Route CausalTune's estimator-level HPO through hiertunehub.create_tuner
so fit() supports flaml, hyperopt and optuna via new framework=/algo=
arguments; optuna is the default. search_space() now returns a
hiertunehub.SearchSpace and parse_tuner_params() maps the tuner settings
to each backend (hyperopt defaults algo to tpe.suggest; optuna/hyperopt
raise on an unbounded search).

Preserve full FLAML-option parity on framework="flaml": cost_attr,
low_cost_partial_config, try_init_configs warm-start (points_to_evaluate)
and resume (evaluated_rewards rebuilt from the previous tuner's results).
On hyperopt/optuna, resume raises NotImplementedError and try_init_configs
warns rather than silently dropping.

Also fix two bugs surfaced by the port:
- best_score_by_estimator iterated a dict and referenced a removed loop
  variable in its error message (NameError); it now takes a list.
- A failed trial returned -inf regardless of metric direction, which a
  minimizing backend (the optuna default) treats as best and so poisons
  best_estimator selection; the failure sentinel is now the worst value
  for the metric direction (+inf for minimized metrics).
  The model property now resolves the fitted estimator from scores, since
  the objective pops it out of the result dict in the non-store_all path.

Ports PR #337 by Yikun Jiang.

Co-authored-by: Yikun Jiang <jiangyikun9663@gmail.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: Egor Kraev <egor.kraev@gmail.com>

Cover parse_tuner_params mapping for all three frameworks (incl. the
num_samples==-1 rule, hyperopt tpe.suggest default, exact key sets and
the unbounded-budget guard), search_space returning a hiertunehub
SearchSpace, the list-based best_score_by_estimator (and its ValueError
on a malformed entry), end-to-end fits per backend, the optuna default
and custom sampler, FLAML parity (cost_attr/warm-start/resume params
actually reaching flaml.tune.run, resume rebuilt from the previous
tuner), the failed-trial-not-selected-as-best regression, and the
best-effort NotImplementedError/warn on non-flaml init/resume.

Co-authored-by: Yikun Jiang <jiangyikun9663@gmail.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: Egor Kraev <egor.kraev@gmail.com>

- test_packaging_dependencies: importorskip tomllib (stdlib only from 3.11)
  so the Python 3.10 CI job doesn't ModuleNotFoundError.
- Rename ct.results.trials -> ct.tuner.trials and trial.last_result ->
  trial.result in the experiment runner and example notebooks, following
  the self.results -> self.tuner move. HierTuneHub's Trial exposes .result.

Co-authored-by: Yikun Jiang <jiangyikun9663@gmail.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: Egor Kraev <egor.kraev@gmail.com>

Describe the optuna (default) / hyperopt / flaml backend choice via the
framework and algo arguments to fit(), note that try_init_configs and
resume are flaml-only, and mention the optional [hyperopt] extra. Also
refresh the stale supported-Python line (3.10-3.12) and add optuna to the
dependency lists.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: Egor Kraev <egor.kraev@gmail.com>

hiertunehub 0.2.1's to_hyperopt() raises a cryptic NameError when the
search space contains only parameterless choices (e.g.
estimator_list=["Dummy"] with outcome_model!="auto"), because it imports
its hyperopt global only while converting a sampler leaf. Raise a clear
ValueError up front instead, pointing at the optuna/flaml alternatives.
Also fix the Mac install guide's stale python=3.9.x.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: Egor Kraev <egor.kraev@gmail.com>

test_random_from_biased compares an ERUPT estimate against an empirical
mean with atol=5e-2, using unseeded global np.random. Each CI process
draws different randomness, so it failed intermittently (1 of 6 matrix
jobs). Seed the RNG in make_dataset for deterministic data; seed 0 leaves
~16x margin on the tightest assertion. Unrelated to the HPO-backend change
but surfaced by this PR's CI run.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: Egor Kraev <egor.kraev@gmail.com>