Mutual Fund Recommender System. Tailor for fund transactions.

JHJu

Last update: May 19, 2022

Related tags

Recommender Systems fund_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

Part I: Fund Recommedation

Training

Supported models

Heuristic
LightFM (CF/Hybrid/Meta)
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 ${EPOCHS} \
   --training_eval_per_epochs 1 \
   --evaluation_diff \
   --evaluation_regular \
   --evaluation_metrics 'P@5' \
   --evaluation_metrics 'R@5' \
   --evaluation_metrics 'mAP@5' \
   --evaluation_metrics 'mRR@5' \
   --use_heuristic 'frequency' \
   --use_heuristic 'volume' \
   --evaluation_results_csv results/lightfm_cf_evaluation_${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 ${EPOCHS} \
   --training_eval_per_epochs 1 \
   --evaluation_diff \
   --evaluation_regular \
   --evaluation_metrics 'P@5' \
   --evaluation_metrics 'R@5' \
   --evaluation_metrics 'mAP@5' \
   --evaluation_metrics 'mRR@5' \
   --evaluation_results_csv results/smore_evaluation_${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 'P@5' \          
   -metric 'R@5' \          
   -metric 'mAP@5' \
   -metric 'mRR@5'

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                     
})
==============================
 P@5     on EXPLORE    0.0001
 R@5     on EXPLORE    0.0004
 mAP@5   on EXPLORE    0.0004
 mRR@5   on EXPLORE    0.0004
 P@5     on EXPLOIT    0.0000
 R@5     on EXPLOIT    0.0001
 mAP@5   on EXPLOIT    0.0001
 mRR@5   on EXPLOIT    0.0001
 P@5     on REGULAR    0.0000
 R@5     on REGULAR    0.0001
 mAP@5   on REGULAR    0.0001
 mRR@5   on REGULAR    0.0001
 P@5     on SOLO       0.0001
 R@5     on SOLO       0.0004
 mAP@5   on SOLO       0.0004
 mRR@5   on SOLO       0.0004
==============================

Results

Methods	P@5	mAP@5	mRR@5	R@5
Collabarative Fiiltering	-	-	-
PersonFreq	-	-	-
PersonVolume	-	-	-

LightFM Meta	-	-	-
LightFM PureCF	-	-	-
LightFM Hybrid	0.000	0.000	0.000	0.000

DGL	-	-	-
GCN	-	-	-

Fund Explanation

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