def test_logistic_gradient(coef, X, y):
p = lr.predict_proba(coef, X)
assert np.linalg.norm(lr.logistic_gradient(coef, X, p)) < 1e-8
assert np.linalg.norm(lr.logistic_gradient(np.array([1, 100]), X, y)) > 1
gradient_norm = np.linalg.norm(lr.logistic_gradient(coef, X, y))
assert abs(gradient_norm - 0.7071067811865) < 1e-8
def y(coef, X):
return lr.predict_proba(coef, X) > 0.5
def test_predict_proba(coef, X):
probabilities = lr.predict_proba(coef, X)
assert abs(probabilities[0] - 0.5) < 1e-8
assert abs(probabilities[1] - 1) < 1e-8
assert abs(probabilities[2]) < 1e-8
def test_has_converged(self, coef, X):
lr_model = lr.LogisticRegression()
p = lr.predict_proba(coef, X)
assert lr_model._has_converged(coef, X, p)
assert not lr_model._has_converged(np.array([1, 1000]), X, p)