Comments (5)
No, kpi_without_optim
should behave similarly between geo and national cases.
Some of the inputs provided would had to be different at some point for those two to provide such different outputs.
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no I tried complete data with extra features and just last 52 weeks and extra features. You can see from below that all other variables are same
Run optimization with the parameters of choice.
solution, kpi_without_optim, previous_budget_allocation = optimize_media.find_optimal_budgets(
n_time_periods=n_time_periods,
media_mix_model=mmm,
extra_features=extra_features,
budget=budget,
prices=prices,
bounds_lower_pct=0.3,
bounds_upper_pct=jnp.array([1,0.3580]),
media_scaler=media_scaler,
target_scaler=target_scaler,
seed=SEED)
from lightweight_mmm.
I would need a reproducible example (can be a colab with mock data) in order to investigate further, its too broad many things play in on this one.
from lightweight_mmm.
please give me an email id so that I can share my colab notebook
from lightweight_mmm.
Im sorry but that is not something we can do. If the error is reproducible, feel free to provide any mock data and code to reproduce and we can look into it.
from lightweight_mmm.
Related Issues (20)
- How can I input future media_data_test for optimization in upcoming periods? HOT 1
- How can channel-wise optimized conversions be obtained?
- Extra features
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- Geo level attribution and response curves
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- add got incompatible shapes for broadcasting: (95,), (90,). HOT 5
- TypeError: add got incompatible shapes for broadcasting: (58,), (54,). HOT 8
- Rendering of several plots not working! HOT 1
- Negative Values in Pre optimization predicted Target vs Post optimization predicted Target
- Budget Optimization
- RuntimeError: Cannot find valid initial parameters. Please check your model again.
- TypeError: where() got some positional-only arguments passed as keyword arguments: 'condition, x, y' HOT 5
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