def test_class(self):
import numpy as np
from kamrecsys.datasets import load_movielens_mini
from kamrecsys.mf.pmf import EventScorePredictor
data = load_movielens_mini()
recommender = EventScorePredictor(C=0.1, k=2, tol=1e-03,
random_state=1234)
self.assertDictEqual(
vars(recommender),
{'C': 0.1, 'n_otypes': 0, 'bu_': None, 'bi_': None, 'k': 2,
'p_': None, 'q_': None, '_coef': None, 'f_loss_': np.inf,
'iid': None, 'i_loss_': np.inf, 'eid': None, 'tol': 1e-03,
'n_objects': None, '_dt': None, 'mu_': None, 'opt_outputs_': None,
'maxiter': 200, 'random_state': 1234, '_rng': None})
recommender.fit(data, disp=False)
self.assertAlmostEqual(recommender.i_loss_,
0.74652578358324106, delta=1e-5)
self.assertAlmostEqual(recommender.f_loss_,
0.025638738121075231, delta=1e-5)
self.assertAlmostEqual(recommender.predict((1, 7)),
3.9873641434545979, delta=1e-5)
self.assertAlmostEqual(recommender.predict((1, 9)),
4.9892118821609106, delta=1e-5)
self.assertAlmostEqual(recommender.predict((1, 11)),
3.6480799850368273, delta=1e-5)
self.assertAlmostEqual(recommender.predict((3, 7)),
3.6336318795279228, delta=1e-5)
self.assertAlmostEqual(recommender.predict((3, 9)),
4.2482001235634943, delta=1e-5)
self.assertAlmostEqual(recommender.predict((3, 11)),
3.7236984083417841, delta=1e-5)
self.assertAlmostEqual(recommender.predict((5, 7)),
3.4141968145802597, delta=1e-5)
self.assertAlmostEqual(recommender.predict((5, 9)),
3.9818882049478654, delta=1e-5)
self.assertAlmostEqual(recommender.predict((5, 11)),
3.4710520150321895, delta=1e-5)
x = np.array([
[1, 7], [1, 9], [1, 11],
[3, 7], [3, 9], [3, 11],
[5, 7], [5, 9], [5, 11]])
assert_allclose(
recommender.predict(x),
[3.98736414, 4.98921188, 3.64807999, 3.63363188, 4.24820012,
3.72369841, 3.41419681, 3.9818882, 3.47105202],
rtol=1e-5)
def test_class(self):
import numpy as np
from kamrecsys.datasets import load_movielens_mini
from kamrecsys.mf.pmf import EventScorePredictor
data = load_movielens_mini()
recommender = EventScorePredictor(C=0.1,
k=2,
tol=1e-03,
random_state=1234)
self.assertDictEqual(
vars(recommender), {
'C': 0.1,
'n_otypes': 0,
'bu_': None,
'bi_': None,
'k': 2,
'p_': None,
'q_': None,
'_coef': None,
'f_loss_': np.inf,
'iid': None,
'i_loss_': np.inf,
'eid': None,
'tol': 1e-03,
'n_objects': None,
'_dt': None,
'mu_': None,
'opt_outputs_': None,
'maxiter': 200,
'random_state': 1234,
'_rng': None
})
recommender.fit(data, disp=False)
self.assertAlmostEqual(recommender.i_loss_,
0.74652578358324106,
delta=1e-5)
self.assertAlmostEqual(recommender.f_loss_,
0.025638738121075231,
delta=1e-5)
self.assertAlmostEqual(recommender.predict((1, 7)),
3.9873641434545979,
delta=1e-5)
self.assertAlmostEqual(recommender.predict((1, 9)),
4.9892118821609106,
delta=1e-5)
self.assertAlmostEqual(recommender.predict((1, 11)),
3.6480799850368273,
delta=1e-5)
self.assertAlmostEqual(recommender.predict((3, 7)),
3.6336318795279228,
delta=1e-5)
self.assertAlmostEqual(recommender.predict((3, 9)),
4.2482001235634943,
delta=1e-5)
self.assertAlmostEqual(recommender.predict((3, 11)),
3.7236984083417841,
delta=1e-5)
self.assertAlmostEqual(recommender.predict((5, 7)),
3.4141968145802597,
delta=1e-5)
self.assertAlmostEqual(recommender.predict((5, 9)),
3.9818882049478654,
delta=1e-5)
self.assertAlmostEqual(recommender.predict((5, 11)),
3.4710520150321895,
delta=1e-5)
x = np.array([[1, 7], [1, 9], [1, 11], [3, 7], [3, 9], [3, 11], [5, 7],
[5, 9], [5, 11]])
assert_allclose(recommender.predict(x), [
3.98736414, 4.98921188, 3.64807999, 3.63363188, 4.24820012,
3.72369841, 3.41419681, 3.9818882, 3.47105202
],
rtol=1e-5)
