我在使用XGBoost来分析特征重要性，希望选择那些贡献了90%重要性的特征。首先，我构建了一个DataFrame，因为我需要它来导出到Excel中，然后我编写了一个循环来评估那些贡献了90%重要性的特征。在这之后，有一个神经网络（但不在下面的代码中）。我知道可能有更简单的方法来做这件事，但它给我抛出了一个错误：

ValueError: could not convert string to float: '0,25691372'

代码如下

  import pandas as pdimport numpy as npfrom sklearn.ensemble import RandomForestRegressorfrom sklearn.feature_selection import SelectFromModelfrom sklearn import preprocessingfrom sklearn.model_selection import train_test_splitfrom xgboost import XGBRegressorfrom matplotlib import pyplot as pltdataset = pd.read_csv('CompleteDataSet_original_Clean_CONC.csv', decimal=',', delimiter = ";")from sklearn.metrics import r2_scorelabel = dataset.iloc[:,-1]features = dataset.drop(columns = ['Label'])y_max_pre_normalize = max(label)y_min_pre_normalize = min(label)def denormalize(y):    final_value = y*(y_max_pre_normalize-y_min_pre_normalize)+y_min_pre_normalize    return final_valueX_train1, X_test1, y_train1, y_test1 = train_test_split(features, label, test_size = 0.20, random_state = 1, shuffle = True)y_test2 = y_test1.to_frame()y_train2 = y_train1.to_frame()scaler1 = preprocessing.MinMaxScaler()scaler2 = preprocessing.MinMaxScaler()X_train = scaler1.fit_transform(X_train1)X_test = scaler2.fit_transform(X_test1)scaler3 = preprocessing.MinMaxScaler()scaler4 = preprocessing.MinMaxScaler()y_train = scaler3.fit_transform(y_train2)y_test = scaler4.fit_transform(y_test2)sel = XGBRegressor(colsample_bytree= 0.7, learning_rate = 0.005, max_depth = 5, min_child_weight = 3, n_estimators = 1000)sel.fit(X_train, y_train)importances = sel.feature_importances_importances = [str(i) for i in importances]importances = [i.replace(".", ",") for i in importances]df1 = pd.DataFrame(features.columns)df1.columns = ['Features']df2 = pd.DataFrame(importances)df2.columns = ['Importances [%]']result = pd.concat([df1,df2],axis = 1)result = result.sort_values(by='Importances [%]', ascending=False)result.to_excel("Feature_Results.xlsx") i = 0somma = 0feature = []while somma <=0.9:    a = result.iloc[i,-1]    somma = float(a) + somma    feature.append(result.iloc[i,-2])    i = i + 1

回答：

你可以使用locale.atof()来处理使用,作为小数点分隔符的情况。

import localelocale.setlocale(locale.LC_ALL, 'fr_FR')...    somma = locale.atof(a) + somma