import os import yaml from common_func import get_db_engine import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.neighbors import NearestNeighbors import numpy as np import sys import pickle import logging from concurrent_log import ConcurrentTimedRotatingFileHandler log_formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s', '%Y/%m/%d %I:%M:%S %p') log_handler = ConcurrentTimedRotatingFileHandler("/data/pythonProject/similar_material/logs/historical_material.log", when="midnight", backupCount=100) log_handler.setFormatter(log_formatter) logger = logging.getLogger('historical_material_logger') logger.addHandler(log_handler) logger.setLevel(logging.DEBUG) logger.info("historical_material_logger started!") print('id of historical_material_logger %s' % id(logger)) def scaler_feature(data, axis=0): """ :param data: pd.DataFrame :param axis: 0-横向归一化 , 1-纵向归一化 :return: z = (x - u) / s """ scaler = StandardScaler() scaler_data = None if axis == 0: scaler_data = scaler.fit_transform(data.T).T if axis == 1: scaler_data = scaler.fit_transform(data) scaler_df = pd.DataFrame(scaler_data) scaler_df.index = data.index scaler_df.columns = [col + '_scaler' for col in data.columns] return scaler_df def get_segment_features(table, signature, dim_col, project_id, col_prefix): # 1 获取原始数据 get_org_data_sql = """ select signature, aclick, bclick, activation, charge, %s from %s where signature in %s and project_id = %s """ % (dim_col, table, tuple(signature), project_id) org_df = pd.read_sql(get_org_data_sql, read_engine) # 2 计算各分段的 行为率、激活率、激活成本,并进行横向归一化 g = org_df.groupby(['signature', dim_col]).agg({'activation': sum, 'aclick': sum, 'bclick': sum, 'charge': sum}).unstack() # 该维度下有哪些分段值:如 gender维度下有:男、女、其他 , 需要去掉 '其他',因为业务含义不明确,没法解释模型效果 segment_cols = g['charge'].columns if '其他' in segment_cols: segment_cols.remove('其他') segment_fea_df = pd.DataFrame() for col in segment_cols: segment_fea_df[col + '_bclick_aclick_pct'] = g[('bclick', col)] / g[('aclick', col)] segment_fea_df[col + '_activation_bclick_pct'] = g[('activation', col)] / g[('bclick', col)] segment_fea_df[col + '_cost'] = g[('charge', col)] / g[('activation', col)] # 3 计算累计激活中各分段的激活个数占比,并进行横向归一化 for col in segment_cols: segment_fea_df[col + '_activation_pct'] = g[('activation', col)] / (g[[('activation', item) for item in segment_cols]].sum(axis=1)) # 4 对 np.nan 和 np.inf 进行处理 # 4-1 cost # np.inf : 分子>0/分母=0, 表示有消耗但是没有激活数,填充为 999 # np.nan :分子=0/分母=0, 表示没有消耗也没有激活数,填充为 -1 cost_cols = [col for col in segment_fea_df.columns if 'cost' in col] segment_fea_df[cost_cols] = segment_fea_df[cost_cols].replace({np.nan: -1, np.inf: 999}) # 4-2 占比类 # np.inf :分子>0/分母=0 ,如行为率:表示有点击但没有曝光,属于数据bug情况。需要剔除该素材并写入日志 # np.nan :分子=0/分母=0 ,如激活率:表示没有激活也没有点击,填充为 0 pct_cols = [col for col in segment_fea_df.columns if 'pct' in col] segment_fea_df[pct_cols] = segment_fea_df[pct_cols].replace({np.nan: 0}) inf_df = np.isinf(segment_fea_df[pct_cols]).sum(axis=1) remove_sig_lst = inf_df[inf_df >= 1].index # TODO 添加日志 记录数据存在问题的素材 if len(remove_sig_lst): print(remove_sig_lst) segment_fea_df = segment_fea_df[~segment_fea_df.index.isin(remove_sig_lst)] # 5 对特征进行横向归一化 segment_fea_scaler_df = pd.DataFrame() for col in ['bclick_aclick_pct', 'activation_bclick_pct', 'cost', 'activation_pct']: tmp_df = segment_fea_df.filter(like=col) scaler_df = scaler_feature(tmp_df, axis=0) segment_fea_scaler_df = pd.concat([segment_fea_scaler_df, scaler_df], axis=1) # 6 col_prefix and index segment_fea_scaler_df.columns = [col_prefix + col for col in segment_fea_scaler_df.columns] segment_fea_scaler_df.index = g.index return segment_fea_scaler_df def get_signature_features(signature, project_id): """ 计算素材整体的 行为率、激活率、激活成本 :param project_id: :param signature: :return: """ get_data_sql = """ select signature, sum(aclick) aclick, sum(bclick) bclick, sum(activation) activation, sum(charge) charge from ctop_kuaishou_audience_report_daily_age_material where signature in %s and project_id = %s group by signature """ % (tuple(signature), project_id) sig_org_df = pd.read_sql(get_data_sql, read_engine) sig_fea_df = pd.DataFrame() sig_fea_df['cost'] = sig_org_df['charge'] / sig_org_df['activation'] sig_fea_df['bclick_aclick_pct'] = sig_org_df['bclick'] / sig_org_df['aclick'] sig_fea_df['activation_bclick_pct'] = sig_org_df['activation'] / sig_org_df['bclick'] # 对特征进行纵向的归一化 sig_fea_df.index = sig_org_df['signature'].values sig_fea_scaler_df = scaler_feature(sig_fea_df, axis=1) return sig_fea_scaler_df if __name__ == '__main__': # 1、读取配置文件 with open('config/config.yaml', mode='r', encoding='utf-8') as f: config = yaml.load(f.read(), Loader=yaml.FullLoader) # 1-1 数据库连接引擎,依据开发环境/生产环境 进行切换 if os.getenv('LYY_DEV', 'unknown') == 'dev': write_engine = get_db_engine(config['devDB']) else: write_engine = get_db_engine(config['productDB']) read_engine = get_db_engine(config['productDB']) # 2、获取历史所有达标的素材 project_id = 458 get_sig_sql = """select signature, sum(activation) activation from ctop_kuaishou_audience_report_daily_age_material where project_id = %s group by signature having sum(activation) >= 100 """ % project_id sig_df = pd.read_sql(get_sig_sql, read_engine) sig_df = sig_df[~sig_df.signature.isnull()] sig_lst = list(sig_df.signature.values) print('sig_lst:', len(sig_lst)) # TODO for test # sig_lst = ['cefd07cf7951ba9674d43c1d9c112f37', # '6f11859acaf4f1dd501706f8d50995d8', # 'bc8fe6ae97db794acaf050acff4386b9', # '59a34e41bfcf321a931a0b204a8609c9', # 'c67604889af726d49401f40283865bb0', # '75237de4427a9ff6da994976bf516f78', # '0ab2a8816b1b34638767862123fc74a6', # '177a0f359eb90623b7ecaf1c8d0b9db4', # 'd3c9bbd98d07afb7be9fe8b1b40133d3'] if sig_lst is None: # TODO 加入 logger sys.exit(0) if len(sig_lst) == 1: # TODO 加入 logger sys.exit(1) # 2、构造特征 # 2-1 获取素材整体的特征情况:行为率、激活率、激活成本 sig_feature_df = get_signature_features(sig_lst, project_id) # 2-2 获取各维度(age,gender)各分段(男、女等)的行为率、激活率、激活成本、激活占比,并进行纵向归一化 # get_segment_features(table, signature, dim_col, project_id) table_dim_tuple = [('ctop_kuaishou_audience_report_daily_age_material', 'age_segment', 'age_'), ('ctop_kuaishou_audience_report_daily_gender_material', 'gender', 'gender_')] segment_feature_df = pd.DataFrame() for table, dim_col, col_prefix in table_dim_tuple: tmp_df = get_segment_features(table, sig_lst, dim_col, project_id, col_prefix) segment_feature_df = pd.concat([segment_feature_df, tmp_df], axis=1) # 2-3 素材整体的特征 和 各维度各分段的特征 进行合并,得到最终的特征DataFrame final_feature_df = pd.concat([sig_feature_df, segment_feature_df], axis=1) # 部分素材在 age 或者 gender 维度上没有数据,特征concat时,会导致空值 # 去掉这部分的素材,防止在调用 NearestNeighbors 时,报错 null_df = final_feature_df.isnull().sum(axis=1) exist_null_fea_sig_lst = null_df[null_df >= 1].index # TODO 添加日志 记录存在数据缺失的素材 if len(exist_null_fea_sig_lst) > 0: print(exist_null_fea_sig_lst) final_feature_df = final_feature_df[~final_feature_df.index.isin(exist_null_fea_sig_lst)] with open('final_feature_df.pkl', 'wb') as f: pickle.dump(final_feature_df, f) # 3 Finding the Nearest Neighbors k = config['nearestNeighbors']['k'] distance_threshold = config['nearestNeighbors']['distanceThreshold'] X = final_feature_df.values nbrs = NearestNeighbors(n_neighbors=k, algorithm='ball_tree').fit(X) distances, indices = nbrs.kneighbors(X) with open('distances.pkl', 'wb') as f: pickle.dump(distances, f) with open('indices.pkl', 'wb') as f: pickle.dump(indices, f) # 3-1 两点之间的距离超过阈值时,不作为相邻素材 # mask 中,为 True 的元素替换为 NaN mask = distances > distance_threshold indices_mask_df = pd.DataFrame(indices).mask(mask) indices_mask_df = indices_mask_df.astype(dtype=pd.Int64Dtype()) # 3-2 把 index 替换为具体的 signatures sig_lst = final_feature_df.index.values indices_mask_df.index = sig_lst indices_mask_df = indices_mask_df.applymap(lambda x: sig_lst[x] if not pd.isnull(x) else x) res_lst = [] for index, row in indices_mask_df.iterrows(): others = [item for item in row if (not pd.isnull(item)) and (item != index)] res_lst.append([index] + others) res_df = pd.DataFrame(res_lst) res_df.columns = ['self'] + ['N' + str(i + 1) for i in range(res_df.shape[1] - 1)] columns = ['self'] + ['N' + str(i + 1) for i in range(k)] res_df = res_df.reindex(columns=columns) with open('res_df.pkl', 'wb') as f: pickle.dump(res_df, f) # 4 TODO 结果入库 pass