example of starting ax experiment from dataframe data (#5098)

[sdaulton]

Summary:

This diff adds a new Ax tutorial notebook demonstrating how to initialize an experiment from pre-existing DataFrame data.

Key Features Covered:

1. Attaching Historical Trials — Shows how to use client.attach_trial() and client.complete_trial() to import historical experiment data (arms with parameters and their observed metric values) from a pandas DataFrame
2. Warm-Starting Bayesian Optimization — After attaching historical data, uses Ax's Client to generate new candidate trials with model-based optimization (BoTorch)
3. Analysis & Visualization — Demonstrates Ax's built-in analysis tools: cross-validation plots, utility progression tracking, and arm effects visualization (both observed and predicted)

Use Cases:

• Migrating experiments run outside of Ax into the Ax framework
• Leveraging existing CSV/database data to warm-start Bayesian optimization
• Building on historical configurations and outcomes

Technical Details:

• Uses the Branin function as a benchmark optimization problem (2 parameters, known global minimum ≈ 0.398)
• Creates 15 quasi-random historical evaluations and attaches them as completed trials
• Generates and evaluates a new candidate using client.get_next_trial()
• Compares predicted vs. observed arm effects

Reviewed By: andycylmeta

Differential Revision: D93760064

[meta-codesync]

@sdaulton has exported this pull request. If you are a Meta employee, you can view the originating Diff in D93760064.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 96.41%. Comparing base (218bf55) to head (5fe7341).

Additional details and impacted files

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  Coverage   96.41%   96.41%           
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  Files         613      613           
  Lines       68131    68131           
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  Hits        65687    65687           
  Misses       2444     2444           

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[meta-codesync]

This pull request has been merged in 7c67537.