rankfx 0.1.0

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ranking

This repo provides framework for using models created with PyTorch with Scikit-learn API.
Main focus is on ranking models, implementations were partially taken from FuxiCTR
Right now following models are implemented:

• DCNv2
• FinalNet

Benchmarks

Datasets:

• Movielens_x1
• Frappe_x1
• KKBox_x1
• Avazu_x1

All quality measurements are done without any tuning and with not much training epochs.

No	Model	AUC Movielens_x1	AUC Frappe_x1	AUC KKBox_x1	AUC Avazu_x1
0	LGBMClassisifer	0.93878	0.98406	0.77265	0.75589
1	DCNv2	0.93801	0.96225	0.78645	0.75401
2	FinalNet	0.94116	0.97601	0.80282	0.75844

Interface

To wrap your own model into provided interface, you should do following steps:

1. Inherit your PyTorch model from models/common/base/model/NNPandasModel
2. Implement following methods:
   a. forward - model forward pass
   b. train_step - train stage step, should take batch of data and return train metrics as dict, MUST have loss key in output
   c. val_step/test_step - validation and test stage steps, should take batch of data and return metrics as dict
   d. inference_step - inference stage step, should take batch of data and return model output
   e. _init_modules - instantiate torch modules based on models/common/features/config/FeaturesConfig, which will be infered from train data

A bit more about FeaturesConfig:

• Will be infered from train data during training
• Contains list of features, each of them has following attributes: description parameters (name, feature_type, feature_size - can be > 1 for sequential features) and embedding_parameters (needs_embed, embedding_size, embedding_vocab_size, embedding_padding_idx)