##注入所需库
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import random
import numpy as np
import time
import shap
# from sklearn.svm import SVC #支持向量机分类器
# # from sklearn.neighbors import KNeighborsClassifier #K近邻分类器
# # from sklearn.linear_model import LogisticRegression #逻辑回归分类器
# import xgboost as xgb #XGBoost分类器
# import lightgbm as lgb #LightGBM分类器
from sklearn.ensemble import RandomForestClassifier #随机森林分类器
# # from catboost import CatBoostClassifier #CatBoost分类器
# # from sklearn.tree import DecisionTreeClassifier #决策树分类器
# # from sklearn.naive_bayes import GaussianNB #高斯朴素贝叶斯分类器
# from skopt import BayesSearchCV
# from skopt.space import Integer
# from deap import base, creator, tools, algorithms
# from sklearn.model_selection import StratifiedKFold, cross_validate # 引入分层 K 折和交叉验证工具
# from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score # 用于评估分类器性能的指标
from sklearn.metrics import classification_report, confusion_matrix #用于生成分类报告和混淆矩阵
from sklearn.metrics import make_scorer#定义函数
# import warnings #用于忽略警告信息
# warnings.filterwarnings("ignore") # 忽略所有警告信息
#聚类
from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score
#3D可视化
from mpl_toolkits.mplot3d import Axes3D
#设置中文字体&负号正确显示
plt.rcParams['font.sans-serif']=['STHeiti']
plt.rcParams['axes.unicode_minus']=True
plt.rcParams['figure.dpi']=100
#读取数据
data=pd.read_csv(r'data.csv')
#数据填补
for i in data.columns:
if data[i].dtype!='object':
if data[i].isnull().sum()>0:
data[i].fillna(data[i].mean(),inplace=True)
else:
if data[i].isnull().sum()>0:
data[i].fillna(data[i].mode()[0],inplace=True)
mapping={'10+ years':0,
'9 years':1,
'8 years':2,
'7 years':3,
'6 years':4,
'5 years':5,
'4 years':6,
'3 years':7,
'2 years':8,
'1 year':9,
'< 1 year':10}
data['Years in current job']=data['Years in current job'].map(mapping)
dummies_list=[]
data2=pd.read_csv(r'data.csv')
data=pd.get_dummies(data=data,drop_first=True)
for i in data.columns:
if i not in data2.columns:
dummies_list.append(i)
for i in dummies_list:
data[i]=data[i].astype(int)
print(f'{data.info()}')
#划分数据集
from sklearn.model_selection import train_test_split
x=data.drop(columns=['Credit Default','Id'],axis=1)
y=data['Credit Default']
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)
#smote
from imblearn.over_sampling import SMOTE
smote=SMOTE(random_state=42)
x_train_smote,y_train_smote=smote.fit_resample(x_train,y_train)
#标准化数据，将自变量标准化，聚类就是从自变量中聚合新的自变量，与因变量无关
scaler=StandardScaler()
x_scaled=scaler.fit_transform(x)
# #KMeans++
# k_range=range(2,5)
# inertia_value=[]
# silhouette_scores=[]
# ch_scores=[]
# db_scores=[]
# start_time=time.time()
# for k in k_range:
# kmeans=KMeans(n_clusters=k,random_state=42)
# kmeans_label=kmeans.fit_predict(x_scaled)#提供了每个数据点所属的簇的信息，用于区分不同簇的数据点
# inertia_value.append(kmeans.inertia_)
# silhouette=silhouette_score(x_scaled,kmeans_label)
# silhouette_scores.append(silhouette)
# ch=calinski_harabasz_score(x_scaled,kmeans_label)
# ch_scores.append(ch)
# db=davies_bouldin_score(x_scaled,kmeans_label)
# db_scores.append(db)
# # print(f'k={k}\n 惯性:{kmeans.inertia_:.2f}\n轮廓系数:{silhouette:.3f}\n CH系数:{ch:.2f}\n DB{db:.3f}')
# end_time=time.time()
# print(f'聚类分析耗时：{end_time-start_time:.4f}')
# #绘制评估指标图
# plt.figure(figsize=(12,6))
# #肘部法则图
# plt.subplot(2,2,1)
# plt.plot(k_range,inertia_value,marker='o')
# plt.title('肘部法则确定最优聚类数 k(惯性，越小越好)')
# plt.xlabel('聚类数 (k)')
# plt.ylabel('惯性')
# plt.grid(True)
# #轮廓系数图
# plt.subplot(2,2,2)
# plt.plot(k_range,silhouette_scores,marker='o',color='orange')
# plt.title('轮廓系数确定最优聚类数 k(越大越好)')
# plt.xlabel('聚类数 (k)')
# plt.ylabel('轮廓系数')
# plt.grid(True)
# #CH指数图
# plt.subplot(2,2,3)
# plt.plot(k_range,ch_scores,marker='o',color='red')
# plt.title('Calinski-Harabasz 指数确定最优聚类数 k(越大越好)')
# plt.xlabel('聚类数 (k)')
# plt.ylabel('CH 指数')
# plt.grid(True)
# #DB指数图
# plt.subplot(2,2,4)
# plt.plot(k_range,db_scores,marker='o',color='yellow')
# plt.xlabel('聚类数 (k)')
# plt.ylabel('DB 指数')
# plt.grid(True)
# plt.tight_layout()
# plt.show()
#选择K值进行聚类
selected_k=3
kmeans=KMeans(n_clusters=selected_k,random_state=42)
kmeans_label=kmeans.fit_predict(x_scaled)
x['KMeans_Cluster']=kmeans_label
##PCA降维
pca=PCA(n_components=3)
x_pca=pca.fit_transform(x_scaled)
# # ##聚类可视化
# # plt.figure(figsize=(6,5))
# # sns.scatterplot(
# # x=x_pca[:,0],
# # y=x_pca[:,1],
# # hue=kmeans_label,
# # palette='viridis'
# # )
# # plt.title(f'KMean Clustering with k={selected_k} (PCA Visualization)')
# # plt.xlabel('PCA Component 1')
# # plt.ylabel('PCA Component 2')
# # plt.show()
# # #3D可视化
# pca=PCA(n_components=3)
# import plotly.express as px
# import plotly.graph_objects as go
# # 准备数据
# df_pca = pd.DataFrame(x_pca, columns=['PC1', 'PC2', 'PC3'])
# df_pca['Cluster'] = kmeans_label
# # 创建3D散点图
# fig = px.scatter_3d(df_pca, x='PC1', y='PC2', z='PC3', color='Cluster',
# color_continuous_scale=px.colors.sequential.Viridis,
# title=f'KMeans Clustering with k={selected_k} (PCA 3D Visualization)')
# # 调整图形
# fig.update_layout(scene=dict(xaxis_title='PCA Component 1',
# yaxis_title='PCA Component 2',
# zaxis_title='PCA Component 3'),
# width=1200, height=1000)
# # 显示图形
# fig.show()
# ##打印KMeans聚类前几行
# print(f'KMeans Cluster labels(k={selected_k}added to x):')
# print(x[['KMeans_Cluster']].value_counts())
start_time=time.time()
x1=x.drop('KMeans_Cluster',axis=1)
y1=x['KMeans_Cluster']
rf1_model=RandomForestClassifier(random_state=42,class_weight='balanced')
rf1_model.fit(x1,y1)
explainer=shap.TreeExplainer(rf1_model)
shap_values=explainer.shap_values(x1)
print(shap_values.shape)
end_time=time.time()
print(f'SHAP分析耗时:{end_time-start_time:.4f}')
# # --- 1. SHAP 特征重要性条形图 (Summary Plot - Bar) ---
# print("--- 1. SHAP 特征重要性条形图 ---")
# shap.summary_plot(shap_values[:,:,0],x1,plot_type='bar',show=False)
# plt.title('shap feature importance (bar plot)')
# plt.tight_layout()
# plt.show()
selected_features=['Purpose_debt consolidation','Home Ownership_Home Mortgage','Purpose_home improvements','Purpose_other']
# for feature in selected_features:
# unique_count=x[feature].nunique()
# print(f'{feature}的唯一值数量:{unique_count}')
# if unique_count<10:
# print(f'{feature}可能是离散型变量')
# else:
# print(f'{feature}可能是连续性变量')
# fig,axes=plt.subplots(2,2,figsize=(10,8))
# axes=axes.flatten()
# for i,feature in enumerate(selected_features):
# axes[i].hist(x[feature],bins=10)
# axes[i].set_title(f'histogram of {feature}')
# axes[i].set_xlabel(feature)
# axes[i].set_ylabel('frequency')
# plt.tight_layout()
# plt.show()
print(x[['KMeans_Cluster']].value_counts())
x_cluster0=x[x['KMeans_Cluster']==0]
x_cluster1=x[x['KMeans_Cluster']==1]
x_cluster2=x[x['KMeans_Cluster']==2]
x_cluster3=x[x['KMeans_Cluster']==3]
# #簇0
# fig,axes=plt.subplots(2,2,figsize=(6,4))
# axes=axes.flatten()
# for i,feature in enumerate(selected_features):
# sns.countplot(x=x_cluster0[feature],ax=axes[i])
# axes[i].set_title(f'countplot of {feature}')
# axes[i].set_xlabel(feature)
# axes[i].set_ylabel('count')
# plt.tight_layout()
# plt.show()
# #簇1
# fig,axes=plt.subplots(2,2,figsize=(6,4))
# axes=axes.flatten()
# for i,feature in enumerate(selected_features):
# sns.countplot(x=x_cluster1[feature],ax=axes[i])
# axes[i].set_title(f'countplot of {feature}')
# axes[i].set_xlabel(feature)
# axes[i].set_ylabel('count')
# plt.tight_layout()
# plt.show()
# #簇2
# fig,axes=plt.subplots(2,2,figsize=(6,4))
# axes=axes.flatten()
# for i,feature in enumerate(selected_features):
# sns.countplot(x=x_cluster2[feature],ax=axes[i])
# axes[i].set_title(f'countplot of {feature}')
# axes[i].set_xlabel(feature)
# axes[i].set_ylabel('count')
# plt.tight_layout()
# plt.show()
print("--- 递归特征消除 (RFE) ---")
from sklearn.feature_selection import RFE
base_model=RandomForestClassifier(random_state=42,class_weight='balanced')
rfe=RFE(base_model,n_features_to_select=3)
rfe.fit(x_train_smote,y_train_smote)
x_train_rfe=rfe.transform(x_train_smote)
x_test_rfe=rfe.transform(x_test)
selected_features_rfe=x_train.columns[rfe.support_]
print(f"RFE筛选后保留的特征数量: {len(selected_features_rfe)}")
print(f"保留的特征: {selected_features_rfe}")
# #3D可视化
import plotly.express as px
import plotly.graph_objects as go
x_selected=x[selected_features_rfe]
df_viz=pd.DataFrame(x_selected)
df_viz['cluster']=x['KMeans_Cluster']
fig=px.scatter_3d(
df_viz,
x=selected_features_rfe[0],
y=selected_features_rfe[1],
z=selected_features_rfe[2],
color='cluster',
color_continuous_scale=px.colors.sequential.Viridis,
title='RFE特征选择的3D可视化'
)
fig.update_layout(
scene=dict(
xaxis_title=selected_features_rfe[0],
yaxis_title=selected_features_rfe[1],
zaxis_title=selected_features_rfe[2]
),
width=1200,
height=1000
)
fig.show()
#训练随机森林模型
rf_model_rfe=RandomForestClassifier(random_state=42,class_weight='balanced')
rf_model_rfe.fit(x_train_rfe,y_train)
rf_pred_rfe=rf_model_rfe.predict(x_test_rfe)
print("\nRFE筛选后随机森林在测试集上的分类报告:")
print(classification_report(y_test, rf_pred_rfe))
print("RFE筛选后随机森林在测试集上的混淆矩阵:")
print(confusion_matrix(y_test, rf_pred_rfe))