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

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

Requires the Ax library to be installed (`pip install ax-platform`).
"""

import time

import numpy as np

from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.search.ax import AxSearch


def hartmann6(x):
    alpha = np.array([1.0, 1.2, 3.0, 3.2])
    A = np.array(
        [
            [10, 3, 17, 3.5, 1.7, 8],
            [0.05, 10, 17, 0.1, 8, 14],
            [3, 3.5, 1.7, 10, 17, 8],
            [17, 8, 0.05, 10, 0.1, 14],
        ]
    )
    P = 10 ** (-4) * np.array(
        [
            [1312, 1696, 5569, 124, 8283, 5886],
            [2329, 4135, 8307, 3736, 1004, 9991],
            [2348, 1451, 3522, 2883, 3047, 6650],
            [4047, 8828, 8732, 5743, 1091, 381],
        ]
    )
    y = 0.0
    for j, alpha_j in enumerate(alpha):
        t = 0
        for k in range(6):
            t += A[j, k] * ((x[k] - P[j, k]) ** 2)
        y -= alpha_j * np.exp(-t)
    return y


def easy_objective(config):
    for i in range(config["iterations"]):
        x = np.array([config.get("x{}".format(i + 1)) for i in range(6)])
        tune.report(
            {
                "timesteps_total": i,
                "hartmann6": hartmann6(x),
                "l2norm": np.sqrt((x**2).sum()),
            }
        )
        time.sleep(0.02)


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()

    algo = AxSearch(
        parameter_constraints=["x1 + x2 <= 2.0"],  # Optional.
        outcome_constraints=["l2norm <= 1.25"],  # Optional.
    )
    # Limit to 4 concurrent trials
    algo = tune.search.ConcurrencyLimiter(algo, max_concurrent=4)
    scheduler = AsyncHyperBandScheduler()
    tuner = tune.Tuner(
        easy_objective,
        run_config=tune.RunConfig(
            name="ax",
            stop={"timesteps_total": 100},
        ),
        tune_config=tune.TuneConfig(
            metric="hartmann6",  # provided in the 'easy_objective' function
            mode="min",
            search_alg=algo,
            scheduler=scheduler,
            num_samples=10 if args.smoke_test else 50,
        ),
        param_space={
            "iterations": 100,
            "x1": tune.uniform(0.0, 1.0),
            "x2": tune.uniform(0.0, 1.0),
            "x3": tune.uniform(0.0, 1.0),
            "x4": tune.uniform(0.0, 1.0),
            "x5": tune.uniform(0.0, 1.0),
            "x6": tune.uniform(0.0, 1.0),
        },
    )
    results = tuner.fit()
    print("Best hyperparameters found were: ", results.get_best_result().config)