Spaces:
Sleeping
Sleeping
| import json | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| class Recommender : | |
| def __init__(self, id_col, title_col, content_col, title_vec_col, content_vec_col): | |
| self.title_vec_col = title_vec_col | |
| self.content_vec_col = content_vec_col | |
| self.title_col = title_col | |
| self.content_col = content_col | |
| self.id_col = id_col | |
| def calculate_recom_scores (self, k, similarities) : | |
| scores = list(enumerate(similarities[0])) | |
| scores = sorted(scores, key=lambda x: x[1], reverse=True) | |
| scores = scores[1: k + 1] | |
| return scores | |
| def str2arr (self, arr) : | |
| output = list() | |
| for string in arr : | |
| data_list = json.loads(string) | |
| # Convert the list to a NumPy array | |
| data_array = np.array(data_list) | |
| output.append(data_array) | |
| return np.array(output) | |
| # def recommend_k (self, table, k, id) : | |
| # data = np.array(list(zip(*table))) | |
| # # print(data.shape, data) | |
| # idx = int(data[0][data[self.id_col]==id].item()) | |
| # titles = self.str2arr(data[self.title_vec_col, :]) | |
| # contents = self.str2arr(data[self.content_vec_col, :]) | |
| # print(titles.shape) | |
| # print(titles[int(idx)].shape) | |
| # titles_sim = cosine_similarity(titles[int(idx)].reshape(1, -1), titles) | |
| # print(titles_sim.shape) | |
| # contents_sim = cosine_similarity(contents[int(idx)].reshape(1, -1), contents) | |
| # titles_scores = self.calculate_recom_scores(k, titles_sim) | |
| # contents_scores = self.calculate_recom_scores(k, contents_sim) | |
| # print(titles_scores) | |
| # union_scores = np.union1d(titles_scores, contents_scores) | |
| # print(type(union_scores)) | |
| # # union_scores = sorted(union_scores.tolist(), key=lambda x: x[1], reverse=True) | |
| # union_scores = sorted(union_scores.tolist(), key=lambda x: x[1] if isinstance(x, tuple) else x, reverse=True) | |
| # indices = [i[0] for i in union_scores] | |
| # result = data[:, np.isin(data[0,:], indices)] | |
| # return result[self.id_col, :].tolist() | |
| def recommend_k(self, table, k, title): | |
| data = np.array(list(zip(*table))) | |
| idx = np.where(data == title)[0].tolist()[0] | |
| titles = self.str2arr(data[self.title_vec_col, :]) | |
| contents = self.str2arr(data[self.content_vec_col, :]) | |
| titles_sim = cosine_similarity(titles[int(idx)].reshape(1, -1), titles) | |
| contents_sim = cosine_similarity(contents[int(idx)].reshape(1, -1), contents) | |
| titles_scores = self.calculate_recom_scores(k, titles_sim) | |
| contents_scores = self.calculate_recom_scores(k, contents_sim) | |
| # union_scores = np.union1d(titles_scores, contents_scores) | |
| union_scores = list(set(titles_scores).union(set(contents_scores))) | |
| union_scores = sorted(union_scores, key=lambda x: x[1] if isinstance(x, tuple) else x, reverse=True) | |
| # indices = [i[0] if isinstance(i, tuple) else i for i in union_scores] | |
| # result = data[:, np.isin(data[0, :], indices)] | |
| unique_dict = {} | |
| for t in union_scores: | |
| if t[0] not in unique_dict or t[1] > unique_dict[t[0]][1]: | |
| unique_dict[t[0]] = t | |
| union_scores = list(unique_dict.values()) | |
| indices = [i[0] for i in union_scores] | |
| titles = '\n'.join(table[self.title_col][indices].astype(str)) | |
| return indices, union_scores, titles | |