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import os
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import sys
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sys.path.append(os.getcwd())
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import pandas as pd
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from datetime import datetime
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import warnings
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import pickle
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from tqdm import tqdm
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from sklearn.preprocessing import MinMaxScaler
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from deepctr.models import DIN
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from deepctr.layers import custom_objects
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from tensorflow.keras import backend as K
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from tensorflow.keras.layers import *
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from tensorflow.keras.models import *
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from tensorflow.keras.callbacks import *
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from tensorflow.python.keras.models import load_model
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import tensorflow as tf
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from config import subject_features_save_path
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from config import subject_model_save_path
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from config import logger
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from config import subject_all_user_item_feats
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from config import subject_bst_rank_dict
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from config import subject_rank_dense_fea
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from config import subject_rank_sparse_fea
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from config import subject_max_seq_len
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from config import mysubjects_data_path
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from utils import get_user
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from matching.subject.recall_comm import get_item_info_df
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from matching.subject.recall_comm import get_all_select_df
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from ranking.subject.bst_ranker_train import get_bst_feats_columns
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from ranking.subject.user_recall_rank_features import init_rank_features
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from ranking.subject.user_recall_rank_features import build_rank_features_offline
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global graph, sess
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = "1"
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K.set_learning_phase(True)
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if tf.__version__ >= '2.0.0':
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tf.compat.v1.disable_eager_execution()
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graph = tf.compat.v1.get_default_graph()
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sess = tf.compat.v1.keras.backend.get_session()
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warnings.filterwarnings('ignore')
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logger.info('获取物品信息')
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item_info_df = get_item_info_df()
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item_info_df = item_info_df[['subject_id', 'subject_name']].reset_index()
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logger.info('加载物品行为数据')
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all_data = get_all_select_df()
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logger.info('生成用户历史选择物品数据')
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hist_select = all_data[['user_id', 'subject_id']].groupby('user_id').agg({list}).reset_index()
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his_behavior_df = pd.DataFrame()
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his_behavior_df['user_id'] = hist_select['user_id']
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his_behavior_df['hist_subject_id'] = hist_select['subject_id']
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his_behavior_df = his_behavior_df.reset_index()
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logger.info('加载BST排序模型')
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bst_model = load_model(subject_model_save_path + 'bst_model.h5', custom_objects)
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def bst_ranker_predict(user_item_feats_df, topk=10, verbose=True):
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"""
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BST模型预测接口
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:param model: 训练保存的BST模型
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:param user_item_feats_df: 根据user_id生成的排序模型特征
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:param topk: 返回排序后的topk个物品
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"""
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start_time = datetime.now()
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# 获取用户的历史选择物品
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user_item_feats_df = user_item_feats_df.merge(his_behavior_df, on='user_id')
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# 稀疏特征
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sparse_fea = subject_rank_sparse_fea
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# 行为特征
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behavior_fea = ['subject_id']
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# 历史行为特征
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hist_behavior_fea = ['hist_subject_id']
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# 稠密连续型特征
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dense_fea = subject_rank_dense_fea
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# 填充缺失值
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user_item_feats_df[dense_fea] = user_item_feats_df[dense_fea].fillna(0, )
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# dense特征进行归一化
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for feat in dense_fea:
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min_max_scaler = pickle.load(open(subject_model_save_path + 'min_max_scaler_' + feat + '.model', 'rb'))
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user_item_feats_df[feat] = min_max_scaler.transform(user_item_feats_df[[feat]])
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# sparse特征LabelEncoder
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for feat in sparse_fea:
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label_encoder = pickle.load(open(subject_model_save_path + feat + '_label_encoder.model', 'rb'))
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user_item_feats_df[feat] = label_encoder.transform(user_item_feats_df[[feat]])
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if feat == 'subject_id':
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subject_id_lable_encoder = label_encoder
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if feat == 'user_id':
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user_id_label_encoder = label_encoder
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x, dnn_feature_columns = get_bst_feats_columns(user_item_feats_df,
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dense_fea,
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sparse_fea,
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behavior_fea,
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hist_behavior_fea,
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max_len=subject_max_seq_len)
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# 模型预测
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# with graph.as_default():
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# with sess.as_default():
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user_item_feats_df['pred_score'] = bst_model.predict(x, verbose=1, batch_size=256)#BST运行环境版本不兼容，改成tf2.x的风格
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# 还原user_id和subject_id
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user_item_feats_df['user_id'] = user_id_label_encoder.inverse_transform(user_item_feats_df[['user_id']])
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user_item_feats_df['subject_id'] = subject_id_lable_encoder.inverse_transform(user_item_feats_df[['subject_id']])
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# 按预测分数降序排序
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rank_results = user_item_feats_df[['user_id', 'subject_id', 'rank', 'pred_score']]
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rank_results['subject_id'] = rank_results['subject_id'].astype(int)
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rank_results = rank_results.merge(item_info_df, how='left', on='subject_id')
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rank_results = rank_results[['user_id', 'subject_id', 'subject_name', 'rank', 'pred_score']]
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rank_results.sort_values(by=['pred_score'], ascending=False, inplace=True)
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rank_results['rank'] = rank_results['pred_score'].rank(ascending=False, method='first').astype(int)
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rank_results = rank_results[:topk]
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# 计算耗时毫秒
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end_time = datetime.utcnow()
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cost_time_millisecond = round(float((end_time - start_time).microseconds / 1000.0), 3)
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if verbose:
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logger.info(f"BST 预测耗时: {cost_time_millisecond} 毫秒")
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return rank_results
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def get_user_his_behavior_df(user_id):
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user_his_behavior_df = his_behavior_df[his_behavior_df['user_id'] == user_id]
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return user_his_behavior_df
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def alluser_bst_ranker_predict():
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"""
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生成所有用户召回物品离线特征排序后的字典
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"""
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init_rank_features()
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recall_rank_list_lict = {}
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all_user_ids = get_user(mysubjects_data_path)
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for user_id in tqdm(all_user_ids):
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user_id = int(user_id)
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try:
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recall_rank_list_lict.setdefault(user_id, [])
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user_item_feats_df = build_rank_features_offline(user_id)
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user_his_behavior_df = get_user_his_behavior_df(user_id)
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if user_his_behavior_df.shape[0] > 0:
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rank_results = bst_ranker_predict(user_item_feats_df,
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topk=user_item_feats_df.shape[0],
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verbose=False)
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for subject_id, subject_name in zip(rank_results['subject_id'], rank_results['subject_name']):
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recall_rank_list_lict[user_id].append((subject_id, subject_name))
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except:
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continue
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pickle.dump(recall_rank_list_lict, open(subject_bst_rank_dict, 'wb'))
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if __name__ == '__main__':
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user_item_feats_df = pd.read_csv(subject_features_save_path + 'user_item_feats_df.csv', sep='\t')
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tmp_user_item_feats_df = user_item_feats_df.merge(item_info_df, how='left', on='subject_id')
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logger.info('BST排序之前的数据:')
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print(tmp_user_item_feats_df[['user_id', 'subject_id', 'subject_name', 'rank']][:20])
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rank_results = bst_ranker_predict(user_item_feats_df,
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topk=user_item_feats_df.shape[0])
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logger.info('BST排序之后的数据:')
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print(rank_results[['user_id', 'subject_id', 'subject_name', 'rank']][:20])
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alluser_bst_ranker_predict()
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print("success!!!") |
