def non_scenario_ami(n_words):
file = open(path + 'result_ami/result_non_scen.txt', 'w')
meet_list = tools.non_scenario_based
file.write('************************' + str(meet_list) + '*************************************' + '\n')
all_documents = tools.all_nonscenario_based_documents
example = tf_idf_class(all_documents)
tfidf_representation = example.tfidf()
for meet in meet_list:
file.write(meet + '\n')
file.write('*****************' + '\n')
c = compare_with_AMI_results(meet)
c.get_resumes()
candidate = c.get_best_k_tfidf(n_words, tfidf_representation[tools.non_scenario_based.index(meet)])
file.write('candidate = ' + str(candidate) + '\n')
bleu_measure = c.bleu_evaluation(candidate)
file.write('score_abstractive = ' + str(bleu_measure[0]) + '\n')
file.write('score_extractive = ' + str(bleu_measure[1]) + '\n')
file.flush()
file.write('\n')
file.flush()
file.close()
example = tf_idf_class(all_documents)
tfidf_representation = example.tfidf()
counter = 0
for meet in meet_list:
print '***********'
print 'meet', meet
meet_block = classification_scenario_based[counter]
file.write(str(meet_block) + '\n')
file.write('*****************' + '\n')
c = compare_with_AMI_results(meet_block)
c.get_resumes()
candidate = c.get_best_k_tfidf(20, tfidf_representation[counter])
file.write('candidate = ' + str(candidate) + '\n')
bleu_measure = c.bleu_evaluation(candidate)
file.write('score_abstractive = ' + str(bleu_measure[0]) + '\n')
file.write('score_extractive = ' + str(bleu_measure[1]) + '\n')
file.write('\n')
file.flush()
counter += 1
# #
# #
meet_list = tools.scenario_based
file.write('************************' + str(meet_list) + '*************************************\n')
all_documents = tools.all_scenario_based_documents
example = tf_idf_class(all_documents)
tfidf_representation = example.tfidf()
for meet in meet_list:
file.write(meet+ '\n')
file.write('*****************'+ '\n')
c = compare_with_AMI_results(meet)
c.get_resumes()
candidate = c.get_best_k_tfidf(20, tfidf_representation[tools.scenario_based.index(meet)])
file.write('candidate = ' + str(candidate)+ '\n')
bleu_measure = c.bleu_evaluation(candidate)
file.write('score_abstractive = ' + str(bleu_measure[0]) + '\n')
file.write(' score_extractive = ' + str(bleu_measure[1])+ '\n')
file.write('\n')
file.flush()
file.close()
def blocks_4_4_ami(n_words, length= None):
"""
this function extracts the first
:return:
"""
file = open(path + 'result_ami/result_4_4.txt', 'w')
adress = path + '/manual_corpus/'
if length is None:
for meet_list in classification_scenario_based:
file.write('************************' + str(meet_list) + '*************************************\n')
all_documents = [tools.text_to_string(adress + i + '.txt') for i in meet_list]
example = tf_idf_class(all_documents) # , document_0)
tfidf_representation = example.tfidf()
for meet in meet_list:
file.write(meet+ '\n')
file.write('*****************\n')
c = compare_with_AMI_results(meet)
c.get_resumes()
candidate = c.get_best_k_tfidf(n_words, tfidf_representation[meet_list.index(meet)])
file.write('candidate = ' + str(candidate)+ '\n')
file.flush()
bleu_measure = c.bleu_evaluation(candidate)
file.write('score_abstractive = ' + str(bleu_measure[0])+ '\n')
file.write('score_extractive = ' + str(bleu_measure[1])+ '\n')
file.flush()
file.write('\n')
file.flush()
else:
for meet_list in classification_scenario_based[0:length]:
file.write('************************' + str(meet_list) + '*************************************\n')
all_documents = [tools.text_to_string(adress + i + '.txt') for i in meet_list]
example = tf_idf_class(all_documents) # , document_0)
tfidf_representation = example.tfidf(length)
for meet in meet_list:
file.write(meet+ '\n')
file.write('*****************\n')
c = compare_with_AMI_results(meet)
c.get_resumes()
candidate = c.get_best_k_tfidf(n_words, tfidf_representation[meet_list.index(meet)])
file.write('candidate = ' + str(candidate)+ '\n')
file.flush()
bleu_measure = c.bleu_evaluation(candidate)
file.write('score_abstractive = ' + str(bleu_measure[0])+ '\n')
file.write('score_extractive = ' + str(bleu_measure[1])+ '\n')
file.flush()
file.write('\n')
file.flush()
file.close()
return
def blocks_ami(n_words, length = None):
#print('we are inside')
file = open(path + 'result_ami/result_4_block_scen.txt', 'w')
adress_ = path + "manual_blocks/"
meet_list = ["block_" + str(i + 1) for i in xrange(34)]
all_documents = [tools.text_to_string(adress_ + i + '.txt') for i in meet_list]
example = tf_idf_class(all_documents)
counter = 0
if length is None:
tfidf_representation = example.tfidf()
for meet in meet_list:
#print '***********'
#print 'meet', meet
meet_block = classification_scenario_based[counter]
file.write(str(meet_block) + '\n')
file.write('*****************' + '\n')
file.flush()
c = compare_with_AMI_results(meet_block)
c.get_resumes()
candidate = c.get_best_k_tfidf(n_words, tfidf_representation[counter])
file.write('candidate = ' + str(candidate) + '\n')
file.flush()
bleu_measure = c.bleu_evaluation(candidate)
file.write('score_abstractive = ' + str(bleu_measure[0]) + '\n')
file.write('score_extractive = ' + str(bleu_measure[1]) + '\n')
file.write('\n')
file.flush()
counter += 1
else:
#print('length is not none')
tfidf_representation = example.tfidf(length)
for meet in meet_list[0:length]:
#print '***********'
#print 'meet', meet
meet_block = classification_scenario_based[counter]
file.write(str(meet_block) + '\n')
file.write('*****************' + '\n')
file.flush()
c = compare_with_AMI_results(meet_block)
c.get_resumes()
candidate = c.get_best_k_tfidf(n_words, tfidf_representation[counter])
file.write('candidate = ' + str(candidate) + '\n')
file.flush()
bleu_measure = c.bleu_evaluation(candidate)
file.write('score_abstractive = ' + str(bleu_measure[0]) + '\n')
file.write('score_extractive = ' + str(bleu_measure[1]) + '\n')
file.write('\n')
file.flush()
counter += 1
file.close()