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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv('../data/ratings.csv')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#df['rating'] = df['rating']-3\n",
"#df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[4. 5. 3. 2. 1. 4.5 3.5 2.5 0.5 1.5]\n",
"['userId' 'movieId' 'rating' 'timestamp']\n",
"[ 1 3 6 ... 160836 163937 163981]\n"
]
}
],
"source": [
"rating = df['rating'].unique()\n",
"print(rating)\n",
"print(df.columns.values)\n",
"\n",
"#df['userId'] = 'u'+df['userId']\n",
"#df['movieId'] = 'm'+df['movieId'].unique()\n",
"\n",
"movie = df['movieId'].unique()\n",
"print(movie)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# garantir que os ids dof filmes e usuários sejam diferentes\n",
"df['movieId'] = df['movieId'] + 610"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import networkx as nx\n",
"from networkx.algorithms import bipartite\n",
"\n",
"#df = df.query(expr='rating>3.5')\n",
"user_id_list = df['userId'].unique()\n",
"movie_id_list = df['movieId'].unique()\n",
"df = df.filter(['userId','movieId','rating'])\n",
"df_b = df.filter(['userId','movieId'])\n",
"df.filter(['userId','movieId'])\n",
"\n",
"B = nx.Graph()\n",
"# Add nodes with the node attribute \"bipartite\"\n",
"B.add_nodes_from(np.array(user_id_list), bipartite=0)\n",
"B.add_nodes_from(np.array(movie_id_list), bipartite=1)\n",
"# Add edges only between nodes of opposite node sets\n",
"B.add_edges_from(np.array(df_b))\n",
"nx.write_gexf(B, 'graph_b.gexf')\n",
"\n",
"df_r45 = df.query(expr='rating>=4').filter(['userId','movieId']) #gostou\n",
"B_r45 = nx.Graph()\n",
"# Add nodes with the node attribute \"bipartite\"\n",
"B_r45.add_nodes_from(np.array(user_id_list), bipartite=0)\n",
"B_r45.add_nodes_from(np.array(movie_id_list), bipartite=1)\n",
"# Add edges only between nodes of opposite node sets\n",
"B_r45.add_edges_from(np.array(df_r45))\n",
"nx.write_gexf(B_r45, 'graph_b_r45.gexf')\n",
"\n",
"df_indiferente = df.query(expr='rating<4 & rating>=2').filter(['userId','movieId']) #indiferente\n",
"B_indiferente = nx.Graph()\n",
"# Add nodes with the node attribute \"bipartite\"\n",
"B_indiferente.add_nodes_from(np.array(user_id_list), bipartite=0)\n",
"B_indiferente.add_nodes_from(np.array(movie_id_list), bipartite=1)\n",
"# Add edges only between nodes of opposite node sets\n",
"B_indiferente.add_edges_from(np.array(df_indiferente))\n",
"nx.write_gexf(B_indiferente, 'graph_b_indiferente.gexf')\n",
"\n",
"df_r01 = df.query(expr='rating<2').filter(['userId','movieId']) #não gostou\n",
"B_r01 = nx.Graph()\n",
"# Add nodes with the node attribute \"bipartite\"\n",
"B_r01.add_nodes_from(np.array(user_id_list), bipartite=0)\n",
"B_r01.add_nodes_from(np.array(movie_id_list), bipartite=1)\n",
"# Add edges only between nodes of opposite node sets\n",
"B_r01.add_edges_from(np.array(df_r01))\n",
"nx.write_gexf(B_r01, 'graph_b_r01.gexf')\n",
"\n",
"# rating centralizado\n",
"df['rating'] = df['rating'] -2.75\n",
"# B= bipartido W=weight/rating\n",
"BW = nx.Graph()\n",
"# Add nodes with the node attribute \"bipartite\"\n",
"BW.add_nodes_from(np.array(user_id_list), bipartite=0)\n",
"BW.add_nodes_from(np.array(movie_id_list), bipartite=1)\n",
"# Add edges only between nodes of opposite node sets\n",
"BW.add_weighted_edges_from(np.array(df))\n",
"nx.write_gexf(BW, 'graph_bw.gexf')\n",
"\n",
"am = bipartite.biadjacency_matrix(B, row_order=user_id_list)\n",
"G = bipartite.from_biadjacency_matrix(am)\n",
"nx.write_gexf(G, 'graph_g.gexf')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"am_user = am.dot(am.T)\n",
"df = pd.DataFrame(am_user)\n",
"G = nx.Graph(am_user)\n",
"nx.write_gexf(G, 'graph_am_user.gexf')\n",
"\n",
"#muito pesado\n",
"#am_movie = am.T.dot(am)\n",
"#df = pd.DataFrame(am_movie)\n",
"#G = nx.Graph(am_movie)\n",
"#nx.write_gexf(G, 'graph_am_movie.gexf')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10334\n",
"9724\n"
]
}
],
"source": [
"print(len(movie_id_list)+len(user_id_list))\n",
"print(len(movie_id_list))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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