def test_binary_sgd_step_size0():
rstate = RandomState(42)
n_features = 20
X, y = get_fake_data(100, n_features, rstate)
clf = get_new_model(n_features, rstate)
best = find_sgd_step_size0(clf, X, y, (.25, .5))
assert_almost_equal(best, -4.9927, decimal=4)
# start a little lower, still works
best = find_sgd_step_size0(clf, X, y, (.125, .25))
assert_almost_equal(best, -4.6180, decimal=4)
# find_sgd_step_size0 does not change clf
assert clf.sgd_step_size0 == 1000.0
def test_binary_sgd_step_size0():
rstate = RandomState(42)
n_features = 20
X, y = get_fake_data(100, n_features, rstate)
clf = get_new_model(n_features, rstate)
best = find_sgd_step_size0(clf, X, y, (.25, .5))
assert_almost_equal(best, -4.9927, decimal=4)
# start a little lower, still works
best = find_sgd_step_size0(clf, X, y, (.125, .25))
assert_almost_equal(best, -4.6180, decimal=4)
# find_sgd_step_size0 does not change clf
assert clf.sgd_step_size0 == 1000.0
def test_binary_fit():
rstate = RandomState(42)
n_features = 20
for L in [100, DEFAULT_MAX_EXAMPLES, int(DEFAULT_MAX_EXAMPLES * 1.5),
int(DEFAULT_MAX_EXAMPLES * 3)]:
clf = get_new_model(n_features, rstate, L)
X, y = get_fake_data(L, n_features, rstate, separation=0.1)
best = find_sgd_step_size0(clf, (X, y))
_clf = binary_fit(clf, (X, y))
assert _clf is clf
assert 0 < clf.sgd_step_size0 <= best
def test_binary_sgd_step_size0():
rstate = RandomState(42)
n_features = 20
X, y = get_fake_data(100, n_features, rstate)
clf = get_new_model(n_features, rstate, 100)
best0 = find_sgd_step_size0(clf, (X, y))
print best0
assert np.allclose(best0, 0.04, atol=.1, rtol=.5)
# find_sgd_step_size0 does not change clf
assert clf.sgd_step_size0 == 1000.0
