Suppose we’ve a following array:
arr = [[ 5.60241616e+02, 1.01946349e+03, 8.61527813e+01],
[ 4.10969632e+02 , 9.77019409e+02 , -5.34489688e+01],
[ 6.10031512e+02, 9.10689615e+01, 1.45066095e+02 ]]How to print it with rounded elements using map() and lamba() functions?
l = list(map(lambda i: list(map(lambda j: round(j, 2), i)), arr))
print(l)The result will be the following:
[[560.24, 1019.46, 86.15],
[410.97, 977.02, -53.45],
[610.03, 91.07, 145.07]]
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in the post will reviewed a number of metrics for evaluating classification and regression models. For that we use the functions we use of the sklearn library. We’ll learn how to generate model data and how to train linear models and evaluate their quality.
In this post we’ll show how to build regression linear models using the sklearn.linear.model module.
See also the post on classification linear models using the sklearn.linear.model module.
The docs on requestQueue.getInfo().
After some unsuccessful tries I could have managed to get the requestQueue info output. Note, we run the function inside the Apify runtime environment:
Apify.main(async () => { ... }
We make the function async and add await to the getInfo() Promise call:
async function printRequestQueue (requestQueue){
var { totalRequestCount, handledRequestCount, pendingRequestCount } = await requestQueue.getInfo();
console.log(`Request Queue info:` );
console.log(' - handled :', handledRequestCount);
console.log(' - pending :', pendingRequestCount);
console.log(' - total:' , totalRequestCount);
}with the following result:
Request Queue info:
- handled : 479
- pending : 312
- total: 791
In this case we do not need to make our function async since we catch the the getInfo() promise result thru .then(response).
function printRequestQueue (requestQueue){
requestQueue.getInfo().then((response)=> {
console.log('total:', response.totalRequestCount);
console.log('handled:', response.handledRequestCount);
console.log('pending:', response.pendingRequestCount);
console.log('\nFull response:\n', response); })
.catch( (error) => console.log(error));
}with the following result:
total: 791
handled: 479
pending: 312
Full response:
{ id: 'queue-name',
name: 'queue-name',
userId: null,
createdAt: 2021-02-26T11:57:00.453Z,
modifiedAt: 2021-02-26T11:58:47.988Z,
accessedAt: 2021-02-26T11:58:47.989Z,
totalRequestCount: 791,
handledRequestCount: 479,
pendingRequestCount: 312
}
let table = $('table');
if ($(table).has('br')) {
$("br").replaceWith(" ");
}Often we need to select certain html DOM elements excluding ones with certain names/ attributes/ attribute values. Let’s show how to do that.
In this post we’ll show how to build classification linear models using the sklearn.linear.model module.
The Regularization is applying a penalty to increasing the magnitude of parameter values in order to reduce overfitting. When you train a model such as a logistic regression model, you are choosing parameters that give you the best fit to the data. This means minimizing the error between what the model predicts for your dependent variable given your data compared to what your dependent variable actually is.
See the practical example how to deal with overfitting by the regularization.
In the post we’ll get to know the Cross-validation strategies as from the Sklearn module. We’ll show the methods of how to perform k-fold cross-validation. All the iPython notebook code is correct for Python 3.6.