阿里云天池大赛赛题（机器学习）——天猫用户重复购买预测（完整代码）(阿里云天池大赛pdf)

阿里云天池大赛赛题（机器学习）——天猫用户重复购买预测（完整代码） 目录赛题背景全代码导入包读取数据（训练数据前10000行，测试数据前100条）读取全部数据获取训练和测试数据切分40%数据用于线下验证交叉验证：评估估算器性能F1验证ShuffleSplit切分数据模型调参模糊矩阵不同的分类模型LR 模型KNN 模型tree树模型bagging模型随机森林模型ExTree模型AdaBoost模型GBDT模型VOTE模型投票lgb 模型xgb 模型自己封装模型Stacking,Bootstrap,Bagging技术实践测试自己封装的模型类天猫复购场景实战读取特征数据设置模型参数模型训练预测结果保存结果赛题背景

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商家一般会在 “双十一”，“双十二” 等节日进行大规模的促销，比如各种折扣券和现金券。然而，被低价、折扣、各种让利吸引的用户往往在这次消费之后就再也没有购买，主要为了“薅羊毛”，针对这些用户的促销并没有带来未来销量的提高，只是增加了相应的营销成本。因此店铺有迫切的需求，想知道哪些用户可能会成为重复购买其店铺商品的忠诚用户，以便对这些有潜力的用户进行精准营销，以降低促销成本，提高投资回报率。 这个赛题的目标就是给一堆数据（用户、店铺的历史行为），然后用训练好的模型预测新用户是否会在6个月内再次从同一店铺购买商品。所以这是一个典型的二分类问题。 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。

导入包import pandas as pdimport numpy as npimport warningswarnings.filterwarnings("ignore") 读取数据（训练数据前10000行，测试数据前100条）train_data = pd.read_csv('train_all.csv',nrows=10000)test_data = pd.read_csv('test_all.csv',nrows=100)train_data.head()test_data.head()

读取全部数据train_data.columns获取训练和测试数据features_columns = [col for col in train_data.columns if col not in ['user_id','label']]train = train_data[features_columns].valuestest = test_data[features_columns].valuestarget =train_data['label'].values切分40%数据用于线下验证from sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierclf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0, n_jobs=-1)X_train, X_test, y_train, y_test = train_test_split(train, target, test_size=0.4, random_state=0)print(X_train.shape, y_train.shape)print(X_test.shape, y_test.shape)clf = clf.fit(X_train, y_train)clf.score(X_test, y_test) 交叉验证：评估估算器性能from sklearn.model_selection import cross_val_scorefrom sklearn.ensemble import RandomForestClassifierclf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0, n_jobs=-1)scores = cross_val_score(clf, train, target, cv=5)print(scores)print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2)) F1验证from sklearn import metricsfrom sklearn.model_selection import cross_val_scorefrom sklearn.ensemble import RandomForestClassifierclf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0, n_jobs=-1)scores = cross_val_score(clf, train, target, cv=5, scoring='f1_macro')print(scores) print("F1: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))ShuffleSplit切分数据from sklearn.model_selection import ShuffleSplitfrom sklearn.model_selection import cross_val_scorefrom sklearn.ensemble import RandomForestClassifierclf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0, n_jobs=-1)cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=0)cross_val_score(clf, train, target, cv=cv) 模型调参from sklearn.model_selection import train_test_splitfrom sklearn.model_selection import GridSearchCVfrom sklearn.metrics import classification_reportfrom sklearn.ensemble import RandomForestClassifier# Split the dataset in two equal partsX_train, X_test, y_train, y_test = train_test_split(train, target, test_size=0.5, random_state=0)# model clf = RandomForestClassifier(n_jobs=-1)# Set the parameters by cross-validationtuned_parameters = { 'n_estimators': [50, 100, 200]# ,'criterion': ['gini', 'entropy']# ,'max_depth': [2, 5]# ,'max_features': ['log2', 'sqrt', 'int']# ,'bootstrap': [True, False]# ,'warm_start': [True, False] }scores = ['precision']for score in scores: print("# Tuning hyper-parameters for %s" % score) print() clf = GridSearchCV(clf, tuned_parameters, cv=5, scoring='%s_macro' % score) clf.fit(X_train, y_train) print("Best parameters set found on development set:") print() print(clf.best_params_) print() print("Grid scores on development set:") print() means = clf.cv_results_['mean_test_score'] stds = clf.cv_results_['std_test_score'] for mean, std, params in zip(means, stds, clf.cv_results_['params']): print("%0.3f (+/-%0.03f) for %r" % (mean, std * 2, params)) print() print("Detailed classification report:") print() print("The model is trained on the full development set.") print("The scores are computed on the full evaluation set.") print() y_true, y_pred = y_test, clf.predict(X_test) print(classification_report(y_true, y_pred)) print()模糊矩阵import itertoolsimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import confusion_matrixfrom sklearn.ensemble import RandomForestClassifier# label nameclass_names = ['no-repeat', 'repeat']# Split the data into a training set and a test setX_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)# Run classifier, using a model that is too regularized (C too low) to see# the impact on the resultsclf = RandomForestClassifier(n_jobs=-1)y_pred = clf.fit(X_train, y_train).predict(X_test)def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): """ This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print("Normalized confusion matrix") else: print('Confusion matrix, without normalization') print(cm) plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) fmt = '.2f' if normalize else 'd' thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.ylabel('True label') plt.xlabel('Predicted label') plt.tight_layout()# Compute confusion matrixcnf_matrix = confusion_matrix(y_test, y_pred)np.set_printoptions(precision=2)# Plot non-normalized confusion matrixplt.figure()plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix, without normalization')# Plot normalized confusion matrixplt.figure()plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True, title='Normalized confusion matrix')plt.show()

from sklearn.metrics import classification_reportfrom sklearn.ensemble import RandomForestClassifier# label nameclass_names = ['no-repeat', 'repeat']# Split the data into a training set and a test setX_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)# Run classifier, using a model that is too regularized (C too low) to see# the impact on the resultsclf = RandomForestClassifier(n_jobs=-1)y_pred = clf.fit(X_train, y_train).predict(X_test)print(classification_report(y_test, y_pred, target_names=class_names))

不同的分类模型LR 模型from sklearn.linear_model import LinearRegressionfrom sklearn.linear_model import LogisticRegressionfrom sklearn.preprocessing import StandardScalerstdScaler = StandardScaler()X = stdScaler.fit_transform(train)# Split the data into a training set and a test setX_train, X_test, y_train, y_test = train_test_split(X, target, random_state=0)clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial').fit(X_train, y_train)clf.score(X_test, y_test)KNN 模型from sklearn.neighbors import KNeighborsClassifierfrom sklearn.preprocessing import StandardScalerstdScaler = StandardScaler()X = stdScaler.fit_transform(train)# Split the data into a training set and a test setX_train, X_test, y_train, y_test = train_test_split(X, target, random_state=0)clf = KNeighborsClassifier(n_neighbors=3).fit(X_train, y_train)clf.score(X_test, y_test)tree树模型from sklearn import tree# Split the data into a training set and a test setX_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)clf = tree.DecisionTreeClassifier()clf = clf.fit(X_train, y_train)clf.score(X_test, y_test)bagging模型from sklearn.ensemble import BaggingClassifierfrom sklearn.neighbors import KNeighborsClassifier# Split the data into a training set and a test setX_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)clf = BaggingClassifier(KNeighborsClassifier(), max_samples=0.5, max_features=0.5)clf = clf.fit(X_train, y_train)clf.score(X_test, y_test)随机森林模型from sklearn.ensemble import RandomForestClassifier# Split the data into a training set and a test setX_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)clf = clf = RandomForestClassifier(n_estimators=10, max_depth=3, min_samples_split=12, random_state=0)clf = clf.fit(X_train, y_train)clf.score(X_test, y_test)ExTree模型from sklearn.ensemble import ExtraTreesClassifier# Split the data into a training set and a test setX_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)clf = ExtraTreesClassifier(n_estimators=10, max_depth=None, min_samples_split=2, random_state=0)clf = clf.fit(X_train, y_train)clf.score(X_test, y_test)clf.n_features_clf.feature_importances_[:10]AdaBoost模型from sklearn.ensemble import AdaBoostClassifier# Split the data into a training set and a test setX_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)clf = AdaBoostClassifier(n_estimators=10)clf = clf.fit(X_train, y_train)clf.score(X_test, y_test)GBDT模型from sklearn.ensemble import GradientBoostingClassifier# Split the data into a training set and a test setX_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)clf = GradientBoostingClassifier(n_estimators=10, learning_rate=1.0, max_depth=1, random_state=0)clf = clf.fit(X_train, y_train)clf.score(X_test, y_test)VOTE模型投票from sklearn import datasetsfrom sklearn.model_selection import cross_val_scorefrom sklearn.linear_model import LogisticRegressionfrom sklearn.naive_bayes import GaussianNBfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.ensemble import VotingClassifierfrom sklearn.preprocessing import StandardScalerstdScaler = StandardScaler()X = stdScaler.fit_transform(train)y = targetclf1 = LogisticRegression(solver='lbfgs', multi_class='multinomial', random_state=1)clf2 = RandomForestClassifier(n_estimators=50, random_state=1)clf3 = GaussianNB()eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard')for clf, label in zip([clf1, clf2, clf3, eclf], ['Logistic Regression', 'Random Forest', 'naive Bayes', 'Ensemble']): scores = cross_val_score(clf, X, y, cv=5, scoring='accuracy') print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))lgb 模型import lightgbmX_train, X_test, y_train, y_test = train_test_split(train, target, test_size=0.4, random_state=0)X_test, X_valid, y_test, y_valid = train_test_split(X_test, y_test, test_size=0.5, random_state=0)clf = lightgbmtrain_matrix = clf.Dataset(X_train, label=y_train)test_matrix = clf.Dataset(X_test, label=y_test)params = { 'boosting_type': 'gbdt', #'boosting_type': 'dart', 'objective': 'multiclass', 'metric': 'multi_logloss', 'min_child_weight': 1.5, 'num_leaves': 2**5, 'lambda_l2': 10, 'subsample': 0.7, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.7, 'learning_rate': 0.03, 'tree_method': 'exact', 'seed': 2017, "num_class": 2, 'silent': True, }num_round = 10000early_stopping_rounds = 100model = clf.train(params, train_matrix, num_round, valid_sets=test_matrix, early_stopping_rounds=early_stopping_rounds)pre= model.predict(X_valid,num_iteration=model.best_iteration)print('score : ', np.mean((pre[:,1]>0.5)==y_valid))xgb 模型import xgboostX_train, X_test, y_train, y_test = train_test_split(train, target, test_size=0.4, random_state=0)X_test, X_valid, y_test, y_valid = train_test_split(X_test, y_test, test_size=0.5, random_state=0)clf = xgboosttrain_matrix = clf.DMatrix(X_train, label=y_train, missing=-1)test_matrix = clf.DMatrix(X_test, label=y_test, missing=-1)z = clf.DMatrix(X_valid, label=y_valid, missing=-1)params = {'booster': 'gbtree', 'objective': 'multi:softprob', 'eval_metric': 'mlogloss', 'gamma': 1, 'min_child_weight': 1.5, 'max_depth': 5, 'lambda': 100, 'subsample': 0.7, 'colsample_bytree': 0.7, 'colsample_bylevel': 0.7, 'eta': 0.03, 'tree_method': 'exact', 'seed': 2017, "num_class": 2 }num_round = 10000early_stopping_rounds = 100watchlist = [(train_matrix, 'train'), (test_matrix, 'eval') ]model = clf.train(params, train_matrix, num_boost_round=num_round, evals=watchlist, early_stopping_rounds=early_stopping_rounds )pre = model.predict(z,ntree_limit=model.best_ntree_limit)print('score : ', np.mean((pre[:,1]>0.3)==y_valid))自己封装模型Stacking,Bootstrap,Bagging技术实践""" 导入相关包"""import pandas as pdimport numpy as npimport lightgbm as lgbfrom sklearn.metrics import f1_scorefrom sklearn.model_selection import train_test_splitfrom sklearn.model_selection import KFoldfrom sklearn.model_selection import StratifiedKFoldclass SBBTree(): """ SBBTree Stacking,Bootstap,Bagging """ def __init__( self, params, stacking_num, bagging_num, bagging_test_size, num_boost_round, early_stopping_rounds ): """ Initializes the SBBTree. Args: params : lgb params. stacking_num : k_flod stacking. bagging_num : bootstrap num. bagging_test_size : bootstrap sample rate. num_boost_round : boost num. early_stopping_rounds : early_stopping_rounds. """ self.params = params self.stacking_num = stacking_num self.bagging_num = bagging_num self.bagging_test_size = bagging_test_size self.num_boost_round = num_boost_round self.early_stopping_rounds = early_stopping_rounds self.model = lgb self.stacking_model = [] self.bagging_model = [] def fit(self, X, y): """ fit model. """ if self.stacking_num > 1: layer_train = np.zeros((X.shape[0], 2)) self.SK = StratifiedKFold(n_splits=self.stacking_num, shuffle=True, random_state=1) for k,(train_index, test_index) in enumerate(self.SK.split(X, y)): X_train = X[train_index] y_train = y[train_index] X_test = X[test_index] y_test = y[test_index] lgb_train = lgb.Dataset(X_train, y_train) lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) gbm = lgb.train(self.params, lgb_train, num_boost_round=self.num_boost_round, valid_sets=lgb_eval, early_stopping_rounds=self.early_stopping_rounds) self.stacking_model.append(gbm) pred_y = gbm.predict(X_test, num_iteration=gbm.best_iteration) layer_train[test_index, 1] = pred_y X = np.hstack((X, layer_train[:,1].reshape((-1,1)))) else: pass for bn in range(self.bagging_num): X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=self.bagging_test_size, random_state=bn) lgb_train = lgb.Dataset(X_train, y_train) lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) gbm = lgb.train(self.params, lgb_train, num_boost_round=10000, valid_sets=lgb_eval, early_stopping_rounds=200) self.bagging_model.append(gbm) def predict(self, X_pred): """ predict test data. """ if self.stacking_num > 1: test_pred = np.zeros((X_pred.shape[0], self.stacking_num)) for sn,gbm in enumerate(self.stacking_model): pred = gbm.predict(X_pred, num_iteration=gbm.best_iteration) test_pred[:, sn] = pred X_pred = np.hstack((X_pred, test_pred.mean(axis=1).reshape((-1,1)))) else: pass for bn,gbm in enumerate(self.bagging_model): pred = gbm.predict(X_pred, num_iteration=gbm.best_iteration) if bn == 0: pred_out=pred else: pred_out+=pred return pred_out/self.bagging_num测试自己封装的模型类""" TEST CODE"""from sklearn.datasets import make_classificationfrom sklearn.datasets import load_breast_cancerfrom sklearn.datasets import make_gaussian_quantilesfrom sklearn import metricsfrom sklearn.metrics import f1_score# X, y = make_classification(n_samples=1000, n_features=25, n_clusters_per_class=1, n_informative=15, random_state=1)X, y = make_gaussian_quantiles(mean=None, cov=1.0, n_samples=1000, n_features=50, n_classes=2, shuffle=True, random_state=2)# data = load_breast_cancer()# X, y = data.data, data.targetX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)params = { 'task': 'train', 'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'auc', 'num_leaves': 9, 'learning_rate': 0.03, 'feature_fraction_seed': 2, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'min_data': 20, 'min_hessian': 1, 'verbose': -1, 'silent': 0 }# test 1model = SBBTree(params=params, stacking_num=2, bagging_num=1, bagging_test_size=0.33, num_boost_round=10000, early_stopping_rounds=200)model.fit(X,y)X_pred = X[0].reshape((1,-1))pred=model.predict(X_pred)print('pred')print(pred)print('TEST 1 ok')# test 1model = SBBTree(params, stacking_num=1, bagging_num=1, bagging_test_size=0.33, num_boost_round=10000, early_stopping_rounds=200)model.fit(X_train,y_train)pred1=model.predict(X_test)# test 2 model = SBBTree(params, stacking_num=1, bagging_num=3, bagging_test_size=0.33, num_boost_round=10000, early_stopping_rounds=200)model.fit(X_train,y_train)pred2=model.predict(X_test)# test 3 model = SBBTree(params, stacking_num=5, bagging_num=1, bagging_test_size=0.33, num_boost_round=10000, early_stopping_rounds=200)model.fit(X_train,y_train)pred3=model.predict(X_test)# test 4 model = SBBTree(params, stacking_num=5, bagging_num=3, bagging_test_size=0.33, num_boost_round=10000, early_stopping_rounds=200)model.fit(X_train,y_train)pred4=model.predict(X_test)fpr, tpr, thresholds = metrics.roc_curve(y_test+1, pred1, pos_label=2)print('auc: ',metrics.auc(fpr, tpr))fpr, tpr, thresholds = metrics.roc_curve(y_test+1, pred2, pos_label=2)print('auc: ',metrics.auc(fpr, tpr))fpr, tpr, thresholds = metrics.roc_curve(y_test+1, pred3, pos_label=2)print('auc: ',metrics.auc(fpr, tpr))fpr, tpr, thresholds = metrics.roc_curve(y_test+1, pred4, pos_label=2)print('auc: ',metrics.auc(fpr, tpr))# auc: 0.7281621243885396# auc: 0.7710471146419509# auc: 0.7894369046305492# auc: 0.8084519474787597天猫复购场景实战读取特征数据import pandas as pdimport numpy as npimport lightgbm as lgbfrom sklearn.metrics import f1_scorefrom sklearn.model_selection import train_test_splitfrom sklearn.model_selection import KFoldfrom sklearn.model_selection import StratifiedKFoldtrain_data = pd.read_csv('train_all.csv',nrows=10000)test_data = pd.read_csv('test_all.csv',nrows=100)features_columns = [col for col in train_data.columns if col not in ['user_id','label']]train = train_data[features_columns].valuestest = test_data[features_columns].valuestarget =train_data['label'].values设置模型参数params = { 'task': 'train', 'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'auc', 'num_leaves': 9, 'learning_rate': 0.03, 'feature_fraction_seed': 2, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'min_data': 20, 'min_hessian': 1, 'verbose': -1, 'silent': 0 }model = SBBTree(params=params, stacking_num=5, bagging_num=3, bagging_test_size=0.33, num_boost_round=10000, early_stopping_rounds=200)模型训练model.fit(train, target)预测结果pred = model.predict(test)df_out = pd.DataFrame()df_out['user_id'] = test_data['user_id'].astype(int)df_out['predict_prob'] = preddf_out.head()保存结果""" 保留数据头，不保存index"""df_out.to_csv('df_out.csv',header=True,index=False)print('save OK!')

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！