问题

这个警告弹出了10次。行号在456和305之间变化：

C:\Users\foo\Anaconda3\lib\site-packages\scipy\optimize_linesearch.py:456: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)

我正在使用以下参数运行一个网格搜索：

logistic_regression_grid = {
"class_weight": ["balanced"],
"max_iter": [100000],
"solver": ["lbfgs", "liblinear", "newton-cg", "newton-cholesky", "sag", "saga"],
"random_state": [0]
}

所以问题是哪个求解器（solver）引发了警告？能够确定吗？

C:\Users\foo\Anaconda3\lib\site-packages\scipy\optimize\_linesearch.py:456: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)

logistic_regression_grid = {
    "class_weight": ["balanced"], 
    "max_iter":     [100000],
    "solver":       ["lbfgs", "liblinear", "newton-cg", "newton-cholesky", "sag", "saga"],
    "random_state": [0]
}

答案1

得分: 1

以下是您提供的代码的中文翻译部分：

我使用了鸢尾花数据集，将`max_iter=10`设置为故意引发收敛警告。由于您只对求解器感兴趣，我在不使用网格搜索的情况下循环遍历了这些求解器，并且能够使用`warnings`库和`sklearn.exceptions`包打印出哪个求解器不收敛。以下是我的代码：

```python
import warnings
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.exceptions import ConvergenceWarning
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 逻辑回归参数网格

logistic_regression_grid = {
    "class_weight": ["balanced"],
    "max_iter": [100000],
    "solver": ["lbfgs", "liblinear", "newton-cg", "sag", "saga"],
    "random_state": [0]
}

# 载入鸢尾花数据集

iris = load_iris()
X, y = iris.data, iris.target

# 将数据拆分为训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 循环遍历求解器并捕获警告

for solver in logistic_regression_grid["solver"]:
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")

        # 使用当前求解器拟合逻辑回归模型

        model = LogisticRegression(class_weight="balanced", max_iter=10, solver=solver, random_state=0)
        model.fit(X_train, y_train)

        # 检查是否生成了任何警告

        if any(issubclass(warning.category, ConvergenceWarning) for warning in w):
            print(f"求解器 '{solver}' 未收敛。")

这是我得到的输出：

求解器 'lbfgs' 未收敛。
求解器 'newton-cg' 未收敛。
求解器 'sag' 未收敛。
求解器 'saga' 未收敛。

希望这能帮助您理解代码的功能和输出。

<details>
<summary>英文:</summary>

I used the iris set and I set `max_iter=10` to purposefully induce a convergence warning. Since you are interested only in the solvers I looped over the solvers without using grid search and I was able to print which solver does not converge using the `warnings` library and the `sklearn.exceptions` package. Here is my code:

import warnings
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.exceptions import ConvergenceWarning
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

Your logistic regression grid

logistic_regression_grid = {
"class_weight": ["balanced"],
"max_iter": [100000],
"solver": ["lbfgs", "liblinear", "newton-cg", "sag", "saga"],
"random_state": [0]
}

Load the Iris dataset

iris = load_iris()
X, y = iris.data, iris.target

Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

Loop over the solvers and capture warnings

for solver in logistic_regression_grid["solver"]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")

    # Fit logistic regression model with the current solver

    model = LogisticRegression(class_weight="balanced", max_iter=10, solver=solver, random_state=0)
    model.fit(X_train, y_train)

    # Check if any warning was generated

    if any(issubclass(warning.category, ConvergenceWarning) for warning in w):
        print(f"Solver '{solver}' did not converge.")

Here is the output I get:

Solver 'lbfgs' did not converge.
Solver 'newton-cg' did not converge.
Solver 'sag' did not converge.
Solver 'saga' did not converge.

</details>