bench_isotonic.py

"""
Benchmarks of isotonic regression performance.

We generate a synthetic dataset of size 10^n, for n in [min, max], and
examine the time taken to run isotonic regression over the dataset.

The timings are then output to stdout, or visualized on a log-log scale
with matplotlib.

This allows the scaling of the algorithm with the problem size to be
visualized and understood.
"""
from __future__ import print_function

import numpy as np
import gc
from datetime import datetime
from sklearn.isotonic import isotonic_regression
from sklearn.utils.bench import total_seconds
import matplotlib.pyplot as plt
import argparse


def generate_perturbed_logarithm_dataset(size):
    return np.random.randint(-50, 50, size=n) \
        + 50. * np.log(1 + np.arange(n))


def generate_logistic_dataset(size):
    X = np.sort(np.random.normal(size=size))
    return np.random.random(size=size) < 1.0 / (1.0 + np.exp(-X))


DATASET_GENERATORS = {
    'perturbed_logarithm': generate_perturbed_logarithm_dataset,
    'logistic': generate_logistic_dataset
}


def bench_isotonic_regression(Y):
    """
    Runs a single iteration of isotonic regression on the input data,
    and reports the total time taken (in seconds).
    """
    gc.collect()

    tstart = datetime.now()
    isotonic_regression(Y)
    delta = datetime.now() - tstart
    return total_seconds(delta)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description="Isotonic Regression benchmark tool")
    parser.add_argument('--iterations', type=int, required=True,
                        help="Number of iterations to average timings over "
                        "for each problem size")
    parser.add_argument('--log_min_problem_size', type=int, required=True,
                        help="Base 10 logarithm of the minimum problem size")
    parser.add_argument('--log_max_problem_size', type=int, required=True,
                        help="Base 10 logarithm of the maximum problem size")
    parser.add_argument('--show_plot', action='store_true',
                        help="Plot timing output with matplotlib")
    parser.add_argument('--dataset', choices=DATASET_GENERATORS.keys(),
                        required=True)

    args = parser.parse_args()

    timings = []
    for exponent in range(args.log_min_problem_size,
                          args.log_max_problem_size):
        n = 10 ** exponent
        Y = DATASET_GENERATORS[args.dataset](n)
        time_per_iteration = \
            [bench_isotonic_regression(Y) for i in range(args.iterations)]
        timing = (n, np.mean(time_per_iteration))
        timings.append(timing)

        # If we're not plotting, dump the timing to stdout
        if not args.show_plot:
            print(n, np.mean(time_per_iteration))

    if args.show_plot:
        plt.plot(*zip(*timings))
        plt.title("Average time taken running isotonic regression")
        plt.xlabel('Number of observations')
        plt.ylabel('Time (s)')
        plt.axis('tight')
        plt.loglog()
        plt.show()