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Merge pull request #1 from adapt-sjtu/dev_badge
Add badges and citations
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1,4 @@ | ||
| commonsense-papers.bib | ||
| commonsense-papers.bib | ||
| README.html | ||
| test_scripts/ | ||
| .vscode/ |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,171 @@ | ||
| import re | ||
| import numpy as np | ||
| import pandas as pd | ||
| from collections import defaultdict, Counter | ||
| import spacy | ||
| nlp = spacy.load("en", disabled=["ner", "parser"]) | ||
| import semanticscholar as sch | ||
| import markdown2 | ||
| import jinja2 | ||
|
|
||
| def str_sim(str1, str2): | ||
| # currently use this simple heuristic | ||
| chars1, chars2 = set(str1), set(str2) | ||
| return len(chars1 & chars2) / max(min(len(chars1), len(chars2)), 1) | ||
|
|
||
| def most_sim_author(str1, author_list): | ||
| best_sim, best_match = -1, None | ||
| for str2 in author_list: | ||
| sim0 = str_sim(str1, str2) | ||
| if sim0 > best_sim: | ||
| best_sim, best_match = sim0, str2 | ||
| return best_match | ||
|
|
||
| allow_search = True | ||
|
|
||
| paper_cnt = 0 | ||
| author_cnt = Counter() | ||
| kwds_cnt = Counter() | ||
| venue_cnt = defaultdict(int) | ||
| my_stopwords = {"commonsense", "knowledge", "natural", "language"} | ||
| lid2badges = defaultdict(str) | ||
| lid2citation_nums = defaultdict(str) | ||
| mention2aid = {} | ||
| aid2url = {} | ||
| curr_authors_info = None | ||
| readme_lines = [] | ||
| with open("README.md", encoding="utf-8") as f: | ||
| for lid, line in enumerate(f): | ||
| line = line.strip() | ||
| readme_lines.append(line) | ||
| if re.search(r"^\*\*([^\*]+)\*\*", line): # title | ||
| title = re.search(r"^\*\*([^\*]+)\*\*", line).group(1) | ||
| keywords = {x.lemma_.lower() for x in nlp(title) if not (x.is_stop or x.is_punct or x.lemma_.lower() in my_stopwords)} | ||
| kwds_cnt.update(keywords) | ||
| paper_cnt += 1 | ||
| # find arxiv id, and get details from semantic scholar API | ||
| if re.search(r"https://arxiv.org/(pdf|abs)/(\d+\.\d+)", line): | ||
| arxiv_id = re.search(r"https://arxiv.org/(pdf|abs)/(\d+\.\d+)", line).group(2) | ||
| alt_badge = f' <div data-badge-popover="right" data-badge-type="2" data-hide-no-mentions="true" class="altmetric-embed" data-arxiv-id="{arxiv_id}" style="float:left"></div> ' | ||
| lid2badges[lid] = alt_badge | ||
| if allow_search: | ||
| paper_info = sch.paper(f'arxiv:{arxiv_id}', timeout=2) | ||
| citations = len(paper_info["citations"]) | ||
| inf_citations = paper_info["influentialCitationCount"] | ||
| if inf_citations > 0: | ||
| lid2citation_nums[lid] = f" (Citations: {citations}, {inf_citations} influential) " | ||
| else: | ||
| lid2citation_nums[lid] = f" (Citations: {citations}) " | ||
| curr_authors_info = paper_info["authors"] | ||
| doi = paper_info.get('doi', None) | ||
| print("DOI", doi) | ||
| if doi is not None: | ||
| # use doi to link to Dimensions Badge | ||
| dim_badge = f' <span class="__dimensions_badge_embed__" data-doi="{doi}" data-style="small_rectangle" style="float:left"></span> ' | ||
| lid2badges[lid] = lid2badges[lid] + dim_badge | ||
| else: | ||
| lid2citation_nums[lid] = " (Citations: ?) " | ||
| else: | ||
| lid2badges[lid] = "" | ||
|
|
||
| try: | ||
| beg = line.rfind(r"** ") + 3 | ||
| end = line.find("[") - 1 | ||
| venue_text = line[beg:end] | ||
| if ")" in venue_text: | ||
| venue_text = venue_text[venue_text.find(")")+1:] | ||
| venue = venue_text.strip().split()[0] | ||
| venue_cnt[venue] += 1 | ||
| except Exception as e: | ||
| pass | ||
| continue | ||
| if re.search(r"^\*.+\*$", line): # author | ||
| authors = [x.strip() for x in line[1:-1].split(", ")] | ||
| author_cnt.update(authors) | ||
| if curr_authors_info != None: | ||
| # match author mention with semantic scholar std name | ||
| # first and last author should be accurately matched | ||
| mention2aid[authors[0]] = curr_authors_info[0]['authorId'] | ||
| mention2aid[authors[-1]] = curr_authors_info[-1]['authorId'] | ||
| tmp_author2id = {} | ||
| for author_info in curr_authors_info: | ||
| aid2url[author_info['authorId']] = author_info['url'] | ||
| tmp_author2id[author_info['name']] = author_info['authorId'] | ||
| if len(authors) > 2: | ||
| for mention0 in authors[1:-1]: | ||
| if len(tmp_author2id) > 0: | ||
| matched_aname = most_sim_author(mention0, tmp_author2id) | ||
| mention2aid[mention0] = tmp_author2id[matched_aname] | ||
| del tmp_author2id[matched_aname] | ||
|
|
||
| curr_authors_info = None | ||
|
|
||
| kwds_cnt = pd.DataFrame(pd.Series(kwds_cnt).sort_values(ascending=False), columns=["count"]) | ||
| author_cnt = pd.DataFrame(pd.Series(author_cnt).sort_values(ascending=False), columns=["count"]) | ||
| venue_cnt = pd.DataFrame(pd.Series(venue_cnt).sort_values(ascending=False), columns=["count"]) | ||
|
|
||
| readme_to_md = "\n".join(readme_lines) | ||
| readme_to_md = re.sub(r'<anchor id="cnt">(.*?)</anchor>', f'<anchor id="cnt">{paper_cnt}</anchor>', readme_to_md) | ||
| html0 = kwds_cnt.head(10).to_html() | ||
| readme_to_md = re.sub(r'<anchor id="keyword">\n(.*?)\n</anchor>', f'<anchor id="keyword">\n{html0}\n</anchor>', readme_to_md, flags=re.DOTALL) | ||
| html0 = author_cnt.head(10).to_html() | ||
| for mention0 in author_cnt.index: | ||
| if mention0 in mention2aid: | ||
| url0 = aid2url[mention2aid[mention0]] | ||
| html0 = html0.replace(mention0, f'<a href="{url0}">{mention0}</a>') | ||
| readme_to_md = re.sub(r'<anchor id="researcher">\n(.*?)\n</anchor>', f'<anchor id="researcher">\n{html0}\n</anchor>', readme_to_md, flags=re.DOTALL) | ||
| html0 = venue_cnt.head(5).to_html() | ||
| readme_to_md = re.sub(r'<anchor id="venue">\n(.*?)\n</anchor>', f'<anchor id="venue">\n{html0}\n</anchor>', readme_to_md, flags=re.DOTALL) | ||
| with open("README.md", "w", encoding="utf-8") as f: | ||
| f.write(readme_to_md) | ||
|
|
||
| # write to website | ||
| for lid, cite_str in lid2citation_nums.items(): | ||
| if cite_str != "": | ||
| readme_lines[lid] += cite_str | ||
| for lid, badge_str in lid2badges.items(): | ||
| if badge_str != "": | ||
| readme_lines[lid] += (badge_str + "<br/>") | ||
| readme_to_html = "\n".join(readme_lines) | ||
| readme_to_html = re.sub(r'<anchor id="cnt">(.*?)</anchor>', f'<anchor id="cnt">{paper_cnt}</anchor>', readme_to_html) | ||
| html0 = kwds_cnt.head(10).to_html() | ||
| readme_to_html = re.sub(r'<anchor id="keyword">\n(.*?)\n</anchor>', f'<anchor id="keyword">\n{html0}\n</anchor>', readme_to_html, flags=re.DOTALL) | ||
| html0 = author_cnt.head(10).to_html() | ||
| for mention0 in author_cnt.index: | ||
| if mention0 in mention2aid: | ||
| url0 = aid2url[mention2aid[mention0]] | ||
| html0 = html0.replace(mention0, f'<a href="{url0}">{mention0}</a>') | ||
| readme_to_html = re.sub(r'<anchor id="researcher">\n(.*?)\n</anchor>', f'<anchor id="researcher">\n{html0}\n</anchor>', readme_to_html, flags=re.DOTALL) | ||
| html0 = venue_cnt.head(5).to_html() | ||
| readme_to_html = re.sub(r'<anchor id="venue">\n(.*?)\n</anchor>', f'<anchor id="venue">\n{html0}\n</anchor>', readme_to_html, flags=re.DOTALL) | ||
| html = markdown2.markdown(readme_to_html) | ||
|
|
||
| template = """ | ||
| <head> | ||
| <script type='text/javascript' charset="utf-8">{{dimensions_badge}}</script> | ||
| <script type='text/javascript' charset="utf-8">{{altmetrics_badge}}</script> | ||
| </head> | ||
| {{main_body}} | ||
| """ | ||
|
|
||
| template = jinja2.Template(template) | ||
|
|
||
| template_vars = { | ||
| "main_body": html, | ||
| "dimensions_badge": open("static/badge.js", encoding="utf-8").read(), | ||
| "altmetrics_badge": open("static/embed.js", encoding="utf-8").read() | ||
| } | ||
|
|
||
| html_out = template.render(template_vars) | ||
| with open("index.html", "w", encoding="utf-8") as f: | ||
| f.write(html_out) | ||
|
|
||
| print("Results (for human read)") | ||
| print("\n--Keyword--\n") | ||
| print(kwds_cnt.head(10)) | ||
| print("\n--Author--\n") | ||
| print(author_cnt.head(10)) | ||
| print("\n--Venue--\n") | ||
| print(venue_cnt.head(5)) | ||
| print(f"Paper count: {paper_cnt}") |
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