242981f4577e5d90dbd6d3925d92eb5cb725e17a,tests/test_metrics.py,,test_accuracy,#,43

Before Change

    Testing accuracy.
    n_samples, n_features, n_classes = 1000, 100, 2

    beta0 = np.random.normal(0.0, 1.0, 1)
    beta = np.random.normal(0.0, 1.0, (n_features, n_classes))

    // sample train and test data
    glm_sim = GLM(distr="binomial", score_metric="accuracy")

After Change

    // sample train and test data
    glm_sim = GLM(distr="binomial", score_metric="accuracy")
    X = np.random.randn(n_samples, n_features)
    y = np.zeros((n_samples, 2))
    for idx, beta in enumerate(betas.T):
        y[:, idx] = simulate_glm(glm_sim.distr, beta0, beta, X)
    y = np.argmax(y, axis=1)
    glm_sim.fit(X, y)

Instances