def test_w0_MCMC(self):
init = Initialisation()
train, test, num_all_attribute = init.train, init.test, init.num_all_attribute
meta = DataMetaInfo(num_all_attribute)
fm = libFM(num_all_attribute, seed=3, method='mcmc', num_iter=1000, dim='1,0,0',
param_regular='0,0,0.1', init_stdev=0.1)
mcmc = MCMC_learn(fm, meta, train, test, 0)
mcmc.learn()
np.testing.assert_array_almost_equal(mcmc.predict(), [ 3.266, 3.266, 3.266, 3.266], decimal=1, err_msg='', verbose=True)
def test_w0_ALS(self):
init = Initialisation()
train, test, num_all_attribute = init.train, init.test, init.num_all_attribute
meta = DataMetaInfo(num_all_attribute)
fm = libFM(num_all_attribute, seed=1, method='als', num_iter=1, dim='1,0,0',
param_regular='0,0,0.1', init_stdev=0.1)
mcmc = MCMC_learn(fm, meta, train, test, 0)
mcmc.learn()
self.assertAlmostEqual(fm.w0, 3.26666666667, places=7, msg=None, delta=None)
np.testing.assert_array_almost_equal(mcmc.predict(), [ 3.26666667, 3.26666667, 3.26666667, 3.26666667], decimal=6, err_msg='', verbose=True)
def test_w_ALS(self):
init = Initialisation()
train, test, num_all_attribute = init.train, init.test, init.num_all_attribute
meta = DataMetaInfo(num_all_attribute)
fm = libFM(num_all_attribute, seed=1, method='als', num_iter=2, dim='0,1,0',
param_regular='0,0,0.1', init_stdev=0.1)
fm.w = np.array( [ 0.16243454, -0.06117564, -0.05281718, -0.10729686, 0.08654076, -0.23015387,
0.17448118, -0.07612069, 0.03190391] )
mcmc = MCMC_learn(fm, meta, train, test, 0)
mcmc.learn()
np.testing.assert_array_almost_equal(mcmc.predict(), [ 2.24725653, 3.91392319, 2.64163236, 3.9749657 ], decimal=5, err_msg='', verbose=True)
def test_v_ALS(self):
init = Initialisation()
train, test, num_all_attribute = init.train, init.test, init.num_all_attribute
meta = DataMetaInfo(num_all_attribute)
fm = libFM(num_all_attribute, seed=1, method='als', num_iter=2, dim='0,0,3',
param_regular='0,0,0.1', init_stdev=0.1)
fm.v = np.asarray([[-0.02493704,0.14621079,-0.20601407,-0.03224172,-0.03840544,0.11337694,-0.10998913,-0.01724282,-0.08778584],
[0.00422137,0.05828152,-0.11006192,0.11447237,0.09015907,0.05024943,0.09008559,-0.06837279,-0.01228902],
[-0.09357694,-0.02678881,0.05303555,-0.06916608,-0.03967535,-0.06871727,-0.08452056,-0.06712461,-0.00126646]])
mcmc = MCMC_learn(fm, meta, train, test, 0)
mcmc.learn()
np.testing.assert_array_almost_equal(mcmc.predict(), [3.71472717, 5.0, 1.0, 5.0], decimal=5, err_msg='', verbose=True)