File size: 2,543 Bytes
8304f29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | from data.data import Data
from util.conf import OptionConf
from util.logger import Log
from os.path import abspath
from time import strftime, localtime, time
class Recommender(object):
def __init__(self, conf, training_set, test_set, **kwargs):
self.config = conf
self.data = Data(self.config, training_set, test_set)
self.model_name = self.config['model.name']
self.ranking = OptionConf(self.config['item.ranking'])
self.emb_size = int(self.config['embbedding.size'])
self.maxEpoch = int(self.config['num.max.epoch'])
self.batch_size = int(self.config['batch_size'])
self.lRate = float(self.config['learnRate'])
self.reg = float(self.config['reg.lambda'])
self.output = OptionConf(self.config['output.setup'])
current_time = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
self.model_log = Log(self.model_name, self.model_name + ' ' + current_time)
self.result = []
self.recOutput = []
def initializing_log(self):
self.model_log.add('### model configuration ###')
for k in self.config.config:
self.model_log.add(k + '=' + self.config[k])
def print_model_info(self):
print('Model:', self.config['model.name'])
print('Training Set:', abspath(self.config['training.set']))
print('Test Set:', abspath(self.config['test.set']))
print('Embedding Dimension:', self.emb_size)
print('Maximum Epoch:', self.maxEpoch)
print('Learning Rate:', self.lRate)
print('Batch Size:', self.batch_size)
print('Regularization Parameter:', self.reg)
parStr = ''
if self.config.contain(self.config['model.name']):
args = OptionConf(self.config[self.config['model.name']])
for key in args.keys():
parStr += key[1:] + ':' + args[key] + ' '
print('Specific parameters:', parStr)
def build(self):
pass
def train(self):
pass
def predict(self, u):
pass
def test(self):
pass
def save(self):
pass
def load(self):
pass
def evaluate(self, rec_list):
pass
def execute(self):
self.initializing_log()
self.print_model_info()
print('Initializing and building model...')
self.build()
print('Training Model...')
self.train()
print('Testing...')
rec_list = self.test()
print('Evaluating...')
self.evaluate(rec_list)
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