metrics.py 文件源码

def f_score(Theta_true, Theta_est, beta=1, eps=1e-6, per_ts=False):
    """Compute f1 score in the same manner as `precision` and `recall`.
    Therefore see those two functions for the respective waiting and per_ts
    explanation.

    Parameters
    ----------
    Theta_true : 3D ndarray, shape (timesteps, n_vertices, n_vertices)
    Theta_est : 3D ndarray, shape (timesteps, n_vertices, n_vertices)
    beta : float (default 1)
        beta value of the F score to be computed
    eps : float
    per_ts : bool
        whether to compute average or per timestep recall

    Returns
    -------
    ndarray or float
        recall list or single precision value
    """
    prec = precision(Theta_true, Theta_est, eps, per_ts=True)
    rec = recall(Theta_true, Theta_est, eps, per_ts=True)
    with np.errstate(divide='ignore', invalid='ignore'):
        nom = (1 + beta**2) * prec * rec
        print(beta**2 * prec)
        den = beta**2 * prec + rec
        f = np.nan_to_num(np.true_divide(nom, den))
    return f if per_ts else np.sum(f) / len(Theta_true)