criterion performance measurements
overview
want to understand this report?
bench/./Curry/Bayes ""
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.540444312825019e-2 | 2.5832117593266927e-2 | 2.6217483420354768e-2 |
| Standard deviation | 7.15504897061197e-4 | 8.638384841341116e-4 | 1.0274087296428841e-3 |
Outlying measurements have slight (9.711472419073436e-2%) effect on estimated standard deviation.
bench/python ProbLog/bayes.py ""
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.14768046342789948 | 0.15556198933294843 | 0.1615998181070955 |
| Standard deviation | 6.901762597912788e-3 | 1.0140332039351364e-2 | 1.4034946728836196e-2 |
Outlying measurements have moderate (0.13213630986596137%) effect on estimated standard deviation.
bench/./WebPPL/node_modules/.bin/webppl WebPPL/bayes.wppl ""
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.5199857404125698 | 1.5332859845851392 | 1.5440622260794044 |
| Standard deviation | 1.0376489867809952e-2 | 1.3776483896974596e-2 | 1.634649107801889e-2 |
Outlying measurements have moderate (0.18749999999999997%) effect on estimated standard deviation.
understanding this report
In this report, each function benchmarked by criterion is assigned a section of its own. The charts in each section are active; if you hover your mouse over data points and annotations, you will see more details.
- The chart on the left is a kernel density estimate (also known as a KDE) of time measurements. This graphs the probability of any given time measurement occurring. A spike indicates that a measurement of a particular time occurred; its height indicates how often that measurement was repeated.
- The chart on the right is the raw data from which the kernel density estimate is built. The x axis indicates the number of loop iterations, while the y axis shows measured execution time for the given number of loop iterations. The line behind the values is the linear regression prediction of execution time for a given number of iterations. Ideally, all measurements will be on (or very near) this line.
Under the charts is a small table. The first two rows are the results of a linear regression run on the measurements displayed in the right-hand chart.
- OLS regression indicates the time estimated for a single loop iteration using an ordinary least-squares regression model. This number is more accurate than the mean estimate below it, as it more effectively eliminates measurement overhead and other constant factors.
- R² goodness-of-fit is a measure of how accurately the linear regression model fits the observed measurements. If the measurements are not too noisy, R² should lie between 0.99 and 1, indicating an excellent fit. If the number is below 0.99, something is confounding the accuracy of the linear model.
- Mean execution time and standard deviation are statistics calculated from execution time divided by number of iterations.
We use a statistical technique called the bootstrap to provide confidence intervals on our estimates. The bootstrap-derived upper and lower bounds on estimates let you see how accurate we believe those estimates to be. (Hover the mouse over the table headers to see the confidence levels.)
A noisy benchmarking environment can cause some or many measurements to fall far from the mean. These outlying measurements can have a significant inflationary effect on the estimate of the standard deviation. We calculate and display an estimate of the extent to which the standard deviation has been inflated by outliers.