def test_column_transformer_sparse_array():
X_sparse = sparse.eye(3, 2).tocsr()
# no distinction between 1D and 2D
X_res_first = X_sparse[:, 0]
X_res_both = X_sparse
for col in [0, [0], slice(0, 1)]:
for remainder, res in [('drop', X_res_first),
('passthrough', X_res_both)]:
ct = ColumnTransformer([('trans', Trans(), col)],
remainder=remainder,
sparse_threshold=0.8)
assert sparse.issparse(ct.fit_transform(X_sparse))
assert_allclose_dense_sparse(ct.fit_transform(X_sparse), res)
assert_allclose_dense_sparse(
ct.fit(X_sparse).transform(X_sparse), res)
for col in [[0, 1], slice(0, 2)]:
ct = ColumnTransformer([('trans', Trans(), col)], sparse_threshold=0.8)
assert sparse.issparse(ct.fit_transform(X_sparse))
assert_allclose_dense_sparse(ct.fit_transform(X_sparse), X_res_both)
assert_allclose_dense_sparse(
ct.fit(X_sparse).transform(X_sparse), X_res_both)
def test_column_transformer_drop_all_sparse_remainder_transformer():
X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T
ct = ColumnTransformer([('trans1', 'drop', [0])],
remainder=SparseMatrixTrans(),
sparse_threshold=0.8)
X_trans = ct.fit_transform(X_array)
assert sparse.issparse(X_trans)
# SparseMatrixTrans creates 3 features for each column, thus:
assert X_trans.shape == (3, 3)
assert_array_equal(X_trans.toarray(), np.eye(3))
assert len(ct.transformers_) == 2
assert ct.transformers_[-1][0] == 'remainder'
assert isinstance(ct.transformers_[-1][1], SparseMatrixTrans)
assert_array_equal(ct.transformers_[-1][2], [1, 2])
def test_column_transformer_remainder_pandas(key):
# test different ways that columns are specified with passthrough
pd = pytest.importorskip('pandas')
if isinstance(key, str) and key == 'pd-index':
key = pd.Index(['first'])
X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
X_df = pd.DataFrame(X_array, columns=['first', 'second'])
X_res_both = X_array
ct = ColumnTransformer([('trans1', Trans(), key)], remainder='passthrough')
assert_array_equal(ct.fit_transform(X_df), X_res_both)
assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both)
assert len(ct.transformers_) == 2
assert ct.transformers_[-1][0] == 'remainder'
assert ct.transformers_[-1][1] == 'passthrough'
assert_array_equal(ct.transformers_[-1][2], [1])
def test_column_transformer_sparse_remainder_transformer():
X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T
ct = ColumnTransformer([('trans1', Trans(), [0])],
remainder=SparseMatrixTrans(),
sparse_threshold=0.8)
X_trans = ct.fit_transform(X_array)
assert sparse.issparse(X_trans)
# SparseMatrixTrans creates 3 features for each column. There is
# one column in ``transformers``, thus:
assert X_trans.shape == (3, 3 + 1)
exp_array = np.hstack((X_array[:, 0].reshape(-1, 1), np.eye(3)))
assert_array_equal(X_trans.toarray(), exp_array)
assert len(ct.transformers_) == 2
assert ct.transformers_[-1][0] == 'remainder'
assert isinstance(ct.transformers_[-1][1], SparseMatrixTrans)
assert_array_equal(ct.transformers_[-1][2], [1, 2])
def test_column_transformer_dataframe():
pd = pytest.importorskip('pandas')
X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
X_df = pd.DataFrame(X_array, columns=['first', 'second'])
X_res_first = np.array([0, 1, 2]).reshape(-1, 1)
X_res_both = X_array
cases = [
# String keys: label based
# scalar
('first', X_res_first),
# list
(['first'], X_res_first),
(['first', 'second'], X_res_both),
# slice
(slice('first', 'second'), X_res_both),
# int keys: positional
# scalar
(0, X_res_first),
# list
([0], X_res_first),
([0, 1], X_res_both),
(np.array([0, 1]), X_res_both),
# slice
(slice(0, 1), X_res_first),
(slice(0, 2), X_res_both),
# boolean mask
(np.array([True, False]), X_res_first),
(pd.Series([True, False], index=['first', 'second']), X_res_first),
]
for selection, res in cases:
ct = ColumnTransformer([('trans', Trans(), selection)],
remainder='drop')
assert_array_equal(ct.fit_transform(X_df), res)
assert_array_equal(ct.fit(X_df).transform(X_df), res)
# callable that returns any of the allowed specifiers
ct = ColumnTransformer([('trans', Trans(), lambda X: selection)],
remainder='drop')
assert_array_equal(ct.fit_transform(X_df), res)
assert_array_equal(ct.fit(X_df).transform(X_df), res)
ct = ColumnTransformer([('trans1', Trans(), ['first']),
('trans2', Trans(), ['second'])])
assert_array_equal(ct.fit_transform(X_df), X_res_both)
assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both)
assert len(ct.transformers_) == 2
assert ct.transformers_[-1][0] != 'remainder'
ct = ColumnTransformer([('trans1', Trans(), [0]),
('trans2', Trans(), [1])])
assert_array_equal(ct.fit_transform(X_df), X_res_both)
assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both)
assert len(ct.transformers_) == 2
assert ct.transformers_[-1][0] != 'remainder'
# test with transformer_weights
transformer_weights = {'trans1': .1, 'trans2': 10}
both = ColumnTransformer([('trans1', Trans(), ['first']),
('trans2', Trans(), ['second'])],
transformer_weights=transformer_weights)
res = np.vstack([
transformer_weights['trans1'] * X_df['first'],
transformer_weights['trans2'] * X_df['second']
]).T
assert_array_equal(both.fit_transform(X_df), res)
assert_array_equal(both.fit(X_df).transform(X_df), res)
assert len(both.transformers_) == 2
assert ct.transformers_[-1][0] != 'remainder'
# test multiple columns
both = ColumnTransformer([('trans', Trans(), ['first', 'second'])],
transformer_weights={'trans': .1})
assert_array_equal(both.fit_transform(X_df), 0.1 * X_res_both)
assert_array_equal(both.fit(X_df).transform(X_df), 0.1 * X_res_both)
assert len(both.transformers_) == 1
assert ct.transformers_[-1][0] != 'remainder'
both = ColumnTransformer([('trans', Trans(), [0, 1])],
transformer_weights={'trans': .1})
assert_array_equal(both.fit_transform(X_df), 0.1 * X_res_both)
assert_array_equal(both.fit(X_df).transform(X_df), 0.1 * X_res_both)
assert len(both.transformers_) == 1
assert ct.transformers_[-1][0] != 'remainder'
# ensure pandas object is passes through
class TransAssert(BaseEstimator):
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
assert isinstance(X, (pd.DataFrame, pd.Series))
if isinstance(X, pd.Series):
X = X.to_frame()
return X
ct = ColumnTransformer([('trans', TransAssert(), 'first')],
remainder='drop')
ct.fit_transform(X_df)
ct = ColumnTransformer([('trans', TransAssert(), ['first', 'second'])])
ct.fit_transform(X_df)
# integer column spec + integer column names -> still use positional
X_df2 = X_df.copy()
X_df2.columns = [1, 0]
ct = ColumnTransformer([('trans', Trans(), 0)], remainder='drop')
assert_array_equal(ct.fit_transform(X_df2), X_res_first)
assert_array_equal(ct.fit(X_df2).transform(X_df2), X_res_first)
assert len(ct.transformers_) == 2
assert ct.transformers_[-1][0] == 'remainder'
assert ct.transformers_[-1][1] == 'drop'
assert_array_equal(ct.transformers_[-1][2], [1])