mirror of
https://github.com/microsoft/autogen.git
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569908fbe6
* console log handler * version update * doc * skippable steps * notebook update * constraint sign * doc for constraints * bug fix: define-by-run and unflatten_hierarchical * const * handle nested space in indexof() * test grid search * test suggestion * model test * >1 ckpts * always increase iter count * log total # iterations * security patch * make iter_per_learner consistent
116 lines
4.2 KiB
Python
116 lines
4.2 KiB
Python
from flaml import BlendSearch, CFO, tune
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def test_define_by_run():
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from flaml.tune.space import (
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unflatten_hierarchical,
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normalize,
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indexof,
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complete_config,
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)
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space = {
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# Sample a float uniformly between -5.0 and -1.0
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"uniform": tune.uniform(-5, -1),
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# Sample a float uniformly between 3.2 and 5.4,
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# rounding to increments of 0.2
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"quniform": tune.quniform(3.2, 5.4, 0.2),
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# Sample a float uniformly between 0.0001 and 0.01, while
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# sampling in log space
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"loguniform": tune.loguniform(1e-4, 1e-2),
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# Sample a float uniformly between 0.0001 and 0.1, while
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# sampling in log space and rounding to increments of 0.00005
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"qloguniform": tune.qloguniform(1e-4, 1e-1, 5e-5),
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# Sample a random float from a normal distribution with
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# mean=10 and sd=2
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# "randn": tune.randn(10, 2),
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# Sample a random float from a normal distribution with
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# mean=10 and sd=2, rounding to increments of 0.2
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# "qrandn": tune.qrandn(10, 2, 0.2),
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# Sample a integer uniformly between -9 (inclusive) and 15 (exclusive)
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"randint": tune.randint(-9, 15),
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# Sample a random uniformly between -21 (inclusive) and 12 (inclusive (!))
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# rounding to increments of 3 (includes 12)
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"qrandint": tune.qrandint(-21, 12, 3),
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# Sample a integer uniformly between 1 (inclusive) and 10 (exclusive),
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# while sampling in log space
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"lograndint": tune.lograndint(1, 10),
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# Sample a integer uniformly between 2 (inclusive) and 10 (inclusive (!)),
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# while sampling in log space and rounding to increments of 2
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"qlograndint": tune.qlograndint(2, 10, 2),
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# Sample an option uniformly from the specified choices
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"choice": tune.choice(["a", "b", "c"]),
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"const": 5,
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}
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choice = {"nested": space}
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bs = BlendSearch(
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space={"c": tune.choice([choice])},
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low_cost_partial_config={"c": choice},
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metric="metric",
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mode="max",
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)
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print(indexof(bs._gs.space["c"], choice))
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print(indexof(bs._gs.space["c"], {"nested": {"const": 1}}))
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config = bs._gs.suggest("t1")
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print(config)
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config = unflatten_hierarchical(config, bs._gs.space)[0]
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print(config)
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print(normalize({"c": [choice]}, bs._gs.space, config, {}, False))
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space["randn"] = tune.randn(10, 2)
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cfo = CFO(
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space={"c": tune.choice([0, choice])},
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metric="metric",
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mode="max",
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)
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for i in range(5):
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cfo.suggest(f"t{i}")
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# print(normalize(config, bs._gs.space, config, {}, False))
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print(complete_config({}, cfo._ls.space, cfo._ls))
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def test_grid():
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from flaml.searcher.variant_generator import (
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generate_variants,
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grid_search,
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TuneError,
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has_unresolved_values,
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)
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from flaml.tune import sample
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space = {
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"activation": grid_search(["relu", "tanh"]),
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"learning_rate": grid_search([1e-3, 1e-4, 1e-5]),
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"c": sample.choice([2, 3]),
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}
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for _, generated in generate_variants({"config": space}):
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config = generated["config"]
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print(config)
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for _, generated in generate_variants({"config": space}, True):
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config = generated["config"]
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print(config)
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space = {
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"activation": grid_search([{"c": sample.choice([2, 3])}]),
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"learning_rate": grid_search([1e-3, 1e-4, 1e-5]),
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}
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try:
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for _, generated in generate_variants({"config": space}, True):
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config = generated["config"]
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print(config)
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except ValueError:
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# The variable `('config', 'activation', 'c')` could not be unambiguously resolved to a single value.
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pass
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space = {
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"c": sample.choice([{"c1": sample.choice([1, 2])}]),
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"a": sample.randint(1, 10),
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"b": sample.choice([sample.uniform(10, 20), sample.choice([1, 2])]),
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}
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for _, generated in generate_variants({"config": space}):
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config = generated["config"]
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print(config)
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space = {"a": grid_search(3)}
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try:
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print(has_unresolved_values(space))
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except TuneError:
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# Grid search expected list of values, got: 3
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pass
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