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| import pandas as pd | |
| import networkx as nx | |
| import numpy as np | |
| # CARREGANDO DATAFRAME | |
| df_rio = pd.read_csv('./dataset.csv') | |
| df_rio['original_id'] = df_rio['original'] + '/' + df_rio['original_photo'] | |
| df_rio['compare_id'] = df_rio['compare'] + '/' + df_rio['compare_photo'] | |
| # CRIANDO GRAFO | |
| G = nx.Graph() | |
| for index, row in df_rio.iterrows(): | |
| if row['original_id'] not in G: | |
| G.add_node(row['original_id']) | |
| if row['compare_id'] not in G: | |
| G.add_node(row['compare_id']) | |
| weight = row['distance'] | |
| G.add_edge(row['original_id'], row['compare_id'], weight=weight) | |
| def get_graph(): | |
| return G | |
| def get_page_rank_graph(): | |
| # CRIANDO GRAFO PARA PAGE RANKING | |
| G_pr = nx.Graph() | |
| for index, row in df_rio.iterrows(): | |
| if row['original_id'] not in G_pr: | |
| G_pr.add_node(row['original_id']) | |
| if row['compare_id'] not in G_pr: | |
| G_pr.add_node(row['compare_id']) | |
| weight = np.abs(row['distance'] - 1) | |
| G_pr.add_edge(row['original_id'], row['compare_id'], weight=weight) | |
| return G_pr | |
| # ALGORITMOS DE RECOMENDAÇÃO | |
| def recommend_dijkstra(graph, original_id): | |
| distances, paths = nx.single_source_dijkstra( | |
| graph, original_id, weight='weight') | |
| recommended_paths = sorted( | |
| paths.items(), key=lambda item: distances[item[0]]) | |
| return recommended_paths | |
| def recommend_floyd_warshall(graph, original_id): | |
| distances = nx.floyd_warshall_numpy(graph, weight='weight') | |
| nodes = list(graph.nodes()) | |
| original_index = nodes.index(original_id) | |
| recommended_indices = sorted( | |
| range(len(nodes)), key=lambda i: distances[original_index][i]) | |
| recommended_paths = [(nodes[i], distances[original_index][i]) | |
| for i in recommended_indices] | |
| return recommended_paths | |
| def recommend_page_rank(graph, start_node): | |
| personalization = {node: 0 for node in graph.nodes()} | |
| personalization[start_node] = 1 | |
| personalized_pagerank_scores = nx.pagerank( | |
| graph, personalization=personalization, weight='weight') | |
| recommended_paths = sorted( | |
| personalized_pagerank_scores.items(), key=lambda item: item[1], reverse=True) | |
| return recommended_paths | |