epicon 0.3.0

Lightweight from-scratch ML library — neural nets, tree-based models, linear models, and more

Epicon

A lightweight, from-scratch machine learning library built on NumPy with optional Numba acceleration. Provides a unified API for neural networks and traditional ML models.

Designed to be minimal yet capable — like Flask for ML.

Quick Start

import epicon
from epicon.datasets import load_iris
from epicon.preprocessing import train_test_split
from epicon.metrics import accuracy_score

# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# Train a model
model = epicon.DecisionTreeClassifier(max_depth=5)
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.4f}")

What's Included

Neural Networks

• Layers: Dense, Dropout, Conv1D
• Activations: ReLU, LeakyReLU, Sigmoid, Softmax, Tanh
• Losses: MSE, BinaryCrossEntropy, CategoricalCrossEntropy
• Optimizers: GradientDescent, Momentum, Adam
• Model — layer-by-layer construction
• Sequential — Keras-style wrapper with string activations

Traditional ML Models

• LinearRegression, Ridge, Lasso, LogisticRegression
• KNeighborsClassifier, KNeighborsRegressor
• GaussianNB
• DecisionTreeClassifier, DecisionTreeRegressor
• RandomForestClassifier, RandomForestRegressor

Utilities

• Preprocessing: StandardScaler, MinMaxScaler, LabelEncoder, OneHotEncoder, train_test_split
• Datasets: load_iris, load_mnist, make_classification, make_regression
• Metrics: accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, mean_squared_error, mean_absolute_error, r2_score

Installation

# Minimal install (NumPy required)
pip install numpy

# Install Epicon from source
pip install -e .

# With Numba (optional, for faster tree/KNN)
pip install numba

Design

• Consistent API: all models follow fit(X, y) / predict(X).
• Minimal dependencies: only NumPy is required.
• Optional acceleration: Numba JIT for tree split search and KNN.
• Educational: readable, fully documented source code.
• Tested: 169+ unit tests with pytest.

Examples

See the examples/ directory:

• example_ml_iris.py — Decision tree on Iris dataset
• example_ml_binary.py — LogisticRegression with L2 penalty
• example_ml_forest.py — RandomForestRegressor on synthetic data
• example_nn_sequential.py — Sequential neural net with Adam on MNIST