ValuError在单簧管imblearn.over_sampling中的计数

Question

由于数据集不平衡，我一直试图对其进行过采样。我正在进行二进制文本分类，并希望在我的两个类之间保持1的比率。我正在尝试用击打装置来解决这个问题。

我遵循了本教程：https://beckernick.github.io/oversampling-modeling/

然而，我遇到一个错误，它说：

ValueError:无法将字符串转换为浮动

这是我的代码：

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)

有人能告诉我我哪里出了问题吗?没有这两条线，我的程序运行得很好。

Answer 1

在对文本数据进行矢量化之后，在对分类器进行拟合之前，您应该进行过度采样。这意味着拆分代码中的管道。代码的相关部分应该如下所示：

nb_pipeline = Pipeline([
    ('vectorizer', CountVectorizer(ngram_range = (1, 10))),
    ('tfidf_transformer', TfidfTransformer())
])

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

    vectorized_text = nb_pipeline.fit_transform(train_text)

    sm = SMOTE(ratio = 1.0)
    train_text_res, train_y_res = sm.fit_sample(vectorized_text, train_y)

    clf = MultinomialNB()
    clf.fit(train_text_res, train_y_res)
    predictions = clf.predict(nb_pipeline.transform(test_text))