def plot_score()

in neural/visuals.py [0:0]

• 18 lines of code
• 7 McCabe index (conditional complexity)

def plot_score(scores, labels, ref=None, path=None, title=None):
    """
        input:
        -- scores : list of scores arrays of shape (S,)
        -- labels: list of labels of same len as scores
        """

    n_models = len(scores)

    bps = []  # list of boxplots

    for idx in range(n_models):

        # current elements
        score = scores[idx]
        if ref is not None:
            score = ref - score

        bp = plt.boxplot(score, positions=[idx])
        bps.append(bp)

    plt.xlim(-0.5, n_models + 1)
    plt.legend([bp["boxes"][0] for bp in bps], [label for label in labels], loc='upper right')
    if title is not None:
        plt.title(title)
    else:
        plt.title("Temporal Correlation between predicted and truth")
    plt.tight_layout()

    if path is not None:
        plt.savefig(os.path.join(path, "scores.png"))