Mutual Fund Recommender System. Tailor for fund transactions.

Related tags

Recommender Systemsfund_recsys
Overview

Explainable Mutual Fund Recommendation

Data

Please see 'DATA_DESCRIPTION.md' for mode detail.

Recommender System Methods

Baseline

  • Collabarative Fiiltering
  • PersonFreq
  • PersonVolume

Stable

  • LightFM Meta
  • LightFM PureCF
  • LightFM Hybrid

Advanced

  • DGL
  • GCN

Part I: Fund Recommedation

Training

Supported models
  1. Heuristic
  2. LightFM (CF/Hybrid/Meta)
  3. SMORe
# Process 3 models in parallel
bash run_all.sh
Arugments

You can also tune the detail parameter settings of each method in training pipeline.

--use_heuristic "> 
# Commonly used arguments 
--model 
    
     
--model_type 
     
      
--model_hidden_dimension 
      
       
--evaluation_metrics 
       
        
--use_heuristic

For example, LightFM with pure-CF method

EPOCHS=10
EMBED_SIZE=64
DATE=20181231

python3 train.py \
   --path_transaction data/${DATE}/transaction_train.csv \
   --path_transaction_eval data/${DATE}/transaction_eval.csv \
   --path_user data/${DATE}/customer.csv \
   --path_item data/${DATE}/product.csv \
   --model 'LightFM' \
   --model_path 'models/lightfm' \
   --model_type 'cf' \
   --model_hidden_dimension ${EMBED_SIZE} \
   --model_max_neg_sample 100 \
   --model_loss 'warp' \
   --training_do_evaluation \
   --training_verbose \
   --training_num_epochs ${EPOCHS} \
   --training_eval_per_epochs 1 \
   --evaluation_diff \
   --evaluation_regular \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --use_heuristic 'frequency' \
   --use_heuristic 'volume' \
   --evaluation_results_csv results/lightfm_cf_evaluation_${DATE}.csv \
   --evaluation_rec_detail_report results/lightfm_cf_rec_detail_${DATE}.tsv \
       > logs/lightfm_cf_exp_${DATE}.log

For another example, SMORe

python3 train.py \
   --path_transaction data/${DATE}/transaction_train.csv \
   --path_transaction_eval data/${DATE}/transaction_eval.csv \
   --path_user data/${DATE}/customer.csv \
   --path_item data/${DATE}/product.csv \
   --model 'SMORe' \
   --model_path 'models/smore' \
   --model_hidden_dimension ${EMBED_SIZE} \
   --model_max_neg_sample 100 \
   --model_loss 'warp' \
   --training_do_ \
   --training_verbose \
   --training_num_epochs ${EPOCHS} \
   --training_eval_per_epochs 1 \
   --evaluation_diff \
   --evaluation_regular \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --evaluation_metrics '[email protected]' \
   --evaluation_results_csv results/smore_evaluation_${DATE}.csv \
   --evaluation_rec_detail_report results/smore_rec_detail_${DATE}.tsv \
       > logs/smore_exp_${DATE}.log

Evaluataion

To use the evaluation pipeline, you need a prediction rec file with the format like the example below:

# prediction rec file 
   
    \t
    
     \t
     
      \t
      
       \t
       
        \t
        
          CFDAXWccjJPoVInuiF0mMg== AG25 EXPLOIT SOLO 0 2 CFDAXWccjJPoVInuiF0mMg== XXXX EXPLOIT SOLO 0 1 CFDAXWccjJPoVInuiF0mMg== JJ15 EXPLOIT REGULAR 0 2 CFDAXWccjJPoVInuiF0mMg== XXXX EXPLOIT REGULAR 0 1 CFDAwH4y/ssuYSedFy8UMw== CC89 EXPLOIT REGULAR 0 2 CFDAwH4y/ssuYSedFy8UMw== XXXX EXPLOIT REGULAR 0 1 CFDA9UDJnLAm4/0txbPMVQ== AP06 EXPLORE NA 0 2 CFDA9UDJnLAm4/0txbPMVQ== XXXX EXPLORE NA 0 1

Later you could directly use the evaluate pipeline

bash rec_convert_eval.sh

In the evaluation pipeline, you need to convert the ground truth interaction into '.rec' format. For xample.

# truth rec file 
   
    \t
    
     \t
     
      \t
      
       \t
       
         CFDAXWccjJPoVInuiF0mMg== AG25 EXPLOIT SOLO 1.0 CFDAXWccjJPoVInuiF0mMg== JJ15 EXPLOIT REGULAR 1.0 CFDAwH4y/ssuYSedFy8UMw== CC89 EXPLOIT REGULAR 1.0 CFDA9UDJnLAm4/0txbPMVQ== AP06 EXPLORE NA 1.0

Convert from the evaluation transaction (includes the preprocess pipeline) by the following code, which will save the corresponding rec file in the defined argument '--path_trainsaction_truth'

DATE=20181231
python3 convert_to_rec.py \
    --path_transaction data/${DATE}/transaction_train.csv \
    --path_transaction_eval data/${DATE}/transaction_eval.csv \
    --path_user data/${DATE}/customer.csv \
    --path_item data/${DATE}/product.csv \
    --path_transaction_truth rec/${DATE}.eval.truth.rec

And evaluate by the code "rec_eval.py"

DATE=20181231
python3 rec_eval.py \
   -truth rec/${DATE}.eval.truth.rec \ 
   -pred rec/pred.rec \     
   -metric '[email protected]' \          
   -metric '[email protected]' \          
   -metric '[email protected]' \
   -metric '[email protected]'

The results would be like

TRUTH REC FILE EXISTED:  'rec/20181231.eval.truth.rec'

EvalDict({                
          SUBSET     USERS     EXAMPLES 
        * EXPLORE    2305      2826     
        * EXPLOIT    33355     62403    
        * REGULAR    31763     59054    
        * SOLO       2747      3349                     
})
==============================
 [email protected]     on EXPLORE    0.0001
 [email protected]     on EXPLORE    0.0004
 [email protected]   on EXPLORE    0.0004
 [email protected]   on EXPLORE    0.0004
 [email protected]     on EXPLOIT    0.0000
 [email protected]     on EXPLOIT    0.0001
 [email protected]   on EXPLOIT    0.0001
 [email protected]   on EXPLOIT    0.0001
 [email protected]     on REGULAR    0.0000
 [email protected]     on REGULAR    0.0001
 [email protected]   on REGULAR    0.0001
 [email protected]   on REGULAR    0.0001
 [email protected]     on SOLO       0.0001
 [email protected]     on SOLO       0.0004
 [email protected]   on SOLO       0.0004
 [email protected]   on SOLO       0.0004
==============================

Results

Methods [email protected] [email protected] [email protected] [email protected]
Collabarative Fiiltering - - -
PersonFreq - - -
PersonVolume - - -
LightFM Meta - - -
LightFM PureCF - - -
LightFM Hybrid 0.000 0.000 0.000 0.000
DGL - - -
GCN - - -

Fund Explanation

Owner
JHJu
Research assistant @ cnc Lab, ASCITI
JHJu
GitHub Repository
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