def test_predict_diff(example_static_selection):
X, y, pool = example_static_selection
static_selection_test = StaticSelection(pool, 0.75)
static_selection_test.fit(X, y)
predicted_labels = static_selection_test.predict(X)
assert np.equal(predicted_labels, 1).all()
def test_predict(example_static_selection, create_pool_classifiers):
X, y, _ = example_static_selection
static_selection_test = StaticSelection(create_pool_classifiers * 10, 0.25)
static_selection_test.fit(X, y)
predicted_labels = static_selection_test.predict(X)
assert np.equal(predicted_labels, 0).all()
def test_predict():
X = X_dsel_ex1
y = y_dsel_ex1
pool_classifiers = create_pool_classifiers_score(
1, 25, 0.5) + create_pool_classifiers_score(0, 25, 1.0)
static_selection_test = StaticSelection(pool_classifiers, 0.25)
static_selection_test.fit(X, y)
predicted_labels = static_selection_test.predict(X)
assert np.equal(predicted_labels, 0).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)
ss = StaticSelection(pool)
ss.fit(X, y)
pred = ss.predict(X)
assert np.isin(ss.classes_, pred).all()
def test_not_fitted():
static_selection_test = StaticSelection(create_pool_classifiers(), 0.25)
with pytest.raises(NotFittedError):
static_selection_test.predict(np.array([1, -1]))
def test_label_encoder(create_label_encoder_test):
X, y, pool = create_label_encoder_test
static = StaticSelection(pool).fit(X, y)
pred = static.predict(X)
assert np.array_equal(pred, y)
def test_not_fitted():
static_selection_test = StaticSelection()
with pytest.raises(NotFittedError):
static_selection_test.predict(np.array([[1, -1]]))
