lightweight_mmm
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google
LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) in () 4 seed=SEED) 5 else: ----> 6 new_predictions = mmm.predict(media=media_scaler.transform(media_data_test), 7 extra_features=extra_features_scaler.transform(extra_features_test), 8 seed=SEED) 17 frames /usr/local/lib/python3.10/dist-packages/lightweight_mmm/lightweight_mmm.py in predict(self, media, extra_features, media_gap,...
Hello, I am using the google provided example code from their github and oddly, when I plot `plot.plot_prior_and_posterior(media_mix_model=mmm)`, the output cart renders perfectly find. However, when I run other plots...
I am getting this error when trying to predict. size of media_test_data is (5,3) 
Dear LMMM Team, I want to express my sincere appreciation for the hard work you've dedicated to creating this remarkable model. It's truly impressive and has proven to be user-friendly...
I get weird plots with extra lines in plot_prior_and_posterior plot I used lag_weight priors: alpha = np.array([2.0] * len(media_channels)) beta = np.ones(len(media_channels))*4.0 custom_priors = {'lag_weight': {'concentration1': alpha, 'concentration0': beta}} 
To generate predictions, we need to provide de media_data_test with impressions or any other media exposure variable. However, how can I provide this data if, for example, impressions for the...
I've used "lambda x: jnp.mean(x[x > 0])" in place of "jnp.mean" `media_scaler = preprocessing.CustomScaler(divide_operation=lambda x: jnp.mean(x[x > 0])) ` It throws "NonConcreteBooleanIndexError: Array boolean indices must be concrete; got ShapedArray(bool[82])"...
Hi all - I am currently using budget allocator from lightweight, however I noticed the Before optimization budget is not correct. Could somebody let me know why this happened? for...
Hi all - I am trying to use lightweight budget allocator functions. however, I am wondering whether we can set up lowerbound and upperbound per channel so that we can...
Could somebody guide me on how to work with outlier/influential points in the data? Since MMM is ultimately a linear regression model, it is quite susceptible to such data points...
