Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
This PR adds implementation of transformation module functions.
Fixes the implementation of response_curve with integration with transformation.
Completes the implementation of generate_plot function of pareto_optmizer
Fixes # (issue)
datamapper, and existing notebook for running pareto optimizer e2e for generating the plot data from mmmdata and outputmodel
Type of change
Implements the core functionality of pareto_optimizer which generates the plot data which will be later on used for clustering and plotting.
How Has This Been Tested?
Using the test data to run the e2e workflow, exported the data right before the call to pareto optimizer into a json file and read that in using datamapper. Used the outputs from the datamapper to then call pareto_optimizer e2e and validated results with R.
R Output:
Project Robyn
This PR adds implementation of transformation module functions. Fixes the implementation of response_curve with integration with transformation. Completes the implementation of generate_plot function of pareto_optmizer
Fixes # (issue) datamapper, and existing notebook for running pareto optimizer e2e for generating the plot data from mmmdata and outputmodel
Type of change
Implements the core functionality of pareto_optimizer which generates the plot data which will be later on used for clustering and plotting.
How Has This Been Tested?
Using the test data to run the e2e workflow, exported the data right before the call to pareto optimizer into a json file and read that in using datamapper. Used the outputs from the datamapper to then call pareto_optimizer e2e and validated results with R. R Output:
Python Output: