Update get_trace to work for preference learning experiments #4553
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…k#4553) Summary: This diff adds a new property to the `Experiment` class called `is_preference_learning_problem`. This property checks if the experiment is a preference learning (BOPE) experiment by checking if the optimization config is a `PreferenceOptimizationConfig` or if there is a `PE_EXPERIMENT` (preference exploration) auxiliary experiment attached. This property is useful for identifying preference learning experiments in Ax. Differential Revision: D87347126
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…k#4553) Summary: This diff adds a new property to the `Experiment` class called `is_preference_learning_problem`. This property checks if the experiment is a preference learning (BOPE) experiment by checking if the optimization config is a `PreferenceOptimizationConfig` or if there is a `PE_EXPERIMENT` (preference exploration) auxiliary experiment attached. This property is useful for identifying preference learning experiments in Ax. Differential Revision: D87347126
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…k#4553) Summary: This diff adds a new property to the `Experiment` class called `is_preference_learning_problem`. This property checks if the experiment is a preference learning (BOPE) experiment by checking if the optimization config is a `PreferenceOptimizationConfig` or if there is a `PE_EXPERIMENT` (preference exploration) auxiliary experiment attached. This property is useful for identifying preference learning experiments in Ax. Differential Revision: D87347126
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…k#4553) Summary: This diff adds a new property to the `Experiment` class called `is_preference_learning_problem`. This property checks if the experiment is a preference learning (BOPE) experiment by checking if the optimization config is a `PreferenceOptimizationConfig` or if there is a `PE_EXPERIMENT` (preference exploration) auxiliary experiment attached. This property is useful for identifying preference learning experiments in Ax. Differential Revision: D87347126
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…k#4553) Summary: This diff adds a new property to the `Experiment` class called `is_preference_learning_problem`. This property checks if the experiment is a preference learning (BOPE) experiment by checking if the optimization config is a `PreferenceOptimizationConfig` or if there is a `PE_EXPERIMENT` (preference exploration) auxiliary experiment attached. This property is useful for identifying preference learning experiments in Ax. Differential Revision: D87347126
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…k#4553) Summary: This diff adds a new property to the `Experiment` class called `is_preference_learning_problem`. This property checks if the experiment is a preference learning (BOPE) experiment by checking if the optimization config is a `PreferenceOptimizationConfig` or if there is a `PE_EXPERIMENT` (preference exploration) auxiliary experiment attached. This property is useful for identifying preference learning experiments in Ax. Differential Revision: D87347126
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…k#4553) Summary: This diff adds a new property to the `Experiment` class called `is_preference_learning_problem`. This property checks if the experiment is a preference learning (BOPE) experiment by checking if the optimization config is a `PreferenceOptimizationConfig` or if there is a `PE_EXPERIMENT` (preference exploration) auxiliary experiment attached. This property is useful for identifying preference learning experiments in Ax. Differential Revision: D87347126
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…k#4553) Summary: This diff adds a new property to the `Experiment` class called `is_preference_learning_problem`. This property checks if the experiment is a preference learning (BOPE) experiment by checking if the optimization config is a `PreferenceOptimizationConfig` or if there is a `PE_EXPERIMENT` (preference exploration) auxiliary experiment attached. This property is useful for identifying preference learning experiments in Ax. Differential Revision: D87347126
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Summary: This diff updates the `get_trace` function in `ax/service/utils/best_point.py` to support preference learning (BOPE) experiments with `PreferenceOptimizationConfig`. When a BOPE experiment has an associated PE_EXPERIMENT auxiliary experiment with preference data, `get_trace` now: 1. Fits a PairwiseGP preference model to the PE_EXPERIMENT data 2. Uses the learned preference model to predict utility values for each arm's metric values 3. Returns a trace based on predicted utilities If the PE_EXPERIMENT is missing or has no data, the function gracefully falls back to standard hypervolume computation for multi-objective optimization. Differential Revision: D87347126
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Summary: This diff updates the `get_trace` function in `ax/service/utils/best_point.py` to support preference learning (BOPE) experiments with `PreferenceOptimizationConfig`. When a BOPE experiment has an associated PE_EXPERIMENT auxiliary experiment with preference data, `get_trace` now: 1. Fits a PairwiseGP preference model to the PE_EXPERIMENT data 2. Uses the learned preference model to predict utility values for each arm's metric values 3. Returns a trace based on predicted utilities If the PE_EXPERIMENT is missing or has no data, the function gracefully falls back to standard hypervolume computation for multi-objective optimization. Differential Revision: D87347126
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Summary: This diff updates the `get_trace` function in `ax/service/utils/best_point.py` to support preference learning (BOPE) experiments with `PreferenceOptimizationConfig`. When a BOPE experiment has an associated PE_EXPERIMENT auxiliary experiment with preference data, `get_trace` now: 1. Fits a PairwiseGP preference model to the PE_EXPERIMENT data 2. Uses the learned preference model to predict utility values for each arm's metric values 3. Returns a trace based on predicted utilities If the PE_EXPERIMENT is missing or has no data, the function gracefully falls back to standard hypervolume computation for multi-objective optimization. Differential Revision: D87347126
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Summary: This diff updates the `get_trace` function in `ax/service/utils/best_point.py` to support preference learning (BOPE) experiments with `PreferenceOptimizationConfig`. When a BOPE experiment has an associated PE_EXPERIMENT auxiliary experiment with preference data, `get_trace` now: 1. Fits a PairwiseGP preference model to the PE_EXPERIMENT data 2. Uses the learned preference model to predict utility values for each arm's metric values 3. Returns a trace based on predicted utilities If the PE_EXPERIMENT is missing or has no data, the function gracefully falls back to standard hypervolume computation for multi-objective optimization. Differential Revision: D87347126
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Summary: This diff updates the `get_trace` function in `ax/service/utils/best_point.py` to support preference learning (BOPE) experiments with `PreferenceOptimizationConfig`. When a BOPE experiment has an associated PE_EXPERIMENT auxiliary experiment with preference data, `get_trace` now: 1. Fits a PairwiseGP preference model to the PE_EXPERIMENT data 2. Uses the learned preference model to predict utility values for each arm's metric values 3. Returns a trace based on predicted utilities If the PE_EXPERIMENT is missing or has no data, the function gracefully falls back to standard hypervolume computation for multi-objective optimization. Differential Revision: D87347126
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Summary: This diff updates the `get_trace` function in `ax/service/utils/best_point.py` to support preference learning (BOPE) experiments with `PreferenceOptimizationConfig`. When a BOPE experiment has an associated PE_EXPERIMENT auxiliary experiment with preference data, `get_trace` now: 1. Fits a PairwiseGP preference model to the PE_EXPERIMENT data 2. Uses the learned preference model to predict utility values for each arm's metric values 3. Returns a trace based on predicted utilities If the PE_EXPERIMENT is missing or has no data, the function gracefully falls back to standard hypervolume computation for multi-objective optimization. Differential Revision: D87347126
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Summary: This diff updates the `get_trace` function in `ax/service/utils/best_point.py` to support preference learning (BOPE) experiments with `PreferenceOptimizationConfig`. When a BOPE experiment has an associated PE_EXPERIMENT auxiliary experiment with preference data, `get_trace` now: 1. Fits a PairwiseGP preference model to the PE_EXPERIMENT data 2. Uses the learned preference model to predict utility values for each arm's metric values 3. Returns a trace based on predicted utilities If the PE_EXPERIMENT is missing or has no data, the function gracefully falls back to standard hypervolume computation for multi-objective optimization. Differential Revision: D87347126
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Summary: This diff updates the `get_trace` function in `ax/service/utils/best_point.py` to support preference learning (BOPE) experiments with `PreferenceOptimizationConfig`. When a BOPE experiment has an associated PE_EXPERIMENT auxiliary experiment with preference data, `get_trace` now: 1. Fits a PairwiseGP preference model to the PE_EXPERIMENT data 2. Uses the learned preference model to predict utility values for each arm's metric values 3. Returns a trace based on predicted utilities If the PE_EXPERIMENT is missing or has no data, the function gracefully falls back to standard hypervolume computation for multi-objective optimization. Differential Revision: D87347126
…book#4566) Summary: `get_trace` will be used in getting trace for plotting New UtilityProgression Analysis (as a part of AxSweep plots migration to Ax Analyses). Following changes have been made to enable trace compution for the new analysis. 1. Add flag to `get_trace` helper: We need to have `status_quo` not be filtered from the trace to match what AxSweep currently shows. 2. Optimize `get_trace` to skip `derelativize_opt_config` when there are no relative constraints: This avoids unnecessary data pivoting that fails with duplicate (trial_index, arm_name) entries common in multi-objective experiments where each arm has multiple metrics. Without this fix, the pandas pivot operation throws "`ValueError:` Index contains duplicate entries, cannot reshape", causing `get_trace` to fail. Since derelativization is only needed for constraints relative to a status quo baseline, it's safely skipped when all constraints are absolute. Differential Revision: D87516104
Summary: This diff updates the `get_trace` function in `ax/service/utils/best_point.py` to support preference learning (BOPE) experiments with `PreferenceOptimizationConfig`. When a BOPE experiment has an associated PE_EXPERIMENT auxiliary experiment with preference data, `get_trace` now: 1. Fits a PairwiseGP preference model to the PE_EXPERIMENT data 2. Uses the learned preference model to predict utility values for each arm's metric values 3. Returns a trace based on predicted utilities If the PE_EXPERIMENT is missing or has no data, the function gracefully falls back to standard hypervolume computation for multi-objective optimization. Differential Revision: D87347126
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Summary: This diff updates the `get_trace` function in `ax/service/utils/best_point.py` to support preference learning (BOPE) experiments with `PreferenceOptimizationConfig`. When a BOPE experiment has an associated PE_EXPERIMENT auxiliary experiment with preference data, `get_trace` now: 1. Fits a PairwiseGP preference model to the PE_EXPERIMENT data 2. Uses the learned preference model to predict utility values for each arm's metric values 3. Returns a trace based on predicted utilities If the PE_EXPERIMENT is missing or has no data, the function gracefully falls back to standard hypervolume computation for multi-objective optimization. Differential Revision: D87347126
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Summary: This diff adds a new property to the
Experimentclass calledis_preference_learning_problem. This property checks if the experiment is a preference learning (BOPE) experiment by checking if the optimization config is aPreferenceOptimizationConfigor if there is aPE_EXPERIMENT(preference exploration) auxiliary experiment attached. This property is useful for identifying preference learning experiments in Ax.Differential Revision: D87347126