def test_select_single_sample():
pool_classifiers = create_pool_classifiers()
des_mi = DESMI(pool_classifiers, pct_accuracy=0.7)
competences = np.array([0.7, 0.2, 1.0])
selected_clf = des_mi.select(competences)
expected = np.array([0, 2])
assert np.array_equal(np.unique(selected_clf), np.unique(expected))
def test_select_single_sample():
des_mi = DESMI(pct_accuracy=0.7)
des_mi.N_ = 2
competences = np.array([0.7, 0.2, 1.0])
selected_clf = des_mi.select(competences)
expected = np.array([0, 2])
assert np.array_equal(np.unique(selected_clf), np.unique(expected))
def test_select_batch_samples():
n_samples = 10
des_mi = DESMI(pct_accuracy=0.7)
des_mi.N_ = 2
competences = np.tile(np.array([0.7, 0.2, 1.0]), (n_samples, 1))
selected_clf = des_mi.select(competences)
expected = np.tile(np.array([0, 2]), (n_samples, 1))
assert np.array_equal(np.unique(selected_clf), np.unique(expected))
def test_classify_with_ds_diff_sizes():
query = np.ones((10, 2))
predictions = np.ones((5, 3))
des_mi = DESMI()
with pytest.raises(ValueError):
des_mi.classify_with_ds(query, predictions)
def test_proba_with_ds_diff_sizes():
query = np.ones((10, 2))
predictions = np.ones((5, 3))
probabilities = np.ones((5, 3, 2))
des_mi = DESMI()
with pytest.raises(ValueError):
des_mi.predict_proba_with_ds(query, predictions, probabilities)
def test_proba_with_ds_diff_sizes():
query = np.ones((10, 2))
predictions = np.ones((5, 3))
probabilities = np.ones((5, 3, 2))
pool_classifiers = create_pool_classifiers()
des_mi = DESMI(pool_classifiers)
with pytest.raises(ValueError):
des_mi.predict_proba_with_ds(query, predictions, probabilities)
def test_classify_with_ds_single_sample():
query = np.ones(2)
# simulated predictions of the pool of classifiers
predictions = np.array([0, 1, 0])
desmi_test = DESMI(DFP=True)
DFP_mask = np.ones((1, 3))
desmi_test.estimate_competence = MagicMock(return_value=(np.ones((1, 3))))
desmi_test.select = MagicMock(return_value=np.array([[0, 2]]))
result = desmi_test.classify_with_ds(query, predictions, DFP_mask=DFP_mask)
assert np.allclose(result, 0)
def test_classify_with_ds_batch_samples():
n_samples = 10
# Passing 10 samples for classification automatically
query = np.ones((n_samples, 2))
pool_classifiers = create_pool_classifiers()
# simulated predictions of the pool of classifiers
predictions = np.tile(np.array([0, 1, 0]), (n_samples, 1))
desmi_test = DESMI(pool_classifiers)
desmi_test.estimate_competence = MagicMock(return_value=(np.ones((n_samples, 3))))
desmi_test.select = MagicMock(return_value=np.tile(np.array([[0, 2]]), (n_samples, 1)))
result = desmi_test.classify_with_ds(query, predictions)
assert np.allclose(result, np.zeros(10))
def test_soft_voting_no_proba(create_X_y):
from sklearn.linear_model import Perceptron
X, y = create_X_y
clf = Perceptron()
clf.fit(X, y)
with pytest.raises(ValueError):
DESMI([clf, clf, clf, clf], voting='soft').fit(X, y)
def test_predict_proba_with_ds():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
desmi_test = DESMI(pool_classifiers, DFP=True)
desmi_test.DFP_mask = np.ones((1, 6))
selected_indices = np.array([[0, 1, 5]])
desmi_test.estimate_competence = MagicMock(return_value=np.ones(6))
desmi_test.select = MagicMock(return_value=selected_indices)
desmi_test.n_classes = 2
expected = np.array([0.61, 0.39])
predictions = []
probabilities = []
for clf in desmi_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
probabilities.append(clf.predict_proba(query)[0])
query = np.atleast_2d(query)
predictions = np.atleast_2d(predictions)
probabilities = np.array(probabilities)
probabilities = np.expand_dims(probabilities, axis=0)
predicted_proba = desmi_test.predict_proba_with_ds(query, predictions,
probabilities)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_classify_with_ds_batch_samples():
n_samples = 10
# simulated predictions of the pool of classifiers
predictions = np.tile(np.array([0, 1, 0]), (n_samples, 1))
desmi_test = DESMI()
desmi_test.n_classes_ = 2
desmi_test.estimate_competence = MagicMock(
return_value=(np.ones((n_samples, 3))))
desmi_test.select = MagicMock(
return_value=np.tile(np.array([[0, 2]]), (n_samples, 1)))
result = desmi_test.classify_with_ds(predictions)
assert np.allclose(result, np.zeros(10))
def test_predict_proba_with_ds_soft(create_pool_classifiers):
expected = np.array([0.61, 0.39])
DFP_mask = np.ones((1, 6))
predictions = np.array([[0, 1, 0, 0, 1, 0]])
probabilities = np.array([[[0.5, 0.5], [1, 0], [0.33, 0.67], [0.5, 0.5],
[1, 0], [0.33, 0.67]]])
pool_classifiers = create_pool_classifiers + create_pool_classifiers
desmi_test = DESMI(pool_classifiers, DFP=True, voting='soft')
desmi_test.n_classes_ = 2
selected_indices = np.array([[0, 1, 5]])
desmi_test.estimate_competence = MagicMock(return_value=np.ones(6))
desmi_test.select = MagicMock(return_value=selected_indices)
predicted_proba = desmi_test.predict_proba_with_ds(predictions,
probabilities,
DFP_mask=DFP_mask)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_require_proba():
X = np.random.randn(5, 5)
y = np.array([0, 1, 0, 0, 0])
clf1 = Perceptron()
clf1.fit(X, y)
DESMI([clf1, clf1, clf1])
def test_check_pct_accuracy_value(pct_accuracy):
pool_classifiers = create_pool_classifiers()
with pytest.raises(ValueError):
desmi = DESMI(pool_classifiers, pct_accuracy=pct_accuracy)
desmi.fit(X_dsel_ex1, y_dsel_ex1)
def test_check_alpha_type(alpha):
pool_classifiers = create_pool_classifiers()
with pytest.raises(TypeError):
desmi = DESMI(pool_classifiers, alpha=alpha)
desmi.fit(X_dsel_ex1, y_dsel_ex1)
def test_check_N_value(alpha):
pool_classifiers = create_pool_classifiers()
with pytest.raises(ValueError):
DESMI(pool_classifiers, pct_accuracy=0.05)
def test_mi(knn_methods):
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
desmi = DESMI(pool_classifiers, alpha=0.9, knn_classifier=knn_methods)
desmi.fit(X_dsel, y_dsel)
assert np.isclose(desmi.score(X_test, y_test), 0.9787234042553191)
def test_mi():
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
desmi = DESMI(pool_classifiers, alpha=0.9)
desmi.fit(X_dsel, y_dsel)
assert np.isclose(desmi.score(X_test, y_test), 0.3500000000)
def test_check_alpha_value(alpha):
pool_classifiers = create_pool_classifiers()
with pytest.raises(ValueError):
DESMI(pool_classifiers, alpha=alpha)
def test_check_estimator():
check_estimator(DESMI())
def test_check_pct_accuracy_value(pct_accuracy, create_X_y):
X, y = create_X_y
with pytest.raises(ValueError):
desmi = DESMI(pct_accuracy=pct_accuracy)
desmi.fit(X, y)
def test_check_alpha_type(alpha, create_X_y):
X, y = create_X_y
with pytest.raises(TypeError):
desmi = DESMI(alpha=alpha)
desmi.fit(X, y)
def test_wrong_voting_value(voting, create_X_y, create_pool_classifiers):
X, y = create_X_y
pool = create_pool_classifiers
with pytest.raises(ValueError):
DESMI(pool, voting=voting).fit(X, y)
