Merge branch '11-text-mining-sw' of...

Merge branch '11-text-mining-sw' of github.com:elixir-europe/BioHackathon-projects-2019 into 11-text-mining-sw

dummy-service/dummyService.py

@@ -2,6 +2,7 @@ import logging
 from typing import List
 
 from interface.TextMiningService import TextMiningService, Publication
+from models.coocurrence import CoOccurrence
 
 logger = logging.getLogger(__name__)
 
@@ -13,7 +14,7 @@ class DummyService(TextMiningService):
     def get_mentions(self, entities: List, limit: int = 20) -> List[Publication]:
         return [Publication(pm_id="00000" + str(i)) for i in range(20)]
 
-    def get_co_occurrences(self, entity: str) -> List[str]:
+    def get_co_occurrences(self, entity: str, limit: int = 20) -> List[CoOccurrence]:
         pass
 
 

interface/TextMiningService.py

 from abc import ABCMeta, abstractmethod
 from typing import List
-from models.publication import Publication
+
 from models.coocurrence import CoOccurrence
+from models.publication import Publication
 
 
 class TextMiningService(metaclass=ABCMeta):

jensenlab-service/__init__.py

+import logging
+
+logger = logging.getLogger(__name__)   
+ 
\ No newline at end of file

jensenlab-service/jensenLabService.py

+import json
+import logging
+from typing import List
+
+import requests
+
+from TextMiningService import TextMiningService, Publication
+
+logger = logging.getLogger(__name__)
+
+
+class JensenLabService(TextMiningService):
+    BASE_URL = "https://api.jensenlab.org"
+    MENTION_URL = BASE_URL + "/Mentions?type={}&id={}&limit={}&format=json"
+    IDS_MAPPING = {
+        "CID": -1,
+        "BTO": -25,
+        "DOID": -26,
+    }
+
+    def __init__(self):
+        super().__init__("JensenLabService", "Text-Mining api available at api.jensenlab.org")
+
+    def get_mentions(self, entities: List, limit: int = 20) -> List[Publication]:
+        entities_and_types = self.guess_types_for_entities(entities)
+        publications_ids = []
+        for (entity, entity_type) in entities_and_types:
+            publications_ids.append(self.get_mentions_for_entity(entity, entity_type, limit))
+        publications_ids_intersection = set.intersection(*publications_ids)
+        return [Publication(pm_id=pid) for pid in publications_ids_intersection]
+
+    def get_co_occurrences(self, entity: str, limit: int = 20) -> List[str]:
+        pass
+
+    @staticmethod
+    def guess_types_for_entities(entities):
+        results = []
+        for entity in entities:
+            entity_type = JensenLabService.guess_type_for_entity(entity)
+            results.append((entity, entity_type))
+        return results
+
+    @staticmethod
+    def guess_type_for_entity(entity):
+        for prefix, entity_type in JensenLabService.IDS_MAPPING.items():
+            if entity.startswith(prefix):
+                return entity_type
+        return -1
+
+    def get_mentions_for_entity(self, entity, entity_type, limit):
+        url_mentions = JensenLabService.MENTION_URL.format(entity_type, entity, limit)
+        results = requests.get(url_mentions)
+        assert results.ok
+        publications_string = results.content.decode().strip().replace('True', 'true').replace('False', 'false')
+        publications_list, has_more = json.loads(publications_string)
+        return set(publications_list)
+
+
+if __name__ == '__main__':
+    text_mining_service = JensenLabService()
+    print("Using service {}".format(text_mining_service.name))
+    publications = text_mining_service.get_mentions(["DOID:10652", "DOID:10654"], limit=1000000)
+    print(", ".join([p.pm_id for p in publications]))