AP-GCN_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import yaml\n",
    "import torch\n",
    "import logging\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.sparse as sp\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import torch.nn.functional as F\n",
    "import seaborn as sns\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "from torch.optim import Adam, Optimizer\n",
    "from collections import defaultdict\n",
    "from torch_geometric.data import Data, InMemoryDataset\n",
    "\n",
    "from model import APGCN\n",
    "from seeds import test_seeds, gen_seeds, quick_seeds\n",
    "from data import get_dataset, set_train_val_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_obj(obj, name):\n",
    "    with open('results/'+ name + '.pkl', 'wb') as f:\n",
    "        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "def load_obj(name):\n",
    "    with open('results/' + name + '.pkl', 'rb') as f:\n",
    "        return pickle.load(f)\n",
    "\n",
    "def summary(results):\n",
    "    report={}\n",
    "    for k, v in results.items():\n",
    "        if k != 'steps' and k != 'probs':\n",
    "            boots_series = sns.algorithms.bootstrap(results[k], func=np.mean, n_boot=1000)\n",
    "            report[k] = np.mean(results[k])\n",
    "            report[f'{k}_ci'] = np.max(np.abs(sns.utils.ci(boots_series, 95) - report[k]))\n",
    "        else:\n",
    "            array = np.array([k.mean().cpu().detach().numpy() for k in results['steps']])\n",
    "            boots_series = sns.algorithms.bootstrap(array, func=np.mean, n_boot=1000)\n",
    "            report[k] = np.mean(array)\n",
    "            report[f'{k}_ci'] = np.max(np.abs(sns.utils.ci(boots_series, 95) - report[k]))\n",
    "    return report\n",
    "\n",
    "def plot_density(results):\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    z =[(x.cpu().numpy()).astype(int) for x in results['steps']]\n",
    "    z = np.vstack(z)\n",
    "    z = np.mean(z,axis=0)\n",
    "\n",
    "    sns.distplot(z, hist = False, kde = True,\n",
    "                 kde_kws = {'shade': True, 'linewidth': 3}, \n",
    "                 ax=ax)\n",
    "    plt.xlabel('Number of Steps')\n",
    "    plt.ylabel('Density')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model: torch.nn.Module, optimizer: Optimizer, data: Data, train_halt, weight_decay: float):\n",
    "    model.train()\n",
    "    \n",
    "    for param in model.prop.parameters():\n",
    "        param.requires_grad = train_halt\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    logits, steps, reminders = model(data)\n",
    "    \n",
    "    loss = F.nll_loss(logits[data.train_mask], data.y[data.train_mask])\n",
    "    l2_reg = sum((torch.sum(param ** 2) for param in model.reg_params))\n",
    "    loss += weight_decay/2 * l2_reg + model.prop_penalty *(\n",
    "            steps[data.train_mask] + reminders[data.train_mask]).mean()\n",
    "    \n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    return\n",
    "\n",
    "def evaluate(model: torch.nn.Module, data: Data, test: bool, weight_decay: float):\n",
    "    model.eval()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        logits, steps, reminders = model(data)\n",
    "        \n",
    "        loss = F.nll_loss(logits[data.train_mask], data.y[data.train_mask])\n",
    "        l2_reg = sum((torch.sum(param ** 2) for param in model.reg_params))\n",
    "        loss += weight_decay/2 * l2_reg + model.prop_penalty *(\n",
    "                steps[data.train_mask] + reminders[data.train_mask]).mean()\n",
    "\n",
    "    eval_dict = {}\n",
    "    keys = ['train','val']\n",
    "    eval_dict['steps'] = steps\n",
    "    for key in keys:\n",
    "        mask = data[f'{key}_mask']\n",
    "        pred = logits[mask].max(1)[1]\n",
    "        acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()\n",
    "        eval_dict[f'{key}_acc'] = acc\n",
    "    return eval_dict, loss\n",
    "\n",
    "\n",
    "def test_acc(model: torch.nn.Module, data: Data):\n",
    "    model.eval()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        logits, steps, reminders = model(data)\n",
    "    mask = data['test_mask']\n",
    "    pred = logits[mask].max(1)[1]\n",
    "    acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(dataset: InMemoryDataset,\n",
    "        model: torch.nn.Module,\n",
    "        seeds: np.ndarray,\n",
    "        test: bool = False,\n",
    "        max_epochs: int = 10000,\n",
    "        patience: int = 100,\n",
    "        lr: float = 0.01,\n",
    "        weight_decay: float = 0.01,\n",
    "        num_development: int = 1500,\n",
    "        device: str = 'cuda'):\n",
    "    \n",
    "\n",
    "    best_dict = defaultdict(list)\n",
    "\n",
    "    for seed in tqdm(seeds):\n",
    "        for _ in range(config['niter_per_seed']):\n",
    "            torch_seed = gen_seeds()\n",
    "            torch.manual_seed(seed=torch_seed)\n",
    "            \n",
    "            dataset.data = set_train_val_test_split(\n",
    "                seed,\n",
    "                dataset.data,\n",
    "                num_development=num_development,\n",
    "                num_per_class=20\n",
    "                ).to(device)\n",
    "            \n",
    "            model.to(device).reset_parameters()\n",
    "            optimizer = Adam(model.parameters(),lr=lr)\n",
    "            \n",
    "            patience_counter = 0\n",
    "            best_loss = 999\n",
    "            tmp_dict = {'val_acc': 0}\n",
    "            \n",
    "            start_time = time.perf_counter()\n",
    "            for epoch in range(1, max_epochs + 1):\n",
    "                if patience_counter == patience:\n",
    "                    break\n",
    "\n",
    "                train(model, optimizer, dataset.data, epoch%5==0, weight_decay)\n",
    "                eval_dict, loss = evaluate(model, dataset.data, test, weight_decay)\n",
    "                                \n",
    "                if(eval_dict['val_acc'] > tmp_dict['val_acc']) or (\n",
    "                  (eval_dict['val_acc'] == tmp_dict['val_acc']) and loss < best_loss):\n",
    "                    patience_counter = 0\n",
    "                    tmp_dict['epoch'] = epoch\n",
    "                    tmp_dict['runtime'] = time.perf_counter() - start_time\n",
    "                    \n",
    "                    for k, v in eval_dict.items():\n",
    "                        tmp_dict[k] = v\n",
    "\n",
    "                    best_state = {key: value.cpu() for key, value\n",
    "                                      in model.state_dict().items()}\n",
    "                \n",
    "                else:\n",
    "                    patience_counter += 1\n",
    "                    \n",
    "                if loss < best_loss:\n",
    "                    best_loss = loss\n",
    "                    patience_counter = 0\n",
    "                            \n",
    "            model.load_state_dict(best_state)\n",
    "            tmp_dict['test_acc'] = test_acc(model,dataset.data)\n",
    "            print(\"Epoch: {:.1f}\"\" Train: {:.2f}\"\" Val: {:.2f}\"\" Test: {:.2f}\".format(\n",
    "                  tmp_dict['epoch'],\n",
    "                  tmp_dict['train_acc'] * 100,\n",
    "                  tmp_dict['val_acc'] * 100,\n",
    "                  tmp_dict['test_acc'] * 100))\n",
    "                \n",
    "            for k, v in tmp_dict.items():\n",
    "                best_dict[k].append(v)\n",
    "\n",
    "    return dict(best_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda'\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.synchronize()\n",
    "#Datasets: 'citeseer', 'cora_ml' 'pubmed' 'ms_academic', 'amazon_electronics_computers', 'amazon_electronics_photo'\n",
    "#Num Developent: 1500,1500,1500,5000,1500,1500\n",
    "# weight_decay 0 for Amazon Datasets 8e-03 for the others\n",
    "config = {'dataset_name': 'amazon_electronics_computers',\n",
    "          'test': True,\n",
    "          'use_lcc': True,\n",
    "          'num_development': 1500,\n",
    "          'niter_per_seed': 5,\n",
    "          'hidden_units': 64,\n",
    "          'lr': 0.01,\n",
    "          'dropout': 0.5,\n",
    "          'weight_decay': 0\n",
    "         }\n",
    "    \n",
    "dataset = get_dataset(\n",
    "    name=config['dataset_name'],\n",
    "    use_lcc=config['use_lcc']\n",
    "    )\n",
    "\n",
    "dataset.data = dataset.data.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "text": "Epoch: 914.0 Train: 99.00 Val: 88.00 Test: 86.74\nEpoch: 1759.0 Train: 99.50 Val: 86.80 Test: 85.35\nEpoch: 844.0 Train: 99.00 Val: 86.80 Test: 85.57\nEpoch: 676.0 Train: 99.50 Val: 86.60 Test: 85.67\nEpoch: 475.0 Train: 98.00 Val: 87.20 Test: 85.40\nEpoch: 1168.0 Train: 100.00 Val: 84.40 Test: 85.64\nEpoch: 1535.0 Train: 100.00 Val: 84.20 Test: 84.82\nEpoch: 1142.0 Train: 100.00 Val: 84.80 Test: 85.21\nEpoch: 1143.0 Train: 100.00 Val: 84.40 Test: 85.71\nEpoch: 1285.0 Train: 100.00 Val: 85.00 Test: 84.99\nEpoch: 1574.0 Train: 100.00 Val: 87.00 Test: 86.03\nEpoch: 666.0 Train: 99.50 Val: 86.00 Test: 85.33\nEpoch: 467.0 Train: 96.00 Val: 86.80 Test: 86.05\nEpoch: 1762.0 Train: 100.00 Val: 86.40 Test: 85.89\nEpoch: 1558.0 Train: 100.00 Val: 86.40 Test: 85.66\nEpoch: 1819.0 Train: 99.50 Val: 85.00 Test: 85.37\nEpoch: 1173.0 Train: 99.50 Val: 83.80 Test: 85.73\nEpoch: 1126.0 Train: 98.00 Val: 85.40 Test: 85.05\nEpoch: 922.0 Train: 98.50 Val: 84.60 Test: 85.93\nEpoch: 1170.0 Train: 99.00 Val: 85.60 Test: 86.40\nEpoch: 1196.0 Train: 99.50 Val: 89.00 Test: 85.60\nEpoch: 683.0 Train: 96.50 Val: 88.80 Test: 84.93\nEpoch: 314.0 Train: 94.00 Val: 89.40 Test: 84.50\nEpoch: 1159.0 Train: 99.00 Val: 88.80 Test: 84.58\nEpoch: 827.0 Train: 97.50 Val: 88.80 Test: 85.97\nEpoch: 732.0 Train: 99.50 Val: 83.40 Test: 83.71\nEpoch: 1141.0 Train: 99.50 Val: 84.80 Test: 84.85\nEpoch: 796.0 Train: 99.00 Val: 84.20 Test: 84.70\nEpoch: 556.0 Train: 99.00 Val: 84.20 Test: 84.43\nEpoch: 1567.0 Train: 99.50 Val: 84.40 Test: 83.63\nEpoch: 1829.0 Train: 99.50 Val: 88.20 Test: 85.03\nEpoch: 989.0 Train: 98.50 Val: 88.20 Test: 84.66\nEpoch: 1039.0 Train: 97.50 Val: 87.60 Test: 83.86\nEpoch: 1586.0 Train: 100.00 Val: 87.40 Test: 83.90\nEpoch: 1199.0 Train: 98.50 Val: 88.40 Test: 84.45\nEpoch: 1103.0 Train: 99.00 Val: 87.20 Test: 85.41\nEpoch: 1143.0 Train: 99.50 Val: 86.80 Test: 85.31\nEpoch: 696.0 Train: 97.00 Val: 86.20 Test: 83.94\nEpoch: 1629.0 Train: 99.50 Val: 86.40 Test: 85.13\nEpoch: 579.0 Train: 99.00 Val: 86.80 Test: 84.96\nEpoch: 934.0 Train: 99.50 Val: 87.40 Test: 85.63\nEpoch: 2712.0 Train: 100.00 Val: 86.40 Test: 86.31\nEpoch: 830.0 Train: 96.50 Val: 87.60 Test: 87.07\nEpoch: 1039.0 Train: 100.00 Val: 86.40 Test: 85.27\nEpoch: 762.0 Train: 98.00 Val: 87.20 Test: 84.82\nEpoch: 1556.0 Train: 100.00 Val: 85.00 Test: 84.99\nEpoch: 885.0 Train: 99.00 Val: 85.20 Test: 85.18\nEpoch: 836.0 Train: 100.00 Val: 85.00 Test: 84.71\nEpoch: 1778.0 Train: 100.00 Val: 85.40 Test: 84.61\nEpoch: 2283.0 Train: 100.00 Val: 84.60 Test: 84.34\nEpoch: 1789.0 Train: 100.00 Val: 85.40 Test: 86.09\nEpoch: 1904.0 Train: 100.00 Val: 85.60 Test: 85.81\nEpoch: 900.0 Train: 98.50 Val: 86.40 Test: 86.61\nEpoch: 1292.0 Train: 100.00 Val: 87.00 Test: 86.12\nEpoch: 678.0 Train: 99.00 Val: 86.20 Test: 86.53\nEpoch: 687.0 Train: 98.00 Val: 83.00 Test: 83.89\nEpoch: 394.0 Train: 95.50 Val: 82.80 Test: 83.74\nEpoch: 672.0 Train: 98.00 Val: 83.40 Test: 83.56\nEpoch: 651.0 Train: 97.50 Val: 83.40 Test: 84.01\nEpoch: 456.0 Train: 97.00 Val: 83.00 Test: 83.10\nEpoch: 978.0 Train: 98.00 Val: 86.60 Test: 86.31\nEpoch: 734.0 Train: 97.00 Val: 86.80 Test: 86.20\nEpoch: 1820.0 Train: 99.00 Val: 85.80 Test: 85.83\nEpoch: 962.0 Train: 98.50 Val: 86.80 Test: 86.74\nEpoch: 959.0 Train: 98.00 Val: 85.80 Test: 86.15\nEpoch: 337.0 Train: 96.50 Val: 88.00 Test: 85.94\nEpoch: 486.0 Train: 98.00 Val: 88.40 Test: 86.10\nEpoch: 566.0 Train: 98.00 Val: 87.60 Test: 85.35\nEpoch: 609.0 Train: 97.00 Val: 86.80 Test: 84.62\nEpoch: 521.0 Train: 97.50 Val: 87.40 Test: 85.91\nEpoch: 525.0 Train: 98.00 Val: 85.40 Test: 84.98\nEpoch: 299.0 Train: 96.00 Val: 86.00 Test: 85.02\nEpoch: 1546.0 Train: 99.50 Val: 85.80 Test: 86.32\nEpoch: 930.0 Train: 99.50 Val: 87.00 Test: 85.90\nEpoch: 642.0 Train: 99.50 Val: 85.80 Test: 84.96\nEpoch: 455.0 Train: 97.50 Val: 86.40 Test: 86.79\nEpoch: 1397.0 Train: 100.00 Val: 87.40 Test: 88.05\nEpoch: 516.0 Train: 97.50 Val: 86.80 Test: 86.42\nEpoch: 764.0 Train: 100.00 Val: 87.80 Test: 87.42\nEpoch: 640.0 Train: 98.50 Val: 86.60 Test: 86.81\nEpoch: 577.0 Train: 97.50 Val: 87.00 Test: 85.51\nEpoch: 512.0 Train: 98.00 Val: 85.40 Test: 84.74\nEpoch: 692.0 Train: 99.00 Val: 86.40 Test: 85.78\nEpoch: 462.0 Train: 97.50 Val: 85.80 Test: 85.41\nEpoch: 595.0 Train: 96.50 Val: 86.80 Test: 86.15\nEpoch: 959.0 Train: 97.00 Val: 82.80 Test: 82.06\nEpoch: 1908.0 Train: 99.50 Val: 82.20 Test: 83.62\nEpoch: 416.0 Train: 96.00 Val: 81.40 Test: 82.22\nEpoch: 271.0 Train: 95.00 Val: 82.00 Test: 83.24\nEpoch: 566.0 Train: 96.00 Val: 82.80 Test: 82.82\nEpoch: 583.0 Train: 98.50 Val: 84.00 Test: 83.73\nEpoch: 1069.0 Train: 98.50 Val: 83.20 Test: 83.86\nEpoch: 939.0 Train: 99.00 Val: 84.20 Test: 84.18\nEpoch: 970.0 Train: 99.50 Val: 85.00 Test: 85.03\nEpoch: 726.0 Train: 100.00 Val: 84.60 Test: 84.66\nEpoch: 1454.0 Train: 100.00 Val: 84.60 Test: 83.34\nEpoch: 1605.0 Train: 99.50 Val: 86.00 Test: 86.13\nEpoch: 1373.0 Train: 99.50 Val: 85.00 Test: 84.53\nEpoch: 1410.0 Train: 100.00 Val: 86.20 Test: 85.83\nEpoch: 1594.0 Train: 99.50 Val: 86.00 Test: 85.58\n\nFINAL\nTrain Accuracy: 98.62 ± 0.28%\nStopping Accuracy: 85.87 ± 0.34%\nTest     Accuracy: 85.19 ± 0.22%\nSteps: 1.74 ± 0.05\nEpochs:  1013.46 ± 107.81\nRuntime: 27.9021 ± 2.5610\n\n"
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mFWaWb2ZPmVmrmX3I7+BE0lFMifkcdu8BVJYUEF64t7V7kIFhrawr2SvRcVA3OOc6gT8DmoBzgC/4FpVIGosuMZ/LMVAAed5g3bCTURPWimSbRBNUeELYdwD3OedO+xSPSNo7lsIWFKhQQnJHognqETN7DVgDPGVmdYC+GZKTjqZoDFRYjQolJEckutzGF4GrgDXOuSGgB7jJz8BE0lVTm1pQInNhOuVH5xMaDxX9nHuSHI9I2kunLj5V8kk2S3S5jZ8DZwNbgXDZkEMJSnKQuvhE5kaiLag1wAXOhVfAmZqZ/YxQ1d9J59yF4+xfC/wWOOA99KBz7uuJvr5IKnQPDNPRNwRAfp5RUTz3C0rHLFyoFpRksUSLJLYD86f52ncBN05xzPPOuYu9m5KTpL1jMetAFRIID0qaQ9EzVxxTgpIslmgLqhbYaWYbgMjAC+fcuyd6gnPuD2bWOKvoRNJMKsdAhVWW5GMGzkFL1wADwyMUBrWyrmSfRBPU13x6/6vM7BXgGPBPzrkd4x1kZrcCtwIsXbrUp1BEptYUff1pjlbSjRcMBKgqKeC0t7Juc3s/jbVaWVeyT6Jl5s8BB4F8b3sjsGWW770FWOacuwj4AfDQJO9/h3NujXNuTV1d3SzfVmTm0qEFBbEzqEcXbYhkk0Tn4vtb4H7gx95Di5gkoSTCOdfpnOv2th8F8s2sdjavKeK3VJeYR947Kjk2tfWmLA4RPyVaJPFJ4M1AJ4Bzbg9QP5s3NrP5ZqErzGZ2hRfLqdm8pojfUl1iHhbTgmpTC0qyU6LXoAacc4NePsEbrDtpybmZ3QesBWrNrAn4Z7w5/ZxzPwJuBj5uZsNAH/CB6ZSxi6TC0RTPIhF57/Kx5NikLj7JUokmqOfM7MtAsZldD3wCeGSyJzjnbpli/+3A7Qm+v0jKDY2McqIrVNZtxA6YnWvRLagmtaAkSyXaxfdFoAV4Ffg74FHgK34FJZKOjnf0E27jV5UWEMxL9OuTfOrik1yQUAvKOTdqZg8BDznnWnyOSSQtRbdUUtl6AqiJSlDHO/sZHhlNacIU8cOkn2gL+ZqZtQKvAa+bWYuZfXVuwhNJH4dP90S261NYYg5QEAxQ6U2zNDLqON6pGSUk+0z1k+tzhKr3LnfO1TjnqoE3Am82s3/wPTqRNLL3ZHdke2FlcQojCYkuNVc3n2SjqRLUR4BbnHPhCV1xzu0HPuTtE8kZ+1rGWlBpkaCiytxVKCHZaKoEle+ca41/0LsONffTOIuk0L6W9GpBaTYJyXZTJajBGe4TySr9QyMcOR2ascGA+RVFqQ0IdfFJ9puqiu8iM+sc53EDUv8NFZkjB0/1MOqVmNeVF1IQTH3FXPRA4aZ2TXck2WfSBOWc0xz+IsC+k2PXnxalQfceaCyUZL/U/wwUyQDpdv0JQi25sGPt/YyOaqYwyS5KUCIJSMcEVZSfR1lhqBNkcGSUlu6BKZ4hklmUoEQSEDsGKn0uv9aVa04+yV5KUCJTGB117E+zMVBhsWOhVCgh2UUJSmQKzZ399A2NAFBeFKSiKH2GAGoslGQzJSiRKeyL7t6blz6tJ4hfWVcJSrKLEpTIFNJtDr5oKjWXbKYEJTKF2Aq+9CmQgLjZJNTFJ1lGCUpkCtEJKl0G6YbFzCbR1otzGgsl2UMJSmQK6TaLebTSgjyK80MTvvQPjXK6R1NkSvZQghKZREffEC1doQGw+XkWc80nHZiZCiUkaylBiUwiuntv/rxiAgFLYTTjU6m5ZCslKJFJxJaYp1eBRFj08vOHTmmwrmQPJSiRSURff1pUlV7Xn8IWRCXO/VEtPpFMpwQlMom9aTxIN2x+dIJq7ZnkSJHMogQlMgHnHK80tUfuL6kuSWE0E4uuLFQLSrKJEpTIBJra+iIVfMX5eSxOsxLzsOrSAgryQl/ltt4h2lRqLllCCUpkApsPtUW2V9aXpWUFH0DALPY6VKtaUZIdlKBEJhCToBrKUxjJ1BZURhdK6DqUZAffEpSZ/czMTprZ9gn2m5l938z2mtk2M7vUr1hEZiI6QZ3TUJbCSKa2IKqAQ4USki38bEHdBdw4yf63Ayu9263AD32MRWRaegaGee14JwAGrKhP9wSlUnPJPr4lKOfcH4DTkxxyE3CPC1kHVJrZAr/iEZmOV460M+rNu7qkuoSSgmBqA5pCbCWfWlCSHVJ5DWoRcCTqfpP32BnM7FYz22Rmm1paWuYkOMlt8QUS6S66BXXoVC8jo5rVXDJfKhPUeCVR436rnHN3OOfWOOfW1NXV+RyWCGw+HH39Kb0LJABKCoJUFoeWoh8cGaWpTVMeSeZLZYJqApZE3V8MHEtRLCIRo6OOLYcyK0GBKvkk+6QyQT0MfMSr5rsS6HDONacwHhEgNIN5Z/8wABVFQRoq0muJjYmokk+yjW9Xfs3sPmAtUGtmTcA/A/kAzrkfAY8C7wD2Ar3Ax/yKRWQ6Nse1nszSc4BuPFXySbbxLUE5526ZYr8DPunX+4vMVCYN0I0WPZmtuvgkG2gmCZE4sQUS6V/BFxZzDUrTHUkWUIISiXK6ZzDS+sgLGGfVZk6Cqi8vIs/rjjzROUD3wHCKIxKZHSUokShP7jwR2T6rtpSCYOZ8RfICFlPQcUDdfJLhMufbJzIHfvfqWCHpFcurUxjJzCyInlFC3XyS4ZSgRDynewb5497WyP0rz6pJYTQzE1vJpxaUZDYlKBHP77cfj0wRtLK+jNqyzBj/FG2hxkJJFlGCEvH8btvYRCZXnZ15rSeIreTbe1JdfJLZlKBEgJauAdbtPwWEJol84/LMTFBLqkoi23tPdtE/NJLCaERmRwlKBPj99ubI8hrnzi+nurQgtQHNUGlhkPkVoVbU0IjjteNdKY5IZOaUoESAR7aNVe9lavde2PK60sj2q03tKYxEZHaUoCTnnejsZ+PB0NqaZnBFY+aVl0c7q3YsQW1r6khhJCKzowQlOe+RV47hvO69CxZUUFmSmd17YWfXjc1+8epRJSjJXEpQktMGhke484UDkfuZ3r0H0FhTGlkNdPeJLvoGVSghmUkJSnLarzYeobmjH4B5xfm8ZUVtiiOaveKCPBZ6M0qMOtjZrFaUZCYlKMlZ/UMj/NszeyP3333RQgqDeSmMKHl0HUqygRKU5KxfrD/Mic4BACpL8rnu/IYUR5Q8Z8VU8ilBSWZSgpKc1Dc4wr8/uy9y/z0XL8qomcunclZUocQ2FUpIhsqeb6TINPx83UFau0Otp+rSAq4+tz7FESXXspoSAl6lxL6Wbq0NJRlJCUpyzvGO/qxuPQEUBvNY7E175BxsVytKMlB2fStFpjAy6vjsL1+mvXcIgLqyQq4+ty7FUfkjulBC16EkEylBSU75wdN7WH9gbNaIj689m2Bedn4NogsldB1KMlF2fjNFxrFu/ym+/9SeyP33XrqY8xdUpDAif0UXSmhOPslESlCSE1q6BvjcL7dGZiy/YEEFf37xotQG5bOl1SXkeZUSB0/10uF1a4pkCiUoyXonu/q55SfrON4ZmjGivCjIJ69eQSBc5pal8vMCLK0eWx9qy+G2FEYjMn1KUJLVTnb2c8sd6yKrywYMPrF2Rcau9zRd0V2Yz75+MoWRiEyfEpRkrROd/XzgjnXsa+kBQsnpU1ev5OIllSmObO5cEvXf+szrLbjwtO0iGUAJSrLSlsNt3HT7H9nfOpacPnPNyqyYrXw6zptfTlF+6Gt++HRvJFmLZAIlKMkqzjn+c90h3v/jlyLXnPLM+My1K3njWbmVnACCeQFWL5oXua9uPskkviYoM7vRzF43s71m9sVx9n/UzFrMbKt3+xs/45Hs1tU/xD/9ZhtfeWg7QyOhrqzSwjy+8LZzeePy3EtOYRcvqYpsP6MEJRkk6NcLm1ke8G/A9UATsNHMHnbO7Yw79FfOuU/5FYfkhuf3tPDFB17laHtf5LFlNSX843XnUF9RlMLIUi/6mtuGA6fpHhimrNC3r75I0vjZgroC2Ouc2++cGwR+Cdzk4/tJDuroHeJLD27jw3duiElOf7KyltvevSrnkxOEJsNdVhMqNx8acbywpzXFEYkkxs8EtQg4EnW/yXss3nvNbJuZ3W9mS8Z7ITO71cw2mdmmlpYWP2KVDDMwPMJPn9/Pn37rGe7bMPYxKysM8ulrVvDxt56dNYsPJkN0NZ+uQ0mm8LOdP94oyPga10eA+5xzA2b298DdwDVnPMm5O4A7ANasWaM62Rw2MDzC715p5ntP7ebI6b6YfW9cXs1H39RIZUlujHGajouXVPHQ1mNA6DqUcw6z7B6oLJnPzwTVBES3iBYDx6IPcM6dirr7E+CbPsbju9FRx3+8eJCXD7dREAxQGAxQWhDk7asXcNmyqqlfQCZ0orOfe9cd4hcbDtPaPRizr6GikA9esYwrllenKLr0t7K+jLLCIN0Dw5zoHGBncyerFs6b+okiKeRngtoIrDSz5cBR4APAB6MPMLMFzrlm7+67gV0+xuO77z25m+8/vfeMx+/84wE+ffUKPnvdOZG50WRqzR19PL79OL/fcZwNB05H5tELKysM8heXLuL68xuydkbyZAkEjDcsnseL+0K/CZ957aQSlKQ93xKUc27YzD4FPA7kAT9zzu0ws68Dm5xzDwOfMbN3A8PAaeCjfsXjt4dePjpucoLQgnHff3ovGw+28a+3XEx9uS7cj2doZJQth9p4bncLz+1uYcexznGPqy4t4PrzG7j+ggZKVY2WsIuXVEYS1IMvH+UTa7N/PkLJbJZpU5+sWbPGbdq0KdVhxNh8qI1bfrKOweFRAC5cWMFbVtYyNOJYt/9UzD+0tWWF/PjDl6nLz9MzMMxzu1v4rx3Heeq1k3T1j780uRGaV+6GCxpY01itlugM9A2O8MlfbKFvaASAuz52OWuzbKl7yUxmttk5tyb+cf38nKWmtl7+7uebIslpUWUx/3D9OZQUhE7tNefW8+DLR3lwSxMOaO0e4JafrOPb77uId120MIWRp87IqOOFva38ZtMRnth5ggHv3MXLM2PVwgouX17NmmVVKn6YpeKCPNaeW8dj248DcNeLB5WgJK0pQc3Sf39oe+SifXlRkC+87dxIcoJQ3//Nly3m3Pnl/ODpPXT1DzM4PMqn73uZg609fOqaFTlTTdXc0ccv1h/m/s1NNHf0j3tMTWkBFy+p5KIllaxaWBFzLmX23rZqPr/ffhwHPPt6C/taujk7amFDkXSib/8svH68i2deD43LMuAfrzuHhgkGhq5eNI//cdOF/Mvjr3GsPfSP87ef2M3O5k6+ftOF1JUXzlXYc8o5x/oDp7nnpYM8vuMEI/GVDsCSqmIub6xmTWM1jTUlOZOwU6GhoohLllZF1oa658WD3HbThSmOSmR8SlCz8NPn90e2L2+s5rwplg9vqCjitndfyPee3B25LvXY9uO8uO8UX3nn+dx82eKs+ce5s3+I/7vlKPeuP8TuE91n7K8oCvKWFbW89dz6mEX1xH9vv3B+JEHdv7mJz7/tXCqK8lMclciZlKBm6GRXP7/dOjas651vWJDQ88oKg3zx7edx94sHeXJXaER/R98QX7h/G/dvbuITV6/gT1fW+p6oBoZHaOka4GTXAK1dA5gZpQV5lBQGmVecz6LKYgqC0yvd7hsciRQ8PLb9eORifLQLFlRww6oGLltapdLwFFm1sILFVcU0tfXRMzjCrzce4W/+5KxUhyVyBiWoGbrnxUMMjoQu7q+sL+OchvKEnxsMBPjrt5zF5Y3V3PnCAU52DQCw/sBp1h/YwIr6Mj76pkbe9YaFzCuZ/S/bkVHHruZOXj7cxsuH23n5SDsHWidfFyhgsKiqmGXVpSypLmFJdTFLq0uoKS0kP88I5gUYHhnlQGsP+1p62H2iixf3tdI/dGbBQ2EwwFtW1PK2VfNZotZSypkZN66az09fOADA3S8d5CNXNU77B4mI31RmPgO9g8O86RtP0947BMDnrls54+Uc+odG+M3mJh7b3kz8/4q8gHFFYzU3rGrgzStqObuuLKHy6oHhEV5r7uLlw228uO8U6w+cpqNvaEbxzcaS6hKuP7+eN6+oVbFDmhkYDpWc9wyEWrmfu24ln7vunBRHJblKZeZJ9BgikE4AAA9wSURBVMDmpkhyqi8v5PJlM59ipyg/jw9fuYwbLmjg9zuO8+zrJyOtkJFRx0v7T/HS/tDgyuL8PC5YWME5DWXMKy6gojhIWWGQrv5hTnUPcrpngD0nu9l9oiuyHtJEzGBecT5VJQXMK87HgP7hEQaGRmnvG+J0z+Ckz5/IospiLm+s4vLGapbXlmbNNbVsUxjM4y8uWczP1x0C4Pan9/K2VfM5f4rrqCJzSS2oaRoZdVz77Wc5eKoXgL+6qpEbL5yftNfvHRzm2ddbeHFfa1KX564szue8BeWsrC9nZX0Zy2pKJ+3SGRwepaVrgOOd/bR09XOia4CWrgF6BoYZGXWMep+buvJCFlYWs3BeMWfVlrKgsjhpMYu/RkcdX3tkB3tOhopYVi+ax//9xJt0bVDmnFpQSfKH3S2R5FTqDXxMppKCIO9YvYB3rF7A6Z5BthxuY+uRdva1dEdabYmYX1HE8tpSzltQzqoF81hYWTSt1kxBMMCiqmIWVSnhZKtAwPi7t57Nlx7cxtCI49WjHfz4D/v55NUrUh2aCKAENW33rj8U2b76vHqK8v1bc6i6tIDrzm/guvMbAGjrHeRAaw+tXQP0Do7QMzhM3+AIxQV5VBTlU1EcpKa0kOW1pZqjThKyqLKYmy9dzH0bQ2tq/euTe1h7bp0mkpW0oH/FpuFoex9Pvza22Nu15zXM6ftXlRRQtVTT/UhyvfMNC9lw8DT7WnoYHBnlw3du4L6/vZJz5ydemSriB3U2T8MvNxyOLPmwetE85s/TrOSS+fICxt+/9WyKvd6A0z2DfPAn63j9eFeKI5NcpwSVoKGRUX65cWxp8XC3m0g2WFxVwpfefl4kSZ1SkpI0oASVoCd3nqDFG1BbWZLPpcsqUxyRSHKtbCg/I0n9+b//kfs2HCbTqn0lOyhBJeje9Ycj29ecW08woFMn2WdlQzlfjEpSvYMjfOnBV/nbezbT2j2Q4ugk1+hf2QQcaO3hhb2tQGiA69XnaQ0dyV7nNJTzz++6gEVRY9qe3HWCG777B/7jjwcYGD5zjkURPyhBJeDuFw9Gti9ZUkVtWXYujSEStqymlP/156t526qxQeinewa57ZGdXPN/nuOBzU3jLp0ikkxKUFM42h5aZC/s+gtUHCG5oSAY4KNvauSLN55HbdnY8Iaj7X18/jev8NZvPcNPn99PV//cz/MouUEJago/eGpPzKzlFy3WAEbJLRctqeTb77uYD1+5jLKoAeBNbX38z/+3i6v+99N87eEdqviTpNNA3Unsb+nmN5ubIvfff/kSTX4qOakgGOAdqxew9tw6Hn21mcd3nKB7YBiA7oFh7nrxIHe9eJBLllZyyxVLeefqBZrNRGZNn6BJfPfJPZF+9gsXVmj6F8l5JQVBbr5sCe+6aCEv7Gnlse3HOdreF9n/8uF2Xj7cztce3sE7Vi/gfZct5orl1fphJzOiBDWBncc6eeSVsRVz33/5khRGI5JeCoN5XHt+A1efV8+OY508/doJNh5si/yg6x0c4f7NTdy/uYkl1cW85+JFvOeSRZxdV5biyCWTKEGNwznHvzz+WuT+mmVVrKjXvGQi8QJmrF40j9WL5tHRN8Tze1p49vWWmFbVkdN9/ODpvfzg6b2sXjSPt6+ez42r5nOWkpVMQetBjeObv3+NHz67DwADvvHeN7BUS5WLJMQ5x76WHp7bfZKX9p2iZ3D8cVPnNJTxpyvruPKsGi5fXs284vw5jlTSxUTrQSlBxfmPPx7gtkd2Ru7fcEEDH3vzct/eTySbDQ6PsvVIOy/sbWHL4fYJx06ZwYq6MlbUh27La0upLy+ipqyAmrLQqs+FQf+WtpHU0oKFCXjklWN8/XdjyemyZVV85KrG1AUkkuEKggGuWF7NFcur6R4Y5uXDbWw4cJpXmtoZGhlLVs7BnpPdkdV9J3qtiqIg5UX5VBTnM8+71ZQWUFdeSF1ZIXUVhcyvKGLBvCLmFeerOCPDKUEBzR19/OjZffxiw2HCDcpzGsr49DUryAvoAy6SDGWFQf5kZR1/srKO/qERdh7rZNfxTnYe6+TAqR6m6swZHB6ltXuQ1u7BhN6vKD9AQ0URDRVFzK8ooqGikNqyQurKC6kpK6SqJJ/K4gLmleRTVhjUdz0N+ZqgzOxG4F+BPOCnzrlvxO0vBO4BLgNOAe93zh30MyaAkVHHkdO97D7RxbO7W7h/U1NkMC6EVhn9wg3nqUtBxCdF+XlcuqyKS5dVAdA3OMKxjj6OtvVxrKOPE539dPYN09E3RGf/ED0Dw0x3ZqX+oVEOnerl0KnehI4vKcijtDBIaUEeRfnhWyD0N5hHsfd4SUH4FqS0MI/SgmDoeYVjj5XkBynMD1AUzKMwP0AwYOQFTC26afItQZlZHvBvwPVAE7DRzB52zu2MOuyvgTbn3Aoz+wDwTeD9fsX0yCvH+PEf9rH3ZDf9Q6PjHrOyvozPXruSsiI1LkXmSnFBHmfXlU1Yhu6cY3BklL7BEXoGR+gZGKZnYJjugVAS6+gbor13iPbeQU73DnK6Z3DC7/hEegdH6B0coSUZ/0ETCCeqgIUTVqgSMmBg3l8IPW7g/R27D0SSXPi58X8D3l/w7gfC+8f2BaJeN3xM+H74Pcbeb+y9iYsh7BvvXU19efIXcPXzX+ErgL3Ouf0AZvZL4CYgOkHdBHzN274fuN3MzPlUuTE4PMr2o53j7jtvfjm3XL6US5ZW6leOSLoqTfzQ3sFhTnUPcqpnkFPdA7T1DtHWO0h77xAdfYN0DQzT3T9MV/8wfUNzM0P78KhjOAsn2R2Y5o+BRPmZoBYBR6LuNwFvnOgY59ywmXUANUBr9EFmditwq3e328xeT3awh4DHZ/cStcTFLdOi8zc7On+zo/M3C0u/Oevzt2y8B/1MUOM1Q+J/OiRyDM65O4A7khGUX8xs03hlkpIYnb/Z0fmbHZ2/2fHr/Pk5m3kTED0/0GLg2ETHmFkQmAec9jEmERHJEH4mqI3ASjNbbmYFwAeAh+OOeRj4K2/7ZuBpv64/iYhIZvGti8+7pvQpQpd28oCfOed2mNnXgU3OuYeBO4Gfm9leQi2nD/gVzxxI6y7IDKDzNzs6f7Oj8zc7vpy/jJvqSEREcoNW1BURkbSkBCUiImlJCWoazOxnZnbSzLZPsN/M7PtmttfMtpnZpXMdYzpL4PytNbMOM9vq3b461zGmMzNbYmbPmNkuM9thZp8d5xh9BieQ4PnTZ3ACZlZkZhvM7BXv/N02zjGFZvYr7/O33swaZ/Oems9neu4Cbic0f+B43g6s9G5vBH7ImYOTc9ldTH7+AJ53zv3Z3ISTcYaBzzvntphZObDZzJ6Imz5Mn8GJJXL+QJ/BiQwA1zjnus0sH3jBzB5zzq2LOiap09epBTUNzrk/MPk4rZuAe1zIOqDSzBbMTXTpL4HzJ5NwzjU757Z4213ALkKzsUTTZ3ACCZ4/mYD3mQqvh5Lv3eKr7G4C7va27weutVnMHacElVzjTe+kL8D0XOV1ITxmZqtSHUy68rpOLgHWx+3SZzABk5w/0GdwQmaWZ2ZbgZPAE865CT9/zrlhIDx93YwoQSVXQlM3yYS2AMuccxcBPwAeSnE8acnMyoAHgM855+JnP9ZncApTnD99BifhnBtxzl1MaGagK8zswrhDkvr5U4JKrkSmd5IJOOc6w10IzrlHgXwzq01xWGnF6/t/ALjXOffgOIfoMziJqc6fPoOJcc61A88CN8btSur0dUpQyfUw8BGvkupKoMM515zqoDKFmc0P91eb2RWEPp+nUhtV+vDOzZ3ALufcdyY4TJ/BCSRy/vQZnJiZ1ZlZpbddDFwHvBZ3WFKnr1MV3zSY2X3AWqDWzJqAfyZ0oRDn3I+AR4F3AHuBXuBjqYk0PSVw/m4GPm5mw0Af8AHNzRjjzcCHgVe96wAAXwaWgj6DCUjk/OkzOLEFwN0WWow2APzaOfc7P6ev01RHIiKSltTFJyIiaUkJSkRE0pISlIiIpCUlKBERSUtKUCIikpaUoCQnmZkzs29H3f8nM/takl77LjO7ORmvNcX7vM+bmfuZuMcD3ozm283sVTPbaGbLvX1f9jsukWRRgpJcNQD8RbrNEuCNMUnUXwOfcM5dHff4+4GFwBucc6uBPwfavX1KUJIxlKAkVw0DdwD/EL8jvgVkZt3e37Vm9pyZ/drMdpvZN8zsL701cl41s7OjXuY6M3veO+7PvOfnmdm3vBbNNjP7u6jXfcbMfgG8Ok48t3ivv93Mvuk99lXgLcCPzOxbcU9ZADQ750YBnHNNzrk2M/sGUOytc3Sv9zof8uLfamY/DidIM+s2s2+b2RYze8rM6rzHP2NmO734fzmD8y6SOOecbrrl3A3oBiqAg4TmC/sn4GvevruAm6OP9f6uJdQSWQAUAkeB27x9nwW+F/X83xP6AbiS0PxkRcCtwFe8YwqBTcBy73V7gOXjxLkQOAzUEZr55WngPd6+Z4E14zxnsffftRX4NnBJ/H+Lt30+8AiQ793/d+Aj3rYD/tLb/ipwu7d9DCj0titT/f9Rt+y+qQUlOcuFZrK+B/jMNJ620YXWFRoA9gH/5T3+KtAYddyvnXOjzrk9wH7gPOAGQvPkbSW0zEMNoQQGsME5d2Cc97sceNY51+JCyxfcC/zpFP9dTcC5wJeAUeApM7t2nEOvBS4DNnoxXQuc5e0bBX7lbf8nodYawDbgXjP7EKFWqIhvNBef5LrvEVpi4T+iHhvG6/72Jg4tiNo3ELU9GnV/lNjvU/wcYo7QUgSfds49Hr3DzNYSakGNZ0aLvXkJ9DHgMTM7AbwHeGqc177bOfelRF7S+/tOQgny3cB/N7NVXuIUSTq1oCSnOedOA78mVHAQdpBQywJCK4Tmz+Cl3+dV051NqFXyOvA4oYlI8wHM7BwzK53iddYDbzWzWu/60C3Ac5M9wcwuNbOF3nYAeANwyNs9FH5/QgnrZjOr946tNrNl3r4AoYlTAT5IaHnvALDEOfcM8N+ASqBsyjMhMkNqQYmErtN8Kur+T4DfmtkGQv+IT9S6mczrhBJJA/D3zrl+M/spoW7ALV7LrIVQy2ZCzrlmM/sS8AyhFs+jzrnfTvHe9cBPzKzQu78BuN3bvgPYZmZbnHN/aWZfAf7LSz5DwCcJJbMeYJWZbSa0Kur7gTzgP81snhfLd11oXSARX2g2cxE5g5l1O+fUOpKUUhefiIikJbWgREQkLakFJSIiaUkJSkRE0pISlIiIpCUlKBERSUtKUCIikpb+P410lGGCtAxrAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "model = APGCN(dataset,10, prop_penalty=0.05)\n",
    "\n",
    "results = run(\n",
    "    dataset,\n",
    "    model,\n",
    "    seeds=test_seeds if config['test'] else val_seeds,\n",
    "    #seeds= quick_seeds,\n",
    "    lr=config['lr'],\n",
    "    weight_decay=config['weight_decay'],\n",
    "    test=config['test'],\n",
    "    num_development=config['num_development'],\n",
    "    device=device\n",
    "    )\n",
    "\n",
    "#save_obj(results,'results_' + config['dataset_name'])\n",
    "report = summary(results)\n",
    "\n",
    "print(\"FINAL\\n\"\n",
    "      \"Train Accuracy: {:.2f} ± {:.2f}%\\n\"\n",
    "      \"Stopping Accuracy: {:.2f} ± {:.2f}%\\n\"\n",
    "      \"Test     Accuracy: {:.2f} ± {:.2f}%\\n\"\n",
    "      \"Steps: {:.2f} ± {:.2f}\\n\" \n",
    "      \"Epochs:  {:.2f} ± {:.2f}\\n\"\n",
    "      \"Runtime: {:.4f} ± {:.4f}\\n\"\n",
    "      .format(\n",
    "          report['train_acc'] * 100,\n",
    "          report['train_acc_ci'] * 100,\n",
    "          report['val_acc'] * 100,\n",
    "          report['val_acc_ci'] * 100,\n",
    "          report['test_acc']*100,\n",
    "          report['test_acc_ci']*100,\n",
    "          report['steps'],\n",
    "          report['steps_ci'],\n",
    "          report['epoch'],\n",
    "          report['epoch_ci'],\n",
    "          report['runtime'],\n",
    "          report['runtime_ci']))\n",
    "\n",
    "plot_density(results)\n",
    "\n",
    "del model, dataset\n",
    "torch.cuda.empty_cache()"
   ]
  }
 ],
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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