LIBOML:Library For Online Machine Learning

Here we implement representative online learning algorithms. We split these algorithms into 3 parts:

• Linear classifiers;
• Kernel classifiers;
• Deep classifiers.

The models we have implement:

Install

pip install liboml

or

git clone https://github.com/Kaslanarian/liboml
cd liboml
python setup.py install

How to use

Our implementation is based on sklearn, so you can easily use it just like this:

from sklearn.dataset import load_breast_cancer
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

from liboml.linear import OGDClassifier

X, y = load_breast_cancer(return_X_y=True)
train_X, test_X, train_y, test_y = train_test_split(
    X, 
    y, 
    train_size=0.8,
	random_state=42,
)
stder = StandardScaler().fit(train_X)
train_X, test_X = stder.transform(train_X), stder.transform(test_X)

model = OGDClassifier(init_lr=0.1)
model.fit(train_X, train_y)
acc = model.score(test_X, test_y)
print(acc) # 0.9649122807017544

Future

• More algorithms.
• Support GPU.