'Kendall\'s Tau',
train_kt, train_pval,
test_kt, test_pval)
print u'{:15} | {:.3f} | {:.3f}'.format(
'Bigram Accuracy',
train_bg, test_bg)
print u'\n'
print 'MODEL NAME: '
print
test_docs = [cnlp.Document(xml)
for xml in data.corenlp_apws_test()]
baseline_dict = data.get_apws_model('fword_synseq12.p')
model = baseline_dict['model']
feats = baseline_dict['features']
doc_cutoff = baseline_dict['doc_cutoff']
trainX = baseline_dict['trainX']
gtrainY = baseline_dict['gtrainY']
ptrainY = baseline_dict['ptrainY']
testX = baseline_dict['testX']
gtestY = baseline_dict['gtestY']
ptestY = baseline_dict['ptestY']
print_model_features(feats)
print
import discourse.data as data
import corenlp as cnlp
test_docs = [cnlp.Document(xml)
for xml in data.corenlp_apws_test()]
baseline_dict = data.get_apws_model('test1.p')
base_model = baseline_dict['model']
base_ptest = baseline_dict['ptest']
base_ptrain = baseline_dict['ptrain']
base_feats = baseline_dict['features']
new_dict = data.get_apws_model('test2.p')
new_model = new_dict['model']
new_ptest = new_dict['ptest']
new_ptrain = new_dict['ptrain']
new_feats = new_dict['features']
import discourse
import discourse.evaluation as evaluation
evaluation.eval_against_baseline(test_docs,
base_ptest,
new_ptest,
base_model,
new_model,
base_feats,
new_feats,
base_ptrain,
new_ptrain
)
