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    Cameron Davidson-Pilon
    @CamDavidsonPilon
    (..., *args, **kwargs) I was thinking about this, too

    Clock-dependent hazards I think are actually pretty common

    Agree, but I feel like the common strategy is to use a regression model or fit N univariate models (i.e. partition the data)

    I think a seasonal model is a great idea, so I want this to work.

    Cameron Davidson-Pilon
    @CamDavidsonPilon
    :wave: new lifelines release: 0.22.0. Some important API changes to take a look at, but some really powerful new regression models: https://github.com/CamDavidsonPilon/lifelines/releases/tag/v0.22.0
    https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Custom%20Regression%20Models.html
    Julian Späth
    @julianspaeth
    Hi all, does lifelines somehow offer a Random Survival Forest? Or is there a specific reason why not? As there is no real python implementation of RSF and I want to implement it for my Master thesis, I was wondering if you are interested in including it into lifelines?
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    Hi Julian, lifelines does not have a RF model. Maybe scikit-learn survival does though.
    lifelines has focused less on purely predictive models, and more on inference
    Julian Späth
    @julianspaeth
    Hi Cameron, thank you for your answer. As far as I can see scikit-survival does not have a RF model. So I guess I need to implement it from scratch to use it in python. Thank you anyways 🙂
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    hm, I thought it was, okay - have fun!
    Pedro Sola
    @pedrosola
    Hi everyone, I'm trying to fit a model onto a recurrent process. I.E: Patient returns to a doctor. Is there a way to do so using lifelines ? So far the closest that I've got was this repo: https://github.com/dunan/MultiVariatePointProcess
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    Lifelines has limited support for recurring events.
    Unfortunately
    mohit
    @mohit-shrma
    Hi, I am using CoxPHFitter with IPS weights and robust=True flag. However, the fit is taking really long time to finish. I have about million instances and 6 features in my dataset. Let me know if slower runtime is expected in weighted version and what can be done to speed it up.
    mohit
    @mohit-shrma

    @CamDavidsonPilon Let me know if you have any suggestions for question below:

    Hi, I am using CoxPHFitter with IPS weights and robust=True flag. However, the fit is taking really long time to finish. I have about million instances and 6 features in my dataset. Let me know if slower runtime is expected in weighted version and what can be done to speed it up.

    Cameron Davidson-Pilon
    @CamDavidsonPilon
    hello! A million is a lot, much more than needed for only 6 features. I would suggest subsampling to 50k or even less, and checking the std. errors.
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    @mohit-shrma, another suggestion is to "collapse" similar rows and use weights. Ex: with only 6 variables, you likely have the same row appear twice. You can group these, assign that row an integer count, and use the weight_col argument in fit
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    :wave: minor version of lifelines released: https://github.com/CamDavidsonPilon/lifelines/releases/tag/v0.22.1
    Robert Green
    @rgreen13_gitlab

    Hi all. I've somewhat new to using lifelines, and in using the CoxPHFitter, when I run check_assumptions, I end up with an error that reads as follows: /RuntimeWarning: overflow encountered in exp scores = weights * np.exp(np.dot(X, self.params_))

    Any suggestions on dealing with this issue? I'm starting down the road of normalization, but I'm not sure if that's 100% correct.

    Cameron Davidson-Pilon
    @CamDavidsonPilon
    @rgreen13_gitlab hi, thanks for reporting this. I'll create a bug issue around it. For now, you can try scaling and normalizing your matrix before calling .fit
    Robert Green
    @rgreen13_gitlab
    Thanks!
    sharmarishika
    @sharmarishika
    Hi! I am currently trying to create mixed cure models using the lifelines fitter. I saw that there is an example code in the GitHub under experiments. I was going to use this as a starting point and then adjust accordingly but I am getting an error when I run that code saying: "AttributeError: 'CureModel' object has no attribute '_primary_parameter_name'
    I don't have a full understanding of the input arguments for _cumulative_hazard so I am not sure what is causing this error. Thank you!
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    @sharmarishika hi there. Are you using lifelines >= 0.22.0?
    If not, try upgrading. Otherwise, if you are still getting the error, can you post the entire stack trace?
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    (Also, make sure you are subclassing ParametricRegressionFitter, and not ParametericAFTRegressionFitter)
    sharmarishika
    @sharmarishika
    @CamDavidsonPilon ah - I think I'm using version 0.19.5! when i install through pip it says 'requirement already satisfied' - would you recommend a different way of upgrading?
    in reference to the subclass my computer doesn't recognize ParametricRegressionFitter as an option but it does recognize ParametericRegressionFitter - perhaps also because of the version?
    mohit
    @mohit-shrma
    hello! A million is a lot, much more than needed for only 6 features. I would suggest subsampling to 50k or even less, and checking the std. errors.
    @CamDavidsonPilon thanks for the advice, I will try that idea.
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    @sharmarishika try pip install -U lifelines or pip install lifelines==0.21.1
    sharmarishika
    @sharmarishika
    @CamDavidsonPilon i get an error saying that there are no matching distributions for lifelines 0.21.1 - in the list provided the most recent version is 0.19.5
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    is this conda?
    sharmarishika
    @sharmarishika
    i was using pip, in my command line typed exactly what i mentioned above - sorry i'm quite new to this!
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    @sharmarishika ah, are you on Python2? 0.19.5 was the latest Py2 release. It's only Python3 now
    sharmarishika
    @sharmarishika
    oh i see! ya i'm using python2.7 - i should look into doing the upgrade! thanks for the help!
    Charlene Chambliss
    @blissfulchar_twitter
    Hi folks, does anyone have experience explaining concordance index to a nontechnical audience (like execs), or even devising an alternative method of presenting model accuracy? I don't think describing the model's predictions in terms of ordered pairs is likely to be of interest - they just want to know how accurate the model is in terms of customer retention/LTV.
    Pedro Sobreiro
    @pesobreiro
    @blissfulchar_twitter personally I like using the survival probabilities to calculate the CLV assuming contractual settings (e.g. Berry & Linof, 2004). I don't like it for a non-technical audience. I normally I try to link survival to CLV for execs. Anyone correct me if I am wrong but concordance is "global" index for validating the predictive ability of a survival model, representing how well the variables allow to predict the survival, e.g. observations with higher survival time has the higher probability of survival predicted by your model.
    Charlene Chambliss
    @blissfulchar_twitter
    @pesobreiro This is a nice idea! And definitely in line with how we're currently asking retention questions at my co. Did you do that by predicting the survival function for each individual and then multiplying by customer LTV accordingly?
    Pedro Sobreiro
    @pesobreiro
    @blissfulchar_twitter we used the survival probabilities under each curve (cohort) and the monthly payment to calculate CLV. We didn’t used individual customer but customers grouped in the survival curves. This option as some limitations but gives us an idea for an estimated CLV. What you say should be very interesting. I think there are other approaches to calculate the predictions of individual CLV.
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    :wave: minor lifelines release. Important thing is that scipy 1.3 can be used with it now: https://github.com/CamDavidsonPilon/lifelines/releases/tag/v0.22.2
    Charlene Chambliss
    @blissfulchar_twitter
    Thanks @pesobreiro, this is helpful :)
    A N
    @aleva85
    Hi, what is the best way to retrieve log likelihood of a fit? it is shown via 'model.print_summary()' but not via 'model.summary', which only shows a summary of the parameters.
    I managed to get it via model._log_likelihood, but had to look into the source code for that.
    Thanks and kudos for the library!
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    Hi @aleva85, that currently is the best way, but you bring up a good observation that it’s not easy to find. Maybe in a future release I’ll promote it and document it well
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    FYI I've been playing around with pure-python & autograd neural nets for better prediction. I may make this it's own experimental package. Need a good name for it though
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    lifelike, lifenets?
    Rob deCarvalho
    @robdmc
    @CamDavidsonPilon maybe a plugin-like architecture? Different project that can attach itself to lifelines hooks
    Diego S
    @diego-s
    Hi, sorry I was wondering if any maintainers/users of lifelines based in Europe would like to do tutorial/workshop or talk about this package in our conference Python in Pharma (PyPharma) in Basel? Apologies for the unrequested advertisement, I will delete it if this is a problem. The conference would be free to attend (under invites) and 100% volunteer run. It will take place in November 21-22 and our target is 100-150 attendees. We would really be happy if lifelines is represented at this event.
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    ^ no need to apologize, this message welcome here. I would love to join, hopefully someone can take my place. There were a few Euro speakers of lifelines already: Linda Uruchurtu , Lorna Brightmore and Elena Sharova have all recently (past few years) given talks on lifelines. You can search for their videoes online.
    Cameron Davidson-Pilon
    @CamDavidsonPilon
    They may be interested in coming? Unfortunately I can't make an introduction, as I don't know them personally
    Unrelated: :wave: new minor (but important) version of lifelines released: https://github.com/CamDavidsonPilon/lifelines/releases/tag/v0.22.3
    Diego S
    @diego-s
    @CamDavidsonPilon Thanks for the suggestions! I will look them up. :)
    harish ramani
    @linkinnation_twitter
    @julianspaeth I don't know if it's late, but checkout pysurvival for conditional forest models. Might be similar to your ask.