数据分类实验的python程序
实验设置要求:
- 数据集:共12个,从本地文件夹中包含若干个以xlsx为后缀的Excel文件,每个文件中有一个小规模数据,有表头,最后一列是分类的类别class,其他列是特征,数值的。
- 实验方法:XGBoost、AdaBoost、SVM (采用rbf核)、Neural Network分类器
- 输出:分类准确率,即十折交叉验证的准确率均值和方差,并重复5次实验,不同数据的实验结果分别保存至各自的一个csv文件。
- 其他要求:SVC增加rbf参数设置,默认为0.001、MLPClassifier为1层隐层神经网络,隐层节点为100. XGBoost和AdaBoost弱分类器设置。 cross_val_score增加数据标准化和n_jobs设置。由于数据的类别可能是非连续的字符形式,增加class的映射
Excel中的数据形式如下:
python程序如下:
import os import pandas as pd import numpy as np from sklearn.model_selection import KFold, cross_val_score from sklearn.svm import SVC from sklearn.ensemble import AdaBoostClassifier from sklearn.neural_network import MLPClassifier import xgboost as xgb from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.pipeline import Pipeline from concurrent.futures import ThreadPoolExecutor # 1. 读取文件夹中的所有.xlsx文件 data_folder = "./分类数据集" file_list = [f for f in os.listdir(data_folder) if f.endswith('.xlsx')] result_folder = "./results" # 定义分类器 classifiers = { "XGBoost": xgb.XGBClassifier(), "AdaBoost": AdaBoostClassifier(n_estimators=50), #https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html "SVM_rbf": SVC(C=1.0, kernel="rbf", gamma='scale'), #gamma默认为1 / (n_features * X.var()), #可以根据数据集进行调整 "Neural_Network": MLPClassifier(hidden_layer_sizes=(100), max_iter=1000) } # # 对每一个文件进行分类实验 # def process_file(file): # 对每一个文件进行分类实验 for file in file_list: df = pd.read_excel(os.path.join(data_folder, file)) X = df.iloc[:, :-1].values y = df.iloc[:, -1].values # 类别映射 le = LabelEncoder() y = le.fit_transform(y) results = { "Classifier": [], "Experiment 1": [], "Experiment 2": [], "Experiment 3": [], "Experiment 4": [], "Experiment 5": [], "Mean Accuracy": [], "Accuracy Variance": [] } # 使用四种分类器 for clf_name, clf in classifiers.items(): all_accuracies = [] # 使用标准化和分类器创建流水线 pipeline = Pipeline([ ('scaler', StandardScaler()), ('classifier', clf) ]) # 重复5次实验 for exp_num in range(1, 6): print(clf_name, exp_num) kf = KFold(n_splits=10, shuffle=True, random_state=None) accuracies = cross_val_score(pipeline, X, y, cv=kf,n_jobs=16) results[f"Experiment {exp_num}"].append(np.mean(accuracies)) all_accuracies.extend(accuracies) results["Classifier"].append(clf_name) results["Mean Accuracy"].append(np.mean(all_accuracies)) results["Accuracy Variance"].append(np.var(all_accuracies)) # 保存到.csv文件 result_df = pd.DataFrame(results) result_df.to_csv(os.path.join(result_folder, f"results_{file.replace('.xlsx', '.csv')}"), index=False) # # 使用多线程处理文件 # with ThreadPoolExecutor() as executor: # executor.map(process_file, file_list) print("Experiments completed!")
标签:python,accuracies,分类,程序,results,Experiment,file,import,sklearn From: https://www.cnblogs.com/huadongw/p/17624646.html