def construct_from_svd(U, s, V, cfg):
T = cfg['T']
Phi = np.zeros((U.shape[0], T))
Theta = np.zeros((T, V.shape[1]))
for i in xrange(T):
x = U[:, i]
y = V[i, :]
xp = np.copy(x)
xp[xp < 0] = 0
xn = (-1)*np.copy(x)
xn[xn < 0] = 0
yp = np.copy(y)
yp[yp < 0] = 0
yn = (-1)*np.copy(y)
yn[yn < 0] = 0
xp_norm = np.linalg.norm(xp, ord=1)
yp_norm = np.linalg.norm(yp, ord=1)
xn_norm = np.linalg.norm(xn, ord=1)
yn_norm = np.linalg.norm(yn, ord=1)
if xp_norm*yp_norm > xn_norm*yn_norm:
Phi[:, i] = np.sqrt(s[i]*xp_norm*yp_norm)*xp/xp_norm
Theta[i, :] = np.sqrt(s[i]*xp_norm*yp_norm)*yp/yp_norm
else:
Phi[:, i] = np.sqrt(s[i]*xn_norm*yn_norm)*xn/xn_norm
Theta[i, :] = np.sqrt(s[i]*xn_norm*yn_norm)*yn/yn_norm
return normalize_cols(Phi), normalize_cols(Theta)
def plsa3D(V, W, H, post='', cfg=config.default_config()):
#print('Probabilistic Latent Semantic Analysis.')
eps = cfg['eps']
(N, M) = V.shape
T = H.shape[0]
V3 = V.reshape(N, M, 1).repeat(T, 2).swapaxes(1, 2)
W3 = W.reshape(N, T, 1).repeat(M, 2)
H3 = H.T.reshape(M, T, 1).repeat(N, 2).swapaxes(0, 2)
Q3 = dot(W, H).reshape(N, M, 1).repeat(T, 2).swapaxes(1, 2)
Z = V3 * W3 * H3 / (Q3 + eps)
W = normalize_cols(sum(Z, 2).reshape(N, T))
H = normalize_cols(sum(Z, 0).reshape(T, M))
return W, H
def plsa3D(V, W, H, post='', cfg=config.default_config()):
#print('Probabilistic Latent Semantic Analysis.')
eps = cfg['eps']
(N, M) = V.shape
T = H.shape[0]
V3 = V.reshape(N, M, 1).repeat(T, 2).swapaxes(1, 2)
W3 = W.reshape(N, T, 1).repeat(M, 2)
H3 = H.T.reshape(M, T, 1).repeat(N, 2).swapaxes(0, 2)
Q3 = dot(W, H).reshape(N, M, 1).repeat(T, 2).swapaxes(1, 2)
Z = V3 * W3 * H3 / (Q3 + eps)
W = normalize_cols(sum(Z, 2).reshape(N, T))
H = normalize_cols(sum(Z, 0).reshape(T, M))
return W, H
def gen_matrix_topic(params):
N, T = params['rows'], params['cols']
phi = np.zeros((N, T))
sparse = params['sparsity'] # sparsness (the main parameter)
if sparse < params['eps']:
sparse = params['eps']
elif sparse > 1:
sparse = 1
nkernel = params['nkernel'] # number of average kernel words in topic
nnoise = params['nnoise'] # number of noise (smooth) topics
ntopic = T - nnoise
kernel = np.maximum(1, np.random.binomial(N, min(1, nkernel / (N*sparse)), ntopic))
s = 0
for i in range(ntopic):
phi[s:s+kernel[i], i] = -np.sort(-np.random.exponential(0.5, kernel[i]))
s = s + int(kernel[i] * sparse)
if i < ntopic-1 and s + kernel[i+1] > N:
kernel[i+1] = max(1, N - s)
s = N - kernel[i+1]
if N-s-kernel[-1]+1 > 0:
if nnoise == 0:
phi[s+kernel[-1]-1:, :] = np.random.random_sample((N-s-kernel[-1]+1, T))
else:
#phi[s+kernel[-1]-1:, ntopic:] = np.random.random_sample((N-s-kernel[-1]+1, nnoise))
phi[:, ntopic:] = np.random.random_sample((N, nnoise))
return normalize_cols(phi)
def gen_matrix_topic(params):
N, T = params['rows'], params['cols']
phi = np.zeros((N, T))
sparse = params['sparsity'] # sparsness (the main parameter)
if sparse < params['eps']:
sparse = params['eps']
elif sparse > 1:
sparse = 1
nkernel = params['nkernel'] # number of average kernel words in topic
nnoise = params['nnoise'] # number of noise (smooth) topics
ntopic = T - nnoise
kernel = np.maximum(
1, np.random.binomial(N, min(1, nkernel / (N * sparse)), ntopic))
s = 0
for i in range(ntopic):
phi[s:s + kernel[i],
i] = -np.sort(-np.random.exponential(0.5, kernel[i]))
s = s + int(kernel[i] * sparse)
if i < ntopic - 1 and s + kernel[i + 1] > N:
kernel[i + 1] = max(1, N - s)
s = N - kernel[i + 1]
if N - s - kernel[-1] + 1 > 0:
if nnoise == 0:
phi[s + kernel[-1] - 1:, :] = np.random.random_sample(
(N - s - kernel[-1] + 1, T))
else:
#phi[s+kernel[-1]-1:, ntopic:] = np.random.random_sample((N-s-kernel[-1]+1, nnoise))
phi[:, ntopic:] = np.random.random_sample((N, nnoise))
return normalize_cols(phi)
def gen_matrix_sparse(params):
rows = params['rows']
cols = params['cols']
sparsity = params['sparsity']
M = np.zeros((rows, cols), dtype='float32')
for i in xrange(cols):
M[:, i] = np.random.dirichlet([sparsity]*rows)
return normalize_cols(M)
def grad_desc(V, W, H, post='', cfg=config.default_config()):
alpha = cfg[post + '_alpha']
step = cfg[post + '_alpha_step']
eps = cfg['eps']
#print('Gradient Descent with alpha={alpha}.'.format(alpha=alpha))
grad_W = dot((V - dot(W, H)), H.T)
grad_H = dot(W.T, (V - dot(W, H)))
#grad_W[grad_W < eps] = 0
#grad_H[grad_H < eps] = 0
W = W + alpha * grad_W
W[(grad_W < eps) & (W < eps)] = 0
W = normalize_cols(W)
H = H + alpha * grad_H
H[(grad_H < eps) & (H < eps)] = 0
H = normalize_cols(H)
alpha = alpha * step
cfg[post + '_alpha'] = alpha
return (W, H)
def grad_desc(V, W, H, post='', cfg=config.default_config()):
alpha = cfg[post + '_alpha']
step = cfg[post + '_alpha_step']
eps = cfg['eps']
#print('Gradient Descent with alpha={alpha}.'.format(alpha=alpha))
grad_W = dot((V - dot(W, H)), H.T)
grad_H = dot(W.T, (V - dot(W, H)))
#grad_W[grad_W < eps] = 0
#grad_H[grad_H < eps] = 0
W = W + alpha * grad_W
W[(grad_W < eps) & (W < eps)] = 0
W = normalize_cols(W)
H = H + alpha * grad_H
H[(grad_H < eps) & (H < eps)] = 0
H = normalize_cols(H)
alpha = alpha * step
cfg[post + '_alpha'] = alpha
return (W, H)
def gen_matrix_sparse(params):
rows = params['rows']
cols = params['cols']
sparsity = params['sparsity']
M = np.zeros((rows, cols), dtype='float32')
sz = int(rows * cols * (1 - sparsity))
idx0_t = [i for i in range(rows) for j in range(cols)]
np.random.shuffle(idx0_t)
idx0 = idx0_t[:sz]
idx1_t = [j for i in range(rows) for j in range(cols)]
np.random.shuffle(idx1_t)
idx1 = idx1_t[:sz]
M[idx0, idx1] = np.random.sample(sz)
if rows < cols:
M[:, :rows] = M[:, :rows] + np.eye(rows) * params['eps']
M[0, rows:] = params['eps']
else:
M[:cols, :] = M[:cols, :] + np.eye(cols) * params['eps']
return normalize_cols(M)
def gen_matrix_sparse(params):
rows = params['rows']
cols = params['cols']
sparsity = params['sparsity']
M = np.zeros((rows, cols), dtype='float32')
sz = int(rows * cols * (1 - sparsity))
idx0_t = [i for i in range(rows) for j in range(cols)]
np.random.shuffle(idx0_t)
idx0 = idx0_t[:sz]
idx1_t = [j for i in range(rows) for j in range(cols)]
np.random.shuffle(idx1_t)
idx1 = idx1_t[:sz]
M[idx0, idx1] = np.random.sample(sz)
if rows < cols:
M[:, :rows] = M[:, :rows] + np.eye(rows) * params['eps']
M[0, rows:] = params['eps']
else:
M[:cols, :] = M[:cols, :] + np.eye(cols) * params['eps']
return normalize_cols(M)
def gen_matrix_normal(params):
return normalize_cols(abs(np.random.randn(params['rows'], params['cols'])))
def gen_matrix_uniform(params):
return normalize_cols(
np.random.uniform(size=(params['rows'], params['cols'])))
def run(V, W, H, W_r=None, H_r=None, cfg=config.default_config()):
T = H.shape[0]
eps = cfg['eps']
schedule = cfg['schedule'].split(',')
meas = cfg['measure'].split(',')
val = np.zeros((cfg['max_iter'] + 2, len(meas)))
hdist = np.zeros((cfg['max_iter'] + 2, 1))
for i, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[0, i] = fun(V, np.dot(W, H))
if cfg['compare_real']:
#m = Munkres()
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[0] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
if cfg['print_lvl'] > 1:
print('Initial loss:', val[0])
status = 0
methods_num = len(schedule)
it = -1
for it in range(cfg['max_iter']):
if cfg['print_lvl'] > 1:
print('Iteration', it + 1)
W_old = deepcopy(W)
H_old = deepcopy(H)
method_name = schedule[it % methods_num]
if cfg['print_lvl'] > 1:
print('Method:', method_name)
method = getattr(methods, method_name)
(W, H) = method(V, W, H, method_name, cfg)
if (it + 1) % cfg['normalize_iter'] == 0:
W = normalize_cols(W)
H = normalize_cols(H)
for j, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[it + 1, j] = fun(V, np.dot(W, H))
if cfg['compare_real']:
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[it + 1] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
if cfg['print_lvl'] > 1:
print(val[it + 1])
if all(val[it, :] < eps):
if cfg['print_lvl'] > 1:
print('By cost.')
status = 1
break
if abs(W_old - W).max() < eps and abs(H_old - H).max() < eps:
if cfg['print_lvl'] > 1:
print('By argument.')
status = 2
break
#del W_old
#del H_old
if cfg['print_lvl'] > 1:
print('Final:')
W = normalize_cols(W)
H = normalize_cols(H)
for j, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[it + 2:, j] = fun(V, np.dot(W, H))
if cfg['compare_real']:
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[it + 2:] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
return (val, hdist, it, W, H, status)
def main(config_file='config.txt', results_file='results.txt', cfg=None):
if cfg == None:
cfg = config.load(config_file)
if cfg['seed'] >= 0:
np.random.seed(cfg['seed'])
else:
np.random.seed(None)
eps = cfg['eps']
N = cfg['N']
T = cfg['T']
M = cfg['M']
vocab = None
W_r = None
H_r = None
if cfg['run_info'] == 'results' or cfg['run_info'] == 1:
cfg['print_lvl'] = 1
elif cfg['run_info'] == 'run' or cfg['run_info'] == 2:
cfg['print_lvl'] = 2
else:
cfg['print_lvl'] = 0
if cfg['print_lvl'] > 0:
print('Generating...')
if cfg['load_data'] == 'uci' or cfg['load_data'] == 2:
V, vocab = load_uci(cfg['data_name'], cfg)
V = normalize_cols(V)
N, M = V.shape
cfg['N'], cfg['M'] = V.shape
print('Size:', N, M)
elif cfg['load_data'] == 'csv' or cfg['load_data'] == 1:
_, W_r, H_r = load_csv(cfg['gen_name'], cfg)
#plt.matshow(1-W_r, cmap=plt.cm.gray)
#plt.title('real')
V, vocab = load_uci(cfg['gen_name'], cfg)
V = normalize_cols(V)
N, M = V.shape
cfg['N'], cfg['M'] = V.shape
print('Size:', N, M)
cfg['T_0'] = W_r.shape[1]
else:
V, W_r, H_r = gen_real(cfg)
print('Checking assumption on V:', np.sum(V, axis=0).max())
#tp = '0_5_100_16_500'
#V_filename = 'datasets/V.' + tp + '.txt.csv'
#W_filename = 'datasets/W.' + tp + '.txt.csv'
#H_filename = 'datasets/H.' + tp + '.txt.csv'
#V = np.loadtxt(V_filename, delimiter=',')
#W_r = np.loadtxt(W_filename, delimiter=',')
#H_r = np.loadtxt(H_filename, delimiter=',')
#show_matrices(W_r, H_r)
#plt.savefig('tm_tests/real' + tp + '.eps', format='eps')
res = [0] * cfg['runs']
finals = [0] * cfg['runs']
hdist_runs = [0] * cfg['runs']
exp_time = [0] * cfg['runs']
meas = cfg['measure'].split(',')
meas_name = [''] * len(meas)
for i, f_name in enumerate(meas):
f = getattr(measure, f_name + '_name')
meas_name[i] = f()
print('Measures:', meas_name)
if cfg['compare_methods']:
methods = cfg['schedule'].split(',')
nmethods = len(methods)
for r in range(cfg['runs']):
if cfg['print_lvl'] > 0:
print('Run', r + 1)
#(W, H) = gen_init(cfg)
if cfg['print_lvl'] > 0:
print(' Starting...')
labels = None
st = time()
if r >= cfg['prepare'] and cfg['prepare'] >= 0 and cfg[
'prepare_method'] > 0:
print('Preparing data...')
if cfg['prepare_method'] == 1:
W = anchor_words(V, 'L2', cfg)
print('Solving for H')
H = linalg.solve(
np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, V))
H[H < eps] = 0
H = normalize_cols(H)
elif cfg['prepare_method'] == 2:
centroids, labels = reduce_cluster(V.T, cfg['T'], cfg)
W = centroids.T
W[W < eps] = 0
W = normalize_cols(W)
print('Solving for H')
H = linalg.solve(
np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, V))
H[H < eps] = 0
H = normalize_cols(H)
elif cfg['prepare_method'] == 3:
centroids, labels = reduce_cluster(V, cfg['num_clusters'], cfg)
W = anchor_words(centroids, 'L2', cfg)
print('Solving for H')
H = linalg.solve(
np.dot(W.T, W) + np.eye(W.shape[1]) * eps,
np.dot(W.T, normalize_cols(centroids)))
H[H < eps] = 0
H = normalize_cols(H)
W = restore_cluster(W, labels, cfg)
elif cfg['prepare_method'] >= 4 and cfg['prepare_method'] <= 6:
if cfg['prepare_method'] == 4:
red = reduce_tsne(V, to_dim=4)
elif cfg['prepare_method'] == 5:
red = reduce_tsne(V, to_dim=3)
elif cfg['prepare_method'] == 6:
red = reduce_tsne(V, to_dim=2)
centroids, labels = reduce_cluster(red, cfg['num_clusters'],
cfg)
nearest_words = find_nearest(red, centroids, labels)
V_reduced = normalize_cols(V[nearest_words, :])
W = anchor_words(V_reduced, 'L2', cfg)
print('Solving for H')
H = linalg.solve(
np.dot(W.T, W) + np.eye(W.shape[1]) * eps,
np.dot(W.T, V_reduced))
H[H < eps] = 0
H = normalize_cols(H)
W = restore_cluster(W, labels, cfg)
elif cfg['prepare_method'] == 10:
centroids, labels = reduce_multi_cluster(
V, cfg['num_clusters'], cfg)
W = anchor_words(centroids, 'L2', cfg)
print('Solving for H')
H = linalg.solve(
np.dot(W.T, W) + np.eye(W.shape[1]) * eps,
np.dot(W.T, normalize_cols(centroids)))
H[H < eps] = 0
H = normalize_cols(H)
#W = restore_multi_cluster(W, labels, cfg)
W = linalg.solve(
dot(H, H.T) + eye(H.shape[0]) * eps, dot(H, V.T)).T
W[W < eps] = 0
W = normalize_cols(W)
else:
(W, H) = gen_init(cfg)
#cur_frob = measure.frobenius(V, np.dot(W, H))
#for init_it in xrange(200):
# W_new, H_new = gen_init(cfg)
# if measure.frobenius(V, np.dot(W_new, H_new)) #< cur_frob:
# W = deepcopy(W_new)
# H = deepcopy(H_new)
se = time() - st
print('Preparing took time:', timedelta(seconds=se))
#labels=None
#print('Preparing data...')
#centroids, labels = reduce_cluster(V, cfg['T'], cfg)
#H = centroids
#H[H < eps] = 0
#H = normalize_cols(H)
#print('Solving for W')
#W = linalg.solve(dot(H, H.T) + eye(H.shape[0]) * eps, dot(H, V.T)).T
#W[W < eps] = 0
#W = normalize_cols(W)
#centroids, labels = reduce_cluster(V, cfg['num_clusters'], cfg)
#W = anchor_words(centroids, 'L2', cfg)
#print('Solving for H')
#H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, normalize_cols(centroids)))
#H[H < eps] = 0
#H = normalize_cols(H)
#W = restore_cluster(W, labels, cfg)
#W = anchor_words(V, 'L2', cfg)
#print('Solving for H')
#H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, V))
#H[H < eps] = 0
#H = normalize_cols(H)
#red = reduce_tsne(V, to_dim=3)
#centroids, labels = reduce_cluster(red, cfg['num_clusters'], cfg)
#print('c', centroids.shape, 'l', labels.shape)
#nearest_words = find_nearest(red, centroids, labels)
#print('nw:', nearest_words.shape)
#V_reduced = V[nearest_words, :]
#print('Vr', V_reduced.shape)
#W = anchor_words(V_reduced, 'L2', cfg)
#print('Solving for H')
#H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, V_reduced))
#H[H < eps] = 0
#H = normalize_cols(H)
#W = restore_cluster(W, labels, cfg)
if cfg['compare_prepare'] > 0:
if r > 0:
print('Preparing data...')
if r == 1:
W = anchor_words(V, 'L2', cfg)
print('Solving for H')
H = linalg.solve(
np.dot(W.T, W) + np.eye(W.shape[1]) * eps,
np.dot(W.T, V))
H[H < eps] = 0
H = normalize_cols(H)
elif r == 2:
centroids, labels = reduce_cluster(V, cfg['T'], cfg)
H = centroids
H[H < eps] = 0
H = normalize_cols(H)
print('Solving for W')
W = linalg.solve(
dot(H, H.T) + eye(H.shape[0]) * eps, dot(H, V.T)).T
W[W < eps] = 0
W = normalize_cols(W)
elif r == 3:
centroids, labels = reduce_cluster(V, cfg['num_clusters'],
cfg)
W = anchor_words(centroids, 'L2', cfg)
print('Solving for H')
H = linalg.solve(
np.dot(W.T, W) + np.eye(W.shape[1]) * eps,
np.dot(W.T, normalize_cols(centroids)))
H[H < eps] = 0
H = normalize_cols(H)
W = restore_cluster(W, labels, cfg)
elif r >= 4 and r <= 6:
if r == 4:
red = reduce_tsne(V, to_dim=4)
elif r == 5:
red = reduce_tsne(V, to_dim=3)
elif r == 6:
red = reduce_tsne(V, to_dim=2)
centroids, labels = reduce_cluster(red,
cfg['num_clusters'],
cfg)
nearest_words = find_nearest(red, centroids, labels)
V_reduced = V[nearest_words, :]
W = anchor_words(V_reduced, 'L2', cfg)
print('Solving for H')
H = linalg.solve(
np.dot(W.T, W) + np.eye(W.shape[1]) * eps,
np.dot(W.T, V_reduced))
H[H < eps] = 0
H = normalize_cols(H)
W = restore_cluster(W, labels, cfg)
if cfg['compare_methods'] > 0:
cfg['schedule'] = methods[r % nmethods]
start = time()
(val, hdist, it, W, H, status) = run(V, W, H, W_r, H_r, cfg)
stop = time()
print('Run time:', timedelta(seconds=stop - start))
exp_time[r] = stop - start
res[r] = val
hdist_runs[r] = hdist
if cfg['print_lvl'] > 0:
print(' Result:', val[-1, :])
for i, fun_name in enumerate(cfg['finals'].split(',')):
#val = np.array([r[:, i] for r in res])
fun = getattr(measure, fun_name)
name, val = fun(W, H)
print(name, ':', val)
print(cfg['experiment'])
if cfg['experiment'] == '':
exp_name = 'test'
else:
exp_name = cfg['experiment']
#if cfg['save_results']:
# if cfg['save_file']:
# results_file = cfg['save_file']
# with open(results_file, 'w') as rf:
# print_head(rf)
# print('# Generated on {}'.format(datetime.today()), file=rf)
# print('# Experiments config:', file=rf)
# print('# Number of experiments: {}'.format(cfg['runs']), file=rf)
# print('# Methods schedule: {}'.format(cfg['schedule']), file=rf)
# print('# Iterations number: {}'.format(cfg['max_iter']), file=rf)
# print('# All experiments done in {}'.format(
# timedelta(seconds=sum(exp_time))), file=rf)
# print_head(rf)
# for r in range(cfg['runs']):
# print('# Run #{}. Done in {}'.format(r+1,
# timedelta(seconds=exp_time[r])), file=rf)
# [print(val, file=rf) for val in res[:, r]]
# print_head(rf)
if cfg['show_results']:
if not os.path.exists(cfg['result_dir']):
os.makedirs(cfg['result_dir'])
np.savetxt(join(cfg['result_dir'], cfg['experiment'] + '_W.csv'), W)
#show_topics(W, 25, vocab=vocab)
save_topics(W,
join(cfg['result_dir'], cfg['experiment'] + '_topics.txt'),
vocab)
#plot_matrix(V, 'Documents', labels=labels, vocab=vocab)
#filename = os.path.join(cfg['result_dir'], cfg['experiment']+'_V.eps')
#plt.savefig(filename, format='eps')
#plot_matrix(W, u'Распределение слов в темах', labels, vocab)
#filename = os.path.join(cfg['result_dir'], cfg['experiment']+'_W.pdf')
#plt.savefig(filename, format='pdf')
for i, fun_name in enumerate(cfg['measure'].split(',')):
val = np.array([r[:, i] for r in res])
fun = getattr(measure, fun_name + '_name')
plot_measure(val.T, fun())
filename = os.path.join(
cfg['result_dir'], cfg['experiment'] + '_' + fun_name + '.pdf')
plt.savefig(filename, format='pdf')
if cfg['compare_real']:
print('Hellinger res:', hdist_runs[0][-1, 0])
plot_measure(
np.array([r[:, 0] for r in hdist_runs]).T,
measure.hellinger_name())
show_matrices_recovered(W_r, H_r, W, H, cfg, permute=True)
#plt.savefig('tm_tests/recovered_cnmf_' + tp + '.eps', format='eps')
#plt.show()
return res
def main(config_file='config.txt', results_file='results.txt', cfg=None):
if cfg == None:
cfg = config.load(config_file)
if cfg['seed'] >= 0:
np.random.seed(cfg['seed'])
else:
np.random.seed(None)
eps = cfg['eps']
N = cfg['N']
T = cfg['T']
M = cfg['M']
vocab = None
W_r = None
H_r = None
if cfg['run_info'] == 'results' or cfg['run_info'] == 1:
cfg['print_lvl'] = 1
elif cfg['run_info'] == 'run' or cfg['run_info'] == 2:
cfg['print_lvl'] = 2
else:
cfg['print_lvl'] = 0
if cfg['print_lvl'] > 0:
print('Generating...')
if cfg['load_data'] == 'uci' or cfg['load_data'] == 2:
V, vocab = load_uci(cfg['data_name'], cfg)
V = normalize_cols(V)
N, M = V.shape
cfg['N'], cfg['M'] = V.shape
print('Size:', N, M)
elif cfg['load_data'] == 'csv' or cfg['load_data'] == 1:
_, W_r, H_r = load_csv(cfg['gen_name'], cfg)
#plt.matshow(1-W_r, cmap=plt.cm.gray)
#plt.title('real')
V, vocab = load_uci(cfg['gen_name'], cfg)
V = normalize_cols(V)
N, M = V.shape
cfg['N'], cfg['M'] = V.shape
print('Size:', N, M)
cfg['T_0'] = W_r.shape[1]
else:
V, W_r, H_r = gen_real(cfg)
print('Checking assumption on V:', np.sum(V, axis=0).max())
#tp = '0_5_100_16_500'
#V_filename = 'datasets/V.' + tp + '.txt.csv'
#W_filename = 'datasets/W.' + tp + '.txt.csv'
#H_filename = 'datasets/H.' + tp + '.txt.csv'
#V = np.loadtxt(V_filename, delimiter=',')
#W_r = np.loadtxt(W_filename, delimiter=',')
#H_r = np.loadtxt(H_filename, delimiter=',')
#show_matrices(W_r, H_r)
#plt.savefig('tm_tests/real' + tp + '.eps', format='eps')
res = [0] * cfg['runs']
finals = [0] * cfg['runs']
hdist_runs = [0] * cfg['runs']
exp_time = [0] * cfg['runs']
meas = cfg['measure'].split(',')
meas_name = [''] * len(meas)
for i, f_name in enumerate(meas):
f = getattr(measure, f_name + '_name')
meas_name[i] = f()
print('Measures:', meas_name)
if cfg['compare_methods']:
methods = cfg['schedule'].split(',')
nmethods = len(methods)
for r in range(cfg['runs']):
if cfg['print_lvl'] > 0:
print('Run', r+1)
#(W, H) = gen_init(cfg)
if cfg['print_lvl'] > 0:
print(' Starting...')
labels = None
st = time()
if r >= cfg['prepare'] and cfg['prepare'] >= 0 and cfg['prepare_method'] > 0:
print('Preparing data...')
if cfg['prepare_method'] == 1:
W = anchor_words(V, 'L2', cfg)
print('Solving for H')
H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, V))
H[H < eps] = 0
H = normalize_cols(H)
elif cfg['prepare_method'] == 2:
centroids, labels = reduce_cluster(V.T, cfg['T'], cfg)
W = centroids.T
W[W < eps] = 0
W = normalize_cols(W)
print('Solving for H')
H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, V))
H[H < eps] = 0
H = normalize_cols(H)
elif cfg['prepare_method'] == 3:
centroids, labels = reduce_cluster(V, cfg['num_clusters'], cfg)
W = anchor_words(centroids, 'L2', cfg)
print('Solving for H')
H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, normalize_cols(centroids)))
H[H < eps] = 0
H = normalize_cols(H)
W = restore_cluster(W, labels, cfg)
elif cfg['prepare_method'] >= 4 and cfg['prepare_method'] <= 6:
if cfg['prepare_method'] == 4:
red = reduce_tsne(V, to_dim=4)
elif cfg['prepare_method'] == 5:
red = reduce_tsne(V, to_dim=3)
elif cfg['prepare_method'] == 6:
red = reduce_tsne(V, to_dim=2)
centroids, labels = reduce_cluster(red, cfg['num_clusters'], cfg)
nearest_words = find_nearest(red, centroids, labels)
V_reduced = normalize_cols(V[nearest_words, :])
W = anchor_words(V_reduced, 'L2', cfg)
print('Solving for H')
H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, V_reduced))
H[H < eps] = 0
H = normalize_cols(H)
W = restore_cluster(W, labels, cfg)
elif cfg['prepare_method'] == 10:
centroids, labels = reduce_multi_cluster(V, cfg['num_clusters'], cfg)
W = anchor_words(centroids, 'L2', cfg)
print('Solving for H')
H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, normalize_cols(centroids)))
H[H < eps] = 0
H = normalize_cols(H)
#W = restore_multi_cluster(W, labels, cfg)
W = linalg.solve(dot(H, H.T) + eye(H.shape[0]) * eps, dot(H, V.T)).T
W[W < eps] = 0
W = normalize_cols(W)
else:
(W, H) = gen_init(cfg)
#cur_frob = measure.frobenius(V, np.dot(W, H))
#for init_it in xrange(200):
# W_new, H_new = gen_init(cfg)
# if measure.frobenius(V, np.dot(W_new, H_new)) #< cur_frob:
# W = deepcopy(W_new)
# H = deepcopy(H_new)
se = time() - st
print('Preparing took time:', timedelta(seconds=se))
#labels=None
#print('Preparing data...')
#centroids, labels = reduce_cluster(V, cfg['T'], cfg)
#H = centroids
#H[H < eps] = 0
#H = normalize_cols(H)
#print('Solving for W')
#W = linalg.solve(dot(H, H.T) + eye(H.shape[0]) * eps, dot(H, V.T)).T
#W[W < eps] = 0
#W = normalize_cols(W)
#centroids, labels = reduce_cluster(V, cfg['num_clusters'], cfg)
#W = anchor_words(centroids, 'L2', cfg)
#print('Solving for H')
#H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, normalize_cols(centroids)))
#H[H < eps] = 0
#H = normalize_cols(H)
#W = restore_cluster(W, labels, cfg)
#W = anchor_words(V, 'L2', cfg)
#print('Solving for H')
#H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, V))
#H[H < eps] = 0
#H = normalize_cols(H)
#red = reduce_tsne(V, to_dim=3)
#centroids, labels = reduce_cluster(red, cfg['num_clusters'], cfg)
#print('c', centroids.shape, 'l', labels.shape)
#nearest_words = find_nearest(red, centroids, labels)
#print('nw:', nearest_words.shape)
#V_reduced = V[nearest_words, :]
#print('Vr', V_reduced.shape)
#W = anchor_words(V_reduced, 'L2', cfg)
#print('Solving for H')
#H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, V_reduced))
#H[H < eps] = 0
#H = normalize_cols(H)
#W = restore_cluster(W, labels, cfg)
if cfg['compare_prepare'] > 0:
if r > 0:
print('Preparing data...')
if r == 1:
W = anchor_words(V, 'L2', cfg)
print('Solving for H')
H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, V))
H[H < eps] = 0
H = normalize_cols(H)
elif r == 2:
centroids, labels = reduce_cluster(V, cfg['T'], cfg)
H = centroids
H[H < eps] = 0
H = normalize_cols(H)
print('Solving for W')
W = linalg.solve(dot(H, H.T) + eye(H.shape[0]) * eps, dot(H, V.T)).T
W[W < eps] = 0
W = normalize_cols(W)
elif r == 3:
centroids, labels = reduce_cluster(V, cfg['num_clusters'], cfg)
W = anchor_words(centroids, 'L2', cfg)
print('Solving for H')
H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, normalize_cols(centroids)))
H[H < eps] = 0
H = normalize_cols(H)
W = restore_cluster(W, labels, cfg)
elif r >= 4 and r <= 6:
if r == 4:
red = reduce_tsne(V, to_dim=4)
elif r == 5:
red = reduce_tsne(V, to_dim=3)
elif r == 6:
red = reduce_tsne(V, to_dim=2)
centroids, labels = reduce_cluster(red, cfg['num_clusters'], cfg)
nearest_words = find_nearest(red, centroids, labels)
V_reduced = V[nearest_words, :]
W = anchor_words(V_reduced, 'L2', cfg)
print('Solving for H')
H = linalg.solve(np.dot(W.T, W) + np.eye(W.shape[1]) * eps, np.dot(W.T, V_reduced))
H[H < eps] = 0
H = normalize_cols(H)
W = restore_cluster(W, labels, cfg)
if cfg['compare_methods'] > 0:
cfg['schedule'] = methods[r % nmethods]
start = time()
(val, hdist, it, W, H, status) = run(V, W, H , W_r, H_r, cfg)
stop = time()
print('Run time:', timedelta(seconds=stop - start))
exp_time[r] = stop - start
res[r] = val
hdist_runs[r] = hdist
if cfg['print_lvl'] > 0:
print(' Result:', val[-1, :])
for i, fun_name in enumerate(cfg['finals'].split(',')):
#val = np.array([r[:, i] for r in res])
fun = getattr(measure, fun_name)
name, val = fun(W, H)
print(name, ':', val)
print(cfg['experiment'])
if cfg['experiment'] == '':
exp_name = 'test'
else:
exp_name = cfg['experiment']
#if cfg['save_results']:
# if cfg['save_file']:
# results_file = cfg['save_file']
# with open(results_file, 'w') as rf:
# print_head(rf)
# print('# Generated on {}'.format(datetime.today()), file=rf)
# print('# Experiments config:', file=rf)
# print('# Number of experiments: {}'.format(cfg['runs']), file=rf)
# print('# Methods schedule: {}'.format(cfg['schedule']), file=rf)
# print('# Iterations number: {}'.format(cfg['max_iter']), file=rf)
# print('# All experiments done in {}'.format(
# timedelta(seconds=sum(exp_time))), file=rf)
# print_head(rf)
# for r in range(cfg['runs']):
# print('# Run #{}. Done in {}'.format(r+1,
# timedelta(seconds=exp_time[r])), file=rf)
# [print(val, file=rf) for val in res[:, r]]
# print_head(rf)
if cfg['show_results']:
if not os.path.exists(cfg['result_dir']):
os.makedirs(cfg['result_dir'])
np.savetxt(join(cfg['result_dir'], cfg['experiment'] + '_W.csv'), W)
#show_topics(W, 25, vocab=vocab)
save_topics(W, join(cfg['result_dir'], cfg['experiment'] + '_topics.txt'), vocab)
#plot_matrix(V, 'Documents', labels=labels, vocab=vocab)
#filename = os.path.join(cfg['result_dir'], cfg['experiment']+'_V.eps')
#plt.savefig(filename, format='eps')
#plot_matrix(W, u'Распределение слов в темах', labels, vocab)
#filename = os.path.join(cfg['result_dir'], cfg['experiment']+'_W.pdf')
#plt.savefig(filename, format='pdf')
for i, fun_name in enumerate(cfg['measure'].split(',')):
val = np.array([r[:, i] for r in res])
fun = getattr(measure, fun_name + '_name')
plot_measure(val.T, fun())
filename = os.path.join(cfg['result_dir'], cfg['experiment']+'_'+fun_name+'.pdf')
plt.savefig(filename, format='pdf')
if cfg['compare_real']:
print('Hellinger res:', hdist_runs[0][-1,0])
plot_measure(np.array([r[:, 0] for r in hdist_runs]).T, measure.hellinger_name())
show_matrices_recovered(W_r, H_r, W, H, cfg, permute=True)
#plt.savefig('tm_tests/recovered_cnmf_' + tp + '.eps', format='eps')
#plt.show()
return res
def plsa(V, W, H, post='', cfg=config.default_config()):
eps = cfg['eps']
tmp = V / maximum(dot(W, H), eps)
H = normalize_cols(H * dot(W.T, tmp))
W = normalize_cols(W * dot(tmp, H.T))
return W, H
def restore_cluster(W_reduced, labels, params):
W = zeros((params['N'], params['T']))
for word, label in enumerate(labels):
W[word, :] = W_reduced[label, :]
return normalize_cols(W)
def run(V, W, H, W_r=None, H_r=None, cfg=config.default_config()):
T = H.shape[0]
eps = cfg['eps']
schedule = cfg['schedule'].split(',')
meas = cfg['measure'].split(',')
val = np.zeros((cfg['max_iter']+2, len(meas)))
hdist = np.zeros((cfg['max_iter']+2, 1))
for i, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[0, i] = fun(V, np.dot(W, H))
if cfg['compare_real']:
#m = Munkres()
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[0] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
if cfg['print_lvl'] > 1:
print('Initial loss:', val[0])
status = 0
methods_num = len(schedule)
it = -1
for it in range(cfg['max_iter']):
if cfg['print_lvl'] > 1:
print('Iteration', it+1)
W_old = deepcopy(W)
H_old = deepcopy(H)
method_name = schedule[it % methods_num]
if cfg['print_lvl'] > 1:
print('Method:', method_name)
method = getattr(methods, method_name)
(W, H) = method(V, W, H, method_name, cfg)
if (it+1) % cfg['normalize_iter'] == 0:
W = normalize_cols(W)
H = normalize_cols(H)
for j, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[it+1, j] = fun(V, np.dot(W, H))
if cfg['compare_real']:
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[it+1] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
if cfg['print_lvl'] > 1:
print(val[it+1])
if all(val[it, :] < eps):
if cfg['print_lvl'] > 1:
print('By cost.')
status = 1
break
if abs(W_old - W).max() < eps and abs(H_old - H).max() < eps:
if cfg['print_lvl'] > 1:
print('By argument.')
status = 2
break
#del W_old
#del H_old
if cfg['print_lvl'] > 1:
print('Final:')
W = normalize_cols(W)
H = normalize_cols(H)
for j, fun_name in enumerate(meas):
fun = getattr(measure, fun_name)
val[it+2:, j] = fun(V, np.dot(W, H))
if cfg['compare_real']:
idx = get_permute(W_r, H_r, W, H, cfg['munkres'])
hdist[it+2:] = hellinger(W[:, idx[:, 1]], W_r[:, idx[:, 0]]) / T
return (val, hdist, it, W, H, status)
def plsa(F, Phi, Theta, post='', cfg=config.default_config()):
eps = cfg['eps']
tmp = F / maximum(dot(Phi, Theta), eps)
Theta, Phi = normalize_cols(Theta * dot(Phi.T, tmp)), normalize_cols(Phi * dot(tmp, Theta.T))
return Phi, Theta
def plsa(V, W, H, post='', cfg=config.default_config()):
eps = cfg['eps']
tmp = V / maximum(dot(W, H), eps)
H = normalize_cols(H * dot(W.T, tmp))
W = normalize_cols(W * dot(tmp, H.T))
return W, H
def gen_matrix_uniform(params):
return normalize_cols(np.random.uniform(size=(params['rows'], params['cols'])))
def initialize_matrices(i, F, cfg=config.default_config()):
"""Initialize matrices Phi Theta.
- Return:
Phi
Theta
- Used params:
prepare_method
"""
if (int(cfg['prepare_method'].split(',')[i]) == 1):
print("Arora")
eps = cfg['eps']
F_norm = normalize_cols(F)
Phi = prepare.anchor_words(F_norm, 'L2', cfg)
print('Solving for Theta')
Theta = np.linalg.solve(np.dot(Phi.T, Phi) + np.eye(Phi.shape[1]) * eps, np.dot(Phi.T, F_norm))
Theta[Theta < eps] = 0
Theta = normalize_cols(Theta)
return Phi, Theta
elif (int(cfg['prepare_method'].split(',')[i]) == 2):
print("Random rare")
cfg['phi_sparsity'] = 0.05
cfg['theta_sparsity'] = 0.1
return gen_init(cfg)
elif (int(cfg['prepare_method'].split(',')[i]) == 3):
print("Random uniform")
cfg['phi_sparsity'] = 1.
cfg['theta_sparsity'] = 1.
return gen_init(cfg)
elif (int(cfg['prepare_method'].split(',')[i]) == 4):
eps = cfg['eps']
F_norm = normalize_cols(F)
print("Clustering of words")
centroids, labels = prepare.reduce_cluster(F_norm, cfg['T'], cfg)
Theta = centroids
Theta[Theta < eps] = 0
Theta = normalize_cols(Theta)
print('Solving for Phi')
Phi = np.transpose(np.linalg.solve(np.dot(Theta, Theta.T) + np.eye((Theta.T).shape[1]) * eps, np.dot(Theta, F_norm.T)))
Phi[Phi < eps] = 0
Phi = normalize_cols(Phi)
return Phi, Theta
elif (int(cfg['prepare_method'].split(',')[i]) == 5):
eps = cfg['eps']
F_norm = normalize_cols(F)
print("SVD init")
U, s, V = np.linalg.svd(F_norm)
Phi, Theta = construct_from_svd(U, s, V, cfg)
return Phi, Theta
elif (int(cfg['prepare_method'].split(',')[i]) == 6):
eps = cfg['eps']
transformer = TfidfTransformer()
transformer.fit(F)
F_tfidf = (transformer.transform(F)).toarray()
print("Clustering of tf-idf")
centroids, labels = prepare.reduce_cluster(F_tfidf, cfg['T'], cfg)
Theta = centroids
Theta[Theta < eps] = 0
Theta = normalize_cols(Theta)
print('Solving for Phi')
Phi = np.transpose(np.linalg.solve(np.dot(Theta, Theta.T) + np.eye((Theta.T).shape[1]) * eps, np.dot(Theta, F_tfidf.T)))
Phi[Phi < eps] = 0
Phi = normalize_cols(Phi)
return Phi, Theta
elif (int(cfg['prepare_method'].split(',')[i]) == 7):
eps = cfg['eps']
F_norm = normalize_cols(F)
print("Clustering of words mixed")
centroids, labels = prepare.reduce_cluster(F_norm, cfg['T'], cfg)
Theta = centroids
Theta[Theta < eps] = 0
Theta = normalize_cols(Theta)
print('Solving for Phi')
Phi = np.transpose(np.linalg.solve(np.dot(Theta, Theta.T) + np.eye((Theta.T).shape[1]) * eps, np.dot(Theta, F_norm.T)))
Phi[Phi < eps] = 0
Phi = normalize_cols(Phi)
cfg['phi_sparsity'] = 1.
cfg['theta_sparsity'] = 1.
Phi1, Theta1 = gen_init(cfg)
zzz = 0.3
return zzz*Phi1+(1.-zzz)*Phi, zzz*Theta1+(1.-zzz)*Theta
elif (int(cfg['prepare_method'].split(',')[i]) == 8):
print("Arora mixed")
eps = cfg['eps']
F_norm = normalize_cols(F)
Phi = prepare.anchor_words(F_norm, 'L2', cfg)
print('Solving for Theta')
Theta = np.linalg.solve(np.dot(Phi.T, Phi) + np.eye(Phi.shape[1]) * eps, np.dot(Phi.T, F_norm))
Theta[Theta < eps] = 0
Theta = normalize_cols(Theta)
cfg['phi_sparsity'] = 1.
cfg['theta_sparsity'] = 1.
Phi1, Theta1 = gen_init(cfg)
zzz = 0.3
return zzz*Phi1+(1.-zzz)*Phi, zzz*Theta1+(1.-zzz)*Theta
elif (int(cfg['prepare_method'].split(',')[i]) == 9):
print("Arora unifrom")
eps = cfg['eps']
F_norm = normalize_cols(F)
Phi = prepare.anchor_words(F_norm, 'L2', cfg)
print('Solving for Theta')
Theta = np.ones((Phi.shape[1], F.shape[1]))
Theta = normalize_cols(Theta)
return Phi, Theta
elif (int(cfg['prepare_method'].split(',')[i]) == 10):
eps = cfg['eps']
F_norm = normalize_cols(F)
print("Clustering of docs")
centroids, labels = prepare.reduce_cluster(F_norm.T, cfg['T'], cfg)
Phi = centroids.T
Phi[Phi < eps] = 0
Phi = normalize_cols(Phi)
print('Solving for Theta')
Theta = np.linalg.solve(np.dot(Phi.T, Phi) + np.eye(Phi.shape[1]) * eps, np.dot(Phi.T, F_norm))
Theta[Theta < eps] = 0
Theta = normalize_cols(Theta)
return Phi, Theta
def restore_multi_cluster(W_reduced, labels, params):
W = zeros((params['N'], params['T']))
for word in xrange(W.shape[0]):
W[word, :] = mean(W_reduced[labels[word, :], :])
return normalize_cols(W)
def gen_matrix_normal(params):
return normalize_cols(abs(np.random.randn(params['rows'], params['cols'])))
