Botorch with cardinality constraint via sampling #301

Waschenbacher · 2024-07-03T20:47:54Z

This PR adds support for cardinality constraints to BotorchRecommender. The core idea is to tackle the problem in an exhaustive search like manner, i.e. by

enumerating the possible combinations of in-/active parameters dictated by the cardinality constraints
optimizing the corresponding restricted subspaces, where the cardinality constraint can then be simply removed since the in-/active sets are fixed within these subspaces
aggregating the optimization results of the individual subspaces into a single recommendation batch.

The PR implements two mechanisms for determining the configuration of inactive parameters:

When the combinatorial list of possible inactive parameter configurations is not too large, we iterate the full list
otherwise, a fixed amount of inactive parameter configurations is randomly selected

The current aggregation step is to simply optimize all subspaces independently of each other and then return the batch from the subspace where the highest acquisition value is achieved. This has the side-effect that the set of inactive parameters is the same across the entire recommendation batch. This can be a desirable property in many use cases but potentially higher acquisition values can be obtained by altering the in-/activity sets across the batch. A simple way to achieve this (though out of scope for this PR) is by generalizing the sequential greedy principle to multiple subspaces.

Out of scope

Fulfilling cardinality constraints by passing them in suitable form as nonlinear constraints to the optimizer
Sequential greedy optimization to achieve varying in-/activity sets (see explanation above)

CHANGELOG.md

AdrianSosic

Hi @Waschenbacher, thanks for the work. This is not yet a review but only a first batch of very high-level comments that I would like you to address before we can go into the actual review process. The reason being that the main functionality brought by this PR is currently rather convoluted and hard to parse, so I'd prefer to to work with a more readable version, tbh

baybe/constraints/continuous.py

baybe/recommenders/pure/bayesian/botorch.py

baybe/searchspace/continuous.py

AdrianSosic

Hi @Waschenbacher, I've finally managed to spend some time on this important PR, thanks again for your preparation work. I've already refactored some parts that were clear to me. However, I'm not yet certain about the design at the remaining places. I have the feeling that it can potentially be simplified a lot, depending on whether it is possible to reuse the idea of reduced subspaces. Have marked the corresponding places with comments. I think we need to discuss this part first before we can continue.

baybe/searchspace/continuous.py

baybe/recommenders/pure/bayesian/botorch.py

…ctive parameters

…rch recommender

AdrianSosic

Hi @AVHopp @Scienfitz. I spent some time an refactored @Waschenbacher's proposal quite a bit, and think that the logic shipped by this PR is now very clear. So from my end, everything seems in good shape and we are ready for a fresh round of review. But I feel we are close to getting it merged. There a few open things to be discussed, I've marked the corresponding parts and also updated the PR description. Let me know what you think

AdrianSosic · 2024-11-01T12:22:10Z

baybe/parameters/numerical.py

@@ -150,5 +150,38 @@ def summary(self) -> dict:
        return param_dict


+@define(frozen=True, slots=False)
+class _FixedNumericalContinuousParameter(ContinuousParameter):


@AVHopp @Scienfitz: The class proved to be useful for this PR, even though we could have implemented the mechanism in a different way where instead of fixing parameter values we could have created reduced spaces where the parameter values are dropped. However, since botorch's optimizers accept a list of fixed values, this was the simpler implementation.

I've kept it private for now because I first want to see if/how things might need to be adjusted. For example, just yesterday, I had a conversation with @julianStreibel about another feature that would be very useful in the future, namely conditioning recommendations onto certain subspaces without having to redefine the searchspace object. I guess we'll use it for that as well, but until then I'll keep it hidden.

Any comments, thoughts? If not, just give thumbs up and I will resolve

AdrianSosic · 2024-11-01T12:36:52Z

baybe/parameters/utils.py

@@ -87,3 +89,51 @@ def get_parameters_from_dataframe(
 def sort_parameters(parameters: Collection[Parameter]) -> tuple[Parameter, ...]:
    """Sort parameters alphabetically by their names."""
    return tuple(sorted(parameters, key=lambda p: p.name))
+
+
+def activate_parameter(


@Scienfitz @AVHopp: This is one of the important open points to discuss. As you probably know by now, the mechanism of this PR is to divide parameters in to active and inactive sets. However, this opens a general question, not only related to this PR:

What do we even mean with an "active continuous parameter", i.e. what is the value range where such a parameter is considered "active"?

The current logic is to assume some small threshold around 0 to discriminate active/inactive ranges. And this function then takes care of "activating" a parameter according to this definition.

Problem to be discussed:
For parameters with bound falling into the threshold range, everything is simple. But how can we guarantee that a parameter with bounds (-a, b) with a,b >> threshold is active? We cannot simply "cut out" the threshold interval since we then end up with a non-continuous range (see comment in docstring). This causes downstream problems in the sense that we cannot strictly guarantee that the "min_cardinality" part of a cardinality constrained will be fulfilled, since any optimizer might decide that – even if we assign the parameter to the "active set" – its value should be driven to zero. And then the cardinality will drop by one. In theory, one could solve this by branching into the two separate subspaces of the non-continuous range of the parameter, but that will end up with yet another exponential blow-up.

TL;DR: I think min-cardinality constraints are really hard to strictly fulfill. On the other hand, I cannot think of any use case where people actually need a min-constraint. So I see two options:

(not preferred) dropping the option of min values in cardinality constraints.

(my suggestion) keeping everything as is and being aware (plus potentially mention it in the docstring) that the min-part of the constraint can be overridden by the optimizer. The min part still reduces the overall search burden in that it rules out certain subspaces to start with. But for the ones left, the min-part could be violated.

Input is appreciated!

Quick follow-up: whatever we decide here, the corresponding logic of the random recommender should also be updated, which does not yet use any concept of activated parameters. It's not as important for the random recommender as it is for optimization based ones, but still ...

First question: Wasn't this sort of a problem one of the main arguments against using Narrow Gaussian Processes? By my understanding, the problem that we are getting here is now basically identical, could you please give a quick heads-up why Narrow Gaussians are not an option/alternative? Don't need an extensive discussion, but rather a "Hey Alex, we already talked about that, let me quickly refresh your memory".

Now to your actual points.

First problem that I see with allowing min_cardinality and just mentioning in the docstring that this might be overwritten by the optimizer seems VERY dangerous to me. As we know by now, people tend to not read documentation at all. So what I see is people thinking "Oh, great, I can use a min_cardinality", stop to read and think at exactly that point, assume that it works as intended and then be angry if it does not work.

This is a potential crucial silent error that people might have at very inconvenient times: Imagine that you really require this lower cardinality, and that it works the first X runs of your optimization, just to fail in run X+1, invalidating potentially your whole set up.

As you say yourself: "I cannot think of any use case where people actually need a min-constraint". We said that things like this should be a strong indicator to not implement stuff.

So I'd propose to drop the support for this.

To first question: let's tackle this topic in a dedicated baybathon
Second: today in dev meeting

As agreed in the baybathon, there are two options how to support the cardinality constraint (especially the minimum cardinality) in Botorch recommender:

Option I: this PR with hard threshold. If possible, the bounds of an active parameter gets updated by excluding the inactive range (-threshold, threshold). But if impossible, e.g. a parameter has bounds (-a, b) with a,b >> threshold (see discussion above), its value may be driven to zero by the optimizer and the min_cardinality might get violated. A warning MinimumCardinalityViolatedWarning will be raised if the minimum cardinality is violated.

Option II: a future PR based on narrow Gaussian kernel = soft-constraint.

AdrianSosic · 2024-11-01T12:37:23Z

baybe/recommenders/pure/bayesian/botorch.py

@@ -76,6 +85,13 @@ class BotorchRecommender(BayesianRecommender):
    optimization. **Does not affect purely discrete optimization**.
    """

+    max_n_subspaces: int = field(default=10, validator=[instance_of(int), ge(1)])


@AVHopp @Scienfitz: To you think this is an appropriate attribute name?

I think that it should be linked to cardinality somehow: For people that are not interested in cardinality constraints, it is only clear after reading the docstring that this is not interesting for them. This would however make the name quite long :/ So although I am not perfectly happy with the name, we could keep it as I do not see a better alternative while keeping it here.

I get your point but I see this emerging as part of a bigger problem, actually, not limited to this particular attribute. Namely that all attributes of the class are linked to particular settings only:

sequential_continuous is relevant for continuous spaces only

hybrid_samples is relevant for hybrid spaces only

sampling_percentage is relevant for hybrid spaces and specific sampling strategies only

In that sense, max_n_subspaces is even a little less problematic since it is strictly speaking not only relevant for cardinality constraints but could also be used for hybrid spaces. And now I also see another clash, since it's functionality basically overlaps with sampling_percentage 🤔

So two TODOs from I see:

Cleverly resolve the duplication somehow

Potentially, in a next PR: find a better structure. We could consider, for example, to capture all the settings in a separate object like a TypedDict or so, that then uses its own validation path

@Scienfitz: Thoughts from your end?

To avoid the duplicates, we could rename sampling_percentage to hybrid_sampling_percentage or something similar, that might already be sufficient. Agree that it would be beneficial to somehow "merge" these two attributes soon.

AdrianSosic · 2024-11-01T12:39:24Z

baybe/recommenders/pure/bayesian/botorch.py

-            ),
-        ]
-        return to_string(self.__class__.__name__, *fields)
+    def _optimize_continuous_subspaces(


@AVHopp @Scienfitz: Just wanted to raise awareness. I've refactored @Waschenbacher's original version quite significantly, but the nice outcome of this is that we now have this useful and general helper that can be used to optimize over arbitrary non-connected subspaces. Thumbs up once you saw it and I'll resolve

Can we potentially leverage this for Discrete Searchspaces or some sort of hybrid recommendation as well?

Yes, the use is by no means limited to cardinality constraints. It's basically the baybe equivalent to botorchs optimize_acqf_mixed, if you will.

AdrianSosic · 2024-11-01T12:40:21Z

baybe/searchspace/continuous.py

@@ -301,6 +334,45 @@ def _drop_parameters(self, parameter_names: Collection[str]) -> SubspaceContinuo
            ],
        )

+    def _enforce_cardinality_constraints_via_assignment(


I'm not yet perfectly happy with the name here. Any better suggestions? One alternative I could imagine would be _enforce_cardinality_constraints_via_bounds. But input is welcome

Since there is only one way of enforcing cardinality consraints, why not simply enforce_cardinality_constraints? If people are interested in the details of the how, they can read the docstring.

can do, but would keep it private 👍🏼. @Scienfitz: thumbs up is sufficient for approval

AVHopp

First high-level review regarding points that we should discuss. Not a full review yet as I think that the code might change depending on what is decided regarding the min_cardinality.

baybe/parameters/numerical.py

AVHopp · 2024-11-04T15:31:26Z

baybe/parameters/utils.py

@@ -87,3 +89,51 @@ def get_parameters_from_dataframe(
 def sort_parameters(parameters: Collection[Parameter]) -> tuple[Parameter, ...]:
    """Sort parameters alphabetically by their names."""
    return tuple(sorted(parameters, key=lambda p: p.name))
+
+
+def activate_parameter(


First question: Wasn't this sort of a problem one of the main arguments against using Narrow Gaussian Processes? By my understanding, the problem that we are getting here is now basically identical, could you please give a quick heads-up why Narrow Gaussians are not an option/alternative? Don't need an extensive discussion, but rather a "Hey Alex, we already talked about that, let me quickly refresh your memory".

AVHopp · 2024-11-04T15:40:30Z

baybe/parameters/utils.py

@@ -87,3 +89,51 @@ def get_parameters_from_dataframe(
 def sort_parameters(parameters: Collection[Parameter]) -> tuple[Parameter, ...]:
    """Sort parameters alphabetically by their names."""
    return tuple(sorted(parameters, key=lambda p: p.name))
+
+
+def activate_parameter(


Now to your actual points.

First problem that I see with allowing min_cardinality and just mentioning in the docstring that this might be overwritten by the optimizer seems VERY dangerous to me. As we know by now, people tend to not read documentation at all. So what I see is people thinking "Oh, great, I can use a min_cardinality", stop to read and think at exactly that point, assume that it works as intended and then be angry if it does not work.

This is a potential crucial silent error that people might have at very inconvenient times: Imagine that you really require this lower cardinality, and that it works the first X runs of your optimization, just to fail in run X+1, invalidating potentially your whole set up.

As you say yourself: "I cannot think of any use case where people actually need a min-constraint". We said that things like this should be a strong indicator to not implement stuff.

So I'd propose to drop the support for this.

AVHopp · 2024-11-04T15:45:17Z

baybe/recommenders/pure/bayesian/botorch.py

@@ -76,6 +85,13 @@ class BotorchRecommender(BayesianRecommender):
    optimization. **Does not affect purely discrete optimization**.
    """

+    max_n_subspaces: int = field(default=10, validator=[instance_of(int), ge(1)])


I think that it should be linked to cardinality somehow: For people that are not interested in cardinality constraints, it is only clear after reading the docstring that this is not interesting for them. This would however make the name quite long :/ So although I am not perfectly happy with the name, we could keep it as I do not see a better alternative while keeping it here.

AVHopp · 2024-11-04T15:46:39Z

baybe/recommenders/pure/bayesian/botorch.py

+        subspace_continuous: SubspaceContinuous,
+        batch_size: int,
+    ) -> tuple[Tensor, Tensor]:
+        """Recommend from a continuous search space with cardinality constraints.


I like it, only some information about how max_n_subspaces comes into play here is still missing for me :)

AVHopp · 2024-11-04T15:48:01Z

baybe/recommenders/pure/bayesian/botorch.py

-            ),
-        ]
-        return to_string(self.__class__.__name__, *fields)
+    def _optimize_continuous_subspaces(


Can we potentially leverage this for Discrete Searchspaces or some sort of hybrid recommendation as well?

AVHopp · 2024-11-04T15:49:23Z

baybe/searchspace/continuous.py

@@ -301,6 +334,45 @@ def _drop_parameters(self, parameter_names: Collection[str]) -> SubspaceContinuo
            ],
        )

+    def _enforce_cardinality_constraints_via_assignment(


Since there is only one way of enforcing cardinality consraints, why not simply enforce_cardinality_constraints? If people are interested in the details of the how, they can read the docstring.

- Replace redandunt function - Ensure near-zero range being an open interval

- Add to-do related to customized error in botorch - Add to-do related to active parameters guarantee in random sampler

…ity constraint

…a_sampling

Waschenbacher · 2024-12-16T13:53:41Z

@AdrianSosic This is the updated PR according to our discussion in the baybathon. The main changes are below:

A threshold near_zero_threshold is added to each parameter NumericalContinuousParameter. This implementation deviates from the original plan of
- Threshold for each constraint or subspace vs threshold for each parameter. I decide for a threshold for one parameter because I think this way is more flexible and more logical
- Threshold ratio (the original suggestion) vs threshold. I decide for threshold since the logic gets complicated with ratio of threshold and there are several doubts when I want to infer the absolute threshold from the ratio of threshold:
- - Is the threshold ratio based on the complete region or just one side?
- - If the bounds are not symmetrical, e.g. a parameter has bounds (a, b)with a!=b. How to infer the absolute threshold values? Do we want to keep a single absolute threshold or two different numbers?
- - How to deal with infinite bounds?
- - The user can infer the desired threshold from any threshold ratio, if needed, and assign them.
Check whether any minimum cardinality constraint is violated in botorch's recommendation and raise the warning MinimumCardinalityViolatedWarning if it occurs.
Added a test catching the MinimumCardinalityViolatedWarning is raised when any minimum cardinality constraint is violated.
Update the test related on cardinality constraint: all maximum cardinality constraints are fulfilled and either all minimum cardinality constraints are fulfilled or a warning is raised if not.
Created a botorch PR (Add InfeasibilityError exception pytorch/botorch#2652) for the dangerous ValueError due to infeasibility and added TODO.

baybe/constraints/continuous.py

baybe/parameters/numerical.py

Scienfitz · 2024-11-15T09:31:33Z

baybe/parameters/utils.py

@@ -87,3 +89,51 @@ def get_parameters_from_dataframe(
 def sort_parameters(parameters: Collection[Parameter]) -> tuple[Parameter, ...]:
    """Sort parameters alphabetically by their names."""
    return tuple(sorted(parameters, key=lambda p: p.name))
+
+
+def activate_parameter(


shouldnt htis be removed given the meeting outcome that these cases of non-guaranteed min_cardinality are acceptable with a warning?

I believe we still need activate_parameters.

When the original bounds of an active parameter is [0, a] or [-a, 0]. with a>threshold, we still need this method to shift the bounds so that it is guaranteed active. I would say especially in this case, the acquisition function tends to have the optimum on the boundary, which is "0". Therefore, activate_parameter helps at least for this case.

When the original bounds of an active parameter is [a, b] with value a<0<b and the range is larger than the threshold, we cannot really activate the parameter. Only in this case, it may occur that the optimizer drives this parameter to 0. If it occurs, the minimum cardinality may not hold. This is the reason why we have the check on minimum cardinality and raise a warning if it gets violated.

second that

Scienfitz · 2024-12-19T08:29:52Z

baybe/constraints/validation.py

@@ -41,6 +49,9 @@ def validate_constraints(  # noqa: DOC101, DOC103
    param_names_discrete = [p.name for p in parameters if p.is_discrete]
    param_names_continuous = [p.name for p in parameters if p.is_continuous]
    param_names_non_numerical = [p.name for p in parameters if not p.is_numerical]
+    params_continuous: list[NumericalContinuousParameter] = [
+        p for p in parameters if isinstance(p, NumericalContinuousParameter)


sry for my confusion, the method exists in this PR https://github.com/emdgroup/baybe/pull/291/files#diff-9b02c8d8e9e86b086ea306806ebc5e47435ac5045557f8eb138a7be22c3cb0e8R461 which is however on hold

Waschenbacher added the new feature New functionality label Jul 3, 2024

Waschenbacher self-assigned this Jul 3, 2024

Waschenbacher requested review from Scienfitz, AdrianSosic and AVHopp as code owners July 3, 2024 20:47

Waschenbacher marked this pull request as draft July 4, 2024 07:38

Waschenbacher force-pushed the feature/cardinality_constraint_to_botorch_via_sampling branch from 0be3285 to 1f7783d Compare July 4, 2024 09:21

Waschenbacher marked this pull request as ready for review July 4, 2024 09:35

AVHopp reviewed Jul 5, 2024

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Waschenbacher force-pushed the feature/cardinality_constraint_to_botorch_via_sampling branch from 1f7783d to 9d28b49 Compare July 12, 2024 07:38

AdrianSosic force-pushed the feature/cardinality_constraint_to_botorch_via_sampling branch from f68ce4f to fc52875 Compare August 15, 2024 12:52

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Waschenbacher and others added 17 commits August 26, 2024 10:29

Enable cardinality constraint in botorch recommender via sampling ina…

d811240

…ctive parameters

Make inactive parameters fixed features

da813f5

Fix bug in test file

adf5cc2

Validate bounds of cardinality constraint parameters

e69ceff

Add second option: iterate through combinatorial list

2270350

Fix type error

6483e4b

Revise botorch+cardinality constraint for enhanced clarity

ae919d4

Fix property names and its docstrings

9ab8fda

Use guard clause

c3831c7

Simplify syntax with 'prod'

2f49f5a

Refactor botorch+cardinality constraint

d46fd60

Make 'n_threshold_inactive_parameters_generator' an attribute of boto…

293e2ef

…rch recommender

Refactor combinatorial properties of cardinality constraint

f8d0713

Refactor combinatorial properties of continuous subspace

76b5d72

Refactor constraint validation

5c92079

Move factory code up

f07a452

Simplify constructor code

c5b014d

AdrianSosic added 7 commits October 28, 2024 18:19

Fix exception types and messages

b8d24d7

Apply minor formatting and documentation fixes

492bb3b

Remove unnecessary len call

584d8d9

Remove unnecessary function layer

5744e31

Extract loop into general function optimizing subspaces

e4afdcc

Simplify multi-space optimization logic

788a5ba

Remove restriction on subspaces without cardinality constraints

046a8e2

AdrianSosic force-pushed the feature/cardinality_constraint_to_botorch_via_sampling branch from 8a7ef15 to 046a8e2 Compare October 29, 2024 17:17

AdrianSosic added 7 commits October 29, 2024 18:19

Move __str__ method to top

7aac4d3

Rename threshold attribute

3c829d0

Add item to README.md

bc697c0

Implement summary method

b9038b8

Update CHANGELOG.md

c2c8b99

Fix tests

a721f21

Explain mechanism of recommending with cardinality constraints

e84dda9

AdrianSosic reviewed Nov 1, 2024

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AVHopp reviewed Nov 4, 2024

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Scienfitz marked this pull request as draft November 26, 2024 12:48

Waschenbacher added 10 commits December 13, 2024 11:51

Add near-zero threshold to continuous numerical parameter

fa13267

Refine activate parameter helper function

7aa7c3f

- Replace redandunt function - Ensure near-zero range being an open interval

Show warnings when any minimum cardinality constraints are violated.

cfdf1e3

Update test related to cardinality constraints

e3c6620

Add to-dos

b85924f

- Add to-do related to customized error in botorch - Add to-do related to active parameters guarantee in random sampler

Add test on catching warning related to violation of minimum cardinal…

22b19f9

…ity constraint

Update CHANGELOG.md

b0dc037

Merge branch 'main' into feature/cardinality_constraint_to_botorch_vi…

3fa8b02

…a_sampling

Clean up merge conflict code

35825b8

Refine logic in counting the near-zero elements

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Botorch with cardinality constraint via sampling #301

Botorch with cardinality constraint via sampling #301

Waschenbacher commented Jul 3, 2024 •

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Botorch with cardinality constraint via sampling #301

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Waschenbacher commented Jul 3, 2024 •

edited by AdrianSosic

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