def test_frienemy_safe_region():
ds_test = BaseDS(create_pool_classifiers(), safe_k=3)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.DSEL_processed_ = dsel_processed_ex1
result = ds_test._frienemy_pruning(np.array([0, 1, 2, 6, 7, 8, 14]))
assert np.array_equal(result, np.array([[1, 1, 1]]))
def test_frienemy_not_all_classifiers_crosses():
ds_test = BaseDS(create_pool_classifiers(), safe_k=3)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.DSEL_processed_ = dsel_processed_ex1
result = ds_test._frienemy_pruning(neighbors_ex1[0, :])
assert np.array_equal(result, np.array([[1, 1, 0]]))
def test_frienemy_all_classifiers_crosses(index):
ds_test = BaseDS(create_pool_classifiers())
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.DSEL_processed_ = dsel_processed_all_ones
result = ds_test._frienemy_pruning(neighbors_ex1[index, :])
assert result.all() == 1.0
def test_DFP_is_used(example_estimate_competence, create_pool_classifiers):
X, y, neighbors, _, dsel_processed, _ = example_estimate_competence
ds_test = BaseDS(create_pool_classifiers, DFP=True, safe_k=3)
ds_test.fit(X, y)
ds_test.DSEL_processed_ = dsel_processed
DFP_mask = ds_test._frienemy_pruning(neighbors[0, :])
assert np.array_equal(DFP_mask, np.atleast_2d([1, 1, 0]))
def test_frienemy_safe_region(example_estimate_competence,
create_pool_classifiers):
X, y, _, _, dsel_processed, _ = example_estimate_competence
ds_test = BaseDS(create_pool_classifiers, safe_k=3)
ds_test.fit(X, y)
ds_test.DSEL_processed_ = dsel_processed
result = ds_test._frienemy_pruning(np.array([0, 1, 2, 6, 7, 8, 14]))
assert np.array_equal(result, np.array([[1, 1, 1]]))
def test_frienemy_all_classifiers_crosses(index, example_all_ones,
create_pool_classifiers):
X, y, neighbors, _, dsel_processed, _ = example_all_ones
ds_test = BaseDS(create_pool_classifiers)
ds_test.fit(X, y)
ds_test.DSEL_processed_ = dsel_processed
result = ds_test._frienemy_pruning(neighbors[index, :])
assert result.all() == 1.0
def test_DFP_is_used():
ds_test = BaseDS(create_pool_classifiers(), DFP=True, safe_k=3)
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
DFP_mask = ds_test._frienemy_pruning(neighbors_ex1[0, :])
assert np.array_equal(DFP_mask, np.atleast_2d([1, 1, 0]))
def test_frienemy_not_all_classifiers_crosses_batch():
expected = np.array([[1, 1, 0], [0, 1, 0], [1, 1, 1]])
ds_test = BaseDS(create_pool_classifiers(), safe_k=3)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.DSEL_processed_ = dsel_processed_ex1
# passing three samples to compute the DFP at the same time
result = ds_test._frienemy_pruning(neighbors_ex1)
assert np.array_equal(result, expected)
def test_frienemy_not_all_classifiers_crosses(example_estimate_competence,
create_pool_classifiers):
expected = np.array([[1, 1, 0], [0, 1, 0], [1, 1, 1]])
X, y, neighbors, _, dsel_processed, _ = example_estimate_competence
ds_test = BaseDS(create_pool_classifiers, safe_k=3)
ds_test.fit(X, y)
ds_test.DSEL_processed_ = dsel_processed
# passing three samples to compute the DFP at the same time
result = ds_test._frienemy_pruning(neighbors)
assert np.array_equal(result, expected)
def test_frienemy_safe_region_batch():
n_samples = 10
n_classifiers = 3
expected = np.ones((n_samples, n_classifiers))
ds_test = BaseDS(create_pool_classifiers(), safe_k=3)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.DSEL_processed_ = dsel_processed_ex1
neighbors = np.tile(np.array([0, 1, 2, 6, 7, 8, 14]), (n_samples, 1))
result = ds_test._frienemy_pruning(neighbors)
assert np.array_equal(result, expected)
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_frienemy_safe_region_batch(example_estimate_competence,
create_pool_classifiers):
X, y, neighbors, _, dsel_processed, _ = example_estimate_competence
n_samples = 10
n_classifiers = 3
expected = np.ones((n_samples, n_classifiers))
ds_test = BaseDS(create_pool_classifiers, safe_k=3)
ds_test.fit(X, y)
ds_test.DSEL_processed_ = dsel_processed
neighbors = np.tile(np.array([0, 1, 2, 6, 7, 8, 14]), (n_samples, 1))
result = ds_test._frienemy_pruning(neighbors)
assert np.array_equal(result, expected)
