| 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) |
|
|
