Getting Started

Installation

Install tlmnet directly from PyPI:

pip install tlmnet

Basic Usage

tlmnet provides an exact MILP classifier compliant with the Scikit-Learn Estimator API.

Because feature values directly multiply discrete weights, feature scaling heavily influences margin penalties. It is highly recommended to wrap the estimator in a standardization pipeline:

from tlmnet import TlmMilpClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

X, y = make_classification(n_samples=100, n_features=10, n_informative=5, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

clf = make_pipeline(
    StandardScaler(),
    TlmMilpClassifier(max_features=5, C=1.0)
)
clf.fit(X_train, y_train)

tlm_model = clf.named_steps["tlmmilpclassifier"]
print("Ternary Coefficients:", tlm_model.coef_)
print("Test Score:", clf.score(X_test, y_test))