def test_ogdlr_learns():
# test that this model learns something
y_pred = ogdlr_before.predict_proba(X)
ll_before = logloss(y, y_pred)
y_pred = ogdlr_after.predict_proba(X)
ll_after = logloss(y, y_pred)
assert ll_before > ll_after
def test_ogdlr_learns():
# test that this model learns something
y_pred = ogdlr_before.predict_proba(X)
ll_before = logloss(y, y_pred)
y_pred = ogdlr_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)
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)
def test_predict_proba(n_samples):
y_prob = ogdlr_after.predict_proba(X[:n_samples])
assert len(y_prob) == n_samples
assert all([isinstance(pr, float) for pr in y_prob])
def test_predict_proba(n_samples):
y_prob = ogdlr_after.predict_proba(X[:n_samples])
assert len(y_prob) == n_samples
assert all([isinstance(pr, float) for pr in y_prob])