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I want to evaluate my Cox model using cross validation for which lifelines package does not support. So I must use the sklearn adapter. However, when I fit my cox model using the sklearn adapter, I get a convergence error. Inexplicably, I do not get a convergence error when I fit my cox model on the exact same data with the exact same parameters using the CoxPHfitter directly.

X = test_data.drop('DxToFollowup', axis=1) 
Y = test_data['DxToFollowup']
CoxRegression = sklearn_adapter(CoxPHFitter, event_col='IsDead')
sk_cph = CoxRegression(penalizer=.1)

scores = cross_val_score(sk_cph, X, Y, cv=3) 

Give me the error

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.

However when I run

cph = CoxPHFitter(penalizer=0.1)
cph.fit(test_data, duration_col='DxToFollowup', event_col='IsDead', show_progress=True)
cph.print_summary()

It converges with no issues. I have even tried reducing the penalty to 0.000001 but this does not help.

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  • $\begingroup$ Likely the reason is the cv=3 - it's splitting the original dataset into smaller datasets, and it's possible that training on these smaller datasets produce a singularity. How large is your dataset? $\endgroup$ Commented May 16, 2020 at 2:52
  • $\begingroup$ 119 observations 104 events. $\endgroup$
    – Mattreex
    Commented May 16, 2020 at 3:51
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    $\begingroup$ Try a higher cv. How many columns are there? $\endgroup$ Commented May 16, 2020 at 13:35
  • $\begingroup$ That's interesting. Choosing cv=5 seems to work. There are 23 columns. I have 6 continuous variables and 5 categorical variables. I found that one variable in particular was causing the issue. I am not sure why 5 works, but with cv=5 I can keep all the variables $\endgroup$
    – Mattreex
    Commented May 16, 2020 at 17:10
  • $\begingroup$ Are you dropping one of the categories in each categorical variable? Another problem could be i) a rare category that isn't present in the training df (i.e. all 0s), or ii) because there are 17 categorical variables, and 78 rows (in cv=3), it's possible that two of the encoded variables become co-linear. $\endgroup$ Commented May 16, 2020 at 18:54

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