---------------------------------------------------------------------------
LinAlgError                               Traceback (most recent call last)
~/code/lifelines/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)
    988             try:
--> 989                 inv_h_dot_g_T = spsolve(-h, g, assume_a="pos", check_finite=False)
    990             except (ValueError, LinAlgError) as e:

~/venvs/data/lib/python3.7/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)

~/venvs/data/lib/python3.7/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-15-f5c8742b8449> in <module>
      1 cph = CoxPHFitter()
----> 2 cph.fit(regression_df, 'T', 'event', formula="president + bs(year, df=3)")
      3 cph.print_summary(3)

~/code/lifelines/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

~/code/lifelines/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)
    284             timeline=timeline,
    285             formula=formula,
--> 286             entry_col=entry_col,
    287         )
    288         return self

~/code/lifelines/lifelines/fitters/coxph_fitter.py in _fit_model(self, *args, **kwargs)
    303     def _fit_model(self, *args, **kwargs):
    304         if self.baseline_estimation_method == "breslow":
--> 305             return self._fit_model_breslow(*args, **kwargs)
    306         elif self.baseline_estimation_method == "spline":
    307             return self._fit_model_spline(*args, **kwargs)

~/code/lifelines/lifelines/fitters/coxph_fitter.py in _fit_model_breslow(self, *args, **kwargs)
    313             penalizer=self.penalizer, l1_ratio=self.l1_ratio, strata=self.strata, alpha=self.alpha, label=self._label
    314         )
--> 315         model.fit(*args, **kwargs)
    316         return model
    317 

~/code/lifelines/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

~/code/lifelines/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)
    726             initial_point=initial_point,
    727             show_progress=show_progress,
--> 728             step_size=step_size,
    729         )
    730 

~/code/lifelines/lifelines/fitters/coxph_fitter.py in _fit_model(self, X, T, E, weights, entries, initial_point, step_size, show_progress)
    845     ):
    846         beta_, ll_, hessian_ = self._newton_rhapson_for_efron_model(
--> 847             X, T, E, weights, entries, initial_point=initial_point, step_size=step_size, show_progress=show_progress
    848         )
    849 

~/code/lifelines/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)
   1000                             CONVERGENCE_DOCS
   1001                         ),
-> 1002                         e,
   1003                     )
   1004                 else:

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.

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-13-9b2470913cd3> in <module>
----> 1 cph.plot_covariate_groups("year", values=np.linspace(1977, 2016, 5), y="cumulative_hazard")

~/code/lifelines/lifelines/fitters/coxph_fitter.py in __getattr__(self, attr)
    292             raise AttributeError("Must call `fit` first.")
    293 
--> 294         if hasattr(self._model, attr):
    295             return getattr(self._model, attr)
    296 

~/code/lifelines/lifelines/fitters/coxph_fitter.py in __getattr__(self, attr)
    290     def __getattr__(self, attr):
    291         if attr == "_model":
--> 292             raise AttributeError("Must call `fit` first.")
    293 
    294         if hasattr(self._model, attr):

AttributeError: Must call `fit` first.