def test_different_input_shape(create_X_y):
X, y = create_X_y
query = np.array([[1.0, 1.0, 2.0]])
ds_test = BaseDS()
ds_test.fit(X, y)
with pytest.raises(ValueError):
ds_test.predict(query)
def test_predict_value(query):
pool_classifiers = create_classifiers_disagree()
ds = BaseDS(pool_classifiers)
X = np.random.rand(10, 2)
y = np.ones(10)
y[:5] = 0
ds.fit(X, y)
with pytest.raises(ValueError):
ds.predict(query)
def test_label_encoder_only_dsel_allagree():
X_dsel_ex1 = np.array([[-1, 1], [-0.75, 0.5], [-1.5, 1.5]])
y_dsel_ex1 = np.array(['cat', 'dog', 'plane'])
query = np.atleast_2d([[1, 0], [-1, -1]])
ds_test = BaseDS(create_pool_classifiers_dog(), k=2)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
predictions = ds_test.predict(query)
assert np.array_equal(predictions, ['dog', 'dog'])
def test_label_encoder_only_dsel():
X_dsel_ex1 = np.array([[-1, 1], [-0.75, 0.5], [-1.5, 1.5]])
y_dsel_ex1 = np.array(['cat', 'dog', 'plane'])
query = np.atleast_2d([[1, 0], [-1, -1]])
ds_test = BaseDS(create_pool_classifiers_dog_cat_plane(), k=2)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.classify_with_ds = Mock()
ds_test.classify_with_ds.return_value = [1, 0]
predictions = ds_test.predict(query)
assert np.array_equal(predictions, ['dog', 'cat'])
def test_IH_is_used(example_estimate_competence, create_pool_classifiers):
X, y, neighbors, distances, dsel_processed, _ = example_estimate_competence
expected = [0, 0, 1]
query = np.ones((3, 2))
ds_test = BaseDS(create_pool_classifiers, with_IH=True, IH_rate=0.5)
ds_test.fit(X, y)
ds_test.DSEL_processed_ = dsel_processed
ds_test._get_region_competence = MagicMock(return_value=(distances,
neighbors))
predicted = ds_test.predict(query)
assert np.array_equal(predicted, expected)
def test_IH_is_used():
expected = [0, 0, 1]
query = np.ones((3, 2))
ds_test = BaseDS(create_pool_classifiers(), with_IH=True, IH_rate=0.5)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.DSEL_processed_ = dsel_processed_ex1
ds_test.DSEL_target_ = y_dsel_ex1
ds_test.DSEL_data_ = X_dsel_ex1
ds_test._get_region_competence = MagicMock(return_value=(distances_ex1, neighbors_ex1))
predicted = ds_test.predict(query)
assert np.array_equal(predicted, expected)
def test_label_encoder_base():
from sklearn.linear_model import LogisticRegression
X_dsel_ex1 = np.array([[-1, 1], [-0.75, 0.5], [-1.5, 1.5]])
y_dsel_ex1 = np.array(['cat', 'dog', 'plane'])
x = [[-2, -2], [2, 2]]
y = ['cat', 'dog']
pool_classifiers = [LogisticRegression().fit(x, y) for _ in range(2)]
query = np.atleast_2d([[1, 0], [-1, -1]])
ds_test = BaseDS(pool_classifiers, k=2)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
predictions = ds_test.predict(query)
assert np.array_equal(predictions, np.array(['dog', 'cat']))
def test_bad_input_X(X_test, create_X_y):
X_train, y_train = create_X_y
ds_test = BaseDS()
ds_test.fit(X_train, y_train)
with pytest.raises(ValueError):
ds_test.predict(X_test)
def test_not_fitted_ds():
query = np.array([[1.0, 1.0]])
ds_test = BaseDS()
with pytest.raises(NotFittedError):
ds_test.predict(query)
def test_bad_input_X(X):
ds_test = BaseDS(create_pool_classifiers())
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
with pytest.raises(ValueError):
ds_test.predict(X)
def test_not_fitted_ds():
query = np.array([[1.0, 1.0]])
ds_test = BaseDS(create_pool_classifiers())
with pytest.raises(NotFittedError):
ds_test.predict(query)
def test_different_input_shape():
query = np.array([[1.0, 1.0, 2.0]])
ds_test = BaseDS(create_pool_classifiers())
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
with pytest.raises(ValueError):
ds_test.predict(query)
