trainedml 0.1.3

Simple ML training framework

trainedml

Modular machine learning framework for Python — train, benchmark, and visualize ML models with a unified API, CLI, and web interface.

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Table of Contents

• Overview
• Installation
• Quickstart
• Features
• API Reference
• CLI Usage
• Architecture
• Testing
• Documentation
• Contributing
• License

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Overview

trainedml est un package Python modulaire pour entraîner, comparer et visualiser des modèles de machine learning sur des jeux de données classiques ou personnalisés. Il offre une API unifiée, une interface en ligne de commande et une application web interactive Streamlit pour des workflows ML complets.

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Installation

pip install trainedml

Or install from source:

git clone https://github.com/diamankayero/trainedml.git
cd trainedml
pip install -e .

Requirements: Python 3.9+

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Quickstart

from trainedml import Trainer

# Train on a built-in dataset
trainer = Trainer(dataset="iris", model="random_forest")
trainer.fit()

# Evaluate
scores = trainer.evaluate()
print(scores)

# Predict
predictions = trainer.predict([[5.1, 3.5, 1.4, 0.2]])
print(predictions)

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Features

• Unified API — train, evaluate, and predict with a single Trainer class
• Built-in datasets — Iris, Wine, and any remote CSV via URL
• Modular models — KNN, Logistic Regression, Random Forest, and more
• Standard metrics — accuracy, precision, recall, F1, R², MSE, RMSE, MAE
• Benchmarking — compare models side-by-side with timing and parallelization
• Exploratory analysis — correlations, distributions, missing values, outliers, multicollinearity
• Visualization — heatmaps, histograms, line plots, boxplots, bivariate charts
• CLI — automate ML pipelines from the terminal
• Streamlit webapp — interactive web interface for exploration and prediction
• Full test coverage — 100% coverage with Sphinx documentation

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API Reference

Trainer

The main entry point for the framework.

from trainedml import Trainer

trainer = Trainer(dataset="iris", model="knn")
trainer.fit()
scores = trainer.evaluate()
predictions = trainer.predict([[5.1, 3.5, 1.4, 0.2]])

Train on a custom remote dataset:

trainer = Trainer(
    url="https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv",
    target="quality",
    model="logistic"
)
trainer.fit()

DataLoader

from trainedml.data.loader import DataLoader

X, y = DataLoader().load_dataset(name="wine")

KNNModel (and other models)

from trainedml.models.knn import KNNModel

model = KNNModel(n_neighbors=3)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Benchmark

from trainedml.benchmark import Benchmark
from trainedml.models.knn import KNNModel
from trainedml.models.random_forest import RandomForestModel

models = {"knn": KNNModel(), "rf": RandomForestModel()}
bench = Benchmark(models)
results = bench.run(X_train, y_train, X_test, y_test)
print(results)

Visualizer

from trainedml.visualization import Visualizer

viz = Visualizer(X)
fig = viz.heatmap()
fig.show()

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CLI Usage

# Show help
python -m trainedml --help

# Benchmark all models on Iris
python -m trainedml --benchmark --dataset iris

# Train KNN on Wine
python -m trainedml --model knn --dataset wine

# Visualize correlation heatmap
python -m trainedml --dataset iris --show

Interactive Webapp

streamlit run trainedml_webapp/src/app.py

Or visit the hosted version: trainedml.streamlit.app

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Architecture

trainedml/
├── src/trainedml/
│   ├── __init__.py        # Trainer API
│   ├── analyzer.py        # Exploratory data analysis
│   ├── benchmark.py       # Model benchmarking
│   ├── cli.py             # CLI interface
│   ├── evaluation.py      # Evaluation metrics
│   ├── figure.py          # Figure generation
│   ├── visualization.py   # Visualization tools
│   ├── data/              # Data loading
│   ├── models/            # ML models (KNN, LR, RF, ...)
│   ├── utils/             # Utility functions
│   └── viz/               # Advanced visualizations
├── doc/                   # Sphinx documentation
├── tests/                 # Unit tests
├── trainedml_webapp/      # Streamlit webapp
└── requirements.txt

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Testing

pytest tests/

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Documentation

• Online docs (GitHub Pages)
• Usage guide
• Webapp docs

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Contributing

Contributions are welcome!

1. Fork the project
2. Create your feature branch: git checkout -b feature/my-feature
3. Commit your changes: git commit -m 'Add my feature'
4. Push to the branch: git push origin feature/my-feature
5. Open a Pull Request

For bugs or suggestions, open an issue.

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License

MIT — see LICENSE for details.