--- a
+++ b/config.py
@@ -0,0 +1,84 @@
+'''
+@Author: your name
+@Date: 2019-12-20 19:02:25
+@LastEditTime: 2020-05-26 20:58:12
+@LastEditors: Please set LastEditors
+@Description: In User Settings Edit
+@FilePath: /matengfei/KGCN_Keras-master/config.py
+'''
+# -*- coding: utf-8 -*-
+
+import os
+
+RAW_DATA_DIR = os.getcwd()+'/raw_data'
+PROCESSED_DATA_DIR = os.getcwd()+'/data'
+LOG_DIR = os.getcwd()+'/log'
+MODEL_SAVED_DIR = os.getcwd()+'/ckpt'
+
+KG_FILE = {
+           'drugbank':os.path.join(RAW_DATA_DIR,'drugbank','train2id.txt'),
+           'kegg':os.path.join(RAW_DATA_DIR,'kegg','train2id.txt')}
+ENTITY2ID_FILE = {
+                    'drugbank':os.path.join(RAW_DATA_DIR,'drugbank','entity2id.txt'),
+                    'kegg':os.path.join(RAW_DATA_DIR,'kegg','entity2id.txt')}
+EXAMPLE_FILE = {
+               'drugbank':os.path.join(RAW_DATA_DIR,'drugbank','approved_example.txt'),
+               'kegg':os.path.join(RAW_DATA_DIR,'kegg','approved_example.txt')}
+SEPARATOR = {'drug':'\t','kegg':'\t'}
+THRESHOLD = {'drug':4,'kegg':4} #添加drug修改
+NEIGHBOR_SIZE = {'drug':4,'kegg':4}
+
+#
+DRUG_VOCAB_TEMPLATE = '{dataset}_drug_vocab.pkl'
+ENTITY_VOCAB_TEMPLATE = '{dataset}_entity_vocab.pkl'
+RELATION_VOCAB_TEMPLATE = '{dataset}_relation_vocab.pkl'
+ADJ_ENTITY_TEMPLATE = '{dataset}_adj_entity.npy'
+ADJ_RELATION_TEMPLATE = '{dataset}_adj_relation.npy'
+TRAIN_DATA_TEMPLATE = '{dataset}_train.npy'
+DEV_DATA_TEMPLATE = '{dataset}_dev.npy'
+TEST_DATA_TEMPLATE = '{dataset}_test.npy'
+#RESULT_LOG='result.txt'
+RESULT_LOG={'drugbank':'drugbank_result.txt','kegg':'kegg_result.txt'}
+PERFORMANCE_LOG = 'kgcn_performance.log'
+DRUG_EXAMPLE='{dataset}_examples.npy'
+
+class ModelConfig(object):
+    def __init__(self):
+        self.neighbor_sample_size = 4 # neighbor sampling size
+        self.embed_dim = 32  # dimension of embedding
+        self.n_depth = 2    # depth of receptive field
+        self.l2_weight = 1e-7  # l2 regularizer weight
+        self.lr = 2e-2  # learning rate
+        self.batch_size = 65536
+        self.aggregator_type = 'sum'
+        self.n_epoch = 50
+        self.optimizer = 'adam'
+
+        self.drug_vocab_size = None
+        self.entity_vocab_size = None
+        self.relation_vocab_size = None
+        self.adj_entity = None
+        self.adj_relation = None
+
+        self.exp_name = None
+        self.model_name = None
+
+        # checkpoint configuration 设置检查点
+        self.checkpoint_dir = MODEL_SAVED_DIR
+        self.checkpoint_monitor = 'val_auc'
+        self.checkpoint_save_best_only = True
+        self.checkpoint_save_weights_only = True
+        self.checkpoint_save_weights_mode = 'max'
+        self.checkpoint_verbose = 1
+
+        # early_stoping configuration
+        self.early_stopping_monitor = 'val_auc'
+        self.early_stopping_mode = 'max'
+        self.early_stopping_patience = 5
+        self.early_stopping_verbose = 1
+        self.dataset='drug'
+        self.K_Fold=1
+        self.callbacks_to_add = None
+
+        # config for learning rating scheduler and ensembler
+        self.swa_start = 3