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automl fit with starting points (#141)

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* add starting point in fit

* add estimator best config

* add test

* add doc string

* when there are multiple points_to_evaluate in CFO, use the best one to start local search; after that use low cost partial config as the start point; then, remove the points whose performance is worse than the converged, and start local search from the remaining ones ordered by their performance.

Co-authored-by: Qingyun Wu <qingyunwu@Qingyuns-MacBook-Pro-2.local>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
This commit is contained in:
Qingyun Wu
2021-07-31 16:39:31 -04:00
committed by GitHub
parent 15fd8adac4
commit e24265ee5d
7 changed files with 230 additions and 48 deletions
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29
test/tune/test_tune.py
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@@ -163,7 +163,7 @@ def _test_xgboost(method='BlendSearch'):
def test_nested():
from flaml import tune
from flaml import tune, CFO
search_space = {
# test nested search space
"cost_related": {
@@ -178,6 +178,27 @@ def test_nested():
tune.report(obj=obj)
tune.report(obj=obj, ab=config["cost_related"]["a"] * config["b"])
analysis = tune.run(
simple_func,
search_alg=CFO(
space=search_space, metric="obj", mode="min",
low_cost_partial_config={
"cost_related": {"a": 1}
},
points_to_evaluate=[
{"b": .99, "cost_related": {"a": 3}},
{"b": .99, "cost_related": {"a": 2}},
{"cost_related": {"a": 8}}
],
metric_constraints=[("ab", "<=", 4)]),
local_dir='logs/',
num_samples=-1,
time_budget_s=.1)
best_trial = analysis.get_best_trial()
logger.info(f"CFO best config: {best_trial.config}")
logger.info(f"CFO best result: {best_trial.last_result}")
analysis = tune.run(
simple_func,
config=search_space,
@@ -189,11 +210,11 @@ def test_nested():
metric_constraints=[("ab", "<=", 4)],
local_dir='logs/',
num_samples=-1,
time_budget_s=1)
time_budget_s=.1)
best_trial = analysis.get_best_trial()
logger.info(f"Best config: {best_trial.config}")
logger.info(f"Best result: {best_trial.last_result}")
logger.info(f"BlendSearch best config: {best_trial.config}")
logger.info(f"BlendSearch best result: {best_trial.last_result}")
def test_xgboost_bs():