RovOpt/rov_train.py

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.preprocessing import MultiLabelBinarizer
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
from scipy import sparse
from scipy.sparse import hstack

import process_feature
import process_tag


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 process_feature.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(getRovfeaturetable(tm, table))
    testdata = pd.concat(testlist)
    testdata.reset_index(inplace=True)
    testdata = testdata.drop(axis=1, columns='index')
    return testdata

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

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 

def today_view_category(predict_data):
### 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 =   predict_data.loc[predict_data['day1viewcount_rank'] > 10000]['day1viewcount'].mean()
    data_test1_view2 =   predict_data.loc[(predict_data['day1viewcount_rank'] > 3000)&(predict_data['day1viewcount_rank'] <= 10000)]['day1viewcount'].mean()
    data_test1_view3 =   predict_data.loc[(predict_data['day1viewcount_rank'] > 1000)&(predict_data['day1viewcount_rank'] <= 3000)]['day1viewcount'].mean()
    data_test1_view4 =   predict_data.loc[(predict_data['day1viewcount_rank'] > 300)&(predict_data['day1viewcount_rank'] <= 1000)]['day1viewcount'].mean()
    data_test1_view5 =   predict_data.loc[(predict_data['day1viewcount_rank'] > 100)&(predict_data['day1viewcount_rank'] <= 300)]['day1viewcount'].mean()
    data_test1_view6 =   predict_data.loc[(predict_data['day1viewcount_rank'] > 30)&(predict_data['day1viewcount_rank'] <= 100)]['day1viewcount'].mean()
    data_test1_view7 =   predict_data.loc[(predict_data['day1viewcount_rank'] > 0)&(predict_data['day1viewcount_rank'] <= 30)]['day1viewcount'].mean()
    
    predict_data.loc[predict_data['day1viewcount_rank'] > 10000, 'todyviewcount'] = data_test1_view1
    predict_data.loc[(predict_data['day1viewcount_rank'] > 3000)&(predict_data['day1viewcount_rank'] <= 10000), 'todyviewcount'] = data_test1_view2
    predict_data.loc[(predict_data['day1viewcount_rank'] > 1000)&(predict_data['day1viewcount_rank'] <= 3000), 'todyviewcount'] = data_test1_view3
    predict_data.loc[(predict_data['day1viewcount_rank'] > 300)&(predict_data['day1viewcount_rank'] <= 1000), 'todyviewcount'] = data_test1_view4
    predict_data.loc[(predict_data['day1viewcount_rank'] > 100)&(predict_data['day1viewcount_rank'] <= 300), 'todyviewcount'] = data_test1_view5
    predict_data.loc[(predict_data['day1viewcount_rank'] > 30)&(predict_data['day1viewcount_rank'] <= 100), 'todyviewcount'] = data_test1_view6
    predict_data.loc[(predict_data['day1viewcount_rank'] > 0)&(predict_data['day1viewcount_rank'] <= 30), 'todyviewcount'] = data_test1_view7
    return predict_data

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[process_feature.features]
    df_target = df_pre['weighted_retrn_log']
    df_new_feature = pd.concat([df_new_feature, df_pre[process_feature.cate_feat],df_pre[process_feature.one_hot_feature]], axis=1)
    return df_new_feature, df_target


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 process_train_predict_data():
    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)
    predict_dt = now_date - diff_1
    predict_day = datetime.datetime.strftime(predict_dt, '%Y%m%d')
    train_dt = now_date - diff_5
    train_day = datetime.datetime.strftime(train_dt, '%Y%m%d')

    train_data = getdatasample(train_day, 30, 'rov_feature_add_v1')
    predict_data = getdatasample(predict_day, 1, 'rov_predict_table_add_v1')
    #TODO save tempt
    import _pickle as cPickle
    with open('train_data.pickle','wb') as output_file:
        cPickle.dump(train_data, output_file)
    with open('predict_data.pickle','wb') as output_file:
        cPickle.dump(predict_data, output_file) 
    exit()
    with open(r"train_data.pickle", "rb") as input_file:
        train_data = cPickle.load(input_file)    
    with open(r"predict_data.pickle", "rb") as input_file:
        predict_data = cPickle.load(input_file)       

    train_data = basic_cal(train_data)
    predict_data = basic_cal(predict_data)

    predict_data = select_recent_video(predict_data)
    predict_data.loc[predict_data['dt'] != int(predict_day), 'futre7dayreturn'] = 0
    predict_data = predict_data.drop(axis=1, columns='rk')

    train_data.drop_duplicates(subset=['videoid', 'dt'], keep='first', inplace=True)
    predict_data.drop_duplicates(subset=['videoid', 'dt'], keep='first', inplace=True)

    train_data = train_data.fillna(0)
    predict_data = predict_data.fillna(0)
    train_data = process_feature.cal_feature(train_data)
    predict_data = process_feature.cal_feature(predict_data)
    predict_data =  today_view_category(predict_data) 

    predict_data['videoid'] = predict_data['videoid'].astype('int')

    df_new_feature,df_target= dataprepare(train_data)
    df_new_feature_predict, df_target_predict = dataprepare(predict_data)

    df_new_feature_part_one = sparse.csr_matrix(np.array(pd.DataFrame(df_new_feature).loc[:,'day1playcount':'videocategory555']))
    df_new_feature_predict_part_one = sparse.csr_matrix(np.array(pd.DataFrame(df_new_feature_predict).loc[:,'day1playcount':'videocategory555']))

    print('value feature generate successfully')

    train_videoid = pd.DataFrame(df_new_feature).loc[:,'videoid']
    predict_videoid = pd.DataFrame(df_new_feature_predict).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()
    predict_videoid_list = pd.DataFrame(df_new_feature_predict).loc[:,'videoid'].to_numpy().reshape(len(pd.DataFrame(df_new_feature_predict).loc[:,'videoid']),1).tolist()


    allvideo_raw = list(set(np.array(pd.concat([train_videoid,predict_videoid])).tolist()))
    allvideo = np.array(allvideo_raw).reshape(len(allvideo_raw),1).tolist()
    

    mlb_model_videoid = MultiLabelBinarizer(sparse_output=True).fit(allvideo)
    train_videoid = mlb_model_videoid.transform(train_videoid_list)
    predict_videoid = mlb_model_videoid.transform(predict_videoid_list)

    print('videoid feature generate successfully')

    #获取tag-one-hot
    tags ,train_tag,predict_tag = process_tag.tag_preprocessing('tag', df_new_feature, df_new_feature_predict)
    #获取tag tfidf
    tag_dict = process_tag.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 = process_tag.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 = predict_tag.multiply(tag_tf_idf_matrix)  
    print('tag tfidf feature generate successfully')
    print('tag dimension:', len(tag_tfidf_list))

    #获取values without tag
    words ,train_words,test_words = process_tag.tag_preprocessing('words_no_tag')
    #获取words tfidf
    words_dict = process_tag.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 = process_tag.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))

    df_new_feature = hstack([df_new_feature_part_one,train_videoid,tag_feature_train, words_feature_train])
    df_new_feature_predict = hstack([df_new_feature_predict_part_one,predict_videoid,tag_feature_test,words_feature_test])
 


def do_train(train_data, predict_data, df_target, df_target_predict, df_new_feature, df_new_feature_predict):

    #target
    df_target_predict = sparse.csr_matrix(pd.DataFrame(df_target_predict).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_predict))
    feature_importance_df = pd.DataFrame()


    # values_lenth = len(process_feature.features + process_feature.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_predict.toarray()))
    change_view = change_view.sort_index()


    for fold_, (trn_idx, val_idx) in enumerate(folds.split(df_new_feature, train_data['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_predict, num_iteration=clf.best_iteration) / folds.n_splits

        fold_importance_df = pd.DataFrame()
        
        # column = process_feature.features+process_feature.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_predict, predictions)))
    print('test_mse:', mean_squared_error(df_target_predict, predictions))


    print('oof_mape:', MAPE(df_target, oof))
    print('test_mape:', MAPE(df_target_predict, predictions))

    print('verification r2:', r2_score(df_target, oof))
    print('test r2:', r2_score(df_target_predict, predictions))

    sub_df_ = pd.DataFrame({"videoid": predict_data["videoid"].values})
    sub_df_['score'] = predictions
    print('regre ranking shape', sub_df_.shape)


if __name__ == '__main__':
    process_train_predict_data()