from core.experiment import experiment2
from core.experiment import run_with_bayes_opt2
from core.util import dirs
#dirs.set_project('imm_labeled', 'framing')
run_with_bayes_opt2.main(['imm_labeled', 'Economic_labels', 'framing'])
"""
experiment2.run_experiment('test', 'Economic_labels', 2, -1,
['ngrams,subdir=stanford,source=words,transform=binarize,min_df=4,lower=0,n=1',
'ngrams,subdir=stanford,source=lemmas,transform=binarize,min_df=6,lower=0,n=2',
'list,subdir=stanford,source=pos,transform=normalizel1',
'list,subdir=stanford,source=dependency_links,transform=normalizel1'],
'LR', 10, 0.1,
weight_col=1,
metric='f1', only_unanimous=True,
save_model=False)
"""
def main():
usage = "%prog project label splits_file"
parser = OptionParser(usage=usage)
parser.add_option('-t', dest='target_col', default=0,
help='Target column; default=%default')
parser.add_option('-w', dest='weight_col', default=-1,
help='weight column; default=%default')
#parser.add_option('-m', dest='model', default='LR',
# help='Model: (LR|SVM|MNB|SVMNB); default=%default')
(options, args) = parser.parse_args()
if len(args) < 2:
sys.exit("Please provide input arguments")
project = args[0]
label_file = args[1]
splits_file = args[2]
dirs.set_project(project, splits_file)
target_col = int(options.target_col)
weight_col = int(options.weight_col)
model_type = 'LR'
reuse = False
verbose = 1
unigrams = ['ngrams,n=1,transform=binarize']
unigrams_and_bigrams = unigrams + ['ngrams,n=2,transform=binarize']
ub_personas_old = unigrams_and_bigrams + ['pkl,subdir=personas,source=personasdpm,transform=binarize']
ub_personas_new = unigrams_and_bigrams + ['pkl,subdir=personas,source=personas,transform=binarize']
ub_personas_and_stories = ub_personas_new + ['pkl,subdir=personas,source=storytypesold,transform=normalizel2']
all_feature_list = [
'ngrams,n=1,transform=binarize',
'ngrams,n=2,transform=binarize,min_df=2',
'list,subdir=brown,source=brown',
'pkl,subdir=lda,source=lda,transform=binarize',
'pkl,subdir=personas,source=personas,transform=binarize',
'list,subdir=stanford,source=pos,transform=binarize',
'list,subdir=stanford,source=ner,transform=binarize',
'list,subdir=stanford,source=dependency_links,transform=binarize,min_df=2,lower=1',
'list,subdir=stanford,source=jkgrams,transform=binarize,min_df=2,lower=1',
'list,subdir=stanford,source=sentiments,transform=binarize',
'list,subdir=semafor,source=frames,transform=binarize,lower=1',
'list,subdir=amalgram,source=ss_tags,transform=binarize,lower=1',
]
#exps = [unigrams, unigrams_and_bigrams, ub_personas_old, ub_personas_new, ub_personas_and_stories, all_feature_list]
#names = ['unigrams', 'bigrams', 'personas_dpm', 'personas_new', 'personas_and_stories', 'all_features']
exps = [unigrams_and_bigrams, ub_personas_old, ub_personas_new, ub_personas_and_stories]
names = ['unigrams_and_bigrams', 'personas_dpm', 'personas_new', 'personas_and_stories']
n_eval_iters = 20
dev_prop = 0.1
for i, features in enumerate(exps):
for t in range(10):
print 'experiment', i, '; test_fold', t
experiment2.run_experiment(name=names[i],
label_file=label_file,
target=target_col,
test_fold=t,
feature_list=features,
model_type=model_type,
n_eval_iters=n_eval_iters,
eval_prop=dev_prop,
reuse=False,
verbose=verbose,
weight_col=weight_col,
best_alphas=None,
additional_label_files=None,
additional_label_weights=None,
metric='f1',
only_unanimous=True) # run experiment
