AttributeError:'str'对象在拟合逻辑回归模型时没有属性'decode'

我目前正在尝试使用逻辑回归创建二元分类。目前我正在确定特征重要性。我已经进行了数据预处理(一次热编码和采样)并使用 XGBoost 和 RandomFOrestClassifier 运行它,没问题

但是,当我尝试拟合 LogisticRegression 模型时(以下是我在 Notebook 中的代码),

from sklearn.linear_model import LogisticRegression

#Logistic Regression
# fit the model
model = LogisticRegression()
# fit the model
model.fit(np.array(X_over), np.array(y_over))
# get importance
importance = model.coef_[0]
# summarize feature importance
df_imp = pd.DataFrame({'feature':list(X_over.columns), 'importance':importance})
display(df_imp.sort_values('importance', ascending=False).head(20))

# plot feature importance
plt.bar(list(X_over.columns), importance)
plt.show()

它给出了一个错误

...
~AppDataLocalContinuumanaconda3libsite-packagesjoblibparallel.py in <listcomp>(.0)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

~AppDataLocalContinuumanaconda3libsite-packagessklearnlinear_model_logistic.py in _logistic_regression_path(X, y, pos_class, Cs, fit_intercept, max_iter, tol, verbose, solver, coef, class_weight, dual, penalty, intercept_scaling, multi_class, random_state, check_input, max_squared_sum, sample_weight, l1_ratio)
    762             n_iter_i = _check_optimize_result(
    763                 solver, opt_res, max_iter,
--> 764                 extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
    765             w0, loss = opt_res.x, opt_res.fun
    766         elif solver == 'newton-cg':

~AppDataLocalContinuumanaconda3libsite-packagessklearnutilsoptimize.py in _check_optimize_result(solver, result, max_iter, extra_warning_msg)
    241                 "    https://scikit-learn.org/stable/modules/"
    242                 "preprocessing.html"
--> 243             ).format(solver, result.status, result.message.decode("latin1"))
    244             if extra_warning_msg is not None:
    245                 warning_msg += "n" + extra_warning_msg

AttributeError: 'str' object has no attribute 'decode'    

我在 google 上搜索了一下,几乎所有的回复都说这个错误是因为 scikit-learn 库试图解码一个已经解码的字符串。但我不知道如何解决我这里的情况。我确保我的所有数据都是整数或 float64,并且没有字符串。

回答

我尝试scikit-learn使用以下命令升级我的,仍然没有解决AttributeError: 'str' object has no attribute 'decode'问题

pip install scikit-learn  -U

最后,下面的代码片段解决了这个问题,将求解器添加为 liblinear

model = LogisticRegression(solver='liblinear')


回答

在最新版本的 scikit-learn(现在是 0.24.1)中,该问题已得到修复,将部分代码包含在我在下面报告的 try-catch 块中:文件是

optimize.py -> _check_optimize_result(solver, result, max_iter=None,
                       extra_warning_msg=None)

代码片段是

if solver == "lbfgs":
    if result.status != 0:
        try:
            # The message is already decoded in scipy>=1.6.0
            result_message = result.message.decode("latin1")
        except AttributeError:
            result_message = result.message
            warning_msg = (
                "{} failed to converge (status={}):n{}.nn"
                "Increase the number of iterations (max_iter) "
                "or scale the data as shown in:n"
                "    https://scikit-learn.org/stable/modules/"
                "preprocessing.html"
            ).format(solver, result.status, result_message)

这只是

if solver == "lbfgs":
    if result.status != 0:
        warning_msg = (
            "{} failed to converge (status={}):n{}.nn"
            "Increase the number of iterations (max_iter) "
            "or scale the data as shown in:n"
            "    https://scikit-learn.org/stable/modules/"
            "preprocessing.html"
        ).format(solver, result.status, result.message.decode("latin1"))

前。所以升级 scikit-learn 就解决了这个问题。


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