micromlkit 1.0.0

Mini machine learning library

microMlKit

microMlKit is a lightweight, educational machine learning library built with NumPy and a scikit-learn-inspired API.

Package/import name: micromlkit (lowercase)

Overview

micromlkit is designed for learning, experimentation, and understanding core ML algorithms through readable implementations.

It provides:

• A familiar estimator workflow (fit, predict, transform)
• Reusable base classes and mixins
• Core algorithms across regression, classification, clustering, decomposition, trees, SVM, ensemble, and neighbors
• Preprocessing, metrics, model selection, and pipeline utilities

Installation

Install from PyPI

pip install micromlkit

Install from source (development)

git clone <repository-url>
cd microMlKit
pip install -e .[dev]

Quick Start

Regression with preprocessing + pipeline

import numpy as np

from micromlkit.linear_model import LinearRegression
from micromlkit.metrics import mean_squared_error, r2_score
from micromlkit.model_selection import train_test_split
from micromlkit.pipeline import Pipeline
from micromlkit.preprocessing import StandardScaler

X = np.array([[1.0], [2.0], [3.0], [4.0], [5.0]])
y = np.array([2.0, 4.1, 6.1, 8.0, 10.2])

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.4, random_state=42
)

model = Pipeline([
    ("scaler", StandardScaler()),
    ("regressor", LinearRegression()),
])

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print("MSE:", mean_squared_error(y_test, y_pred))
print("R2:", r2_score(y_test, y_pred))

Classification example

import numpy as np

from micromlkit.linear_model import LogisticRegression
from micromlkit.metrics import accuracy_score

X = np.array([[0.1], [0.3], [0.6], [0.8]])
y = np.array([0, 0, 1, 1])

clf = LogisticRegression(learning_rate=0.1, n_iterations=2000)
clf.fit(X, y)
pred = clf.predict(X)

print("Accuracy:", accuracy_score(y, pred))

Implemented Modules

Module	Key Components
micromlkit.base	BaseEstimator, BaseModel, BaseTransformer, mixins, BasePipeline
micromlkit.linear_model	LinearRegression, Ridge, Lasso, LogisticRegression
micromlkit.cluster	KMeans, DBSCAN, AgglomerativeClustering
micromlkit.tree	DecisionTreeClassifier, DecisionTreeRegressor
micromlkit.ensemble	RandomForestClassifier, RandomForestRegressor, GradientBoostingClassifier, GradientBoostingRegressor
micromlkit.neighbors	KNNClassifier, KNNRegressor
micromlkit.svm	SVC, SVR
micromlkit.decomposition	PCA
micromlkit.preprocessing	StandardScaler, SimpleImputer, LabelEncoder
micromlkit.metrics	classification + regression metrics
micromlkit.model_selection	train_test_split, KFold, StratifiedKFold, cross_val_score, ParameterGrid, GridSearchCV
micromlkit.pipeline	Pipeline
micromlkit.utils	kernel/math and validation helpers

Project Scope

• Goal: educational readability and practical experimentation
• Runtime dependency: NumPy
• Python version: >=3.9
• Production note: this project is intended for learning and small-scale experimentation rather than production-grade ML workloads

Testing

Run the test suite:

pytest -ra

Latest local verification in this repository: 157 passed.

Repository Structure

micromlkit/
├── base.py
├── cluster/
├── decomposition/
├── ensemble/
├── linear_model/
├── metrics/
├── model_selection/
├── neighbors/
├── pipeline/
├── preprocessing/
├── svm/
├── tree/
└── utils/

License

See the LICENSE file in the repository root.