# Authors: The jax-sklearn developers
# SPDX-License-Identifier: BSD-3-Clause

import time

import matplotlib.pyplot as plt

from xlearn.metrics.pairwise import pairwise_distances, pairwise_kernels
from xlearn.utils import check_random_state


def plot(func):
    random_state = check_random_state(0)
    one_core = []
    multi_core = []
    sample_sizes = range(1000, 6000, 1000)

    for n_samples in sample_sizes:
        X = random_state.rand(n_samples, 300)

        start = time.time()
        func(X, n_jobs=1)
        one_core.append(time.time() - start)

        start = time.time()
        func(X, n_jobs=-1)
        multi_core.append(time.time() - start)

    plt.figure("jax-sklearn parallel %s benchmark results" % func.__name__)
    plt.plot(sample_sizes, one_core, label="one core")
    plt.plot(sample_sizes, multi_core, label="multi core")
    plt.xlabel("n_samples")
    plt.ylabel("Time (s)")
    plt.title("Parallel %s" % func.__name__)
    plt.legend()


def euclidean_distances(X, n_jobs):
    return pairwise_distances(X, metric="euclidean", n_jobs=n_jobs)


def rbf_kernels(X, n_jobs):
    return pairwise_kernels(X, metric="rbf", n_jobs=n_jobs, gamma=0.1)


plot(euclidean_distances)
plot(rbf_kernels)
plt.show()
