raise ValueError( ValueError: For evaluating multiple scores, use sklearn.model_selection.cross_validate instead. ['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score', 'average_precision', 'balanced_accuracy', 'completeness_score', 'explained_variance', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'fowlkes_mallows_score', 'homogeneity_score', 'jaccard', 'jaccard_macro', 'jaccard_micro', 'jaccard_samples', 'jaccard_weighted', 'matthews_corrcoef', 'max_error', 'mutual_info_score', 'neg_brier_score', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_absolute_percentage_error', 'neg_mean_gamma_deviance', 'neg_mean_poisson_deviance', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error', 'neg_negative_likelihood_ratio', 'neg_root_mean_squared_error', 'normalized_mutual_info_score', 'positive_likelihood_ratio', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'rand_score', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc', 'roc_auc_ovo', 'roc_auc_ovo_weighted', 'roc_auc_ovr', 'roc_auc_ovr_weighted', 'top_k_accuracy', 'v_measure_score'] was passed.

这个错误是因为你在调用某个函数时传入了多个评估指标，而该函数不支持同时对多个指标进行评估。建议使用 sklearn.model_selection.cross_validate 函数来对多个指标进行评估。你可以将评估指标作为参数传递给该函数。例如：

from sklearn.model_selection import cross_validate
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification

X, y = make_classification(random_state=0)
clf = LogisticRegression(random_state=0)

scoring = ['accuracy', 'precision_macro', 'recall_macro']
scores = cross_validate(clf, X, y, scoring=scoring)

这样就可以同时对 accuracy、precision_macro 和 recall_macro 三个指标进行评估了。

相关问题

报错ValueError: np.nan is an invalid document, expected byte or unicode string. 怎么修改import pandas as pd from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # 读取电影评论数据集 data = pd.read_csv(r'D:\shujukexue\review_data.csv', encoding='gbk') x = v.fit_transform(df['eview'].apply(lambda x: np.str_(x))) # 分割数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42) # 创建CountVectorizer对象进行词频统计和向量化 count_vectorizer = CountVectorizer() X_train_count = count_vectorizer.fit_transform(X_train) X_test_count = count_vectorizer.transform(X_test) # 创建TfidfVectorizer对象进行TF-IDF计算和向量化 tfidf_vectorizer = TfidfVectorizer() X_train_tfidf = tfidf_vectorizer.fit_transform(X_train) X_test_tfidf = tfidf_vectorizer.transform(X_test) # 创建逻辑回归分类器并在CountVectorizer上进行训练和预测 classifier_count = LogisticRegression() classifier_count.fit(X_train_count, y_train) y_pred_count = classifier_count.predict(X_test_count) accuracy_count = accuracy_score(y_test, y_pred_count) print("Accuracy using CountVectorizer:", accuracy_count) # 创建逻辑回归分类器并在TfidfVectorizer上进行训练和预测 classifier_tfidf = LogisticRegression() classifier_tfidf.fit(X_train_tfidf, y_train) y_pred_tfidf = classifier_tfidf.predict(X_test_tfidf) accuracy_tfidf = accuracy_score(y_test, y_pred_tfidf) print("Accuracy using TfidfVectorizer:", accuracy_tfidf)

在代码中，变量名应该是 'data' 而不是 'df'，所以在使用 CountVectorizer 进行向量化的时候，应该使用 'data' 而不是 'df'，修改代码如下所示：

import pandas as pd 
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer 
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LogisticRegression 
from sklearn.metrics import accuracy_score 

# 读取电影评论数据集 
data = pd.read_csv(r'D:\shujukexue\review_data.csv', encoding='gbk') 

# 分割数据集为训练集和测试集 
X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42) 

# 创建CountVectorizer对象进行词频统计和向量化 
count_vectorizer = CountVectorizer() 
X_train_count = count_vectorizer.fit_transform(X_train) 
X_test_count = count_vectorizer.transform(X_test) 

# 创建TfidfVectorizer对象进行TF-IDF计算和向量化 
tfidf_vectorizer = TfidfVectorizer() 
X_train_tfidf = tfidf_vectorizer.fit_transform(X_train) 
X_test_tfidf = tfidf_vectorizer.transform(X_test) 

# 创建逻辑回归分类器并在CountVectorizer上进行训练和预测 
classifier_count = LogisticRegression() 
classifier_count.fit(X_train_count, y_train) 
y_pred_count = classifier_count.predict(X_test_count) 
accuracy_count = accuracy_score(y_test, y_pred_count) 
print("Accuracy using CountVectorizer:", accuracy_count) 

# 创建逻辑回归分类器并在TfidfVectorizer上进行训练和预测 
classifier_tfidf = LogisticRegression() 
classifier_tfidf.fit(X_train_tfidf, y_train) 
y_pred_tfidf = classifier_tfidf.predict(X_test_tfidf) 
accuracy_tfidf = accuracy_score(y_test, y_pred_tfidf) 
print("Accuracy using TfidfVectorizer:", accuracy_tfidf)

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.neural_network import MLPClassifier import joblib # 读取数据 Xtrain = pd.read_excel('sj_final.xlsx') ytrain = pd.read_excel('water_heater_log.xlsx') test = pd.read_excel('test_data.xlsx') # 训练集测试集区分。 x_train, x_test, y_train, y_test = Xtrain.iloc[:,5:],test.iloc[:,4:-1],ytrain.iloc[:,-1],test.iloc[:,-1] # 标准化 stdScaler = StandardScaler().fit(x_train) x_stdtrain = stdScaler.transform(x_train) x_stdtest = stdScaler.transform(x_test) # 建立模型 bpnn = MLPClassifier(hidden_layer_sizes = (17,10), max_iter = 200, solver = 'lbfgs',random_state=45) bpnn.fit(x_stdtrain, y_train) # 保存模型 joblib.dump(bpnn,'water_heater_nnet.m') print('构建的模型为：\n',bpnn) 报错如下ValueError: The feature names should match those that were passed during fit. Feature names must be in the same order as they were in fit.

这个错误提示是因为在进行模型训练时，数据集的特征名称顺序与测试数据集的特征名称顺序不一致。你需要检查一下训练集和测试集的特征名称顺序是否一致，如果不一致，可以通过重新调整列的顺序来解决这个问题。你可以使用下面的代码来重新调整列的顺序：

x_train = x_train[x_test.columns]

这样可以将训练集中的特征按照测试集的特征顺序重新排列。然后再重新训练模型即可。