def test_large_semi_nmf(self):
auv = sio.loadmat(mat_file)
a, u, v = auv['a'], auv['u'], auv['v']
old_loss = np_frobenius_norm(a, u @ v)
a_ph = tf.placeholder(tf.float64, shape=a.shape)
u_ph = tf.placeholder(tf.float64, shape=u.shape)
v_ph = tf.placeholder(tf.float64, shape=v.shape)
tf_u, tf_v = tf.py_func(semi_nmf, [a_ph, u_ph, v_ph],
[tf.float64, tf.float64])
init = tf.global_variables_initializer()
with tf.Session() as sess:
init.run()
start_time = time.time()
_u, _v = sess.run([tf_u, tf_v],
feed_dict={
a_ph: a,
u_ph: u,
v_ph: v
})
end_time = time.time()
duration = end_time - start_time
new_loss = np_frobenius_norm(a, _u @ _v)
assert a.shape == (_u @ _v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print('solve semi-NMF\n\t'
'old loss {0}\n\t'
'new loss {1}\n\t'
'process duration {2}'.format(old_loss, new_loss, duration))
def test_original_biased_nonlin_semi_nmf(self):
auv = sio.loadmat(mat_file)
u, v = auv['u'], auv['v']
a = relu(u @ v)
bias_v = np.vstack((v, np.ones((1, v.shape[1]))))
old_loss = np_frobenius_norm(a, u @ v)
a_ph = tf.placeholder(tf.float64, shape=a.shape)
u_ph = tf.placeholder(tf.float64, shape=u.shape)
bias_v_ph = tf.placeholder(tf.float64, shape=bias_v.shape)
tf_bias_u, tf_v = nonlin_semi_nmf(a_ph, u_ph, bias_v_ph, use_bias=True, use_tf=True, num_calc_v=0)
init = tf.global_variables_initializer()
with tf.Session() as sess:
init.run()
start_time = time.time()
_u, _bias_v = sess.run([tf_bias_u, tf_v], feed_dict={a_ph: a, u_ph: u, bias_v_ph: bias_v})
end_time = time.time()
duration = end_time - start_time
_bias_u = np.hstack((_u, np.ones((_u.shape[0], 1))))
new_loss = np_frobenius_norm(a, relu(_bias_u @ _bias_v))
assert a.shape == (_bias_u @ _bias_v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print_format('TensorFlow', 'biased Nonlinear semi-NMF(NOT CALC v)', a, _bias_u, _bias_v, old_loss, new_loss,
duration)
def test_np_not_calc_v_biased_nonlin_semi_nmf(self):
auv = sio.loadmat(mat_file)
a, u, v = auv['a'], auv['u'], auv['v']
old_loss = np_frobenius_norm(a, u @ v)
biased_u = np.hstack((u, np.ones((u.shape[0], 1))))
start_time = time.time()
biased_u, v = nonlin_semi_nmf(a,
biased_u,
v,
use_bias=True,
num_calc_v=0)
end_time = time.time()
duration = end_time - start_time
bias_v = np.vstack((v, np.ones((1, v.shape[1]))))
new_loss = np_frobenius_norm(a, relu(biased_u @ bias_v))
assert a.shape == (biased_u @ bias_v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print('\n[Numpy]Solve biased Nonlinear semi-NMF(NOT CALCULATE v)\n\t'
'old loss {0}\n\t'
'new loss {1}\n\t'
'process duration {2}'.format(old_loss, new_loss, duration))
def test_u_neg_nonlin_semi_nmf(self):
mat_file = python_path.joinpath('./u_neg.mat').as_posix()
auv = sio.loadmat(mat_file)
a, u, v = auv['a'], auv['u'], auv['v']
old_loss = np_frobenius_norm(a, u @ v)
a_ph = tf.placeholder(tf.float64, shape=a.shape)
u_ph = tf.placeholder(tf.float64, shape=u.shape)
v_ph = tf.placeholder(tf.float64, shape=v.shape)
tf_u, tf_v = tf.py_func(nonlin_semi_nmf, [a_ph, u_ph, v_ph],
[tf.float64, tf.float64])
tf_loss = frobenius_norm(a_ph, tf.nn.relu(tf_u @ tf_v))
init = tf.global_variables_initializer()
with tf.Session() as sess:
init.run()
start_time = time.time()
_u, _v, new_loss = sess.run([tf_u, tf_v, tf_loss],
feed_dict={
a_ph: a,
u_ph: u,
v_ph: v
})
end_time = time.time()
duration = end_time - start_time
assert a.shape == (_u @ _v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print('solve Nonlinear semi-NMF\n\t'
'old loss {0}\n\t'
'new loss {1}\n\t'
'process duration {2}'.format(old_loss, new_loss, duration))
def test_tf_not_calc_v_nonlin_semi_nmf(self):
auv = sio.loadmat(mat_file)
a, u, v = auv['a'], auv['u'], auv['v']
old_loss = np_frobenius_norm(a, u @ v)
# [1000, 500]
a_ph = tf.placeholder(tf.float64, shape=a.shape)
# [1000, 201]
u_ph = tf.placeholder(tf.float64, shape=u.shape)
# [200, 500]
v_ph = tf.placeholder(tf.float64, shape=v.shape)
tf_u, tf_v = nonlin_semi_nmf(a_ph, u_ph, v_ph, use_tf=True, use_bias=False, num_calc_v=0, num_calc_u=1)
tf_loss = frobenius_norm(a_ph, tf.nn.relu(tf.matmul(tf_u, tf_v)))
init = tf.global_variables_initializer()
with tf.Session() as sess:
init.run()
start_time = time.time()
_u, _v, new_loss = sess.run([tf_u, tf_v, tf_loss], feed_dict={a_ph: a, u_ph: u, v_ph: v})
end_time = time.time()
duration = end_time - start_time
assert a.shape == (_u @ _v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print_format('TensorFlow', 'Nonlinear semi-NMF(NOT CALCLATE v)', a, u, v, old_loss, new_loss, duration)
def test_tf_vanilla_semi_nmf(self):
a = np.random.uniform(-1., 1., size=(100, 100))
u = np.random.uniform(0., 1., size=(100, 300))
v = np.random.uniform(-1., 1., size=(300, 100))
old_loss = np_frobenius_norm(a, u @ v)
# [1000, 500]
a_ph = tf.placeholder(tf.float64, shape=a.shape)
# [1000, 201]
u_ph = tf.placeholder(tf.float64, shape=u.shape)
# [200, 500]
v_ph = tf.placeholder(tf.float64, shape=v.shape)
tf_u, tf_v = semi_nmf(a_ph, u_ph, v_ph, use_tf=True, use_bias=False)
tf_loss = frobenius_norm(a_ph, tf.matmul(tf_u, tf_v))
init = tf.global_variables_initializer()
with tf.Session() as sess:
init.run()
start_time = time.time()
_u, _v, new_loss = sess.run([tf_u, tf_v, tf_loss], feed_dict={a_ph: a, u_ph: u, v_ph: v})
assert np.min(_u) > 0, np.min(_u)
end_time = time.time()
duration = end_time - start_time
assert a.shape == (_u @ _v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print('\n[TensorFlow]Solve semi-NMF\n\t'
'old loss {0}\n\t'
'new loss {1}\n\t'
'process duration {2}'.format(old_loss, new_loss, duration))
print_format('TensorFlow', 'semi-NMF', a, u, v, old_loss, new_loss, duration)
def test_tf_vanilla_semi_nmf(self):
auv = sio.loadmat(mat_file)
a, u, v = auv['a'], auv['u'], auv['v']
old_loss = np_frobenius_norm(a, u @ v)
# [1000, 500]
a_ph = tf.placeholder(tf.float64, shape=a.shape)
# [1000, 201]
u_ph = tf.placeholder(tf.float64, shape=u.shape)
# [200, 500]
v_ph = tf.placeholder(tf.float64, shape=v.shape)
tf_u, tf_v = semi_nmf(a_ph, u_ph, v_ph, use_tf=True, use_bias=False)
tf_loss = frobenius_norm(a_ph, tf.matmul(tf_u, tf_v))
init = tf.global_variables_initializer()
with tf.Session() as sess:
init.run()
start_time = time.time()
_u, _v, new_loss = sess.run([tf_u, tf_v, tf_loss],
feed_dict={
a_ph: a,
u_ph: u,
v_ph: v
})
end_time = time.time()
duration = end_time - start_time
assert a.shape == (_u @ _v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print('\n[TensorFlow]Solve semi-NMF\n\t'
'old loss {0}\n\t'
'new loss {1}\n\t'
'process duration {2}'.format(old_loss, new_loss, duration))
def test_np_not_calc_v_vanilla_nonlin_semi_nmf(self):
a = np.random.uniform(0., 1., size=(100, 100))
u = np.random.uniform(0., 1., size=(100, 300))
v = np.random.uniform(-1., 1., size=(300, 100))
old_loss = np_frobenius_norm(a, u @ v)
start_time = time.time()
u, v = nonlin_semi_nmf(a, u, v, use_bias=False, num_calc_v=0)
assert np.min(u) > 0, np.min(u)
end_time = time.time()
duration = end_time - start_time
new_loss = np_frobenius_norm(a, relu(u @ v))
assert a.shape == (u @ v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print_format('Numpy', 'Nonlinear semi-NMF(NOT CALCULATE v)', a, u, v, old_loss, new_loss, duration)
def test_np_u_neg_matlab_semi_nmf(self):
a = np.random.uniform(size=(100, 100))
u = np.random.uniform(-1., 1., size=(100, 50))
v = np.random.uniform(0., 1., size=(50, 100))
old_loss = np_frobenius_norm(a, u @ v)
start_time = time.time()
u, v = semi_nmf(a, u, v, use_bias=False, data_format=False)
assert np.min(u) < 0, np.min(u)
assert np.min(v) > 0, np.min(v)
end_time = time.time()
duration = end_time - start_time
new_loss = np_frobenius_norm(a, u @ v)
assert a.shape == (u @ v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print_format('Numpy', 'semi-NMF', a, u, v, old_loss, new_loss, duration)
def test_np_not_calc_v_vanilla_nonlin_semi_nmf(self):
auv = sio.loadmat(mat_file)
a, u, v = auv['a'], auv['u'], auv['v']
old_loss = np_frobenius_norm(a, u @ v)
start_time = time.time()
u, v = nonlin_semi_nmf(a, u, v, use_bias=False, num_calc_v=0)
end_time = time.time()
duration = end_time - start_time
new_loss = np_frobenius_norm(a, relu(u @ v))
assert a.shape == (u @ v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print('\n[Numpy]Solve Nonlinear semi-NMF(NOT CALCULATE v)\n\t'
'old loss {0}\n\t'
'new loss {1}\n\t'
'process duration {2}'.format(old_loss, new_loss, duration))
def test_np_vanilla_semi_nmf(self):
auv = sio.loadmat(mat_file)
a, u, v = auv['a'], auv['u'], auv['v']
old_loss = np_frobenius_norm(a, u @ v)
start_time = time.time()
u, v = semi_nmf(a, u, v, use_bias=False)
end_time = time.time()
duration = end_time - start_time
new_loss = np_frobenius_norm(a, u @ v)
assert a.shape == (u @ v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print('\n[Numpy]Solve semi-NMF\n\t'
'old loss {0}\n\t'
'new loss {1}\n\t'
'process duration {2}'.format(old_loss, new_loss, duration))
def test_np_biased_semi_nmf(self):
a = np.random.uniform(-1., 1., size=(100, 100))
u = np.random.uniform(0., 1., size=(100, 300))
v = np.random.uniform(-1., 1., size=(300, 100))
old_loss = np_frobenius_norm(a, u @ v)
bias_v = np.vstack((v, np.ones((1, v.shape[1]))))
start_time = time.time()
u, bias_v = semi_nmf(a, u, bias_v, use_bias=True)
assert np.min(u) > 0, np.min(u)
end_time = time.time()
duration = end_time - start_time
bias_u = np.hstack((u, np.ones((u.shape[0], 1))))
new_loss = np_frobenius_norm(a, bias_u @ bias_v)
assert a.shape == (bias_u @ bias_v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print_format('Numpy', 'biased semi-NMF', a, bias_u, bias_v, old_loss, new_loss, duration)
def test_np_softmax_nmf(self):
a = np.random.uniform(-1., 1., size=(1000, 100))
u = np.random.uniform(0., 1., size=(1000, 2000))
v = np.random.uniform(-1., 1., size=(2000, 100))
old_loss = np_frobenius_norm(a, u @ v)
start_time = time.time()
u, v = softmax_nmf(a, u, v, use_bias=False)
assert np.min(v) < 0, np.min(v)
assert np.min(u) > 0, np.min(u)
u_sum = u.sum(axis=1)
assert u_sum[u_sum == 1].any(), u_sum
end_time = time.time()
duration = end_time - start_time
new_loss = np_frobenius_norm(a, u @ v)
assert a.shape == (u @ v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print_format('Numpy', 'semi-NMF', a, u, v, old_loss, new_loss, duration)
def test_tf_not_calc_v_biased_nonlin_semi_nmf(self):
auv = sio.loadmat(mat_file)
a, u, v = auv['a'], auv['u'], auv['v']
bias_u = np.hstack((u, np.ones((u.shape[0], 1))))
old_loss = np_frobenius_norm(a, u @ v)
a_ph = tf.placeholder(tf.float64, shape=a.shape)
bias_u_ph = tf.placeholder(tf.float64, shape=bias_u.shape)
v_ph = tf.placeholder(tf.float64, shape=v.shape)
tf_bias_u, tf_v = nonlin_semi_nmf(a_ph,
bias_u_ph,
v_ph,
num_calc_v=0,
use_bias=True,
use_tf=True)
init = tf.global_variables_initializer()
with tf.Session() as sess:
init.run()
start_time = time.time()
_bias_u, _v = sess.run([tf_bias_u, tf_v],
feed_dict={
a_ph: a,
bias_u_ph: bias_u,
v_ph: v
})
end_time = time.time()
duration = end_time - start_time
_bias_v = np.vstack((_v, np.ones((1, v.shape[1]))))
new_loss = np_frobenius_norm(a, relu(_bias_u @ _bias_v))
assert a.shape == (_bias_u @ _bias_v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print(
'\n[TensorFlow]Solve biased Nonlinear semi-NMF(NOT CALCULATE v)\n\t'
'old loss {0}\n\t'
'new loss {1}\n\t'
'process duration {2}'.format(old_loss, new_loss, duration))
def test_np_biased_semi_nmf(self):
auv = sio.loadmat(mat_file)
a, u, v = auv['a'], auv['u'], auv['v']
old_loss = np_frobenius_norm(a, u @ v)
u = np.hstack((u, np.ones((u.shape[0], 1))))
start_time = time.time()
u, v = semi_nmf(a, u, v, use_bias=True)
end_time = time.time()
duration = end_time - start_time
bias_v = np.vstack((v, np.ones((1, v.shape[1]))))
new_loss = np_frobenius_norm(a, u @ bias_v)
assert a.shape == (u @ bias_v).shape
assert new_loss < old_loss, "new loss should be less than old loss."
print('\n[Numpy]Solve biased semi-NMF\n\t'
'old loss {0}\n\t'
'new loss {1}\n\t'
'process duration {2}'.format(old_loss, new_loss, duration))
