def setUpClass(self):
# Fake models! Only made so we can do unittests
vm = shVM('tests/w2vModels/*.w2v', useCache=False)
results, links = vm.trackClouds('x')
agg = shVA(yearsInInterval=1)
aggResults, aggMetadata = agg.aggregate(results)
self.embedded = doSpaceEmbedding(vm, results, aggMetadata)
def trackWord(terms):
'''VocabularyMonitor.trackClouds service. Expects a list of terms to be
sent to the Vocabulary monitor, and returns a JSON representation of the
response.'''
params = app.config['trackParser'].parse_args()
termList = terms.split(',')
termList = [ term.strip() for term in termList ]
termList = [ term.lower() for term in termList ]
results, links = \
app.config['vm'].trackClouds(termList, maxTerms=params['maxTerms'],
maxRelatedTerms=params['maxRelatedTerms'],
startKey=params['startKey'],
endKey=params['endKey'],
minSim=params['minSim'],
wordBoost=params['wordBoost'],
forwards=params['forwards'],
sumSimilarity=params['boostMethod'],
algorithm=params['algorithm'],
cleaningFunction=app.config['cleaningFunction'] if params[
'doCleaning'] else None
)
agg = VocabularyAggregator(weighF=params['aggWeighFunction'],
wfParam=params['aggWFParam'],
yearsInInterval=params['aggYearsInInterval'],
nWordsPerYear=params['aggWordsPerYear']
)
aggResults, aggMetadata = agg.aggregate(results)
embedded = doSpaceEmbedding(app.config['vm'], results, aggMetadata)
networks = yearlyNetwork(aggMetadata, aggResults, results, links)
return jsonify(stream=yearTuplesAsDict(aggResults),
networks=networks,
embedded=embedded,
vocabs=links)
def trackWord(terms):
'''VocabularyMonitor.trackClouds service. Expects a list of terms to be
sent to the Vocabulary monitor, and returns a JSON representation of the
response.'''
params = app.config['trackParser'].parse_args()
termList = terms.split(',')
termList = [term.strip() for term in termList]
termList = [term.lower() for term in termList]
results, links = \
app.config['vm'].trackClouds(termList, maxTerms=params['maxTerms'],
maxRelatedTerms=params['maxRelatedTerms'],
startKey=params['startKey'],
endKey=params['endKey'],
minSim=params['minSim'],
wordBoost=params['wordBoost'],
forwards=params['forwards'],
sumSimilarity=params['boostMethod'],
algorithm=params['algorithm'],
cleaningFunction=app.config['cleaningFunction'] if params[
'doCleaning'] else None
)
agg = VocabularyAggregator(weighF=params['aggWeighFunction'],
wfParam=params['aggWFParam'],
yearsInInterval=params['aggYearsInInterval'],
nWordsPerYear=params['aggWordsPerYear'])
aggResults, aggMetadata = agg.aggregate(results)
embedded = doSpaceEmbedding(app.config['vm'], results, aggMetadata)
networks = yearlyNetwork(aggMetadata, aggResults, results, links)
return jsonify(stream=yearTuplesAsDict(aggResults),
networks=networks,
embedded=embedded,
vocabs=links)
def setUpClass(self):
# Fake models! Only made so we can do unittests
vm = shVM('tests/w2vModels/*.w2v', useCache=False)
results, links = vm.trackClouds('x')
agg = shVA(yearsInInterval=1)
aggResults, aggMetadata = agg.aggregate(results)
self.embedded = doSpaceEmbedding(vm, results, aggMetadata)