machine-learning - 为什么多标签分类(二元相关性)起作用？

Question

我是使用二进制相关性进行多标签分类的新手，并且在解释结果时遇到一些问题:

结果是: [[ 0.0.] [2.2.]]

这是否意味着第一个案例属于[0,0]，第二个案例属于[2,2]？这看起来一点都不好。或者我还缺少其他东西吗？

在绅士们回答后，我收到以下错误因为 y_train 标签 [2**,0,**3,4] 因为零

Traceback (most recent call last):
File "driver.py", line 22, in <module>
clf_dict[i] = clf.fit(x_train, y_tmp)
File "C:\Users\BaderEX\Anaconda22\lib\site-packages\sklearn\linear_model\logistic.py", line 1154, in fit
self.max_iter, self.tol, self.random_state)
File "C:\Users\BaderEX\Anaconda22\lib\site-packages\sklearn\svm\base.py", line 885, in _fit_liblinear
" class: %r" % classes_[0])
ValueError: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1

更新的代码:

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import *

numer_classes = 5

x_train = np.array([[1,2,3,4],[0,1,2,1],[1,2,0,3]])
y_train = [[0],[1,0,3],[2,0,3,4]]

x_test = np.array([[1,2,3,4],[0,1,2,1],[1,2,0,3]])
y_test = [[0],[1,0,3],[2,0,3,4]]

clf_dict = {}
for i in range(numer_classes):
    y_tmp = []
    for j in range(len(y_train)):
        if i in y_train[j]:
            y_tmp.append(1)
        else:
            y_tmp.append(0)
    clf = LogisticRegression()
    clf_dict[i] = clf.fit(x_train, y_tmp)

prediction_matrix = np.zeros((len(x_test),numer_classes))
for i in range(numer_classes):
    prediction = clf_dict[i].predict(x_test)
    prediction_matrix[:,i] = prediction    

print('Predicted')
print(prediction_matrix)

谢谢

Answer 1

对于二进制相关性，您应该为每个标签创建指示符类别:0 或 1。 scikit-multilearn提供与 scikit 兼容的分类器实现。

设置:

def to_indicator_matrix(y_list):
    y_train_matrix = np.zeros(shape=(len(y_list), max(map(len, y_list))+1), dtype='i8')
    for i, y in enumerate(y_list):
        y_train_matrix[i][y] = 1
    return y_train_matrix

给定您的 y_train 和 y_test，运行:

y_train = to_indicator_matrix(y_train)
y_test =  to_indicator_matrix(y_test)

您的 y_train 现在是:

array([[1, 1, 0],
       [0, 1, 1],
       [1, 0, 1]])

这应该可以解决您的问题。不过，使用 scikit-multilearn BinaryRelevance 比使用您自己的代码更舒服。尝试一下!

运行

pip install scikit-multilearn

然后尝试

from skmultilearn.problem_transform import BinaryRelevance
from sklearn.linear_model import LogisticRegression
import sklearn.metrics

# assume data is loaded using
# and is available in X_train/X_test, y_train/y_test

# initialize Binary Relevance multi-label classifier
# with gaussian naive bayes base classifier
classifier = BinaryRelevance(LogisticRegression(C=40,class_weight='balanced'), require_dense)

# train
classifier.fit(X_train, y_train)

# predict
predictions = classifier.predict(X_test)

# measure
print(sklearn.metrics.hamming_loss(y_test, predictions))