如何用生命线包来估计cox模型？

Question

我想估计cox模型，但是当我试图运行代码时，我有一个错误。似乎这个关于coxphfitter().does的问题解决了这个问题。我认为生命线库不能用ML方法计算系数，这里是.So，我复制错误和示例代码，.I应该说我只写了代码，比如输入，而不是全部。

码

df_l=df[['Observed','HighLTV','Liquidation']]
    df_c=df[['Observed','HighLTV','Cure']]
    cph_l=CoxPHFitter()
    cph_c=CoxPHFitter()
    cph_l.fit(df_l,'Observed',event_col='Liquidation')
    cph_c.fit(df_c,'Observed',event_col='Cure')
    beta_cure=float('{:.3f}'.format((cph_c.params_[0])))
    beta_liquidation=float('{:.3f}'.format((cph_l.params_[0])))

误差

LinAlgError                               Traceback (most recent call last)
~\anaconda3\lib\site-packages\lifelines\fitters\coxph_fitter.py in _newton_rhapson_for_efron_model(self, X, T, E, weights, entries, initial_point, step_size, precision, show_progress, max_steps)
   1497             try:
-> 1498                 inv_h_dot_g_T = spsolve(-h, g, assume_a="pos", check_finite=False)
   1499             except (ValueError, LinAlgError) as e:

~\anaconda3\lib\site-packages\scipy\linalg\basic.py in solve(a, b, sym_pos, lower, overwrite_a, overwrite_b, debug, check_finite, assume_a, transposed)
    247                            overwrite_b=overwrite_b)
--> 248         _solve_check(n, info)
    249         rcond, info = pocon(lu, anorm)

~\anaconda3\lib\site-packages\scipy\linalg\basic.py in _solve_check(n, info, lamch, rcond)
     28     elif 0 < info:
---> 29         raise LinAlgError('Matrix is singular.')
     30 

LinAlgError: Matrix is singular.

During handling of the above exception, another exception occurred:

ConvergenceError                          Traceback (most recent call last)
<ipython-input-145-7cb92b8db8fe> in <module>
      8     k.append(list(map(lambda x: random.choice(o),range(10))))
      9     s=pd.DataFrame(k[i],columns=df.columns)
---> 10     c.append(CCR(s))

<ipython-input-144-da506c585def> in CCR(data)
     30     cph_c=CoxPHFitter()
     31     cph_l.fit(df_l,'Observed',event_col='Liquidation')
---> 32     cph_c.fit(df_c,'Observed',event_col='Cure')
     33     beta_cure=float('{:.3f}'.format((cph_c.params_[0])))
     34     beta_liquidation=float('{:.3f}'.format((cph_l.params_[0])))

~\anaconda3\lib\site-packages\lifelines\utils\__init__.py in f(model, *args, **kwargs)
     52         def f(model, *args, **kwargs):
     53             cls.set_censoring_type(model, cls.RIGHT)
---> 54             return function(model, *args, **kwargs)
     55 
     56         return f

~\anaconda3\lib\site-packages\lifelines\fitters\coxph_fitter.py in fit(self, df, duration_col, event_col, show_progress, initial_point, strata, step_size, weights_col, cluster_col, robust, batch_mode, timeline, formula, entry_col)
    274         """
    275         self.strata = utils.coalesce(strata, self.strata)
--> 276         self._model = self._fit_model(
    277             df,
    278             duration_col,

~\anaconda3\lib\site-packages\lifelines\fitters\coxph_fitter.py in _fit_model(self, *args, **kwargs)
    595     def _fit_model(self, *args, **kwargs):
    596         if self.baseline_estimation_method == "breslow":
--> 597             return self._fit_model_breslow(*args, **kwargs)
    598         elif self.baseline_estimation_method == "spline":
    599             return self._fit_model_spline(*args, **kwargs)

~\anaconda3\lib\site-packages\lifelines\fitters\coxph_fitter.py in _fit_model_breslow(self, *args, **kwargs)
    608         )
    609         if utils.CensoringType.is_right_censoring(self):
--> 610             model.fit(*args, **kwargs)
    611             return model
    612         else:

~\anaconda3\lib\site-packages\lifelines\utils\__init__.py in f(model, *args, **kwargs)
     52         def f(model, *args, **kwargs):
     53             cls.set_censoring_type(model, cls.RIGHT)
---> 54             return function(model, *args, **kwargs)
     55 
     56         return f

~\anaconda3\lib\site-packages\lifelines\fitters\coxph_fitter.py in fit(self, df, duration_col, event_col, show_progress, initial_point, strata, step_size, weights_col, cluster_col, robust, batch_mode, timeline, formula, entry_col)
   1225         )
   1226 
-> 1227         params_, ll_, variance_matrix_, baseline_hazard_, baseline_cumulative_hazard_, model = self._fit_model(
   1228             X_norm,
   1229             T,

~\anaconda3\lib\site-packages\lifelines\fitters\coxph_fitter.py in _fit_model(self, X, T, E, weights, entries, initial_point, step_size, show_progress)
   1353         show_progress: bool = True,
   1354     ):
-> 1355         beta_, ll_, hessian_ = self._newton_rhapson_for_efron_model(
   1356             X, T, E, weights, entries, initial_point=initial_point, step_size=step_size, show_progress=show_progress
   1357         )

~\anaconda3\lib\site-packages\lifelines\fitters\coxph_fitter.py in _newton_rhapson_for_efron_model(self, X, T, E, weights, entries, initial_point, step_size, precision, show_progress, max_steps)
   1505                     )
   1506                 elif isinstance(e, LinAlgError):
-> 1507                     raise exceptions.ConvergenceError(
   1508                         """Convergence halted due to matrix inversion problems. Suspicion is high collinearity. {0}""".format(
   1509                             CONVERGENCE_DOCS

ConvergenceError: Convergence halted due to matrix inversion problems. Suspicion is high collinearity. Please see the following tips in the lifelines documentation: https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-modelMatrix is singular.

Answer 1

该文件清楚地说明了这个问题：

ConvergenceError:由于矩阵求逆问题，收敛停止。怀疑是高度共线性的。请参阅生命线文档中的以下技巧：https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-modelMatrix是单数。

没有真实的数据，我无法给出任何进一步的建议。但是生命线文档在这个问题上给出了很多建议：

由于矩阵反演问题，

收敛停止:这意味着您的数据集中存在很高的共线性。也就是说，一列等于一个或多个其他列的线性组合。造成此错误的一个常见原因是虚拟分类变量，但不删除列或数据集中的某种层次结构。尝试通过:在模型中添加一个简化器( ex: CoxPHFitter(penalizer=0.1).fit(…) )来寻找这种关系。直到模型会聚。在print_summary()中，具有较高共线性的系数在coefs列中具有较大的(绝对)幅值。使用方差通货膨胀因子(VIF)寻找冗余变量。查看数据集的相关矩阵，或

这很可能不是由生命线引起的错误，而是您的数据或如何将模型应用于您的数据。