cybooster 0.1.2

A high-performance gradient boosting implementation using Cython

CyBooster: A Gradient Boosting Library

CyBooster is a high-performance generic gradient boosting (any based learner can be used) library designed for classification and regression tasks. It is built on Cython for speed and efficiency, making it suitable for large datasets and complex models.

Each base learner is augmented with a randomized neural network (a generalization of https://www.researchgate.net/publication/346059361_LSBoost_gradient_boosted_penalized_nonlinear_least_squares to any base learner), which allows the model to learn complex patterns in the data. The library supports both classification and regression tasks, making it versatile for various machine learning applications.

CyBooster is born from mlsauce, that might be difficult to install on some systems. This version will also be more GPU friendly, thanks to JAX.

Installation

To install CyBooster, you can use pip:

pip install cybooster --verbose

From GitHub:

pip install git+https://github.com/Techtonique/cybooster.git --verbose

Usage

from cybooster import BoosterClassifier, BoosterRegressor
from sklearn.datasets import load_iris, load_diabetes, load_breast_cancer, load_digits, load_wine
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, mean_squared_error, root_mean_squared_error
from sklearn.linear_model import LinearRegression
from time import time 


# Regression Example
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
regressor = BoosterRegressor(obj=LinearRegression(), n_estimators=100, learning_rate=0.1,
                             n_hidden_features=10, verbose=1, seed=42)
start = time()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
print(f"Elapsed: {time() - start} s")
rmse = root_mean_squared_error(y_test, y_pred)
print(f"RMSE for regression: {rmse:.4f}")

# Classification Example
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
classifier = BoosterClassifier(obj=LinearRegression(), n_estimators=100, learning_rate=0.1,
                               n_hidden_features=10, verbose=1, seed=42)
start = time()
try: 
    classifier.fit(X_train, y_train)
except Exception as e: # this is for Windows users
    y_train = y_train.astype('int32')
    classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
print(f"Elapsed: {time() - start} s")
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy for classification: {accuracy:.4f}")

X, y = load_wine(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
classifier = BoosterClassifier(obj=LinearRegression(), n_estimators=100, learning_rate=0.1,
                               n_hidden_features=10, verbose=1, seed=42)
start = time()
try:
    classifier.fit(X_train, y_train)
except Exception as e: # this is for Windows users
    y_train = y_train.astype('int32')
    classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
print(f"Elapsed: {time() - start} s")
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy for classification: {accuracy:.4f}")

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
classifier = BoosterClassifier(obj=LinearRegression(), n_estimators=100, learning_rate=0.1,
                               n_hidden_features=10, verbose=1, seed=42)
start = time()
try: 
    classifier.fit(X_train, y_train)
except Exception as e: # this is for Windows users
    y_train = y_train.astype('int32')
    classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
print(f"Elapsed: {time() - start} s")
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy for classification: {accuracy:.4f}")

X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
classifier = BoosterClassifier(obj=LinearRegression(), n_estimators=100, learning_rate=0.1,
                               n_hidden_features=10, verbose=1, seed=42)
start = time()
try: 
    classifier.fit(X_train, y_train)
except Exception as e: # this is for Windows users
    y_train = y_train.astype('int32')
    classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
print(f"Elapsed: {time() - start} s")
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy for classification: {accuracy:.4f}")