smlr-learn 0.1.2

Python Package of Simplex-based Multinomial Logistic Regression

SMLR: Simplex-based Multinomial Logistic Regression

Currently in v0.1.2

Introduction

This is a project of SMLR (Simplex-based Multinomial Logistic Regression) + Penalty. You may learn this model from paper by Fu et al. [1]

Starter Guidance

By pip

To create a virtual env in your device, use:

python3 -m venv .venv

Then use pip to install:

pip install smlr-learn

Fit SMLR

You can use SMLR by sklearn-like API:

from smlr import SMLR
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report


model = SMLR(penalty='l1', gamma=1.0)

X, y = make_classification(
    n_informative=10,
    n_samples=1000,
    random_state=42
)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=42)

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(classification_report(y_pred, y_test))

Then you can see:

Iteration 1000, loss: 0.335493
Converged after 1786 iterations, loss: 0.335087
              precision    recall  f1-score   support

           0       0.78      0.88      0.83       135
           1       0.89      0.79      0.84       165

    accuracy                           0.83       300
   macro avg       0.83      0.84      0.83       300
weighted avg       0.84      0.83      0.83       300

References

[1] Fu, S., Chen, P., Liu, Y., & Ye, Z. (2023). Simplex-based multinomial logistic regression with diverging numbers of categories and covariate. Statistica Sinica, 33(4), 2463-2493.

Update Notice

0.1.2

• Fix CMLR1 and CMLR2. Now CMLR1 performs correctly.
• Enhance SMLR computation by change gradient descent optimization method to proximal gradient descent.
• Add examples in repository.