illoominate 0.9.0

A scalable library designed to compute data importance scores for interaction data in sequential kNN-based recommender 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.
• 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

Ensure Python >= 3.10 is installed.

Installing Illoominate

Illoominate can be installed via pip from PyPI.

pip install illoominate

Note

It is recommended to install and run Illoominate from a virtual environment. If you are using a virtual environment, activate it before running the installation command.

python -m venv venv       # Create the virtual environment (Linux/macOS/Windows)   
source venv/bin/activate  # Activate the virtualenv (Linux/macOS)  
venv\Scripts\activate     # Activate the virtualenv (Windows)  

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
)

# 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()

# Identify potentially corrupted sessions
negative = shapley_values[shapley_values.score < 0]
corrupt_sessions = train_df.merge(negative, on='session_id')

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()

Example 4: Increasing the Sustainability of Recommendations via Data Pruning

Illoominate supports metrics to include a sustainability term that expresses the number of sustainable products in a given recommendation. SustainableMRR@t as 0.8·MRR@t + 0.2· st . This utility combines the MRR@t with the “sustainability coverage term” st , where s denotes the number of sustainable items among the t recommended items.

The function call remains the same, you only change the metric to SustainableMRR, SustainableNDCG or st (sustainability coverage term) and provide a list of items that are considered sustainable.

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

train_df = pd.read_csv('data/rsc15_100k/processed/train.csv', sep='\t')
validation_df = pd.read_csv('data/rsc15_100k/processed/valid.csv', sep='\t')
# rsc15 items considered sustainable. (Randomly chosen for this dataset) 
sustainable_df = pd.read_csv('data/rsc15_100k/processed/sustainable.csv', sep='\t')

importance = illoominate.data_loo_values(
    train_df=train_df,
    validation_df=validation_df,
    model='vmis',
    metric='sustainablemrr@20',
    params={'m':500, 'k':100, 'seed': 42},
    sustainable_df=sustainable_df,
)

plt.hist(importance['score'], density=False, bins=100)
plt.title('Distribution of Data Leave-One-Out Values')
plt.yscale('log')
plt.ylabel('Frequency')
plt.xlabel('Data Leave-One-Out Values')
plt.savefig('data/rsc15_100k/processed/loo_responsiblemrr.png', dpi=300)
plt.show()

# Prune the training data
threshold = importance['score'].quantile(0.05)  # 5th percentile threshold
filtered_importance_values = importance[importance['score'] >= threshold]
train_df_pruned = train_df.merge(filtered_importance_values, on='session_id')

This demonstrates the pruned training dataset, where less impactful or irrelevant interactions have been removed to focus on high-quality data points for model training.

print(train_df_pruned)
	session_id	item_id	timestamp	score
0	3	214716935	1.396437e+09	0.000000
1	3	214832672	1.396438e+09	0.000000
2	7	214826835	1.396414e+09	-0.000003
3	7	214826715	1.396414e+09	-0.000003
4	11	214821275	1.396515e+09	0.000040
...	...	...	...	...
47933	31808	214820441	1.396508e+09	0.000000
47934	31812	214662819	1.396365e+09	-0.000002
47935	31812	214836765	1.396365e+09	-0.000002
47936	31812	214836073	1.396365e+09	-0.000002
47937	31812	214662819	1.396365e+09	-0.000002

Supported Recommendation models and Metrics

model (str): Name of the model to use. Supported values:

• vmis: Session-based recommendation VMIS-kNN.
• tifu: Next-basket recommendation TIFU-kNN.

metric (str): Evaluation metric to calculate importance. Supported values:

• mrr
• ndcg
• st
• hitrate
• f1
• precision
• recall
• sustainablemrr
• sustainablendcg

params (dict): Model specific parameters

sustainable_df (pd.DataFrame):

• This argument is only mandatory for the sustainable related metrics st, sustainablemrr or sustainablendcg

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.

Releasing a new version of Illoominate

Increment the version number in pyproject.toml

Trigger a build using the CI pipeline in Github, via either:

• A push is made to the main branch with a tag matching -rc (e.g., v1.0.0-rc1).
• A pull request is made to the main branch.
• A push occurs on a branch that starts with branch-*.

Download the wheels mentioned in the CI job output and place them in a directory. Navigate to that directory and then

twine upload dist/* -u __token__ -p pypi-SomeSecretAPIToken123

This will upload all files in the dist/ directory to PyPI. dist/ is the directory where the wheel files will be located after you unpack the artifact from GitHub Actions.