PySRSequenceRegressor

[wenbang24]

Resolve #94

Adds ability to use symbolic regression on 1D recurrent array.
Added new keyword argument to PySRRegressor called recursive_history_length. Must be at least 1, or will throw a ValueError.

Here an example of how it works:
Suppose you have the array [1,2,3,4,5,6,7,8,9] and recursive_history_length = 3.
Then the feature array will be

[[1 2 3]
 [2 3 4]
 [3 4 5]
 [4 5 6]
 [5 6 7]
 [6 7 8]]

And the target array will be:

[4, 5, 6, 7, 8, 9]

And it's the same from there.

[MilesCranmer]

Thanks! Regarding the design, I wonder if this shouldn’t go into PySRRegressor itself, but rather be a separate input transform that you would compose with scikit-learn’s Pipeline: https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline

This would be nice because it would automatically update .predict as well without extra internal code.

Basically I think PySRRegressor is already too large and I’d like to break it into smaller, composable pieces, so it’s easier for users to implement custom behavior.

Alternatively maybe this could be a separate class PySRSequenceRegressor that inherits from PySRRegressor but wraps the methods with transforms.

Also please add some tests, as well as tests for how this interacts with X_units, variable_names, weights, complexity_of_variables, etc.

[wenbang24]

test_sequence_weighted_bumper test still isn't passing; how do the weights work?

[wenbang24]

One test (test_sequence_noisy_builtin_variable_names) isn't passing because set_params doesn't work when PySRSequenceRegressor calls super().__init__()

[MilesCranmer]

General comment: should PySRSequenceRegressor allow for multiple features? Right now it is just a scalar feature over time. But what if you want to have two features over time, like X_t and Z_t, and get recursive information for both of them?

(Just wondering why/why not)

[MilesCranmer]

I think for weights, the time series approach should simply weight by time. So the weights should be shape (n_times,). It should be one weight for every predicted value.

[MilesCranmer]

I think stuff breaks without np.array(X)

Weird, normally scikit-learn's checking function should automatically convert to a numpy array. The normal PySRRegressor you can pass it lists or pandas dataframes and it will get converted by scikit-learn to a numpy array. Is there a reason scikit-learn's conversion doesn't work here?

[wenbang24]

yeah it works mb
but the examples look a bit weird with X.append(X[-1][0] + X[-2][0])
should i do that or X = np.append(X, X[-1] + X[-2])

[wenbang24]

also theres a test failing in TestDimensionalConstraints and idk what's going on with it, but I think its something to do with a display_variable_names parameter messing up positional arguments

[wenbang24]

ok i stopped the error; is that the right way to fix it?

[wenbang24]

how do we fix the typing issue??
im stumped

[wenbang24]

yo all checks passed 🎉

[wenbang24]

@MilesCranmer anything else I need to do before merging?

[MilesCranmer]

Sorry for not reviewing the latest yet, got back from holiday recently and still catching up.

In the meantime you might consider some “dogfooding” (https://en.m.wikipedia.org/wiki/Eating_your_own_dog_food) of PySRSequenceRegressor on some real problems that you have in mind? Just by using it on some real problems you will be able to find ways of improving the API. Maybe look up some math sequences or simple physics problems and try it out, and see what breaks and what works well, and what parts of the API are easy and what parts are confusing. (This is how I develop PySR in general - I’m both a user and a dev!)

Anyways I hope to get to reviewing soon.