def _collabrativeFiltering(query,models,issues):
# collaborative filtering through cos-sim scoring
result = {}
scores = cosSim.scoreEntries(query,models)
for issue in issues:
tools.updateDictValues(issue['entries'],scores[issue['_id']],additive=False)
tools.mergeDicts(result,issue['entries'])
return result
def selectCandidates(profile, storage, cf=True):
def prepareQueryMatrix(topics, wordBag, pool):
# standardization
words = topics.viewkeys() | wordBag.viewkeys()
query = deepcopy(topics)
bag = deepcopy(wordBag)
tools.updateDictValues(query,
1.0 / sum(query.values()),
additive=False) # nornmalize query
tools.completeDict(query, words, default=0)
tools.completeDict(bag, words, default=0)
matrix = _getMatrix(pool, words)
return query, matrix, bag
topics = profile['topics']
entries = storage.getLatestEntries()
wordBag = storage.getKeywordWeights()
topicBag = storage.getTopicWeights()
query, matrix, wordWeights = prepareQueryMatrix(topics, wordBag,
_getPool(entries))
import languageModel as lang
import cosSim as cos
import ensemble
candidates = {}
candidates['cosSim'] = cos.scoreEntries(query, matrix)
candidates['languageModel'] = lang.scoreEntries(query, matrix, wordWeights)
if 'model' not in profile or not profile[
'model']: # if profile is not a topic model
models = _getPool(storage.getLatestProfiles(), model=True)
issues = storage.getLatestIssues()
query, models = prepareQueryMatrix(topics, topicBag, models)
import collabrativeFiltering as coFilter
candidtates['collabrativeFiltering'] = coFilter.scoreEntries(
query, models, issues)
return ensemble.scoreEntries(candidates, topics, topicBag, pool, wordBag)
def selectCandidates(profile, storage, cf=True):
def prepareQueryMatrix(topics, wordBag, pool):
# standardization
words = topics.viewkeys() | wordBag.viewkeys()
query = deepcopy(topics)
bag = deepcopy(wordBag)
tools.updateDictValues(query, 1.0 / sum(query.values()), additive=False) # nornmalize query
tools.completeDict(query, words, default=0)
tools.completeDict(bag, words, default=0)
matrix = _getMatrix(pool, words)
return query, matrix, bag
topics = profile["topics"]
entries = storage.getLatestEntries()
wordBag = storage.getKeywordWeights()
topicBag = storage.getTopicWeights()
query, matrix, wordWeights = prepareQueryMatrix(topics, wordBag, _getPool(entries))
import languageModel as lang
import cosSim as cos
import ensemble
candidates = {}
candidates["cosSim"] = cos.scoreEntries(query, matrix)
candidates["languageModel"] = lang.scoreEntries(query, matrix, wordWeights)
if "model" not in profile or not profile["model"]: # if profile is not a topic model
models = _getPool(storage.getLatestProfiles(), model=True)
issues = storage.getLatestIssues()
query, models = prepareQueryMatrix(topics, topicBag, models)
import collabrativeFiltering as coFilter
candidtates["collabrativeFiltering"] = coFilter.scoreEntries(query, models, issues)
return ensemble.scoreEntries(candidates, topics, topicBag, pool, wordBag)
