389 lines
15 KiB
Python
389 lines
15 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# Author: Jayant Jain <jayant@rare-technologies.com>
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# Copyright (C) 2016 RaRe Technologies
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"""This script using for extracting plain text out of a raw Wikipedia dump. Input is an xml.bz2 file provided
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by MediaWiki that looks like <LANG>wiki-<YYYYMMDD>-pages-articles.xml.bz2 or <LANG>wiki-latest-pages-articles.xml.bz2
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(e.g. 14 GB of https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2).
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It streams through all the XML articles using multiple cores (#cores - 1, by default),
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decompressing on the fly and extracting plain text from the articles and their sections.
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For each extracted article, it prints its title, section names and plain text section contents, in json-line format.
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How to use
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----------
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#. Process Wikipedia dump with this script ::
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python -m gensim.scripts.segment_wiki -i -f enwiki-latest-pages-articles.xml.bz2 -o enwiki-latest.json.gz
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#. Read output in simple way
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>>> from smart_open import smart_open
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>>> import json
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>>>
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>>> # iterate over the plain text data we just created
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>>> for line in smart_open('enwiki-latest.json.gz'):
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>>> # decode each JSON line into a Python dictionary object
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>>> article = json.loads(line)
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>>>
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>>> # each article has a "title", a mapping of interlinks and a list of "section_titles" and "section_texts".
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>>> print("Article title: %s" % article['title'])
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>>> print("Interlinks: %s" + article['interlinks'])
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>>> for section_title, section_text in zip(article['section_titles'], article['section_texts']):
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>>> print("Section title: %s" % section_title)
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>>> print("Section text: %s" % section_text)
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Notes
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-----
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Processing the entire English Wikipedia dump takes 1.7 hours (about 3 million articles per hour,
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or 10 MB of XML per second) on an 8 core Intel i7-7700 @3.60GHz.
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Command line arguments
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----------------------
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.. program-output:: python -m gensim.scripts.segment_wiki --help
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:ellipsis: 0, -10
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"""
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import argparse
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import json
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import logging
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import multiprocessing
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import re
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import sys
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from xml.etree import cElementTree
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from functools import partial
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from gensim.corpora.wikicorpus import IGNORED_NAMESPACES, WikiCorpus, filter_wiki, find_interlinks, get_namespace, utils
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from smart_open import smart_open
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logger = logging.getLogger(__name__)
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def segment_all_articles(file_path, min_article_character=200, workers=None, include_interlinks=False):
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"""Extract article titles and sections from a MediaWiki bz2 database dump.
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Parameters
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----------
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file_path : str
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Path to MediaWiki dump, typical filename is <LANG>wiki-<YYYYMMDD>-pages-articles.xml.bz2
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or <LANG>wiki-latest-pages-articles.xml.bz2.
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min_article_character : int, optional
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Minimal number of character for article (except titles and leading gaps).
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workers: int or None
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Number of parallel workers, max(1, multiprocessing.cpu_count() - 1) if None.
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include_interlinks: bool
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Whether or not interlinks should be included in the output
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Yields
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------
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(str, list of (str, str), (Optionally) dict of str: str)
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Structure contains (title, [(section_heading, section_content), ...], (Optionally) {interlinks}).
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"""
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with smart_open(file_path, 'rb') as xml_fileobj:
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wiki_sections_corpus = _WikiSectionsCorpus(
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xml_fileobj, min_article_character=min_article_character, processes=workers,
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include_interlinks=include_interlinks)
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wiki_sections_corpus.metadata = True
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wiki_sections_text = wiki_sections_corpus.get_texts_with_sections()
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for article in wiki_sections_text:
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yield article
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def segment_and_write_all_articles(file_path, output_file, min_article_character=200, workers=None,
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include_interlinks=False):
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"""Write article title and sections to `output_file` (or stdout, if output_file is None).
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The output format is one article per line, in json-line format with 4 fields::
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'title' - title of article,
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'section_titles' - list of titles of sections,
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'section_texts' - list of content from sections,
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(Optional) 'section_interlinks' - list of interlinks in the article.
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Parameters
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----------
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file_path : str
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Path to MediaWiki dump, typical filename is <LANG>wiki-<YYYYMMDD>-pages-articles.xml.bz2
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or <LANG>wiki-latest-pages-articles.xml.bz2.
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output_file : str or None
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Path to output file in json-lines format, or None for printing to stdout.
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min_article_character : int, optional
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Minimal number of character for article (except titles and leading gaps).
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workers: int or None
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Number of parallel workers, max(1, multiprocessing.cpu_count() - 1) if None.
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include_interlinks: bool
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Whether or not interlinks should be included in the output
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"""
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if output_file is None:
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outfile = getattr(sys.stdout, 'buffer', sys.stdout) # we want write bytes, so for py3 we used 'buffer'
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else:
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outfile = smart_open(output_file, 'wb')
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try:
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article_stream = segment_all_articles(file_path, min_article_character, workers=workers,
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include_interlinks=include_interlinks)
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for idx, article in enumerate(article_stream):
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article_title, article_sections = article[0], article[1]
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if include_interlinks:
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interlinks = article[2]
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output_data = {
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"title": article_title,
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"section_titles": [],
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"section_texts": [],
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}
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if include_interlinks:
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output_data["interlinks"] = interlinks
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for section_heading, section_content in article_sections:
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output_data["section_titles"].append(section_heading)
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output_data["section_texts"].append(section_content)
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if (idx + 1) % 100000 == 0:
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logger.info("processed #%d articles (at %r now)", idx + 1, article_title)
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outfile.write((json.dumps(output_data) + "\n").encode('utf-8'))
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finally:
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if output_file is not None:
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outfile.close()
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def extract_page_xmls(f):
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"""Extract pages from a MediaWiki database dump.
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Parameters
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----------
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f : file
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File descriptor of MediaWiki dump.
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Yields
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------
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str
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XML strings for page tags.
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"""
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elems = (elem for _, elem in cElementTree.iterparse(f, events=("end",)))
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elem = next(elems)
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namespace = get_namespace(elem.tag)
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ns_mapping = {"ns": namespace}
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page_tag = "{%(ns)s}page" % ns_mapping
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for elem in elems:
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if elem.tag == page_tag:
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yield cElementTree.tostring(elem)
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# Prune the element tree, as per
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# http://www.ibm.com/developerworks/xml/library/x-hiperfparse/
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# except that we don't need to prune backlinks from the parent
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# because we don't use LXML.
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# We do this only for <page>s, since we need to inspect the
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# ./revision/text element. The pages comprise the bulk of the
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# file, so in practice we prune away enough.
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elem.clear()
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def segment(page_xml, include_interlinks=False):
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"""Parse the content inside a page tag
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Parameters
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----------
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page_xml : str
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Content from page tag.
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include_interlinks : bool
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Whether or not interlinks should be parsed.
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Returns
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-------
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(str, list of (str, str), (Optionally) dict of (str: str))
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Structure contains (title, [(section_heading, section_content), ...], (Optionally) {interlinks}).
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"""
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elem = cElementTree.fromstring(page_xml)
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filter_namespaces = ('0',)
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namespace = get_namespace(elem.tag)
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ns_mapping = {"ns": namespace}
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text_path = "./{%(ns)s}revision/{%(ns)s}text" % ns_mapping
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title_path = "./{%(ns)s}title" % ns_mapping
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ns_path = "./{%(ns)s}ns" % ns_mapping
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lead_section_heading = "Introduction"
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top_level_heading_regex = r"\n==[^=].*[^=]==\n"
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top_level_heading_regex_capture = r"\n==([^=].*[^=])==\n"
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title = elem.find(title_path).text
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text = elem.find(text_path).text
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ns = elem.find(ns_path).text
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if ns not in filter_namespaces:
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text = None
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if text is not None:
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if include_interlinks:
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interlinks = find_interlinks(text)
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section_contents = re.split(top_level_heading_regex, text)
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section_headings = [lead_section_heading] + re.findall(top_level_heading_regex_capture, text)
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section_headings = [heading.strip() for heading in section_headings]
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assert len(section_contents) == len(section_headings)
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else:
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interlinks = []
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section_contents = []
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section_headings = []
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section_contents = [filter_wiki(section_content) for section_content in section_contents]
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sections = list(zip(section_headings, section_contents))
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if include_interlinks:
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return title, sections, interlinks
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else:
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return title, sections
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class _WikiSectionsCorpus(WikiCorpus):
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"""Treat a wikipedia articles dump (<LANG>wiki-<YYYYMMDD>-pages-articles.xml.bz2
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or <LANG>wiki-latest-pages-articles.xml.bz2) as a (read-only) corpus.
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The documents are extracted on-the-fly, so that the whole (massive) dump can stay compressed on disk.
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"""
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def __init__(self, fileobj, min_article_character=200, processes=None,
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lemmatize=utils.has_pattern(), filter_namespaces=('0',), include_interlinks=False):
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"""
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Parameters
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----------
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fileobj : file
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File descriptor of MediaWiki dump.
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min_article_character : int, optional
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Minimal number of character for article (except titles and leading gaps).
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processes : int, optional
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Number of processes, max(1, multiprocessing.cpu_count() - 1) if None.
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lemmatize : bool, optional
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If `pattern` package is installed, use fancier shallow parsing to get token lemmas.
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Otherwise, use simple regexp tokenization.
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filter_namespaces : tuple of int, optional
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Enumeration of namespaces that will be ignored.
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include_interlinks: bool
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Whether or not interlinks should be included in the output
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"""
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self.fileobj = fileobj
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self.filter_namespaces = filter_namespaces
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self.metadata = False
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if processes is None:
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processes = max(1, multiprocessing.cpu_count() - 1)
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self.processes = processes
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self.lemmatize = lemmatize
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self.min_article_character = min_article_character
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self.include_interlinks = include_interlinks
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def get_texts_with_sections(self):
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"""Iterate over the dump, returning titles and text versions of all sections of articles.
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Notes
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-----
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Only articles of sufficient length are returned (short articles & redirects
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etc are ignored).
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Note that this iterates over the **texts**; if you want vectors, just use
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the standard corpus interface instead of this function::
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>>> for vec in wiki_corpus:
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>>> print(vec)
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Yields
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------
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(str, list of (str, str), dict of (str: str))
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Structure contains (title, [(section_heading, section_content), ...], (Optionally){interlinks}).
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"""
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skipped_namespace, skipped_length, skipped_redirect = 0, 0, 0
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total_articles, total_sections = 0, 0
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page_xmls = extract_page_xmls(self.fileobj)
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pool = multiprocessing.Pool(self.processes)
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# process the corpus in smaller chunks of docs, because multiprocessing.Pool
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# is dumb and would load the entire input into RAM at once...
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for group in utils.chunkize(page_xmls, chunksize=10 * self.processes, maxsize=1):
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for article in pool.imap(partial(segment, include_interlinks=self.include_interlinks),
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group): # chunksize=10): partial(merge_names, b='Sons')
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article_title, sections = article[0], article[1]
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# article redirects are pruned here
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if any(article_title.startswith(ignore + ':') for ignore in IGNORED_NAMESPACES): # filter non-articles
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skipped_namespace += 1
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continue
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if not sections or sections[0][1].lstrip().lower().startswith("#redirect"): # filter redirect
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skipped_redirect += 1
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continue
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if sum(len(body.strip()) for (_, body) in sections) < self.min_article_character:
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# filter stubs (incomplete, very short articles)
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skipped_length += 1
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continue
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total_articles += 1
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total_sections += len(sections)
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if self.include_interlinks:
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interlinks = article[2]
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yield (article_title, sections, interlinks)
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else:
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yield (article_title, sections)
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logger.info(
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"finished processing %i articles with %i sections (skipped %i redirects, %i stubs, %i ignored namespaces)",
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total_articles, total_sections, skipped_redirect, skipped_length, skipped_namespace)
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pool.terminate()
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self.length = total_articles # cache corpus length
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if __name__ == "__main__":
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logging.basicConfig(format='%(asctime)s - %(module)s - %(levelname)s - %(message)s', level=logging.INFO)
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parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter, description=__doc__[:-136])
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default_workers = max(1, multiprocessing.cpu_count() - 1)
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parser.add_argument('-f', '--file', help='Path to MediaWiki database dump (read-only).', required=True)
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parser.add_argument(
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'-o', '--output',
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help='Path to output file (stdout if not specified). If ends in .gz or .bz2, '
|
||
|
|
'the output file will be automatically compressed (recommended!).')
|
||
|
|
parser.add_argument(
|
||
|
|
'-w', '--workers',
|
||
|
|
help='Number of parallel workers for multi-core systems. Default: %(default)s.',
|
||
|
|
type=int,
|
||
|
|
default=default_workers
|
||
|
|
)
|
||
|
|
parser.add_argument(
|
||
|
|
'-m', '--min-article-character',
|
||
|
|
help="Ignore articles with fewer characters than this (article stubs). Default: %(default)s.",
|
||
|
|
default=200
|
||
|
|
)
|
||
|
|
parser.add_argument(
|
||
|
|
'-i', '--include-interlinks',
|
||
|
|
help='Include a mapping for interlinks to other articles in the dump. The mappings format is: '
|
||
|
|
'"interlinks": {"article_title_1": "interlink_text_1", "article_title_2": "interlink_text_2", ...}',
|
||
|
|
action='store_true'
|
||
|
|
)
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
logger.info("running %s", " ".join(sys.argv))
|
||
|
|
segment_and_write_all_articles(
|
||
|
|
args.file, args.output,
|
||
|
|
min_article_character=args.min_article_character,
|
||
|
|
workers=args.workers,
|
||
|
|
include_interlinks=args.include_interlinks
|
||
|
|
)
|
||
|
|
|
||
|
|
logger.info("finished running %s", sys.argv[0])
|