Comments (8)
Hello, I have the same question. Have you got it?
from learning2rank.
@whuissyxa I have the same question too , have you got it?
from learning2rank.
It's on README. https://github.com/shiba24/learning2rank#usage
X is numpy array with the shape of (num_samples, num_features) and y is numpy array with the shape of (num_samples, ). y is the score which you would like to rank based on (e.g., Sales of the products, page view, etc).
if you have any further questions, feel free to post it here.
@jiasudemotuohe
from learning2rank.
no qid/ group id is required?
from learning2rank.
@shiba24 , I'm also interested in the answer to the question @Royisaboy is asking. How can we do "listwise" training without being able to specify lists? Are we supposed to call "fit" multiple times in a row for all the lists we have? Or is it meant to be trained on a single very long list? Thank you
from learning2rank.
我也有疑问,希望能给出示例,谢谢。
from learning2rank.
I have the same question. And I have read README.
Model.fit(X, y)
Here, X is numpy array with the shape of (num_samples, num_features) and y is numpy array with the shape of (num_samples, ). y is the score which you would like to rank based on (e.g., Sales of the products, page view, etc).
In my recommendation system, I got a recommended list. And I want to re-rank the list by adding more info. But I don't understand the data format of X, y in your code.
Can I directly set X with N_features, and y with only probabilities (such as 0/1), if the data is not divided into groups. Thank you.
from learning2rank.
@shiba24, I have a question : Each problem has several documents. Does X contain all the document features of a problem, or can it contain all the document features of all problems? That is to say, when you train, do you just train one problem at a time or do you train all problems together. What would the X form look like if we could train it together. Suppose I now have two questions, qid1 contains four documents, each with a feature, and qid2 contains three documents, can I type X and y like this? X = np. Array ([[[1], [2], [3], [4]], [[2], [3], [5]]]), y = np. Array ([,2,1,1 [2], [1 0]])
from learning2rank.
Related Issues (20)
- How to input query and document ?
- Getting following error while running this code HOT 1
- Does this work with (almost) binary y's? HOT 1
- Invalid value encountered
- What kind of data format for x and y? HOT 2
- simple regression example HOT 1
- Installation & Setup
- IndexError: list index out of range HOT 3
- model initiating error
- ListNet Loss Function HOT 3
- ListNet predict nan HOT 1
- Runtime Warning and unchanged loss
- setup and installation
- Any possibility to transfer&continue this project on python3.x?
- Merge from betterenvi repo HOT 2
- how to specify different query? HOT 2
- It looks like just a logistic package HOT 3
- How to run/use this code? HOT 2
- input/output HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from learning2rank.