from collections import defaultdict
from time import time

import numpy as np
from numpy import random as nr

from sklearn.cluster import KMeans, MiniBatchKMeans


def compute_bench(samples_range, features_range):
    it = 0
    results = defaultdict(lambda: [])
    chunk = 100

    max_it = len(samples_range) * len(features_range)
    for n_samples in samples_range:
        for n_features in features_range:
            it += 1
            print("==============================")
            print("Iteration %03d of %03d" % (it, max_it))
            print("==============================")
            print()
            data = nr.randint(-50, 51, (n_samples, n_features))

            print("K-Means")
            tstart = time()
            kmeans = KMeans(init="k-means++", n_clusters=10).fit(data)

            delta = time() - tstart
            print("Speed: %0.3fs" % delta)
            print("Inertia: %0.5f" % kmeans.inertia_)
            print()

            results["kmeans_speed"].append(delta)
            results["kmeans_quality"].append(kmeans.inertia_)

            print("Fast K-Means")
            # let's prepare the data in small chunks
            mbkmeans = MiniBatchKMeans(
                init="k-means++", n_clusters=10, batch_size=chunk
            )
            tstart = time()
            mbkmeans.fit(data)
            delta = time() - tstart
            print("Speed: %0.3fs" % delta)
            print("Inertia: %f" % mbkmeans.inertia_)
            print()
            print()

            results["MiniBatchKMeans Speed"].append(delta)
            results["MiniBatchKMeans Quality"].append(mbkmeans.inertia_)

    return results


def compute_bench_2(chunks):
    results = defaultdict(lambda: [])
    n_features = 50000
    means = np.array(
        [
            [1, 1],
            [-1, -1],
            [1, -1],
            [-1, 1],
            [0.5, 0.5],
            [0.75, -0.5],
            [-1, 0.75],
            [1, 0],
        ]
    )
    X = np.empty((0, 2))
    for i in range(8):
        X = np.r_[X, means[i] + 0.8 * np.random.randn(n_features, 2)]
    max_it = len(chunks)
    it = 0
    for chunk in chunks:
        it += 1
        print("==============================")
        print("Iteration %03d of %03d" % (it, max_it))
        print("==============================")
        print()

        print("Fast K-Means")
        tstart = time()
        mbkmeans = MiniBatchKMeans(init="k-means++", n_clusters=8, batch_size=chunk)

        mbkmeans.fit(X)
        delta = time() - tstart
        print("Speed: %0.3fs" % delta)
        print("Inertia: %0.3fs" % mbkmeans.inertia_)
        print()

        results["MiniBatchKMeans Speed"].append(delta)
        results["MiniBatchKMeans Quality"].append(mbkmeans.inertia_)

    return results


if __name__ == "__main__":
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import axes3d  # register the 3d projection  # noqa: F401

    samples_range = np.linspace(50, 150, 5).astype(int)
    features_range = np.linspace(150, 50000, 5).astype(int)
    chunks = np.linspace(500, 10000, 15).astype(int)

    results = compute_bench(samples_range, features_range)
    results_2 = compute_bench_2(chunks)

    max_time = max(
        [max(i) for i in [t for (label, t) in results.items() if "speed" in label]]
    )
    max_inertia = max(
        [max(i) for i in [t for (label, t) in results.items() if "speed" not in label]]
    )

    fig = plt.figure("scikit-learn K-Means benchmark results")
    for c, (label, timings) in zip("brcy", sorted(results.items())):
        if "speed" in label:
            ax = fig.add_subplot(2, 2, 1, projection="3d")
            ax.set_zlim3d(0.0, max_time * 1.1)
        else:
            ax = fig.add_subplot(2, 2, 2, projection="3d")
            ax.set_zlim3d(0.0, max_inertia * 1.1)

        X, Y = np.meshgrid(samples_range, features_range)
        Z = np.asarray(timings).reshape(samples_range.shape[0], features_range.shape[0])
        ax.plot_surface(X, Y, Z.T, cstride=1, rstride=1, color=c, alpha=0.5)
        ax.set_xlabel("n_samples")
        ax.set_ylabel("n_features")

    i = 0
    for c, (label, timings) in zip("br", sorted(results_2.items())):
        i += 1
        ax = fig.add_subplot(2, 2, i + 2)
        y = np.asarray(timings)
        ax.plot(chunks, y, color=c, alpha=0.8)
        ax.set_xlabel("Chunks")
        ax.set_ylabel(label)

    plt.show()

from collections import defaultdict
from time import time

import numpy as np
from numpy import random as nr

from sklearn.cluster import KMeans, MiniBatchKMeans


def compute_bench(samples_range, features_range):
    it = 0
    results = defaultdict(lambda: [])
    chunk = 100

    max_it = len(samples_range) * len(features_range)
    for n_samples in samples_range:
        for n_features in features_range:
            it += 1
            print("==============================")
            print("Iteration %03d of %03d" % (it, max_it))
            print("==============================")
            print()
            data = nr.randint(-50, 51, (n_samples, n_features))

            print("K-Means")
            tstart = time()
            kmeans = KMeans(init="k-means++", n_clusters=10).fit(data)

            delta = time() - tstart
            print("Speed: %0.3fs" % delta)
            print("Inertia: %0.5f" % kmeans.inertia_)
            print()

            results["kmeans_speed"].append(delta)
            results["kmeans_quality"].append(kmeans.inertia_)

            print("Fast K-Means")
            # let's prepare the data in small chunks
            mbkmeans = MiniBatchKMeans(
                init="k-means++", n_clusters=10, batch_size=chunk
            )
            tstart = time()
            mbkmeans.fit(data)
            delta = time() - tstart
            print("Speed: %0.3fs" % delta)
            print("Inertia: %f" % mbkmeans.inertia_)
            print()
            print()

            results["MiniBatchKMeans Speed"].append(delta)
            results["MiniBatchKMeans Quality"].append(mbkmeans.inertia_)

    return results


def compute_bench_2(chunks):
    results = defaultdict(lambda: [])
    n_features = 50000
    means = np.array(
        [
            [1, 1],
            [-1, -1],
            [1, -1],
            [-1, 1],
            [0.5, 0.5],
            [0.75, -0.5],
            [-1, 0.75],
            [1, 0],
        ]
    )
    X = np.empty((0, 2))
    for i in range(8):
        X = np.r_[X, means[i] + 0.8 * np.random.randn(n_features, 2)]
    max_it = len(chunks)
    it = 0
    for chunk in chunks:
        it += 1
        print("==============================")
        print("Iteration %03d of %03d" % (it, max_it))
        print("==============================")
        print()

        print("Fast K-Means")
        tstart = time()
        mbkmeans = MiniBatchKMeans(init="k-means++", n_clusters=8, batch_size=chunk)

        mbkmeans.fit(X)
        delta = time() - tstart
        print("Speed: %0.3fs" % delta)
        print("Inertia: %0.3fs" % mbkmeans.inertia_)
        print()

        results["MiniBatchKMeans Speed"].append(delta)
        results["MiniBatchKMeans Quality"].append(mbkmeans.inertia_)

    return results


if __name__ == "__main__":
    from mpl_toolkits.mplot3d import axes3d  # noqa register the 3d projection
    import matplotlib.pyplot as plt

    samples_range = np.linspace(50, 150, 5).astype(int)
    features_range = np.linspace(150, 50000, 5).astype(int)
    chunks = np.linspace(500, 10000, 15).astype(int)

    results = compute_bench(samples_range, features_range)
    results_2 = compute_bench_2(chunks)

    max_time = max(
        [max(i) for i in [t for (label, t) in results.items() if "speed" in label]]
    )
    max_inertia = max(
        [max(i) for i in [t for (label, t) in results.items() if "speed" not in label]]
    )

    fig = plt.figure("scikit-learn K-Means benchmark results")
    for c, (label, timings) in zip("brcy", sorted(results.items())):
        if "speed" in label:
            ax = fig.add_subplot(2, 2, 1, projection="3d")
            ax.set_zlim3d(0.0, max_time * 1.1)
        else:
            ax = fig.add_subplot(2, 2, 2, projection="3d")
            ax.set_zlim3d(0.0, max_inertia * 1.1)

        X, Y = np.meshgrid(samples_range, features_range)
        Z = np.asarray(timings).reshape(samples_range.shape[0], features_range.shape[0])
        ax.plot_surface(X, Y, Z.T, cstride=1, rstride=1, color=c, alpha=0.5)
        ax.set_xlabel("n_samples")
        ax.set_ylabel("n_features")

    i = 0
    for c, (label, timings) in zip("br", sorted(results_2.items())):
        i += 1
        ax = fig.add_subplot(2, 2, i + 2)
        y = np.asarray(timings)
        ax.plot(chunks, y, color=c, alpha=0.8)
        ax.set_xlabel("Chunks")
        ax.set_ylabel(label)

    plt.show()
