Refactor statistics computations
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parent
55f5964867
commit
ada2317435
156
src/build.py
156
src/build.py
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@ -128,6 +128,24 @@ def build_session_page(config):
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content=config["SESSION_PAGE"],
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citeblock="")
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def reverse_statistics_dict(stats, reverse=True):
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"""
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Transforms a dictionary mapping titles to a value into a list of values
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and lists of titles. The list is sorted by the value, and the titles are
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sorted alphabetically.
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"""
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rev = {}
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for key, value in stats.items():
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if value not in rev:
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rev[value] = []
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rev[value].append(key)
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for key, value in rev.items():
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rev[key] = sorted(value, key=lambda t: utils.titlesort(t))
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return sorted(rev.items(), key=lambda x:x[0], reverse=reverse)
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def itemize(stats_list):
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return map(lambda x: "{0} – {1}".format(x[0], "; ".join(x[1])), stats_list)
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def build_statistics_page(articles, config):
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"""
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Builds the full HTML of the statistics page.
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@ -136,61 +154,58 @@ def build_statistics_page(articles, config):
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cite_map = {
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article.title : [
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cite_tuple[1]
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for cite_tuple in article.citations.values()]
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for cite_tuple
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in article.citations.values()
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]
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for article in articles}
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# Pages by pagerank
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Top 10 pages by page rank:</u><br>\n"
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# Top pages by pagerank
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# Compute pagerank for each article
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G = networkx.Graph()
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for citer, citeds in cite_map.items():
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for cited in citeds:
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G.add_edge(citer, cited)
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ranks = networkx.pagerank(G)
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sranks = sorted(ranks.items(), key=lambda x: x[1], reverse=True)
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ranking = list(enumerate(map(lambda x: x[0], sranks)))
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content += "<br>\n".join(map(lambda x: "{0} – {1}".format(x[0]+1, x[1]), ranking[:10]))
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content += "</p>\n"
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content += "</div>\n"
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rank_by_article = networkx.pagerank(G)
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# Get the top ten articles by pagerank
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top_pageranks = reverse_statistics_dict(rank_by_article)[:10]
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# Replace the pageranks with ordinals
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top_ranked = enumerate(map(lambda x: x[1], top_pageranks), start=1)
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# Format the ranks into strings
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top_ranked_items = itemize(top_ranked)
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# Write the statistics to the page
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Top 10 pages by page rank:</u><br>\n"
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content += "<br>\n".join(top_ranked_items)
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content += "</p>\n</div>\n"
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# Top number of citations made
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citations_made = { title : len(cites) for title, cites in cite_map.items() }
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top_citations = reverse_statistics_dict(citations_made)[:3]
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top_citations_items = itemize(top_citations)
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Most citations made from:</u><br>\n"
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citation_tally = [(kv[0], len(kv[1])) for kv in cite_map.items()]
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citation_count = defaultdict(list)
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for title, count in citation_tally: citation_count[count].append(title)
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content += "<br>\n".join(map(
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lambda kv: "{0} – {1}".format(
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kv[0],
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"; ".join(sorted(
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kv[1],
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key=lambda t: utils.titlesort(t)))),
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sorted(citation_count.items(), reverse=True)[:3]))
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content += "</p>\n"
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content += "</div>\n"
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content += "<br>\n".join(top_citations_items)
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content += "</p>\n</div>\n"
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# Top number of times cited
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Most citations made to:</u><br>\n"
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all_cited = set([title for cites in cite_map.values() for title in cites])
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# Build a map of what cites each article
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all_cited = set([title for citeds in cite_map.values() for title in citeds])
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cited_by_map = {
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cited: [
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citer
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for citer in cite_map.keys()
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if cited in cite_map[citer]]
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for cited in all_cited }
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cited_tally = [(kv[0], len(kv[1])) for kv in cited_by_map.items()]
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cited_count = defaultdict(list)
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for title, count in cited_tally: cited_count[count].append(title)
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content += "<br>\n".join(map(
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lambda kv: "{0} – {1}".format(kv[0], "; ".join(sorted(kv[1]))),
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sorted(cited_count.items(), reverse=True)[:3]))
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content += "</p>\n"
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content += "</div>\n"
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# Compute the number of citations to each article
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citations_to = { title : len(cites) for title, cites in cited_by_map.items() }
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top_cited = reverse_statistics_dict(citations_to)[:3]
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top_cited_items = itemize(top_cited)
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Most citations made to:</u><br>\n"
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content += "<br>\n".join(top_cited_items)
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content += "</p>\n</div>\n"
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# Top article length, roughly by words
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Longest article:</u><br>\n"
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article_length = {}
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for article in articles:
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format_map = {
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@ -198,61 +213,66 @@ def build_statistics_page(articles, config):
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for format_id, cite_tuple in article.citations.items()
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}
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plain_content = article.content.format(**format_map)
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words = len(plain_content.split())
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article_length[article.title] = words
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content += "<br>\n".join(map(
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lambda kv: "{0} – {1}".format(kv[1], kv[0]),
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sorted(article_length.items(), reverse=True, key=lambda t: t[1])[:3]))
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content += "</p>\n"
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content += "</div>\n"
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wordcount = len(plain_content.split())
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article_length[article.title] = wordcount
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top_length = reverse_statistics_dict(article_length)[:3]
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top_length_items = itemize(top_length)
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Longest article:</u><br>\n"
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content += "<br>\n".join(top_length_items)
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content += "</p>\n</div>\n"
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# Total word count
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Total word count:</u><br>\n"
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content += str(sum(article_length.values())) + "</p>"
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content += "</p>\n</div>\n"
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# Player pageranks
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Player total page rank:</u><br>\n"
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players = sorted(set([article.player for article in articles if article.player is not None]))
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articles_by = {
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articles_by_player = {
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player : [
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a
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for a in articles
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if a.player == player]
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for player in players}
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player_rank = {
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player : sum(map(lambda a: ranks[a.title] if a.title in ranks else 0, articles))
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for player, articles in articles_by.items()}
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content += "<br>\n".join(map(
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lambda kv: "{0} – {1}".format(kv[0], round(kv[1], 3)),
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sorted(player_rank.items(), key=lambda t:t[1], reverse=True)))
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content += "</p>\n"
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content += "</div>\n"
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pagerank_by_player = {
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player : round(
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sum(map(
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lambda a: rank_by_article[a.title] if a.title in rank_by_article else 0,
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articles)),
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3)
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for player, articles
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in articles_by_player.items()}
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player_rank = reverse_statistics_dict(pagerank_by_player)
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player_rank_items = itemize(player_rank)
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Player total page rank:</u><br>\n"
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content += "<br>\n".join(player_rank_items)
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content += "</p>\n</div>\n"
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# Player citations made
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Citations made by player</u><br>\n"
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player_cite_count = {
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player : sum(map(lambda a:len(a.wcites | a.pcites), articles))
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for player, articles in articles_by.items()}
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content += "<br>\n".join(map(
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lambda kv: "{0} – {1}".format(kv[0], kv[1]),
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sorted(player_cite_count.items(), key=lambda t:t[1], reverse=True)))
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content += "</p>\n"
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content += "</div>\n"
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for player, articles in articles_by_player.items()}
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player_cites_made_ranks = reverse_statistics_dict(player_cite_count)
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player_cites_made_items = itemize(player_cites_made_ranks)
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Citations made by player</u><br>\n"
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content += "<br>\n".join(player_cites_made_items)
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content += "</p>\n</div>\n"
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# Player cited count
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Citations made to player</u><br>\n"
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cited_times = {player : 0 for player in players}
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for article in articles:
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if article.player is not None:
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cited_times[article.player] += len(article.citedby)
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content += "<br>\n".join(map(
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lambda kv: "{0} – {1}".format(kv[0], kv[1]),
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sorted(cited_times.items(), key=lambda t:t[1], reverse=True)))
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content += "</p>\n"
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content += "</div>\n"
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cited_times_ranked = reverse_statistics_dict(cited_times)
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cited_times_items = itemize(cited_times_ranked)
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content += "<div class=\"moveable\">\n"
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content += "<p><u>Citations made to player</u><br>\n"
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content += "<br>\n".join(cited_times_items)
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content += "</p>\n</div>\n"
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# Fill in the entry skeleton
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entry_skeleton = utils.load_resource("entry-page.html")
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