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c4ee601bf4
This PR adds the checkmetrics scripts that will be used for the kata metrics CI. Fixes #7160 Signed-off-by: Gabriela Cervantes <gabriela.cervantes.tellez@intel.com>
109 lines
3.9 KiB
Go
109 lines
3.9 KiB
Go
// Copyright (c) 2023 Intel Corporation
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//
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// SPDX-License-Identifier: Apache-2.0
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package main
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import (
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"github.com/montanaflynn/stats"
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log "github.com/sirupsen/logrus"
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)
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type statistics struct {
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Results []float64 // Result array converted to floats
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Iterations int // How many results did we gather
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Mean float64 // The 'average'
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Min float64 // Smallest value we saw
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Max float64 // Largest value we saw
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Range float64 // Max - Min
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RangeSpread float64 // (Range/Min) * 100
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SD float64 // Standard Deviation
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CoV float64 // Co-efficient of Variation
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}
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// metrics represents the repository under test
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// The members are Public so the toml reflection can see them, but I quite
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// like the lower case toml naming, hence we use the annotation strings to
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// get the parser to look for lower case.
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type metrics struct {
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// Generic to JSON files
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// Generally mandatory
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Name string `toml:"name"` //Used to locate the results file
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Description string `toml:"description"`
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// Optional config entries
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Type string `toml:"type"` //Default is JSON
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// Processing related entries
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CheckType string `toml:"checktype"` //Result val to calculate: mean, median, min, max
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// default: mean
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CheckVar string `toml:"checkvar"` //JSON: which var to (extract and) calculate on
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// is a 'jq' query
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stats statistics // collection of our stats data, calculated from Results
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// For setting 'bounds', you can either set a min/max value pair,
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// or you can set a mid-range value and a 'percentage gap'.
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// You should set one or the other. Setting both will likely result
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// in one of them being chosen first.
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// The range we expect the processed result to fall within
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// (MinVal <= Result <= MaxVal) == pass
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MinVal float64 `toml:"minval"`
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MaxVal float64 `toml:"maxval"`
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// If we are doing a percentage range check then you need to set
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// both a mid-value and a percentage range to check.
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MidVal float64 `toml:"midval"`
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MinPercent float64 `toml:"minpercent"`
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MaxPercent float64 `toml:"maxpercent"`
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// Vars that are not in the toml file, but are filled out later
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// dynamically
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Gap float64 // What is the % gap between the Min and Max vals
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}
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// Calculate the statistics from the stored Results data
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// Although the calculations can fail, we don't fail the function
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func (m *metrics) calculate() {
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// First we check/calculate some non-stats values to fill out
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// our base data.
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// We should either have a Min/Max value pair or a percentage/MidVal
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// set. If we find a non-0 percentage set, then calculate the Min/Max
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// values from them, as the rest of the code base works off the Min/Max
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// values.
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if (m.MinPercent + m.MaxPercent) != 0 {
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m.MinVal = m.MidVal * (1 - (m.MinPercent / 100))
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m.MaxVal = m.MidVal * (1 + (m.MaxPercent / 100))
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// The rest of the system works off the Min/Max value
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// pair - so, if your min/max percentage values are not equal
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// then **the values you see in the results table will not look
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// like the ones you put in the toml file**, because they are
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// based off the mid-value calculation below.
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// This is unfortunate, but it keeps the code simpler overall.
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}
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// the gap is the % swing around the midpoint.
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midpoint := (m.MinVal + m.MaxVal) / 2
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m.Gap = (((m.MaxVal / midpoint) - 1) * 2) * 100
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// And now we work out the actual stats
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m.stats.Iterations = len(m.stats.Results)
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m.stats.Mean, _ = stats.Mean(m.stats.Results)
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m.stats.Min, _ = stats.Min(m.stats.Results)
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m.stats.Max, _ = stats.Max(m.stats.Results)
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m.stats.Range = m.stats.Max - m.stats.Min
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m.stats.RangeSpread = (m.stats.Range / m.stats.Min) * 100.0
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m.stats.SD, _ = stats.StandardDeviation(m.stats.Results)
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m.stats.CoV = (m.stats.SD / m.stats.Mean) * 100.0
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log.Debugf(" Iters is %d", m.stats.Iterations)
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log.Debugf(" Min is %f", m.stats.Min)
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log.Debugf(" Max is %f", m.stats.Max)
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log.Debugf(" Mean is %f", m.stats.Mean)
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log.Debugf(" SD is %f", m.stats.SD)
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log.Debugf(" CoV is %.2f", m.stats.CoV)
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}
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