This PR adds support for local models and datasets. The following scenarios are supported:
global model and global dataset -- as previously
local models and local datasets -- one-to-one mapping
global model and local datasets -- many-to-one mapping
The initial dataset can be a global dataset sampled from the whole search space. This data will be replicated to each of the regions on the first iteration and subsequently each region will have an associated local dataset. For batch-TR algorithm, the dataset for each region are filtered after each iteration to only contain the points in the region (but TREGO doesn't do this).
Note: this replication of initial data can potentially cause an issue when a global model is being used, as the points may be repeated. This will only be an issue if regions overlap and both contain that initial data-point (as filtering would otherwise remove duplicates). The main way to avoid this issue is to provide local initial datasets, instead of a global initial dataset.
The trust_region notebook contains a new temporary TEST section, just to show how local models can be used in the notebook. It is worth noting in the gif that the query-points are filtered to be only inside the boxes for each iteration. This section is only for demonstration and will be removed before merging this PR. A follow-on PR will update the TURBO section to use local models and demonstrate this functionality.
Fully backwards compatible: no
The BatchTrustRegion rule acquisition returns rank 3 points, instead of rank 2 as for other rules (and previous trust-region rules). This means the users should use the new batched observer with this rule. That is already taken care of in BayesianOptimizer. However, with AskTellOptimizer the users should use the batched observer as follows:
Related issue(s)/PRs: #782
Summary
This PR adds support for local models and datasets. The following scenarios are supported:
The initial dataset can be a global dataset sampled from the whole search space. This data will be replicated to each of the regions on the first iteration and subsequently each region will have an associated local dataset. For batch-TR algorithm, the dataset for each region are filtered after each iteration to only contain the points in the region (but
TREGOdoesn't do this).Note: this replication of initial data can potentially cause an issue when a global model is being used, as the points may be repeated. This will only be an issue if regions overlap and both contain that initial data-point (as filtering would otherwise remove duplicates). The main way to avoid this issue is to provide local initial datasets, instead of a global initial dataset.
Thetrust_regionnotebook contains a new temporaryTESTsection, just to show how local models can be used in the notebook. It is worth noting in thegifthat the query-points are filtered to be only inside the boxes for each iteration. This section is only for demonstration and will be removed before merging this PR. A follow-on PR will update theTURBOsection to use local models and demonstrate this functionality.Fully backwards compatible: no
The
BatchTrustRegionrule acquisition returns rank 3 points, instead of rank 2 as for other rules (and previous trust-region rules). This means the users should use the new batched observer with this rule. That is already taken care of inBayesianOptimizer. However, withAskTellOptimizerthe users should use the batched observer as follows:PR checklist