LGNN_Semi-supervised_multi_dataset.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"“LGNN_Semi-supervised.ipynb”的副本","provenance":[{"file_id":"https://github.com/mamoonmasud/graph_community/blob/main/LGNN_Semi_supervised.ipynb","timestamp":1639283384194}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"egX1FxdfD8Xb","executionInfo":{"status":"ok","timestamp":1639332709264,"user_tz":360,"elapsed":6046,"user":{"displayName":"David Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}}},"outputs":[],"source":["import torch"]},{"cell_type":"code","source":["! pip install dgl-cu111 -f https://data.dgl.ai/wheels/repo.html "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bW5DoNAPEVGH","outputId":"38d6221a-a71b-4487-f660-8d86539ffd33","executionInfo":{"status":"ok","timestamp":1639332737508,"user_tz":360,"elapsed":21092,"user":{"displayName":"David Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}}},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in links: https://data.dgl.ai/wheels/repo.html\n","Collecting dgl-cu111\n","  Downloading https://data.dgl.ai/wheels/dgl_cu111-0.7.2-cp37-cp37m-manylinux1_x86_64.whl (165.0 MB)\n","\u001b[K     |████████████████████████████████| 165.0 MB 35 kB/s \n","\u001b[?25hRequirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from dgl-cu111) (2.23.0)\n","Requirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.7/dist-packages (from dgl-cu111) (2.6.3)\n","Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from dgl-cu111) (1.4.1)\n","Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from dgl-cu111) (1.19.5)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->dgl-cu111) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->dgl-cu111) (2021.10.8)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->dgl-cu111) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->dgl-cu111) (1.24.3)\n","Installing collected packages: dgl-cu111\n","Successfully installed dgl-cu111-0.7.2\n"]}]},{"cell_type":"code","source":["import dgl\n","import dgl.function as fn\n","from dgl import DGLGraph\n","from dgl.data import citation_graph as citegrh\n","from dgl.data import DGLDataset\n","# pytorch\n","import torch as th\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import pandas as pd\n","# other\n","import time\n","import numpy as np\n","import random as rng\n","import scipy.sparse as ss\n","import networkx as nx\n","import pickle\n","from sklearn import metrics as skmetrics\n","from scipy import sparse as sp\n","from sklearn.metrics import accuracy_score as acc\n","from math import log\n"],"metadata":{"id":"Q43A-4KgEfvw","executionInfo":{"status":"ok","timestamp":1639332738783,"user_tz":360,"elapsed":1279,"user":{"displayName":"David Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}},"outputId":"94d6a168-ff60-4f95-ea4b-f0aca98a208b","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["DGL backend not selected or invalid.  Assuming PyTorch for now.\n","Using backend: pytorch\n"]},{"output_type":"stream","name":"stdout","text":["Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable.  Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n"]}]},{"cell_type":"code","source":["from google.colab import files\n","def upload_files():\n","  from google.colab import files\n","  uploaded = files.upload()\n","  for k, v in uploaded.items():\n","    open(k, 'wb').write(v)\n","  return list(uploaded.keys())\n","\n","upload_files()\n","#Upload LGNN.py, performance.py and SparseMax.py"],"metadata":{"colab":{"resources":{"http://localhost:8080/nbextensions/google.colab/files.js":{"data":"Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7CgpmdW5jdGlvbiBfdXBsb2FkRmlsZXMoaW5wdXRJZCwgb3V0cHV0SWQpIHsKICBjb25zdCBzdGVwcyA9IHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCk7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICAvLyBDYWNoZSBzdGVwcyBvbiB0aGUgb3V0cHV0RWxlbWVudCB0byBtYWtlIGl0IGF2YWlsYWJsZSBmb3IgdGhlIG5leHQgY2FsbAogIC8vIHRvIHVwbG9hZEZpbGVzQ29udGludWUgZnJvbSBQeXRob24uCiAgb3V0cHV0RWxlbWVudC5zdGVwcyA9IHN0ZXBzOwoKICByZXR1cm4gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpOwp9CgovLyBUaGlzIGlzIHJvdWdobHkgYW4gYXN5bmMgZ2VuZXJhdG9yIChub3Qgc3VwcG9ydGVkIGluIHRoZSBicm93c2VyIHlldCksCi8vIHdoZXJlIHRoZXJlIGFyZSBtdWx0aXBsZSBhc3luY2hyb25vdXMgc3RlcHMgYW5kIHRoZSBQeXRob24gc2lkZSBpcyBnb2luZwovLyB0byBwb2xsIGZvciBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcC4KLy8gVGhpcyB1c2VzIGEgUHJvbWlzZSB0byBibG9jayB0aGUgcHl0aG9uIHNpZGUgb24gY29tcGxldGlvbiBvZiBlYWNoIHN0ZXAsCi8vIHRoZW4gcGFzc2VzIHRoZSByZXN1bHQgb2YgdGhlIHByZXZpb3VzIHN0ZXAgYXMgdGhlIGlucHV0IHRvIHRoZSBuZXh0IHN0ZXAuCmZ1bmN0aW9uIF91cGxvYWRGaWxlc0NvbnRpbnVlKG91dHB1dElkKSB7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICBjb25zdCBzdGVwcyA9IG91dHB1dEVsZW1lbnQuc3RlcHM7CgogIGNvbnN0IG5leHQgPSBzdGVwcy5uZXh0KG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSk7CiAgcmV0dXJuIFByb21pc2UucmVzb2x2ZShuZXh0LnZhbHVlLnByb21pc2UpLnRoZW4oKHZhbHVlKSA9PiB7CiAgICAvLyBDYWNoZSB0aGUgbGFzdCBwcm9taXNlIHZhbHVlIHRvIG1ha2UgaXQgYXZhaWxhYmxlIHRvIHRoZSBuZXh0CiAgICAvLyBzdGVwIG9mIHRoZSBnZW5lcmF0b3IuCiAgICBvdXRwdXRFbGVtZW50Lmxhc3RQcm9taXNlVmFsdWUgPSB2YWx1ZTsKICAgIHJldHVybiBuZXh0LnZhbHVlLnJlc3BvbnNlOwogIH0pOwp9CgovKioKICogR2VuZXJhdG9yIGZ1bmN0aW9uIHdoaWNoIGlzIGNhbGxlZCBiZXR3ZWVuIGVhY2ggYXN5bmMgc3RlcCBvZiB0aGUgdXBsb2FkCiAqIHByb2Nlc3MuCiAqIEBwYXJhbSB7c3RyaW5nfSBpbnB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIGlucHV0IGZpbGUgcGlja2VyIGVsZW1lbnQuCiAqIEBwYXJhbSB7c3RyaW5nfSBvdXRwdXRJZCBFbGVtZW50IElEIG9mIHRoZSBvdXRwdXQgZGlzcGxheS4KICogQHJldHVybiB7IUl0ZXJhYmxlPCFPYmplY3Q+fSBJdGVyYWJsZSBvZiBuZXh0IHN0ZXBzLgogKi8KZnVuY3Rpb24qIHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IGlucHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKGlucHV0SWQpOwogIGlucHV0RWxlbWVudC5kaXNhYmxlZCA9IGZhbHNlOwoKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIG91dHB1dEVsZW1lbnQuaW5uZXJIVE1MID0gJyc7CgogIGNvbnN0IHBpY2tlZFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgaW5wdXRFbGVtZW50LmFkZEV2ZW50TGlzdGVuZXIoJ2NoYW5nZScsIChlKSA9PiB7CiAgICAgIHJlc29sdmUoZS50YXJnZXQuZmlsZXMpOwogICAgfSk7CiAgfSk7CgogIGNvbnN0IGNhbmNlbCA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2J1dHRvbicpOwogIGlucHV0RWxlbWVudC5wYXJlbnRFbGVtZW50LmFwcGVuZENoaWxkKGNhbmNlbCk7CiAgY2FuY2VsLnRleHRDb250ZW50ID0gJ0NhbmNlbCB1cGxvYWQnOwogIGNvbnN0IGNhbmNlbFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgY2FuY2VsLm9uY2xpY2sgPSAoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9OwogIH0pOwoKICAvLyBXYWl0IGZvciB0aGUgdXNlciB0byBwaWNrIHRoZSBmaWxlcy4KICBjb25zdCBmaWxlcyA9IHlpZWxkIHsKICAgIHByb21pc2U6IFByb21pc2UucmFjZShbcGlja2VkUHJvbWlzZSwgY2FuY2VsUHJvbWlzZV0pLAogICAgcmVzcG9uc2U6IHsKICAgICAgYWN0aW9uOiAnc3RhcnRpbmcnLAogICAgfQogIH07CgogIGNhbmNlbC5yZW1vdmUoKTsKCiAgLy8gRGlzYWJsZSB0aGUgaW5wdXQgZWxlbWVudCBzaW5jZSBmdXJ0aGVyIHBpY2tzIGFyZSBub3QgYWxsb3dlZC4KICBpbnB1dEVsZW1lbnQuZGlzYWJsZWQgPSB0cnVlOwoKICBpZiAoIWZpbGVzKSB7CiAgICByZXR1cm4gewogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgICAgfQogICAgfTsKICB9CgogIGZvciAoY29uc3QgZmlsZSBvZiBmaWxlcykgewogICAgY29uc3QgbGkgPSBkb2N1bWVudC5jcmVhdGVFbGVtZW50KCdsaScpOwogICAgbGkuYXBwZW5kKHNwYW4oZmlsZS5uYW1lLCB7Zm9udFdlaWdodDogJ2JvbGQnfSkpOwogICAgbGkuYXBwZW5kKHNwYW4oCiAgICAgICAgYCgke2ZpbGUudHlwZSB8fCAnbi9hJ30pIC0gJHtmaWxlLnN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Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}}},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","     <input type=\"file\" id=\"files-7d5c3eee-01b0-4282-abdb-98b0e39811cb\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-7d5c3eee-01b0-4282-abdb-98b0e39811cb\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script src=\"/nbextensions/google.colab/files.js\"></script> "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving git_edges.csv to git_edges.csv\n"]},{"output_type":"execute_result","data":{"text/plain":["['git_edges.csv']"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["upload_files()"],"metadata":{"id":"i9chf0l0W5W7","executionInfo":{"status":"ok","timestamp":1639333183384,"user_tz":360,"elapsed":15157,"user":{"displayName":"David Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}},"outputId":"5e3210c2-d8fd-4f8c-b32b-b3df18d8ca35","colab":{"resources":{"http://localhost:8080/nbextensions/google.colab/files.js":{"data":"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","ok":true,"headers":[["content-type","application/javascript"]],"status":200,"status_text":""}},"base_uri":"https://localhost:8080/","height":90}},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","     <input type=\"file\" id=\"files-9bbc0ae0-7dc4-4a2d-b9f8-c31e2905c370\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-9bbc0ae0-7dc4-4a2d-b9f8-c31e2905c370\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script src=\"/nbextensions/google.colab/files.js\"></script> "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving papers.csv to papers.csv\n"]},{"output_type":"execute_result","data":{"text/plain":["['papers.csv']"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["pip install LGNN"],"metadata":{"id":"WWpUt3l7vflq","executionInfo":{"status":"ok","timestamp":1639180017358,"user_tz":360,"elapsed":1044,"user":{"displayName":"","photoUrl":"","userId":""}},"outputId":"911bc043-0419-4274-bef9-03bfa5b9adb8","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[31mERROR: Could not find a version that satisfies the requirement LGNN (from versions: none)\u001b[0m\n","\u001b[31mERROR: No matching distribution found for LGNN\u001b[0m\n"]}]},{"cell_type":"code","source":["import LGNN\n","#from LGNN import LGNN_Net\n","import importlib\n","importlib.reload(LGNN)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":427},"id":"XXKBSr1xE4rV","outputId":"7999c862-f229-49bc-f380-51c3d3fb657a","executionInfo":{"status":"error","timestamp":1639180002389,"user_tz":360,"elapsed":257,"user":{"displayName":"","photoUrl":"","userId":""}}},"execution_count":null,"outputs":[{"output_type":"error","ename":"ModuleNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","\u001b[0;32m<ipython-input-5-cd95e86c4b1b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mLGNN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#from LGNN import LGNN_Net\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLGNN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'LGNN'","","\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"],"errorDetails":{"actions":[{"action":"open_url","actionText":"Open Examples","url":"/notebooks/snippets/importing_libraries.ipynb"}]}}]},{"cell_type":"code","source":["# LGNN Definition\n","\n","\n","class LGNN_Net(nn.Module):\n","    \"\"\"This is a whole LGNN\"\"\"\n","\n","    def __init__(\n","        self, g, in_feats, hidden_size, hidden_layers, out_feats, dropout, batchnorm, lg, radius\n","    ):\n","        super(LGNN_Net, self).__init__()\n","        self.g = g\n","        self.lg = lg\n","        in_feats = [in_feats] + [hidden_size] * hidden_layers\n","        self.module_list = nn.ModuleList(\n","            [LGNNModule(m, n, radius, batchnorm) for m, n in zip(in_feats[:-1], in_feats[1:])]\n","        )\n","        self.linear = nn.Linear(in_feats[-1], out_feats)\n","        self.dropout = nn.Dropout(dropout)\n","\n","        self.pmpd = self.g.edges()[0]\n","\n","        # compute the degrees\n","        self.deg_g = self.g.in_degrees().float().unsqueeze(1)\n","        self.deg_lg = self.lg.in_degrees().float().unsqueeze(1)\n","\n","    def forward(self, features):\n","        (h, lg_h) = features\n","        for i, module in enumerate(self.module_list):\n","            if i == len(self.module_list) - 1:\n","                h = module(\n","                    self.g, self.lg, h, lg_h, self.deg_g, self.deg_lg, self.pmpd, last=True\n","                )  # the last pass should only return h, otherwhise we get a memory leak\n","                h = self.dropout(h)\n","            else:\n","                h, lg_h = module(self.g, self.lg, h, lg_h, self.deg_g, self.deg_lg, self.pmpd)\n","                h = self.dropout(h)\n","                lg_h = self.dropout(lg_h)\n","        h = self.linear(h)\n","        h = F.log_softmax(h, 1)\n","        return h\n"],"metadata":{"id":"qRdpjAsqKeBA","executionInfo":{"status":"ok","timestamp":1639333185673,"user_tz":360,"elapsed":98,"user":{"displayName":"David Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["\n","class LGNNModule(nn.Module):\n","    \"\"\"This is a graph network block of LGNN\"\"\"\n","\n","    def __init__(self, in_feats, out_feats, radius, batchnorm):\n","        super().__init__()\n","        self.out_feats = out_feats\n","        self.radius = radius\n","\n","        new_linear = lambda: nn.Linear(in_feats, out_feats)\n","        new_linear_list = lambda: nn.ModuleList([new_linear() for i in range(radius)])\n","\n","        self.theta_x, self.theta_deg, self.theta_y = new_linear(), new_linear(), new_linear()\n","        self.theta_list = new_linear_list()\n","\n","        self.gamma_y, self.gamma_deg, self.gamma_x = new_linear(), new_linear(), new_linear()\n","        self.gamma_list = new_linear_list()\n","\n","        self.batchnorm = batchnorm\n","        self.bn_x = nn.BatchNorm1d(out_feats)\n","        self.bn_y = nn.BatchNorm1d(out_feats)\n","\n","    def aggregate(self, a_graph, z):\n","        z_list = []\n","        a_graph.ndata['z'] = z\n","        # ntid = a_graph.get_ntype_id(None)\n","        #a_graph._set_n_repr(ntid, u= z, data = {\"z\": z})\n","        a_graph.update_all(fn.copy_src(src=\"z\", out=\"m\"), fn.sum(msg=\"m\", out=\"z\"))\n","        z_list.append(a_graph.ndata[\"z\"])\n","        for i in range(self.radius - 1):\n","            for j in range(2 ** i):\n","                a_graph.update_all(fn.copy_src(src=\"z\", out=\"m\"), fn.sum(msg=\"m\", out=\"z\"))\n","            z_list.append(a_graph.ndata[\"z\"])\n","        return z_list\n","        \n","\n","    def forward(self, g, lg, x, y, deg_g, deg_lg, pm_pd, last=False):\n","        pmpd_x = F.embedding(pm_pd, x)\n","\n","        sum_x = sum(theta(z) for theta, z in zip(self.theta_list, self.aggregate(g, x)))\n","\n","        g.edata[\"y\"] = y\n","        g.update_all(fn.copy_edge(edge=\"y\", out=\"m\"), fn.sum(\"m\", \"pmpd_y\"))\n","        pmpd_y = g.ndata.pop(\"pmpd_y\")\n","\n","        x = self.theta_x(x) + self.theta_deg(deg_g * x) + sum_x + self.theta_y(pmpd_y)\n","        n = self.out_feats // 2\n","        x = th.cat([x[:, :n], F.relu(x[:, n:])], 1)\n","        if self.batchnorm:\n","            x = self.bn_x(x)\n","\n","        if last:\n","            return x  # do not compute the rest of the lgnn part to avoid memory leak\n","\n","        sum_y = sum(gamma(z) for gamma, z in zip(self.gamma_list, self.aggregate(lg, y)))\n","\n","        y = self.gamma_y(y) + self.gamma_deg(deg_lg * y) + sum_y + self.gamma_x(pmpd_x)\n","        y = th.cat([y[:, :n], F.relu(y[:, n:])], 1)\n","        if self.batchnorm:\n","            y = self.bn_y(y)\n","\n","        return x, y"],"metadata":{"id":"lcNbENCYKlNB","executionInfo":{"status":"ok","timestamp":1639333187129,"user_tz":360,"elapsed":233,"user":{"displayName":"David Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}}},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":["## Loading the dataset"],"metadata":{"id":"OwZ0MYq3F1fp"}},{"cell_type":"code","source":["#Loading CORA\n","data = citegrh.load_cora()\n","\n","g = data[0]\n","num_class = data.num_classes\n","\n","features = g.ndata['feat']\n","\n","train_mask = g.ndata['train_mask']\n","val_mask = g.ndata['val_mask']\n","test_mask = g.ndata['test_mask']\n","labels = g.ndata['label']\n","\n","# add self loop\n","#g.remove_edges_from(nx.selfloop_edges(g))\n","g  = dgl.remove_self_loop(g)\n","#g = DGLGraph(g)\n","#g.add_edges(g.nodes(), g.nodes())\n","\n","comb_mask = th.zeros(len(train_mask), dtype=torch.bool)\n","\n","for i in range(len(comb_mask)):\n","    if test_mask[i] or train_mask[i] or val_mask[i]:\n","        comb_mask[i] = True\n","\n","lg_feat = np.vstack([features[e[0],:]*features[e[1],:] for e in data.graph.edges])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fMUUBl9BE-oq","outputId":"677a1a4a-b0de-4b44-b621-8dda8cf96e8e","executionInfo":{"status":"ok","timestamp":1639241299344,"user_tz":360,"elapsed":1038,"user":{"displayName":"","photoUrl":"","userId":""}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading /root/.dgl/cora_v2.zip from https://data.dgl.ai/dataset/cora_v2.zip...\n","Extracting file to /root/.dgl/cora_v2\n","Finished data loading and preprocessing.\n","  NumNodes: 2708\n","  NumEdges: 10556\n","  NumFeats: 1433\n","  NumClasses: 7\n","  NumTrainingSamples: 140\n","  NumValidationSamples: 500\n","  NumTestSamples: 1000\n","Done saving data into cached files.\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/dgl/data/utils.py:286: UserWarning: Property dataset.graph will be deprecated, please use dataset[0] instead.\n","  warnings.warn('Property {} will be deprecated, please use {} instead.'.format(old, new))\n"]}]},{"cell_type":"code","source":["#load pubmed\n","from dgl.data import PubmedGraphDataset as pubmed\n","import networkx as nx\n","\n","data = pubmed()\n","\n","# features = th.FloatTensor(data.features)\n","# labels = th.LongTensor(data.labels)\n","# mask = th.ByteTensor(data.train_mask)\n","#g1 = data.graph\n","\n","##\n","g = data[0]\n","num_class = data.num_classes\n","\n","features = g.ndata['feat']\n","\n","train_mask = g.ndata['train_mask']\n","val_mask = g.ndata['val_mask']\n","test_mask = g.ndata['test_mask']\n","labels = g.ndata['label']\n","\n","# add self loop\n","#g.remove_edges_from(nx.selfloop_edges(g))\n","g  = dgl.remove_self_loop(g)\n","#g = DGLGraph(g)\n","#g.add_edges(g.nodes(), g.nodes())\n","\n","comb_mask = torch.zeros(len(train_mask), dtype=torch.bool)\n","\n","for i in range(len(comb_mask)):\n","    if test_mask[i] or train_mask[i] or val_mask[i]:\n","        comb_mask[i] = True\n","lg_feat = np.vstack([features[e[0],:]*features[e[1],:] for e in data.graph.edges])"],"metadata":{"id":"prNWu4ItWAzw","executionInfo":{"status":"ok","timestamp":1639241316380,"user_tz":360,"elapsed":3458,"user":{"displayName":"","photoUrl":"","userId":""}},"outputId":"b0f8f24b-cdb0-4857-df4c-acd6c4488915","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["  NumNodes: 19717\n","  NumEdges: 88651\n","  NumFeats: 500\n","  NumClasses: 3\n","  NumTrainingSamples: 60\n","  NumValidationSamples: 500\n","  NumTestSamples: 1000\n","Done loading data from cached files.\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/dgl/data/utils.py:286: UserWarning: Property dataset.graph will be deprecated, please use dataset[0] instead.\n","  warnings.warn('Property {} will be deprecated, please use {} instead.'.format(old, new))\n"]}]},{"cell_type":"code","source":["# load github\n","# Generating a dgl graph from the given csv files of github dataset at: http://snap.stanford.edu/data/github-social.html\n","\n","class GitHubDataset(DGLDataset):\n","    def __init__(self):\n","        super().__init__(name='github_dataset')\n","\n","    def process(self):\n","        nodes_data = pd.read_csv('papers.csv')\n","        edges_data = pd.read_csv('git_edges.csv')\n","        node_target = pd.read_csv('musae_git_target.csv')\n","        node_features = torch.from_numpy(nodes_data.to_numpy())\n","        node_labels = torch.from_numpy(node_target[\"ml_target\"].astype('category').cat.codes.to_numpy())\n","        #edge_features = torch.from_numpy(edges_data['Weight'].to_numpy())\n","        edges_src = torch.from_numpy(edges_data['source'].to_numpy())\n","        edges_dst = torch.from_numpy(edges_data['target'].to_numpy())\n","\n","        self.graph = dgl.graph((edges_src, edges_dst), num_nodes=nodes_data.shape[0])\n","        self.graph.ndata['feat'] = node_features\n","        self.graph.ndata['label'] = node_labels\n","        #self.graph.edata['weight'] = edge_features\n","        # If your dataset is a node classification dataset, you will need to assign\n","        # masks indicating whether a node belongs to training, validation, and test set.\n","        n_nodes = nodes_data.shape[0]\n","        n_train = int(n_nodes * 0.6)\n","        n_val = int(n_nodes * 0.2)\n","        train_mask = torch.zeros(n_nodes, dtype=torch.bool)\n","        val_mask = torch.zeros(n_nodes, dtype=torch.bool)\n","        test_mask = torch.zeros(n_nodes, dtype=torch.bool)\n","        train_mask[:n_train] = True\n","        val_mask[n_train:n_train + n_val] = True\n","        test_mask[n_train + n_val:] = True\n","        self.graph.ndata['train_mask'] = train_mask\n","        self.graph.ndata['val_mask'] = val_mask\n","        self.graph.ndata['test_mask'] = test_mask\n","\n","    def __getitem__(self, i):\n","        return self.graph\n","\n","    def __len__(self):\n","        return 1\n","\n","data = GitHubDataset()\n","graph = data[0]\n","\n","num_class = 2\n","\n","features = graph.ndata['feat']\n","\n","train_mask = graph.ndata['train_mask']\n","val_mask = graph.ndata['val_mask']\n","test_mask = graph.ndata['test_mask']\n","labels = graph.ndata['label']\n","\n","# add self loop\n","#g.remove_edges_from(nx.selfloop_edges(g))\n","graph = dgl.remove_self_loop(graph)\n","#g = DGLGraph(g)\n","graph.add_edges(graph.nodes(), graph.nodes())\n","\n","comb_mask = torch.zeros(len(train_mask), dtype=torch.bool)\n","\n","for i in range(len(comb_mask)):\n","    if test_mask[i] or train_mask[i] or val_mask[i]:\n","        comb_mask[i] = True"],"metadata":{"id":"qCByqhroLmr_","executionInfo":{"status":"ok","timestamp":1639333195423,"user_tz":360,"elapsed":884,"user":{"displayName":"David Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"3d639d8f-695c-4376-e7d4-29ea32aa9aa7"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ../torch/csrc/utils/tensor_numpy.cpp:189.)\n","  del sys.path[0]\n"]}]},{"cell_type":"code","source":["# Generating a dgl graph from the given csv files of github dataset at: http://snap.stanford.edu/data/github-social.html\n","\n","# class GitHubDataset(DGLDataset):\n","#     def __init__(self):\n","#         super().__init__(name='github_dataset')\n","\n","#     def process(self):\n","#         nodes_data = pd.read_csv('drive/MyDrive/CSCE689/data_git/papers.csv')\n","#         edges_data = pd.read_csv('drive/MyDrive/CSCE689/data_git/git_edges.csv')\n","#         node_target = pd.read_csv('drive/MyDrive/CSCE689/data_git/musae_git_target.csv')\n","#         node_features = torch.from_numpy(nodes_data.to_numpy())\n","#         node_labels = torch.from_numpy(node_target[\"ml_target\"].astype('category').cat.codes.to_numpy())\n","#         #edge_features = torch.from_numpy(edges_data['Weight'].to_numpy())\n","#         edges_src = torch.from_numpy(edges_data['source'].to_numpy())\n","#         edges_dst = torch.from_numpy(edges_data['target'].to_numpy())\n","\n","#         self.graph = dgl.graph((edges_src, edges_dst), num_nodes=nodes_data.shape[0])\n","#         self.graph.ndata['feat'] = node_features\n","#         self.graph.ndata['label'] = node_labels\n","#         #self.graph.edata['weight'] = edge_features\n","\n","#         # If your dataset is a node classification dataset, you will need to assign\n","#         # masks indicating whether a node belongs to training, validation, and test set.\n","#         n_nodes = nodes_data.shape[0]\n","#         n_train = int(n_nodes * 0.6)\n","#         n_val = int(n_nodes * 0.2)\n","#         train_mask = torch.zeros(n_nodes, dtype=torch.bool)\n","#         val_mask = torch.zeros(n_nodes, dtype=torch.bool)\n","#         test_mask = torch.zeros(n_nodes, dtype=torch.bool)\n","#         train_mask[:n_train] = True\n","#         val_mask[n_train:n_train + n_val] = True\n","#         test_mask[n_train + n_val:] = True\n","#         self.graph.ndata['train_mask'] = train_mask\n","#         self.graph.ndata['val_mask'] = val_mask\n","#         self.graph.ndata['test_mask'] = test_mask\n","\n","#     def __getitem__(self, i):\n","#         return self.graph\n","\n","#     def __len__(self):\n","#         return 1\n","\n","# dataset = GitHubDataset()\n","# graph = dataset[0]"],"metadata":{"id":"HPOYO_UUY6Qp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# num_class = 2\n","\n","# features = graph.ndata['feat']\n","\n","# train_mask = graph.ndata['train_mask']\n","# val_mask = graph.ndata['val_mask']\n","# test_mask = graph.ndata['test_mask']\n","# labels = graph.ndata['label']\n","\n","# # add self loop\n","# #g.remove_edges_from(nx.selfloop_edges(g))\n","# graph = dgl.remove_self_loop(graph)\n","# #g = DGLGraph(g)\n","# graph.add_edges(graph.nodes(), graph.nodes())\n","\n","# comb_mask = torch.zeros(len(train_mask), dtype=torch.bool)\n","\n","# for i in range(len(comb_mask)):\n","#     if test_mask[i] or train_mask[i] or val_mask[i]:\n","#         comb_mask[i] = True\n"],"metadata":{"id":"mUwA1qM1Z5vF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# create line graph for all dataset\n","#graph = g\n","lg = graph.line_graph(backtracking=False)"],"metadata":{"id":"r6zQuGzWMCBR","executionInfo":{"status":"ok","timestamp":1639333297606,"user_tz":360,"elapsed":86156,"user":{"displayName":"David Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["print('The graph has %d nodes.' % graph.number_of_nodes())\n","print('The graph has %d edges.' % graph.number_of_edges())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HASijI-vHjvl","outputId":"cbd281cf-80bd-4867-85d6-fb427837c6c5","executionInfo":{"status":"ok","timestamp":1639333209073,"user_tz":360,"elapsed":79,"user":{"displayName":"David Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}}},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["The graph has 37700 nodes.\n","The graph has 326703 edges.\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"D3ajTR01Yi8N"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#partition for cora\n","# Training set selection\n","\n","percentage_train = 0.1\n","percentage_val = 0.1\n","\n","#with open(\"drive/MyDrive/CSCE689/data/cora_permutation1.pickle\",\"rb\") as f:\n","#    perm1 = pickle.load(f)\n","#from itertools import permutations\n","\n","num_node = [i for i in range(1,g.number_of_nodes()+1)]\n","perm1 = num_node #list(permutations(num_node))\n","mask_train = np.zeros(g.number_of_nodes())\n","mask_val = np.zeros(g.number_of_nodes())\n","\n","i_train = int(percentage_train*g.number_of_nodes())\n","i_val = i_train + int(percentage_val*g.number_of_nodes())\n","mask_train[perm1[0:i_train)]] = 1\n","mask_val[perm1[range(i_train,i_val)]] = 1\n","mask_train = th.BoolTensor(mask_train)\n","mask_val = th.BoolTensor(mask_val)"],"metadata":{"id":"t7PpQJ_EIKHD","executionInfo":{"status":"error","timestamp":1639241126743,"user_tz":360,"elapsed":114,"user":{"displayName":"","photoUrl":"","userId":""}},"outputId":"44e1d9b9-b9e4-4376-a408-8205186f4fd4","colab":{"base_uri":"https://localhost:8080/","height":131}},"execution_count":null,"outputs":[{"output_type":"error","ename":"SyntaxError","evalue":"ignored","traceback":["\u001b[0;36m  File \u001b[0;32m\"<ipython-input-13-54dcfecf6475>\"\u001b[0;36m, line \u001b[0;32m17\u001b[0m\n\u001b[0;31m    mask_train[perm1[0:i_train)]] = 1\u001b[0m\n\u001b[0m                              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"]}]},{"cell_type":"code","source":["#partition for pubmed mask\n","\n","percentage_train = 0.1\n","percentage_val = 0.1\n","with open(\"pubmed_permutation6.pickle\",\"rb\") as f:\n","    perm1 = pickle.load(f)\n","mask_train = np.zeros(g.number_of_nodes())\n","mask_val = np.zeros(g.number_of_nodes())\n","\n","i_train = int(percentage_train*g.number_of_nodes())\n","i_val = i_train + int(percentage_val*g.number_of_nodes())\n","mask_train[perm1[range(0,i_train)]] = 1\n","mask_val[perm1[range(i_train,i_val)]] = 1\n","mask_train = th.BoolTensor(mask_train)\n","mask_val = th.BoolTensor(mask_val)"],"metadata":{"id":"-UQ4w-kcXF0T"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#partition for github\n","percentage_train = 0.6\n","percentage_val = 0.2\n","\n","with open(\"git_permutation4.pickle\",\"rb\") as f:\n","    perm1 = pickle.load(f)\n","mask_train = np.zeros(graph.number_of_nodes())\n","mask_val = np.zeros(graph.number_of_nodes())\n","\n","i_train = int(percentage_train*graph.number_of_nodes())\n","i_val = i_train + int(percentage_val*graph.number_of_nodes())\n","mask_train[perm1[range(0,i_train)]] = 1\n","mask_val[perm1[range(i_train,i_val)]] = 1\n","mask_train = th.BoolTensor(mask_train)\n","mask_val = th.BoolTensor(mask_val)"],"metadata":{"id":"DiMA6WbfMWw4","executionInfo":{"status":"error","timestamp":1639333297957,"user_tz":360,"elapsed":7,"user":{"displayName":"David Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}},"colab":{"base_uri":"https://localhost:8080/","height":236},"outputId":"77e60643-654f-445c-aa38-eebcbfda54ee"},"execution_count":13,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-13-bef8ffad11f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpercentage_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"git_permutation4.pickle\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mperm1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mmask_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber_of_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'git_permutation4.pickle'"]}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"Lozv21kT4MVD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Training Block\n","\n","# create the model\n","degrees = False\n","if degrees:\n","    # use degree as the input feature\n","    # compute the degrees\n","    g_feat = g.in_degrees().float().unsqueeze(1)\n","    lg_feat = lg.in_degrees().float().unsqueeze(1)  \n","else:\n","    # use astracts as input feature\n","    g_feat = features\n","    lg_feat = th.FloatTensor(np.vstack([features[e[0],:] for e in data.graph.edges]))\n","    \n","net = LGNN_Net(g=g,in_feats=g_feat.shape[1],hidden_size=21,hidden_layers=2,out_feats=7,\n","               dropout=0.1,batchnorm=False,lg=lg,radius=2)\n","\n","# define the optimizer\n","optimizer = th.optim.Adam(net.parameters(), lr=0.01, weight_decay=0.1)\n","\n","all_logits = []\n","all_losses = []\n","all_losses_val = []\n","\n","# train\n","for epoch in range(1000):\n","\n","    # Compute loss for test nodes (only for validation, not used by optimizer)\n","    net.eval()\n","    prediction = F.log_softmax(net((g_feat, lg_feat)),1)\n","    val_loss = F.nll_loss(prediction.detach()[mask_val], labels[mask_val])\n","    all_losses_val.append(val_loss.item())\n","    val_acc = acc(labels[mask_val],prediction.detach()[mask_val].numpy().argmax(axis=1))\n","    net.train()\n","\n","    logits = net((g_feat, lg_feat))\n","    \n","    # Save logits for visualization later\n","    all_logits.append(logits.detach())\n","    logp = F.log_softmax(logits, 1)\n","\n","    # Compute loss for train nodes\n","    loss = F.nll_loss(logp[mask_train], labels[mask_train])\n","    all_losses.append(loss.detach().item())\n","\n","    optimizer.zero_grad()\n","    loss.backward()\n","    optimizer.step()\n","    \n","    print('Epoch %d | Loss: %.4f | Total: %.4f | Acc: %.4f' % (epoch, loss.item(), val_loss.item(),val_acc))"],"metadata":{"id":"TE6YZ-rGIioj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import time\n","g= graph\n","# create the model\n","degrees = True\n","if degrees:\n","    # use degree as the input feature\n","    # compute the degrees\n","    g_feat = g.in_degrees().float().unsqueeze(1)\n","    lg_feat = lg.in_degrees().float().unsqueeze(1)  \n","else:\n","    # use astracts as input feature\n","    g_feat = th.FloatTensor(data.features)\n","    lg_feat = th.FloatTensor(np.vstack([data.features[e[0],:] for e in data.graph.edges]))\n","features = (g_feat,lg_feat)\n","    \n","net = LGNN_Net(g=g,in_feats=g_feat.shape[1],hidden_size=21,hidden_layers=2,out_feats=7,\n","               dropout=0.1,batchnorm=False,lg=lg,radius=2)\n","#print(net)\n","\n","optimizer = th.optim.Adam(net.parameters(), lr=1e-2, weight_decay=1e-2)\n","net.train() # Set to training mode (use dropout)\n","\n","dur = []\n","loss_ev = []\n","current_best = 0 #arbitrarily high\n","current_best_epoch = 0\n","current_best_params = None\n","no_improvement_for = 0\n","\n","for epoch in range(10000):\n","    if epoch >=3:\n","        t0 = time.time()\n","\n","    # Compute loss for test nodes (only for validation, not used by optimizer)\n","    net.eval()\n","    prediction = net(features)\n","    train_rand=pf.rand_score(labels[mask_train].numpy(),np.argmax(prediction[mask_train].detach().numpy(), axis=1))\n","    validation_rand=pf.rand_score(labels[mask_val].numpy(),np.argmax(prediction[mask_val].detach().numpy(), axis=1))\n","    if train_rand>current_best:\n","        current_best = train_rand\n","        current_best_epoch = epoch\n","        current_best_params = copy.deepcopy(net.state_dict())\n","        no_improvement_for = 0\n","    else: no_improvement_for += 1\n","    \n","    if no_improvement_for>100:\n","        break\n","    \n","    net.train()\n","\n","    # Compute loss for train nodes\n","    logits = net(features)\n","\n","    loss = loss_function.approximate_loss(logits,mask_train,nclasses=7)\n","    loss_ev.append(loss.detach().item())\n","\n","    #loss = F.nll_loss(logits[mask_train], labels[mask_train])\n","    \n","    optimizer.zero_grad()\n","    loss.backward()\n","    optimizer.step()\n","\n","    if epoch >=3:\n","        dur.append(time.time() - t0)\n","        print(f\"Epoch {epoch:05d} | Loss {loss.item():.4f} | Train.Rand {train_rand:.4f} | Valid.Rand {validation_rand:.4f} | Time(s) {np.mean(dur):.4f}\")\n","    else:\n","        print(f\"Epoch {epoch:05d} | Loss {loss.item():.4f} | Train.Rand {train_rand:.4f} | Valid.Rand {validation_rand:.4f} | Time(s) unknown\")\n","        \n","net.load_state_dict(current_best_params)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":270},"id":"YLX_pB0x5SXb","outputId":"ec9f7454-4997-43f6-ef4f-012407573a73","executionInfo":{"status":"error","timestamp":1639291512345,"user_tz":360,"elapsed":3506,"user":{"displayName":"David Xiang","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjtJKfUVG2DYaiEewusXcRLqc2hPIHM-T_hDZl5=s64","userId":"02261116886270133472"}}},"execution_count":17,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-17-16bfe17df2fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m     \u001b[0mtrain_rand\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask_train\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask_train\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m     \u001b[0mvalidation_rand\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask_val\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask_val\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtrain_rand\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0mcurrent_best\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'pf' is not defined"]}]},{"cell_type":"code","source":["import seaborn as sns\n","sns.lineplot(x=range(len(loss_ev)), y=loss_ev)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"KHEIcmUzAI9H","outputId":"d08a951b-6e5d-453e-ba60-daf8e347a71a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["import performance as pf\n","import copy\n","loss_function = pf.perm_inv_loss(labels)\n"],"metadata":{"id":"6gu-RankIqLc"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":[""],"metadata":{"id":"aI_Eda_75R7K"}},{"cell_type":"code","source":["net.load_state_dict(current_best_params)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xgvzhkEhM66I","outputId":"c40b03e5-3df5-4028-eab0-6b4f2eb0c493"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<All keys matched successfully>"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","source":["# Visualise predictions\n","net.eval() # Set net to evaluation mode (deactivates dropout)\n","final_prediction = net(features).detach()\n","a = np.transpose(np.vstack([final_prediction[comb_mask].numpy().argmax(axis=1),labels[comb_mask].numpy()]))\n","a[a[:,0].argsort()][np.random.choice(range(a.shape[0]),size=10)]\n"],"metadata":{"id":"4oAx0ebrNVs9","colab":{"base_uri":"https://localhost:8080/"},"outputId":"06cba69e-02e5-4e5e-abb5-3518906a5f29"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0, 5],\n","       [3, 3],\n","       [5, 2],\n","       [1, 0],\n","       [4, 6],\n","       [4, 0],\n","       [2, 1],\n","       [6, 6],\n","       [3, 3],\n","       [5, 2]])"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["# Performnace evaluation functions\n","\n","def variation_of_information_score(labels, preds):\n","    def mi(x, y):\n","        contingency = skmetrics.cluster.contingency_matrix(x, y, sparse=True)\n","        # print(contingency.todense())\n","        nzx, nzy, nz_val = sp.find(contingency)\n","        contingency_sum = contingency.sum()\n","\n","        pi = np.ravel(contingency.sum(axis=1))\n","        pj = np.ravel(contingency.sum(axis=0))\n","        # print(nz_val)\n","        log_contingency_nm = np.log(nz_val)\n","        # print(log_contingency_nm)\n","        contingency_nm = nz_val / contingency_sum\n","        # print(contingency_nm)\n","\n","        # Don't need to calculate the full outer product, just for non-zeroes\n","        outer = pi.take(nzx).astype(np.int64, copy=False) * pj.take(nzy).astype(\n","            np.int64, copy=False\n","        )\n","        # print(outer)\n","        log_outer = -np.log(outer) + log(pi.sum()) + log(pj.sum())\n","        # print(log_outer)\n","        mi = (\n","            contingency_nm * (log_contingency_nm - log(contingency_sum))\n","            + contingency_nm * log_outer\n","        )\n","        # print(mi)\n","        return mi.sum()\n","\n","    return mi(labels, labels) + mi(preds, preds) - 2 * mi(labels, preds)\n","\n","\n","def mutual_info_score(labels, preds):\n","    return skmetrics.adjusted_mutual_info_score(labels, preds, average_method=\"arithmetic\")\n","\n","def compute_performance(labels, logits, splits):\n","    logits = logits.detach().numpy()\n","    preds = np.argmax(logits, axis=1)\n","    labels = labels.numpy()\n","    pred_sets = {\n","        \"All \": preds,\n","        \"Train\": preds[mask_train],\n","        \"Val\": preds[mask_val],\n","        \"Test\": preds[test_mask],\n","    }\n","    label_sets = {\n","        \"All \": labels,\n","        \"Train\": labels[mask_train],\n","        \"Val\": labels[mask_val],\n","        \"Test\": labels[test_mask],\n","    }\n","    eval_functions = {\n","        \"Rand-Index\": rand_score,\n","        \"Mutual Information\": mutual_info_score,\n","        \"Variation of Information\": variation_of_information_score,\n","    }\n","    scores = {\n","        subset: {\n","            name: func(label_sets[subset], pred_sets[subset])\n","            for name, func in eval_functions.items()\n","        }\n","        for subset in pred_sets.keys()\n","    }\n","    return scores\n","\n","def performance_as_df(labels, logits, splits):\n","    scores = compute_performance(labels, logits, splits)\n","    return pd.DataFrame(scores)\n","\n","def rand_score(labels, preds):\n","    return skmetrics.adjusted_rand_score(labels, preds)"],"metadata":{"id":"2NBAt05H5OFL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["net.eval() # Set net to evaluation mode (deactivates dropout)\n","final_prediction = net(features).detach()\n","performance_as_df(labels,final_prediction,comb_mask)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":142},"id":"n1Shdi8dASUN","outputId":"ce78215f-5cff-4aaa-9171-66070cf97371"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>All</th>\n","      <th>Train</th>\n","      <th>Val</th>\n","      <th>Test</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Rand-Index</th>\n","      <td>0.558294</td>\n","      <td>0.971305</td>\n","      <td>0.492320</td>\n","      <td>0.554135</td>\n","    </tr>\n","    <tr>\n","      <th>Mutual Information</th>\n","      <td>0.532625</td>\n","      <td>0.969968</td>\n","      <td>0.495712</td>\n","      <td>0.532490</td>\n","    </tr>\n","    <tr>\n","      <th>Variation of Information</th>\n","      <td>1.691030</td>\n","      <td>0.105132</td>\n","      <td>1.753864</td>\n","      <td>1.678290</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                              All      Train       Val      Test\n","Rand-Index                0.558294  0.971305  0.492320  0.554135\n","Mutual Information        0.532625  0.969968  0.495712  0.532490\n","Variation of Information  1.691030  0.105132  1.753864  1.678290"]},"metadata":{},"execution_count":39}]},{"cell_type":"code","source":[""],"metadata":{"id":"ogieeOM-ATxQ"},"execution_count":null,"outputs":[]}]}