Trapping part 2/3 #493
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Trapping part 2/3 #493
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This is much faster than fitting the features, and can be used by trapping to replace some constraint index calculations. Also: Add docstring/type annotations Rename a variable so it doesn't shadow newly-imported name
There are several major differences from the version in example 8: * Coupled with PolynomialLibrary logic using PolynomialLibrary.powers_. Previously, this coupling was implicit and actually tied to the specific order in Example 8, not the order users would expect. * Instead of handling pre-flattened 2D array, constraints are built with target and feature axes separately, letting the caller handle formatting the constraints as "target" or "feature". This gives us flexibility to later clean up the constraint logic outside this function without needing to return here and removes the need for striding calculations like: n_tgts * (n_tgts + k - 1) + i + n_tgts * int(j * (2 * n_tgts - j - 3) / 2.0) Since constraints are built directly from the terms and exponents in the library, its much easier to read and debug the code. * Renamed "r" as "n_tgts", etc. Single-letter index variables are removed. When they occur in homogenous iterators, they are concatenated with what they're iterating (e.g. tgt_i, tgt_j) * Broke out the different kinds of constraints into helper functions to make it easier to test. This allows follow on work to auto-attach constraints to a problem.
(other than degree, which has to be 2)
Previously, TrappingSR3 could only use constraints passed to it, and only then a limited set of constraints. It also didn't apply the trapping constraints automatically, because constraints were required at __init__, and actually shaping them requires knowledge about the number of features, typically not known until fit (unless the user is a developer who knows how the feature libraries work internally 😉) WIP, Spawned issue #452
And simplify reorder_constraints
Read the fucking diff backport-to: constraint-order
Also: * (constrained_sr3)clean up creating cvxpy constraints * (constrained_sr3)Clone the cvxpy problem in case of except statements, because prob.solve() mutates prob in a mathematically significant way
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## master #493 +/- ##
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- Coverage 94.81% 94.68% -0.14%
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Trapping part 2/3
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This is part 2/3, clarifying the way constraints are applied and allowing other constraints to be added to Trapping problems, by dispatching all to Constrainted SR3. It also solves some bugs in cvxpy
probs. Part 3/3 will be the Mai's local trapping bit.Currently there is a small hack - constraints are added at initialization, but in order to add trapping constraints, we need to know the size of the problem, which are data-dependent parameters. Solution in the interim is to add arguments to init that begin with an underscore.