recommendation_module.py 文件源码

def construct_k_nearest_matrix(self, dt_matrix, k):
        tmp = np.array(1 - pairwise_distances(dt_matrix[dt_matrix.columns[1:]], metric = "cosine"))
        similarity_matrix = pd.DataFrame(tmp, index = dt_matrix.index.tolist(), columns = dt_matrix.index.tolist())
        for i in similarity_matrix.index:
            tmp = [int(i),[]]
            j = 0
            while j < k:
                max_col = similarity_matrix.loc[i].idxmax(axis = 1)
                similarity_matrix.loc[i][max_col] =  -1
                if max_col != i:
                    tmp[1].append(int(max_col)) #max column name
                    j += 1
            self.k_nearest.append(tmp)