skilearn 0.1.0

A FastAPI-based package for implementing Candidate Elimination, Find-S, and Hypothesis Testing algorithms

Skilearn - Machine Learning Algorithms Package

A FastAPI-based Python package implementing fundamental machine learning algorithms including Candidate Elimination, Find-S, and Hypothesis Testing.

Installation

Install from PyPI (when published):

pip install skilearn

Install from source:

git clone https://github.com/yourusername/skilearn.git
cd skilearn
pip install -e .

Features

• Candidate Elimination Algorithm: Find the version space of hypotheses consistent with training data
• Find-S Algorithm: Find the most specific hypothesis consistent with positive examples
• Hypothesis Testing: Statistical hypothesis testing utilities

Usage

Import the algorithms

from skilearn import CandidateElimination, FindS, HypothesisTesting
import pandas as pd

# Load your data
df = pd.read_csv('your_data.csv')

# Use Find-S algorithm
find_s = FindS()
result = find_s.fit(df[['feature1', 'feature2']], df['label'])
print(f"Hypothesis: {result}")

# Use Candidate Elimination
ce = CandidateElimination()
version_space = ce.fit(df[['feature1', 'feature2']], df['label'])
print(f"Version Space Size: {len(version_space)}")

FastAPI Server

Run the FastAPI server:

uvicorn skilearn.api:app --reload

Then access the interactive API documentation at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

API Endpoints

• POST /api/find-s: Apply Find-S algorithm
• POST /api/candidate-elimination: Apply Candidate Elimination algorithm
• POST /api/hypothesis-testing: Perform statistical hypothesis testing

Development

Install with development dependencies:

pip install -e ".[dev]"

Run tests:

pytest

License

MIT License