import warnings warnings.filterwarnings("ignore") from sklearn.metrics import r2_score import os import pandas as pd import gc import math import numpy as np import time from sklearn.linear_model import SGDRegressor from sklearn.linear_model import SGDClassifier import lightgbm as lgb from sklearn.model_selection import train_test_split from sklearn.model_selection import StratifiedKFold from sklearn import metrics import pickle from sklearn.metrics import mean_squared_error import seaborn as sns import matplotlib.pylab as plt from odps import ODPS from odps.df import DataFrame as odpsdf from datetime import datetime as dt import datetime now_date = datetime.date.today() # day = datetime.datetime.strftime(now_date, '%Y%m%d') diff_1 = datetime.timedelta(days=1) diff_5 = datetime.timedelta(days=7) input_dt = now_date - diff_1 input_day = datetime.datetime.strftime(input_dt, '%Y%m%d') now_day = datetime.datetime.strftime(now_date, '%Y%m%d') train_dt = now_date - diff_5 train_day = datetime.datetime.strftime(train_dt, '%Y%m%d') def getRovfeaturetable(dt, table): odps = ODPS('LTAI4FtW5ZzxMvdw35aNkmcp', '0VKnydcaHK3ITjylbgUsLubX6rwiwc', 'usercdm', endpoint='http://service.cn.maxcompute.aliyun.com/api', connect_timeout=3000, \ read_timeout=500000, pool_maxsize=1000, pool_connections=1000) featureArray = [] for record in odps.read_table(table, partition='dt=%s' % dt): valueFeature = {} for i in featurename: if i == 'dt': valueFeature[i] = dt else: valueFeature[i] = record[i] featureArray.append(valueFeature) featureArray = pd.DataFrame(featureArray) print(dt, table, 'feature table finish') return featureArray def getdatasample(date, max_range, table): new_date = dt.strptime(date, '%Y%m%d') datelist = [] testlist = [] for i in range(0, max_range): delta = datetime.timedelta(days=i) tar_dt = new_date - delta datelist.append(tar_dt.strftime("%Y%m%d")) print(datelist) for tm in datelist: testlist.append(getRovtestable(tm, table)) testdata = pd.concat(testlist) testdata.reset_index(inplace=True) testdata = testdata.drop(axis=1, columns='index') return testdata traindata = getrainingdata(train_day, 30, 'rov_feature_add_v1') data_test_ori_rk = getestingdata(input_day, 1, 'rov_predict_table_add_v1') def select_recent_video(df): """对每一个视频添加row number，按照日期排序，最后选取最近的那一天""" df['dt'] = df['dt'].astype(int) df['rk'] = df['dt'].groupby(df['videoid']).rank(ascending=0, method='first') df = df[df['rk'] == 1] return df data_test_ori = select_recent_video(data_test_ori_rk) data_test_ori.loc[data_test_ori['dt'] != int(input_day), 'futre7dayreturn'] = 0 data_test_ori = data_test_ori.drop(axis=1, columns='rk') traindata.drop_duplicates(subset=['videoid', 'dt'], keep='first', inplace=True) data_test_ori.drop_duplicates(subset=['videoid', 'dt'], keep='first', inplace=True) def basic_cal(df): df['weighted_retrn'] = df['futre7dayreturn'].astype('int') df['weighted_retrn_log'] = df.apply(lambda x: np.log(x['weighted_retrn'] + 1),axis=1) ## 设置回流大于thresh， label就是1， 没有分享或有分享但是回流数是零的标为0 df['return_back'] = df.apply(lambda x:1 if x['weighted_retrn']> 0 else 0,axis=1) return df data_train = basic_cal(traindata) data_test = basic_cal(data_test_ori) def today_view_category(df): ### 对当天的曝光量分三个级别，未来三天的曝光量分3个级别，添加Category feaure data_test1_view1 = df.loc[data_test['day1viewcount_rank'] > 10000]['day1viewcount'].mean() data_test1_view2 = df.loc[(data_test['day1viewcount_rank'] > 3000)&(data_test['day1viewcount_rank'] <= 10000)]['day1viewcount'].mean() data_test1_view3 = df.loc[(data_test['day1viewcount_rank'] > 1000)&(data_test['day1viewcount_rank'] <= 3000)]['day1viewcount'].mean() data_test1_view4 = df.loc[(data_test['day1viewcount_rank'] > 300)&(data_test['day1viewcount_rank'] <= 1000)]['day1viewcount'].mean() data_test1_view5 = df.loc[(data_test['day1viewcount_rank'] > 100)&(data_test['day1viewcount_rank'] <= 300)]['day1viewcount'].mean() data_test1_view6 = df.loc[(data_test['day1viewcount_rank'] > 30)&(data_test['day1viewcount_rank'] <= 100)]['day1viewcount'].mean() data_test1_view7 = df.loc[(data_test['day1viewcount_rank'] > 0)&(data_test['day1viewcount_rank'] <= 30)]['day1viewcount'].mean() df.loc[df['day1viewcount_rank'] > 10000, 'todyviewcount'] = data_test1_view1 df.loc[(data_test['day1viewcount_rank'] > 3000)&(data_test['day1viewcount_rank'] <= 10000), 'todyviewcount'] = data_test1_view2 df.loc[(data_test['day1viewcount_rank'] > 1000)&(data_test['day1viewcount_rank'] <= 3000), 'todyviewcount'] = data_test1_view3 df.loc[(data_test['day1viewcount_rank'] > 300)&(data_test['day1viewcount_rank'] <= 1000), 'todyviewcount'] = data_test1_view4 df.loc[(data_test['day1viewcount_rank'] > 100)&(data_test['day1viewcount_rank'] <= 300), 'todyviewcount'] = data_test1_view5 df.loc[(data_test['day1viewcount_rank'] > 30)&(data_test['day1viewcount_rank'] <= 100), 'todyviewcount'] = data_test1_view6 df.loc[(data_test['day1viewcount_rank'] > 0)&(data_test['day1viewcount_rank'] <= 30), 'todyviewcount'] = data_test1_view7 return df data_test = today_view_category(data_test) def dataprepare(df_pre): # 直接将特征送进去，不加交叉特征。 # 是否对数据补零 df_pre = df_pre.fillna(0) df_new_feature = df_pre[features] df_target = df_pre['weighted_retrn_log'] df_new_feature = pd.concat([df_new_feature, df_pre[cate_feat],df_pre[one_hot_feature]], axis=1) return df_new_feature, df_target data_test['videoid'] = data_test['videoid'].astype('int') data_train = data_train[data_train['weighted_retrn'] > 0] print(data_train.shape, 'train shape') data_test = pd.merge(data_test, recall_video_stage_one, on=['videoid'], how='inner') print('score>0.5 video_count:', data_test.shape) df_new_feature,df_target= dataprepare(data_train) df_new_feature_test, df_target_test = dataprepare(data_test) #数值 from scipy import sparse df_new_feature_part_one = sparse.csr_matrix(np.array(pd.DataFrame(df_new_feature).loc[:,'day1playcount':'videocategory555'])) df_new_feature_test_part_one = sparse.csr_matrix(np.array(pd.DataFrame(df_new_feature_test).loc[:,'day1playcount':'videocategory555'])) print('value feature generate successfully') train_videoid = pd.DataFrame(df_new_feature).loc[:,'videoid'] test_videoid = pd.DataFrame(df_new_feature_test).loc[:,'videoid'] train_videoid_list = pd.DataFrame(df_new_feature).loc[:,'videoid'].to_numpy().reshape(len(pd.DataFrame(df_new_feature).loc[:,'videoid']),1).tolist() test_videoid_list = pd.DataFrame(df_new_feature_test).loc[:,'videoid'].to_numpy().reshape(len(pd.DataFrame(df_new_feature_test).loc[:,'videoid']),1).tolist() allvideo_raw = list(set(np.array(pd.concat([train_videoid,test_videoid])).tolist())) allvideo = np.array(allvideo_raw).reshape(len(allvideo_raw),1).tolist() from sklearn.preprocessing import MultiLabelBinarizer mlb_model_videoid = MultiLabelBinarizer(sparse_output=True).fit(allvideo) train_videoid = mlb_model_videoid.transform(train_videoid_list) test_videoid = mlb_model_videoid.transform(test_videoid_list) print('videoid feature generate successfully') #获取tag-one-hot tags ,train_tag,test_tag = tag_preprocessing('tag') #获取tag tfidf tag_dict = get_tag_tfidf('20200305','video_tag_tf_idf') print('lenth tag_dict:',len(tag_dict)) #获取tfidf_tag 稀疏矩阵 tag_corpus = tags.tolist() #corpus tag_tfidf_list = ttfidf_list_generation(tag_corpus,tag_dict ) tag_tf_idf_matrix = sparse.csr_matrix(np.array(tag_tfidf_list)) tag_feature_train = train_tag.multiply(tag_tf_idf_matrix) tag_feature_test = test_tag.multiply(tag_tf_idf_matrix) print('tag tfidf feature generate successfully') print('tag dimension:', len(tag_tfidf_list)) # In[28]: #获取values without tag words ,train_words,test_words = tag_preprocessing('words_no_tag') #获取words tfidf words_dict = get_tag_tfidf('20200305','video_words_without_tags_tfidf') print('lenth words_dict:',len(words_dict)) #获取tfidf_tag 稀疏矩阵 words_corpus = words.tolist() #corpus words_tfidf_list = ttfidf_list_generation(words_corpus,words_dict ) words_tf_idf_matrix = sparse.csr_matrix(np.array(words_tfidf_list)) words_feature_train = train_words.multiply(words_tf_idf_matrix) words_feature_test = test_words.multiply(words_tf_idf_matrix) print('tag tfidf feature generate successfully') print('words dimension:', len(words_tfidf_list)) def featureImportance(fold1_df,fold2_df,fold3_df,fold4_df,values_lenth,video_id_lenth,tag_length,word_length): Feature_Data= pd.DataFrame() for df in (fold1_df,fold2_df,fold3_df,fold4_df): fold1_df1 = df.iloc[0:values_lenth,:] videoid_fold1_importance = df.iloc[values_lenth:values_lenth+video_id_lenth,:]['importance'].sum() fold1_df2 = pd.DataFrame([{'Feature':'videoid','importance':videoid_fold1_importance,'fold':1}]) tag_fold1_importance = df.iloc[values_lenth+video_id_lenth:values_lenth+video_id_lenth+tag_length,:]['importance'].sum() fold1_df3 = pd.DataFrame([{'Feature':'tags','importance':tag_fold1_importance,'fold':1}]) words_fold1_importance = df.iloc[values_lenth+video_id_lenth+tag_length:values_lenth+video_id_lenth+tag_length+word_length,:]['importance'].sum() fold1_df4 = pd.DataFrame([{'Feature':'words','importance':words_fold1_importance,'fold':1}]) Feature_Data = pd.concat([Feature_Data,fold1_df1,fold1_df2,fold1_df3,fold1_df4]) return Feature_Data def MAPE(true, pred): true = np.array(true) sum_ = 0 count = 0 for i in range(len(true)): if true[i] != 0: sum_ = sum_ + np.abs(true[i] - pred[i]) / true[i] count = count + 1 else: continue return sum_ / count def do_train(): from scipy.sparse import hstack df_new_feature = hstack([df_new_feature_part_one,train_videoid,tag_feature_train, words_feature_train]) df_new_feature_test = hstack([df_new_feature_test_part_one,test_videoid,tag_feature_test,words_feature_test]) #target df_target_test = sparse.csr_matrix(pd.DataFrame(df_target_test).values).toarray() df_target = sparse.csr_matrix(pd.DataFrame(df_target).values).toarray() param = {'num_leaves': 18, 'min_data_in_leaf': 60, 'objective': 'regression', 'max_depth': -1, 'learning_rate': 0.01, "min_child_samples": 30, "boosting": "gbdt", "feature_fraction": 0.8, "bagging_freq": 1, "bagging_fraction": 0.8, "bagging_seed": 11, "metric": 'rmse', "lambda_l1": 0.1, "verbosity": -1, "nthread": 4, # 'max_bin': 512, "random_state": 4590} folds = StratifiedKFold(n_splits=4, shuffle=True, random_state=4590) oof = np.zeros(len(pd.DataFrame(df_new_feature.toarray()))) predictions = np.zeros(len(df_target_test)) feature_importance_df = pd.DataFrame() values_lenth = len(features + cate_feat) video_id_lenth = len(mlb_model_videoid.classes_) tag_length = len(tag_tfidf_list) word_length = len(words_tfidf_list) change_view = pd.DataFrame(pd.DataFrame(df_new_feature_test.toarray())) change_view = change_view.sort_index() for fold_, (trn_idx, val_idx) in enumerate(folds.split(df_new_feature, data_train['return_back'].values)): print("folds {}".format(fold_)) trn_data = lgb.Dataset(df_new_feature.tocsr()[trn_idx,:], label=pd.DataFrame(df_target).iloc[trn_idx]) val_data = lgb.Dataset(df_new_feature.tocsr()[val_idx,:], label=pd.DataFrame(df_target).iloc[val_idx]) num_round = 10000 clf = lgb.train(param, trn_data, num_round, valid_sets=[trn_data, val_data], verbose_eval=100, early_stopping_rounds=200) oof[val_idx] = clf.predict(df_new_feature.tocsr()[val_idx,:], num_iteration=clf.best_iteration) predictions += clf.predict(df_new_feature_test, num_iteration=clf.best_iteration) / folds.n_splits fold_importance_df = pd.DataFrame() column = features+cate_feat+mlb_model_videoid.classes_.tolist()+ tag_corpus + words_corpus fold_importance_df["Feature"] = np.array(column) fold_importance_df["importance"] = clf.feature_importance() fold_importance_df["fold"] = fold_ + 1 feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0) fold1_df = feature_importance_df.loc[feature_importance_df['fold']==1] fold2_df = feature_importance_df.loc[feature_importance_df['fold']==2] fold3_df = feature_importance_df.loc[feature_importance_df['fold']==3] fold4_df = feature_importance_df.loc[feature_importance_df['fold']==4] feature_importance_df = featureImportance(fold1_df,fold2_df,fold3_df,fold4_df,values_lenth,video_id_lenth,tag_length,word_length) print('oof_rmse:', np.sqrt(mean_squared_error(df_target, oof))) print('oof_mse:', mean_squared_error(df_target, oof)) print('test_rmse:', np.sqrt(mean_squared_error(df_target_test, predictions))) print('test_mse:', mean_squared_error(df_target_test, predictions)) print('oof_mape:', MAPE(df_target, oof)) print('test_mape:', MAPE(df_target_test, predictions)) print('verification r2:', r2_score(df_target, oof)) print('test r2:', r2_score(df_target_test, predictions)) sub_df_ = pd.DataFrame({"videoid": data_test["videoid"].values}) sub_df_['score'] = predictions print('regre ranking shape', sub_df_.shape)