ixplore 0.1.3

Iterative Probabilistic Logistic Regression Embedding

IXPLORE

Iterative Probabilistic Logistic Regression Embedding: A Python package for embedding users and questionnaire items in a shared 2D latent space.

Applicable data

IXPLORE is designed for user-item reaction matrices commonly found in political questionnaires. It's well suited for binary data (e.g., agree/disagree) and Likert-scale responses, where each question measures different preferences dimensions. Use IXPLORE when you want a compact, interpretable 2D visualization of the latent political landscape or when you need interpretable imputation of missing responses based on users' latent positions.

Features

IXPLORE jointly learns a posterior distribution for each user and a logistic regression model for each questionnaire item. It visualizes the political landscpae in a two dimensional space. In inference, it can impute missing values and generate answers based on any latent position.

• User Embedding: Compute posterior distributions for users based on their reactions
• Item Models: Define decision boundaries for each questions with logistic regression models
• Iterative Refinement: Jointly optimize user embeddings and item models through iterative updates
• Flexible Initialization: Initialize embeddings via PCA, random values, or load pretrained embeddings
• Missing Data Handling: Robust to missing values in the user-item reaction matrix
• Answer Imputation: Predict answers based on positions in latent space
• New User Embedding: Embed new users in the latent space with uncertainty quantification
• Visualization Tools: Built-in plotting functions for embeddings, posteriors, and item decision boundaries

Installation

pip install ixplore

Or install from source:

git clone https://github.com/fsvbach/ixplore.git
cd ixplore
pip install -e .

Quick Start

import pandas as pd
from ixplore import IXPLORE

# Load reaction data (users × items matrix, values in {0, 1})
reactions = pd.read_csv('../data/binary_reactions.csv', index_col=0)

# Initialize and fit the model
model = IXPLORE(reactions, pca_initialization=True)

# Get user embeddings
embedding  = model.get_embedding()        # User positions (N × 2)
parameters = model.get_item_parameters()  # Item parameters (K × 3)

# Embed a new user based on their answers
new_user_answers = pd.Series({'Q1': 0.8, 'Q2': 0.2, 'Q3': 0.6}, name='new_user')
position = model.embed_new_user(new_user_answers)

# Predict all answers for a user
predicted = model.predict_all_answers(new_user_answers)

Custom Configuration

import numpy as np

model = IXPLORE(
    reactions,
    prior_mean=np.array([0, 0]),                # Prior center
    prior_cov=np.array([[0.1, 0], [0, 0.1]]),   # Prior covariance
    sampling_resolution=200,                    # Grid resolution for posteriors
    xlimits=(-1, 1),                            # X-axis bounds
    ylimits=(-1, 1),                            # Y-axis bounds
    pca_initialization=True,                    # Initialize with PCA
    random_state=17                             # For reproducibility
)

Loading Pretrained Models

# Load pretrained embedding and model parameters
model = IXPLORE(
    reactions,
    pretrained_embedding='../data/pretrained_embedding.csv',
)

Visualization

from ixplore.visualization import plot_overview

# Load user metadata (e.g., colors for plotting)
users = pd.read_csv('../data/synthetic_users.csv', index_col=0)

# Plot user embeddings
plot_overview(model, question='Q12', user='0', colors=users.color, figsize=(7,2.5))

IXPLORE Class

Constructor Parameters

Parameter	Type	Default	Description
reactions	pd.DataFrame	required	User-item reaction matrix (users as index, items as columns)
prior_mean	np.ndarray	[0, 0]	Mean of the prior distribution
prior_cov	np.ndarray	[[0.1, 0], [0, 0.1]]	Covariance of the prior distribution
sampling_resolution	int	200	Grid resolution for posterior computation
xlimits	tuple	(-1, 1)	X-axis limits of the latent space
ylimits	tuple	(-1, 1)	Y-axis limits of the latent space
pca_initialization	bool	True	Initialize embeddings with PCA
random_state	int	0	Random seed for reproducibility

Key Methods

Method	Description
iterate(n_iterations)	Run n iterations of posterior fitting and model updating
fit_posteriors()	Compute posterior distributions for all users
fit_models()	Fit logistic regression models for all items
get_embedding()	Return current user embeddings as DataFrame
get_item_parameters()	Return item model parameters
embed_new_user(answers)	Embed a new user given their answers
predict_all_answers(answers)	Predict answers for all items given partial answers
impute_remaining_answers(answers)	Impute missing answers for a user
evaluate()	Return MAE and accuracy on training data

Dependencies

• numpy
• pandas
• scikit-learn
• scipy
• matplotlib

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use IXPLORE in your research, please cite:

@software{bachmann2026ixplore,
  author       = {Bachmann, Fynn},
  title        = {IXPLORE: Iterative Probabilistic Logistic Regression Embedding},
  year         = {2026},
  publisher    = {GitHub},
  url          = {https://github.com/fsvbach/ixplore}
}

Or in text format:

Bachmann, F. (2026). IXPLORE: Iterative Probabilistic Logistic Regression Embedding. GitHub. https://github.com/fsvbach/ixplore