def test_FTRL_learns(): # test that this model learns something y_pred = ftrl_before.predict_proba(X) ll_before = logloss(y, y_pred) y_pred = ftrl_after.predict_proba(X) ll_after = logloss(y, y_pred) assert ll_before > ll_after
def test_models_same_predictions(): # for lambda1, lambda2 = 0, OGDLR and FTRLprox should generate the # same result. The same goes if hashing is used (except for the # rare case of hash collisions. A neural net does not necessarily # predict exactly the same outcome. y_f = ftrl_after.predict_proba(X) y_o = ogdlr_after.predict_proba(X) y_h = hash_after.predict_proba(X) assert np.allclose(y_f, y_o, atol=1e-15) assert np.allclose(y_f, y_h, atol=1e-15)