F3licity / yart

Yet another regression toolkit

Yet another regression toolkit

YART implements linear, logistic and ordinal regression with L2 regularization.

LBFGS is used to minimize the loss functions.

Linear regression example

from sklearn import datasets, cross_validation, metrics
from scipy.sparse import csr_matrix
from yart import LinearRegression
diabetes = datasets.load_diabetes()
X = csr_matrix(diabetes.data)
y = diabetes.target
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.5)
model = LinearRegression(l2=0.1)
model.fit(X_train, y_train)
print('MAE: {}'.format(metrics.mean_absolute_error(model.predict(X_test), y_test)))

Logistic regression example

from sklearn import datasets, cross_validation, metrics
from scipy.sparse import csr_matrix
from yart import LogisticRegression
digits = datasets.load_digits()
X = csr_matrix(digits.data)
y = digits.target
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.5)
model = LogisticRegression(l2=0.1)
model.fit(X_train, y_train)
print('Accuracy: {}'.format(metrics.accuracy_score(model.predict(X_test), y_test)))

Ordinal regression example

import numpy
from sklearn import datasets, cross_validation, metrics
from scipy.sparse import csr_matrix
from yart import OrdinalRegression
boston = datasets.load_boston()
X = csr_matrix(boston.data)
y = numpy.round(boston.target/10)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.5)
model = OrdinalRegression(l2=0.1)
model.fit(X_train, y_train)
print('MAE: {}'.format(metrics.mean_absolute_error(model.predict(X_test), y_test)))