Skip to content
This repository has been archived by the owner on Jun 28, 2024. It is now read-only.

Latest commit

 

History

History
51 lines (42 loc) · 3.5 KB

CHANGELOG.md

File metadata and controls

51 lines (42 loc) · 3.5 KB
Raw

Changelog

0.11.3

  • a version bump for conda packaging

0.11.2

  • some convergence improvements

0.11.1

  • bump the Pandas requirements to >= 0.24.0. This should have been done in 0.11.0
  • suppress some warnings from autograd.

0.11.0

  • Move most models (all but Pareto) to autograd for automatic differentiation of their likelihood. This results in faster (at least 3x) and more successful convergence, plus allows for some really exciting extensions (coming soon).
  • GammaGammaFitter, BetaGeoFitter, ModifiedBetaGeoFitter and BetaGeoBetaBinomFitter have three new attributes: confidence_interval_, variance_matrix_ and standard_errors_
  • params_ on fitted models is not longer an OrderedDict, but a Pandas Series
  • GammaGammaFitter can accept a weights argument now.
  • customer_lifelime_value in GammaGamma now accepts a frequency argument.
  • fixed a bug that was causing ParetoNBDFitter to generate data incorrectly.

0.10.1

  • performance improvements to generate_data.py for large datasets #195
  • performance improvements to summary_data_from_transaction_data, thanks @MichaelSchreier
  • Previously, GammaGammaFitter would have an infinite mean when its q parameter was less than 1. This was possible for some datasets. In 0.10.1, a new argument is added to GammaGammaFitter to constrain that q is greater than 1. This can be done with q_constraint=True in the call to GammaGammaFitter.fit. See issue #146. Thanks @vruvora
  • Stop support of scipy < 1.0.
  • Stop support of < Python 3.5.

0.10.0

  • BetaGeoBetaBinomFitter.fit has replaced n_custs with the more appropriately named weights (to align with other statisical libraries). By default and if unspecified, weights is equal to an array of 1s.
  • The conditional_ methods on BetaGeoBetaBinomFitter have been updated to handle exogenously provided recency, frequency and periods.
  • Performance improvements in BetaGeoBetaBinomFitter. fit takes about 50% less time than previously.
  • BetaGeoFitter, ParetoNBDFitter, and ModifiedBetaGeoFitter both have a new weights argument in their fit. This can be used to reduce the size of the data (collapsing subjects with the same recency, frequency, T).

0.9.1

  • Added a data generation method, generate_new_data to BetaGeoBetaBinomFitter. @zscore
  • Fixed a bug in summary_data_from_transaction_data that was casting values to int prematurely. This was solved by including a new param freq_multiplier to be used to scale the resulting durations. See #100 for the original issue. @aprotopopov
  • Performance and bug fixes in utils.expected_cumulative_transactions. @aprotopopov
  • Fixed a bug in utils.calculate_alive_path that was causing a difference in values compared to summary_from_transaction_data. @DaniGate

0.9.0

  • fixed many of the numpy warnings as the result of fitting
  • added optional initial_params to all models
  • Added conditional_probability_of_n_purchases_up_to_time to ParetoNBDFitter
  • Fixed a bug in expected_cumulative_transactions and plot_cumulative_transactions

0.8.1

  • adding new save_model and load_model functions to all fitters. This will save the model locally as a pickle file.
  • observation_period_end in summary_data_from_transaction_data and calibration_and_holdout_data now defaults to the max date in the dataset, instead of current time.
  • improved stability of estimators.
  • improve Runtime warnings.
  • All fitters are now in a local file. This doesn't change the API however.