emotion_name = filename.emotions['LJ40K'][emotion_id]
logger.info('training model for emotion "%s"' % emotion_name)
X_train, y_train = fused_dataset.get_dataset(emotion_name, 'train')
X_dev, y_dev = fused_dataset.get_dataset(emotion_name, 'dev')
import pdb; pdb.set_trace()
if not args.no_scaling:
scaler = StandardScaler(with_mean=True, with_std=True)
logger.debug("applying standard scaling")
X_train = scaler.fit_transform(X_train)
X_dev = scaler.transform(X_dev)
fpath = os.path.join(args.output_folder, filename.get_scaler_filename(emotion_name, 'pkl'))
logger.info('dumpping scaler to %s' % (fpath))
utils.save_pkl_file(scaler, fpath)
best_res[emotion_name] = {}
best_score = 0
if args.parameter_file != None:
Cs = [param_dict[emotion_name][0]]
gammas = [param_dict[emotion_name][1]]
else:
Cs = args.c
gammas = args.gamma
for c in Cs:
for g in gammas:
trainer.set(X=X_train, y=y_train, feature_name=fused_dataset.get_feature_name())
loglevel = logging.DEBUG
elif args.verbose:
loglevel = logging.INFO
else:
loglevel = logging.ERROR
logging.basicConfig(format='[%(levelname)s][%(name)s] %(message)s', level=loglevel)
logger = logging.getLogger(__name__)
# get number of files in each emotion folder
n_doc = get_and_check_files(args.corpus_folder)
n_train = n_doc * args.percent_train / 100;
n_dev = n_doc * args.percent_dev / 100;
n_test = n_doc * args.percent_test / 100;
# remaining as training data
n_train += (n_doc - n_train - n_dev - n_test)
random_list = range(n_doc)
random.shuffle(random_list)
idx_dict = {}
idx_dict['train'] = random_list[0:n_train]
idx_dict['dev'] = random_list[n_train:n_train+n_dev]
idx_dict['test'] = random_list[n_train+n_dev:n_train+n_dev+n_test]
logger.info("dumping file to %s" % (args.output_filename))
utils.save_pkl_file(idx_dict, args.output_filename)
def dump_local(self, e_ID, save_path):
utils.save_pkl_file(self.LocalInfo[e_ID], save_path)
def dump_global(self):
utils.save_pkl_file(self.GlobalInfo,
self.global_dumppath + '/GlobalInfo.pkl')
utils.save_pkl_file(self.DatasetInfo,
self.global_dumppath + '/DatasetInfo.pkl')
logger.info("load features from %s", fpath)
Xy = utils.load_pkl_file(fpath)
idxs = all_idxs['train'][emotion_name][emotion_name] if all_idxs is not None else range(len(Xy))
X = np.zeros((len(idxs), Xy[0]['X'].shape[1]), dtype="float32")
logger.info('X.shape = (%u, %u)' % (X.shape[0], X.shape[1]))
for i in idxs:
# make sure only one feature vector in each doc
assert Xy[i]['X'].shape[0] == 1
X[i] = Xy[i]['X']
tsvd = TruncatedSVD(n_components=args.target_n_component)
logger.info('start fitting for "%s"' % (emotion_name))
start_time = time.time()
X_new = tsvd.fit_transform(X)
end_time = time.time()
logger.info('fit time = %f' % (end_time-start_time))
fname = os.path.basename(fpath)
fpath = os.path.join(args.output_folder, fname)
logger.info('ouputing %s' % (fpath))
utils.save_pkl_file(X_new, fpath)
fname = '%s_tsvd.pkl' % (emotion_name)
fpath = os.path.join(args.output_folder, fname)
logger.info('ouputing %s' % (fpath))
utils.save_pkl_file(tsvd, fpath)
def dump_results(self, filename):
utils.save_pkl_file(self.results, filename)
def dump(self, filename):
utils.save_pkl_file(self, filename)
def dump_results(self, filename):
utils.save_pkl_file(self.results, filename)
def dump(self, filename):
utils.save_pkl_file(self, filename)
