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)
'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'.
format(T, 0., theta_alpha)))
#manager.perform_experiment(args_list[0])
#manager.perform_experiment(args_list[1])
manager.perform_experiment(args_list[2])
#Pool(processes=5).map(manager.perform_experiment, args_list)
from pyartm import regularizers
from pyartm_datasets import main_cases
from pyartm.optimizations import timed_default
import manager
if __name__ == '__main__':
manager.perform_experiment(
main_cases.get_nips()[0], timed_default.Optimizer(
regularization_list=[regularizers.Additive(0., 0.)] * 100,
return_counters=True
), 10, 100
)
from pyartm_datasets import main_cases
import manager
if __name__ == '__main__':
(train_n_dw_matrix, test_n_dw_matrix, _,
num_2_token) = main_cases.get_twitter_sentiment140(train_proportion=0.8,
min_docs_occurrences=3)
manager.perform_experiment(train_n_dw_matrix, test_n_dw_matrix, 10,
num_2_token)
from pyartm import regularizers
from pyartm_datasets import main_cases
from pyartm.optimizations import timed_default
import manager
if __name__ == '__main__':
manager.perform_experiment(
main_cases.get_twitter_sentiment140(min_docs_occurrences=3,
train_proportion=0.8)[0],
timed_default.Optimizer(
regularization_list=[regularizers.Additive(0., 0.)] * 100,
return_counters=True), 10, 100)