我一直在尝试对数据集进行过采样，因为它不平衡。我正在进行二元文本分类，并希望保持两个类之间的比率为1。我尝试使用SMOTE机制来解决问题。
我按照以下教程进行操作: https://beckernick.github.io/oversampling-modeling/ 然而，我遇到了一个错误，错误信息如下:
ValueError: could not convert string to float
以下是我的代码:
我按照以下教程进行操作: https://beckernick.github.io/oversampling-modeling/ 然而，我遇到了一个错误，错误信息如下:
ValueError: could not convert string to float
以下是我的代码:
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.model_selection import KFold
from sklearn.metrics import confusion_matrix, f1_score
from imblearn.over_sampling import SMOTE
data = pd.read_csv("dataset.csv")
nb_pipeline = Pipeline([
('vectorizer', CountVectorizer(ngram_range = (1, 10))),
('tfidf_transformer', TfidfTransformer()),
('classifier', MultinomialNB())
])
k_fold = KFold(n_splits = 10)
nb_f1_scores = []
nb_conf_mat = np.array([[0, 0], [0, 0]])
for train_indices, test_indices in k_fold.split(data):
train_text = data.iloc[train_indices]['sentence'].values
train_y = data.iloc[train_indices]['isRelevant'].values
test_text = data.iloc[test_indices]['sentence'].values
test_y = data.iloc[test_indices]['isRelevant'].values
sm = SMOTE(ratio = 1.0)
train_text_res, train_y_res = sm.fit_sample(train_text, train_y)
nb_pipeline.fit(train_text, train_y)
predictions = nb_pipeline.predict(test_text)
nb_conf_mat += confusion_matrix(test_y, predictions)
score1 = f1_score(test_y, predictions)
nb_f1_scores.append(score1)
print("F1 Score: ", sum(nb_f1_scores)/len(nb_f1_scores))
print("Confusion Matrix: ")
print(nb_conf_mat)
请问有人可以告诉我哪里出了问题吗？去掉SMOTE的两行代码后，我的程序可以正常运行。