def test_macau_dense(self):
Y = scipy.sparse.rand(15, 10, 0.2)
Yt = scipy.sparse.rand(15, 10, 0.1)
F = np.random.randn(15, 2)
smurff.macau(Y,
Ytest=Yt,
side_info=[F, None],
num_latent=5,
burnin=200,
nsamples=200,
verbose=verbose)
def test_macau_side_bin(self):
X = scipy.sparse.rand(15, 10, 0.2)
Xt = scipy.sparse.rand(15, 10, 0.1)
F = scipy.sparse.rand(15, 2, 0.5)
F.data[:] = 1
smurff.macau(X,
Ytest=Xt,
side_info=[F, None],
num_latent=5,
burnin=200,
nsamples=200,
verbose=verbose)
def test_macau_tensor(self):
shape = [30, 4, 2]
A = np.random.randn(shape[0], 2)
B = np.random.randn(shape[1], 2)
C = np.random.randn(shape[2], 2)
idx = list(
itertools.product(np.arange(shape[0]), np.arange(shape[1]),
np.arange(shape[2])))
df = pd.DataFrame(np.asarray(idx), columns=["A", "B", "C"])
df["value"] = np.array(
[np.sum(A[i[0], :] * B[i[1], :] * C[i[2], :]) for i in idx])
Ytrain, Ytest = smurff.make_train_test_df(df, 0.2, shape=shape)
Acoo = scipy.sparse.coo_matrix(A)
predictions = smurff.macau(Ytrain=Ytrain,
Ytest=Ytest,
side_info=[Acoo, None, None],
num_latent=4,
verbose=verbose,
burnin=200,
nsamples=200)
rmse = smurff.calc_rmse(predictions)
self.assertTrue(rmse < 0.5,
msg="Tensor factorization gave RMSE above 0.5 (%f)." %
rmse)
def test_macau_univariate(self):
Y = scipy.sparse.rand(10, 20, 0.2)
Y, Ytest = smurff.make_train_test(Y, 0.5)
side1 = scipy.sparse.coo_matrix(np.random.rand(10, 2))
side2 = scipy.sparse.coo_matrix(np.random.rand(20, 3))
predictions = smurff.macau(Y,
Ytest=Ytest,
side_info=[side1, side2],
univariate=True,
num_latent=4,
verbose=verbose,
burnin=200,
nsamples=200)
self.assertEqual(Ytest.nnz, len(predictions))
def test_macau(self):
Ydense = np.random.rand(10, 20)
r = np.random.permutation(
10 * 20)[:40] # 40 random samples from 10*20 matrix
side1 = Ydense[:, 1:2]
side2 = Ydense[1:2, :].transpose()
Y = scipy.sparse.coo_matrix(Ydense) # convert to sparse
Y = scipy.sparse.coo_matrix((Y.data[r], (Y.row[r], Y.col[r])),
shape=Y.shape)
Y, Ytest = smurff.make_train_test(Y, 0.5)
predictions = smurff.macau(Y,
Ytest=Ytest,
side_info=[side1, side2],
num_latent=4,
verbose=verbose,
burnin=200,
nsamples=200)
self.assertEqual(Ytest.nnz, len(predictions))