Store everest results in ERT storage #9161
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## main #9161 +/- ##
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+ Coverage 91.67% 91.84% +0.16%
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Files 424 424
Lines 26511 26748 +237
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+ Hits 24305 24566 +261
+ Misses 2206 2182 -24
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verveerpj
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src/everest/everest_storage.py
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| mapping = {} | ||
| for d in dummy_df.select("realization", "simulation_id").to_dicts(): | ||
| # Currently we work with str, but should maybe not be done in future |
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verveerpj
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src/everest/everest_storage.py
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| def _store_gradient_results(self, results: FunctionResults) -> _GradientResults: | ||
| perturbation_objectives = polars.from_pandas( | ||
| results.to_dataframe("evaluations").reset_index() | ||
| ).drop("plan_id") |
src/everest/everest_storage.py
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| # expected to be None? | ||
| batch_objective_gradient = polars.from_pandas( | ||
| results.to_dataframe("gradients").reset_index() | ||
| ).drop("plan_id") |
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src/everest/everest_storage.py
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| def _enforce_dtypes(df: polars.DataFrame) -> polars.DataFrame: | ||
| dtypes = { | ||
| "batch_id": polars.UInt32, | ||
| "result_id": polars.UInt32, |
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Could you drop all instances of storing result_id? I do not think it is needed anywhere and I am considering changing/dropping it in ropt, so it would be better not to depend on it.
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Issue
Resolves #8811
Base idea/documentation:
Store datasets by
[batch, realization, perturbation] x [controls, objectives, constraints, objective_gradient, constraint_gradient]:Exhaustive list of data stored PER BATCH :
batch.json- contains info about the batch, batch_id and whether it is an improvement (aka merit flag, but the concepts are now unified for dakota and non-dakota runs)batch_constraintsconstraint values (and violations) for constraints, batch-widebatch_objectivesobjective values, batch-widerealization_controls- control values for geo-realizations, also includessimulation_idrealization_objectives- objective values per geo-realizationrealization_constraints- constraint values per geo-realizationperturbation_objectives- objective and control values per perturbationperturbation_constraints- constraint and control values per perturbation (Note/discussion point: control values could be pulled into separate table to avoid redundancy)batch_objective_gradient- Partial derivatives of objectives, given different controls. This dataset has one column per objective, and one row per control value, and the intersecting cells represent the partial derivative of the objective wrt that control value.batch_constraint_gradient- Partial derivatives of constraints, given different controls. This dataset has one column per constraint, and one row per control value, and the intersecting cells represent the partial derivative of the constraint wrt that control value.Example data from
math_func/config_advanced.yml(json format)Exhaustive list of data stored PER OPTIMIZATION
controls.json- control values for this batchrealization_weights.json- realization weightsnonlinear_constraints- conditions for constraints to satisfy (on average over the batch)objective_functions- objective function names, weights, and normalizationExample data from
math_func/config_advanced.ymlPotential simplifications
The
everest_data_apiis currently used for plotting, but could be used (probably expanded a bit) to avoid doing direct (polars) dataframe manipulations elsewhere in the code, but currently they are done directly in the code.