def test_embed_dim(estimator, build_dataset):
# Checks that the the dimension of the output space is as expected
input_data, labels, _, X = build_dataset()
model = clone(estimator)
set_random_state(model)
model.fit(*remove_y_quadruplets(estimator, input_data, labels))
assert model.transform(X).shape == X.shape
# assert that ValueError is thrown if input shape is 1D
context = make_context(estimator)
err_msg = ("2D array of formed points expected{}. Found 1D array "
"instead:\ninput={}. Reshape your data and/or use a "
"preprocessor.\n".format(context, X[0]))
with pytest.raises(ValueError) as raised_error:
model.score_pairs(model.transform(X[0, :]))
assert str(raised_error.value) == err_msg
# we test that the shape is also OK when doing dimensionality reduction
if hasattr(model, 'n_components'):
model.set_params(n_components=2)
model.fit(*remove_y_quadruplets(estimator, input_data, labels))
assert model.transform(X).shape == (X.shape[0], 2)
# assert that ValueError is thrown if input shape is 1D
with pytest.raises(ValueError) as raised_error:
model.transform(model.transform(X[0, :]))
assert str(raised_error.value) == err_msg
def test_embed_dim(estimator, build_dataset):
# Checks that the the dimension of the output space is as expected
input_data, labels, _, X = build_dataset()
model = clone(estimator)
set_random_state(model)
model.fit(*remove_y_quadruplets(estimator, input_data, labels))
assert model.transform(X).shape == X.shape
# assert that ValueError is thrown if input shape is 1D
context = make_context(estimator)
err_msg = ("2D array of formed points expected{}. Found 1D array "
"instead:\ninput={}. Reshape your data and/or use a "
"preprocessor.\n".format(context, X[0]))
with pytest.raises(ValueError) as raised_error:
model.score_pairs(model.transform(X[0, :]))
assert str(raised_error.value) == err_msg
# we test that the shape is also OK when doing dimensionality reduction
if type(model).__name__ in {'LFDA', 'MLKR', 'NCA', 'RCA'}:
# TODO:
# avoid this enumeration and rather test if hasattr n_components
# as soon as we have made the arguments names as such (issue #167)
model.set_params(num_dims=2)
model.fit(*remove_y_quadruplets(estimator, input_data, labels))
assert model.transform(X).shape == (X.shape[0], 2)
# assert that ValueError is thrown if input shape is 1D
with pytest.raises(ValueError) as raised_error:
model.transform(model.transform(X[0, :]))
assert str(raised_error.value) == err_msg
def test_error_message_tuple_size(estimator, _):
"""Tests that if a tuples learner is not given the good number of points
per tuple, it throws an error message"""
estimator = clone(estimator)
set_random_state(estimator)
invalid_pairs = np.ones((2, 5, 2))
y = [1, 1]
with pytest.raises(ValueError) as raised_err:
estimator.fit(*remove_y(estimator, invalid_pairs, y))
expected_msg = (
"Tuples of {} element(s) expected{}. Got tuples of 5 "
"element(s) instead (shape=(2, 5, 2)):\ninput={}.\n".format(
estimator._tuple_size, make_context(estimator), invalid_pairs))
assert str(raised_err.value) == expected_msg
def test_error_message_t_score_pairs(estimator, _):
"""tests that if you want to score_pairs on triplets for instance, it returns
the right error message
"""
estimator = clone(estimator)
set_random_state(estimator)
estimator.check_preprocessor()
triplets = np.array([[[1.3, 6.3], [3., 6.8], [6.5, 4.4]],
[[1.9, 5.3], [1., 7.8], [3.2, 1.2]]])
with pytest.raises(ValueError) as raised_err:
estimator.score_pairs(triplets)
expected_msg = ("Tuples of 2 element(s) expected{}. Got tuples of 3 "
"element(s) instead (shape=(2, 3, 2)):\ninput={}.\n"
.format(make_context(estimator), triplets))
assert str(raised_err.value) == expected_msg
def test_error_message_tuple_size(estimator, _):
"""Tests that if a tuples learner is not given the good number of points
per tuple, it throws an error message"""
estimator = clone(estimator)
set_random_state(estimator)
invalid_pairs = np.array([[[1.3, 6.3], [3., 6.8], [6.5, 4.4]],
[[1.9, 5.3], [1., 7.8], [3.2, 1.2]]])
y = [1, 1]
with pytest.raises(ValueError) as raised_err:
estimator.fit(*remove_y_quadruplets(estimator, invalid_pairs, y))
expected_msg = ("Tuples of {} element(s) expected{}. Got tuples of 3 "
"element(s) instead (shape=(2, 3, 2)):\ninput={}.\n"
.format(estimator._tuple_size, make_context(estimator),
invalid_pairs))
assert str(raised_err.value) == expected_msg
def test_error_message_tuple_size(estimator):
"""Tests that if a tuples learner is not given the good number of points
per tuple, it throws an error message"""
estimator = clone(estimator)
set_random_state(estimator)
invalid_pairs = np.array([[[1.3, 6.3], [3., 6.8], [6.5, 4.4]],
[[1.9, 5.3], [1., 7.8], [3.2, 1.2]]])
y = [1, 1]
with pytest.raises(ValueError) as raised_err:
estimator.fit(invalid_pairs, y)
expected_msg = (
"Tuples of {} element(s) expected{}. Got tuples of 3 "
"element(s) instead (shape=(2, 3, 2)):\ninput={}.\n".format(
estimator._tuple_size, make_context(estimator), invalid_pairs))
assert str(raised_err.value) == expected_msg
def test_error_message_t_score_pairs(estimator, _):
"""tests that if you want to score_pairs on triplets for instance, it returns
the right error message
"""
estimator = clone(estimator)
set_random_state(estimator)
estimator.check_preprocessor()
triplets = np.array([[[1.3, 6.3], [3., 6.8], [6.5, 4.4]],
[[1.9, 5.3], [1., 7.8], [3.2, 1.2]]])
with pytest.raises(ValueError) as raised_err:
estimator.score_pairs(triplets)
expected_msg = ("Tuples of 2 element(s) expected{}. Got tuples of 3 "
"element(s) instead (shape=(2, 3, 2)):\ninput={}.\n"
.format(make_context(estimator), triplets))
assert str(raised_err.value) == expected_msg
def test_score_pairs_dim(estimator, build_dataset):
# scoring of 3D arrays should return 1D array (several tuples),
# and scoring of 2D arrays (one tuple) should return an error (like
# scikit-learn's error when scoring 1D arrays)
input_data, labels, _, X = build_dataset()
model = clone(estimator)
set_random_state(model)
model.fit(*remove_y_quadruplets(estimator, input_data, labels))
tuples = np.array(list(product(X, X)))
assert model.score_pairs(tuples).shape == (tuples.shape[0], )
context = make_context(estimator)
msg = ("3D array of formed tuples expected{}. Found 2D array "
"instead:\ninput={}. Reshape your data and/or use a preprocessor.\n"
.format(context, tuples[1]))
with pytest.raises(ValueError) as raised_error:
model.score_pairs(tuples[1])
assert str(raised_error.value) == msg
def test_preprocess_points_invalid_message(estimator):
"""Checks that if the preprocessor does some weird stuff, the preprocessed
input is detected as weird."""
context = make_context(estimator) + (' after the preprocessor '
'has been applied')
def preprocessor(sequence):
return np.ones((len(sequence), 2, 2)) # returns a 3D array instead of 2D
with pytest.raises(ValueError) as raised_error:
check_input(np.ones((3,)), type_of_inputs='classic',
preprocessor=preprocessor, estimator=estimator)
expected_msg = ("2D array of formed points expected{}. "
"Found 3D array instead:\ninput={}. Reshape your data{}.\n"
.format(context, np.ones((3, 2, 2)),
' and/or use a preprocessor' if preprocessor
is not None else ''))
assert str(raised_error.value) == expected_msg
def test_preprocess_points_invalid_message(estimator):
"""Checks that if the preprocessor does some weird stuff, the preprocessed
input is detected as weird."""
context = make_context(estimator) + (' after the preprocessor '
'has been applied')
def preprocessor(sequence):
return np.ones((len(sequence), 2, 2)) # returns a 3D array instead of 2D
with pytest.raises(ValueError) as raised_error:
check_input(np.ones((3,)), type_of_inputs='classic',
preprocessor=preprocessor, estimator=estimator)
expected_msg = ("2D array of formed points expected{}. "
"Found 3D array instead:\ninput={}. Reshape your data{}.\n"
.format(context, np.ones((3, 2, 2)),
' and/or use a preprocessor' if preprocessor
is not None else ''))
assert str(raised_error.value) == expected_msg
def test_make_context(estimator, expected): """test the make_name function""" assert make_context(estimator) == expected
def test_make_context(estimator, expected):
"""test the make_name function"""
assert make_context(estimator) == expected
