y_test，sklearn多标签分类上的形状误差不一致MultiLabelBinarizer [英] inconsistent shape error MultiLabelBinarizer on y_test, sklearn multi-label classification

问题描述

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，因为在用MultiLabelBinarizer进行fit_transform后，Y_test给出了不同的矩阵尺寸.

I can't seems to do any measurements since Y_test gives a different matrix dimension after fit_transform with MultiLabelBinarizer.

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

查看打印的Y_test，形状与其余形状不同.我在做什么错，为什么MultiLabelBinarizer为Y_test返回不同的大小? 感谢您的提前帮助！

Looking at the printed Y_test, the shape is different than the rest. What am i doing wrong and why does MultiLabelBinarizer return a different size for Y_test? Thanks for the help in advance!

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'

这是y_test的样子:

This is how y_test looks like:

print(y_test)

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

推荐答案

您应仅对测试数据调用transform().切勿使用fit()或其变体(例如fit_transform()或fit_predict()等).只能在训练数据上使用它们.

You should only call transform() on test data. Never fit() or its variations like fit_transform() or fit_predict() etc. They should be used only on training data.

因此更改行:

Y_test = mlb.fit_transform(y_test)

到

Y_test = mlb.transform(y_test)

说明:

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的标签可能因您的情况而有所不同时，这可能会出现问题.

When you call fit() or fit_transform(), the mlb forgets its previous learnt data and learn the new supplied data. This can be problematic when Y_train and Y_test may have difference in labels as your case have.

在您的情况下，Y_train具有49种不同的标签，而Y_test仅具有42种不同的标签.但这并不意味着Y_test比Y_train短7个标签. Y_test可能具有完全不同的标签集，当二值化结果为42列时，这会影响结果.

In your case, Y_train have 49 different kinds of labels, whereas Y_test have only 42 different labels. But this doesn't mean that Y_test is 7 labels short of Y_train. It can be possible that Y_test may have entirely different set of labels, which when binarized results in 42 columns, and that will affect the results.

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