from __future__ import division

from __future__ import print_function

import gc

import sys

import optparse

from datetime import datetime

import collections

import numpy as np

import scipy.sparse as sp

from sklearn import clone

from sklearn.externals.six.moves import xrange

from sklearn.random_projection import (SparseRandomProjection,

GaussianRandomProjection,

johnson_lindenstrauss_min_dim)

def type_auto_or_float(val):

return float(val)

def type_auto_or_int(val):

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, transfomer):

return time_to_fit, time_to_transform

def make_sparse_random_data(n_samples, n_features, n_nonzeros,

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 statics')

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 xrange(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("")