illoominate 0.1.4

A Rust-powered library for scoring data importance in sequential knn-recommendation systems

Illoominate - Data Importance for Recommender Systems

Illoominate is a scalable library designed to compute data importance scores for interaction data in recommender systems. It supports the computation of Data Shapley values (DSV) and leave-one-out (LOO) scores, offering insights into the relevance and quality of data in large-scale sequential kNN-based recommendation models. This library is tailored for sequential kNN-based algorithms including session-based recommendation and next-basket recommendation tasks, and it efficiently handles real-world datasets with millions of interactions.

Key Features

• Scalable: Optimized for large datasets with millions of interactions.
• Efficient Computation: Uses the KMC-Shapley algorithm to speed up the estimation of Data Shapley values, making it suitable for real-world sequential kNN-based recommendation systems.
• Customizable: Supports multiple recommendation models, including VMIS-kNN (session-based) and TIFU-kNN (next-basket), and supports popular metrics such as MRR, NDCG, Recall, F1 etc.
• Visualization: Easily visualize the distribution of Data Shapley values to analyze data quality and identify potential issues.
• Real-World Application: Focuses on practical use cases, including debugging, data pruning, and improving sustainability in recommendations.

Illoominate Framework

This repository contains the code for the illoominate framework, which accompanies the scientific manuscript which is under review.

Overview

Illoominate is implemented in Rust with a Python frontend. It is optimized to scale with datasets containing millions of interactions, commonly found in real-world recommender systems. The library includes KNN-based models VMIS-kNN and TIFU-kNN, used for session-based recommendations and next-basket recommendations.

By leveraging the Data Shapley value, Illoominate helps data scientists and engineers:

• Debug potentially corrupted data
• Improve recommendation quality by identifying impactful data points
• Prune training data for sustainable item recommendations

Installation

• Python >= 3.10

pip install illoominate

Example Use Cases

Example 1: Computing Data Shapley Values for Session-Based Recommendations

Illoominate computes Data Shapley values to assess the contribution of each data point to the recommendation performance. Below is an example using the public Now Playing 1M dataset.

import illoominate
import matplotlib.pyplot as plt
import pandas as pd

# Load training and validation datasets
train_df = pd.read_csv("data/nowplaying1m/train.csv", sep='\t')
validation_df = pd.read_csv("data/nowplaying1m/valid.csv", sep='\t')

# Compute Data Shapley values
shapley_values = illoominate.data_shapley_values(
    train_df=train_df,
    validation_df=validation_df,
    model='vmis',  # Model to be used (e.g., 'vmis' for VMIS-kNN)
    metric='mrr@20',  # Evaluation metric (e.g., Mean Reciprocal Rank at 20)
    params={'m':100, 'k':100, 'seed': 42},  # Model-specific parameters
)

negative = shapley_values[shapley_values.score < 0]
corrupt_sessions = train_df.merge(negative, on='session_id')

# Visualize the distribution of Data Shapley values
plt.hist(shapley_values['score'], density=False, bins=100)
plt.title('Distribution of Data Shapley Values')
plt.yscale('log')
plt.ylabel('Frequency')
plt.xlabel('Data Shapley Values')
plt.savefig('images/shapley.png', dpi=300)
plt.show()

Sample Output

The distribution of Data Shapley values can be visualized or used for further analysis.

print(corrupt_sessions)

    session_id	item_id	timestamp	score
0	5076	64	1585507853	-2.931978e-05
1	13946	119	1584189394	-2.606203e-05
2	13951	173	1585417176	-6.515507e-06
3	3090	199	1584196605	-2.393995e-05
4	5076	205	1585507872	-2.931978e-05
...	...	...	...	...
956	13951	5860	1585416925	-6.515507e-06
957	447	3786	1584448579	-5.092383e-06
958	7573	14467	1584450303	-7.107826e-07
959	5123	47	1584808576	-4.295939e-07
960	11339	4855	1585391332	-1.579517e-06
961 rows × 4 columns

Example 2: Data Shapley values for Next-Basket Recommendations with TIFU-kNN

To compute Data Shapley values for next-basket recommendations, use the Tafeng dataset.

# Load training and validation datasets
train_df = pd.read_csv('data/tafeng/processed/train.csv', sep='\t')
validation_df = pd.read_csv('data/tafeng/processed/valid.csv', sep='\t')

# Compute Data Shapley values
shapley_values = illoominate.data_shapley_values(
train_df=train_df,
validation_df=validation_df,
model='vmis',
metric='mrr@20',
params={'m':500, 'k':100, 'seed': 42, 'convergence_threshold': .1},
)


# Visualize the distribution of Data Shapley values
plt.hist(shapley_values['score'], density=False, bins=100)
plt.title('Distribution of Data Shapley Values')
plt.yscale('log')
plt.ylabel('Frequency')
plt.xlabel('Data Shapley Values')
plt.savefig('images/shapley.png', dpi=300)
plt.show()

Example 3: Data Leave-One-Out values for Next-Basket Recommendations with TIFU-kNN

# Load training and validation datasets
train_df = pd.read_csv('data/tafeng/processed/train.csv', sep='\t')
validation_df = pd.read_csv('data/tafeng/processed/valid.csv', sep='\t')

#  Data Leave-One-Out values
loo_values = illoominate.data_loo_values(
    train_df=train_df,
    validation_df=validation_df,
    model='tifu',
    metric='ndcg@10',
    params={'m':7, 'k':100, 'r_b': 0.9, 'r_g': 0.7, 'alpha': 0.7, 'seed': 42},
)

# Visualize the distribution of Data Leave-One-Out Values
plt.hist(shapley_values['score'], density=False, bins=100)
plt.title('Distribution of Data LOO Values')
plt.yscale('log')
plt.ylabel('Frequency')
plt.xlabel('Data Leave-One-Out Values')
plt.savefig('images/loo.png', dpi=300)
plt.show()

How KMC-Shapley Optimizes DSV Estimation

KMC-Shapley (K-nearest Monte Carlo Shapley) enhances the efficiency of Data Shapley value computations by leveraging the sparsity and nearest-neighbor structure of the data. It avoids redundant computations by only evaluating utility changes for impactful neighbors, reducing computational overhead and enabling scalability to large datasets.

Development Installation

To get started with developing Illoominate or conducting the experiments from the paper, follow these steps:

Requirements:

• Rust >= 1.82
• Python >= 3.10

1. Clone the repository:

git clone https://github.com/bkersbergen/illoominate.git
cd illoominate

2. Create the python wheel by:

pip install -r requirements.txt
maturin develop --release

Conduct experiments from paper

The experiments from the paper are available in Rust code.

Prepare a config file for a dataset, describing the model, model parameters and the evaluation metric.

$ cat config.toml
[model]
name = "vmis"

[hpo]
k = 50
m = 500

[metric]
name="MRR"
length=20

The software expects the config file for the experiment in the same directory as the data files.

DATA_LOCATION=data/tafeng/processed CONFIG_FILENAME=config.toml cargo run --release --bin removal_impact

Licensing and Copyright

This code is made available exclusively for peer review purposes. Upon acceptance of the accompanying manuscript, the repository will be released under the Apache License 2.0. © 2024 Barrie Kersbergen. All rights reserved.

Notes

For any queries or further support, please refer to the scientific manuscript under review. Contributions and discussions are welcome after open-source release.