halfspace.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import timeit\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import logging\n",
    "from hype.sn import Embedding \n",
    "from hype import train\n",
    "from hype.graph import load_edge_list, eval_reconstruction\n",
    "from hype.rsgd import RiemannianSGD\n",
    "from hype.Poincare import PoincareManifold\n",
    "from hype.Halfspace import HalfspaceManifold\n",
    "import sys\n",
    "import json\n",
    "import torch.multiprocessing as mp\n",
    "from hype.graph_dataset import BatchedDataset\n",
    "\n",
    "device = torch.device('cpu')\n",
    "torch.set_default_tensor_type('torch.DoubleTensor')\n",
    "#torch.set_default_tensor_type('torch.HalfTensor')\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## parameters; these are global in the notebook!\n",
    "opt_maxnorm = 500000; opt_debug = False; opt_epochs = 1000; \n",
    "opt_dim = 2; opt_com_n = 1;\n",
    "opt_negs = 50; opt_batchsize = 10; opt_eval_each = 20;\n",
    "opt_sparse = True; opt_ndproc = 4;  opt_burnin = 20;\n",
    "opt_dampening = 0.75; opt_neg_multiplier = 1.0; \n",
    "opt_burnin_multiplier = 0.01; opt_lr = 0.3;\n",
    "# opt_manifold = \"Poincare\"\n",
    "opt_manifold = \"Halfspace\"\n",
    "#######################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "MANIFOLDS = {\n",
    "    'Poincare': PoincareManifold,\n",
    "    'Halfspace': HalfspaceManifold,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing logging and data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using edge list dataloader\n"
     ]
    }
   ],
   "source": [
    "log_level = logging.DEBUG if opt_debug else logging.INFO\n",
    "log = logging.getLogger('MCF')\n",
    "logging.basicConfig(level=log_level, format='%(message)s', stream=sys.stdout)\n",
    "log.info('Using edge list dataloader')\n",
    "idx, objects, weights = load_edge_list(\"wordnet/mammal_closure.csv\", False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_model(manifold, idx, objects, weights, sparse=True):\n",
    "    conf = []\n",
    "    model_name = '%s_dim%d%com_n'\n",
    "    mname = model_name % (opt_manifold, opt_dim, opt_com_n)\n",
    "    data = BatchedDataset(idx, objects, weights, opt_negs, opt_batchsize,\n",
    "        opt_ndproc, opt_burnin > 0, opt_dampening)\n",
    "    model = Embedding(len(data.objects), opt_dim, manifold, sparse=sparse, com_n=opt_com_n)\n",
    "    data.objects = objects\n",
    "    return model, data, mname, conf\n",
    "\n",
    "def adj_matrix(data):\n",
    "  adj = {}\n",
    "  for inputs, _ in data:\n",
    "    for row in inputs:\n",
    "        x = row[0].item()\n",
    "        y = row[1].item()\n",
    "        if x in adj:\n",
    "            adj[x].add(y)\n",
    "        else:\n",
    "            adj[x] = {y}\n",
    "  return adj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_loader_lr(data, epoch, progress = False):\n",
    "  data.burnin = False \n",
    "  lr = opt_lr\n",
    "  if epoch < opt_burnin:\n",
    "    data.burnin = True\n",
    "    lr = opt_lr * train._lr_multiplier\n",
    "  loader_iter = tqdm(data) if progress else data\n",
    "  return loader_iter, lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(device, model, data, optimizer, progress=False):  \n",
    "  epoch_loss = torch.Tensor(len(data))\n",
    "  LOSS = np.zeros(opt_epochs)\n",
    "  \n",
    "  for epoch in range(opt_epochs):\n",
    "    largest_weight_emb = round(torch.abs(model.lt.weight.data).max().item(), 6)\n",
    "    print(largest_weight_emb, \"is the largest absolute weight in the embedding\")\n",
    "    \n",
    "    epoch_loss.fill_(0)\n",
    "    t_start = timeit.default_timer()\n",
    "    # handling burnin, get loader_iter and learning rate\n",
    "    loader_iter, lr = data_loader_lr(data, epoch, progress = progress)\n",
    "    \n",
    "    for i_batch, (inputs, targets) in enumerate(loader_iter):\n",
    "      elapsed = timeit.default_timer() - t_start\n",
    "      inputs = inputs.to(device); targets = targets.to(device)      \n",
    "      optimizer.zero_grad()\n",
    "      preds = model(inputs)\n",
    "      loss = model.loss(preds, targets, size_average=True)\n",
    "      loss.backward()\n",
    "      optimizer.step(lr=lr)\n",
    "      epoch_loss[i_batch] = loss.cpu().item()\n",
    "      \n",
    "    LOSS[epoch] = torch.mean(epoch_loss).item()\n",
    "    # since only one thread is used:\n",
    "    log.info('json_stats: {' f'\"epoch\": {epoch}, ' \\\n",
    "    f'\"elapsed\": {elapsed}, ' f'\"loss\": {LOSS[epoch]}, ' '}')\n",
    "  return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>>>> The size of embedding: Embedding(1180, 2, sparse=True)\n",
      "the total dimension 2 com_n 1\n",
      "1.0001 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 0, \"elapsed\": 0.7073092339996947, \"loss\": 3.9310063038169822, }\n",
      "1.007937 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 1, \"elapsed\": 0.6546156990007148, \"loss\": 3.9289632965430576, }\n",
      "1.015105 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 2, \"elapsed\": 0.7056052059997455, \"loss\": 3.9268504104365176, }\n",
      "1.022333 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 3, \"elapsed\": 0.666222685998946, \"loss\": 3.924738146599456, }\n",
      "1.029571 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 4, \"elapsed\": 0.6578724759965553, \"loss\": 3.9226144791975717, }\n",
      "1.036596 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 5, \"elapsed\": 0.668765058995632, \"loss\": 3.920505856988451, }\n",
      "1.043794 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 6, \"elapsed\": 0.6484292480017757, \"loss\": 3.9183906321627804, }\n",
      "1.051153 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 7, \"elapsed\": 0.6614683700026944, \"loss\": 3.9162583009759033, }\n",
      "1.058485 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 8, \"elapsed\": 0.6453616639992106, \"loss\": 3.914168236202392, }\n",
      "1.065892 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 9, \"elapsed\": 0.6798683070010156, \"loss\": 3.9120705856346865, }\n",
      "1.073251 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 10, \"elapsed\": 0.6462594640033785, \"loss\": 3.9099947762743565, }\n",
      "1.080773 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 11, \"elapsed\": 0.6468196340065333, \"loss\": 3.907906034052175, }\n",
      "1.088028 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 12, \"elapsed\": 0.6738870469998801, \"loss\": 3.9058300758941087, }\n",
      "1.095458 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 13, \"elapsed\": 0.6846895290000248, \"loss\": 3.9037411528772585, }\n",
      "1.102745 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 14, \"elapsed\": 0.6446618039990426, \"loss\": 3.9016806220122953, }\n",
      "1.110092 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 15, \"elapsed\": 0.6692303990057553, \"loss\": 3.8996061176834513, }\n",
      "1.117451 is the largest absolute weight in the embedding\n",
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      "1.124892 is the largest absolute weight in the embedding\n",
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      "1.132304 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 18, \"elapsed\": 0.650592225996661, \"loss\": 3.893505406419843, }\n",
      "1.139542 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 19, \"elapsed\": 0.6733403070029453, \"loss\": 3.891469632938995, }\n",
      "1.146845 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 20, \"elapsed\": 0.7490879670003778, \"loss\": 3.889853298878368, }\n",
      "1.330872 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 21, \"elapsed\": 0.7558104380004806, \"loss\": 3.794215845239146, }\n",
      "1.634304 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 22, \"elapsed\": 0.8014141940002446, \"loss\": 3.6999093738441613, }\n",
      "1.919326 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 23, \"elapsed\": 0.7815623269998468, \"loss\": 3.6134333614783585, }\n",
      "2.235445 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 24, \"elapsed\": 0.8012225150014274, \"loss\": 3.5348133927562295, }\n",
      "2.659726 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 25, \"elapsed\": 0.7508881570029189, \"loss\": 3.4616561349724857, }\n",
      "3.09321 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 26, \"elapsed\": 0.7946829789943877, \"loss\": 3.3970534498497202, }\n",
      "3.592646 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 27, \"elapsed\": 0.7261412770021707, \"loss\": 3.336140002017071, }\n",
      "4.150414 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 28, \"elapsed\": 0.7445073300041258, \"loss\": 3.278857370273631, }\n",
      "4.869561 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 29, \"elapsed\": 0.7452121759997681, \"loss\": 3.2257879613218248, }\n",
      "5.552188 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 30, \"elapsed\": 0.7480102629997418, \"loss\": 3.178408816448049, }\n",
      "6.429938 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 31, \"elapsed\": 0.8050026769997203, \"loss\": 3.1327777783528523, }\n",
      "7.345376 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 32, \"elapsed\": 0.7844511450020946, \"loss\": 3.0899430459667294, }\n",
      "8.305922 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 33, \"elapsed\": 0.7738189719966613, \"loss\": 3.05518149413091, }\n",
      "9.345158 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 34, \"elapsed\": 0.7610252000013134, \"loss\": 3.0171597554209333, }\n",
      "10.559497 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 35, \"elapsed\": 0.8684101059989189, \"loss\": 2.9851296648816144, }\n",
      "11.928234 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 36, \"elapsed\": 0.7562163929978851, \"loss\": 2.9521434182025175, }\n",
      "13.341494 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 37, \"elapsed\": 0.7562621229953947, \"loss\": 2.9224826759414384, }\n",
      "15.032572 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 38, \"elapsed\": 0.7794760590040823, \"loss\": 2.8946485833300866, }\n",
      "16.481154 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 39, \"elapsed\": 0.7750999209965812, \"loss\": 2.8681571740980756, }\n",
      "18.268391 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 40, \"elapsed\": 0.7466452189983102, \"loss\": 2.839985810039261, }\n",
      "20.447072 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 41, \"elapsed\": 0.7534446950012352, \"loss\": 2.816007996480102, }\n",
      "22.684154 is the largest absolute weight in the embedding\n",
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      "24.805213 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 43, \"elapsed\": 0.7390519539985689, \"loss\": 2.7692996458781765, }\n",
      "27.573897 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 44, \"elapsed\": 0.7452441150016966, \"loss\": 2.7474705955780534, }\n",
      "30.035645 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 45, \"elapsed\": 0.7391254950052826, \"loss\": 2.7230032790498218, }\n",
      "32.85356 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 46, \"elapsed\": 0.7240074539950001, \"loss\": 2.710029821962301, }\n",
      "35.057399 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 47, \"elapsed\": 0.7211221699981252, \"loss\": 2.691366990938633, }\n",
      "37.827535 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 48, \"elapsed\": 0.7325226290049613, \"loss\": 2.674079795743614, }\n",
      "40.327019 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 49, \"elapsed\": 0.7151834100004635, \"loss\": 2.6577838952227846, }\n",
      "42.61643 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 50, \"elapsed\": 0.7187471880024532, \"loss\": 2.6339464658085947, }\n",
      "44.059839 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 51, \"elapsed\": 0.7274653470012709, \"loss\": 2.627032770965358, }\n",
      "47.413088 is the largest absolute weight in the embedding\n",
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      "50.497689 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 53, \"elapsed\": 0.7222076500038384, \"loss\": 2.595414593678848, }\n",
      "54.313996 is the largest absolute weight in the embedding\n",
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      "56.401114 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 55, \"elapsed\": 0.7423384369976702, \"loss\": 2.563710981353292, }\n",
      "60.247276 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 56, \"elapsed\": 0.7258169029955752, \"loss\": 2.560372336063375, }\n",
      "65.668134 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 57, \"elapsed\": 0.7284672769965255, \"loss\": 2.547217509573776, }\n",
      "68.415579 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 58, \"elapsed\": 0.7173323090028134, \"loss\": 2.5330568110371625, }\n",
      "72.682871 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 59, \"elapsed\": 0.7309850599995116, \"loss\": 2.516362468199175, }\n",
      "76.399355 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 60, \"elapsed\": 0.7185383629985154, \"loss\": 2.5065511319356824, }\n",
      "77.727446 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 61, \"elapsed\": 0.7108759980037576, \"loss\": 2.4925365527257326, }\n",
      "81.99287 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 62, \"elapsed\": 0.7499929729965515, \"loss\": 2.4864232108293716, }\n",
      "87.226456 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 63, \"elapsed\": 0.7178382870042697, \"loss\": 2.471229832771365, }\n",
      "90.144627 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 64, \"elapsed\": 0.7292374699973152, \"loss\": 2.4617991095975746, }\n",
      "95.3198 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 65, \"elapsed\": 0.7144619309983682, \"loss\": 2.4616804231530725, }\n",
      "98.939768 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 66, \"elapsed\": 0.7215224910032703, \"loss\": 2.4474859726910045, }\n",
      "103.100166 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 67, \"elapsed\": 0.7233598140010145, \"loss\": 2.427001314178262, }\n",
      "108.204015 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 68, \"elapsed\": 0.7449879699997837, \"loss\": 2.4194252498928543, }\n",
      "111.898849 is the largest absolute weight in the embedding\n",
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      "113.898792 is the largest absolute weight in the embedding\n",
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      "114.249816 is the largest absolute weight in the embedding\n",
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      "120.179426 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 72, \"elapsed\": 0.7539998290012591, \"loss\": 2.384597115980653, }\n",
      "122.7711 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 73, \"elapsed\": 0.7426632530041388, \"loss\": 2.376307354253098, }\n",
      "129.078346 is the largest absolute weight in the embedding\n",
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      "139.777792 is the largest absolute weight in the embedding\n",
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      "145.050887 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 76, \"elapsed\": 0.7606260100001236, \"loss\": 2.340156089458696, }\n",
      "152.587848 is the largest absolute weight in the embedding\n",
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      "159.866047 is the largest absolute weight in the embedding\n",
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      "172.547292 is the largest absolute weight in the embedding\n",
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      "181.786882 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 81, \"elapsed\": 0.7103096630016807, \"loss\": 2.3025668316482126, }\n",
      "193.504604 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 82, \"elapsed\": 0.7142918999961694, \"loss\": 2.2926173563341705, }\n",
      "202.694694 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 83, \"elapsed\": 0.7169574029976502, \"loss\": 2.2844134339713866, }\n",
      "209.587335 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 84, \"elapsed\": 0.7151414249965455, \"loss\": 2.2750247949868285, }\n",
      "215.33153 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 85, \"elapsed\": 0.722269325000525, \"loss\": 2.2674559051885677, }\n",
      "221.663588 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 86, \"elapsed\": 0.7189548069945886, \"loss\": 2.2628209018835617, }\n",
      "228.706506 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 87, \"elapsed\": 0.7298955400037812, \"loss\": 2.2526936000634508, }\n",
      "234.044185 is the largest absolute weight in the embedding\n",
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      "2069.975104 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 953, \"elapsed\": 0.7136102559961728, \"loss\": 0.1065501357663347, }\n",
      "2069.975106 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 954, \"elapsed\": 0.724944333996973, \"loss\": 0.10985677601168477, }\n",
      "2069.975111 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 955, \"elapsed\": 0.7299153599960846, \"loss\": 0.10362563173653216, }\n",
      "2069.975111 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 956, \"elapsed\": 0.7024792279989924, \"loss\": 0.10177947486295547, }\n",
      "2069.975114 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 957, \"elapsed\": 0.7394249839999247, \"loss\": 0.10989102158198935, }\n",
      "2069.975116 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 958, \"elapsed\": 0.7174067879968788, \"loss\": 0.10312023817161675, }\n",
      "2069.975119 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 959, \"elapsed\": 0.7194525770028122, \"loss\": 0.11415328614327108, }\n",
      "2069.97512 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 960, \"elapsed\": 0.7201270060031675, \"loss\": 0.10782996118538858, }\n",
      "2069.97512 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 961, \"elapsed\": 0.7243796629991266, \"loss\": 0.10316026269507358, }\n",
      "2069.975121 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 962, \"elapsed\": 0.7269896470024833, \"loss\": 0.10581214439355335, }\n",
      "2069.975124 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 963, \"elapsed\": 0.7285534809998353, \"loss\": 0.10805158433039885, }\n",
      "2069.975124 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 964, \"elapsed\": 0.7172662790035247, \"loss\": 0.10766263127964688, }\n",
      "2069.975123 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 965, \"elapsed\": 0.7165195240013418, \"loss\": 0.10299629669081714, }\n",
      "2069.975123 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 966, \"elapsed\": 0.7208524060042691, \"loss\": 0.10754132158039789, }\n",
      "2069.975124 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 967, \"elapsed\": 0.7171467499938444, \"loss\": 0.10670646065107459, }\n",
      "2069.975125 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 968, \"elapsed\": 0.7105547180035501, \"loss\": 0.09809300972476087, }\n",
      "2069.975128 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 969, \"elapsed\": 0.7162523790029809, \"loss\": 0.10805555503250062, }\n",
      "2069.975133 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 970, \"elapsed\": 0.7066360600001644, \"loss\": 0.10543080707867246, }\n",
      "2069.975133 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 971, \"elapsed\": 0.7224900799992611, \"loss\": 0.10696311991349411, }\n",
      "2069.975134 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 972, \"elapsed\": 0.7205875409999862, \"loss\": 0.10394567639160876, }\n",
      "2069.975136 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 973, \"elapsed\": 0.7138220099950559, \"loss\": 0.10765992530490807, }\n",
      "2069.975136 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 974, \"elapsed\": 0.7184533119943808, \"loss\": 0.10968472534607598, }\n",
      "2069.975136 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 975, \"elapsed\": 0.717025508005463, \"loss\": 0.10763046969297915, }\n",
      "2069.975138 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 976, \"elapsed\": 0.7106265229958808, \"loss\": 0.10276081532608665, }\n",
      "2069.97514 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 977, \"elapsed\": 0.7069645449955715, \"loss\": 0.10967946872902869, }\n",
      "2069.975143 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 978, \"elapsed\": 0.7217453549965285, \"loss\": 0.10555274383376594, }\n",
      "2069.975144 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 979, \"elapsed\": 0.7152832749998197, \"loss\": 0.10054252619422725, }\n",
      "2069.975145 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 980, \"elapsed\": 0.7245721979998052, \"loss\": 0.09947909160141849, }\n",
      "2069.975147 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 981, \"elapsed\": 0.7107401319954079, \"loss\": 0.10798656196988266, }\n",
      "2069.975147 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 982, \"elapsed\": 0.7070386049963417, \"loss\": 0.10477332977720182, }\n",
      "2069.975148 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 983, \"elapsed\": 0.768284030003997, \"loss\": 0.10556596548552524, }\n",
      "2069.975148 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 984, \"elapsed\": 0.7324488680023933, \"loss\": 0.10872237098773876, }\n",
      "2069.975151 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 985, \"elapsed\": 0.7334926729963627, \"loss\": 0.1031952183259523, }\n",
      "2069.975151 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 986, \"elapsed\": 0.7074535299980198, \"loss\": 0.10626788780122551, }\n",
      "2069.975151 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 987, \"elapsed\": 0.7203662469983101, \"loss\": 0.10179901923705272, }\n",
      "2069.975151 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 988, \"elapsed\": 0.7165660790051334, \"loss\": 0.10009112208898886, }\n",
      "2069.975153 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 989, \"elapsed\": 0.7260606620038743, \"loss\": 0.10645492214779718, }\n",
      "2069.975153 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 990, \"elapsed\": 0.7155293489995529, \"loss\": 0.10637981459178264, }\n",
      "2069.975154 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 991, \"elapsed\": 0.7211487400054466, \"loss\": 0.10323000916150654, }\n",
      "2069.975155 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 992, \"elapsed\": 0.7455060399952345, \"loss\": 0.10210420074224186, }\n",
      "2069.975155 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 993, \"elapsed\": 0.7357302020027419, \"loss\": 0.10477130382585277, }\n",
      "2069.975154 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 994, \"elapsed\": 0.7133190009990358, \"loss\": 0.10198640612904934, }\n",
      "2069.975153 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 995, \"elapsed\": 0.7587859120030771, \"loss\": 0.10602392411636519, }\n",
      "2069.975154 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 996, \"elapsed\": 0.7187140379974153, \"loss\": 0.10227848954920127, }\n",
      "2069.975154 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 997, \"elapsed\": 0.7267600419945666, \"loss\": 0.10100639319747182, }\n",
      "2069.975156 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 998, \"elapsed\": 0.7629500990005909, \"loss\": 0.10492824517287165, }\n",
      "2069.975156 is the largest absolute weight in the embedding\n",
      "json_stats: {\"epoch\": 999, \"elapsed\": 0.7208778159983922, \"loss\": 0.10261301962844345, }\n",
      "Total training time is: 729.8673656419996\n"
     ]
    }
   ],
   "source": [
    "# setup model\n",
    "# manifold = PoincareManifold(\n",
    "#     debug=opt_debug, max_norm=opt_maxnorm, com_n=opt_com_n)\n",
    "manifold = MANIFOLDS[opt_manifold](\n",
    "        debug=opt_debug, max_norm=opt_maxnorm, com_n=opt_com_n)\n",
    "model, data, model_name, conf = init_model(\n",
    "    manifold, idx, objects, weights, sparse=opt_sparse)\n",
    "data.neg_multiplier = opt_neg_multiplier\n",
    "train._lr_multiplier = opt_burnin_multiplier\n",
    "model = model.to(device)\n",
    "print('the total dimension', model.lt.weight.data.size(-1), 'com_n', opt_com_n)\n",
    "\n",
    "# setup optimizer\n",
    "optimizer = RiemannianSGD(model.optim_params(manifold), lr= opt_lr)\n",
    "# get adjacency matrix\n",
    "adj = adj_matrix(data) \n",
    "# begin training\n",
    "start_time = timeit.default_timer()\n",
    "train(device, model, data, optimizer, progress=False )\n",
    "print(\"Total training time is:\", timeit.default_timer() - start_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "json_stats final test: \n",
      "{\"sqnorm_min\": 0.44194, \"sqnorm_avg\": 0.998845, \"sqnorm_max\": 1.0, \n",
      "\"mean_rank\": 1.790061, \"map\": 0.880847, }\n",
      "tensor([-1.7542e-01,  1.0284e-07])\n"
     ]
    }
   ],
   "source": [
    "meanrank, maprank = eval_reconstruction(\n",
    "    adj, model.lt.weight.data.clone(), manifold.distance, workers=opt_ndproc)\n",
    "sqnorms = manifold.pnorm(model.lt.weight.data.clone())\n",
    "log.info(\n",
    "        'json_stats final test: \\n{' \n",
    "        f'\"sqnorm_min\": {round(sqnorms.min().item(),6)}, '\n",
    "        f'\"sqnorm_avg\": {round(sqnorms.mean().item(),6)}, '\n",
    "        f'\"sqnorm_max\": {round(sqnorms.max().item(),6)}, \\n'\n",
    "        f'\"mean_rank\": {round(meanrank,6)}, '\n",
    "        f'\"map\": {round(maprank,6)}, '\n",
    "        '}'\n",
    "    )\n",
    "print(model.lt.weight.data[0])"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "poincare_new",
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