def test_predict_hard_voting():
X = np.array([[1, 2, 3], [4, 5, 6]])
y = np.array([0, 1])
model1 = LogisticRegression(solver="liblinear")
model2 = LogisticRegression(solver="lbfgs")
model1.fit(X, y)
model2.fit(X, y)
clf = postprocessing.SimpleVoter([("model1", model1), ("model2", model2)],
voting="hard",
classes=model1.classes_)
pred = clf.predict(X)
assert np.allclose(pred, y)
def test_predict_strings():
X = np.array([[1, 2, 3], [4, 5, 6]])
y = np.array(["pizza", "tacos"])
model1 = LogisticRegression(solver="liblinear")
model2 = LogisticRegression(solver="lbfgs")
model1.fit(X, y)
model2.fit(X, y)
clf = postprocessing.SimpleVoter([("model1", model1), ("model2", model2)],
voting="hard",
classes=model1.classes_)
pred = clf.predict(X)
assert list(pred) == list(y)
def test_predict():
X = np.array([[1, 2, 3], [4, 5, 6]])
y = np.array([0, 1])
model1 = LogisticRegression(solver="liblinear")
model2 = LogisticRegression(solver="lbfgs")
model1.fit(X, y)
model2.fit(X, y)
clf = postprocessing.SimpleVoter([("model1", model1), ("model2", model2)],
voting="soft",
classes=model1.classes_)
pred = clf.predict(X)
probs = clf.predict_proba(X)
assert pred.shape == y.shape