utils.py 文件源码

def analyze_false(validData,validDataNumbers,validLabels,model):    
    'Calculating precision and recall for best model...'
    predictions = np.squeeze((model.predict(validDataNumbers) > 0.5).astype('int32'))
    c1_inds = np.where(validLabels == 1)[0]
    pos_inds = np.where((predictions+validLabels) == 2)[0] #np.squeeze(predictions) == validLabels
    neg_inds = np.setdiff1d(c1_inds,pos_inds)
    seq_lengths = np.zeros((validData.shape[0]))
    for ind,row in np.ndenumerate(validData):
            seq_lengths[ind] = len(wordpunct_tokenize(row.lower().strip())) 

    mean_true_length = np.mean(seq_lengths[pos_inds])   
    mean_false_length = np.mean(seq_lengths[neg_inds])

    return mean_false_length,mean_true_length