glassboxml 0.2.0

A transparent machine learning framework focused on explainability and diagnostics.

GlassBoxML

Machine Learning you can actually see.

Overview

GlassBoxML is a theory-first machine learning library built from scratch using pure NumPy.

Unlike traditional libraries that prioritize abstraction and convenience, GlassBoxML emphasizes transparency and understanding. Every model exposes:

what it learns how it learns where it fails

This project bridges the gap between mathematical learning theory and practical implementation.

Philosophy

Most ML libraries behave like black boxes:

model.fit(X, y)
# magic happens

GlassBoxML is different:

model.fit(X, y)

model.loss_history
model.gradients
model.assumptions
model.failure_modes
model.generalization_estimate

You don't just train models — you inspect learning itself.

Goals Implement core ML algorithms from first principles Expose optimization behavior during training Make model assumptions explicit Demonstrate overfitting and generalization Provide educational transparency without sacrificing code quality Non-Goals Competing with high-performance libraries like scikit-learn GPU acceleration Massive algorithm coverage Production deployment pipelines

This is a learning and reasoning library, not a benchmarking tool.

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Implemented / Planned Algorithms

Core Models

• Linear Regression
• Logistic Regression
• k‑Nearest Neighbors
• Ridge Regression
• Lasso Regression
• Decision Trees
• Random Forest
• SVM

Optimization

• Batch Gradient Descent
• Stochastic Gradient Descent
• Momentum

Diagnostics

• Loss curves
• Bias–variance indicators
• Overfitting detection
• Condition number warnings

Theory Tools

• Generalization estimates — implemented as model.generalization_estimate, a lightweight, capacity-vs-sample-size heuristic that works without a held-out validation set
• Capacity indicators
• Noise sensitivity analysis

Feature Extraction & Pipelines

• TF-IDF Vectorizer (sparse and dense modes)
• Sparse Random Projection
• Pipeline for chaining preprocessing and models

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Example

from glassboxml import LinearRegression

model = LinearRegression()
model.fit(X, y)

print(model.loss_history)
print(model.explain())
print(model.diagnose())  # dataset profile, training error, failure modes, and generalization_estimate

Project Structure

glassboxml/
│
├── core/               # optimizers, model selection, Pipeline, base classes
├── models/             # ML algorithms
├── diagnostics/        # overfitting & model insights
├── datasets/           # synthetic data generators
├── metrics/            # evaluation metrics
├── preprocessing/      # scaling and transformations
├── feature_extraction/ # TF-IDF vectorizer
├── tuning/             # hyperparameter search
└── examples/           # demos & experiments

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Installation

From PyPI:

pip install glassboxml

From source:

git clone https://github.com/hogwarts-coder10/GlassBox-ML.git
cd GlassBox-ML
pip install -r requirements.txt

Dependencies are intentionally minimal:

• numpy
• matplotlib
• scipy

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Why This Project Exists

Modern ML education often teaches usage before understanding.

This creates developers who can:

train models ❌ but not explain, debug, or trust them ❌

GlassBoxML reverses that:

Understand → Implement → Experiment → Trust

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Contributing

This project values clarity over cleverness.

Contributions should:

Prefer readable, math-aligned code Include explanation comments Demonstrate failure cases, not just success

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