y_test 上的形状不一致错误 MultiLabelBinarizer，sklearn 多标签分类

Question

import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import LinearSVC
from sklearn.linear_model import SGDClassifier
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.svm import SVC

data = r'C:\Users\...\Downloads\news_v1.xlsx'

df = pd.read_excel(data)
df = pd.DataFrame(df.groupby(["id", "doc"]).label.apply(list)).reset_index()

X = np.array(df.doc)
y = np.array(df.label)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

mlb = preprocessing.MultiLabelBinarizer()
Y_train = mlb.fit_transform(y_train)

classifier = Pipeline([
('vectorizer', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC()))])

classifier.fit(X_train, Y_train)
predicted = classifier.predict(X_test)

Y_test = mlb.fit_transform(y_test)

print("Y_train: ", Y_train.shape)
print("Y_test: ", Y_test.shape)
print("Predicted: ", predicted.shape)
print("Accuracy Score: ", accuracy_score(Y_test, predicted))

我似乎无法进行任何测量，因为 Y_test 在 fit_transform 之后使用 MultiLabelBinarizer 给出了不同的矩阵维数。

结果和错误：

Y_train:  (1278, 49)
Y_test:  (630, 42)
Predicted:  (630, 49)
Traceback (most recent call last):
  File "C:/Users/../PycharmProjects/MultiAutoTag/classifier.py", line 41, in <module>
    print("Accuracy Score: ", accuracy_score(Y_test, predicted))
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\classification.py", line 174, in accuracy_score
    differing_labels = count_nonzero(y_true - y_pred, axis=1)
  File "C:\ProgramData\Anaconda3\lib\site-packages\scipy\sparse\compressed.py", line 361, in __sub__
    raise ValueError("inconsistent shapes")
ValueError: inconsistent shapes

看打印出来的Y_test，形状跟其他的不一样。我做错了什么，为什么 MultiLabelBinarizer return 与 Y_test 的大小不同？ 提前感谢您的帮助！

编辑 新错误：

Traceback (most recent call last):
  File "C:/Users/../PycharmProjects/MultiAutoTag/classifier.py", line 47, in <module>
    Y_test = mlb.transform(y_test)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py", line 763, in transform
    yt = self._transform(y, class_to_index)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py", line 787, in _transform
    indices.extend(set(class_mapping[label] for label in labels))
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py", line 787, in <genexpr>
    indices.extend(set(class_mapping[label] for label in labels))
KeyError: 'Sanction'

这就是 y_test 的样子：

print(y_test)

[['App'] ['Contract'] ['Pay'] ['App'] 
 ['App'] ['App']
 ['Reports'] ['Reports'] ['Executive', 'Pay']
 ['Change'] ['Reports']
 ['Reports'] ['Issue']]

Answer 1

您应该只对测试数据调用 transform()。永远不要 fit() 或其变体，如 fit_transform() 或 fit_predict() 等。它们应该只用于训练数据。

所以改行：

Y_test = mlb.fit_transform(y_test)

到

Y_test = mlb.transform(y_test)

解释:

当您调用 fit() 或 fit_transform() 时，mlb 会忘记其先前学习的数据并学习新提供的数据。当 Y_train 和 Y_test 的标签可能与您的情况不同时，这可能会出现问题。

在您的例子中，Y_train 有 49 种不同的标签，而 Y_test 只有 42 种不同的标签。但这并不意味着 Y_test 比 Y_train 少 7 个标签。 Y_test 可能具有完全不同的标签集，当二值化时产生 42 列，这将影响结果。