用python实现一些简单的加密流程

import pandas as pd from sklearn import preprocessing from sklearn.feature_selection import mutual_info_classif from sklearn.feature_selection import SelectKBest from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score data_train=pd.read_csv("Train_data.csv") data_test=pd.read_csv("Test_data.csv") def encoding(df): for col in df.columns: if df[col].dtype == 'object': label_encoder = preprocessing.LabelEncoder() df[col] = label_encoder.fit_transform(df[col]) #LabelEncoder对列进行编码转换 encoding(data_train) #encoding函数对data_train数据进行编码处理 X = data_train.drop(["class"], axis=1) y = data_train["class"] #，分别表示特征数据和目标标签数据，通过data_train.drop(["class"], axis=1)和data_train["class"]来实现 select_best_cols = SelectKBest(mutual_info_classif, k=25) #创建SelectKBest对象select_best_cols，并指定使用mutual_info_classif方法来选择最佳特征，特征数量为25个 select_best_cols.fit(X, y) #使用select_best_cols对象对X和y进行特征选择，并通过get_support()方法获取所选特征的索引 selected_features = X.columns[select_best_cols.get_support()] # 根据所选特征的索引，从X中选取相应的特征，创建变量selected_features X = X[selected_features] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) sc = StandardScaler() X_train=sc.fit_transform(X_train) X_test=sc.transform(X_test) def classalgo_test(x_train, x_test, y_train, y_test): rfc = RandomForestClassifier() rfc.fit(x_train, y_train) y_test_pred = rfc.predict(x_test) test_acc = format(accuracy_score(y_test, y_test_pred), ".2f") return test_acc a = classalgo_test(X_train, X_test, y_train, y_test) print(a) # 定义了一个名为classalgo_test的函数，该函数用于评估基于随机森林分类器的分类性能。 # 创建随机森林分类器对象rfc，并使用训练集对模型进行训练。 # 使用测试集对模型进行预测，并计算预测结果与真实标签的准确率。 # 调用classalgo_test函数来计算测试集上的准确率，并将结果存储在变量a中。 # 打印输出测试集准确率的结果。