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model.py
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346 lines (288 loc) · 13.3 KB
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import os
import re
import io
import numpy as np
import pickle
from elasticsearch import Elasticsearch
from ranker import *
main_path = os.path.dirname(os.path.realpath(__file__))
static_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'static')
slides_path = os.path.join(main_path, 'pdf.js/static/slides')
related_slides_path = os.path.join(main_path, 'pdf.js/static/ranking.csv')
#related_slides_path = os.path.join(static_path, 'ranking_results.csv')
vocab_path = os.path.join(static_path, 'tf_idf_outputs', 'vocabulary_list.p')
tfidfs_path = os.path.join(static_path, 'tf_idf_outputs', 'normalized_tfidfs.npy')
title_tfidfs_path = os.path.join(static_path, 'tf_idf_outputs', 'normalized_title_tfidfs.npy')
ss_corpus_path = os.path.join(static_path, 'tf_idf_outputs', 'ss_corpus.p')
paras_folder = os.path.join(main_path, 'para_idx_data')
cfg = os.path.join(main_path, 'para_idx_data', 'config.toml')
related_dict = {}
slide_names = open(os.path.join(main_path, 'pdf.js/static/slide_names.txt'), 'r').readlines()
slide_names = [name.strip() for name in slide_names]
# slide_titles = io.open(os.path.join(static_path, 'slide_titles.txt'), 'r', encoding='utf-8').readlines()
# slide_titles = [t.strip() for t in slide_titles]
title_mapping = dict(zip(slide_names, slide_names))
print('Building or loading index...')
idx = metapy.index.make_inverted_index(cfg)
mu = 2500
alpha = 0.34
ranker_obj = load_ranker(cfg, mu)
with open(cfg, 'r') as fin:
cfg_d = pytoml.load(fin)
vocabulary_list = pickle.load(open(vocab_path, 'rb'))
tfidfs = np.load(tfidfs_path)
title_tfidfs = np.load(title_tfidfs_path)
ss_corpus = pickle.load(open(ss_corpus_path, 'rb'))
log_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'log', 'log.txt')
es = Elasticsearch()
def log(ip, to_slide, action, start_time):
with open(log_path, 'a+') as f:
f.write('{},{},{},{}\n'.format(ip, to_slide, action, start_time))
def get_snippet_sentences(slide_name, matching_keywords):
idx = slide_names.index(slide_name)
content = ss_corpus[idx].split(' ')
include = [0] * len(content)
for c in range(len(content)):
if content[c] in matching_keywords:
for i in range(max(0, c - 2), min(c + 3, len(content))):
include[i] = 1
text = ''
for c in range(len(content)):
if include[c] == 1:
if c != 0 and include[c - 1] == 0:
text += '......'
text += content[c] + ' '
text += '......'
return text
def trim_name(slide_name):
name = slide_name.split(' ')
new_name = []
for i, n in enumerate(name):
if (len(re.findall('[0-9\.]+', n)) != 0 and i > 0 and name[i - 1].lower() == 'part') or (
len(re.findall('[0-9\.]+', n)) == 0):
if (n == 'Lesson') or (n in new_name) or (len(re.findall('[0-9\.]+', n)) == 0 and len(n) <= 2):
continue
new_name += [n]
return ' '.join(new_name)
def get_color(slide_course_name, related_slide_course_name):
if slide_course_name == related_slide_course_name:
return "blue"
else:
return "brown"
def get_snippet(slide_name, related_slide_name):
no_keywords = False
related_slide_name = related_slide_name.replace('----', '##')[:-4]
slide_name = slide_name.replace('----', '##')[:-4]
idx1 = slide_names.index(slide_name)
idx2 = slide_names.index(related_slide_name)
title_tfidf1 = title_tfidfs[idx1, :]
title_tfidf2 = title_tfidfs[idx2, :]
tfidf1 = tfidfs[idx1, :]
tfidf2 = tfidfs[idx2, :]
term_sims = 2.8956628 * (title_tfidf1 * title_tfidf2) + 5.92724651 * (tfidf1 * tfidf2)
top_terms_indeces = np.argsort(term_sims)[::-1][:5]
# print related_slide_name
# print np.sort(term_sims)[::-1][:10]
top_terms_indeces = filter(lambda l: term_sims[l] > 0, top_terms_indeces)
matching_words = [vocabulary_list[t] for t in top_terms_indeces]
# matching_words = [(vocabulary_list[t],vec.idf_[t]) for t in top_terms_indeces]
# matching_words = sorted(matching_words, key = lambda l :l[1], reverse = True)
# matching_words = map(lambda l : l[0], matching_words)
if len(matching_words) == 0:
no_keywords = True
keywords = ', '.join(matching_words)
snippet_sentence = get_snippet_sentences(related_slide_name, matching_words)
return (('Slide title : ' + title_mapping[related_slide_name][
:-1] + '\n' + 'Matching keywords: ' + keywords + '\n' + 'Snippet:' + snippet_sentence),
no_keywords)
# idx = metapy.index.make_inverted_index('slides-config.toml')
# ranker = metapy.index.OkapiBM25()
# slide_titles = []
# with open(os.path.join('./slides/slides.dat.labels')) as f:
# for line in f:
# slide_titles.append(line[:-1])
def get_course_names():
course_names = sorted(os.listdir(slides_path))
cn_cpy = list(course_names)
for cn in cn_cpy:
if cn == '.DS_Store':
course_names.remove(cn)
num_course = len(course_names)
return course_names, num_course
def load_related_slides():
global related_dict
with open(related_slides_path, 'r') as f:
related_slides = f.readlines()
for row in related_slides:
cols = row.split(',')
key = cols[0].replace('##', '----') + '.pdf'
related_dict[key] = []
for col_num in range(1, len(cols), 2):
pdf_name = cols[col_num].replace('##', '----') + '.pdf'
if cols[col_num + 1].strip() != '':
score = float(cols[col_num + 1].strip())
if score < 0.03:
break
name_comp = pdf_name.split('----')
course_name = name_comp[0]
# if course_name == 'cs-410':
# continue
lec_name = '----'.join(name_comp[1:-1])
# if os.path.exists(os.path.join(slides_path, course_name, lec_name, pdf_name)):
related_dict[key].append(pdf_name)
# print('=========')
# print(key)
# print(related_dict[key])
def sort_slide_names(l):
""" Sort the given iterable in the way that humans expect."""
convert = lambda text: int(text) if text.isdigit() else text
alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
return sorted(l, key=alphanum_key)
def get_slide(course_name, slide, lno):
lectures = sort_slide_names(os.listdir(os.path.join(slides_path, course_name)))
lno = int(lno)
ses_disp_str = get_disp_str(slide)
related_slides_info = get_related_slides(slide)
return slide, lno, lectures[lno], related_slides_info, lectures, range(len(lectures)), ses_disp_str
def get_disp_str(slide_name):
slide_no = slide_name.split('----')[-1][:-4].title()
comp = slide_name.split('----')
slide_name = ' '.join(comp[-2].replace('.txt', '').replace('_', '-').split('-')).title()
return ' '.join(comp[0].replace('_', '-').split('-')).title() + ' : ' + trim_name(slide_name) + ', ' + slide_no
def get_next_slide(course_name, lno, curr_slide=None):
print('cn', course_name)
lectures = sort_slide_names(os.listdir(os.path.join(slides_path, course_name)))
lno = int(lno)
slides = sort_slide_names(os.listdir(os.path.join(slides_path, course_name, lectures[lno])))
if curr_slide is not None:
idx = slides.index(curr_slide)
slides = slides[idx + 1:]
if len(slides) > 0:
next_slide = slides[0]
else:
if lno == len(lectures) - 1:
return None, None, None, (None, None, None, None, None, None, None, None), None, None, None
else:
next_slide = sort_slide_names(os.listdir(os.path.join(slides_path, course_name, lectures[lno + 1])))[0]
lno += 1
ses_disp_str = get_disp_str(next_slide)
related_slides_info = get_related_slides(next_slide)
return next_slide, lno, lectures[lno], related_slides_info, lectures, range(len(lectures)), ses_disp_str
def get_prev_slide(course_name, lno, curr_slide):
lectures = sort_slide_names(os.listdir(os.path.join(slides_path, course_name)))
lno = int(lno)
slides = sort_slide_names(os.listdir(os.path.join(slides_path, course_name, lectures[lno])))
idx = slides.index(curr_slide)
if idx == 0:
if lno == 0:
return None, None, None, (None, None, None, None, None, None, None, None), None, None, None
else:
prev_slide = sort_slide_names(os.listdir(os.path.join(slides_path, course_name, lectures[lno - 1])))[-1]
lno -= 1
else:
prev_slide = slides[:idx][-1]
ses_disp_str = get_disp_str(prev_slide)
related_slides_info = get_related_slides(prev_slide)
return prev_slide, lno, lectures[lno], related_slides_info, lectures, range(len(lectures)), ses_disp_str
def get_related_slides(slide_name):
if related_dict == {}:
load_related_slides()
filtered_related_slides = []
disp_strs = []
disp_colors = []
disp_snippets = []
course_names = []
lnos = []
slide_comp = slide_name.split('----')
related_slide_trim_names = []
lec_names = []
if slide_name in related_dict:
related_slides = related_dict[slide_name]
filtered_related_slides = []
for r in related_slides:
comp = r.split('----')
# disp_strs.append(' '.join(comp[0].replace('_','-').split('-')).title() + ' : ' + ' '.join(comp[-2].replace('.txt','').replace('_','-').split('-')).title() + ' , ' + ' '.join(comp[-1].replace('.pdf','').split('-')).title())
related_slide_name = ' '.join(comp[-2].replace('.txt', '').replace('_', '-').split('-')).title()
slide_course_name = ' '.join(slide_comp[0].replace('_', '-').split('-')).title()
related_slide_course_name = ' '.join(comp[0].replace('_', '-').split('-')).title()
trimmed_name = ' '.join(comp[0].replace('_', '-').split('-')).title() + ' : ' + trim_name(
related_slide_name)
if trimmed_name in related_slide_trim_names:
continue
else:
related_slide_trim_names += [trimmed_name]
color = get_color(slide_course_name, related_slide_course_name)
snippet, no_keywords = get_snippet(slide_name, r)
if no_keywords == True:
continue
filtered_related_slides.append(r)
disp_strs.append(
' '.join(comp[0].replace('_', '-').split('-')).title() + ' : ' + trim_name(related_slide_name))
disp_snippets.append(snippet)
disp_colors.append(color)
course_names.append(comp[0])
lectures = sort_slide_names(os.listdir(os.path.join(slides_path, comp[0])))
lname = '----'.join(comp[1:-1])
lnos.append(lectures.index(lname))
lec_names.append(lname)
else:
filtered_related_slides = []
return len(disp_strs), filtered_related_slides, disp_strs, course_names, lnos, lec_names, disp_colors, disp_snippets
def format_string(matchobj):
return '<span style="background-color: #bddcf5">' + matchobj.group(0) + '</span>'
def get_search_results(search):
# query = metapy.index.Document()
# query.content(search)
# print (query,idx,ranker,search)
# top_docs = ranker.score(idx, query, num_results=50)
# top_docs = [slide_titles[x[0]] for x in top_docs]
top_docs = []
res = es.search(index='slides', body={"query": {'match': {'content': search}}}, size=50)
# print(res)
for d in res['hits']['hits']:
top_docs.append(d[u'_source'][u'label'])
results = []
disp_strs = []
course_names = []
snippets = []
lnos = []
top_slide_trim_names = []
lec_names = []
for r in top_docs:
comp = r.split('##')
lectures = sort_slide_names(os.listdir(os.path.join(slides_path, comp[0])))
lname = '----'.join(comp[1:-1])
try:
lnos.append(lectures.index(lname))
except ValueError: # not an "actual" slide
continue
if len(results) < 10:
disp_strs.append(' '.join(comp[0].replace('_', '-').split('-')).title() + ' : ' + trim_name(
' '.join(comp[-2].replace('.txt', '').replace('_', '-').split('-')).title()) + ', ' + ' '.join(
comp[-1].replace('.pdf', '').split('-')).title())
course_names.append(comp[0])
lec_names.append(lname)
results.append(r)
snippets.append(get_snippet_sentences(r, search))
for x in range(len(results)):
results[x] = results[x].replace('##', '----') + '.pdf'
return len(results), results, disp_strs, course_names, lnos, snippets, lec_names
def get_explanation(search_string, top_k=1):
query = metapy.index.Document()
query.content(search_string)
print(query)
# score2(ranker,idx,query)
file_id_tups, fn_dict = score2(ranker_obj, idx, query, top_k, alpha)
# print(file_id_tups,fn_dict)
explanation = ''
file_names = []
for fn, _ in sorted(fn_dict.items(), key=lambda k: k[1], reverse=True)[:top_k]:
with open(os.path.join(paras_folder, fn), 'r') as f:
explanation += f.read().strip()
file_names.append(fn)
formatted_exp = explanation
for w in search_string.lower().split():
(sub_str, cnt) = re.subn(re.compile(r"\b{}\b".format(w), re.I), format_string, formatted_exp)
if cnt > 0:
formatted_exp = sub_str
return formatted_exp, '#'.join(file_names)