aydie-mllib 1.2.1

A Python library to automate machine learning model training and tuning using a simple configuration file.

aydie-mllib

aydie-mllib is a Python library designed to automate and simplify the process of training and tuning machine learning models. By leveraging a simple YAML configuration file, you can easily test multiple algorithms, perform hyperparameter tuning with GridSearchCV, and find the best model for your data without writing repetitive boilerplate code.

This library is built to be extensible and supports any model that follows the scikit-learn API, including popular libraries like XGBoost.

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Features

• Configuration-Driven: Define your entire model training pipeline in a single YAML file.
• Automated Grid Search: Automatically performs hyperparameter tuning for multiple models.
• Model Agnostic: Works with any scikit-learn compatible model (e.g., RandomForestRegressor, SVR, XGBClassifier).
• Find the Best: Compares the tuned models and returns the one with the highest score.
• Easy to Use: Includes a helper function to generate a sample configuration file to get you started instantly.

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Installation

You can install aydie-mllib directly from PyPI:

pip install aydie-mllib

Or, install it directly from the source for the latest version:

git clone [https://github.com/aydiegithub/aydie-mllib.git](https://github.com/aydiegithub/aydie-mllib.git)
cd aydie-mllib
pip install .

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Quickstart Guide

Here's how to get up and running with aydie-mllib in just a few steps.

1. Generate the Configuration File

First, create a Python script to generate a sample model_config.yaml file. This will be the blueprint for your training pipeline.

generate_config.py

from aydie_mllib.config import generate_sample_model_config

# This will create a 'config' directory and place 'model_config.yaml' inside it.
file_path = generate_sample_model_config(export_dir="config")

print(f"Sample config file has been generated at: {file_path}")

2. Customize model_config.yaml

Now, open the newly created config/model_config.yaml and customize it for the models you want to test. Let's set it up to compare a RandomForestRegressor and an XGBRegressor.

grid_search:
  class: GridSearchCV
  module: sklearn.model_selection
  params:
    cv: 5
    verbose: 1
model_selection:
  module_0:
    class: RandomForestRegressor
    module: sklearn.ensemble
    params:
      n_estimators: 100
      random_state: 42
    search_param_grid:
      n_estimators:
        - 100
        - 200
      max_depth:
        - 5
        - 10
        - null
  module_1:
    class: XGBRegressor
    module: xgboost
    params:
      objective: reg:squarederror
    search_param_grid:
      n_estimators:
        - 50
        - 100
      learning_rate:
        - 0.05
        - 0.1

3. Find the Best Model

Finally, use the ModelBuilder to load your configuration, train the models, and find the best one.

run_training.py

import pandas as pd
from sklearn.model_selection import train_test_split
from aydie_mllib import ModelBuilder

# --- 1. Load your data ---
# As an example, let's create some dummy data
from sklearn.datasets import make_regression
X, y = make_regression(n_samples=1000, n_features=20, noise=0.1, random_state=42)
X = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])


# --- 2. Initialize the ModelBuilder ---
# Point it to your configuration file
model_builder = ModelBuilder(model_config_path="config/model_config.yaml")


# --- 3. Get the best model ---
# The get_best_model method runs the entire pipeline
best_model_detail = model_builder.get_best_model(X=X, y=y, base_accuracy=0.6)


# --- 4. Print the results ---
print("\n--- Best Model Found ---")
print(f"Model Class: {best_model_detail.best_model.__class__.__name__}")
print(f"Best Score (R^2): {best_model_detail.best_score:.4f}")
print(f"Best Parameters: {best_model_detail.best_parameters}")

# You can now use this best model for predictions
# best_model = best_model_detail.best_model
# predictions = best_model.predict(X)

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How it Works

The library is centered around the ModelBuilder class, which orchestrates the entire process based on your model_config.yaml file.

• grid_search section: Defines the hyperparameter search strategy. By default, it uses sklearn.model_selection.GridSearchCV. You can customize its parameters like cv (cross-validation folds).
• model_selection section: This is a dictionary where each key (e.g., module_0) represents a model to be evaluated.
  • module: The Python module where the model class is located (e.g., sklearn.ensemble or xgboost).
  • class: The name of the model class (e.g., RandomForestRegressor).
  • params: A dictionary of fixed parameters that will be passed to the model's constructor.
  • search_param_grid: The dictionary of hyperparameters to be tuned by the grid search.

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Connect with Me

• 🌐 Website: aydie.in
• 💼 LinkedIn: @aydiemusic
• 🐦 X (Twitter): @aydiemusic
• 📸 Instagram: @aydiemusic
• 📺 YouTube: @aydiemusic
• 📧 Contact: [e-mail]

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Contributing

Contributions are welcome! If you have ideas for improvements or find a bug, please open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.