def test_predict(create_X_y, create_pool_classifiers):
X, y = create_X_y
oracle_test = Oracle(create_pool_classifiers)
oracle_test.fit(X, y)
predicted_labels = oracle_test.predict(X, y)
assert np.equal(predicted_labels, y).all()
assert oracle_test.score(X, y) == 1.0
def test_predict_all_same(create_X_y, create_pool_all_agree):
X, y = create_X_y
expected = y
oracle_test = Oracle(create_pool_all_agree)
oracle_test.fit(X, y)
expected[expected == 1] = 0
predicted_labels = oracle_test.predict(X, y)
assert np.equal(predicted_labels, expected).all()
def test_predict():
X = X_dsel_ex1
y = y_dsel_ex1
oracle_test = Oracle(create_pool_classifiers())
oracle_test.fit(X, y)
predicted_labels = oracle_test.predict(X, y)
assert np.equal(predicted_labels, y).all()
assert oracle_test.score(X, y) == 1.0
def test_predict_all_same():
X = X_dsel_ex1
y = np.copy(y_dsel_ex1)
expected = y
oracle_test = Oracle(create_pool_all_agree(return_value=0, size=10))
oracle_test.fit(X, y)
expected[expected == 1] = 0
predicted_labels = oracle_test.predict(X, y)
assert np.equal(predicted_labels, expected).all()
def test_label_encoder_base_ensemble():
from sklearn.ensemble import RandomForestClassifier
X, y = make_classification()
y[y == 1] = 2
y = y.astype(np.float)
pool = RandomForestClassifier().fit(X, y)
oracle = Oracle(pool)
oracle.fit(X, y)
pred = oracle.predict(X, y)
assert np.isin(oracle.classes_, pred).all()
def test_oracle():
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
oracle = Oracle(pool_classifiers)
oracle.fit(X_dsel, y_dsel)
assert np.isclose(oracle.score(X_test, y_test), 0.99468085106382975)