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# Copyright (C) 2021 - 2022 Advanced Micro Devices, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
"""Performance analysis routines."""
import random
import numpy as np
import statistics
from perflib.utils import Run
from dataclasses import dataclass
from typing import List
def confidence_interval(vals, measure, confidence, alpha=0.95, nboot=2000):
"""Compute the alpha-confidence interval for the given values using boot-strap resampling."""
if confidence == "bootstrap":
medians = []
for iboot in range(nboot):
resample = []
for i in range(len(vals)):
resample.append(vals[random.randrange(len(vals))])
if measure == "median":
medians.append(np.median(resample))
elif measure == "mean":
medians.append(np.mean(resample))
medians = sorted(medians)
low = medians[int(np.floor(nboot * 0.5 * (1.0 - alpha)))]
high = medians[int(np.ceil(nboot * (1.0 - 0.5 * (1.0 - alpha))))]
elif confidence == "stdev":
mean = np.mean(vals)
stdev = np.mean(vals)
# NB: assumes alpha \approx 95
low = mean - 2 * stdev
high = mean + 2 * stdev
else:
print("invalid value for confidence:", confidence)
import sys
sys.exit(1)
return low, high
def ratio_confidence_interval(Avals, Bvals, alpha=0.95, nboot=2000):
"""Compute the alpha-confidence interval for the ratio of the given sets of values using boot-strap resampling."""
ratios = []
for i in range(nboot):
ratios.append(Avals[random.randrange(len(Avals))] /
Bvals[random.randrange(len(Bvals))])
ratios = sorted(ratios)
low = ratios[int(np.floor(len(ratios) * 0.5 * (1.0 - alpha)))]
high = ratios[int(np.ceil(len(ratios) * (1.0 - 0.5 * (1.0 - alpha))))]
return low, high
@dataclass
class MoodsResult:
pval: float
medians: List[float]
def moods(reference: Run, others: List[Run]):
"""Perform Moods analysis..."""
import scipy.stats
import numpy
pvals = {}
for rname, rdat in reference.dats.items():
for other in others:
odat = other.dats[rname]
for length in rdat.samples.keys():
s1 = rdat.samples[length].times
s2 = odat.samples[length].times
if arguments.method == 'median':
m1 = statistics.median(s1)
m2 = statistics.median(s2)
elif arguments.mesaure == "mean":
m1 = numpy.mean(s1)
m2 = numpy.mean(s2)
if arguments.method == 'moods':
_, pval, _, _ = scipy.stats.median_test(s1, s2)
elif arguments.method == 'ttest':
_, pval = scipy.stats.ttest_ind(s1, s2)
elif arguments.method == 'mwu':
_, pval = scipy.stats.mannwhitneyu(s1, s2)
else:
print("unsupported statistical method")
sys.exit(1)
pvals[other.path.name, rname,
length] = MoodsResult(pval, [m1, m2])
return pvals
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