EduCoder_Study_RS/matching/subject/pinsage_recall_train.py

import os
import sys
sys.path.append(os.getcwd())
from tqdm import tqdm
import warnings
import tensorflow as tf
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import Model
import collections
import pickle
from libreco.algorithms import PinSage
from libreco.data import DatasetFeat
from config import logger, offline_mode
from config import need_metric_recall
from config import subject_pinsage_model_path
from config import subject_pinsage_recall_dict
from matching.subject.recall_comm import get_all_select_df
from matching.subject.recall_comm import get_user_info_df,get_item_info_df
from matching.subject.recall_comm import metrics_pinsage_recall,get_all_hist_and_last_select

K.set_learning_phase(True)
if tf.__version__ >= '2.0.0':
    tf.compat.v1.disable_eager_execution()

tqdm.pandas()
warnings.filterwarnings('ignore')

def reset_state(name):
    tf.compat.v1.reset_default_graph()
    print("\n", "=" * 30, name, "=" * 30)


def pinsage_recall_train(): 
    """
    pinsage召回训练
    """

    # 需要召回的数量
    recall_item_num = 100

    logger.info("加载物品行为数据")
    all_select_df = get_all_select_df(offline=offline_mode)   
    
    logger.info("获取物品信息数据")
    item_info = get_item_info_df()
    
    logger.info("获取用户信息数据")
    users_info = get_user_info_df()

    all_select_df = all_select_df.merge(users_info, on='user_id')
    all_select_df = all_select_df.merge(item_info,on='subject_id')

    # 为了召回评估，提取最后一次选择作为召回评估
    # 如果不需要做召回评估直接使用全量的训练集进行召回     
    if need_metric_recall:
        logger.info('获取物品行为数据历史和最后一次选择') 
        train_hist_select_df, train_last_select_df = get_all_hist_and_last_select(all_select_df)
        train_hist_select_df['label'] = 1
        train_last_select_df['label'] = 1
        # pinsage模型需要
        train_hist_select_df.rename(columns={'user_id': 'user','subject_id': 'item'}, inplace=True)
        train_last_select_df.rename(columns={'user_id': 'user','subject_id': 'item'}, inplace=True)
    else:
        train_hist_select_df = all_select_df
        train_hist_select_df['label'] = 1     
        train_hist_select_df.rename(columns={'user_id': 'user','subject_id': 'item'}, inplace=True)   
        train_last_select_df = all_select_df.sample(frac=0.001)  
        train_last_select_df['label'] = 1 
        train_last_select_df.rename(columns={'user_id': 'user','subject_id': 'item'}, inplace=True)  
                            
    #调试程序简单采样，注意删除临时文件
    # train_hist_select_df= train_hist_select_df.sample(frac=0.001)
    # print(train_hist_select_df.head())

    # 定义特征,指定完整列信息
    # sparse_col = ['user_id','subject_id']
    dense_col = ['gender', 'identity', 'edu_background','logins','grade','experience',
                 'visits','stages_count','stage_shixuns_count','shixuns_count','study_count',
                 'course_study_count','passed_count','challenge_count','evaluate_count',
                 'study_pdf_attachment_count','averge_star']
    user_col = ['gender', 'identity', 'edu_background','logins','grade','experience']
    item_col = ['visits','stages_count','stage_shixuns_count','shixuns_count','study_count',
                'course_study_count','passed_count','challenge_count','evaluate_count',
                'study_pdf_attachment_count','averge_star']
    train_data = train_hist_select_df[['user', 'item','gender', 'identity', 'edu_background','logins','grade','experience',
                                     'visits','stages_count','stage_shixuns_count','shixuns_count','study_count',
                                     'course_study_count','passed_count','challenge_count','evaluate_count',
                                     'study_pdf_attachment_count','averge_star','label']]
    eval_data = train_last_select_df[['user', 'item','gender', 'identity', 'edu_background','logins','grade','experience',
                                    'visits','stages_count','stage_shixuns_count','shixuns_count','study_count',
                                    'course_study_count','passed_count','challenge_count','evaluate_count',
                                    'study_pdf_attachment_count','averge_star','label']]
    # print(type(train_data))
    # print(type(eval_data))
    # input()
    train_data, data_info = DatasetFeat.build_trainset(
        train_data, user_col, item_col, dense_col,seed=2023,shuffle=False
    )
    eval_data = DatasetFeat.build_testset(eval_data)

    reset_state("PinSage")
    
    pinsage = PinSage(
        "ranking",
        data_info,
        loss_type="cross_entropy",
        paradigm="u2i",
        embed_size=32,
        n_epochs=10,
        lr=3e-4,
        lr_decay=False,
        reg=None,
        batch_size=256,
        num_neg=1,
        dropout_rate=0.01,
        remove_edges=False,
        num_layers=1,
        num_neighbors=10,
        num_walks=10,
        neighbor_walk_len=2,
        sample_walk_len=5,
        termination_prob=0.5,
        margin=1.0,
        sampler="random",
        start_node="random",
        focus_start=False,
        seed=2023,
    )
    pinsage.fit(
        train_data,
        neg_sampling=True,
        verbose=2,
        shuffle=True

    )

    # save data_info, 指定模型保存文件夹
    data_info.save(path=subject_pinsage_model_path, model_name="pinsage_model")
    
    # 设置 manual=True 使用 `numpy` 保存模型
    # 设置 manual=False 使用 `tf.train.Saver` 保存模型
    # 设置 inference=True 将只保留预测和推荐所需的变量
    pinsage.save(
        path=subject_pinsage_model_path, model_name="pinsage_model", manual=True, inference_only=True
    )
    print("训练结束，模型已保存")
    user_recall_items_dict = collections.defaultdict(dict)
    
    logger.info('生成item所有用户的召回列表有得分')
    for user_id in tqdm(train_hist_select_df['user'].unique()):
        item_list = list(pinsage.recommend_user(user=user_id, n_rec=recall_item_num).values())[0].tolist()

        score = []
        for i in item_list:
            score.append(pinsage.predict(user=user_id, item=i)[0])
        user_recall_items_dict[user_id] = list(zip(item_list,tuple(score)))

    logger.info('保存pinsage召回结果')
    pickle.dump(user_recall_items_dict, open(subject_pinsage_recall_dict, 'wb'))

    logger.info('pinsage召回效果评估')      
    metrics_pinsage_recall(user_recall_items_dict, train_last_select_df, topk=recall_item_num)   


if __name__ == '__main__':   
    print("召回开始训练")
    pinsage_recall_train()