pyppur: Python Projection Pursuit Unsupervised Reduction

pyppur is a Python package that implements projection pursuit methods for dimensionality reduction. Unlike traditional methods such as PCA, pyppur focuses on finding interesting non-linear projections by minimizing either reconstruction loss or distance distortion.

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Features

  • Two optimization objectives:

    • Distance Distortion: Preserves pairwise distances between data points (with optional nonlinearity)

    • Reconstruction: Minimizes reconstruction error using ridge functions (tied or untied weights)

  • Multiple initialization strategies (PCA-based and random)

  • A scikit-learn transformer: subclasses TransformerMixin / BaseEstimator, passes check_estimator in full, and works in Pipeline, GridSearchCV and cross_val_score

  • Supports standardization and custom weighting

  • Mathematical flexibility with configurable ridge functions and regularization

Quick Start

Installation

pip install pyppur

Basic Usage

import numpy as np
from pyppur import ProjectionPursuit, Objective
from sklearn.datasets import load_digits

# Load data
digits = load_digits()
X = digits.data
y = digits.target

# Projection pursuit with distance distortion
pp_dist = ProjectionPursuit(
    n_components=2,
    objective=Objective.DISTANCE_DISTORTION,
    alpha=1.5,
    n_init=3
)

# Fit and transform
X_transformed = pp_dist.fit_transform(X)

# Evaluate the embedding
metrics = pp_dist.evaluate(X, y)
print(f"Trustworthiness: {metrics['trustworthiness']:.3f}")

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