def loadOrTrainLSHModel(forceGenerate=False):
lshModel = None
if os.path.exists("./pickle_files/lshModel.pickle") and not forceGenerate:
print("LSH model found on disk")
pickleIn = open("./pickle_files/lshModel.pickle", "rb")
lshModel = pickle.load(pickleIn)
else:
print("Training LSH model")
trainAudioDataAndRateArray = loadAllFiles("train", '')
trainingData = generateData(trainAudioDataAndRateArray)
lshModel = LSH()
for data in trainingData:
print("fileName", data[1])
validFrameList = extractValidFrames(data[0])
for i in range(0, validFrameList.shape[0]):
reshapedValidFrame = validFrameList[i].reshape(1, -1)
# print("reshapedValidFrame", reshapedValidFrame)
lshModel.train(reshapedValidFrame, {
"name": data[1] + "_" + str(i),
"frameIndex": i
})
# hr.train(validFrames[1:2], data[1])
# print("lshModel", lshModel)
pickleOut = open("./pickle_files/lshModel.pickle", "wb")
pickle.dump(lshModel, pickleOut)
pickleOut.close()
return lshModel
if "-h" in sys.argv or "--help" in sys.argv:
print(
"Usage: ./nn.py [OPTION] \n\n"
" -h | --help Show this help message and exit \n"
" --fetch <plugin> Fetch new data with proprietary plugin \n"
" --train Train LSH model \n"
" --query <ID> Nearest Neighbor query \n")
exit(0)
if "--fetch" in sys.argv:
pluginName = sys.argv[2].replace('.py', '')
Dimport("%s" % pluginName, pluginName,
FULLNAME('plugins'))(CONST.DATASET)
if "--train" in sys.argv:
dataframe = pd.read_csv(CONST.DATASET)
corpus = TfidfVectorizer().fit_transform(dataframe['content'])
lsh = LSH(corpus)
model = lsh.train()
pickle.dump(model, open(CONST.MODEL, 'wb'))
if "--query" in sys.argv:
print(query(sys.argv[2]))
# eof
