8000 Allowed trans='passthrough' to handle scalar column input. by lrjball · Pull Request #14495 · scikit-learn/scikit-learn · GitHub
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Allowed trans='passthrough' to handle scalar column input. #14495

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Allowed trans='passthrough' to handle scalar column input. #14495

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dc8b679
Allowed trans='passthrough' to handle scalar column input.
lrjball Jul 28, 2019
fa304be
Merge branch 'master' into passthrough_trans
lrjball Jul 28, 2019
03470a9
Fixed flake8 issues
lrjball Jul 28, 2019
238da64
Fixed flake8 issues
lrjball Jul 28, 2019
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17 changes: 15 additions & 2 deletions sklearn/compose/_column_transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -251,8 +251,9 @@ def _iter(self, fitted=False, replace_strings=False):
# skip in case of 'drop'
if trans == 'passthrough':
trans = FunctionTransformer(
accept_sparse=True, check_inverse=False
)
func=_passthrough_func,
accept_sparse=True,
check_inverse=False)
elif trans == 'drop':
continue
elif _is_empty_column_selection(column):
Expand Down Expand Up @@ -707,3 +708,15 @@ def make_column_transformer(*transformers, **kwargs):
remainder=remainder,
sparse_threshold=sparse_threshold,
verbose=verbose)


def _passthrough_func(X):
"""
Function used in the FunctionTransformer for 'passthrough' columns
to ensure the correct shape.
"""
if not getattr(X, 'ndim', 0) == 2:
if hasattr(X, 'values'):
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wouldn't simply X = np.asarray(X) be better here?

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it's better but it's unclear to me whether either is good ;)

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X = np.asarray(X) would work on anything that has __array__ (NamedArray for instance), but values is specific to pandas.

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also values does a copy.

X = X.values
return X.reshape(-1, 1)
return X
43 changes: 43 additions & 0 deletions sklearn/compose/tests/test_column_transformer.py
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Expand Up @@ -719,6 +719,49 @@ def test_column_transformer_special_strings():
ct.fit, X_array)


def test_column_transformer_passthrough_transform():
# check that when trans='passthrough', both lists and scalars can be passed
X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
ct = ColumnTransformer([('pass', 'passthrough', 0)],
remainder='passthrough')
X_pass = ct.fit_transform(X_array)
assert_array_equal(X_pass, X_array)

ct = ColumnTransformer([('pass', 'passthrough', [0])],
remainder='passthrough')
X_pass = ct.fit_transform(X_array)
assert_array_equal(X_pass, X_array)

ct = ColumnTransformer([('pass', 'passthrough', [0, 1])])
X_pass = ct.fit_transform(X_array)
assert_array_equal(X_pass, X_array)

pd = pytest.importorskip('pandas')
df = pd.DataFrame(X_array, columns=['col0', 'col1'])

ct = ColumnTransformer([('pass', 'passthrough', 0)],
remainder='passthrough')
X_pass = ct.fit_transform(df)
assert_array_equal(X_pass, df.values)

ct = ColumnTransformer([('pass', 'passthrough', [0])],
remainder='passthrough')
X_pass = ct.fit_transform(df)
assert_array_equal(X_pass, df.values)

ct = ColumnTransformer([('pass', 'passthrough', [0, 1])])
X_pass = ct.fit_transform(df)
assert_array_equal(X_pass, df.values)

ct = ColumnTransformer([('pass', 'passthrough', 'col0')])
X_pass = ct.fit_transform(df)
assert_array_equal(X_pass, df[['col0']].values)

ct = ColumnTransformer([('pass', 'passthrough', ['col1', 'col0'])])
X_pass = ct.fit_transform(df)
assert_array_equal(X_pass, df[['col1', 'col0']].values)


def test_column_transformer_remainder():
X_array = np.array([[0, 1, 2], [2, 4, 6]]).T

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