Unique benchmark testing12/5/2023 Random splitting typically results in test sequences that are closely related or even identical to training sequence, which leads to artifactual overestimation of performance. In computational biology, families of biological sequences are not independent because they are related by evolution. In this case a standard approach is to randomly split available data into a training and a test set, fit a model to the training set, and evaluate the model on the test set. In many areas of machine learning and statistical inference, data samples can be thought of as approximately independent samples from some unknown distribution describing the data. These algorithms can successfully produce dissimilar training and test sets for more protein families than a previous approach, allowing us to include more families in benchmark datasets for biological sequence analysis tasks.Ĭomputational methods are typically benchmarked on test data that is separate from the data that were used to train the method. To this end, we used ideas from computer science involving graph algorithms to design two new methods for splitting sequence data into dissimilar training and test sets. This motivates the design of strategies for dividing sequence families into dissimilar training and test sets. In this case, applying a standard approach of randomly splitting the data into training and test sets could yield test sequences that are nearly identical to some sequence in the training set, and the resultant benchmark may overstate the model’s performance. Biological sequences (such as protein or RNA) within a particular family are related by evolution and therefore may be very similar to each other. This ensures that the reported performance accurately reflects how well the method would do on previously unseen data. We’ll determine the performance of the following function, which computes all of the prime numbers between one and an integer: // main.goįor j := 2 j <= int(math.Typically, machine learning and statistical inference models are trained on a “training” dataset and evaluated on an separate “test” set. Let’s demonstrate the fundamentals of benchmarking in Go by writing a simple benchmark. Lastly, it’s crucial to isolate the code being benchmarked from the rest of the program, for example, by mocking network requests. However, if you don’t have access to a reserved machine, you should close as many programs as possible before running the benchmark, minimizing the effect of other processes on the benchmark’s results.Īdditionally, to ensure more stable results, you should run the benchmark several times before recording measurements, ensuring that the system is sufficiently warmed up. When possible, you should use either a physical machine or a remote server where nothing else is running to perform your benchmarks. Therefore, we need to minimize the environmental impact as much as possible. The effects of power management, background processes, and thermal management can impact the test results, making them inaccurate and unstable. Let’s get started! Setting the right conditions for benchmarkingįor benchmarking to be useful, the results must be consistent and similar for each execution, otherwise, it will be difficult to gauge the true performance of the code being tested.īenchmarking results can be greatly affected by the state of the machine on which the benchmark is running. To follow along with this tutorial, you’ll need a basic knowledge of the Go syntax and a working installation of Go on your computer. In this tutorial, we’ll introduce some best practices for running consistent and accurate benchmarks in Go, covering the fundamentals of writing benchmark functions and interpreting the results. Golang includes built-in tools for writing benchmarks in the testing package and the go tool, so you can write useful benchmarks without installing any dependencies. Benchmarking in Golang: Improving function performanceĪ benchmark is a type of function that executes a code segment multiple times and compares each output against a standard, assessing the code’s overall performance level. I'm currently working on my own products and teaching programming via my website freshman.tech. Ayooluwa Isaiah Follow I'm a software developer from Nigeria with a keen interest in web technologies, security, and performance.
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