Comments (4)
Hello @chsheth !
If you are using the national model and coming from a pd.DataFrame
you can just take the values of the dataframe directly, utils.dataframe_to_jax()
is only needed for geo models.
Something like the following should suffice:
dataframe = pd.DataFrame(YOUR DATA HERE)
media = jnp.array(dataframe[media_columns].values)
extra_features = jnp.array(dataframe[extra_feature_columns].values)
Yes, trying to fit a geo model with national data and a "faked" geo column might lead to weird outputs. Hopefully trying with the correct model helps!
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Sorry for the late response, I missed this one.
- Yes the model documentation and custom priors documentation should shed some light about all parameters in the model.
- For specifying bounds per channel you need to pass an array, eg. if you have three chanenls your bounds could be
jnp.array([0.25, 0.3, 0.15])
and the parameters are:bounds_lower_pct
andbounds_upper_pct
.
from lightweight_mmm.
Thanks @pabloduque0
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Related Issues (20)
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