Request: Force the first set of evaluated parameters/ save info to trials

[sshleifer]

I know the best params for my model but hyperopt over a parameter space centered around my best params has thus far failed to discover params with a better objective than mine. I want to guide tpe.suggest towards good parameters by telling it "check out these params". Have already run:

trials = Trials()
best = fmin(objective,
            space=rnn_space,
    algo=tpe.suggest,
    max_evals=50,
    trials=trials)

and then

best_params={dropout_frac=1,...}
baseline_result = objective(best_params)

Now I just need to somehow inject this information into the optimization process, or restart the optimization process with more guidance.

[sshleifer]

insert_trial_docs seems relevant, but documentation for how to create a doc from a dictionary does not exist yet

[github-actions[bot]]

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