ywjawmw/clepr

This is our Pytorch implementation for CLEPR model

CLEPR: Contrastive Learning Enhanced Prescription Recommendation

This is our Pytorch implementation for the paper:

Introduction

Contrastive Learning Enhanced Prescription Recommendation (CLEPR) is a two-stage method to model the TCM treatment process as a four-partite graph, which can effectively capture the relationship among symptoms, syndromes, therapeutic methods, and herbs.

Requirement

The code has been tested running under Python 3.7.0. The required packages are as follows:

• torch == 1.10.0
• numpy == 1.17.4
• scipy == 1.21.6
• temsorboardX == 2.0

Usage

The hyperparameter search range and optimal settings have been clearly stated in the codes (see the 'utils' dict in parser.py).

• Train

bash CLEPR_train.sh 

• Pretrain Train File

\weights-CLEPR\Herb\CLEPR_norm_CLEPR_l2\64-128\32\date_2022-09-18_60_ori_emb_seed1234\model.pkl

Retraining:

bash CLEPR_train.sh

the parameters should be notice:

• --attention 0

• --use_S1 0

• --alg_type CLEPR(S1)

• --batch_size 1024

• Test

bash CLEPR_test.sh 

Some important hyperparameters:

• lrs

  • It indicates the learning rates.
  • The learning rate is default as 2e-5.

• mess_dropouts

  • It indicates the message dropout ratio, which randomly drops out the outgoing messages.
  • The message dropout is default as '[0.0,0.0]'.

• steps

  • It indicates the length of the hard-negative sample.
  • We search it in {2, 4, 5, 6, 8, 10, 16, 24}.

• max_step_lens

  • It indicates the search length of the hard-negative sample.
  • We search it in {16, 24, 25, 26, 27, 28, 29}.

• ts

  • temperature parameter.
  • We search it in {0.05, 0.1, 0.2, 0.3, 0.5, 0.7, 1.0}.

• hard_neg

  • It indicates that whether utilizing the hard-negative sample.

• use_S1

  • It indicates that whether using the first stage to pretrain.

Dataset

We provide one public TCM dataset: TCM. \data\Herb

• train_id.txt

  • Train file.
  • Each line is 'symptom ID1 symptom ID2 ... \t herb ID1 herb ID2 ...\n'.
  • Every observed interaction means symptom s once interacted herb h, symptom set sc once interacted prescription p

• valid_id.txt

  • Validation file.
  • Each line is 'symptom ID1 symptom ID2 ...\t herb ID1 herb ID2 ...\n'.

• test_id.txt

  • Test file.
  • Each line is 'symptom ID1 symptom ID2 ...\t herb ID1 herb ID2 ...\n'.

• x.npz

• entity interaction matrix