Training on derived features that won't be present in a test set So I have an odd real-world problem in which the data that's going to be fed to a categorical predictive model has (a few) certain features, and when building a model I can bolt on additional datasets that add context to some of those features, but when it's going to be running live I won't be able to use any of that additional data - the model itself needs to already "know" what's important about specific values of the original data and be able to operate just on those attributes.

The major features here are time(/date/DOW) and location (lat/long) within a city.  So, the additional datasets are context about that location (zoning, demographics, transit) or time (traffic, commercial activity, etc).  But for performance reasons (join cost, etc) we can't use them live, we only have the time and location numbers (plus easily parse-able derivatives).

I'm imagining a process whereby the model uses those additional features to learn which subsets or demarcations of time-and-location actually matter, and can then continue to use those weightings and rules or whatever even in the absence of the additional features, just relying on the numerical values.  But it's also possible that there's no such approach.  Can anyone suggest an algorithm or approach that could let us retain some of that value?
Thats not how it works, there is not juice to be extracted if the data is missing from the test set.

You will have features in train that might be discriminative, but missing in test. Then when you try to predict there wont be any inputs that you can map to the outputs.

This beeing said I should mention that NaiveBayesclassifier will not use missing features, so there would be no harm in including them but you wont get any additional information, just more unnecessary complexity.