ixxi-dante/nw2vec

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train nw2vec with the original VGAE architecture for Cora embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Train on CPU (hide GPU) due to memory constraints\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = \"\"\n",
    "\n",
    "import contextlib\n",
    "\n",
    "import numpy as np\n",
    "import keras\n",
    "from keras_tqdm import TQDMNotebookCallback as TQDMCallback\n",
    "\n",
    "from nw2vec import layers\n",
    "from nw2vec import ae\n",
    "from nw2vec import utils\n",
    "from nw2vec import batching\n",
    "import settings\n",
    "\n",
    "from gae.input_data import load_data\n",
    "from gae.preprocessing import sparse_to_tuple, mask_test_edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "@contextlib.contextmanager\n",
    "def gae_directory():\n",
    "    working_directory = os.path.abspath(os.curdir)\n",
    "    try:\n",
    "        # Move to the GAE directory\n",
    "        os.chdir('../../gae')\n",
    "        yield\n",
    "    finally:\n",
    "        # Move back\n",
    "        os.chdir(working_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "with gae_directory():\n",
    "    adj, features = load_data('cora')\n",
    "    features = features.toarray()\n",
    "\n",
    "#adj_train = mask_test_edges(adj)[0]\n",
    "adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges(adj)\n",
    "assert adj_train.diagonal().sum() == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dims = (features.shape[1], 32, 8, 8)\n",
    "n_ξ_samples =1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_p_builder(dims, adj_kernel=None, use_bias=False, with_l1=True):\n",
    "\n",
    "    # Extract the dimensions we use.\n",
    "    dim_data, dim_l1, _, _ = dims\n",
    "\n",
    "    def p_builder(p_input):\n",
    "        if with_l1:\n",
    "            # Unshared intermediate layer.\n",
    "            p_penultimate_adj = keras.layers.Dense(\n",
    "                dim_l1, use_bias=use_bias, activation='relu',\n",
    "                kernel_regularizer='l2', bias_regularizer='l2',\n",
    "                name='p_layer1_adj'\n",
    "            )(p_input)\n",
    "        else:\n",
    "            # No intermediate layer.\n",
    "            p_penultimate_adj = p_input\n",
    "\n",
    "        # Prepare kwargs for the Bilinear adj decoder, then build it.\n",
    "        adj_kwargs = {}\n",
    "        if adj_kernel is not None:\n",
    "            adj_kwargs['fixed_kernel'] = adj_kernel\n",
    "        else:\n",
    "            adj_kwargs['kernel_regularizer'] = 'l2'\n",
    "        p_adj = layers.Bilinear(0, use_bias=use_bias, name='p_adj',\n",
    "                                bias_regularizer='l2',\n",
    "                                **adj_kwargs)([p_penultimate_adj, p_penultimate_adj])\n",
    "\n",
    "        return ([p_adj], ('SigmoidBernoulliScaledAdjacency',))\n",
    "\n",
    "    return p_builder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_model, q_codecs = ae.build_q(dims, fullbatcher=batching.fullbatches)\n",
    "p_builder = build_p_builder(dims, adj_kernel=np.eye(16), with_l1=False)\n",
    "\n",
    "vae, vae_codecs = ae.build_vae(\n",
    "    (q_model, q_codecs),\n",
    "    p_builder,\n",
    "    n_ξ_samples,\n",
    "    [1.0, 1.0]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_func(batch_adj, required_nodes, final_nodes):\n",
    "    return [\n",
    "        np.zeros(1),  # ignored\n",
    "        utils.expand_dims_tile(\n",
    "            utils.expand_dims_tile(batch_adj + np.eye(batch_adj.shape[0]),\n",
    "                                   0, n_ξ_samples),\n",
    "            0, 1\n",
    "        )\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slerique/anaconda3/envs/base36/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:100: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f8cf30359394702aa5e0fc3a1dffb48",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Training', max=200), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 200\n",
    "\n",
    "history = vae.fit_fullbatches(batcher_kws={'adj': adj_train, 'features': features, 'target_func': target_func},\n",
    "                              epochs=n_epochs,\n",
    "                              verbose=0, callbacks=[TQDMCallback(show_inner=False)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = {'history': history.history}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, len(history['history']), figsize=(len(history['history']) * 5, 4))\n",
    "for i, (title, values) in enumerate(history['history'].items()):\n",
    "    axes[i].plot(np.array(values))\n",
    "    axes[i].set_title(title)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import average_precision_score\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_roc_score(edges_pos, edges_neg, adj_pred):\n",
    "    # Predict on test set of edges\n",
    "    preds = []\n",
    "    pos = []\n",
    "    for e in edges_pos:\n",
    "        preds.append(adj_pred[e[0], e[1]])\n",
    "        pos.append(adj[e[0], e[1]])\n",
    "\n",
    "    preds_neg = []\n",
    "    neg = []\n",
    "    for e in edges_neg:\n",
    "        preds_neg.append(adj_pred[e[0], e[1]])\n",
    "        neg.append(adj[e[0], e[1]])\n",
    "\n",
    "    preds_all = np.hstack([preds, preds_neg])\n",
    "    labels_all = np.hstack([np.ones(len(preds)), np.zeros(len(preds))])\n",
    "    roc_score = roc_auc_score(labels_all, preds_all)\n",
    "    ap_score = average_precision_score(labels_all, preds_all)\n",
    "\n",
    "    return roc_score, ap_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_preds, adj_preds = zip(*[vae.predict_fullbatch(adj=adj_train, features=features) for _ in range(10)])\n",
    "\n",
    "q_preds = np.array(q_preds)\n",
    "adj_preds = np.array(adj_preds)\n",
    "\n",
    "q_pred = q_preds.mean(0)\n",
    "adj_pred = scipy.special.expit(adj_preds[:, 0, :, :, :]).mean(1).mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9033518285810989, 0.9101242349344631)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_roc_score(test_edges, test_edges_false, adj_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TKknVNa832K7e62jgocr+VuCUWdI7D/i3QTftNChqv+DYSL6GM3S+ms+S3TGgraJe2el5ofQbwFrgJYNu2nVJEcvQECXFIFuBYyv7xwDbZtxP+hXgz4CX2B44rSJtimjXaNsUtwGrJT1X0oHAOcDG6gWSTgI+BJxle3uTRDsJCknrJN0jaYukC7vIQy+S7pf03+V0+M2Dv7Ggeek1Zf8wSTdL+k75s++cs5bzNcSjBKOb+2R7D3ABsAn4FnCt7bskXSzprPKyvwWeDvxrmbeNfZLb9zu65Qc+ym60bwMvoyj+bgPW27671Yz0IOl+iu67zgfIJL0Y+CFwle0TymPvAXbavqT8Y3Ko7beMQb4uAn5o++8Gff+QlUf75JN+v9G9vnDr22/vYlymi5JiYDdaFFP2gZ21w2cDV5afrwRe1Wqm6JuvIRIoHkdtsnWli6Do1Y12dAf56MXATZJul3R+15np4QjbDwOUP5/dcX6qGj1KABSPozbZOtJFUDTuRuvAqbZfSDFC+oayqhCDNX6UABj7qeNdBEWjbrQu2N5W/twOXE9R1Rsnj0zNUC5/NupNWWi2H7E9YXsSuJwB/900Odlo60oXQTGwG60Lkg6WtHLqM/Byxm9K/Ebg3PLzucBnOszLXkM9SmCKwbsmW0daH7yzvUfSVDfaCuAK23e1nY8ejgCulwTFf5erbX+uq8z0mbJ/CXCtpPOAB4HXjEm+Gj9KIDzKwbsF0cmIdvn2j7F6A4jte4EXdJ2PKX2m7AOc3mpGakbyKEGCIqImQRFRMdWmGGMJimhdlz1LTSQoomXdDsw1kaCIdi2CFywnKKJ94117SlBE+zJOEVGXoIiosGFivOtPCYpoX0qKiJoERUTF1AuWx1iCIlpmcNoUEfuYNLQjZkibIqImQRFRlQmBEdMZyNTxiJqUFBFVmeYRMZ3BGaeIqMmIdkRN2hQRFXZ6nyJmSEkRUWU8MdF1JmaVoIh2Zep4RA9j3iWb1VGjVQY86UZbE4MWFZX0FEmfLM9/TdLxg9JMUES7XD5k1GQboFxU9DKKlafWAOslralddh6wy/ZPA5cCfzMo3QRFtM4TE422BposKlpdPPM64HSVi5D0kzZFtGo3uzZ93tetanj5QbX1zDfY3lDZ77Wo6Cm1NPZeUy4Y9H3gWUDfZaETFNEq2+tGmFyTRUWHXng01adYzJosKrr3Gkn7A89gwDrgCYpYzJosKlpdPPPVwL/bsw+pp/oUi1a/RUUlXQxstr2RYj2+j0naQlFCnDMoXQ0ImohlJ9WniJoERURNgiKiJkERUZOgiKhJUETUJCgiav4fdNYTdKDAoWYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for layer in vae.layers:\n",
    "    if hasattr(layer, 'kernel'):\n",
    "        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 4))#, sharey=True)\n",
    "        im1 = ax1.imshow(K.eval(layer.kernel).T)\n",
    "        ax1.set_title('{} kernel'.format(layer.name))\n",
    "        plt.colorbar(im1, ax=ax1)\n",
    "        if hasattr(layer, 'bias') and layer.bias is not None:\n",
    "            im2 = ax2.imshow(K.eval(K.expand_dims(layer.bias, -1)))\n",
    "            ax2.set_title('{} bias'.format(layer.name))\n",
    "            plt.colorbar(im2, ax=ax2)\n",
    "        else:\n",
    "            ax2.set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "import scipy.sparse as sp\n",
    "import seaborn as sb\n",
    "\n",
    "from sklearn.manifold import MDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Downscale the embeddings\n",
    "mds = MDS(n_jobs=-2, )\n",
    "mds.fit(q_pred[:, :dims[-1]].astype(np.float64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the ground truth labels\n",
    "\n",
    "def parse_index_file(filename):\n",
    "    index = []\n",
    "    for line in open(filename):\n",
    "        index.append(int(line.strip()))\n",
    "    return index\n",
    "\n",
    "with gae_directory():\n",
    "    ty = pickle.load(open(\"data/ind.cora.ty\", 'rb'), encoding='latin1')\n",
    "    ally = pickle.load(open(\"data/ind.cora.ally\", 'rb'), encoding='latin1')\n",
    "    test_idx_reorder = parse_index_file(\"data/ind.cora.test.index\")\n",
    "test_idx_range = np.sort(test_idx_reorder)\n",
    "\n",
    "labels = sp.vstack((ally, ty)).toarray()\n",
    "labels[test_idx_reorder, :] = labels[test_idx_range, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn labels into colors\n",
    "palette = sb.color_palette(n_colors=labels.shape[1])\n",
    "colors = np.array(palette)[np.argmax(labels, axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the downscaled embeddings and the links\n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "edges = np.array([[mds.embedding_[i], mds.embedding_[j]] for (i, j) in sparse_to_tuple(sp.triu(adj))[0]])\n",
    "edges = edges.transpose([2, 1, 0])\n",
    "ax.plot(edges[0], edges[1], color='lightgrey', zorder=1)\n",
    "ax.scatter(mds.embedding_[:, 0], mds.embedding_[:, 1], c=colors, zorder=2)"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}