mirror of
https://github.com/explosion/spaCy.git
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get vector functionality + unit test
This commit is contained in:
svlandeg
parent
a63d15a142
commit
4086c6ff60
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@ -2,6 +2,7 @@
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from __future__ import unicode_literals
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import os
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import random
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import re
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import bz2
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import datetime
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@ -27,21 +28,23 @@ def _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=N
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Read the XML wikipedia data to parse out training data:
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raw text data + positive instances
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"""
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title_regex = re.compile(r'(?<=<title>).*(?=</title>)')
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id_regex = re.compile(r'(?<=<id>)\d*(?=</id>)')
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title_regex = re.compile(r"(?<=<title>).*(?=</title>)")
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id_regex = re.compile(r"(?<=<id>)\d*(?=</id>)")
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read_ids = set()
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entityfile_loc = training_output / ENTITY_FILE
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with open(entityfile_loc, mode="w", encoding='utf8') as entityfile:
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with open(entityfile_loc, mode="w", encoding="utf8") as entityfile:
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# write entity training header file
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_write_training_entity(outputfile=entityfile,
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article_id="article_id",
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alias="alias",
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entity="WD_id",
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start="start",
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end="end")
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_write_training_entity(
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outputfile=entityfile,
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article_id="article_id",
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alias="alias",
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entity="WD_id",
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start="start",
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end="end",
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)
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with bz2.open(wikipedia_input, mode='rb') as file:
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with bz2.open(wikipedia_input, mode="rb") as file:
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line = file.readline()
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cnt = 0
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article_text = ""
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@ -51,7 +54,12 @@ def _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=N
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reading_revision = False
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while line and (not limit or cnt < limit):
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if cnt % 1000000 == 0:
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print(datetime.datetime.now(), "processed", cnt, "lines of Wikipedia dump")
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print(
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datetime.datetime.now(),
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"processed",
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cnt,
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"lines of Wikipedia dump",
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)
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clean_line = line.strip().decode("utf-8")
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if clean_line == "<revision>":
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@ -69,12 +77,23 @@ def _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=N
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elif clean_line == "</page>":
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if article_id:
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try:
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_process_wp_text(wp_to_id, entityfile, article_id, article_title, article_text.strip(),
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training_output)
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_process_wp_text(
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wp_to_id,
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entityfile,
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article_id,
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article_title,
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article_text.strip(),
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training_output,
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)
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except Exception as e:
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print("Error processing article", article_id, article_title, e)
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print(
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"Error processing article", article_id, article_title, e
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)
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else:
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print("Done processing a page, but couldn't find an article_id ?", article_title)
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print(
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"Done processing a page, but couldn't find an article_id ?",
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article_title,
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)
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article_text = ""
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article_title = None
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article_id = None
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@ -98,7 +117,9 @@ def _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=N
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if ids:
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article_id = ids[0]
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if article_id in read_ids:
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print("Found duplicate article ID", article_id, clean_line) # This should never happen ...
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print(
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"Found duplicate article ID", article_id, clean_line
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) # This should never happen ...
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read_ids.add(article_id)
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# read the title of this article (outside the revision portion of the document)
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@ -111,10 +132,12 @@ def _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=N
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cnt += 1
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text_regex = re.compile(r'(?<=<text xml:space=\"preserve\">).*(?=</text)')
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text_regex = re.compile(r"(?<=<text xml:space=\"preserve\">).*(?=</text)")
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def _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_text, training_output):
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def _process_wp_text(
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wp_to_id, entityfile, article_id, article_title, article_text, training_output
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):
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found_entities = False
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# ignore meta Wikipedia pages
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@ -141,11 +164,11 @@ def _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_te
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entity_buffer = ""
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mention_buffer = ""
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for index, letter in enumerate(clean_text):
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if letter == '[':
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if letter == "[":
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open_read += 1
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elif letter == ']':
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elif letter == "]":
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open_read -= 1
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elif letter == '|':
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elif letter == "|":
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if reading_text:
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final_text += letter
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# switch from reading entity to mention in the [[entity|mention]] pattern
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@ -163,7 +186,7 @@ def _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_te
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elif reading_text:
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final_text += letter
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else:
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raise ValueError("Not sure at point", clean_text[index-2:index+2])
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raise ValueError("Not sure at point", clean_text[index - 2 : index + 2])
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if open_read > 2:
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reading_special_case = True
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@ -175,7 +198,7 @@ def _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_te
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# we just finished reading an entity
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if open_read == 0 and not reading_text:
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if '#' in entity_buffer or entity_buffer.startswith(':'):
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if "#" in entity_buffer or entity_buffer.startswith(":"):
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reading_special_case = True
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# Ignore cases with nested structures like File: handles etc
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if not reading_special_case:
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@ -185,12 +208,14 @@ def _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_te
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end = start + len(mention_buffer)
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qid = wp_to_id.get(entity_buffer, None)
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if qid:
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_write_training_entity(outputfile=entityfile,
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article_id=article_id,
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alias=mention_buffer,
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entity=qid,
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start=start,
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end=end)
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_write_training_entity(
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outputfile=entityfile,
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article_id=article_id,
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alias=mention_buffer,
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entity=qid,
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start=start,
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end=end,
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)
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found_entities = True
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final_text += mention_buffer
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@ -203,29 +228,35 @@ def _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_te
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reading_special_case = False
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if found_entities:
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_write_training_article(article_id=article_id, clean_text=final_text, training_output=training_output)
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_write_training_article(
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article_id=article_id,
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clean_text=final_text,
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training_output=training_output,
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)
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info_regex = re.compile(r'{[^{]*?}')
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htlm_regex = re.compile(r'<!--[^-]*-->')
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category_regex = re.compile(r'\[\[Category:[^\[]*]]')
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file_regex = re.compile(r'\[\[File:[^[\]]+]]')
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ref_regex = re.compile(r'<ref.*?>') # non-greedy
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ref_2_regex = re.compile(r'</ref.*?>') # non-greedy
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info_regex = re.compile(r"{[^{]*?}")
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htlm_regex = re.compile(r"<!--[^-]*-->")
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category_regex = re.compile(r"\[\[Category:[^\[]*]]")
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file_regex = re.compile(r"\[\[File:[^[\]]+]]")
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ref_regex = re.compile(r"<ref.*?>") # non-greedy
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ref_2_regex = re.compile(r"</ref.*?>") # non-greedy
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def _get_clean_wp_text(article_text):
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clean_text = article_text.strip()
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# remove bolding & italic markup
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clean_text = clean_text.replace('\'\'\'', '')
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clean_text = clean_text.replace('\'\'', '')
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clean_text = clean_text.replace("'''", "")
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clean_text = clean_text.replace("''", "")
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# remove nested {{info}} statements by removing the inner/smallest ones first and iterating
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try_again = True
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previous_length = len(clean_text)
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while try_again:
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clean_text = info_regex.sub('', clean_text) # non-greedy match excluding a nested {
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clean_text = info_regex.sub(
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"", clean_text
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) # non-greedy match excluding a nested {
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if len(clean_text) < previous_length:
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try_again = True
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else:
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@ -233,14 +264,14 @@ def _get_clean_wp_text(article_text):
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previous_length = len(clean_text)
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# remove HTML comments
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clean_text = htlm_regex.sub('', clean_text)
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clean_text = htlm_regex.sub("", clean_text)
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# remove Category and File statements
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clean_text = category_regex.sub('', clean_text)
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clean_text = file_regex.sub('', clean_text)
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clean_text = category_regex.sub("", clean_text)
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clean_text = file_regex.sub("", clean_text)
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# remove multiple =
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while '==' in clean_text:
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while "==" in clean_text:
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clean_text = clean_text.replace("==", "=")
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clean_text = clean_text.replace(". =", ".")
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@ -249,43 +280,56 @@ def _get_clean_wp_text(article_text):
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clean_text = clean_text.replace(" =", "")
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# remove refs (non-greedy match)
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clean_text = ref_regex.sub('', clean_text)
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clean_text = ref_2_regex.sub('', clean_text)
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clean_text = ref_regex.sub("", clean_text)
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clean_text = ref_2_regex.sub("", clean_text)
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# remove additional wikiformatting
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clean_text = re.sub(r'<blockquote>', '', clean_text)
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clean_text = re.sub(r'</blockquote>', '', clean_text)
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clean_text = re.sub(r"<blockquote>", "", clean_text)
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clean_text = re.sub(r"</blockquote>", "", clean_text)
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# change special characters back to normal ones
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clean_text = clean_text.replace(r'<', '<')
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clean_text = clean_text.replace(r'>', '>')
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clean_text = clean_text.replace(r'"', '"')
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clean_text = clean_text.replace(r'&nbsp;', ' ')
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clean_text = clean_text.replace(r'&', '&')
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clean_text = clean_text.replace(r"<", "<")
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clean_text = clean_text.replace(r">", ">")
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clean_text = clean_text.replace(r""", '"')
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clean_text = clean_text.replace(r"&nbsp;", " ")
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clean_text = clean_text.replace(r"&", "&")
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# remove multiple spaces
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while ' ' in clean_text:
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clean_text = clean_text.replace(' ', ' ')
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while " " in clean_text:
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clean_text = clean_text.replace(" ", " ")
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return clean_text.strip()
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def _write_training_article(article_id, clean_text, training_output):
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file_loc = training_output / str(article_id) + ".txt"
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with open(file_loc, mode='w', encoding='utf8') as outputfile:
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with open(file_loc, mode="w", encoding="utf8") as outputfile:
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outputfile.write(clean_text)
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def _write_training_entity(outputfile, article_id, alias, entity, start, end):
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outputfile.write(article_id + "|" + alias + "|" + entity + "|" + str(start) + "|" + str(end) + "\n")
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outputfile.write(
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article_id
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+ "|"
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+ alias
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+ "|"
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+ entity
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+ "|"
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+ str(start)
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+ "|"
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+ str(end)
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+ "\n"
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)
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def is_dev(article_id):
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return article_id.endswith("3")
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def read_training(nlp, training_dir, dev, limit):
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# This method provides training examples that correspond to the entity annotations found by the nlp object
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def read_training(nlp, training_dir, dev, limit, kb=None):
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""" This method provides training examples that correspond to the entity annotations found by the nlp object.
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When kb is provided, it will include also negative training examples by using the candidate generator.
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When kb=None, it will only include positive training examples."""
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entityfile_loc = training_dir / ENTITY_FILE
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data = []
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@ -296,24 +340,34 @@ def read_training(nlp, training_dir, dev, limit):
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skip_articles = set()
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total_entities = 0
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with open(entityfile_loc, mode='r', encoding='utf8') as file:
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with open(entityfile_loc, mode="r", encoding="utf8") as file:
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for line in file:
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if not limit or len(data) < limit:
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fields = line.replace('\n', "").split(sep='|')
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fields = line.replace("\n", "").split(sep="|")
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article_id = fields[0]
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alias = fields[1]
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wp_title = fields[2]
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wd_id = fields[2]
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start = fields[3]
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end = fields[4]
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if dev == is_dev(article_id) and article_id != "article_id" and article_id not in skip_articles:
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if (
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dev == is_dev(article_id)
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and article_id != "article_id"
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and article_id not in skip_articles
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):
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if not current_doc or (current_article_id != article_id):
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# parse the new article text
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file_name = article_id + ".txt"
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try:
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with open(os.path.join(training_dir, file_name), mode="r", encoding='utf8') as f:
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with open(
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os.path.join(training_dir, file_name),
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mode="r",
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encoding="utf8",
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) as f:
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text = f.read()
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if len(text) < 30000: # threshold for convenience / speed of processing
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if (
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len(text) < 30000
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): # threshold for convenience / speed of processing
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current_doc = nlp(text)
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current_article_id = article_id
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ents_by_offset = dict()
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@ -321,7 +375,11 @@ def read_training(nlp, training_dir, dev, limit):
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sent_length = len(ent.sent)
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# custom filtering to avoid too long or too short sentences
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if 5 < sent_length < 100:
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ents_by_offset[str(ent.start_char) + "_" + str(ent.end_char)] = ent
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ents_by_offset[
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str(ent.start_char)
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+ "_"
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+ str(ent.end_char)
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] = ent
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else:
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skip_articles.add(article_id)
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current_doc = None
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@ -332,7 +390,7 @@ def read_training(nlp, training_dir, dev, limit):
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# repeat checking this condition in case an exception was thrown
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if current_doc and (current_article_id == article_id):
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found_ent = ents_by_offset.get(start + "_" + end, None)
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found_ent = ents_by_offset.get(start + "_" + end, None)
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if found_ent:
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if found_ent.text != alias:
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skip_articles.add(article_id)
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@ -342,7 +400,26 @@ def read_training(nlp, training_dir, dev, limit):
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# currently feeding the gold data one entity per sentence at a time
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gold_start = int(start) - found_ent.sent.start_char
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gold_end = int(end) - found_ent.sent.start_char
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gold_entities = [(gold_start, gold_end, wp_title)]
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# add both positive and negative examples (in random order just to be sure)
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if kb:
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gold_entities = {}
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candidate_ids = [
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c.entity_ for c in kb.get_candidates(alias)
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]
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candidate_ids.append(
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wd_id
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) # in case the KB doesn't have it
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random.shuffle(candidate_ids)
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for kb_id in candidate_ids:
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entry = (gold_start, gold_end, kb_id)
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if kb_id != wd_id:
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gold_entities[entry] = 0.0
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else:
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gold_entities[entry] = 1.0
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else:
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gold_entities = {}
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gold = GoldParse(doc=sent, links=gold_entities)
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data.append((sent, gold))
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total_entities += 1
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|
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@ -203,6 +203,15 @@ cdef class KnowledgeBase:
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for (entry_index, prob) in zip(alias_entry.entry_indices, alias_entry.probs)
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if entry_index != 0]
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def get_vector(self, unicode entity):
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cdef hash_t entity_hash = self.vocab.strings.add(entity)
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# Return an empty list if this entity is unknown in this KB
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if entity_hash not in self._entry_index:
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return []
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entry_index = self._entry_index[entity_hash]
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return self._vectors_table[self._entries[entry_index].vector_index]
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def dump(self, loc):
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cdef Writer writer = Writer(loc)
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|
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@ -15,20 +15,25 @@ def nlp():
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def test_kb_valid_entities(nlp):
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"""Test the valid construction of a KB with 3 entities and two aliases"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
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# adding entities
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mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
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mykb.add_entity(entity='Q2', prob=0.5, entity_vector=[2])
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mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
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mykb.add_entity(entity="Q1", prob=0.9, entity_vector=[8, 4, 3])
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mykb.add_entity(entity="Q2", prob=0.5, entity_vector=[2, 1, 0])
|
||||
mykb.add_entity(entity="Q3", prob=0.5, entity_vector=[-1, -6, 5])
|
||||
|
||||
# adding aliases
|
||||
mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.8, 0.2])
|
||||
mykb.add_alias(alias='adam', entities=['Q2'], probabilities=[0.9])
|
||||
mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.2])
|
||||
mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
|
||||
|
||||
# test the size of the corresponding KB
|
||||
assert(mykb.get_size_entities() == 3)
|
||||
assert(mykb.get_size_aliases() == 2)
|
||||
assert mykb.get_size_entities() == 3
|
||||
assert mykb.get_size_aliases() == 2
|
||||
|
||||
# test retrieval of the entity vectors
|
||||
assert mykb.get_vector("Q1") == [8, 4, 3]
|
||||
assert mykb.get_vector("Q2") == [2, 1, 0]
|
||||
assert mykb.get_vector("Q3") == [-1, -6, 5]
|
||||
|
||||
|
||||
def test_kb_invalid_entities(nlp):
|
||||
|
|
@ -36,13 +41,15 @@ def test_kb_invalid_entities(nlp):
|
|||
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
|
||||
|
||||
# adding entities
|
||||
mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
|
||||
mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
|
||||
mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
|
||||
mykb.add_entity(entity="Q1", prob=0.9, entity_vector=[1])
|
||||
mykb.add_entity(entity="Q2", prob=0.2, entity_vector=[2])
|
||||
mykb.add_entity(entity="Q3", prob=0.5, entity_vector=[3])
|
||||
|
||||
# adding aliases - should fail because one of the given IDs is not valid
|
||||
with pytest.raises(ValueError):
|
||||
mykb.add_alias(alias='douglas', entities=['Q2', 'Q342'], probabilities=[0.8, 0.2])
|
||||
mykb.add_alias(
|
||||
alias="douglas", entities=["Q2", "Q342"], probabilities=[0.8, 0.2]
|
||||
)
|
||||
|
||||
|
||||
def test_kb_invalid_probabilities(nlp):
|
||||
|
|
@ -50,13 +57,13 @@ def test_kb_invalid_probabilities(nlp):
|
|||
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
|
||||
|
||||
# adding entities
|
||||
mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
|
||||
mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
|
||||
mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
|
||||
mykb.add_entity(entity="Q1", prob=0.9, entity_vector=[1])
|
||||
mykb.add_entity(entity="Q2", prob=0.2, entity_vector=[2])
|
||||
mykb.add_entity(entity="Q3", prob=0.5, entity_vector=[3])
|
||||
|
||||
# adding aliases - should fail because the sum of the probabilities exceeds 1
|
||||
with pytest.raises(ValueError):
|
||||
mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.8, 0.4])
|
||||
mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.4])
|
||||
|
||||
|
||||
def test_kb_invalid_combination(nlp):
|
||||
|
|
@ -64,13 +71,15 @@ def test_kb_invalid_combination(nlp):
|
|||
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
|
||||
|
||||
# adding entities
|
||||
mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
|
||||
mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
|
||||
mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
|
||||
mykb.add_entity(entity="Q1", prob=0.9, entity_vector=[1])
|
||||
mykb.add_entity(entity="Q2", prob=0.2, entity_vector=[2])
|
||||
mykb.add_entity(entity="Q3", prob=0.5, entity_vector=[3])
|
||||
|
||||
# adding aliases - should fail because the entities and probabilities vectors are not of equal length
|
||||
with pytest.raises(ValueError):
|
||||
mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.3, 0.4, 0.1])
|
||||
mykb.add_alias(
|
||||
alias="douglas", entities=["Q2", "Q3"], probabilities=[0.3, 0.4, 0.1]
|
||||
)
|
||||
|
||||
|
||||
def test_kb_invalid_entity_vector(nlp):
|
||||
|
|
@ -78,11 +87,11 @@ def test_kb_invalid_entity_vector(nlp):
|
|||
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
|
||||
|
||||
# adding entities
|
||||
mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1, 2, 3])
|
||||
mykb.add_entity(entity="Q1", prob=0.9, entity_vector=[1, 2, 3])
|
||||
|
||||
# this should fail because the kb's expected entity vector length is 3
|
||||
with pytest.raises(ValueError):
|
||||
mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
|
||||
mykb.add_entity(entity="Q2", prob=0.2, entity_vector=[2])
|
||||
|
||||
|
||||
def test_candidate_generation(nlp):
|
||||
|
|
@ -90,18 +99,18 @@ def test_candidate_generation(nlp):
|
|||
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
|
||||
|
||||
# adding entities
|
||||
mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
|
||||
mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
|
||||
mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
|
||||
mykb.add_entity(entity="Q1", prob=0.9, entity_vector=[1])
|
||||
mykb.add_entity(entity="Q2", prob=0.2, entity_vector=[2])
|
||||
mykb.add_entity(entity="Q3", prob=0.5, entity_vector=[3])
|
||||
|
||||
# adding aliases
|
||||
mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.8, 0.2])
|
||||
mykb.add_alias(alias='adam', entities=['Q2'], probabilities=[0.9])
|
||||
mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.2])
|
||||
mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
|
||||
|
||||
# test the size of the relevant candidates
|
||||
assert(len(mykb.get_candidates('douglas')) == 2)
|
||||
assert(len(mykb.get_candidates('adam')) == 1)
|
||||
assert(len(mykb.get_candidates('shrubbery')) == 0)
|
||||
assert len(mykb.get_candidates("douglas")) == 2
|
||||
assert len(mykb.get_candidates("adam")) == 1
|
||||
assert len(mykb.get_candidates("shrubbery")) == 0
|
||||
|
||||
|
||||
def test_preserving_links_asdoc(nlp):
|
||||
|
|
@ -109,24 +118,26 @@ def test_preserving_links_asdoc(nlp):
|
|||
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
|
||||
|
||||
# adding entities
|
||||
mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
|
||||
mykb.add_entity(entity='Q2', prob=0.8, entity_vector=[1])
|
||||
mykb.add_entity(entity="Q1", prob=0.9, entity_vector=[1])
|
||||
mykb.add_entity(entity="Q2", prob=0.8, entity_vector=[1])
|
||||
|
||||
# adding aliases
|
||||
mykb.add_alias(alias='Boston', entities=['Q1'], probabilities=[0.7])
|
||||
mykb.add_alias(alias='Denver', entities=['Q2'], probabilities=[0.6])
|
||||
mykb.add_alias(alias="Boston", entities=["Q1"], probabilities=[0.7])
|
||||
mykb.add_alias(alias="Denver", entities=["Q2"], probabilities=[0.6])
|
||||
|
||||
# set up pipeline with NER (Entity Ruler) and NEL (prior probability only, model not trained)
|
||||
sentencizer = nlp.create_pipe("sentencizer")
|
||||
nlp.add_pipe(sentencizer)
|
||||
|
||||
ruler = EntityRuler(nlp)
|
||||
patterns = [{"label": "GPE", "pattern": "Boston"},
|
||||
{"label": "GPE", "pattern": "Denver"}]
|
||||
patterns = [
|
||||
{"label": "GPE", "pattern": "Boston"},
|
||||
{"label": "GPE", "pattern": "Denver"},
|
||||
]
|
||||
ruler.add_patterns(patterns)
|
||||
nlp.add_pipe(ruler)
|
||||
|
||||
el_pipe = nlp.create_pipe(name='entity_linker', config={"context_width": 64})
|
||||
el_pipe = nlp.create_pipe(name="entity_linker", config={"context_width": 64})
|
||||
el_pipe.set_kb(mykb)
|
||||
el_pipe.begin_training()
|
||||
el_pipe.context_weight = 0
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user