
(column_number_exclude_timedelta, num_transformer)
],
input_df=True,
df_out=True)
return preprocessor