It's been a longstanding issue in the legacy Lifetimes library that the plot_calibration_purchases_vs_holdout_purchases function in plotting.py cannot be used without first running data through calibration_and_holdout_data in utils.py. This is a useful plotting function for model evaluation, but for those of us working with huge volumes of transaction data, utils.py just isn't going to cut it and the RFM aggregations must be done in a separate service like Spark or Snowflake, effectively blocking the use of this function.
Replacing the _calibration_holdoutmatrix argument with separate _calibrationmatrix and _holdoutmatrix arguments should be enough to resolve this matter.
It's been a longstanding issue in the legacy Lifetimes library that the
plot_calibration_purchases_vs_holdout_purchasesfunction inplotting.pycannot be used without first running data throughcalibration_and_holdout_datainutils.py. This is a useful plotting function for model evaluation, but for those of us working with huge volumes of transaction data,utils.pyjust isn't going to cut it and the RFM aggregations must be done in a separate service like Spark or Snowflake, effectively blocking the use of this function.Replacing the _calibration_holdoutmatrix argument with separate _calibrationmatrix and _holdoutmatrix arguments should be enough to resolve this matter.