def perform_experiment(train_n_dw_matrix, test_n_dw_matrix, T, num_2_token):
train_corpus = [zip(row.indices, row.data) for row in train_n_dw_matrix]
for seed in [42, 7, 777, 12]:
model = LdaModel(train_corpus,
alpha='auto',
id2word=num_2_token,
num_topics=T,
iterations=500,
random_state=seed)
gensim_phi = exp_common.get_phi(model)
gensim_theta = exp_common.get_theta(train_corpus, model)
print('gensim perplexity')
print(np.exp(-model.log_perplexity(train_corpus)))
D, W = train_n_dw_matrix.shape
random_gen = np.random.RandomState(seed)
phi = common.get_prob_matrix_by_counters(
random_gen.uniform(size=(T, W)).astype(np.float64))
theta = common.get_prob_matrix_by_counters(
np.ones(shape=(D, T)).astype(np.float64))
phi, theta = default.Optimizer([regularizers.Additive(0.1, 0.)] * 100,
verbose=False).run(
train_n_dw_matrix, phi, theta)
callback = experiments.default_callback(
train_n_dw_matrix=train_n_dw_matrix,
test_n_dw_matrix=test_n_dw_matrix,
top_pmi_sizes=[5, 10, 20, 30],
top_avg_jaccard_sizes=[10, 50, 100, 200],
measure_time=True)
callback.start_launch()
callback(0, phi, theta)
callback(1, gensim_phi, gensim_theta)
print('artm')
for name, values in callback.launch_result.items():
print('\t{}: {}'.format(name, values[0]))
print('gensim')
for name, values in callback.launch_result.items():
print('\t{}: {}'.format(name, values[1]))
from pyartm import regularizers
from pyartm_datasets import main_cases
from pyartm.optimizations import timed_default
import manager
if __name__ == '__main__':
n_dw_matrix = main_cases.get_20newsgroups([
'rec.autos', 'rec.motorcycles', 'rec.sport.baseball',
'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med',
'sci.space'
])[0]
manager.perform_experiment(
n_dw_matrix,
timed_default.Optimizer(
regularization_list=[regularizers.Additive(0., 0.)] * 100,
return_counters=True), 10, 100)
ITERS_COUNT = 100
SAMPLES = 5
if __name__ == '__main__':
train_n_dw_matrix, test_n_dw_matrix = main_cases.get_20newsgroups(
[
'rec.autos', 'rec.motorcycles', 'rec.sport.baseball',
'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med',
'sci.space'
],
train_proportion=0.8)[:2]
args_list = list()
for T in [10, 25]:
for theta_alpha in [0.1, 0.01, 0.1]:
regularization_list = [regularizers.Additive(0, theta_alpha)
] * ITERS_COUNT
args_list.append(
(train_n_dw_matrix, test_n_dw_matrix,
default.Optimizer(regularization_list), T, SAMPLES,
'20news_experiment/20news_{}t_default_{}_{}.pkl'.format(
T, 0., theta_alpha)))
args_list.append(
(train_n_dw_matrix, test_n_dw_matrix,
thetaless.Optimizer(regularization_list), T, SAMPLES,
'20news_experiment/20news_{}t_thetaless_{}_{}.pkl'.format(
T, 0., theta_alpha)))
args_list.append(
(train_n_dw_matrix, test_n_dw_matrix,
transfer_thetaless.Optimizer(regularization_list), T, SAMPLES,
'20news_experiment/20news_{}t_transfer_thetaless_{}_{}.pkl'.
'talk.politics.guns',
])
topic_0_indices, topic_1_indices = [], []
for index, target in enumerate(doc_targets):
if target == 0:
topic_0_indices.append(index)
elif target == 1:
topic_1_indices.append(index)
thetaless_rels = []
lda_rels = []
for balance in range(10, 201, 10):
print(balance)
n_dw_matrix = _n_dw_matrix[topic_0_indices +
topic_1_indices * balance, :]
regularization_list = [regularizers.Additive(-0.1, 0.)] * 100
lda_phi, lda_theta = experiments.default_sample(
n_dw_matrix,
T=2,
seed=42,
optimizer=default.Optimizer(regularization_list, verbose=False))
thetaless_phi, thetaless_theta = experiments.default_sample(
n_dw_matrix,
T=2,
seed=42,
optimizer=thetaless.Optimizer(regularization_list, verbose=False))
# print(np.argmax(thetaless_theta[:len(topic_0_indices), :2], axis=1).mean())
# print(np.argmax(thetaless_theta[len(topic_0_indices):, :2], axis=1).mean())
# print('!')
# for topic_set in metrics.get_top_words(thetaless_phi, 10):
# print('\n\t'.join(map(num_2_token.get, topic_set)))
import manager
ITERS_COUNT = 100
SAMPLES = 50
if __name__ == '__main__':
train_n_dw_matrix, test_n_dw_matrix = main_cases.get_nips(
train_proportion=0.8)[:2]
args_list = list()
for T in [20, 50]:
for phi_alpha in [-0.1, 0., 0.1]:
for theta_alpha in [-0.1, 0., 0.1]:
regularization_list = [
regularizers.Additive(phi_alpha, theta_alpha)
] * ITERS_COUNT
args_list.append(
(train_n_dw_matrix, test_n_dw_matrix,
default.Optimizer(regularization_list), T, SAMPLES,
'nips_experiment/NIPS_{}t_base_{}_{}.pkl'.format(
T, phi_alpha, theta_alpha)))
args_list.append(
(train_n_dw_matrix, test_n_dw_matrix,
naive_thetaless.Optimizer(regularization_list), T,
SAMPLES,
'nips_experiment/NIPS_{}t_naive_{}_{}.pkl'.format(
T, phi_alpha, theta_alpha)))
for use_B_cheat in [False, True]:
args_list.append(
(train_n_dw_matrix, test_n_dw_matrix,
from pyartm.optimizations import default
import manager
if __name__ == '__main__':
train_n_dw_matrix, test_n_dw_matrix = main_cases.get_20newsgroups(
[
'rec.autos', 'rec.motorcycles', 'rec.sport.baseball',
'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med',
'sci.space'
],
train_proportion=0.8)[:2]
args_list = list()
for T in [10, 30]:
for tau in [1e7, 1e8, 1.5e8, 2e8, 2.5e8, 3e8, 3.5e8, 4e8, 4.5e8, 5e8]:
for use_old_phi in [True]: # [False, True]
regularization_list = [
regularizers.Combination(
regularizers.Decorrelator(tau, use_old_phi),
regularizers.Additive(-0.01, -0.01),
)
] * 500
args_list.append(
(train_n_dw_matrix, test_n_dw_matrix,
default.Optimizer(regularization_list), T, 10,
'20news_experiment/20news_{}t_{}_{}.pkl'.format(
T, int(tau), use_old_phi)))
Pool(processes=8).map(manager.perform_experiment, args_list)
def get_optimizer(phi_alpha, iters_count):
return default.Optimizer(
[regularizers.Additive(phi_alpha, 0.)] * iters_count
)
SAMPLES = 100
INIT_ITERS = 100
if __name__ == '__main__':
train_n_dw_matrix, test_n_dw_matrix = main_cases.get_20newsgroups(
[
'rec.autos', 'rec.motorcycles', 'rec.sport.baseball',
'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med',
'sci.space'
],
train_proportion=0.8)[:2]
args_list = list()
for T in [10, 25]:
plsa_list = [regularizers.Trivial()] * ITERS_COUNT
sparse_lda_list = [regularizers.Additive(-1, 0.)] * ITERS_COUNT
args_list.append(
(train_n_dw_matrix, test_n_dw_matrix,
default.Optimizer(sparse_lda_list), T, SAMPLES, INIT_ITERS,
'20news_experiment/20news_{}t_post_lda.pkl'.format(T)))
args_list.append(
(train_n_dw_matrix, test_n_dw_matrix,
obd.Optimizer(
plsa_list,
gamma_tw_min_delta=1,
), T, SAMPLES, INIT_ITERS,
'20news_experiment/20news_{}t_post_obd_limited.pkl'.format(T)))
args_list.append(
(train_n_dw_matrix, test_n_dw_matrix,
naive_obd.Optimizer(
from pyartm import regularizers
from pyartm.optimizations import default
from pyartm.optimizations import thetaless
from pyartm.optimizations import naive_thetaless
from pyartm.optimizations import obd
if __name__ == '__main__':
# train_n_dw_matrix = sparse.csr_matrix(np.array([
# [1, 1, 1, 0],
# [1, 0, 1, 1],
# [1, 1, 0, 1],
# ]))
train_n_dw_matrix = sparse.csr_matrix(
np.random.RandomState(42).uniform(0, 1, size=(100, 1000)) < 0.3)
regularization_list = [regularizers.Trivial()] * 500
extra_opt = obd.Optimizer([regularizers.Additive(0, 0)] * 500,
verbose=False)
for module in [default, thetaless, naive_thetaless]:
print(module.__name__)
optimizer = module.Optimizer(regularization_list, verbose=False)
D, W = train_n_dw_matrix.shape
T = 4
random_gen = np.random.RandomState(47)
phi_matrix = common.get_prob_matrix_by_counters(
random_gen.uniform(size=(T, W)).astype(np.float64))
theta_matrix = common.get_prob_matrix_by_counters(
np.ones(shape=(D, T)).astype(np.float64))
phi_matrix, theta_matrix = optimizer.run(train_n_dw_matrix, phi_matrix,
theta_matrix)
mod_phi_matrix, mod_theta_matrix = extra_opt.run(