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150 lines
5.9 KiB
150 lines
5.9 KiB
import os
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import sys
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sys.path.append(os.getcwd())
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from datetime import datetime
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import warnings
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from tqdm import tqdm
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import pickle
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import pandas as pd
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from matching.subject.recall_comm import get_item_info_df
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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_model_save_path
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from config import logger
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from config import subject_xdeepfm_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 mysubjects_data_path
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from config import subject_features_save_path
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from utils import get_user
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from ranking.subject.xdeepfm_ranker_train import get_xdeepfm_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xDeepFM排序模型')
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xdeepfm_model = load_model(subject_model_save_path + 'xdeepfm_model.h5', custom_objects)
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def xdeepfm_ranker_predict(user_item_feats_df, topk=10, verbose=True):
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"""
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xdeepfm模型预测接口
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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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# 稀疏特征
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sparse_fea = subject_rank_sparse_fea
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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, linear_feature_columns, dnn_feature_columns = get_xdeepfm_feats_columns(
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user_item_feats_df, dense_fea, sparse_fea)
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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'] = xdeepfm_model.predict(x, verbose=1, batch_size=256)
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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', 'pred_score']]
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rank_results['user_id'] = rank_results['user_id'].astype(int)
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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', 'pred_score']]
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rank_results.sort_values(by=['pred_score'], ascending=False, inplace=True)
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rank_results['pred_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"xDeepFM 预测耗时: {cost_time_millisecond} 毫秒")
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return rank_results
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def alluser_xdeepfm_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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# print(all_user_ids[:10])
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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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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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if user_item_feats_df.shape[0] == 0:
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continue
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rank_results = xdeepfm_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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pickle.dump(recall_rank_list_lict, open(subject_xdeepfm_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('xDeepFM排序之前的数据:')
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print(tmp_user_item_feats_df[['user_id', 'subject_id', 'subject_name', 'score', 'rank']][:20])
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rank_results = xdeepfm_ranker_predict(user_item_feats_df, topk=user_item_feats_df.shape[0])
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logger.info('xDeepFM排序之后的数据:')
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print(rank_results[['user_id', 'subject_id', 'subject_name', 'pred_score', 'pred_rank']][:20])
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alluser_xdeepfm_ranker_predict()
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