def clusterKmeans(self, force):
print 'Clustering'
keys = self.data.columns[0:len(self.data.columns)-1]
self.kmeans.fit(self.data[keys])
labels = self.kmeans.labels_
self.data['cluster'] = labels
if(os.path.isfile('/Users/igor/songAtributesClustered.csv')):
if(force):
os.remove('/Users/igor/songAtributesClustered.csv')
self.data.to_csv('/Users/igor/songAtributesClustered.csv', sep=',', encoding='utf-8')
else:
self.data.to_csv('/Users/igor/songAtributesClustered.csv', sep=',', encoding='utf-8')
db = Database()
for index, row in self.data.iterrows():
db.saveCluster(remoteId=row['remote_id'], cluster=labels[index])
print 'Finished clustering'
return (True, 'KMeans')
def suggest_similar():
print 'Suggest similar'
if request.json:
#get song name
data = request.json # will be
songName = data.get("songName")
global ml
response = {}
response['success'] = False
response['message'] = 'Oops, something went wrong. We couldn\'t find song: ' + songName
#get song
services = Services()
s = services.getSongBySongName(songName)
#print 'trazena pesam: \n' + s.toString()
if(s != None):
#classify and save cluster
db = Database()
persistedSong = db.getSongByRemoteId(s.remoteId)
if(persistedSong == None):
print 'Pesma nije bila u bazi'
s.id = db.insertSongs([s])[0]
s = ml.classifyDecisionTreeClassifier(s)
print 'Dodeljen klaster: ' + str(s.cluster)
db.saveCluster(s.remoteId, s.cluster)
else:
print 'Pesma je bila u bazi'
s.cluster = persistedSong.cluster
s.id = persistedSong.id
#get songs from same cluster
songsFromCluster = db.getSongsFromSameCluster(s)
criteriaKey = data.get("criteriaKey")
criteriaValue = data.get("criteriaValue")
featureWeights = data.get("featureWeights")
print "feature weights"
print featureWeights
if criteriaKey=="year" and criteriaValue!=None:
years = criteriaValue.split("-")
suggestedSong = ml.getSimilarSongFromYearRange(songsFromCluster, s, int(years[0]), int(years[1]))
#return "not supported yet"
elif(criteriaKey =="genre" and criteriaValue!=None):
suggestedSong = ml.getSimilarSongFromGenre(songsFromCluster, s, criteriaValue)
else:
suggestedSong = ml.getSimilarSong(songsFromCluster, s, featureWeights)
if suggestedSong == None:
response['message'] = "Oops, something went wrong. We couldn't find a recommendation. "
return jsonify(response)
response['success'] = True
response['request_song'] = s.toDictionary()
response['suggested_song'] = suggestedSong.toDictionary()
response['message'] = 'Suggestion successful'
print response
return jsonify(response)
response = {}
response['success'] = False
response['message'] = 'Bad request.'
return jsonify(response)