User Guide

Make a classification problem

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
from sklearn.metrics import roc_auc_score
from calf_milp import CalfMilp

seed = 45
X, y = make_classification(
    n_samples=100,
    n_features=5,
    n_informative=2,
    n_redundant=2,
    n_classes=2,
    random_state=seed
)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed)

Train the classifier

cls = CalfMilp().fit(X_train, y_train)

Get the score for class prediction on unseen data

cls.score(X_test, y_test)

0.92

Class probabilities

We vertically stack the ground truth on the top with the probabilities of class 1 on the bottom. The first five entries are shown.

np.round(np.vstack((y_train, cls.predict_proba(X_train).T))[:, 0:5], 2)

array([[1.  , 1.  , 0.  , 0.  , 0.  ],
       [0.23, 0.45, 1.  , 0.78, 0.75],
       [0.77, 0.55, 0.  , 0.22, 0.25]])

roc_auc_score(y_true=y_train, y_score=cls.predict_proba(X_train)[:, 1])

0.9751066856330014

Predict the classes

The ground truth is on the top and the predicted classes are on the bottom. The first five entries are shown.

y_pred = cls.predict(X_test)
np.vstack((y_test, y_pred))[:, 0:5]

array([[0, 0, 0, 1, 0],
       [0, 0, 0, 1, 0]])

The class prediction is expected to be lower than the auc prediction.

roc_auc_score(y_true=y_test, y_score=y_pred)

0.9198717948717948