def test_macau_tensor_univariate(self):
A = np.random.randn(15, 2)
B = np.random.randn(3, 2)
C = np.random.randn(2, 2)
idx = list(
itertools.product(np.arange(A.shape[0]), np.arange(B.shape[0]),
np.arange(C.shape[0])))
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)
Acoo = scipy.sparse.coo_matrix(A)
predictions = smurff.MacauSession(Ytrain=Ytrain,
Ytest=Ytest,
side_info=[Acoo, None, None],
univariate=True,
num_latent=4,
verbose=0,
burnin=200,
nsamples=400).run()
rmse = smurff.calc_rmse(predictions)
self.assertTrue(rmse < 0.5,
msg="Tensor factorization gave RMSE above 0.5 (%f)." %
rmse)
def test_macau_dense_probit(self):
A = np.random.randn(25, 2)
B = np.random.randn(3, 2)
idx = list(
itertools.product(np.arange(A.shape[0]), np.arange(B.shape[0])))
df = pd.DataFrame(np.asarray(idx), columns=["A", "B"])
df["value"] = (np.array([np.sum(A[i[0], :] * B[i[1], :])
for i in idx]) > 0.0).astype(np.float64)
Ytrain, Ytest = smurff.make_train_test_df(df, 0.2)
threshold = 0.5 # since we sample from mu(0,1)
trainSession = smurff.TrainSession(priors=['macau', 'normal'],
num_latent=4,
threshold=threshold,
burnin=20,
nsamples=20,
verbose=False)
trainSession.addTrainAndTest(Ytrain, Ytest,
smurff.ProbitNoise(threshold))
trainSession.addSideInfo(0, A, direct=True)
predictions = trainSession.run()
rmse = smurff.calc_rmse(predictions)
self.assertTrue(
rmse > 0.55,
msg=
"Probit factorization (with dense side) gave AUC below 0.55 (%f)."
% rmse)
def test_bpmf_tensor2(self):
A = np.random.randn(15, 2)
B = np.random.randn(20, 2)
C = np.random.randn(3, 2)
idx = list(
itertools.product(np.arange(A.shape[0]), np.arange(B.shape[0]),
np.arange(C.shape[0])))
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)
predictions = smurff.smurff(Ytrain,
Ytest=Ytest,
priors=['normal', 'normal', 'normal'],
num_latent=4,
verbose=False,
burnin=20,
nsamples=20)
rmse = smurff.calc_rmse(predictions)
self.assertTrue(rmse < 0.5,
msg="Tensor factorization gave RMSE above 0.5 (%f)." %
rmse)
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_bpmf_tensor3(self):
A = np.random.randn(15, 2)
B = np.random.randn(20, 2)
C = np.random.randn(1, 2)
idx = list( itertools.product(np.arange(A.shape[0]), np.arange(B.shape[0]), np.arange(C.shape[0])) )
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)
predictions = smurff.bpmf(Ytrain,
Ytest=Ytest,
num_latent=4,
verbose=verbose,
burnin=20,
nsamples=20)
rmse = smurff.calc_rmse(predictions)
self.assertTrue(rmse < 0.5,
msg="Tensor factorization gave RMSE above 0.5 (%f)." % rmse)
Ytrain_df = Ytrain.data
Ytest_df = Ytest.data
Ytrain_sp = sp.coo_matrix( (Ytrain_df.value, (Ytrain_df.A, Ytrain_df.B) ) )
Ytest_sp = sp.coo_matrix( (Ytest_df.value, (Ytest_df.A, Ytest_df.B) ) )
results_mat = smurff.bpmf(Ytrain_sp,
Ytest=Ytest_sp,
num_latent=4,
verbose=verbose,
burnin=20,
nsamples=20)
def test_macau_dense_probit(self):
A = np.random.randn(25, 2)
B = np.random.randn(3, 2)
idx = list(
itertools.product(np.arange(A.shape[0]), np.arange(B.shape[0])))
df = pd.DataFrame(np.asarray(idx), columns=["A", "B"])
df["value"] = (np.array([np.sum(A[i[0], :] * B[i[1], :])
for i in idx]) > 0.0).astype(np.float64)
Ytrain, Ytest = smurff.make_train_test_df(df, 0.2)
predictions = smurff.smurff(
Ytrain,
Ytest=Ytest,
priors=['macau', 'normal'],
#prior_noises=[('probit', None, None, None, 0.5), ('fixed', 1.0, None, None, None)],
side_info=[A, None],
num_latent=4,
burnin=20,
nsamples=20,
verbose=False)
self.assertTrue(
rmse > 0.55,
msg=
"Probit factorization (with dense side) gave AUC below 0.55 (%f)."
% rmse)
def test_make_train_test_df(self):
idx = list( itertools.product(np.arange(10), np.arange(8), np.arange(3) ))
df = pd.DataFrame( np.asarray(idx), columns=["A", "B", "C"])
df["value"] = np.arange(10.0 * 8.0 * 3.0)
Ytr, Yte = smurff.make_train_test_df(df, 0.4)
self.assertEqual(Ytr.data.shape[0], df.shape[0] * 0.6)
self.assertEqual(Yte.data.shape[0], df.shape[0] * 0.4)
A1 = np.zeros( (10, 8, 3) )
A2 = np.zeros( (10, 8, 3) )
A1[df.A, df.B, df.C] = df.value
A2[Ytr.data.A, Ytr.data.B, Ytr.data.C] = Ytr.data.value
A2[Yte.data.A, Yte.data.B, Yte.data.C] = Yte.data.value
self.assertTrue(np.allclose(A1, A2))
