From 453ff9dcd842c1ae386e24990f796de115aada91 Mon Sep 17 00:00:00 2001
From: Carlos Vega <carlos.vega@uni.lu>
Date: Wed, 20 Nov 2019 10:49:03 +0100
Subject: [PATCH] comments added
---
pmc_europe/textminingservice_pmc_europe/pmc_europe.py | 9 ++++++---
1 file changed, 6 insertions(+), 3 deletions(-)
diff --git a/pmc_europe/textminingservice_pmc_europe/pmc_europe.py b/pmc_europe/textminingservice_pmc_europe/pmc_europe.py
index 4f3ff16..a5ba62f 100644
--- a/pmc_europe/textminingservice_pmc_europe/pmc_europe.py
+++ b/pmc_europe/textminingservice_pmc_europe/pmc_europe.py
@@ -43,7 +43,6 @@ class PMC_Europe_Service(TextMiningService):
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())
@@ -90,6 +89,8 @@ class PMC_Europe_Service(TextMiningService):
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.
+
+ Right now this does not work well because the pageSize is to small, hence the mentions retrieval time is too slow
"""
white_list = None
article_list = []
@@ -109,13 +110,15 @@ class PMC_Europe_Service(TextMiningService):
publications = np.array(publications)
scores = np.array(scores)
inds = scores.argsort()[::-1]
- return publications[inds]
+ return publications[inds][:limit]
def get_co_occurrences(self, entity: str, limit: int = 20, types: List[str] = None) -> List[CoOccurrence]:
- pass
+ """Returns a list of co-occurrences from a given entity
+ """
if __name__ == "__main__":
pmc = PMC_Europe_Service()
+ # get mentions for a single
for pub in pmc.get_mentions(['P53', 'PRDM1']):
print(pub)
--
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