Comments (4)
@virithavanama Thanks for requesting this!
CSV might not be the format we would return it directly but either an array or DataFrame could be, which is easily converted to CSV.
We will look into it.
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can we get the dataframe for recreating the 'plot_pre_post_budget_allocation_comparison' and 'plot_model_fit' plots
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Hi @pabloduque0 , just checking on this request. Even I would like to know how can we get data in any format (csv/array etc) for all model outputs.
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hi virithavanama and shekharkhandelwal1983!
For the plot_model_fit plot, both the target curve and the posterior predictions are produced by lines 662-666 of plot.py, so you can just replicate those in a Colab to get the data.
For the plot_pre_post_budget_allocation_comparison, the data are passed as inputs to those functions so you should already have them, but lines 843-845 convert them to fractions if that's easier to work with.
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Related Issues (20)
- how to treat zero cost channels and attribution hints HOT 5
- cost and media data is not clear from the documentation HOT 1
- Single-channel use issue HOT 15
- Why Pre and Post Budget Allocation do not change? HOT 6
- Nested modeling HOT 2
- Multivariate input for media data HOT 1
- Response curve questions
- Loading saved model
- Saturation + Carryover transformation HOT 2
- Baseline Contribution Area Plot colours are non-unique HOT 1
- Feature request: Extend to time-varying coefficients (uber's orbit model)
- Question: How to model time dependent features in the fit and predict method? HOT 2
- how to understand the media contribution percentage? HOT 2
- Media contribution percentage sums up to 105% HOT 1
- Have issues with plot.plot_media_channel_posteriors(media_mix_model=mmm) HOT 2
- Budget Allocator - Solution X returns incorrect allocation HOT 1
- limitations in the use of extra features HOT 4
- Anyway to save mmm.print_summary()? HOT 1
- Hierarchical Partially Pooled Media
- media_priors HOT 1
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