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##### Activity
Cameron Davidson-Pilon
@CamDavidsonPilon
Hi @dmitryuk, sure that can be done. Queue times fit perfectly into survival analysis. Since you suggest that when the client uploaded the doc is important, I would suggest that you use that feature (mapped to a cyclic variable¹) in a regression model. Ex:
from lifelines import WeibullAFTFitter

df['start_time'] = df['start_time'].map(map_to_seconds)
df['sin_start_time'] = np.sin(2*np.pi*df['start_time']/seconds_in_day)
df['cos_start_time'] = np.cos(2*np.pi*df['start_time']/seconds_in_day)
df = df.drop('start_time', axis=1)

wf = WeibullAFTFitter().fit(df, "duration")

wf.predict_survival_function(df)
wf.predict_median(df)
Since you want how long left to wait, you probably want to use the conditional_after kwarg in the predict_* methods as well
Vilane.
@vgs549
@CamDavidsonPilon have you considered counterfactual analysis in lifelines?
Cameron Davidson-Pilon
@CamDavidsonPilon
@vgs549 mmm not much - are you thinking about casual inference techniques?
Alexander Dmitryuk
@dmitryuk
This way I prepared the data as
id(doc id)|start_from_week_seconds(seconds past from start of week after client uploaded doc)|duration(seconds spent to check the doc)
After code line executed wf = WeibullAFTFitter().fit(df, "duration") exception throw
"StatisticalWarning: The diagonal of the variancematrix has negative values. This could be a problem with WeibullFitter's fit to the data."
Could you help to understand what is wrong in the code?
Vilane.
@vgs549
@CamDavidsonPilon yes, causal inference.
Cameron Davidson-Pilon
@CamDavidsonPilon
@vgs549 I've thought some about it, however I've left most of the burden on the user to choose models and inference appropriately. I would suggest checking out the Zepid package for more causal inference assistance
@dmitryuk ah, ignore it, I need to suppress that. Also, make sure to drop the id col in your model
Cameron Davidson-Pilon
@CamDavidsonPilon
:wave: minor lifelines release. Better support for pickling! https://github.com/CamDavidsonPilon/lifelines/releases
Vilane.
@vgs549
@CamDavidsonPilon Thanks, I will have a look.
Niranjan Ravichandra
@nravic
Hello! I'm trying to predict failure of a few robots with a pretty substantial time-series dataset, and I've been looking at lifelines as a potential method for doing so. The time series data has a few instances of failure, and I'm trying to correlate a number of other variables we have data on (such as forward velocity, number of stationary hours, etc) with failure. In short, I'm trying to get a window in which to predict possible failure based on historical data. Should I be using survival regression for this?
Niranjan Ravichandra
@nravic
Also going off the survival regression chapter in the wiki, each of my observations are obtained daily. Does the fact that the duration in my data is just 1 matter?
Cameron Davidson-Pilon
@CamDavidsonPilon
@nravic I think you can use lifelines, but you're in the realm of recurrent events, which lifelines has only a little support for (there may be another package out there?). Since you have daily snapshots, you probably want to use time-varying regression: https://lifelines.readthedocs.io/en/latest/Time%20varying%20survival%20regression.html
Niranjan Ravichandra
@nravic
Great, thanks @CamDavidsonPilon ! I'll look into this and also see if there's anything around for recurrent events.
Niranjan Ravichandra
@nravic
@CamDavidsonPilon I have data granular down to the second too however. would that see a better use from lifelines or am I better off looking elsewhere?
d-seki
@d-seki
Thank you very much for creating this wonderful tool for statistics. Let me ask you a subtle question. What null hypothesis are you assuming for CoxTimeVaryingFitter? I guess it is for beta to be zero. Best,
Julian Späth
@julianspaeth
Hey, I have a question concerning the concordance_index. I want to use my predicted cumulative hazard functions to compute the concordance_index and use them as predicted_scores. Is it the right way to sum up the chf of each sample and take the negative of it to compute the concordance_index on the basis of the cumulative hazard functions?
Youyang
@zxclcsq
Hello. I'm trying to replicate the Weibull AFT model prediction section in the lifelines docs, but the return is all NANs from the predict_survival_function. Any thoughts on this? The code I used is :
from lifelines import WeibullAFTFitter

aft = WeibullAFTFitter()
aft.fit(rossi_dataset, duration_col='week', event_col='arrest')

X = rossi_dataset.loc[:10]

aft.predict_survival_function(X)
Cameron Davidson-Pilon
@CamDavidsonPilon
Ahh sorry about the delay folks! I don't check this daily, and gitter didn't end me emails
@d-seki yes that's right, NH is that beta == 0

@julianspaeth depends on the model. Recall that the c-index only depends on ranking of values. For the Cox model, the summing the cumulative hazard won't change the ranking, so it won't matter what you use. For an AFT model, it may change the ranking.

Alternatively, you can choose a point in time, and use the CHF at that

@zxclcsq not good! Looks like I broke something...
I'll investigate asap
Cameron Davidson-Pilon
@CamDavidsonPilon
@zxclcsq for now, you must specify the times argument in predict_survival_function
Cameron Davidson-Pilon
@CamDavidsonPilon
The fix is in master: CamDavidsonPilon/lifelines@085258e
hpham04
@hpham04
Hello everyone, i just tried to play with lifelines. I look into some examples but still do not understand. As far as I understand, after training we should have a way to save the model, then we can use this model immediately without re-training model. Can you please help to advise
hpham04
@hpham04
Cameron Davidson-Pilon
@CamDavidsonPilon
@hpham04 yup that is - let me know if you have other questions or that doesn't work.
Cameron Davidson-Pilon
@CamDavidsonPilon
Is anyone experiencing problems installing / upgrading lifelines? Let me know!
d-seki
@d-seki
@CamDavidsonPilon Thanks very much!
Cameron Davidson-Pilon
@CamDavidsonPilon
I got conda forge working again, so we should start to see simultaneous conda & pypi releases again
Cameron Davidson-Pilon
@CamDavidsonPilon
:wave: Also, new minor release with some useful bug fixes: https://github.com/CamDavidsonPilon/lifelines/releases/tag/v0.22.9
Bojan Kostic
@bkos
@CamDavidsonPilon I see there are estimators for cumulative hazard function, and it is as well in your mathematical links between entities diagram (nice one, BTW). What's the point (/advantage?) of introducing/estimating CHF in our survival analysis? It seems that all we need is hazard and survival functions, which have a direct transform. I can't explain the meaning of CHF, it doesn't bring anything, seems redundant... I'm reading about deep survival models (there's lots of papers and code lately) and they hardly mention it...
Cameron Davidson-Pilon
@CamDavidsonPilon
@bkos good question. A few points / advantages: i) The CHF is easier to estimate (less variance) than the hazard ii) The CHF, and the HF, are present in the likelihood equation for survival models, see equation (2.5) in https://cran.r-project.org/web/packages/flexsurv/vignettes/flexsurv.pdf iii) because of the "ease of differentiation" vs "hardness of integration", specifying the CHF and working out the HF is easier than the other way around, iv) it's 1-1 with the SF, that is, SF = exp(-CHF).
Bojan Kostic
@bkos
Thanks a lot, @CamDavidsonPilon! Is the equation you mentioned used in lifelines for some models? With it we don't lose any information, but it's different from the Cox partial likelihood, which includes only uncensored observations and softmax terms...
i completely missed that one, thx a lot!
mitchgallerstein-toast
@mitchgallerstein-toast
Has anyone had the issue where you get a "ZeroDivisionError: float division by zero" when using the CoxTimeVaryingFitter?
We originally thought it had to do with having multiple events with the same duration but that doesnt seem to be the case.
mitchgallerstein-toast
@mitchgallerstein-toast
This seems to be the problem! Does anyone know how we would get around this until it is fixed?
Cameron Davidson-Pilon
@CamDavidsonPilon
@bkos yup, that equation is the basis of parametric models (you're right that it's not used in the Cox model)
@mitchgallerstein-toast hm, this sounds similar to the issue here: CamDavidsonPilon/lifelines#768
$ll(\beta) = \sum_{i:C_i = 1} X_i \beta - \log{\sum_{j: Y_i \ge Y_j} \theta_j}$