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space -> main (#148)

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* subspace in flow2

* search space and trainable from AutoML

* experimental features: multivariate TPE, grouping, add_evaluated_points

* test experimental features

* readme

* define by run

* set time_budget_s for bs

Co-authored-by: liususan091219 <Xqq630517>

* version

* acl

* test define_by_run_func

* size

* constraints

Co-authored-by: Chi Wang <wang.chi@microsoft.com>
This commit is contained in:
Xueqing Liu
2021-08-02 19:10:26 -04:00
committed by GitHub
parent 46752083a2
commit eeaf5b5963
15 changed files with 516 additions and 170 deletions
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27
test/tune/test_tune.py
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@@ -1,5 +1,6 @@
'''Require: pip install flaml[test,ray]
'''
from flaml.searcher.blendsearch import BlendSearch
import time
import os
from sklearn.model_selection import train_test_split
@@ -199,6 +200,28 @@ def test_nested():
logger.info(f"CFO best config: {best_trial.config}")
logger.info(f"CFO best result: {best_trial.last_result}")
analysis = tune.run(
simple_func,
search_alg=BlendSearch(
experimental=True,
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"BlendSearch exp best config: {best_trial.config}")
logger.info(f"BlendSearch exp best result: {best_trial.last_result}")
analysis = tune.run(
simple_func,
config=search_space,
@@ -222,7 +245,7 @@ def test_run_training_function_return_value():
# Test dict return value
def evaluate_config_dict(config):
metric = (round(config['x'])-85000)**2 - config['x']/config['y']
metric = (round(config['x']) - 85000)**2 - config['x'] / config['y']
return {"metric": metric}
tune.run(
@@ -236,7 +259,7 @@ def test_run_training_function_return_value():
# Test scalar return value
def evaluate_config_scalar(config):
metric = (round(config['x'])-85000)**2 - config['x']/config['y']
metric = (round(config['x']) - 85000)**2 - config['x'] / config['y']
return metric
tune.run(