def main():
f_clf = bc3_eval()
print('over')
title = 'david_chalmers_how_do_you_explain_consciousness'
#process_document(title,sen2=1,exist=0)
sample_vector = []
vec,important,title_sim = process_document(title,sen2=1)
topic_similarity = lda_process(vec)
maxs = 0
maxi = 0
phrase_list,vec_phrase_index = phrase_extraction(vec)
word_list = word_score_calculate(phrase_list)
vec_phrase_score = important_word(vec,word_list,phrase_list,vec_phrase_index)
cue_word_score = cue_word(vec)
#print(important)
for j in range(len(vec)):
tmp = f_clf.predict([important[j],title_sim[j],topic_similarity[j],vec_phrase_score[j],cue_word_score[j]])
if(tmp>=maxs):
maxs = tmp
maxi = j
print(vec[maxi].sentence)
def bc3_eval():
corpus = 'bc3/bc3corpus.1.0/corpus.xml'
annotation = 'bc3/bc3corpus.1.0/annotation.xml'
mails = parse_file(corpus)
mails = parse_anno(annotation,mails)
sample_vector = []
target_vector = []
precision_vector = []
recall_vector = []
F_measure_vector = []
imp = 0
ti_s = 0
to_s = 0
train = 20
index =0
for i in mails:
if(index>=train):
break
index += 1
vec,important,title_sim = process_mail(i)
topic_similarity = lda_process(vec)
phrase_list,vec_phrase_index = phrase_extraction(vec)
word_list = word_score_calculate(phrase_list)
vec_phrase_score = important_word(vec,word_list,phrase_list,vec_phrase_index)
tmp_produce = []
standard_summary = [anno(i)]
standard_name = [[]]
cue_word_score = cue_word(vec)
for j in range(len(standard_summary[0])):
standard_name[0].append('PythonROUGE/'+i.name+'/'+str(j)+'_standard.txt')
#print(standard_name)
#standard_name[0].append('PythonROUGE/'+i.name+'/'+str(0)+'_standard.txt')
#print(standard_name)
#standard_name = [['PythonROUGE/'+i+'_standard.txt']]
newpath = 'PythonROUGE/'+i.name
if not os.path.exists(newpath):
os.makedirs(newpath)
for j in range(len(vec)):
produce_name = ['PythonROUGE/'+i.name+'/'+str(j)+'.txt']
produce_summary = [[vec[j].sentence]]
sample_vector.append([important[j],title_sim[j],topic_similarity[j],vec_phrase_score[j],cue_word_score[j]])
imp += important[j]
ti_s += title_sim[j]
to_s += topic_similarity[j]
recall,precision,F_measure = summary_eval(standard_summary,standard_name,produce_summary,produce_name)
target_vector.append(recall[0]*precision[0]*F_measure[0])
recall_vector.append(recall[0])
precision_vector.append(precision[0])
F_measure_vector.append(F_measure[0])
shutil.rmtree(newpath)
for i in sample_vector:
i[0] = i[0]/imp
i[1] = i[1]/ti_s
i[2] = i[2]/to_s
x_recall_train, x_recall_test, y_recall_train, y_recall_test = cross_validation.train_test_split(sample_vector,recall_vector, test_size=0.2, random_state=0)
x_precision_train, x_precision_test, y_precision_train, y_precision_test = cross_validation.train_test_split(sample_vector,precision_vector, test_size=0.2, random_state=0)
x_fmeasure_train, x_fmeasure_test, y_fmeasure_train, y_fmeasure_test = cross_validation.train_test_split(sample_vector,F_measure_vector, test_size=0.2, random_state=0)
#parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-1,1e-2,1e-3, 1e-4,1e-5],
'C': [1, 10, 100, 1000]},
{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
r_clf = GridSearchCV(SVR(C=1,epsilon=0.2), tuned_parameters, cv=5)
p_clf = GridSearchCV(SVR(C=1,epsilon=0.2), tuned_parameters, cv=5)
f_clf = GridSearchCV(SVR(C=1,epsilon=0.2), tuned_parameters, cv=5)
r_clf.fit(sample_vector,recall_vector)
p_clf.fit(sample_vector,precision_vector)
f_clf.fit(sample_vector,F_measure_vector)
index = 0
produce_summary = []
produce_name = []
standard_summary = []
standard_name = []
lex_summary = []
lex_name = []
for i in mails:
if(index<train):
index += 1
continue
if(i.name == 'Re:_StarOffice' or i.name == 'Try_Unsubscribing––You_Can\'t'):
continue
#print(i.name)
vec,important,title_sim = process_mail(i)
topic_similarity = lda_process(vec)
phrase_list,vec_phrase_index = phrase_extraction(vec)
word_list = word_score_calculate(phrase_list)
vec_phrase_score = important_word(vec,word_list,phrase_list,vec_phrase_index)
cue_word_score = cue_word(vec)
#word_list = word_score_calculate(phrase_extraction(vec))
#print(word_list)
tmp_produce = []
standard_summary.append(anno(i))
tmp_name = []
for j in range(len(standard_summary[-1])):
tmp_name.append('PythonROUGE/'+i.name+'/'+str(j)+'_standard.txt')
standard_name.append(tmp_name)
newpath = 'PythonROUGE/'+i.name
if not os.path.exists(newpath):
os.makedirs(newpath)
maxs = 0
maxi = 0
tmp_summary = []
predict_rouge = []
for j in range(len(vec)):
#tmp = r_clf.predict([important[j],title_sim[j],topic_similarity[j],vec_phrase_score[j]])*p_clf.predict([important[j],title_sim[j],topic_similarity[j],vec_phrase_score[j]])*f_clf.predict([important[j],title_sim[j],topic_similarity[j],vec_phrase_score[j]])
tmp = f_clf.predict([important[j],title_sim[j],topic_similarity[j],vec_phrase_score[j],cue_word_score[j]])
predict_rouge.append(tmp)
sort_index = numpy.argsort(predict_rouge)[::-1]
sort_index2 = numpy.argsort(important)[::-1]
'''
for j in range(10):
tmp_summary.append(vec[sort_index[j]].sentence)
tmp_name.append('PythonROUGE/'+i.name+'/'+str(j)+'.txt')
'''
lex_summary.append(vec[sort_index2[0]].sentence)
tmp_summary.append(vec[sort_index[0]].sentence)
produce_name.append('PythonROUGE/'+i.name+'/'+str(j)+'.txt')
lex_name.append('PythonROUGE/'+i.name+'/'+str(j)+'_lex'+'.txt')
produce_summary.append(tmp_summary)
#print(standard_name)
recall,precision,F_measure = summary_eval(standard_summary,standard_name,produce_summary,produce_name)
print('recall:')
print(recall)
print('precision:')
print(precision)
print('F_measure:')
print(F_measure)
recall,precision,F_measure = summary_eval(standard_summary,standard_name,lex_summary,lex_name)
print('lex_recall:')
print(recall)
print('lex_precision:')
print(precision)
print('lex_F_measure:')
print(F_measure)
return f_clf
