赛题背景

O2O行业天然关联着数亿消费者，各类App每天记录了超过百亿调用户行位和位置记录，以优惠券盘活老用户或吸引新用户进店消费是O2O的一种重要营销方式，但是随机投放优惠券不仅对多数用户造成无意义的干扰，还会降低商家的品牌声誉，并提高营销成本。所以个性化投放是调高投资率的重要技术。
本赛题提供用户在2016年1月1日至2016年6月30日之间真实线上线下消费行为的数据，我们需要根据这些数据训练出一个模型，预测用户在2016年7月领取优惠券后15天内的使用情况，即投放的优惠券是否核销。故此问题是典型的二分类问题。
常见的分类算法：朴素贝叶斯，决策树，支持向量机，KNN，逻辑回归等等；
集成学习：随机森林，GBDT（梯度提升决策树），Adaboot，XGBoost，LightGBM，CatBoost等等；
神经网络：MLP（多层神经网络），DL（深度学习）等。
本赛题的数据量不大，一把用不到深度学习，根据赛题特点，集成算法，尤其是XGBoost，LightGBM，CatBoost等算法效果会比较好。

全代码

一个典型的机器学习实战算法基本包括 1) 数据处理，2) 特征选取、优化，和 3) 模型选取、验证、优化。因为 “数据和特征决定了机器学习的上限，而模型和算法知识逼近这个上限而已。” 所以在解决一个机器学习问题时大部分时间都会花在数据处理和特征优化上。
大家最好在jupyter notebook上一段一段地跑下面的代码，加深理解。
机器学习的基本知识可以康康我的其他文章哦好康的。

算法包及全局变量


from sklearn import metrics
import numpy as np
import pandas as pd
import datetime
from sklearn.model_selection import StratifiedKFold
import lightgbm as lgb
import xgboost as xgb
import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
from sklearn.model_selection import GridSearchCV
#########部分SKLearn 集成的算法###############
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn import tree
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from sklearn.naive_bayes import MultinomialNB
import warnings
warnings.filterwarnings("ignore")%matplotlib inline
#########SKLearn 集成的算法###############
############全局参数#################################
id_col_names=['user_id','coupon_id','date_received']
target_col_name='label'
id_target_cols=['user_id','coupon_id','date_received','label']
myeval='roc_auc'
pred_result_col='predicted'
#cvscore=0
############目录定义#################################
datapath = '../data/'
featurepath = '../feature/'
resultpath = '../result/'
tmppath = '../tmp/'
scorepath = '../score/'

工具函数

###########工具函数#############################################
#返回ID列
def get_id_df(df):return df[id_col_names]#返回Target列
def get_target_df(df):return df[target_col_name]#返回特征列
def get_predictors_df(df):predictors = [f for f in df.columns if f not in id_target_cols]return df[predictors]#按特征名读取训练集
def read_featurefile_train(featurename): df=pd.read_csv(featurepath+'train_'+featurename+'.csv', sep=',' , encoding = "utf-8")df.fillna(0,inplace=True)return df#按特征名读取测试集
def read_featurefile_test(featurename): df=pd.read_csv(featurepath+'test_'+featurename+'.csv', sep=',' , encoding = "utf-8")df.fillna(0,inplace=True)return df# 将特征归一化
def standize_df(train_data,test_data):from sklearn import preprocessing features_columns = [f for f in test_data.columns if f not in id_target_cols]min_max_scaler = preprocessing.MinMaxScaler()min_max_scaler = min_max_scaler.fit(train_data[features_columns])train_data_scaler = min_max_scaler.transform(train_data[features_columns])test_data_scaler = min_max_scaler.transform(test_data[features_columns])train_data_scaler = pd.DataFrame(train_data_scaler)train_data_scaler.columns = features_columnstest_data_scaler = pd.DataFrame(test_data_scaler)test_data_scaler.columns = features_columnstrain_data_scaler['label'] = train_data['label']train_data_scaler[id_col_names] = train_data[id_col_names]test_data_scaler[id_col_names] = test_data[id_col_names]return train_data_scaler,test_data_scaler#按特征名读取数据
def read_data(featurename): traindf = read_featurefile_train(featurename)testdf = read_featurefile_test(featurename)#return traindf,testdf  return standize_df(traindf,testdf)

训练及结果输出

####################使用sklearn的统一代码框架##########################
#提供的函数包括：
#classifier_single(featurename,classifier,cvnum)
#按满减情况分别预测
#classifier_single_sep_fd(featurename,classifier,cvnum):
####################整合在sklearn的分类算法###############
def get_sklearn_model(model_name,param=None):#朴素贝叶斯if model_name=='NB':model = MultinomialNB(alpha=0.01)#逻辑回归elif model_name=='LR':model = LogisticRegression(penalty='l2') # KNN  elif model_name=='KNN':model = KNeighborsClassifier()  #随机森林elif model_name=='RF':model = RandomForestClassifier()  #决策树elif model_name=='DT':model = tree.DecisionTreeClassifier()  #向量机elif model_name=='SVC':model = SVC(kernel='rbf')#GBDTelif model_name=='GBDT':model = GradientBoostingClassifier()#XGBoostelif model_name=='XGB':model = XGBClassifier()#lightGBMelif model_name=='LGB':model = LGBMClassifier()else:print("wrong model name!")returnif param is not None:model.set_params(**param)return model#性能评价函数
#本赛题目标是预测投放的优惠券是否核销。
#针对此任务及一些相关背景知识，使用优惠券核销预测的平均AUC（ROC曲线下面积）作为评价标准。
#即对每个优惠券coupon_id单独计算核销预测的AUC值，再对所有优惠券的AUC值求平均作为最终的评价标准。
# coupon平均auc计算
def myauc(test):testgroup = test.groupby(['coupon_id'])aucs = []for i in testgroup:coupon_df = i[1]#测算AUC必须大于1个类别if len(coupon_df['label'].unique()) < 2:continueauc = metrics.roc_auc_score(coupon_df['label'], coupon_df['predicted'])aucs.append(auc)return np.average(aucs)#预测方式：按照购买概率进行预测
def proba_predict(model,df):pred = model.predict_proba(df)return pred[:,1] #预测，
def classifier_pred(traindf,classifier,param=None):model = get_sklearn_model(classifier,param)if classifier in ['LGB']:model.fit(get_predictors_df(traindf), get_target_df(traindf),eval_metric=myeval)if classifier in ['XGB']:model.fit(get_predictors_df(traindf), get_target_df(traindf),eval_metric='auc')else:model.fit(get_predictors_df(traindf), get_target_df(traindf))return model#不分折进行预测
def fit_once(train_feat, test_feat,classifier,param=None):model=classifier_pred(train_feat,classifier,param)predicted = pd.DataFrame(proba_predict(model,get_predictors_df(test_feat)))return predicted,get_target_df(train_feat)#分折进行预测
def fit_cv(train_feat, test_feat,classifier,cvnum,param=None):       print('开始CV '+str(cvnum)+'折训练...')train_preds = np.zeros(train_feat.shape[0])test_preds = np.zeros((test_feat.shape[0], cvnum))i=0kf = StratifiedKFold(n_splits = cvnum, shuffle=True, random_state=520)for train_index, test_index in kf.split(get_predictors_df(train_feat),get_target_df(train_feat)):print('第{}次训练...'.format(i+1))train_feat1 = train_feat.iloc[train_index]train_feat2 = train_feat.iloc[test_index]model=classifier_pred(train_feat1,classifier,param)train_preds[test_index] += proba_predict(model,get_predictors_df(train_feat2))test_preds[:,i] = proba_predict(model,get_predictors_df(test_feat))i=i+1#    print('CV训练用时{}秒'.format(time.time() - t0))test_y=test_preds.mean(axis=1)    #test_y_1 = pd.Series(test_y).apply(lambda x : 1 if x>0.5 else 0)#submission = pd.DataFrame({'pred':test_preds.mean(axis=1)})return pd.DataFrame(test_y),pd.DataFrame(train_preds)def classifier_df(train_feat, test_feat,classifier,cvnum,param=None): if cvnum<=1:predicted,train_preds = fit_once(train_feat, test_feat,classifier,param)else:predicted,train_preds = fit_cv(train_feat,test_feat,classifier,cvnum,param)print('output')#predicted=predicted.round(3)return predicted,train_preds#输出结果
def output_predicted(predicted,resultfile,test_feat):predicted=round(predicted,3)resultdf=get_id_df(test_feat).copy()resultdf['Probability']=predictedresultdf.to_csv(resultfile,header=False,index=False,sep=',')#预测函数
def classifier_df_simple(train_feat, test_feat,classifier,param=None):model = get_sklearn_model(classifier,param)    model.fit(get_predictors_df(train_feat), get_target_df(train_feat))    predicted = pd.DataFrame(model.predict_proba(get_predictors_df(test_feat))[:,1])return predicted

算法分析

########################训练曲线################################################
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,n_jobs=1, train_sizes=[0.005,0.01,0.02,0.04,0.1,0.2,0.5]):plt.figure()plt.title(title)if ylim is not None:plt.ylim(*ylim)plt.xlabel("Training examples")plt.ylabel("Score")train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv,scoring=myeval, n_jobs=n_jobs, train_sizes=train_sizes)train_scores_mean = np.mean(train_scores, axis=1)train_scores_std = np.std(train_scores, axis=1)test_scores_mean = np.mean(test_scores, axis=1)test_scores_std = np.std(test_scores, axis=1)plt.grid()plt.fill_between(train_sizes, train_scores_mean - train_scores_std,train_scores_mean + train_scores_std, alpha=0.1,color="r")plt.fill_between(train_sizes, test_scores_mean - test_scores_std,test_scores_mean + test_scores_std, alpha=0.1, color="g")plt.plot(train_sizes, train_scores_mean, 'o-', color="r",label="Training score")plt.plot(train_sizes, test_scores_mean, 'o-', color="g",label="Cross-validation score")plt.legend(loc="best")return plt#画算法的学习曲线,为加快画图速度，最多选30%数据
def plot_curve_single(featurename,classifier,cvnum,train_sizes=[0.002,0.005,0.01,0.02,0.05,0.1,0.2,0.3]): traindf,testdf= read_data(featurename)X=get_predictors_df(traindf)y=get_target_df(traindf)title = "learning curve of feature:"+featurename+", model "+classifier+", cv:"+str(cvnum)estimator = get_sklearn_model(classifier)    #建模plot_learning_curve(estimator, title, X, y, ylim=(0, 1.01), cv=cvnum, train_sizes=train_sizes)
##########################算法分析###############################################
#对算法进行分析整体AUC
def classifier_df_score_auc(train_x,train_y,classifier,cvnum,param=None):from sklearn.model_selection import cross_val_scoremodel=get_sklearn_model(classifier,param)#loss  = make_scorer(my_custom_loss_func, greater_is_better=False)#score = make_scorer(my_custom_loss_func, greater_is_better=True)scores = cross_val_score(model,train_x,train_y,scoring=myeval,cv=cvnum)return scoresdef classifier_single_score(featurename,classifier,cvnum,param=None): print('reading training data...')traindf,testdf= read_data(featurename)train_x=get_predictors_df(traindf)train_y=get_target_df(traindf)scores=classifier_df_score(train_x,train_y,classifier,cvnum,param)print(scores)print(classifier+":")print(scores.mean())#对算法进行分析Coupon AUC
def classifier_df_score(train_feat,classifier,cvnum,param=None):  clf=get_sklearn_model(classifier,param)train = train_feat.copy()target = get_target_df(train_feat).copy()kf = StratifiedKFold(n_splits=cvnum)scores=[]score_coupons=[]for k, (train_index, test_index) in enumerate(kf.split(train,target)):train_data,test_data,train_target,test_target = train.iloc[train_index],train.iloc[test_index],target[train_index],target[test_index]clf.fit(get_predictors_df(train_data), train_target) test_pred=clf.predict_proba(get_predictors_df(test_data))[:,1]score_test = metrics.roc_auc_score(test_target, test_pred)test_data[pred_result_col]=test_predscore_coupon_test=myauc(test_data)scores.append(score_test)score_coupons.append(score_coupon_test)print (classifier+"总体AUC:",scores)print (classifier+"Coupon AUC:",score_coupons)

调参

##########################网格调参###############################################
#对进行网格调参
def grid_search(train_feat, test_feat,classifier,cvnum,search_scope,param=None):parameters= search_scopeclf = GridSearchCV(get_sklearn_model(classifier,param), param_grid=parameters,scoring='roc_auc',verbose=2)clf.fit(get_predictors_df(train_feat), get_target_df(train_feat))print("最优参数")print(clf.best_params_)test_df=get_predictors_df(test_feat)predicted = pd.DataFrame(clf.predict(test_df))return predicteddef grid_search_single(featurename,classifier,cvnum,search_scope,param=None):train_feat,test_feat= read_data(featurename)predicted=grid_search(train_feat, test_feat,classifier,cvnum,search_scope,param)resultfile=resultpath+featurename+'_'+str(cvnum)+'_'+classifier+'_grid_'+format(datetime.datetime.now().strftime('%Y%m%d_%H%M%S'))+'.csv'output_predicted(predicted,resultfile,test_feat)#对进行网格调参
def grid_plot(train_feat,classifier,cvnum,param_range,param_name,param=None):from sklearn.model_selection import validation_curvetrain_scores, test_scores = validation_curve(get_sklearn_model(classifier,param), get_predictors_df(train_feat), get_target_df(train_feat), param_name=param_name, param_range=param_range,cv=cvnum, scoring='roc_auc', n_jobs=1)train_scores_mean = np.mean(train_scores, axis=1)train_scores_std = np.std(train_scores, axis=1)test_scores_mean = np.mean(test_scores, axis=1)test_scores_std = np.std(test_scores, axis=1)plt.title("Validation Curve with "+param_name)plt.xlabel(param_name)plt.ylabel("Score")plt.ylim(0.0, 1.1)plt.semilogx(param_range, train_scores_mean, label="Training score", color="r")plt.fill_between(param_range, train_scores_mean - train_scores_std,train_scores_mean + train_scores_std, alpha=0.2, color="r")plt.semilogx(param_range, test_scores_mean, label="Cross-validation score",color="g")plt.fill_between(param_range, test_scores_mean - test_scores_std,test_scores_mean + test_scores_std, alpha=0.2, color="g")plt.legend(loc="best")plt.show()def grid_plot_single(featurename,classifier,cvnum,param_range,param_name,param=None):train_feat,test_feat= read_data(featurename)grid_plot(train_feat,classifier,cvnum,param_range,param_name,param=None)

整合及输出结果

######################以下为最终输出函数##########################################
#只运行一种算法
def classifier_single(featurename,classifier,cvnum,param=None): traindf,testdf= read_data(featurename)predicted,train_preds=classifier_df(traindf,testdf,classifier,cvnum,param)if cvnum>1:traindf[pred_result_col]=train_preds    score =myauc(traindf)print('线下成绩：    {}'.format(score))resultfile=resultpath+featurename+'_'+str(cvnum)+'_'+classifier+'_'+format(datetime.datetime.now().strftime('%Y%m%d_%H%M%S'))+'_'+str(round(score,3))+'.csv'else:resultfile=resultpath+featurename+'_'+str(cvnum)+'_'+classifier+'_'+format(datetime.datetime.now().strftime('%Y%m%d_%H%M%S'))+'.csv'output_predicted(predicted,resultfile,testdf)# 平均融合classifier_multi的一种融合方式，可以写多个类似的,作为classifier_multi的参数
def multi_mean(train_multi,test_multi,pred_names):i=0for pred in pred_names:i=i+1if i==1:train_multi[pred_result_col]=train_multi[pred]test_multi[pred_result_col]=test_multi[pred]else:train_multi[pred_result_col]=train_multi[pred_result_col]+train_multi[pred]test_multi[pred_result_col]=train_multi[pred_result_col]+test_multi[pred]train_multi[pred_result_col]=train_multi[pred_result_col]/itest_multi[pred_result_col]=test_multi[pred_result_col]/ireturn train_multi,test_multi#运行多种算法
#sum_func为对多种算法结果的整合函数，要求最终的输出列为'predicted'
def classifier_multi(featurename,classifiers,cvnum,sum_func,param=None): traindf,testdf= read_data(featurename)train_multi=traindf.copy()test_multi=testdf.copy()notes=''pred_names=[]for classifier in classifiers:print('开始'+classifier+'训练')notes=notes+'-'+classifierpred_names.append(classifier+'_pred')predicted,train_preds=classifier_df(traindf,testdf,classifier,cvnum,param)train_multi[classifier+'_pred']=train_predstest_multi[classifier+'_pred']=predictedtrain_result,test_result=sum_func(train_multi,test_multi,pred_names)#score = metrics.roc_auc_score(get_target_df(train_result),train_result['predicted'])#print('线下得分：    {}'.format(score))score =myauc(train_result)print('线下成绩：    {}'.format(score))if cvnum>1:resultfile=resultpath+featurename+'_'+str(cvnum)+'_'+notes+'_'+format(datetime.datetime.now().strftime('%Y%m%d_%H%M%S'))+'_'+str(round(score,3))+'.csv'else:resultfile=resultpath+featurename+'_'+str(cvnum)+'_'+notes+'_'+format(datetime.datetime.now().strftime('%Y%m%d_%H%M%S'))+'.csv'output_predicted(test_result['predicted'],resultfile,test_result)#按满减情况分别预测
def classifier_single_sep_fd(featurename,classifier,cvnum,param=None): trainalldf,testalldf= read_data(featurename)test_result=pd.DataFrame()train_result=pd.DataFrame()#按满减情况分类for fd in range(0,2):traindf=trainalldf[trainalldf.if_fd==fd].copy()testdf=testalldf[testalldf.if_fd==fd].copy()predicted,train_preds=classifier_df(traindf,testdf,classifier,cvnum,param)predicted=round(predicted,3)if fd==0:test_result=get_id_df(testdf).copy().reset_index(drop=True)test_result['predicted']=predictedtrain_result=traindf.copy().reset_index(drop=True)train_result['predicted']=train_predselse:dft1=get_id_df(testdf).copy().reset_index(drop=True)dft1['predicted']=predictedtest_result=pd.concat([test_result,dft1], axis=0).reset_index(drop=True)dfv1=traindf.copy().reset_index(drop=True)dfv1['predicted']=train_predstrain_result=pd.concat([train_result,dfv1], axis=0).reset_index(drop=True)if cvnum>1:#score = metrics.roc_auc_score(get_target_df(train_result),train_result['predicted'])score = round(myauc(train_result),3)print('线下得分：    {}'.format(score))    resultfile=resultpath+featurename+'_sepfd_'+str(cvnum)+'_'+classifier+'_'+str(score)+'.csv'else:resultfile=resultpath+featurename+'_sepfd_'+str(cvnum)+'_'+classifier+'.csv'test_result.to_csv(resultfile,header=False,index=False,sep=',')

赛题实践

结果生成

# 采用f2版本特征，LightGBM，5折，默认参数
classifier_single('sf2','LGB',5)# 采用f3版本特征，LightGBM，5折，默认参数
classifier_single('sf3','LGB',5)

# 采用f3版本特征，LightGBM，5折，优化后参数params={'boosting_type':'gbdt','objective': 'binary','eval_metric':'auc','n_estimators':200,'max_depth':5,'num_leaves':40,'max_bin':400,'min_data_in_leaf':120,'learning_rate':0.05,'lambda_l1': 1e-05,'lambda_l2':1e-05,'min_split_gain':0.0,'bagging_freq':4,'bagging_fraction': 0.9,'feature_fraction':0.6,'seed':1024,'n_thread':12}
classifier_single('sf3','LGB',5,params)

# 采用f3版本特征，LightGBM+XGBoost融合，5折，默认参数
classifier_multi('sf3',['XGB','LGB'],5,multi_mean)

# 采用f3版本特征，LightGBM，5折，默认参数，根据是否为满减分别训练
classifier_single_sep_fd('sf3','LGB',5)

# 采用f3版本特征，LightGBM，5折，优化后参数，根据是否为满减分别训练params={'boosting_type':'gbdt','objective': 'binary','eval_metric':'auc','n_estimators':200,'max_depth':5,'num_leaves':40,'max_bin':400,'min_data_in_leaf':120,'learning_rate':0.05,'lambda_l1': 1e-05,'lambda_l2':1e-05,'min_split_gain':0.0,'bagging_freq':4,'bagging_fraction': 0.9,'feature_fraction':0.6,'seed':1024,'n_thread':12}classifier_single_sep_fd('sf3','LGB',5,params)

绘制学习曲线

plot_curve_single('f1','LR',5,[0.01,0.02,0.05,0.1,0.2,0.3])

参数调优

grid_plot_single('sf3','LGB',3,[0.1,0.2,0.5,0.7,0.8],'colsample_bytree')

grid_search_single('f1','LGB',5,[{'gamma':[0,0.3,0.5,0.7,0.9]}])

以上内容和代码全部来自于《阿里云天池大赛赛题解析(机器学习篇)》这本好书，十分推荐大家去阅读原书！

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天池平台已经举办了超过 200 场来自真实业务场景的竞赛,每场赛事沉淀的课题和数据集,将在天池保留和开放.天池平台已成为在校学生踏入职场前的虚拟实践基地,也成为聚集40万数据人才,孵化2000余家数据 ...
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