benchmarks/bench_random_projections.py - main - githubmirror/scikit-learn

benchmarks/bench_random_projections.py
304 строки8 KB
Dan Schult
Enable config setting `sparse_interface` to control sparray and spmatrix creation (#31177)
11 мар 2026, 09:04
11 мар 2026, 09:040169f83
"""
===========================
Random projection benchmark
===========================

Benchmarks for random projections.

"""

import collections
import gc
import optparse
import sys
from datetime import datetime

import numpy as np
import scipy.sparse as sp

from sklearn import clone
from sklearn.random_projection import (
    GaussianRandomProjection,
    SparseRandomProjection,
    johnson_lindenstrauss_min_dim,
)


def type_auto_or_float(val):
    if val == "auto":
        return "auto"
    else:
        return float(val)


def type_auto_or_int(val):
    if val == "auto":
        return "auto"
    else:
        return int(val)


def compute_time(t_start, delta):
    mu_second = 0.0 + 10**6  # number of microseconds in a second

    return delta.seconds + delta.microseconds / mu_second


def bench_scikit_transformer(X, transformer):
    gc.collect()

    clf = clone(transformer)

    # start time
    t_start = datetime.now()
    clf.fit(X)
    delta = datetime.now() - t_start
    # stop time
    time_to_fit = compute_time(t_start, delta)

    # start time
    t_start = datetime.now()
    clf.transform(X)
    delta = datetime.now() - t_start
    # stop time
    time_to_transform = compute_time(t_start, delta)

    return time_to_fit, time_to_transform


# Make some random data with uniformly located non zero entries with
# Gaussian distributed values
def make_sparse_random_data(n_samples, n_features, n_nonzeros, random_state=None):
    rng = np.random.RandomState(random_state)
    data_coo = sp.coo_array(
        (
            rng.randn(n_nonzeros),
            (
                rng.randint(n_samples, size=n_nonzeros),
                rng.randint(n_features, size=n_nonzeros),
            ),
        ),
        shape=(n_samples, n_features),
    )
    return data_coo.toarray(), data_coo.tocsr()


def print_row(clf_type, time_fit, time_transform):
    print(
        "%s | %s | %s"
        % (
            clf_type.ljust(30),
            ("%.4fs" % time_fit).center(12),
            ("%.4fs" % time_transform).center(12),
        )
    )


if __name__ == "__main__":
    ###########################################################################
    # Option parser
    ###########################################################################
    op = optparse.OptionParser()
    op.add_option(
        "--n-times",
        dest="n_times",
        default=5,
        type=int,
        help="Benchmark results are average over n_times experiments",
    )

    op.add_option(
        "--n-features",
        dest="n_features",
        default=10**4,
        type=int,
        help="Number of features in the benchmarks",
    )

    op.add_option(
        "--n-components",
        dest="n_components",
        default="auto",
        help="Size of the random subspace. ('auto' or int > 0)",
    )

    op.add_option(
        "--ratio-nonzeros",
        dest="ratio_nonzeros",
        default=10**-3,
        type=float,
        help="Number of features in the benchmarks",
    )

    op.add_option(
        "--n-samples",
        dest="n_samples",
        default=500,
        type=int,
        help="Number of samples in the benchmarks",
    )

    op.add_option(
        "--random-seed",
        dest="random_seed",
        default=13,
        type=int,
        help="Seed used by the random number generators.",
    )

    op.add_option(
        "--density",
        dest="density",
        default=1 / 3,
        help=(
            "Density used by the sparse random projection. ('auto' or float (0.0, 1.0]"
        ),
    )

    op.add_option(
        "--eps",
        dest="eps",
        default=0.5,
        type=float,
        help="See the documentation of the underlying transformers.",
    )

    op.add_option(
        "--transformers",
        dest="selected_transformers",
        default="GaussianRandomProjection,SparseRandomProjection",
        type=str,
        help=(
            "Comma-separated list of transformer to benchmark. "
            "Default: %default. Available: "
            "GaussianRandomProjection,SparseRandomProjection"
        ),
    )

    op.add_option(
        "--dense",
        dest="dense",
        default=False,
        action="store_true",
        help="Set input space as a dense matrix.",
    )

    (opts, args) = op.parse_args()
    if len(args) > 0:
        op.error("this script takes no arguments.")
        sys.exit(1)
    opts.n_components = type_auto_or_int(opts.n_components)
    opts.density = type_auto_or_float(opts.density)
    selected_transformers = opts.selected_transformers.split(",")

    ###########################################################################
    # Generate dataset
    ###########################################################################
    n_nonzeros = int(opts.ratio_nonzeros * opts.n_features)

    print("Dataset statistics")
    print("===========================")
    print("n_samples \t= %s" % opts.n_samples)
    print("n_features \t= %s" % opts.n_features)
    if opts.n_components == "auto":
        print(
            "n_components \t= %s (auto)"
            % johnson_lindenstrauss_min_dim(n_samples=opts.n_samples, eps=opts.eps)
        )
    else:
        print("n_components \t= %s" % opts.n_components)
    print("n_elements \t= %s" % (opts.n_features * opts.n_samples))
    print("n_nonzeros \t= %s per feature" % n_nonzeros)
    print("ratio_nonzeros \t= %s" % opts.ratio_nonzeros)
    print("")

    ###########################################################################
    # Set transformer input
    ###########################################################################
    transformers = {}

    ###########################################################################
    # Set GaussianRandomProjection input
    gaussian_matrix_params = {
        "n_components": opts.n_components,
        "random_state": opts.random_seed,
    }
    transformers["GaussianRandomProjection"] = GaussianRandomProjection(
        **gaussian_matrix_params
    )

    ###########################################################################
    # Set SparseRandomProjection input
    sparse_matrix_params = {
        "n_components": opts.n_components,
        "random_state": opts.random_seed,
        "density": opts.density,
        "eps": opts.eps,
    }

    transformers["SparseRandomProjection"] = SparseRandomProjection(
        **sparse_matrix_params
    )

    ###########################################################################
    # Perform benchmark
    ###########################################################################
    time_fit = collections.defaultdict(list)
    time_transform = collections.defaultdict(list)

    print("Benchmarks")
    print("===========================")
    print("Generate dataset benchmarks... ", end="")
    X_dense, X_sparse = make_sparse_random_data(
        opts.n_samples, opts.n_features, n_nonzeros, random_state=opts.random_seed
    )
    X = X_dense if opts.dense else X_sparse
    print("done")

    for name in selected_transformers:
        print("Perform benchmarks for %s..." % name)

        for iteration in range(opts.n_times):
            print("\titer %s..." % iteration, end="")
            time_to_fit, time_to_transform = bench_scikit_transformer(
                X_dense, transformers[name]
            )
            time_fit[name].append(time_to_fit)
            time_transform[name].append(time_to_transform)
            print("done")

    print("")

    ###########################################################################
    # Print results
    ###########################################################################
    print("Script arguments")
    print("===========================")
    arguments = vars(opts)
    print(
        "%s \t | %s "
        % (
            "Arguments".ljust(16),
            "Value".center(12),
        )
    )
    print(25 * "-" + ("|" + "-" * 14) * 1)
    for key, value in arguments.items():
        print("%s \t | %s " % (str(key).ljust(16), str(value).strip().center(12)))
    print("")

    print("Transformer performance:")
    print("===========================")
    print("Results are averaged over %s repetition(s)." % opts.n_times)
    print("")
    print(
        "%s | %s | %s"
        % ("Transformer".ljust(30), "fit".center(12), "transform".center(12))
    )
    print(31 * "-" + ("|" + "-" * 14) * 2)

    for name in sorted(selected_transformers):
        print_row(name, np.mean(time_fit[name]), np.mean(time_transform[name]))

    print("")
    print("")