Question

A data scientist runs Hyperopt's fmin() on a single-node cluster to tune an XGBoost model, passing a Trials() object as the trials argument. After optimization completes, they want to programmatically inspect the loss and hyperparameters tried for every evaluation, not just the best result returned by fmin(). Which approach lets them access the full history of all trials?

Accepted Answer

Iterate over the Trials object's .results and .trials attributes, which store the returned dictionary and parameters for each evaluation

Answer

The return value of fmin() already contains a list of every trial's loss and parameters, so no further object is needed

Answer

Re-run fmin() with return_argmin=False to have it return the complete evaluation history instead of the best point

Answer

Replace Trials() with SparkTrials(), which is the only Trials type that records per-evaluation history