hyperopt_example

"""This example demonstrates the usage of HyperOpt with Ray Tune.

It also checks that it is usable with a separate scheduler.
"""
import time

import ray
from ray import tune
from ray.tune.suggest import ConcurrencyLimiter
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest.hyperopt import HyperOptSearch


def evaluation_fn(step, width, height):
    time.sleep(0.1)
    return (0.1 + width * step / 100)**(-1) + height * 0.1


def easy_objective(config):
    # Hyperparameters
    width, height = config["width"], config["height"]

    for step in range(config["steps"]):
        # Iterative training function - can be any arbitrary training procedure
        intermediate_score = evaluation_fn(step, width, height)
        # Feed the score back back to Tune.
        tune.report(iterations=step, mean_loss=intermediate_score)


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--smoke-test", action="store_true", help="Finish quickly for testing")
    args, _ = parser.parse_known_args()
    ray.init(configure_logging=False)

    current_best_params = [
        {
            "width": 1,
            "height": 2,
            "activation": "relu"  # Activation will be relu
        },
        {
            "width": 4,
            "height": 2,
            "activation": "tanh"  # Activation will be tanh
        }
    ]

    algo = HyperOptSearch(points_to_evaluate=current_best_params)
    algo = ConcurrencyLimiter(algo, max_concurrent=4)

    scheduler = AsyncHyperBandScheduler()
    analysis = tune.run(
        easy_objective,
        search_alg=algo,
        scheduler=scheduler,
        metric="mean_loss",
        mode="min",
        num_samples=10 if args.smoke_test else 1000,
        config={
            "steps": 100,
            "width": tune.uniform(0, 20),
            "height": tune.uniform(-100, 100),
            # This is an ignored parameter.
            "activation": tune.choice(["relu", "tanh"])
        })

    print("Best hyperparameters found were: ", analysis.best_config)