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108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
from functools import partial
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import time
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def evaluation_fn(step, width, height):
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return (0.1 + width * step / 100) ** (-1) + height * 0.1
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def easy_objective(use_raytune, config):
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if use_raytune:
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from ray import tune
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else:
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from flaml import tune
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# Hyperparameters
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width, height = config["width"], config["height"]
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for step in range(config["steps"]):
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# Iterative training function - can be any arbitrary training procedure
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intermediate_score = evaluation_fn(step, width, height)
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# Feed the score back back to Tune.
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try:
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tune.report(iterations=step, mean_loss=intermediate_score)
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except StopIteration:
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return
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def test_tune_scheduler(smoke_test=True, use_ray=True, use_raytune=False):
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import numpy as np
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from flaml.tune.searcher.blendsearch import BlendSearch
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np.random.seed(100)
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easy_objective_custom_tune = partial(easy_objective, use_raytune)
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if use_raytune:
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try:
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from ray import tune
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except ImportError:
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print("ray[tune] is not installed, skipping test")
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return
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searcher = BlendSearch(
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space={
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"steps": 100,
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"width": tune.uniform(0, 20),
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"height": tune.uniform(-100, 100),
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# This is an ignored parameter.
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"activation": tune.choice(["relu", "tanh"]),
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"test4": np.zeros((3, 1)),
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}
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)
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analysis = tune.run(
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easy_objective_custom_tune,
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search_alg=searcher,
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metric="mean_loss",
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mode="min",
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num_samples=10 if smoke_test else 100,
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scheduler="asynchyperband",
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config={
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"steps": 100,
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"width": tune.uniform(0, 20),
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"height": tune.uniform(-100, 100),
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# This is an ignored parameter.
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"activation": tune.choice(["relu", "tanh"]),
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"test4": np.zeros((3, 1)),
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},
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)
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else:
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from flaml import tune
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searcher = BlendSearch(
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space={
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"steps": 100,
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"width": tune.uniform(0, 20),
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"height": tune.uniform(-100, 100),
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# This is an ignored parameter.
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"activation": tune.choice(["relu", "tanh"]),
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"test4": np.zeros((3, 1)),
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}
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)
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analysis = tune.run(
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easy_objective_custom_tune,
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search_alg=searcher,
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metric="mean_loss",
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mode="min",
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num_samples=10 if smoke_test else 100,
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scheduler="asynchyperband",
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resource_attr="iterations",
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max_resource=99,
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# min_resource=1,
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# reduction_factor=4,
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config={
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"steps": 100,
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"width": tune.uniform(0, 20),
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"height": tune.uniform(-100, 100),
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# This is an ignored parameter.
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"activation": tune.choice(["relu", "tanh"]),
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"test4": np.zeros((3, 1)),
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},
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use_ray=use_ray,
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)
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print("Best hyperparameters found were: ", analysis.best_config)
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print("best results", analysis.best_result)
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if __name__ == "__main__":
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test_tune_scheduler(smoke_test=True, use_ray=True, use_raytune=True)
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test_tune_scheduler(smoke_test=True, use_ray=True)
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test_tune_scheduler(smoke_test=True, use_ray=False)
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