'''

@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