实现功能：
python机器学习案例-梯度提升模型搭建及评估。
实现代码：
# 导入需要的库from warnings import simplefiltersimplefilter(action='ignore', category=FutureWarning)import pandas as pdfrom sklearn.model_selection import train_test_splitimport seaborn as snsimport matplotlib.pyplot as pltfrom sklearn import metricsfrom sklearn.metrics import roc_curve, aucimport numpy as npfrom sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier# =============读取数据===========def Read_data(file):dt = pd.read_csv(file)dt.columns = ['age', 'sex', 'chest_pain_type', 'resting_blood_pressure', 'cholesterol','fasting_blood_sugar', 'rest_ecg', 'max_heart_rate_achieved','exercise_induced_angina','st_depression', 'st_slope', 'num_major_vessels', 'thalassemia', 'target']data =dtreturn data# ===========数据清洗==============def data_clean(data):# 重复值处理print('存在' if any(data.duplicated()) else '不存在', '重复观测值')data.drop_duplicates()# 缺失值处理print('不存在' if any(data.isnull()) else '存在', '缺失值')data.dropna() # 直接删除记录data.fillna(method='ffill') # 前向填充data.fillna(method='bfill') # 后向填充data.fillna(value=2) # 值填充data.fillna(value={'resting_blood_pressure': data['resting_blood_pressure'].mean()}) # 统计值填充# 异常值处理data1 = data['resting_blood_pressure']# 标准差监测xmean = data1.mean()xstd = data1.std()print('存在' if any(data1 > xmean + 2 * xstd) else '不存在', '上限异常值')print('存在' if any(data1 < xmean - 2 * xstd) else '不存在', '下限异常值')# 箱线图监测q1 = data1.quantile(0.25)q3 = data1.quantile(0.75)up = q3 + 1.5 * (q3 - q1)dw = q1 - 1.5 * (q3 - q1)print('存在' if any(data1 > up) else '不存在', '上限异常值')print('存在' if any(data1 < dw) else '不存在', '下限异常值')data1[data1 > up] = data1[data1 < up].max()data1[data1 < dw] = data1[data1 > dw].min()return data#==============数据编码=============def data_encoding(data):data = data[["age", 'sex', "chest_pain_type", "resting_blood_pressure", "cholesterol","fasting_blood_sugar", "rest_ecg","max_heart_rate_achieved", "exercise_induced_angina","st_depression", "st_slope", "num_major_vessels","thalassemia","target"]]Discretefeature=['sex',"chest_pain_type", "fasting_blood_sugar", "rest_ecg","exercise_induced_angina", "st_slope", "thalassemia"]Continuousfeature=["age", "resting_blood_pressure", "cholesterol","max_heart_rate_achieved","st_depression","num_major_vessels"]df = pd.get_dummies(data,columns=Discretefeature)df[Continuousfeature]=(df[Continuousfeature]-df[Continuousfeature].mean())/(df[Continuousfeature].std())df["target"]=data[["target"]]return df#=============数据集划分==============def data_partition(data):# 1.4查看样本是否平衡print(data["target"].value_counts())# X提取变量特征；Y提取目标变量X = data.drop('target', axis=1)y = data['target']X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2,random_state=10)feature=list(X.columns)return X_train, y_train, X_test, y_test,feature#===========绘制ROC曲线================def Draw_ROC(list1,list2):fpr_model,tpr_model,thresholds=roc_curve(list1,list2,pos_label=1)roc_auc_model=auc(fpr_model,tpr_model)font = {'family': 'Times New Roman','size': 12,}sns.set(font_scale=1.2)plt.rc('font',family='Times New Roman')plt.plot(fpr_model,tpr_model,'blue',label='AUC = %0.2f'% roc_auc_model)plt.legend(loc='lower right',fontsize = 12)plt.plot([0,1],[0,1],'r--')plt.ylabel('True Positive Rate',fontsize = 14)plt.xlabel('Flase Positive Rate',fontsize = 14)plt.show()return#============梯度提升===========def GB(X_train, y_train, X_test, y_test,feature):gb1 = GradientBoostingClassifier(max_depth=4,random_state=0)gb1.fit(X_train, y_train)print("\nFinally results of GB fitting:")print("Accuracy on training set: {:.3f}".format(gb1.score(X_train, y_train)))print("Accuracy on test set: {:.3f}".format(gb1.score(X_test, y_test)))print("Feature importance:\n{}".format(gb1.feature_importances_))predict_target=gb1.predict(X_test)predict_target_prob=gb1.predict_proba(X_test) # 输出分类概率predict_target_prob_gb=predict_target_prob[:,1]df = pd.DataFrame({'prob':predict_target_prob_gb,'target':predict_target,'labels':list(y_test)})print(predict_target_prob_gb)print('正确预测样本数:')print(sum(predict_target==y_test))print('GB测试集:')print(metrics.classification_report(y_test,predict_target))print(metrics.confusion_matrix(y_test, predict_target))print('GB训练集:')predict_Target=gb1.predict(X_train)print(metrics.classification_report(y_train,predict_Target))print(metrics.confusion_matrix(y_train, predict_Target))id=np.argwhere(gb1.feature_importances_>=0)id=[i for item in id for i in item]dic={}for i in id:dic.update({feature[i]:gb1.feature_importances_[i]})df=pd.DataFrame.from_dict(dic,orient='index',columns=['权重'])df=df.reset_index().rename(columns={'index':'特征'})df=df.sort_values(by='权重',ascending=False)data_hight=df['权重'].values.tolist()print(data_hight)data_x=df['特征'].values.tolist()print(data_x)font = {'family': 'Times New Roman', 'size': 12}sns.set(font_scale=1.2)plt.rc('font',family='Times New Roman')plt.figure(figsize=(8,8))plt.barh(range(len(data_x)), data_hight, color='#6699CC')plt.yticks(range(len(data_x)),data_x,fontsize=12)plt.tick_params(labelsize=12)plt.xlabel('Feature importance',fontsize=14)plt.title("RF feature importance analysis",fontsize = 14)plt.show()return list(y_test), list(predict_target_prob_gb)if __name__=="__main__":data1=Read_data("F:\数据杂坛\\0504\heartdisease\Heart-Disease-Data-Set-main\\UCI Heart Disease Dataset.csv")data1=data_clean(data1)data2=data_encoding(data1)X_train, y_train, X_test, y_test,feature= data_partition(data2)y_test,predict_target_prob_gb=GB(X_train, y_train, X_test, y_test,feature)Draw_ROC(y_test,predict_target_prob_gb)
实现效果：
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