From a8b44a0fd6a0d43764c74237084ce4b06c21e229 Mon Sep 17 00:00:00 2001
From: Carlos Vega <carlos.vega@uni.lu>
Date: Wed, 20 Nov 2019 09:44:38 +0100
Subject: [PATCH] moved file to python package
---
.../pmc_europe.py | 121 ++++++++++++++++++
1 file changed, 121 insertions(+)
create mode 100644 pmc_europe/textminingservice_pmc_europe/pmc_europe.py
diff --git a/pmc_europe/textminingservice_pmc_europe/pmc_europe.py b/pmc_europe/textminingservice_pmc_europe/pmc_europe.py
new file mode 100644
index 0000000..4f3ff16
--- /dev/null
+++ b/pmc_europe/textminingservice_pmc_europe/pmc_europe.py
@@ -0,0 +1,121 @@
+import logging
+from typing import List, Set, DefaultDict
+from collections import defaultdict
+
+import itertools
+import requests
+import json
+import numpy as np
+
+from textminingservice.TextMiningService import TextMiningService
+from textminingservice.models.coocurrence import CoOccurrence
+from textminingservice.models.publication import Publication
+
+logger = logging.getLogger(__name__)
+
+
+class PMC_Europe_Service(TextMiningService):
+ """[summary]
+
+ Arguments:
+ TextMiningService {[type]} -- [description]
+ """
+ MAX_PAGE_SIZE = 8
+ BASE_URL = 'https://www.ebi.ac.uk'
+ MENTION_URL = BASE_URL + \
+ '/europepmc/annotations_api/annotationsByEntity?entity={}&filter={}&format={}&cursorMark={}&pageSize={}'
+
+ def __init__(self):
+ super().__init__('PCM Europe',
+ 'This client communicates with PCM Europe API.')
+
+ def _get_single_entity_mentions(self, entity: str):
+ """
+ Generator that yields each article and article id that mentions the given entity
+
+ See https://europepmc.org/AnnotationsApi#!/annotations45api45controller/getAnnotationsArticlesByEntityUsingGET
+
+ The articles come up sorted by number of mentions
+ """
+ prevCursorMark = -1
+ cursorMark = 0
+ counter = 0
+ while cursorMark != prevCursorMark:
+ url = PMC_Europe_Service.MENTION_URL.format(
+ entity, 1, 'ID_LIST', cursorMark, PMC_Europe_Service.MAX_PAGE_SIZE)
+ print(f'Get {counter}')
+ results = requests.get(url)
+ assert results.ok
+ data = json.loads(results.content.decode().strip())
+ prevCursorMark = cursorMark
+ cursorMark = data['nextCursorMark']
+ for article in data['articles']:
+ counter += 1
+ yield article, article['extId']
+
+ def _incremental_intersection(self, entity: str, white_list: DefaultDict[str, float] = None):
+ """Takes the given entity and returns every article with an ID contained in the given white list as well as a new white list with the new ids and recalculated scores.
+ The score is the normalized position. (1-pos/length) giving more importance to the first article.
+ The articles are given my PMC Europe sorted by the number of occurrences.
+
+ The returned white list will be smaller or equal in size to the given white list.
+ The scores are added up to the previous ones.
+ """
+
+ if white_list is None:
+ white_list = defaultdict(float)
+
+ new_article_list = []
+ new_scores = []
+ new_ids = []
+ index = 0
+ for article, article_id in self._get_single_entity_mentions(entity):
+ index += 1
+ # if no white list, keep all, otherwise, only those in white_list
+ if len(white_list) == 0 or article_id in white_list:
+ new_article_list.append(article)
+ new_scores.append(index)
+ new_ids.append(article_id)
+
+ new_scores = 1-np.array(new_scores)/index # normalize position score
+ new_white_list = dict(zip(new_ids, new_scores))
+ # update scores of the intersection
+ for id in new_ids:
+ new_white_list[id] += white_list[id]
+
+ return new_article_list, new_white_list
+
+ def get_mentions(self, entities: List[str], limit: int = 20) -> List[Publication]:
+ """
+ This method returns a list of publications sorted by importance.
+ Since PMC Europe sorts the publications based on the number of occurrences,
+ this new score could be seen as the degree of co-occurrence.
+ """
+ white_list = None
+ article_list = []
+ for entity in entities:
+ article_list, white_list = self._incremental_intersection(
+ entity, white_list=white_list)
+ print(article_list)
+ # last iteration contains the final intersection
+ scores = []
+ publications = []
+ for article in article_list:
+ article_id = article['extId']
+ pub = Publication(pm_id=article_id, pmc_id=article['pmcid'])
+ publications.append(pub)
+ scores.append(white_list[article_id])
+
+ publications = np.array(publications)
+ scores = np.array(scores)
+ inds = scores.argsort()[::-1]
+ return publications[inds]
+
+ def get_co_occurrences(self, entity: str, limit: int = 20, types: List[str] = None) -> List[CoOccurrence]:
+ pass
+
+
+if __name__ == "__main__":
+ pmc = PMC_Europe_Service()
+ for pub in pmc.get_mentions(['P53', 'PRDM1']):
+ print(pub)
--
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