<s> """<STR_LIT>""" <EOL> from __future__ import print_function <EOL> import numpy as np <EOL> import gc <EOL> from datetime import datetime <EOL> from sklearn . isotonic import isotonic_regression <EOL> from sklearn . utils . bench import total_seconds <EOL> import matplotlib . pyplot as plt <EOL> import argparse <EOL> def generate_perturbed_logarithm_dataset ( size ) : <EOL> return np . random . randint ( - <NUM_LIT:50> , <NUM_LIT:50> , size = n ) + <NUM_LIT> * np . log ( <NUM_LIT:1> + np . arange ( n ) ) <EOL> def generate_logistic_dataset ( size ) : <EOL> X = np . sort ( np . random . normal ( size = size ) ) <EOL> return np . random . random ( size = size ) < <NUM_LIT:1.0> / ( <NUM_LIT:1.0> + np . exp ( - X ) ) <EOL> DATASET_GENERATORS = { <EOL> '<STR_LIT>' : generate_perturbed_logarithm_dataset , <EOL> '<STR_LIT>' : generate_logistic_dataset <EOL> } <EOL> def bench_isotonic_regression ( Y ) : <EOL> """<STR_LIT>""" <EOL> gc . collect ( ) <EOL> tstart = datetime . now ( ) <EOL> isotonic_regression ( Y ) <EOL> delta = datetime . now ( ) - tstart <EOL> return total_seconds ( delta ) <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> parser = argparse . ArgumentParser ( <EOL> description = "<STR_LIT>" ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , required = True , <EOL> help = "<STR_LIT>" <EOL> "<STR_LIT>" ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , required = True , <EOL> help = "<STR_LIT>" ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , required = True , <EOL> help = "<STR_LIT>" ) <EOL> parser . add_argument ( '<STR_LIT>' , action = '<STR_LIT:store_true>' , <EOL> help = "<STR_LIT>" ) <EOL> parser . add_argument ( '<STR_LIT>' , choices = DATASET_GENERATORS . keys ( ) , <EOL> required = True ) <EOL> args = parser . parse_args ( ) <EOL> timings = [ ] <EOL> for exponent in range ( args . log_min_problem_size , <EOL> args . log_max_problem_size ) : <EOL> n = <NUM_LIT:10> ** exponent <EOL> Y = DATASET_GENERATORS [ args . dataset ] ( n ) <EOL> time_per_iteration = [ bench_isotonic_regression ( Y ) for i in range ( args . iterations ) ] <EOL> timing = ( n , np . mean ( time_per_iteration ) ) <EOL> timings . append ( timing ) <EOL> if not args . show_plot : <EOL> print ( n , np . mean ( time_per_iteration ) ) <EOL> if args . show_plot : <EOL> plt . plot ( * zip ( * timings ) ) <EOL> plt . title ( "<STR_LIT>" ) <EOL> plt . xlabel ( '<STR_LIT>' ) <EOL> plt . ylabel ( '<STR_LIT>' ) <EOL> plt . axis ( '<STR_LIT>' ) <EOL> plt . loglog ( ) <EOL> plt . show ( ) </s>
