def test_copy_data_works(self):
ohe = preprocessing.OneHotEncoderFromDataFrame(categorical_feature_names=["one", "three"],
copy=True)
ohe.fit(self.data)
transformed_data = ohe.transform(self.data)
expected_data = pd.DataFrame({"one": [1, 2, 3, 1],
"two": [2, 2, 2, 5],
"three": [0, 5, 0, 5]})
pd.util.testing.assert_frame_equal(self.data.sort_index(axis=1),
expected_data.sort_index(axis=1))
def test_map_to_int_to_onehot(self):
fit_df = pd.DataFrame({"quarter": ["Q1", "Q1", "Q1", "Q2", "Q2"]})
transform_df = fit_df.copy()
mti = preprocessing.MapToInt("quarter", copy=True)
ohe = preprocessing.OneHotEncoderFromDataFrame(categorical_feature_names=["quarter"], copy=True)
pipe = Pipeline(steps=[("one", mti), ("two", ohe)])
pipe.fit(fit_df)
output_df = pipe.transform(transform_df)
expected_df = pd.DataFrame({"onehot_col1": [1.0, 1, 1, 0, 0], "onehot_col2": [0.0, 0, 0, 1, 1]})
pd.util.testing.assert_frame_equal(output_df, expected_df)
def test_encode_some_columns(self):
ohe = preprocessing.OneHotEncoderFromDataFrame(categorical_feature_names=["one", "three"])
ohe.fit(self.data)
transformed_data = ohe.transform(self.data)
expected_data = pd.DataFrame({"two": [2, 2, 2, 5],
"onehot_col1": [1., 0, 0, 1],
"onehot_col2": [0., 1, 0, 0],
"onehot_col3": [0., 0, 1, 0],
"onehot_col4": [1., 0, 1, 0],
"onehot_col5": [0., 1, 0, 1]})
pd.util.testing.assert_frame_equal(transformed_data.sort_index(axis=1),
expected_data.sort_index(axis=1))
def test_inplace_transform_works(self):
ohe = preprocessing.OneHotEncoderFromDataFrame(categorical_feature_names=["one", "three"],
copy=False)
data = self.data.copy()
ohe.fit(self.data)
ohe.transform(self.data)
expected_data = pd.DataFrame({"two": [2, 2, 2, 5],
"onehot_col1": [1., 0, 0, 1],
"onehot_col2": [0., 1, 0, 0],
"onehot_col3": [0., 0, 1, 0],
"onehot_col4": [1., 0, 1, 0],
"onehot_col5": [0., 1, 0, 1]})
pd.util.testing.assert_frame_equal(self.data.sort_index(axis=1),
expected_data.sort_index(axis=1))
def test_encoding_subset_columns(self):
ohe = preprocessing.OneHotEncoderFromDataFrame(categorical_feature_names=["one", "three"],
copy=True)
shifted_data = self.data[2:]
ohe.fit(shifted_data)
transformed_data = ohe.transform(shifted_data)
self.data = pd.DataFrame({"one": [1, 2, 3, 1],
"two": [2, 2, 2, 5],
"three": [0, 5, 0, 5]})
expected_data = pd.DataFrame({"two": [2, 5],
"onehot_col1": [0., 1],
"onehot_col2": [1., 0],
"onehot_col3": [1., 0],
"onehot_col4": [0., 1]},
index=[2, 3])
print(transformed_data)
print(expected_data)
pd.util.testing.assert_frame_equal(transformed_data.sort_index(axis=1),
expected_data.sort_index(axis=1))
def test_correct_handle_unknown_string_passed(self):
ohe = preprocessing.OneHotEncoderFromDataFrame(handle_unknown="ignore")
assert ohe.handle_unknown == "ignore"
def test_correct_dtype_passed(self):
ohe = preprocessing.OneHotEncoderFromDataFrame(dtype=np.int)
assert ohe.dtype == np.int