evaluate.py 文件源码

def getAccSentiment(model,words,f):
    f = open(f,'r')
    lines = f.readlines()
    preds = []
    golds = []
    seq1 = []
    ct = 0
    for i in lines:
        i = i.split("\t")
        p1 = i[0]; score = i[1]
        X1 = getSeq(p1,words)
        seq1.append(X1)
        ct += 1
        if ct % 100 == 0:
            x1,m1 = utils.prepare_data(seq1)
            scores = model.scoring_function(x1,m1)
            scores = np.squeeze(scores)
            preds.extend(scores.tolist())
            seq1 = []
        golds.append(score)
    if len(seq1) > 0:
        x1,m1 = utils.prepare_data(seq1)
        scores = model.scoring_function(x1,m1)
        scores = np.squeeze(scores)
        preds.extend(scores.tolist())
    return accSentiment(preds,golds)