Ejemplo n.º 1
0
 def train_level1(self, select_param=False):
     self.l1_param = {
         "bst:max_depth": 10,
         "bst:min_child_weight": 4,
         "bst:subsample": 0.5,
         "bst:colsample_bytree": 0.8,
         "bst:eta": 0.05,
     }
     other = {
         "silent": 1,
         "objective": "multi:softprob",
         "num_class": 9,
         "nthread": 4,
         "eval_metric": "mlogloss",
         "seed": 0,
     }
     other["num_class"] = len(self.l1_lbl_enc.classes_)
     self.l1_num_round = 400
     if select_param:
         results = otto_utils.select_xgb_params(self.l1_train, self.l1_labels, depth_range=(7, 12))
         self.l1_results = results
         print results[0]
         self.l1_num_round = results[0][1]
         param = results[0][2]
     dtrain = xgb.DMatrix(self.l1_train, label=self.l1_labels)
     full_param = other.copy()
     full_param.update(self.l1_param)
     bst, loss, ntree = xgb.train(full_param, dtrain, self.l1_num_round, [(dtrain, "train")])
     self.l1_model = bst
     pass
Ejemplo n.º 2
0
 def train_level2(self, select_param=False):
     self.l2_models = []
     self.l2_params = []
     self.l2_num_rounds = []
     self.l2_results = []
     for i in xrange(len(self.cls_sets)):
         param = {
             "bst:max_depth": 10,
             "bst:min_child_weight": 4,
             "bst:subsample": 0.5,
             "bst:colsample_bytree": 0.8,
             "bst:eta": 0.05,
         }
         other = {
             "silent": 1,
             "objective": "multi:softprob",
             "num_class": 9,
             "nthread": 4,
             "eval_metric": "mlogloss",
             "seed": 0,
         }
         other["num_class"] = len(self.cls_sets[i])
         num_round = 400
         if select_param:
             results = otto_utils.select_xgb_params(self.l2_train[i], self.l2_labels[i], depth_range=(8, 13))
             self.l2_results.append(results)
             print results[0]
             num_round = results[0][1]
             param = results[0][2]
         dtrain = xgb.DMatrix(self.l2_train[i], self.l2_labels[i])
         self.l2_params.append(param)
         self.l2_num_rounds.append(num_round)
         full_param = other.copy()
         full_param.update(param)
         bst, loss, ntree = xgb.train(full_param, dtrain, num_round, [(dtrain, "train")])
         self.l2_models.append(bst)
     pass