mgp-rnn-fit.py 文件源码

def get_probs_and_accuracy(preds,O):
    """
    helper function. we have a prediction for each MC sample of each observation
    in this batch.  need to distill the multiple preds from each MC into a single
    pred for this observation.  also get accuracy. use true probs to get ROC, PR curves in sklearn
    """
    all_probs = tf.exp(preds[:,1] - tf.reduce_logsumexp(preds, axis = 1)) #normalize; and drop a dim so only prob of positive case
    N = tf.cast(tf.shape(preds)[0]/n_mc_smps,tf.int32) #actual number of observations in preds, collapsing MC samples                    

    #predicted probability per observation; collapse the MC samples
    probs = tf.zeros([0]) #store all samples in a list, then concat into tensor at end
    #setup tf while loop (have to use this bc loop size is variable)
    def cond(i,probs):
        return i < N
    def body(i,probs):
        probs = tf.concat([probs,[tf.reduce_mean(tf.slice(all_probs,[i*n_mc_smps],[n_mc_smps]))]],0)
        return i+1,probs    
    i = tf.constant(0)
    i,probs = tf.while_loop(cond,body,loop_vars=[i,probs],shape_invariants=[i.get_shape(),tf.TensorShape([None])])

    #compare to truth; just use cutoff of 0.5 for right now to get accuracy
    correct_pred = tf.equal(tf.cast(tf.greater(probs,0.5),tf.int32), O)
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) 
    return probs,accuracy