Macey  Kling

Macey Kling

1598793360

Hierarchical Performance Metrics and Where to Find Them

Hierarchical machine learning models are one top-notch trick. As discussed in previous posts, considering the natural taxonomy of the data when designing our models can be well worth our while. Instead of flattening out and ignoring those inner hierarchies, we’re able to use them, making our models smarter and more accurate.

“More accurate”, I say — are they, though? How can we tell? We are people of science, after all, and we expect bold claims to be be supported by the data. This is why we have performance metrics. Whether it’s precision, f1-score, or any other lovely metric we’ve got our eye on — if using hierarchy in our models improves their performance, the metrics should show it.

Problem is, if we use regular performance metrics — the ones designed for flat, one-level classification — we go back to ignoring that natural taxonomy of the data.

If we do hierarchy, let’s do it all the way. If we’ve decided to celebrate our data’s taxonomy and build our model in its image, this needs to also be a part of measuring its performance.

How do we do this? The answer lies below.

Before We Dive In

This post is about measuring the performance of machine learning models designed for hierarchical classification. It kind of assumes you know what all those words mean. If you don’t, check out my previous posts on the topic. Especially the one introducing the subject. Really. You’re gonna want to know what hierarchical classification is before learning how to measure it. That’s kind of an obvious one.

Throughout this post, I’ll be giving examples based on this taxonomy of common house pets:

Image for post

The taxonomy of common house pets. My neighbor just adopted the cutest baby Pegasus.

Oh So Many Metrics

So we’ve got a whole ensemble of hierarchically-structured local classifiers, ready to do our bidding. How do we evaluate them?

That is not a trivial problem, and the solution is not obvious. As we’ve seen in previous problems in this series, different projects require different treatment. The best metric could differ depending on the specific requirements and limitations of your project.

All in all, there are three main options to choose from. Let’s introduce them, shall we?

The contestants, in all their grace and glory:

#machine-learning #hierarchical #performance-metrics #ensemble-learning #metrics

What is GEEK

Buddha Community

Hierarchical Performance Metrics and Where to Find Them
Macey  Kling

Macey Kling

1598793360

Hierarchical Performance Metrics and Where to Find Them

Hierarchical machine learning models are one top-notch trick. As discussed in previous posts, considering the natural taxonomy of the data when designing our models can be well worth our while. Instead of flattening out and ignoring those inner hierarchies, we’re able to use them, making our models smarter and more accurate.

“More accurate”, I say — are they, though? How can we tell? We are people of science, after all, and we expect bold claims to be be supported by the data. This is why we have performance metrics. Whether it’s precision, f1-score, or any other lovely metric we’ve got our eye on — if using hierarchy in our models improves their performance, the metrics should show it.

Problem is, if we use regular performance metrics — the ones designed for flat, one-level classification — we go back to ignoring that natural taxonomy of the data.

If we do hierarchy, let’s do it all the way. If we’ve decided to celebrate our data’s taxonomy and build our model in its image, this needs to also be a part of measuring its performance.

How do we do this? The answer lies below.

Before We Dive In

This post is about measuring the performance of machine learning models designed for hierarchical classification. It kind of assumes you know what all those words mean. If you don’t, check out my previous posts on the topic. Especially the one introducing the subject. Really. You’re gonna want to know what hierarchical classification is before learning how to measure it. That’s kind of an obvious one.

Throughout this post, I’ll be giving examples based on this taxonomy of common house pets:

Image for post

The taxonomy of common house pets. My neighbor just adopted the cutest baby Pegasus.

Oh So Many Metrics

So we’ve got a whole ensemble of hierarchically-structured local classifiers, ready to do our bidding. How do we evaluate them?

That is not a trivial problem, and the solution is not obvious. As we’ve seen in previous problems in this series, different projects require different treatment. The best metric could differ depending on the specific requirements and limitations of your project.

All in all, there are three main options to choose from. Let’s introduce them, shall we?

The contestants, in all their grace and glory:

#machine-learning #hierarchical #performance-metrics #ensemble-learning #metrics

Kasey  Turcotte

Kasey Turcotte

1623991200

Graph-indexed Pandas DataFrames for analyzing Hierarchical Performance Data

Hatchet

Hatchet is a Python-based library that allows Pandas dataframes to be indexed by structured tree and graph data. It is intended for analyzing performance data that has a hierarchy (for example, serial or parallel profiles that represent calling context trees, call graphs, nested regions’ timers, etc.). Hatchet implements various operations to analyze a single hierarchical data set or compare multiple data sets, and its API facilitates analyzing such data programmatically.

To use hatchet, install it with pip:

$ pip install hatchet

Or, if you want to develop with this repo directly, run the install script from

the root directory, which will build the cython modules and add the cloned

directory to your PYTHONPATH:

$ source install.sh

#data analysis #pandas dataframes #pandas #hierarchical performance data #graph-indexed pandas dataframes for analyzing hierarchical performance data

Carmen  Grimes

Carmen Grimes

1595505720

How to Test Mobile App Performance: 3 Key Components - DZone Performance

You’ve probably interacted with an app on your phone or tablet that’s slow, takes a long time to load, freezes, or even crashes on you altogether.

Frustrating, right?

On the flip side, you can probably think of an app that you love to use because from day one, it’s never given you any trouble.

Or maybe you never paid any mind to an app that works quickly, because isn’t that how it’s supposed to be?

So, what causes one app to be crash-prone and another, fast, and reliable?

Whether an app has good or bad performance depends on three factors: the backend, the network, and the app itself running on the device.

A developer or mobile tester can measure the performance of an application in different scenarios.

For example, they can test for when there’s a concurrency of users on the app at the same time, on different devices (which vary in hardware resources and screen sizes), and multiple networks such as 3G, 4G, Wifi, and more.

The reality is that many variables affect the performance of a mobile application. Moreover, a user may have a very bad experience with your app and the cause might not even have anything to do with the code or its implementation.

But, by running performance tests for each of these three factors, you’ll be able to identify problems and optimize your app for the best user experience possible.

Keep reading as we’ll cover the different types of tests for each factor, what to measure, and what tools are available to help you along the way.

1st Mobile Performance Factor: The Backend

A mobile app’s backend architecture is generally based on an application server, a web server, and a database.

**When it comes to the backend, the things related to performance that are important to know when an app is under load are the server’s response times, database queries times, and the server’s resource usage. **

Using this information, it’s easier to detect issues such as:

  • High server response times
  • Bottlenecks or breakpoints in the database and application server resources
  • Poor implementation of escalation policies

So what kind of tests are normally run to check the app’s backend performance? Load tests.

This is when you simulate load on the backend in different ways, whether it be through stress testing, peak testing, endurance testing, load testing, etc.

In general, the objective of these tests is to understand how the backend systems of an app behave and handle a certain volume of concurrent users.

Several tools allow you to load test your mobile app. The most commonly used ones include:

Apache JMeter – the number one open-source load testing tool

Gatling– a developer-friendly, open-source load testing tool with scripts written in Scala

BlazeMeter – a cloud performance testing platform that scales your JMeter or Gatling tests for a greater amount of concurrent users

2nd Mobile Performance Factor: The Network

With regards to the network that the device is connected to, there are two key things to measure: latency and bandwidth.

  • Latency is the time that elapses when information is sent on the network (measured in milliseconds).
  • Bandwidth is the maximum capacity (the amount of data) that can be transmitted through the network (measured in bits per second).

For mobile performance, the lower the latency and the higher the bandwidth, the better.

An app’s performance can vary depending on, for example, whether it’s connected to a 3G network or a 4G network, and unfortunately, this is beyond an app developer or tester’s control.

But, it is possible to incorporate the network during the mobile app performance testing process, simulating the different types of networks and measuring their impact on the response times, both on the server-side and the client-side.

#tutorial #performance #mobile apps #load testing #mobile testing #mobile app performance #client side performance

Joe  Hoppe

Joe Hoppe

1595905879

Best MySQL DigitalOcean Performance – ScaleGrid vs. DigitalOcean Managed Databases

HTML to Markdown

MySQL is the all-time number one open source database in the world, and a staple in RDBMS space. DigitalOcean is quickly building its reputation as the developers cloud by providing an affordable, flexible and easy to use cloud platform for developers to work with. MySQL on DigitalOcean is a natural fit, but what’s the best way to deploy your cloud database? In this post, we are going to compare the top two providers, DigitalOcean Managed Databases for MySQL vs. ScaleGrid MySQL hosting on DigitalOcean.

At a glance – TLDR
ScaleGrid Blog - At a glance overview - 1st pointCompare Throughput
ScaleGrid averages almost 40% higher throughput over DigitalOcean for MySQL, with up to 46% higher throughput in write-intensive workloads. Read now

ScaleGrid Blog - At a glance overview - 2nd pointCompare Latency
On average, ScaleGrid achieves almost 30% lower latency over DigitalOcean for the same deployment configurations. Read now

ScaleGrid Blog - At a glance overview - 3rd pointCompare Pricing
ScaleGrid provides 30% more storage on average vs. DigitalOcean for MySQL at the same affordable price. Read now

MySQL DigitalOcean Performance Benchmark
In this benchmark, we compare equivalent plan sizes between ScaleGrid MySQL on DigitalOcean and DigitalOcean Managed Databases for MySQL. We are going to use a common, popular plan size using the below configurations for this performance benchmark:

Comparison Overview
ScaleGridDigitalOceanInstance TypeMedium: 4 vCPUsMedium: 4 vCPUsMySQL Version8.0.208.0.20RAM8GB8GBSSD140GB115GBDeployment TypeStandaloneStandaloneRegionSF03SF03SupportIncludedBusiness-level support included with account sizes over $500/monthMonthly Price$120$120

As you can see above, ScaleGrid and DigitalOcean offer the same plan configurations across this plan size, apart from SSD where ScaleGrid provides over 20% more storage for the same price.

To ensure the most accurate results in our performance tests, we run the benchmark four times for each comparison to find the average performance across throughput and latency over read-intensive workloads, balanced workloads, and write-intensive workloads.

Throughput
In this benchmark, we measure MySQL throughput in terms of queries per second (QPS) to measure our query efficiency. To quickly summarize the results, we display read-intensive, write-intensive and balanced workload averages below for 150 threads for ScaleGrid vs. DigitalOcean MySQL:

ScaleGrid MySQL vs DigitalOcean Managed Databases - Throughput Performance Graph

For the common 150 thread comparison, ScaleGrid averages almost 40% higher throughput over DigitalOcean for MySQL, with up to 46% higher throughput in write-intensive workloads.

#cloud #database #developer #digital ocean #mysql #performance #scalegrid #95th percentile latency #balanced workloads #developers cloud #digitalocean droplet #digitalocean managed databases #digitalocean performance #digitalocean pricing #higher throughput #latency benchmark #lower latency #mysql benchmark setup #mysql client threads #mysql configuration #mysql digitalocean #mysql latency #mysql on digitalocean #mysql throughput #performance benchmark #queries per second #read-intensive #scalegrid mysql #scalegrid vs. digitalocean #throughput benchmark #write-intensive

DORA Metrics to Measure DevOps Performance

Learn about DORA metrics, see why they’re important to track, explore the value stream management, and more.

Look, we know the software development process is not an easy one to measure and manage, particularly as it becomes more complex and more decentralized. In many companies, there are multiple teams working on smaller parts of a big project-and these teams are spread all over the world. It’s challenging to tell who is doing what and when, where the blockers are and what kind of waste has delayed the process. Without a reliable set of data points to track across teams, it’s virtually impossible to see how each piece of the application development process puzzle fits together. DORA metrics can help shed light on how your teams are performing in DevOps.

What Are DORA Metrics?

Well, these metrics didn’t just come out of thin air. DORA metrics are a result of six years’ worth of surveys conducted by the DORA (DevOps Research and Assessments) team, that, among other data points, specifically measure deployment frequency (DF), mean lead time for changes (MLT), mean time to recover (MTTR) and change failure rate (CFR). These metrics serve as a guide to how well the engineering teams are performing and how successful a company is at DevOps, ranging from “low performers” to “elite performers.” They help answer the question: Are we better at DevOps now than we were a year ago?

The DORA research results and data have become a standard of measurement for those people who are responsible for tracking DevOps performance in their organization. Engineering and DevOps leaders need to understand these metrics in order to manage DevOps performance and improve over time.

#devops #devops performance #dora metrics