logistic.py 文件源码

def _logistic_loss_and_grad(w, X, y, alpha, sample_weight=None):
    """Computes the logistic loss and gradient.
    Parameters
    ----------
    w : ndarray, shape (n_features,) or (n_features + 1,)
        Coefficient vector.
    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        Training data.
    y : ndarray, shape (n_samples,)
        Array of labels.
    alpha : float
        Regularization parameter. alpha is equal to 1 / C.
    sample_weight : array-like, shape (n_samples,) optional
        Array of weights that are assigned to individual samples.
        If not provided, then each sample is given unit weight.
    Returns
    -------
    out : float
        Logistic loss.
    grad : ndarray, shape (n_features,) or (n_features + 1,)
        Logistic gradient.
    """
    _, n_features = X.shape
    grad = np.empty_like(w)

    w, c, yz = _intercept_dot(w, X, y)

    if sample_weight is None:
        sample_weight = np.ones(y.shape[0])

    # Logistic loss is the negative of the log of the logistic function.
    out = -np.sum(sample_weight * log_logistic(yz)) + .5 * alpha * np.dot(w, w)

    z = expit(yz)
    z0 = sample_weight * (z - 1) * y

    grad[:n_features] = safe_sparse_dot(X.T, z0) + alpha * w

    # Case where we fit the intercept.
    if grad.shape[0] > n_features:
        grad[-1] = z0.sum()
    return out, grad