使用LogisticRegression.fit时遇AttributeError：numpy无matrix属性

问题场景 #

运行以下LogisticRegression相关代码时触发错误：

import pandas as pd
import matplotlib
import matplotlib.pyplot as plt 
import numpy as np
%matplotlib inline 
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
import seaborn as sns 
from numpy import matrix
logisticreg = LogisticRegression()
logisticreg.fit(xtrain, ytrain)

报错详情 #

File ...\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:1303, in LogisticRegression.fit(self, X, y, sample_weight)
   1300 else:
   1301     n_threads = 1
-> 1303 fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, prefer=prefer)(
   1304     path_func(
   1305         X,
   1306         y,
   1307         pos_class=class_,
   1308         Cs=[C_],
...
-> 1871     if isinstance(a, np.matrix):
   1872         return asarray(a).ravel(order=order)
   1873     else:

AttributeError: module 'numpy' has no attribute 'matrix'

额外测试 #

当前使用Numpy版本1.26.2，尝试运行LinearRegression示例代码，仍出现相同错误：

import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
# y = 1 * x_0 + 2 * x_1 + 3
y = np.dot(X, np.array([1, 2])) + 3
reg = LinearRegression().fit(X, y)
reg.score(X, y)
reg.coef_
reg.intercept_
reg.predict(np.array([[3, 5]]))

解决方案 #

• 核心原因：Numpy 1.26.x版本正式移除了np.matrix相关旧API，而你的scikit-learn版本过旧，尚未适配这一变更。
• 修复步骤：
  1. 升级scikit-learn到兼容Numpy 1.26+的版本，执行命令：

     pip install --upgrade scikit-learn
     

  2. 删除代码中多余的from numpy import matrix行，现在推荐使用np.array替代所有np.matrix的使用场景。

内容的提问来源于stack exchange，提问作者Apple