def test_imputer_with_none():
X = pd.DataFrame({
"int with None": [1, 0, 5, None],
"float with None": [0.1, 0.0, 0.5, None],
"category with None":
pd.Series(["b", "a", "a", None], dtype='category'),
"boolean with None": [True, None, False, True],
"object with None": ["b", "a", "a", None],
"all None": [None, None, None, None]
})
y = pd.Series([0, 0, 1, 0, 1])
imputer = Imputer()
imputer.fit(X, y)
transformed = imputer.transform(X, y)
expected = pd.DataFrame({
"int with None": [1, 0, 5, 2],
"float with None": [0.1, 0.0, 0.5, 0.2],
"category with None":
pd.Series(["b", "a", "a", "a"], dtype='category'),
"boolean with None": [True, True, False, True],
"object with None":
pd.Series(["b", "a", "a", "a"], dtype='category')
})
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
imputer = Imputer()
transformed = imputer.fit_transform(X, y)
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
def test_categorical_only_input(imputer_test_data):
X = imputer_test_data[[
"categorical col", "object col", "bool col", "categorical with nan",
"object with nan", "bool col with nan", "all nan cat"
]]
y = pd.Series([0, 0, 1, 0, 1])
imputer = Imputer()
imputer.fit(X, y)
transformed = imputer.transform(X, y)
expected = pd.DataFrame({
"categorical col":
pd.Series(["zero", "one", "two", "zero", "three"], dtype='category'),
"object col":
pd.Series(["b", "b", "a", "c", "d"], dtype='category'),
"bool col": [True, False, False, True, True],
"categorical with nan":
pd.Series(["0", "1", "0", "0", "3"], dtype='category'),
"object with nan":
pd.Series(["b", "b", "b", "c", "b"], dtype='category'),
"bool col with nan": [True, True, False, True, True]
})
imputer = Imputer()
transformed = imputer.fit_transform(X, y)
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
def test_categorical_and_numeric_input(imputer_test_data):
X = imputer_test_data
y = pd.Series([0, 0, 1, 0, 1])
imputer = Imputer()
imputer.fit(X, y)
transformed = imputer.transform(X, y)
expected = pd.DataFrame({
"categorical col":
pd.Series(["zero", "one", "two", "zero", "three"], dtype='category'),
"int col": [0, 1, 2, 0, 3],
"object col":
pd.Series(["b", "b", "a", "c", "d"], dtype='category'),
"float col": [0.0, 1.0, 0.0, -2.0, 5.],
"bool col": [True, False, False, True, True],
"categorical with nan":
pd.Series(["0", "1", "0", "0", "3"], dtype='category'),
"int with nan": [0.5, 1.0, 0.0, 0.0, 1.0],
"float with nan": [0.0, 1.0, 0, -1.0, 0.],
"object with nan":
pd.Series(["b", "b", "b", "c", "b"], dtype='category'),
"bool col with nan": [True, True, False, True, True]
})
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
imputer = Imputer()
transformed = imputer.fit_transform(X, y)
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
def test_imputer_fill_value(imputer_test_data):
X = imputer_test_data[[
"int with nan", "categorical with nan", "float with nan",
"object with nan", "bool col with nan"
]]
y = pd.Series([0, 0, 1, 0, 1])
imputer = Imputer(categorical_impute_strategy="constant",
numeric_impute_strategy="constant",
categorical_fill_value="fill",
numeric_fill_value=-1)
imputer.fit(X, y)
transformed = imputer.transform(X, y)
expected = pd.DataFrame({
"int with nan": [-1, 1, 0, 0, 1],
"categorical with nan":
pd.Series(["fill", "1", "fill", "0", "3"], dtype='category'),
"float with nan": [0.0, 1.0, -1, -1.0, 0.],
"object with nan":
pd.Series(["b", "b", "fill", "c", "fill"], dtype='category'),
"bool col with nan":
pd.Series([True, "fill", False, "fill", True], dtype='category')
})
assert_frame_equal(expected, transformed.to_dataframe(), check_dtype=False)
imputer = Imputer(categorical_impute_strategy="constant",
numeric_impute_strategy="constant",
categorical_fill_value="fill",
numeric_fill_value=-1)
transformed = imputer.fit_transform(X, y)
assert_frame_equal(expected, transformed.to_dataframe(), check_dtype=False)
def test_drop_all_columns(imputer_test_data):
X = imputer_test_data[["all nan cat", "all nan"]]
y = pd.Series([0, 0, 1, 0, 1])
imputer = Imputer()
imputer.fit(X, y)
transformed = imputer.transform(X, y)
expected = X.drop(["all nan cat", "all nan"], axis=1)
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
imputer = Imputer()
transformed = imputer.fit_transform(X, y)
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
def test_imputer_empty_data(data_type, make_data_type):
X = pd.DataFrame()
y = pd.Series()
X = make_data_type(data_type, X)
y = make_data_type(data_type, y)
expected = pd.DataFrame(index=pd.Index([]), columns=pd.Index([]))
imputer = Imputer()
imputer.fit(X, y)
transformed = imputer.transform(X, y)
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
imputer = Imputer()
transformed = imputer.fit_transform(X, y)
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
def test_typed_imputer_numpy_input():
X = np.array([[1, 2, 2, 0], [np.nan, 0, 0, 0], [1, np.nan, np.nan,
np.nan]])
y = pd.Series([0, 0, 1])
imputer = Imputer()
imputer.fit(X, y)
transformed = imputer.transform(X, y)
expected = pd.DataFrame(
np.array([[1, 2, 2, 0], [1, 0, 0, 0], [1, 1, 1, 0]]))
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
imputer = Imputer()
transformed = imputer.fit_transform(X, y)
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
def test_imputer_datetime_input():
X = pd.DataFrame({'dates': ['20190902', '20200519', '20190607', np.nan],
'more dates': ['20190902', '20201010', '20190921', np.nan]})
X['dates'] = pd.to_datetime(X['dates'], format='%Y%m%d')
X['more dates'] = pd.to_datetime(X['more dates'], format='%Y%m%d')
y = pd.Series()
imputer = Imputer()
imputer.fit(X, y)
transformed = imputer.transform(X, y)
assert_frame_equal(transformed.to_dataframe(), X, check_dtype=False)
imputer = Imputer()
transformed = imputer.fit_transform(X, y)
assert_frame_equal(transformed.to_dataframe(), X, check_dtype=False)
def test_numeric_only_input(imputer_test_data):
X = imputer_test_data[["int col", "float col",
"int with nan", "float with nan", "all nan"]]
y = pd.Series([0, 0, 1, 0, 1])
imputer = Imputer(numeric_impute_strategy="median")
imputer.fit(X, y)
transformed = imputer.transform(X, y)
expected = pd.DataFrame({
"int col": [0, 1, 2, 0, 3],
"float col": [0.0, 1.0, 0.0, -2.0, 5.],
"int with nan": [0.5, 1.0, 0.0, 0.0, 1.0],
"float with nan": [0.0, 1.0, 0, -1.0, 0.]
})
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
imputer = Imputer()
transformed = imputer.fit_transform(X, y)
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
def test_imputer_no_nans(imputer_test_data):
X = imputer_test_data[["categorical col", "object col", "bool col"]]
y = pd.Series([0, 0, 1, 0, 1])
imputer = Imputer(categorical_impute_strategy="constant", numeric_impute_strategy="constant",
categorical_fill_value="fill", numeric_fill_value=-1)
imputer.fit(X, y)
transformed = imputer.transform(X, y)
expected = pd.DataFrame({
"categorical col": pd.Series(["zero", "one", "two", "zero", "three"], dtype='category'),
"object col": pd.Series(["b", "b", "a", "c", "d"], dtype='category'),
"bool col": [True, False, False, True, True],
})
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
imputer = Imputer(categorical_impute_strategy="constant", numeric_impute_strategy="constant",
categorical_fill_value="fill", numeric_fill_value=-1)
transformed = imputer.fit_transform(X, y)
assert_frame_equal(transformed.to_dataframe(), expected, check_dtype=False)
def test_imputer_empty_data(data_type):
if data_type == 'pd':
X = pd.DataFrame()
y = pd.Series()
expected = pd.DataFrame(index=pd.Index([]), columns=pd.Index([]))
elif data_type == 'ww':
X = ww.DataTable(pd.DataFrame())
y = ww.DataColumn(pd.Series())
expected = pd.DataFrame(index=pd.Index([]), columns=pd.Index([]))
else:
X = np.array([[]])
y = np.array([])
expected = pd.DataFrame(index=pd.Index([0]), columns=pd.Int64Index([]))
imputer = Imputer()
imputer.fit(X, y)
transformed = imputer.transform(X, y)
assert_frame_equal(transformed, expected, check_dtype=False)
imputer = Imputer()
transformed = imputer.fit_transform(X, y)
assert_frame_equal(transformed, expected, check_dtype=False)
