This repository is used to share some L2R algorithms implemted by Python.

So far, this repository contains RankNet , LambdaRank and LambdaMART

I utilize Pytorch to implement the network structure.

In order to use the interface, you should input following parameters:

• n_feaure: int, features numble
• h1_units: int, the unit numbers of hidden layer1
• h2_units: int, the unit numbers of hidden layer2
• epoch: int, iteration times
• learning_rate: float, learning rate
• plot: boolean, whether plot the loss.

The usage is similar with RankNet.

This is a Python version of LambdaMART.

I implement it based on the code of lezzago

‼️I have made some modification because I think there is a mistake on calculating $\lambda$ in lezzago's code.

The dataset is the same as that of lezzago. I have preprocessed it and store in train.npy and test.npy.

You can directly used np.load() to import dataset.

The first column is label, the second column is qid, and the following columns are features (total 46 features).