main.ipynb

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  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "from data import read_graph, get_data\n",
    "from gcn import GCNNet\n",
    "from train import train\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "g = read_graph('dataset/graphs.lg')\n",
    "nx.draw(g, with_labels = True, font_weight = 'bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj = torch.load('saved/adj.pt')\n",
    "features = torch.load('saved/features.pt')\n",
    "model = torch.load('saved/model.pt')\n",
    "labels = torch.load('saved/labels.pt')\n",
    "edges = torch.load('saved/edges.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = model(features, adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedd2d = TSNE().fit_transform(embeddings.detach().cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = labels.detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y = []\n",
    "for edge in edges:\n",
    "    x.append(embedd2d[edge[0]])\n",
    "    y.append(embedd2d[edge[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x576 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8, 8))\n",
    "plt.scatter(embedd2d[:, 0], embedd2d[:, 1], c = labels, cmap = 'tab20')\n",
    "# plt.plot(y, x)\n",
    "# plt.plot(x, y)\n",
    "fig.tight_layout()\n",
    "plt.axis('off')\n",
    "plt.savefig('visual.png')"
   ]
  }
 ]
}