103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
"""
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Benchmarks of isotonic regression performance.
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We generate a synthetic dataset of size 10^n, for n in [min, max], and
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examine the time taken to run isotonic regression over the dataset.
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The timings are then output to stdout, or visualized on a log-log scale
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with matplotlib.
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This allows the scaling of the algorithm with the problem size to be
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visualized and understood.
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"""
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from __future__ import print_function
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import numpy as np
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import gc
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from datetime import datetime
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from sklearn.isotonic import isotonic_regression
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from scipy.special import expit
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import matplotlib.pyplot as plt
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import argparse
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def generate_perturbed_logarithm_dataset(size):
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return (np.random.randint(-50, 50, size=size) +
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50. * np.log(1 + np.arange(size)))
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def generate_logistic_dataset(size):
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X = np.sort(np.random.normal(size=size))
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return np.random.random(size=size) < expit(X)
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def generate_pathological_dataset(size):
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# Triggers O(n^2) complexity on the original implementation.
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return np.r_[np.arange(size),
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np.arange(-(size - 1), size),
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np.arange(-(size - 1), 1)]
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DATASET_GENERATORS = {
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'perturbed_logarithm': generate_perturbed_logarithm_dataset,
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'logistic': generate_logistic_dataset,
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'pathological': generate_pathological_dataset,
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}
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def bench_isotonic_regression(Y):
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"""
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Runs a single iteration of isotonic regression on the input data,
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and reports the total time taken (in seconds).
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"""
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gc.collect()
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tstart = datetime.now()
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isotonic_regression(Y)
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return (datetime.now() - tstart).total_seconds()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(
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description="Isotonic Regression benchmark tool")
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parser.add_argument('--seed', type=int,
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help="RNG seed")
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parser.add_argument('--iterations', type=int, required=True,
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help="Number of iterations to average timings over "
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"for each problem size")
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parser.add_argument('--log_min_problem_size', type=int, required=True,
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help="Base 10 logarithm of the minimum problem size")
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parser.add_argument('--log_max_problem_size', type=int, required=True,
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help="Base 10 logarithm of the maximum problem size")
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parser.add_argument('--show_plot', action='store_true',
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help="Plot timing output with matplotlib")
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parser.add_argument('--dataset', choices=DATASET_GENERATORS.keys(),
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required=True)
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args = parser.parse_args()
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np.random.seed(args.seed)
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timings = []
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for exponent in range(args.log_min_problem_size,
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args.log_max_problem_size):
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n = 10 ** exponent
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Y = DATASET_GENERATORS[args.dataset](n)
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time_per_iteration = \
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[bench_isotonic_regression(Y) for i in range(args.iterations)]
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timing = (n, np.mean(time_per_iteration))
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timings.append(timing)
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# If we're not plotting, dump the timing to stdout
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if not args.show_plot:
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print(n, np.mean(time_per_iteration))
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if args.show_plot:
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plt.plot(*zip(*timings))
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plt.title("Average time taken running isotonic regression")
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plt.xlabel('Number of observations')
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plt.ylabel('Time (s)')
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plt.axis('tight')
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plt.loglog()
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plt.show()
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