def query(query, subSet, useFeatures, usePageRank, useTFIDF, useClustering, useRecommend, topN=100):
results = {}
if not useFeatures and not usePageRank and not useTFIDF and not useClustering and not useRecommend:
return None
query = normalizeString(query, datasets.STOPWORDS, lemmatization).split()
print "Querying with: %r" % query
if useFeatures:
results['IF'] = features.queryFeatures(query, subSet)
prevMethod = 'IF'
if usePageRank:
results['PR'] = pagerank.queryPageRank(query, subSet)
if useTFIDF:
results['TI'] = TFIDF.queryTFIDF(query, subSet)
if useClustering:
results['CL'] = clustering.queryClustering(query, subSet)
prevMethod = 'CL'
if useRecommend:
results['RE'] = recommend.queryRecommend(query, subSet)
prevMethod = 'RE'
endresults = collections.defaultdict(float)
# Loop through all pmid's after having ID set sliced like subSet prescribes
for pmid in [pmid for pmid in datasets.IDS if str(pmid).startswith(subSet)]:
i = 0
if useFeatures and results['IF'].has_key(pmid):
i += 1
endresults[pmid] += results['IF'][pmid] * 1.0 # Weighted modifier, if required
if usePageRank and results['PR'].has_key(pmid):
i += 1
endresults[pmid] += results['PR'][pmid] * 1.0 # Weighted modifier, if required
if useTFIDF and results['TI'].has_key(pmid):
i += 1
endresults[pmid] += results['TI'][pmid] * 1.0 # Weighted modifier, if required
if useClustering and results['CL'].has_key(pmid):
i += 1
endresults[pmid] += results['CL'][pmid] * 1.0 # Weighted modifier, if required
if useRecommend and results['RE'].has_key(pmid):
i += 1
endresults[pmid] += results['RE'][pmid] * 1.0 # Weighted modifier, if required
if i > 0:
endresults[pmid] /= i # Divide by the amount of succesful techniques ran on this pmid
normalizeScore(endresults)
# Return the topN results, sorted descending by score
return sorted(endresults.items(), key=operator.itemgetter(1), reverse=True)[:topN]
def prepareQuery(query):
return normalizeString(query, datasets.STOPWORDS, lemmatization).split()
