# -*- coding: utf-8 -*-

"""

Standard Experimental Design (Grid Search)

==========================================

**Author(s)**: Romain Egele.

This example demonstrates how to generate points from standard experimental designs (e.g., random, grid, lhs).

"""

from deephyper.analysis._matplotlib import update_matplotlib_rc

update_matplotlib_rc()

# %%

# First we define the hyperparameter search space.

from deephyper.problem import HpProblem

problem = HpProblem()

problem.add_hyperparameter((0.0001, 100.0, "log-uniform"), "x")

problem.add_hyperparameter((0.0, 100.0), "y")

problem.add_hyperparameter([1, 2, 3], "z")

problem

problem

# %%

# Then we define the black-box function to optimize.

def run(job):

config = job.parameters

objective = config["x"] + config["y"]

return objective

# %%

# Then we define the search. In this example, we use the `ExperimentalDesignSearch` class to generate points from a grid design. The `Evaluator` can also be used with this class to parallelize evalutions.

# Note that `n_points` and `max_evals` take the same value here.

from deephyper.search.hps import ExperimentalDesignSearch

max_evals = 200

search = ExperimentalDesignSearch(problem, run, n_points=max_evals, design="grid")

results = search.search(max_evals)

# %%

# Finally, we plot the results from the collected DataFrame.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

ax.scatter(results["p:x"], results["p:y"], c=results["p:z"], alpha=0.3)

ax.set_xscale("log")

plt.xlabel("x")

plt.ylabel("y")

plt.show()