如何正确标注混淆矩阵？

Question

尝试做一些 NLP（从语料库中逐字提取并用特定主题标记它们作为标签）。

我在创建混淆矩阵的部分，但我不知道如何正确标记矩阵，以便将标签归因于矩阵的正确部分。

具体来说，如果我有以下混淆矩阵，我怎么知道标签应该放在哪里正确居住？

 whats label 1? [ 1  0  0  0  0  0  0  0  0  0  0  0  0]
 whats label 2? [ 0  5  0  0  0  0  3  0  0  0  0  0  0]
 etc..          [ 0  0  0  0  0  0  0  0  0  0  0  0  0]
                [ 0  1  0  6  0  1  1  0  0  0  0  0  0]
                [ 0  0  0  0  1  0  0  0  0  1  0  0  0]
                [ 0  0  0  1  0  1  0  0  0  0  0  0  0]
                [ 0  1  0  0  0  0 13  0  0  0  0  0  0]
                [ 0  0  0  0  0  0  1  0  0  0  0  0  0]
                [ 0  0  1  0  0  0  1  0  5  0  0  0  0]
                [ 0  0  0  0  0  0  0  0  0  0  0  0  0]
                [ 0  0  0  0  1  0  1  0  0  0  0  0  1]
                [ 0  0  0  0  0  0  1  0  0  0  0  2  0]
                [ 0  0  0  0  0  0  0  1  0  0  0  0  0]

这是我的代码：

#bag of words ---------------------------------------------------------------

from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

def cv(data):
    count_vectorizer = CountVectorizer()

    emb = count_vectorizer.fit_transform(data)

    return emb, count_vectorizer

list_corpus = questions_and_labels['clean_text_lemmed'].tolist()
list_labels = questions_and_labels["category_1_level_1"].tolist()

X_train, X_test, y_train, y_test = train_test_split(list_corpus, list_labels, test_size=0.2, 
                                                                                random_state=40)

X_train_counts, count_vectorizer = cv(X_train)
X_test_counts = count_vectorizer.transform(X_test)

# Confusion Matrix ---------------------------------------------------------------
import numpy as np
import itertools
from sklearn.metrics import confusion_matrix

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.winter):
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title, fontsize=30)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, fontsize=10, rotation=90)
    plt.yticks(tick_marks, classes, fontsize=10)
    
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.

    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", 
                 color="white" if cm[i, j] < thresh else "black", fontsize=40)
    
    plt.tight_layout()
    plt.ylabel('True label', fontsize=30)
    plt.xlabel('Predicted label', fontsize=30)

    return plt

    cm = confusion_matrix(y_test, y_predicted_counts)
    fig = plt.figure(figsize=(20, 20))
    plot = plot_confusion_matrix(cm, classes = HOW_DO_I_GET_THIS?, normalize=False, title='Confusion matrix')
    plt.show()
    print(cm)

额外的上下文，我一直在关注并模仿这个例子：https://github.com/hundredblocks/concrete_NLP_tutorial/blob/master/NLP_notebook.ipynb

但在他们的示例中，他们对标签进行了硬编码...我不确定他们是如何得出正确顺序的

Answer 1

找到答案。

sklearn.metrics 的 confusion_matrix 有一个名为 labels 的协议。

例如，如果您将所有不同的标签放在一个列表中，则列表中标签的顺序将传播并决定混淆矩阵的顺序。

我的例子：

class_names = df.labels.unique() # Here im making the unique labels object the order here will dictate the order in confusion matrix

cm = confusion_matrix(y_test, y_predicted_counts, labels=class_names) #here i tell the cm to use my class_names object for ordering

fig = plt.figure(figsize=(20, 20))
plot = plot_confusion_matrix(cm, classes = class_names, normalize=False, title='Confusion matrix')
plt.show()

如何正确标注混淆矩阵？

How to correctly label confusion matrix?

python

matplotlib

nlp

confusion-matrix