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)