TypeError: object of type 'NoneType' has no len() #23

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1. open-risk

   self-assigned this

   on Jun 25, 2021
2. open-risk

   added

   buga documented buga documented bug

   on Jun 25, 2021
3. open-risk

   added this to the 0.5 PyPI Release milestone on Jun 25, 2021
4. open-risk commented on Jun 25, 2021

   

   open-risk

   on Jun 25, 2021

   Owner

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   Thanks for reporting these issues. The current PyPI release is a bit dated. The immediate next milestone is to further test the current version and have a 0.5 release

   👍React with 👍2Reacted by Tarık Karakaş and Konstantinos
5. KonScanner commented on Dec 13, 2021

   

   KonScanner

   on Dec 13, 2021

   Contributor

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   It seems that
   CohortEstimator(states=myState, ci={'method': 'goodman', 'alpha': 0.05})
   when initialized:
   def __init__(self, cohort_bounds=None, states=None, ci=None)

   So it expects cohort_bounds. When not passed in the default is None.

   I've tried modifying the fit() function to work with the old method:

    # Old way of enumerating cohort intervals was using labels
   cohort_labels = data[timestep_label].unique()
   cohort_dim = len(cohort_labels) - 1
   

   But it causes further issues with:
   tmn_count[(entity_state[i], entity_state[i + 1], event_time[i])] += 1

   at least in my usecase.
6. KonScanner commented on Dec 13, 2021

   

   KonScanner

   on Dec 13, 2021

   Contributor

   More actions

   More specifically:

   State Space
   --------------------------------------------------------------------------------
   State Index and Label:  0 ,  0
   State Index and Label:  1 ,  1
   State Index and Label:  2 ,  2
   State Index and Label:  3 ,  3
   State Index and Label:  4 ,  4
   State Index and Label:  5 ,  5
   State Index and Label:  6 ,  6
   State Index and Label:  7 ,  8
   

   Example df post datetime_to_float

       ID  Time State  EventTime  Count
   0  1         0     0   0.000000    1.0
   1  1         1     0   0.063655    1.0
   2  1         2     0   0.248460    3.0
   3  1         3     0   0.373717    2.0
   4  1         4     0   0.498973    2.0
   ...
   

   When trying to do the following:

   cohort_data, cohort_intervals = tm.utils.bin_timestamps(data, cohorts=8)
   myEstimator = es.CohortEstimator(states=myState, ci={'method': 'goodman', 'alpha': 0.05})
   print(cohort_data.head())
   result = myEstimator.fit(cohort_data)
   

   it fails on the estimator.fit as such:

   tmn_count[(entity_state[i], entity_state[i + 1], event_time[i])] += 1
   IndexError: index 8 is out of bounds for axis 1 with size 8
   

   I am not sure why it is miss-indexing.

   Maybe in my given state space, it doesn't initialize some parameters correctly.?

   Any help would be appreciated!!!

   P.S.
   For cohort_bounds I've restored this:

   # Old way of enumerating cohort intervals was using labels
   cohort_labels = data[timestep_label].unique()
   cohort_dim = len(cohort_labels) - 1