Python学习Day10

学习来源：@浙大疏锦行

知识点：

1. 数据集的划分
2. 机器学习模型建模的三行代码
3. 机器学习模型分类问题的评估

• 对心脏病数据集采用机器学习模型建模和评估

  import pandas as pd 
  file_path = "heart.csv"
  data = pd.read_csv(file_path)
  data.info()
  data.isnull().sum()
  
  
  # 划分训练集和测试机
  from sklearn.model_selection import train_test_split
  X = data.drop(['target'], axis=1)  # 特征，axis=1表示按列删除
  y = data['target']  # 标签
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # 划分数据集，20%作为测试集，随机种子为42
  # 训练集和测试集的形状
  print(f"训练集形状: {X_train.shape}, 测试集形状: {X_test.shape}")  # 打印训练集和测试集的形状
  
  
  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 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
  import warnings #用于忽略警告信息
  warnings.filterwarnings("ignore") # 忽略所有警告信息
  
  # SVM
  svm_model = SVC(random_state=42)
  svm_model.fit(X_train, y_train)
  svm_pred = svm_model.predict(X_test)
  
  print("\nSVM 分类报告：")
  print(classification_report(y_test, svm_pred))  # 打印分类报告
  print("SVM 混淆矩阵：")
  print(confusion_matrix(y_test, svm_pred))  # 打印混淆矩阵
  
  # 计算 SVM 评估指标，这些指标默认计算正类的性能
  svm_accuracy = accuracy_score(y_test, svm_pred)
  svm_precision = precision_score(y_test, svm_pred)
  svm_recall = recall_score(y_test, svm_pred)
  svm_f1 = f1_score(y_test, svm_pred)
  print("SVM 模型评估指标：")
  print(f"准确率: {svm_accuracy:.4f}")
  print(f"精确率: {svm_precision:.4f}")
  print(f"召回率: {svm_recall:.4f}")
  print(f"F1 值: {svm_f1:.4f}")
  # KNN
  knn_model = KNeighborsClassifier()
  knn_model.fit(X_train, y_train)
  knn_pred = knn_model.predict(X_test)
  
  print("\nKNN 分类报告：")
  print(classification_report(y_test, knn_pred))
  print("KNN 混淆矩阵：")
  print(confusion_matrix(y_test, knn_pred))
  
  knn_accuracy = accuracy_score(y_test, knn_pred)
  knn_precision = precision_score(y_test, knn_pred)
  knn_recall = recall_score(y_test, knn_pred)
  knn_f1 = f1_score(y_test, knn_pred)
  print("KNN 模型评估指标：")
  print(f"准确率: {knn_accuracy:.4f}")
  print(f"精确率: {knn_precision:.4f}")
  print(f"召回率: {knn_recall:.4f}")
  print(f"F1 值: {knn_f1:.4f}")
  # 逻辑回归
  logreg_model = LogisticRegression(random_state=42)
  logreg_model.fit(X_train, y_train)
  logreg_pred = logreg_model.predict(X_test)
  
  print("\n逻辑回归 分类报告：")
  print(classification_report(y_test, logreg_pred))
  print("逻辑回归 混淆矩阵：")
  print(confusion_matrix(y_test, logreg_pred))
  
  logreg_accuracy = accuracy_score(y_test, logreg_pred)
  logreg_precision = precision_score(y_test, logreg_pred)
  logreg_recall = recall_score(y_test, logreg_pred)
  logreg_f1 = f1_score(y_test, logreg_pred)
  print("逻辑回归 模型评估指标：")
  print(f"准确率: {logreg_accuracy:.4f}")
  print(f"精确率: {logreg_precision:.4f}")
  print(f"召回率: {logreg_recall:.4f}")
  print(f"F1 值: {logreg_f1:.4f}")
  
  # 朴素贝叶斯
  nb_model = GaussianNB()
  nb_model.fit(X_train, y_train)
  nb_pred = nb_model.predict(X_test)
  
  print("\n朴素贝叶斯 分类报告：")
  print(classification_report(y_test, nb_pred))
  print("朴素贝叶斯 混淆矩阵：")
  print(confusion_matrix(y_test, nb_pred))
  
  nb_accuracy = accuracy_score(y_test, nb_pred)
  nb_precision = precision_score(y_test, nb_pred)
  nb_recall = recall_score(y_test, nb_pred)
  nb_f1 = f1_score(y_test, nb_pred)
  print("朴素贝叶斯 模型评估指标：")
  print(f"准确率: {nb_accuracy:.4f}")
  print(f"精确率: {nb_precision:.4f}")
  print(f"召回率: {nb_recall:.4f}")
  print(f"F1 值: {nb_f1:.4f}")
  
  # 决策树
  dt_model = DecisionTreeClassifier(random_state=42)
  dt_model.fit(X_train, y_train)
  dt_pred = dt_model.predict(X_test)
  
  print("\n决策树 分类报告：")
  print(classification_report(y_test, dt_pred))
  print("决策树 混淆矩阵：")
  print(confusion_matrix(y_test, dt_pred))
  
  dt_accuracy = accuracy_score(y_test, dt_pred)
  dt_precision = precision_score(y_test, dt_pred)
  dt_recall = recall_score(y_test, dt_pred)
  dt_f1 = f1_score(y_test, dt_pred)
  print("决策树 模型评估指标：")
  print(f"准确率: {dt_accuracy:.4f}")
  print(f"精确率: {dt_precision:.4f}")
  print(f"召回率: {dt_recall:.4f}")
  print(f"F1 值: {dt_f1:.4f}")
  
  
  # 随机森林
  rf_model = RandomForestClassifier(random_state=42)
  rf_model.fit(X_train, y_train)
  rf_pred = rf_model.predict(X_test)
  
  print("\n随机森林 分类报告：")
  print(classification_report(y_test, rf_pred))
  print("随机森林 混淆矩阵：")
  print(confusion_matrix(y_test, rf_pred))
  
  rf_accuracy = accuracy_score(y_test, rf_pred)
  rf_precision = precision_score(y_test, rf_pred)
  rf_recall = recall_score(y_test, rf_pred)
  rf_f1 = f1_score(y_test, rf_pred)
  print("随机森林 模型评估指标：")
  print(f"准确率: {rf_accuracy:.4f}")
  print(f"精确率: {rf_precision:.4f}")
  print(f"召回率: {rf_recall:.4f}")
  print(f"F1 值: {rf_f1:.4f}")
  
  # XGBoost
  xgb_model = xgb.XGBClassifier(random_state=42)
  xgb_model.fit(X_train, y_train)
  xgb_pred = xgb_model.predict(X_test)
  
  print("\nXGBoost 分类报告：")
  print(classification_report(y_test, xgb_pred))
  print("XGBoost 混淆矩阵：")
  print(confusion_matrix(y_test, xgb_pred))
  
  xgb_accuracy = accuracy_score(y_test, xgb_pred)
  xgb_precision = precision_score(y_test, xgb_pred)
  xgb_recall = recall_score(y_test, xgb_pred)
  xgb_f1 = f1_score(y_test, xgb_pred)
  print("XGBoost 模型评估指标：")
  print(f"准确率: {xgb_accuracy:.4f}")
  print(f"精确率: {xgb_precision:.4f}")
  print(f"召回率: {xgb_recall:.4f}")
  print(f"F1 值: {xgb_f1:.4f}")
  
  # LightGBM
  lgb_model = lgb.LGBMClassifier(random_state=42)
  lgb_model.fit(X_train, y_train)
  lgb_pred = lgb_model.predict(X_test)
  
  print("\nLightGBM 分类报告：")
  print(classification_report(y_test, lgb_pred))
  print("LightGBM 混淆矩阵：")
  print(confusion_matrix(y_test, lgb_pred))
  
  lgb_accuracy = accuracy_score(y_test, lgb_pred)
  lgb_precision = precision_score(y_test, lgb_pred)
  lgb_recall = recall_score(y_test, lgb_pred)
  lgb_f1 = f1_score(y_test, lgb_pred)
  print("LightGBM 模型评估指标：")
  print(f"准确率: {lgb_accuracy:.4f}")
  print(f"精确率: {lgb_precision:.4f}")
  print(f"召回率: {lgb_recall:.4f}")
  print(f"F1 值: {lgb_f1:.4f}")