python打卡DAY24

##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.manifold import TSNE

from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score

#3D可视化

from mpl_toolkits.mplot3d import Axes3D

import plotly.express as px

import plotly.graph_objects as go

# 导入 Pipeline 和相关预处理工具

from imblearn.over_sampling import SMOTE

from sklearn.pipeline import Pipeline # 用于创建机器学习工作流

from sklearn.compose import ColumnTransformer # 用于将不同的预处理应用于不同的列

from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, StandardScaler # 用于数据预处理（有序编码、独热编码、标准化）

from sklearn.impute import SimpleImputer # 用于处理缺失值

#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')

x=data.drop(['Id','Credit Default'],axis=1)

y=data['Credit Default']

# 定义pipeline相关定义与处理步骤

object_cols = x.select_dtypes(include=['object']).columns.tolist()

numeric_cols = x.select_dtypes(exclude=['object']).columns.tolist()

print(f'object_cols:{object_cols}\nnumeric_cols:{numeric_cols}')

ordinal_features = ['Years in current job']

ordinal_categories = [['< 1 year', '1 year', '2 years', '3 years', '4 years', '5 years', '6 years', '7 years', '8 years', '9 years', '10+ years']] # Years in current job 的顺序 (对应1-11)

ordinal_transformer = Pipeline(steps=[

('imputer', SimpleImputer(strategy='most_frequent')),

('encoder', OrdinalEncoder(categories=ordinal_categories, handle_unknown='use_encoded_value', unknown_value=-1))

])

print("有序特征处理 Pipeline 定义完成。")

nominal_features = ['Home Ownership', 'Purpose', 'Term']

nominal_transformer = Pipeline(steps=[

('imputer', SimpleImputer(strategy='most_frequent')),

('onehot', OneHotEncoder(handle_unknown='ignore'))

])

print("标称特征处理 Pipeline 定义完成。")

continuous_features = x.columns.difference(object_cols).tolist()

print(continuous_features)

continuous_transformer = Pipeline(steps=[

('imputer', SimpleImputer(strategy='mean'))

])

print("连续特征处理 Pipeline 定义完成。")

# --- 构建 ColumnTransformer ---

preprocessor = ColumnTransformer(

transformers=[

('ordinal', ordinal_transformer, ordinal_features),

('nominal', nominal_transformer, nominal_features),

('continuous', continuous_transformer, continuous_features)

], remainder='passthrough'

)

print("\nColumnTransformer (预处理器) 定义完成。")

pipeline = Pipeline(steps=[

('preprocessor', preprocessor),

])

print("\n完整的 Pipeline 定义完成。")

print("\n开始对原始数据进行预处理...")

start_time = time.time()

x_processed=pipeline.fit_transform(x)

end_time=time.time()

print(f"预处理完成，耗时: {end_time - start_time:.4f} 秒")

feature_names=preprocessor.get_feature_names_out()

x_processed_df=pd.DataFrame(x_processed,columns=feature_names)

print(x_processed_df.info())

#划分数据集

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test=train_test_split(x_processed_df,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

scaler=StandardScaler()

x_scaled=scaler.fit_transform(x_processed_df)

# ##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系数图

# 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系数图

# plt.subplot(2,2,4)

# plt.plot(k_range,db_scores,marker='o',color='yellow')

# plt.ylabel('DB 指数')

# plt.grid(True)

# plt.tight_layout()

# plt.show()

#选择K值进行聚类

selected_k=20

kmeans=KMeans(n_clusters=selected_k,random_state=42)

kmeans_label=kmeans_label=kmeans.fit_predict(x_scaled)

x['KMeans_Cluster']=kmeans_label

# ##PCA降维

# print(f"\n--- 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

# #准备数据

# df_pca=pd.DataFrame(x_pca)

# df_pca['cluster']=x['KMeans_Cluster']

# fig=px.scatter_3d(

# df_pca,x=0,y=1,z=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='pca_0',

# yaxis_title='pca_1',

# zaxis_title='pca_2'

# ),

# width=1200,

# height=1000

# )

# fig.show()

print(f"\n---t-SNE 降维 ---")

n_components_tsne=3

# 对训练集进行 fit_transform

tsne=TSNE(

n_components=n_components_tsne,

perplexity=1000,

n_iter=5000,

learning_rate='auto',

random_state=42,

n_jobs=-1

)

# )

print("正在对训练集进行 t-SNE fit_transform...")

start_time = time.time()

x_tsne=tsne.fit_transform(x_scaled)

end_time=time.time()

print(f"训练集 t-SNE耗时: {end_time - start_time:.2f} 秒")

# ##3D可视化

# #准备数据

# df_tsne=pd.DataFrame(x_tsne)

# df_tsne['cluster']=x['KMeans_Cluster']

# fig=px.scatter_3d(

# df_tsne,

# x=0,y=1,z=2,

# color='cluster',

# color_continuous_scale=px.colors.sequential.Viridis,

# title='T-SNE特征选择的3D可视化'

# )

# fig.update_layout(

# scene=dict(

# xaxis_title='pca_0',

# yaxis_title='pca_1',

# zaxis_title='pca_2'

# ),

# width=1200,

# height=1000

# )

# 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()

rf1_model=RandomForestClassifier(random_state=42,class_weight='balanced')

rf1_model.fit(x_train_smote,y_train_smote)

explainer=shap.TreeExplainer(rf1_model)

shap_value=explainer.shap_values(x_processed)

print(shap_value.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_value[:,:,0],x_processed_df,plot_type='bar',show=False)

# plt.title('shap feature importance (bar plot)')

# plt.tight_layout()

# plt.show()

selected_features=['Credit Score','Term','Current Loan Amount']

# fig,axes=plt.subplots(2,2,figsize=(10,8))

# axes=axes.flatten()

# for i,feature in enumerate(selected_features):

# unique_count=x[feature].nunique()

# 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')

# sns.histplot(x=x[feature],ax=axes[i])

# axes[i].set_title(f'countplot 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]

# ##簇0

# fig,axes=plt.subplots(2,2,figsize=(6,4))

# axes=axes.flatten()

# for i,feature in enumerate(selected_features):

# sns.histplot(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.histplot(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.histplot(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

start_time=time.time()

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}")

end_time=time.time()

print(f'RFE分析耗时:{end_time-start_time:.4f}')

# #3D可视化

# x_selected=x_processed_df[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_smote)

# 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))