QAQwhq的博客

2022一些论文与使用的指标一.评分1.RMSE(Root Mean Squard Error)、MAE(Mean Absolute Error)二.推荐列表准确率（Precision）和召回率（Recall）AUC命中率HR(Hits Ratio)三.排序归一化折损累计增益(Normalized Discounted Cumulative Gain，NDCG)平均倒数排名(Mean Reciprocal Rank, MRR)MAP(Mean Average Precision，平均准确率)e.g K=5

preprocessing.LabelEncoder()使用e.g. 1：from sklearn import preprocessingle = preprocessing.LabelEncoder()arr_gf = [1,2,3,'wom','wom','中文','中文']le.fit(arr_gf)one_hot_gf = le.transform(arr_gf)print(one_hot_gf)输出：[0 1 2 3 3 4 4]e.g. 2:csv_path = './

def load_pretrain_embedding(goods_path, seller_path): model = build_model() goods_model = tf.keras.models.load_model(goods_path) # 预训练模型 for i, pretrain_layer in enumerate(goods_model.layers): if isinstance(pretrain_layer, tf.keras

载入数据input1 = np.load('/root/whq/input/one_hot_1400/xf_p_n.npy')input2 = np.load('/root/whq/input/one_hot_1400/gf_p_n.npy')output_ = np.load('/root/whq/input/one_hot_1400/label_p_n_-1.npy')output_0 = np.load('/root/whq/input/one_hot_1400/label_p_n_0.npy

2022.01.231.Missing Completely at Random(MCAR)2.Missing at Random(MAR)3.Missing Not at Random(MNAR)参考：AI for Medical Prognosis以医生是否为每个病人记录血压为例，讲解三种缺失。1.Missing Completely at Random(MCAR)missingness not depend on anything，no bias (通过抛硬币，完全独立)p(missin