# Dump raw values to csv
with open('../dump/results_test.csv', 'w') as csv_file:
writer = csv.writer(csv_file)
for key, value in results_dict.items():
li = []
li.append(key)
for v in value:
li.append(v)
li.append(1)
writer.writerow(li)
print("Results saved in results.csv file!")
print("Calculating A and C1")
essays_list = kd_reader.read_essays(data_path_train, train_files)
essay_words, essay_pos = kd_reader.gen_word_pos(essays_list)
essay_tokenized = kd_reader.gen_sent(essays_list)
essay_parsed = kd_reader.gen_parse(essay_tokenized, stanford_path)
length_train = Length.count_sent(essay_pos,
essay_tokenized) #no. of sentences a
c_1_count_train = c_1.count_c1(
essay_parsed) # number of subj-verb agreement mistakes in each essay
essays_list = kd_reader.read_essays(data_path_test, test_files)
essay_words, essay_pos = kd_reader.gen_word_pos(essays_list)
essay_tokenized = kd_reader.gen_sent(essays_list)
essay_parsed = kd_reader.gen_parse(essay_tokenized, stanford_path)
length_test = Length.count_sent(essay_pos,
essay_tokenized) #no. of sentences a
c_1_count_test = c_1.count_c1(essay_parsed)
# -*- coding: utf-8 -*-
import kd_reader
import a
import c_1
import c_3
import d_1
stanford_path = r'C:\Users\Kashyap\stanford-corenlp-full-2017-06-09' #path to stanford-corenlp-folder
file_path = r"essays_dataset\essays" # path to essays
essays_list = kd_reader.read_essays(file_path)
essay_words, essay_pos = kd_reader.gen_word_pos(essays_list)
essay_tokenized = kd_reader.gen_sent(essays_list)
essay_parsed = kd_reader.gen_parse(essay_tokenized, stanford_path)
length = a.count_sent(essay_pos, essay_tokenized) #no. of sentences a
c_1_count = c_1.count_c1(
essay_parsed) # number of subj-verb agreement mistakes in each essay
#Recent additions
print("Doing c3")
c_3_count = c_3.count_c3(essay_parsed)
sent_pos = d_1.gen_sent_pos(essay_tokenized)
print("Doing d1")
d_1_count = d_1.count_d1(sent_pos)