Lifetime value in Python
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 SeriesGammaGammaFitter
can accept a weights
argument now.customer_lifelime_value
in GammaGamma
now accepts a frequency argument.ParetoNBDFitter
to generate data incorrectly.generate_data.py
for large datasets #195summary_data_from_transaction_data
, thanks @MichaelSchreierGammaGammaFitter
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 @vruvoraBetaGeoBetaBinomFitter.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.conditional_
methods on BetaGeoBetaBinomFitter
have been updated to handle exogenously provided recency, frequency and periods.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).generate_new_data
to BetaGeoBetaBinomFitter
. @zscoresummary_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. @aprotopopovutils.expected_cumulative_transactions
. @aprotopopovutils.calculate_alive_path
that was causing a difference in values compared to summary_from_transaction_data
. @DaniGate