def test_column_transformer_invalid_columns(remainder):
X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
# general invalid
for col in [1.5, ['string', 1], slice(1, 's'), np.array([1.])]:
ct = ColumnTransformer([('trans', Trans(), col)], remainder=remainder)
assert_raise_message(ValueError, "No valid specification", ct.fit,
X_array)
# invalid for arrays
for col in ['string', ['string', 'other'], slice('a', 'b')]:
ct = ColumnTransformer([('trans', Trans(), col)], remainder=remainder)
assert_raise_message(ValueError, "Specifying the columns", ct.fit,
X_array)
# transformed n_features does not match fitted n_features
col = [0, 1]
ct = ColumnTransformer([('trans', Trans(), col)], remainder=remainder)
ct.fit(X_array)
X_array_more = np.array([[0, 1, 2], [2, 4, 6], [3, 6, 9]]).T
msg = ("Given feature/column names or counts do not match the ones for "
"the data given during fit.")
with pytest.warns(DeprecationWarning, match=msg):
ct.transform(X_array_more) # Should accept added columns, for now
X_array_fewer = np.array([
[0, 1, 2],
]).T
err_msg = 'Number of features'
with pytest.raises(ValueError, match=err_msg):
ct.transform(X_array_fewer)
def test_column_transformer_sparse_stacking():
X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
col_trans = ColumnTransformer([('trans1', Trans(), [0]),
('trans2', SparseMatrixTrans(), 1)],
sparse_threshold=0.8)
col_trans.fit(X_array)
X_trans = col_trans.transform(X_array)
assert sparse.issparse(X_trans)
assert X_trans.shape == (X_trans.shape[0], X_trans.shape[0] + 1)
assert_array_equal(X_trans.toarray()[:, 1:], np.eye(X_trans.shape[0]))
assert len(col_trans.transformers_) == 2
assert col_trans.transformers_[-1][0] != 'remainder'
col_trans = ColumnTransformer([('trans1', Trans(), [0]),
('trans2', SparseMatrixTrans(), 1)],
sparse_threshold=0.1)
col_trans.fit(X_array)
X_trans = col_trans.transform(X_array)
assert not sparse.issparse(X_trans)
assert X_trans.shape == (X_trans.shape[0], X_trans.shape[0] + 1)
assert_array_equal(X_trans[:, 1:], np.eye(X_trans.shape[0]))
def test_column_transformer_reordered_column_names_remainder(explicit_colname):
"""Regression test for issue #14223: 'Named col indexing fails with
ColumnTransformer remainder on changing DataFrame column ordering'
Should raise error on changed order combined with remainder.
Should allow for added columns in `transform` input DataFrame
as long as all preceding columns match.
"""
pd = pytest.importorskip('pandas')
X_fit_array = np.array([[0, 1, 2], [2, 4, 6]]).T
X_fit_df = pd.DataFrame(X_fit_array, columns=['first', 'second'])
X_trans_array = np.array([[2, 4, 6], [0, 1, 2]]).T
X_trans_df = pd.DataFrame(X_trans_array, columns=['second', 'first'])
tf = ColumnTransformer([('bycol', Trans(), explicit_colname)],
remainder=Trans())
tf.fit(X_fit_df)
err_msg = 'Column ordering must be equal'
warn_msg = ("Given feature/column names or counts do not match the ones "
"for the data given during fit.")
with pytest.raises(ValueError, match=err_msg):
tf.transform(X_trans_df)
# No error for added columns if ordering is identical
X_extended_df = X_fit_df.copy()
X_extended_df['third'] = [3, 6, 9]
with pytest.warns(DeprecationWarning, match=warn_msg):
tf.transform(X_extended_df) # No error should be raised, for now
# No 'columns' AttributeError when transform input is a numpy array
X_array = X_fit_array.copy()
err_msg = 'Specifying the columns'
with pytest.raises(ValueError, match=err_msg):
tf.transform(X_array)
def test_feature_name_validation():
"""Tests if the proper warning/error is raised if the columns do not match
during fit and transform."""
pd = pytest.importorskip("pandas")
X = np.ones(shape=(3, 2))
X_extra = np.ones(shape=(3, 3))
df = pd.DataFrame(X, columns=['a', 'b'])
df_extra = pd.DataFrame(X_extra, columns=['a', 'b', 'c'])
tf = ColumnTransformer([('bycol', Trans(), ['a', 'b'])])
tf.fit(df)
msg = ("Given feature/column names or counts do not match the ones for "
"the data given during fit.")
with pytest.warns(DeprecationWarning, match=msg):
tf.transform(df_extra)
tf = ColumnTransformer([('bycol', Trans(), [0])])
tf.fit(df)
with pytest.warns(DeprecationWarning, match=msg):
tf.transform(X_extra)
with warnings.catch_warnings(record=True) as warns:
tf.transform(X)
assert not warns
tf = ColumnTransformer([('bycol', Trans(), ['a'])], remainder=Trans())
tf.fit(df)
with pytest.warns(DeprecationWarning, match=msg):
tf.transform(df_extra)
tf = ColumnTransformer([('bycol', Trans(), [0, -1])])
tf.fit(df)
msg = "At least one negative column was used to"
with pytest.raises(RuntimeError, match=msg):
tf.transform(df_extra)
tf = ColumnTransformer([('bycol', Trans(), slice(-1, -3, -1))])
tf.fit(df)
with pytest.raises(RuntimeError, match=msg):
tf.transform(df_extra)
with warnings.catch_warnings(record=True) as warns:
tf.transform(df)
assert not warns
