1598524800
This article is in continuation of my previous article that explained how target encoding actually works. The article explained the encoding method on a binary classification task through theory and an example, and how category-encoders library gives incorrect results for multi-class target. This article shows when _TargetEncoder of category_encoders fails, gives a snip of the theory behind encoding multi-class target, _and provides the correct code, along with an example.
Look at this data. Color is a feature, and Target is well… target. Our aim is to encode Color based on Target.
Let’s do the usual target encoding on this.
import category_encoders as ce
ce.TargetEncoder(smoothing=0).fit_transform(df.Color,df.Target)
Hmm, that doesn’t look right, does it? All the colors were replaced with 1. Why? Because TargetEncoder takes mean of all the Target values for each color, instead of probability.
#multi-class #categorical-variable #categorical-data #target-encoding #multiclass-classification #big data
1662107520
Superdom
You have dom
. It has all the DOM virtually within it. Use that power:
// Fetch all the page links
let links = dom.a.href;
// Links open in a new tab
dom.a.target = '_blank';
Only for modern browsers
Simply use the CDN via unpkg.com:
<script src="https://unpkg.com/superdom@1"></script>
Or use npm or bower:
npm|bower install superdom --save
It always returns an array with the matched elements. Get all the elements that match the selector:
// Simple element selector into an array
let allLinks = dom.a;
// Loop straight on the selection
dom.a.forEach(link => { ... });
// Combined selector
let importantLinks = dom['a.important'];
There are also some predetermined elements, such as id
, class
and attr
:
// Select HTML Elements by id:
let main = dom.id.main;
// by class:
let buttons = dom.class.button;
// or by attribute:
let targeted = dom.attr.target;
let targeted = dom.attr['target="_blank"'];
Use it as a function or a tagged template literal to generate DOM fragments:
// Not a typo; tagged template literals
let link = dom`<a href="https://google.com/">Google</a>`;
// It is the same as
let link = dom('<a href="https://google.com/">Google</a>');
Delete a piece of the DOM
// Delete all of the elements with the class .google
delete dom.class.google; // Is this an ad-block rule?
You can easily manipulate attributes right from the dom
node. There are some aliases that share the syntax of the attributes such as html
and text
(aliases for innerHTML
and textContent
). There are others that travel through the dom such as parent
(alias for parentNode) and children
. Finally, class
behaves differently as explained below.
The fetching will always return an array with the element for each of the matched nodes (or undefined if not there):
// Retrieve all the urls from the page
let urls = dom.a.href; // #attr-list
// ['https://google.com', 'https://facebook.com/', ...]
// Get an array of the h2 contents (alias of innerHTML)
let h2s = dom.h2.html; // #attr-alias
// ['Level 2 header', 'Another level 2 header', ...]
// Get whether any of the attributes has the value "_blank"
let hasBlank = dom.class.cta.target._blank; // #attr-value
// true/false
You also use these:
innerHTML
): retrieve a list of the htmlstextContent
): retrieve a list of the htmlsparentNode
): travel up one level// Set target="_blank" to all links
dom.a.target = '_blank'; // #attr-set
dom.class.tableofcontents.html = `
<ul class="tableofcontents">
${dom.h2.map(h2 => `
<li>
<a href="#${h2.id}">
${h2.innerHTML}
</a>
</li>
`).join('')}
</ul>
`;
To delete an attribute use the delete
keyword:
// Remove all urls from the page
delete dom.a.href;
// Remove all ids
delete dom.a.id;
It provides an easy way to manipulate the classes.
To retrieve whether a particular class is present or not:
// Get an array with true/false for a single class
let isTest = dom.a.class.test; // #class-one
For a general method to retrieve all classes you can do:
// Get a list of the classes of each matched element
let arrays = dom.a.class; // #class-arrays
// [['important'], ['button', 'cta'], ...]
// If you want a plain list with all of the classes:
let flatten = dom.a.class._flat; // #class-flat
// ['important', 'button', 'cta', ...]
// And if you just want an string with space-separated classes:
let text = dom.a.class._text; // #class-text
// 'important button cta ...'
// Add the class 'test' (different ways)
dom.a.class.test = true; // #class-make-true
dom.a.class = 'test'; // #class-push
// Remove the class 'test'
dom.a.class.test = false; // #class-make-false
Did we say it returns a simple array?
dom.a.forEach(link => link.innerHTML = 'I am a link');
But what an interesting array it is; indeed we are also proxy'ing it so you can manipulate its sub-elements straight from the selector:
// Replace all of the link's html with 'I am a link'
dom.a.html = 'I am a link';
Of course we might want to manipulate them dynamically depending on the current value. Just pass it a function:
// Append ' ^_^' to all of the links in the page
dom.a.html = html => html + ' ^_^';
// Same as this:
dom.a.forEach(link => link.innerHTML = link.innerHTML + ' ^_^');
Note: this won't work
dom.a.html += ' ^_^';
for more than 1 match (for reasons)
Or get into genetics to manipulate the attributes:
dom.a.attr.target = '_blank';
// Only to external sites:
let isOwnPage = el => /^https?\:\/\/mypage\.com/.test(el.getAttribute('href'));
dom.a.attr.target = (prev, i, element) => isOwnPage(element) ? '' : '_blank';
You can also handle and trigger events:
// Handle click events for all <a>
dom.a.on.click = e => ...;
// Trigger click event for all <a>
dom.a.trigger.click;
We are using Jest as a Grunt task for testing. Install Jest and run in the terminal:
grunt watch
Author: franciscop
Source Code: https://github.com/franciscop/superdom
License: MIT license
1598524800
This article is in continuation of my previous article that explained how target encoding actually works. The article explained the encoding method on a binary classification task through theory and an example, and how category-encoders library gives incorrect results for multi-class target. This article shows when _TargetEncoder of category_encoders fails, gives a snip of the theory behind encoding multi-class target, _and provides the correct code, along with an example.
Look at this data. Color is a feature, and Target is well… target. Our aim is to encode Color based on Target.
Let’s do the usual target encoding on this.
import category_encoders as ce
ce.TargetEncoder(smoothing=0).fit_transform(df.Color,df.Target)
Hmm, that doesn’t look right, does it? All the colors were replaced with 1. Why? Because TargetEncoder takes mean of all the Target values for each color, instead of probability.
#multi-class #categorical-variable #categorical-data #target-encoding #multiclass-classification #big data
1597940040
Recently I did a project wherein the target was multi-class. It was a simple prediction task and the dataset involved both categorical as well as numerical features.
For those of you who are wondering what multi-class classification is: If you want to answer in ‘0 vs 1’, ‘clicked vs not-clicked’ or ‘cat vs dog’, your classification problem is binary; if you want to answer in ‘red vs green vs blue vs yellow’ or ‘sedan vs hatch vs SUV’, then the problem is multi-class.
Therefore, I was researching suitable ways to encode the categorical features. No points for guessing, I was taken to medium articles enumerating benefits of mean target encoding and how it outperforms other methods and how you can use category_encoders library to do the task in just 2 lines of code. However, to my surprise, I found that no article demonstrated this on multi-class target. I went to the documentation of category_encoders and found that it does not say anything about supporting multi-class targets. I dug deeper, scouring through the source code and realized that the library only works for binary or continuous targets.
So I thought: “Inside of every problem lies an opportunity.” — Robert Kiposaki
Going deep, I went straight for the original paper by _Daniele Micci-Barreca _that introduced mean target encoding. Not only for regression problem, the paper gives the solution for both binary classification as well as multi-class classification. This is the same paper that category_encoders cites for target encoding as well.
While there are several articles explaining target encoding for regression and binary classification problems, my aim is to implement target encoding for multi-class variables. However, before that, we need to understand how it’s done for binary targets. In this article, I cover an overview of the paper that introduced target encoding, and show by example how target encoding works for binary problems.
#multiclass-classification #target-encoding #categorical-data #binary-classification #multi-class #data analytic
1621833780
As you all know laravel 8 already released and you can see there are many changes and update in laravel 8 version. many laravel users are facing issue in their new Laravel 8 version when they try to load their routes in web.php and they run into an Exception that says something like “Target class postController does not exist”.
#target class does not exist in laravel 8 #error #target class controller does not exist #target class not found #laravel #target class does not exist error solved
1594162500
A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.
Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.
By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.
However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.
Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.
Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.
Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.
Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.
The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.
For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.
#cloud computing services #all #hybrid cloud #cloud #multi-cloud strategy #cloud spend #multi-cloud spending #multi cloud adoption #why multi cloud #multi cloud trends #multi cloud companies #multi cloud research #multi cloud market