layeredlearning 1.3.1

A library for Augmented Model Stacking and ensemble with a focus on flexibility and performance.

LayerLearn — Flexible Model Library

layerlearn is a small Python package that makes it easy to build stacked estimators (regressors and classifiers) around scikit-learn models. this library is designed to provide a flexible and easy-to-use interface for building stacked models, allowing users to combine multiple models to improve performance. with this library you can stack any scikit-learn compatible models and also you can use the default models provided by the library.

Features

• Flexible Stacking: Easily stack any scikit-learn compatible models.
• Regression and Classification: Supports both regression and classification tasks.
• Customizable: Allows customization of the base and meta models.
• Easy to Use: Simple API for building and training stacked models.

Requirements

• Python 3.8+
• scikit-learn
• numpy
• xgboost
• catboost
• lightgbm

Installation

From PyPI:

pip install layeredlearning

From source (recommended for development):

git clone https://github.com/Mr-J12/newalgo.git
cd newalgo
pip install -e .

Examples & tests

See example scripts in the repository:

• testing/regression_default_dataset.py
• testing/classification_default_dataset.py
• testing/instantiation_checking.py

Quick examples

Regression:

from layerlearn.flexiblestacked import FlexibleStackedRegressor
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split

X, y = make_regression(n_samples=200, n_features=10, noise=10)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

base = LinearRegression()
meta = RandomForestRegressor(random_state=0)
stack = FlexibleStackedRegressor(base, meta)
stack.fit(X_train, y_train)
preds = stack.predict(X_test)
print(preds[:5])

Classification:

from layerlearn.flexiblestacked import FlexibleStackedClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

X, y = make_classification(n_samples=200, n_features=10, n_classes=2, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

base = LogisticRegression(max_iter=1000)
meta = RandomForestClassifier(random_state=0)
stack = FlexibleStackedClassifier(base, meta)
stack.fit(X_train, y_train)
preds = stack.predict(X_test)
print(preds[:5])

Visualization

Regression Default Dataset Report

Classification Default Dataset Report

Development

• Install development requirements (scikit-learn, numpy).
• Run example scripts to verify behavior: python testing/regression_default_dataset.py

License

See LICENSE for details.