libscores.py 文件源码

def binarize_predictions(array, task='binary.classification'):
    ''' Turn predictions into decisions {0,1} by selecting the class with largest 
    score for multiclass problems and thresholding at 0.5 for other cases.'''
    # add a very small random value as tie breaker (a bit bad because this changes the score every time)
    # so to make sure we get the same result every time, we seed it    
    #eps = 1e-15
    #np.random.seed(sum(array.shape))
    #array = array + eps*np.random.rand(array.shape[0],array.shape[1])
    bin_array = np.zeros(array.shape)
    if (task != 'multiclass.classification') or (array.shape[1]==1): 
        bin_array[array>=0.5] = 1
    else:        
        sample_num=array.shape[0]
        for i in range(sample_num):
            j = np.argmax(array[i,:])
            bin_array[i,j] = 1        
    return bin_array