rkakamilan/explainable_images_for_mols_chainer-chemistry.ipynb

## explainable_images_for_mols_chainer-chemistry.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit WARNING: [17:09:00] Enabling RDKit 2019.09.3 jupyter extensions\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chainer: 7.0.0\n",
      "Chainer-chemistry: 0.7.0\n",
      "RDKit: 2019.09.3\n"
     ]
    }
   ],
   "source": [
    "import base64\n",
    "import functools\n",
    "from io import BytesIO\n",
    "from logging import getLogger\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "import chainer\n",
    "from chainer import iterators, optimizers, training\n",
    "import chainer.functions as F\n",
    "from chainer.functions import relu, dropout\n",
    "from chainer.training import extensions as E\n",
    "\n",
    "import chainer_chemistry\n",
    "from chainer_chemistry.dataset.converters import concat_mols\n",
    "from chainer_chemistry.dataset.preprocessors.ggnn_preprocessor import GGNNPreprocessor\n",
    "from chainer_chemistry.datasets.molnet import get_molnet_dataset\n",
    "from chainer_chemistry.datasets.numpy_tuple_dataset import NumpyTupleDataset\n",
    "from chainer_chemistry.link_hooks.variable_monitor_link_hook import VariableMonitorLinkHook\n",
    "\n",
    "from chainer_chemistry.models.prediction import set_up_predictor\n",
    "from chainer_chemistry.models.prediction.classifier import Classifier\n",
    "from chainer_chemistry.models.prediction.regressor import Regressor\n",
    "\n",
    "from chainer_chemistry.saliency.calculator.calculator_utils import GaussianNoiseSampler\n",
    "# from chainer_chemistry.saliency.calculator.gradient_calculator import GradientCalculator\n",
    "from chainer_chemistry.saliency.calculator.integrated_gradients_calculator import IntegratedGradientsCalculator\n",
    "from chainer_chemistry.saliency.visualizer import abs_max_scaler, min_max_scaler\n",
    "from chainer_chemistry.saliency.visualizer.mol_visualizer import is_visible\n",
    "from chainer_chemistry.saliency.visualizer.visualizer_utils import red_blue_cmap\n",
    "\n",
    "\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
    "from ipywidgets import interact\n",
    "\n",
    "\n",
    "from matplotlib import cm\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import rdkit\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem,  DataStructs, Draw, rdBase, rdCoordGen, rdDepictor\n",
    "from rdkit.Chem.Draw import IPythonConsole, rdMolDraw2D, SimilarityMaps\n",
    "# from rdkit.ML.Descriptors import MoleculeDescriptors\n",
    "import scipy\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "print(f'Chainer: {chainer.__version__}')\n",
    "print(f'Chainer-chemistry: {chainer_chemistry.__version__}')\n",
    "print(f'RDKit: {rdBase.rdkitVersion}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def activation_relu_dropout(h):\n",
    "    return dropout(relu(h), ratio=0.25)\n",
    "\n",
    "def fit(model, dataset, batchsize=16, epoch=10, out='results/tmp', device=-1, converter=concat_mols, alpha=1e-4):\n",
    "    train_iter = iterators.SerialIterator(dataset, batchsize)\n",
    "    optimizer = optimizers.Adam(alpha=alpha)\n",
    "    optimizer.setup(model)\n",
    "\n",
    "    updater = training.StandardUpdater(\n",
    "        train_iter, optimizer, device=device, converter=converter)\n",
    "    trainer = training.Trainer(updater, (epoch, 'epoch'), out=out)\n",
    "    #trainer.extend(E.Evaluator(val_iter, classifier,\n",
    "    #                           device=device, converter=concat_mols))\n",
    "    trainer.extend(E.LogReport(), trigger=(1, 'epoch'))\n",
    "    trainer.extend(E.PrintReport([\n",
    "        'epoch', 'main/loss', 'main/accuracy', 'validation/main/loss',\n",
    "        'validation/main/accuracy', 'elapsed_time']))\n",
    "    trainer.run()\n",
    "    \n",
    "def eval_fun(x, adj, t):\n",
    "    pred =  predictor(x, adj)\n",
    "    pred_summed = F.sum(pred)\n",
    "    return pred_summed\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_unit = 32\n",
    "conv_layers = 4\n",
    "class_num = 1\n",
    "device = -1  # -1 for CPU, 0 for GPU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1128/1128 [00:00<00:00, 1984.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set up GGNN predictor...\n",
      "epoch       main/loss   main/accuracy  validation/main/loss  validation/main/accuracy  elapsed_time\n",
      "\u001b[J1           10.479                                                                     1.99063       \n",
      "\u001b[J2           4.20776                                                                    3.82372       \n",
      "\u001b[J3           2.24927                                                                    5.62311       \n",
      "\u001b[J4           1.23745                                                                    7.37174       \n",
      "\u001b[J5           2.36576                                                                    9.10258       \n",
      "\u001b[J6           2.04462                                                                    10.8956       \n",
      "\u001b[J7           1.15073                                                                    12.6401       \n",
      "\u001b[J8           1.8237                                                                     14.3761       \n",
      "\u001b[J9           1.17838                                                                    16.2506       \n",
      "\u001b[J10          1.69403                                                                    18.0746       \n",
      "\u001b[J11          1.41097                                                                    19.9187       \n",
      "\u001b[J12          0.936554                                                                   21.706        \n",
      "\u001b[J13          1.2655                                                                     23.5215       \n",
      "\u001b[J14          1.3291                                                                     25.3388       \n",
      "\u001b[J15          0.857926                                                                   27.119        \n",
      "\u001b[J16          2.13975                                                                    28.9098       \n",
      "\u001b[J17          1.71933                                                                    30.7402       \n",
      "\u001b[J18          0.579888                                                                   32.6043       \n",
      "\u001b[J19          1.51189                                                                    34.5079       \n",
      "\u001b[J20          1.0376                                                                     36.4345       \n",
      "\u001b[J21          0.822612                                                                   38.3138       \n",
      "\u001b[J22          1.07519                                                                    40.1996       \n",
      "\u001b[J23          1.07342                                                                    42.1325       \n",
      "\u001b[J24          1.49164                                                                    44.0162       \n",
      "\u001b[J25          0.733307                                                                   45.8738       \n",
      "\u001b[J26          0.597865                                                                   47.7003       \n",
      "\u001b[J27          0.96453                                                                    49.5796       \n",
      "\u001b[J28          0.920341                                                                   51.3895       \n",
      "\u001b[J29          0.562004                                                                   53.2519       \n",
      "\u001b[J30          0.503057                                                                   55.1831       \n",
      "\u001b[J31          0.625452                                                                   57.0729       \n",
      "\u001b[J32          0.772168                                                                   58.9548       \n",
      "\u001b[J33          0.629643                                                                   60.8548       \n",
      "\u001b[J34          0.998746                                                                   62.8096       \n",
      "\u001b[J35          0.778005                                                                   64.6935       \n",
      "\u001b[J36          1.03227                                                                    66.5282       \n",
      "\u001b[J37          1.18359                                                                    68.3638       \n",
      "\u001b[J38          0.903051                                                                   70.2129       \n",
      "\u001b[J39          0.508287                                                                   72.013        \n",
      "\u001b[J40          0.64486                                                                    73.8491       \n"
     ]
    }
   ],
   "source": [
    "np.random.seed(20200106)\n",
    "preprocessor = GGNNPreprocessor()\n",
    "result = get_molnet_dataset('delaney', preprocessor, labels=None, return_smiles=True)\n",
    "train = result['dataset'][0]\n",
    "smiles = result['smiles'][0]\n",
    "\n",
    "predictor = set_up_predictor('ggnn',  n_unit=n_unit, conv_layers=conv_layers,\n",
    "    class_num=class_num, postprocess_fn=activation_relu_dropout)\n",
    "regressor = Regressor(predictor, device=device)\n",
    "fit(regressor, train, batchsize=16, epoch=40, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:09<00:00,  9.84s/it]\n",
      "100%|██████████| 5/5 [00:50<00:00, 10.16s/it]\n",
      "100%|██████████| 5/5 [00:52<00:00, 10.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before aggregation: (1, 902, 55, 32) -> After: (902, 55)\n",
      "Before aggregation: (5, 902, 55, 32) -> After: (902, 55)\n",
      "Before aggregation: (5, 902, 55, 32) -> After: (902, 55)\n",
      "CPU times: user 7min 17s, sys: 8.76 s, total: 7min 26s\n",
      "Wall time: 1min 53s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "calculator = IntegratedGradientsCalculator(\n",
    "    predictor, steps=5, eval_fun=eval_fun, \n",
    "    target_extractor=VariableMonitorLinkHook(predictor.graph_conv.embed, timing='post'),\n",
    "    device=device\n",
    ")\n",
    "\n",
    "M = 5\n",
    "saliency_samples_vanilla = calculator.compute(train, M=1, converter=concat_mols)\n",
    "saliency_samples_smooth = calculator.compute(train, M=M, converter=concat_mols, noise_sampler=GaussianNoiseSampler())\n",
    "saliency_samples_bayes = calculator.compute(train, M=M, converter=concat_mols, train=True)\n",
    "\n",
    "method = 'raw'\n",
    "saliency_agg_vanilla = calculator.aggregate(saliency_samples_vanilla, ch_axis=3, method=method)\n",
    "print(f'Before aggregation: {saliency_samples_vanilla.shape} -> After: {saliency_agg_vanilla.shape}')\n",
    "saliency_agg_smooth = calculator.aggregate(saliency_samples_smooth, ch_axis=3, method=method)\n",
    "print(f'Before aggregation: {saliency_samples_smooth.shape} -> After: {saliency_agg_smooth.shape}')\n",
    "saliency_agg_bayes = calculator.aggregate(saliency_samples_bayes, ch_axis=3, method=method)\n",
    "print(f'Before aggregation: {saliency_samples_bayes.shape} -> After: {saliency_agg_bayes.shape}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ref https://github.com/chainer/chainer-chemistry/blob/master/chainer_chemistry/saliency/visualizer/mol_visualizer.py\n",
    "def color_bond(bond, saliency, color_fn):\n",
    "    begin = saliency[bond.GetBeginAtomIdx()]\n",
    "    end = saliency[bond.GetEndAtomIdx()]\n",
    "    return color_fn(is_visible(begin, end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moltopng(mol, atom_colors, bond_colors, molSize=(450,150),kekulize=True):\n",
    "    mc = Chem.Mol(mol.ToBinary())\n",
    "    if kekulize:\n",
    "        try:\n",
    "            Chem.Kekulize(mc)\n",
    "        except:\n",
    "            mc = Chem.Mol(mol.ToBinary())\n",
    "    if not mc.GetNumConformers():\n",
    "        rdDepictor.Compute2DCoords(mc)\n",
    "    drawer = rdMolDraw2D.MolDraw2DCairo(molSize[0],molSize[1])\n",
    "    drawer.drawOptions().useBWAtomPalette()\n",
    "    drawer.drawOptions().padding = .2\n",
    "    drawer.DrawMolecule(\n",
    "        mc,\n",
    "        highlightAtoms=[i for i in range(len(atom_colors))], \n",
    "        highlightAtomColors=atom_colors, \n",
    "        highlightBonds=[i for i in range(len(bond_colors))],\n",
    "        highlightBondColors=bond_colors,\n",
    "        highlightAtomRadii={i: .5 for i in range(len(atom_colors))}\n",
    "    )\n",
    "    drawer.FinishDrawing()\n",
    "    return drawer.GetDrawingText()\n",
    "\n",
    "def label_cat(label):\n",
    "    return '$+$' if bool(label!=0) else '$\\cdot$'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://iwatobipen.wordpress.com/2017/02/25/draw-molecule-with-atom-index-in-rdkit/\n",
    "def mol_with_atom_index(mol):\n",
    "    import copy\n",
    "    atoms = mol.GetNumAtoms()\n",
    "    molwithidx = copy.deepcopy(mol)\n",
    "    for idx in range( atoms ):\n",
    "        molwithidx.GetAtomWithIdx( idx ).SetProp('molAtomMapNumber', str( molwithidx.GetAtomWithIdx( idx ).GetIdx() ))\n",
    "    return molwithidx\n",
    "\n",
    "def draw_mols_with_idx(mol, atom_colors, bond_colors):\n",
    "    images = []\n",
    "    _img = moltopng(mol, atom_colors, bond_colors, molSize=(600, 400))\n",
    "    output = BytesIO()\n",
    "    output.write(_img)\n",
    "    encoded_string = base64.b64encode(output.getvalue()).decode()\n",
    "    images.append(f\"<img style='width: 480px; margin: 0px; float: left; border: 0px solid black;' src='data:_img/png;base64,{encoded_string}' />\")\n",
    "    _img = moltopng(mol_with_atom_index(mol), {}, {}, molSize=(600, 400))\n",
    "    output = BytesIO()\n",
    "    output.write(_img)\n",
    "    encoded_string = base64.b64encode(output.getvalue()).decode()\n",
    "    images.append(f\"<img style='width: 480px; margin: 0px; float: left; border: 0px solid black;' src='data:_img/png;base64,{encoded_string}' />\")\n",
    "\n",
    "    images = ''.join(images)\n",
    "    display(HTML(images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_mol_saliency(mol, saliency, scaler=abs_max_scaler, atoms=['C', 'N', 'O', 'S', 'F', 'Cl', 'P', 'Br']):\n",
    "    n_atom = mol.GetNumAtoms()\n",
    "    symbols = [f'{mol.GetAtomWithIdx(i).GetSymbol()}_{i}' for i in range(mol.GetNumAtoms())]\n",
    "    df = pd.DataFrame(columns=atoms)\n",
    "    saliency = saliency[:n_atom]\n",
    "    num_atoms = mol.GetNumAtoms()\n",
    "    if scaler is not None:\n",
    "        vmax = np.max(np.abs(saliency))\n",
    "        weights = scaler(saliency)\n",
    "    else:\n",
    "        weights, vmax = SimilarityMaps.GetStandardizedWeights(saliency)\n",
    "    \n",
    "    arr = np.zeros((num_atoms, len(atoms)))\n",
    "    for i in range(mol.GetNumAtoms()):\n",
    "        _a = mol.GetAtomWithIdx(i).GetSymbol()\n",
    "        arr[i,atoms.index(_a)] = weights[i]\n",
    "    df = pd.DataFrame(arr, index=symbols, columns=atoms)\n",
    "    \n",
    "    vmin = - vmax\n",
    "    atom_colors = {i: red_blue_cmap(e) for i, e in enumerate(weights)}\n",
    "    # bondlist = [bond.GetIdx() for bond in mol.GetBonds()]\n",
    "    bond_colors = {i: color_bond(bond, weights, red_blue_cmap) for i, bond in enumerate(mol.GetBonds())}\n",
    "    draw_mols_with_idx(mol, atom_colors, bond_colors)\n",
    "    \n",
    "    fig = plt.figure(figsize=(18, 9))\n",
    "    grid = plt.GridSpec(15, 10)\n",
    "    ax = fig.add_subplot(grid[1:, -1])\n",
    "    ax.barh(range(mol.GetNumAtoms()), np.maximum(0, df.values).sum(axis=1), color='C3')\n",
    "    ax.barh(range(mol.GetNumAtoms()), np.minimum(0, df.values).sum(axis=1), color='C0')\n",
    "    ax.set_yticks(range(mol.GetNumAtoms()))\n",
    "    ax.set_ylim(-.5, mol.GetNumAtoms()-.5)\n",
    "    ax.axvline(0, color='k', linestyle='-', linewidth=.5)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.tick_params(axis='both', which='both', left=False, labelleft=False)\n",
    "\n",
    "    ax = fig.add_subplot(grid[1:, :-1], sharey=ax)\n",
    "    im = ax.imshow(df.values, cmap='bwr', vmin=vmin, vmax=vmax, aspect='auto')\n",
    "    ax.set_yticks(range(mol.GetNumAtoms()))\n",
    "    ax.set_ylim(mol.GetNumAtoms() -.5, -.5)\n",
    "    symbols= {i: f'${mol.GetAtomWithIdx(i).GetSymbol()}_{{{i}}}$' for i in range(mol.GetNumAtoms())}\n",
    "    ax.set_yticklabels(symbols.values())\n",
    "    ax.set_xticks(range(len(atoms)))\n",
    "\n",
    "    ax.set_xlim(-.5, len(atoms) -.5)\n",
    "    ax.set_xticklabels(atoms, rotation=90)\n",
    "    ax.set_ylabel('Node')\n",
    "\n",
    "    for (j,i),label in np.ndenumerate(df.values):\n",
    "        ax.text(i,j, label_cat(label) ,ha='center',va='center')\n",
    "    ax.tick_params(axis='both', which='both', bottom=True, labelbottom=True, top=False, labeltop=False)\n",
    "    ax.grid(b=None)\n",
    "\n",
    "\n",
    "    ax = fig.add_subplot(grid[0, :-1])\n",
    "    fig.colorbar(mappable=im, cax=ax, orientation='horizontal')\n",
    "    ax.tick_params(axis='both', which='both', bottom=False, labelbottom=False, top=True, labeltop=True)\n",
    "\n",
    "    display(fig)\n",
    "    plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _fixed_val_scaler(saliency, max_val, logger=None):\n",
    "    try: \n",
    "        xp = cuda.get_array_module(saliency)\n",
    "    except:\n",
    "        xp = saliency\n",
    "    maxv = max_val\n",
    "    if maxv <= 0:\n",
    "        logger = logger or getLogger(__name__)\n",
    "        logger.info('All saliency value is 0')\n",
    "        return xp.zeros_like(saliency)\n",
    "    else:\n",
    "        return saliency / maxv\n",
    "\n",
    "def fixed_val_scaler(max_val):\n",
    "    return functools.partial(_fixed_val_scaler, max_val=max_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison between abs_max_scaler and min_max_scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<img style='width: 480px; margin: 0px; float: left; border: 0px solid black;' 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' 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' 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 699\n",
    "mol = Chem.MolFromSmiles(smiles[i])\n",
    "plot_mol_saliency(mol, saliency=saliency_agg_bayes[i], scaler=abs_max_scaler, atoms=['C', 'N', 'O', 'S', 'F', 'Cl', 'P', 'Br'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='width: 480px; margin: 0px; float: left; border: 0px solid black;' 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' 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' 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 699\n",
    "mol = Chem.MolFromSmiles(smiles[i])\n",
    "plot_mol_saliency(mol, saliency=saliency_agg_bayes[i], scaler=min_max_scaler, atoms=['C', 'N', 'O', 'S', 'F', 'Cl', 'P', 'Br'])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "from chainer_chemistry.saliency.visualizer import normalize_scaler\n",
    "i = 699\n",
    "mol = Chem.MolFromSmiles(smiles[i])\n",
    "plot_mol_saliency(mol, saliency=saliency_agg_bayes[i], scaler=normalize_scaler, atoms=['C', 'N', 'O', 'S', 'F', 'Cl', 'P', 'Br'])\n",
    "\"\"\"saliency array contains negative number, which is unexpected!\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fn(i, saliency, scaler):\n",
    "    saliency_dict ={\n",
    "        'vanilla': saliency_agg_vanilla,\n",
    "        'smooth': saliency_agg_smooth,\n",
    "        'bayes': saliency_agg_bayes,\n",
    "    }\n",
    "    max_val = np.max(saliency_dict[saliency])\n",
    "    scaler_dict = {\n",
    "        'none': None,\n",
    "        'abs_max': abs_max_scaler,\n",
    "        'min_max': min_max_scaler,\n",
    "        'fixed_val': fixed_val_scaler(max_val)\n",
    "    }  \n",
    "    mol = Chem.MolFromSmiles(smiles[i])\n",
    "    plot_mol_saliency(mol, saliency=saliency_dict[saliency][i], scaler=scaler_dict[scaler], atoms=['C', 'N', 'O', 'S', 'F', 'Cl', 'P', 'Br'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling by the Absolute Maximum Value in Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1827564"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_val = np.max(np.abs(saliency_agg_bayes))\n",
    "max_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((455, 10), 1.1827564)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The index of molecule with the absolute maximum value\n",
    "np.unravel_index(np.argmax(np.abs(saliency_agg_bayes)), saliency_agg_bayes.shape), np.max(np.abs(saliency_agg_bayes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='width: 480px; margin: 0px; float: left; border: 0px solid black;' 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' 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' 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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1296x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fn(455, 'bayes', 'abs_max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='width: 480px; margin: 0px; float: left; border: 0px solid black;' 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' 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' 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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1296x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fn(455, 'bayes', 'fixed_val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(501, 0.0738166)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The index of molecule with the absolute minimum value\n",
    "np.argmin(np.max(np.abs(saliency_agg_bayes), axis=1)), np.min(np.max(np.abs(saliency_agg_bayes), axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='width: 480px; margin: 0px; float: left; border: 0px solid black;' 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fn(501, 'bayes', 'abs_max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='width: 480px; margin: 0px; float: left; border: 0px solid black;' 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fn(501, 'bayes', 'fixed_val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(762, 0.11422418)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The index of molecule with the second absolute minimum value\n",
    "cpid_2nd_min = np.argsort(np.max(np.abs(saliency_agg_bayes), axis=1))[1]\n",
    "cpid_2nd_min, np.max(np.abs(saliency_agg_bayes[cpid_2nd_min]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='width: 480px; margin: 0px; float: left; border: 0px solid black;' 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7zTKeSJxpqtURF0aZNma9y2223JSUl9e3bt3Ci4ciRI2WiYbGCd7zopWw2Tp3ydxFC+JkEoefq1KF5c99t71e37jWvv769T58Jdrtt/vxJ06f3yspKKfo2rZbwcLp2RevRv22NGjW+/vrrd955Jzw8fNGiRe3bt9+3z/sr3QQ9RQnKefRFhYezf7+/ixDCzyQIy6VZMxo39t194cWJhtHR1fbuXTt5cuukpDWXvkGjISyMW28lLMzzqxRONNy9e3erVq1+//33Dh06yETDK6WlefiLRhExoIENlx/sBF0v/LkW9CxyVksoY7d3MWw2jhzxSktCBC8JwvJq1Yqrr/bptu8dOgx46609hRMNZ8zofXGioVaLyUS3bt6Z6diyZcsdO3ZMmDAhPz9/0qRJ99xzz9mzZ73QLpCRwaFD7NrF1q1s3crOnRw8SHp6MPU0/v13uX7XuETh9zwObJcfVC75c9EtT5yXvKFc8vJk0VEhZLCMdxw7xr59Pt2BQFGcq1bNXbDgSYej4Kqr2v/73wlNmzbp0sVbn8//+OKLLx566KHMzMx69eotWbKka9eupZ9zhT/+4Oef+fln9u8nJQWdDoMBrfay5LPbsdmIjaV5czp1on17Wrb01l2X961Zw5AhXhlmUhmyAXgZpl442BEM8BMANaElfH/5WddABPxS/ssDI0awcKFXWhIiSEkQek1GxvndeHy5Z+Hhwz/PmnVfaurRyMhK//vff4cNu0+Nqxw/fnzYsGFbtmzR6XTPP/+8WzsaKgo7d7J8OevW4XDgcJRhkdbwcAwGnE5uuYWBA7npJt/1Prvpiy944AGys8vfUmXoA3sgGX6DJgB0BD1sAXwQhP368fXXXmlJiCAVqL9xB6Fq1ejZk+rVfdpN2qxZx9mzf+nff4jFkj18+LCRI0eazWavX+XiREPgpZdeuummm46VMFTEbOaDD+jShUceYcUKsrOxWMq2VLnVitlMbi6rVzNpEh06MGsWGRnl/j68p6DAix254fABWOHxYt5wBr65/CvHW9cGbLbS3yNEhSZB6E3h4dx0E61bYzD44h5Gr6dmTQYMqPzllwkLFiyIjIxctGhRu3bt9uzZo8K19NOmTVu3bl2dOnW2b9/epk2bzz777Mo3mc28/TadOzN3LunpmM3lvTtWFMxmsrP56CNuuYUXX8RbzynLSav17noKN8FYWA2Jrl79Ffpd/uXN9Q4Ctv9ZCF+R/we8r359evWiUSN0OrU+ZPR6KlWiSxc6dSI8HGDkyJG7du1q3br1oUOHbrzxxjfeeMOpQhdtt27dkpKS+vXrd+7cuSFDhvwz0VBRWLaMrl35+GPy8sjL8/KFrVasVj7/nFtv5X//8/928CaT1xcWeh3qwSRXd3ud4a/Lv5p48cKq7jctRDCQIFSFwcB113HHHecnV3irs1SjQa+nShU6dqRHD6pdvllh8+bNt23bNmHCBJvN9vTTT/fq1SslxcVEw3KqXr36V1999b///c9kMhVONPx17VruvptXXsFsVnG3RsBmIy+PefO44w4OHFDxQiXIz2flSt59tzwPCE/Cu/DO5Qej4b9w6pIhMxcZoe7lX95c261mTS82JkQwksEyqrPbOXmSI0fIyUGj8eRmRqs939Farx6NG1OpUinv/+qrr8aMGZORkREbG7tgwYJevXp5Undp9u/fP3To0H379hk1mtdr1JhYallepNFgNDJpEqNH+2gjpLw81q8nMZGvvvI4Ak/ACkiEbeCE6nAaqsE98PGF99wHyyACrvPNYJmwMF59lcmTy9+SEMFLgtB3rFZSUjh1iowM7HZ0OhTFdS4WJp+i4HQSHU2tWtSsSZUqZfjMT01NHTly5Nq1azUazfjx42fOnBnm9XkVDkfe1KlPv//+nIwMYEBk5EexsVV9ObzTZKJzZ2bNcmtlcc9kZbFuHStXsmIFno5C+gOWw+ew+8IRE/SGeyAeql8ehOlwDWRAZ98EYeXKzJ/PPfeUvyUhgpcEoX/YbJw7h8VCbi55edhs54eVGAyEhxMZiclEdDRRUZ7f8CiKMmfOnCeffLKgoKB9+/ZLly696qqrvPYN5Ofz8MMkJZGXt9xsfjAtLdPprKfXL46Lu1ntnYsvZTTSsCGLF1O5sjebPXuWb74hMZG1az0eVLkfvoGVF/IMMMFtEA93w8XncjFwD8y/5MTFMAK6XJhHWAtawno4A1lQ+E/YAiIuSVbPRUfz889cc035WxIieEkQVnA7duwYOnTo0aNHK1WqNG/evGHDhnmh0bw8hg3j8OGLTwRP2O3DUlJ+ys/Xwb+rVJletarBZ1u3GwzUqsWyZVc+MvXAmTOsWkViImvWUFDgWRv7IRGWwcVnmFWgL/SDOyHS09L2QS+oBjvAm/e/YWFYLD6d8SNE4JEgrPiys7MfffTRhIQEYMSIEfPmzYuKivK8OauV4cM5cACr9dLDdkWZkZk54+xZB3QyGpfGxTXy2XZ9ej116vDFFx7eF544wfLlfPEFW7d6PN+jMP8S4I8LR6rBnRAPd0D5e6Ut0B4OwOMwt9yt/aN1a1SYbCNEcJEgDBULFy4cN26cxWJp2rRpQkJC27ZtPWnF6eTRR9m2rbjRoZvy8oanpp602ytrte/Fxg4pT+KWSVgYTZvy2WdlWGIuOZmvviIxka1bPZsd74StkAhfXDKxrwb0gnjo5d2xnfAbdIA8+BL6e6VFg4EpU5g+3SuNCRG8JAhDyMGDB4cOHZqUlGQwGJ599tkXXnhBW9Z5jm+/zYIFlLhJYZbT+XBaWqLZDIyIjv5vjRqRvpmybTTSsydvv13K244eZeXK8uSfA7ZBIiTC6QsH68MAiIfOas5Jmg2ToAokQf3yNxcdzbp1dOxY/paECGoShKHFarU+9dRTc+fOVRSlR48eCxcurFWrlrsn//ADjz/u5mT5hTk5Y9PSchXlmrCwhLi46wun/avNZOK55xg0yMVL+/eTmEhiIr//7lnbVvgRVsKnkHbhYCPoB/HQBXzwUFSBAfA13AwboLwjdKtWJT1dVpYRQoIwFK1du3bUqFEpKSmxsbGffPJJ7969Sz8nM5MePco0he53m21ISso+m82o0bxerdqEmBhfjJ+JiGDlSho0OP+fhfn36accOuRZe3mwHhLhazh34WAL6Ad94SYvVFw26dAaTsF0eL48DRkMjB2L7DQphARhyEpNTR01atSaNWvcnWg4bhybN5d1LkG+okzJyJiTlQX0j4z8KDa2mtoTDXU6rrmGJ57g22/5/HOPd53Nhe8hEb68ZM2zFhAPg8G/sw02Q3fQwCbo4nErJhO7d9O8uffqEiJYSRCGrksnGrZr1y4hIaHYiYbbtvHIIx6vILrCYnkwLe2sw1FXr18cF3eL2hMNFYW0NHI82aEhE1bCN7AKLBcOFubfMLjai0WWzzMX1iZNgqoenK/R0KkTW7d6vTAhgpEEYajbuXPn0KFDjxw5Eh0dPW/evOHDh1/5DqeTnj3LuY/5X3b7sNTUH/PytPB4TMxb1aqpO9HQ4SA52f2xMBnwLSTC2gs7xWvhRoiHe6GOenV6yg43wzYYCJ97cH5UFCtW0KOH1wsTIhhJEAqys7PHjRu3ZMkSID4+/v3334+Jifnn5W++4fnnsViKPd89Dph+9mzhRMOORuPSuLjG6k00dDrJyip1z6Z0+A4SYTUULnWng04QD4PA7UFE/nEU2sI5+AAeLNOZGg1t2rDbC+vSCFExSBCK8xYuXPjYY4+ZzeaGDRsuXbr0xhtvBFAUbruNv//21lW25+ffl5p6rKCgslb73xo1hqq3B1DxN4VXLH4NhENX6AtDIVatgrwvEQaBCXaV6bGlycSGDTJrQoiLZOS0OK9wR8M2bdokJyfffPPN06ZNczqdbN1KZqYXr9LJaNxTr97gqKhzTud9qakjU1MtKuybCKDVXrHT3jGYDTdBQ5gEWyAc+sICSIN1MNGzFKxXjzvuUHHh7+LFw/2QC4PA3ee34eHcdZekoBCXkjtCcRmr1friiy/OnDnT6XT26NFjYfXqtXbv9mzieckuTjRsHhaWEBfXWo2JhjYbJ06UsPj1PeD5yjcNGtC/P/HxdOmCRkO/fqxbd8Wycz5ggXZwECaAWzMhYmL4808vrMsqRAUiQShcWLdu3ciRI1NSUmrodJ/Exd1pMqlxlQM225CUlF/VmWi432ZLzMlZlpV14MJP+MXFr/uA599P48b07ftP/l2UkUGzZmRklLNsD+yDDmCFFaUuvWYysWwZffr4pjAhgoUEoXAtLS1tVK9eq/fs0cD4mJiZ1aqFqTDO89KJhndFRs4v90TD/TZbotm8NCfn8IXtI7yz+HWLFsTHM2gQLVoU+54ffqB375LXn1PJ2zC51KXXIiN54AHmzPFlYUIEBQlCUSzlgQfmrFz5VEaGTVFuCA9PqFnzanXGeV7c0bCuXr+8Vq32ZewmdcLW/PxEs/kLs/nkhZ2Oa+h0vUymeKOxd3q655sMFebf0KE0a+bW++fM4dlnyz/CtqwU6A8r4Rb4vril1666ioMH8eXOyUIECQlCUQynk+uvJz9/p9V6X0rKnwUF0VrtW9WrP1ypkhpX+8tuH56aus9q3VO/fgP3tsdzwLb8/ESzeVlOTorDUXiwgV7fPyoqPiqqs9GoBRSFY8fKtrmSVsuNN9KvHwMH4sFWxuPH8/HHvs/CdLgeTsMr8KzLdxiN/Pwz113n48KECHwShKIYf/zBoEGFH+jZTudj6emLc3KA+Kio92NjY1RYqdkBB222lqXto2RVlB/z8lbm5n6ak5N2If8aGQz9IiPjo6K6GI2XdeA6naSkuNVdqddzyy0MHMjdd1OzpqffBCgK99/PF1/4Pgs3QXfQwmbo7PIdLVuycydqr+wjRLCRIBTFWL6cl166NEIW5uQ8lp5udjobGgxL4uI6+3bCQJ6irM/NTTSbv7JYsi/c4bUIC4uPiuoXGXlDcb2pikJmZkkz63U6OnUiPp4hQ4iL806tisKjj7Jkie+zcAq8CfVgL1Rx+Y7HHuPdd31clRABToJQFOOVV1iw4IqJE8cKCu5LTd2en6/XaJ6rUuWFqlXVnoiaqyjf5+Ymms0rLBbz5fk3JCqquTvb8ObmcurUlQeNRnr0ID6e/v093Ne+VDNm8NprPh47Y4eusB3uhcTi3rRiBQMG+LIqIQKcBKEoxogRbN9e9HCBorySmTn97FkndI+IWBgXV9u9R3plkul0rrRYEs3mdbm5VkUBtNAmPLxvZOSw6OiyjdkpKOD48fN/joige3fi4xkwAHUedl5m5Uruu4/8fC4M4fGBo9AGsuEjeMDlO6pUISmJ+l7Y2VeIikGCUBSjZ0+Sk4t7cX1u7si0tNN2ew2d7uPY2D6RkV65ZobD8W1ubqLZvDY313Yh/240GuOjou6NiqrjWeI6nZw9S8+e9O3LPfcQ5fkcek8kJzNwIIcO+bKbdBkMhkjYBa63WeralY0bZQSpEIUkCEUxOnQoeXG1NIfj/tTU73JzCycavlmtWrinEw3/tttX5eautFhW5+baFYXCxa+NxvioqMHR0TXL+Xmt0bBvH2qsXOMmp5M5c5g6FZutrBs6emwULIRrYQe4fpb78stMneqbYoQIcBKEohjXXkt+fslvUWBOVlbhRMO24eEJNWs2LUunZbbT+X529hdm88/5+YU/hUaN5naTaWBUVL/IyCreGpiq1/PLL/4fKpmaypQpLFuG3c6Fyf7qscANcAgmwSyX79DpWL+eW29VuxIhAp8EoShGixZufl7vslqHpqT8WVAQodG8Vq3axEu3cCrR6tzc/qdP2xQlQqPpbjLFR0UNiIys5PWJGWFhbNvmiyeC7jh6lJdfZtkyNBrvjKPR63E6XU6U3A2doQC+hLtcnluvHklJVPVkZ18hKhIJQlGMVq3cX0I6x+l8LD19UU4OcG9U1Puxse7cz1U6ejTH6Vxcs+Y9kZER6u3TazCwY4evHw2W7OxZFi1i3jz++svDRNTrMZnQahk+nD59GDSInEqW2h8AACAASURBVJyi73oLnoQakAS1XbYzcCCfe7KzrxAViQShKEbbti4/W0uQaDY/nJaW5XQ20OuX1KzZpbSJhoVBeK5xY+/fBV5Ko+HAgQAdGHLsGN98w4oV7NyJ04nBgM1GnqstlQyG8727+fk0a0afPud3Uyr8q5s/nzFjip6kwF3wTclLr73/Pg895L1vSYjgI0EoinHTTaSmlvWk5IKC+1JTt7k30dBHQRgWxv79KrbvLSdO8PvvHD7M8eOcPElWFnY7Wi2RkdSsScOGNGpE8+Zccw0uR88OG8bSpUUPp0FrOA2vwdMur2s0smMH117r3e9GiCAiQSiKcffd/PabB+fZFWXGhYmGt0VELCp+oqGPgjA2li1bSn9bsDt3jjZtOHas6CtroTdo4Qe40eW5rVqxY4f/xxMJ4SeyQ70oRoMGnp2n12imVa26tk6dWnr9hry86//66xufrzR2mfIsHBpEKlfms89wNWr3dngC7DAczrk897ffmDJF5fqECFwShKIYLVu6/FR1U/eIiL316t1pMp1xOO46fXrimTNWf/U9lLCDYAXTvj3Tprl85VXoCEeh2IeB777LV1+pVZgQgU2CUBSjRQvKt6x2DZ3um9q136le3aDRzMnKuuGvv37z1XTyf5hMXH+9ry/qR08/TY8eRQ8bYAlUgkT4xOWJisLo0Zw4oW55QgQkCUJRDDcm1JdKAxNjYrbUrXuVwbDfZvvR5XhItbVu7YeL+otWy+LFLrfRaAJzAXgcDro8NzOTESO4sLOVEKFDglAUo1Ilaruee1ZW7cLDf6lXb3b16o+qtM9DCbRamjTx9UX9Ky6Ojz/G1bzMkTAcLDAMXE8R/eEHXn9d5fqECDgShKJ4d9zheqR+2UVrtRNiYlSbM18MjYauXV1GQgXXuzfjx7t85b/QFH4pbhd7YNo0tm5VrTIhApEEoSher17+XKu6/CIj6dfP30X4yZtv0qZN0cNRsBTCYBasdHmi3c6QISVtZSxEhSNBKIp37bWBtTJZWTkc3Hyzv4vwk/BwPvuM6Oiir9wA00GBMXDa5bl//cUjj6hcnxABRIJQlGj48HKOHfUbvZ677gruO9pyuvpq3n7b5StPQh9Ih/vAxXLdwOef89FHahYnRACRIBQlGjyYIF17SK/nAdc7tIeQBx9k6NCihzUwH2rCJniruHMnTuTAARVrEyJgSBCKElWpwl13lWdmvX9oNLRtS+PG/q4jAPz3vzRqVPRwLHwCGngOtrs80WJh0CDXK4ALUbFIEIrSTJgQoFs3lCA8XNYMO69yZT791OWvMnfAv8AOwyDb5bm//cYzz6hcnxD+J4tuCze8+ioJCeWfX38FtRbd1uvp1o1587zZZrCbMYOpU4seLoCu8DMMgs9cnqjR8OWX3FVkZ1+nE0VBUc7vCXzFx0jhlBWNBo0Grfb8H0LV2bMcPszRoxw7RmoqOTlYrdjtABoNYWGYTMTEULcuTZrQuDGNGwdfF0ywkyAUbrBYuOUWzrlesdljagWhycTatS5XVwldTie338733xd95U9oCzmwAEa6PLd6dfbupXbt87FX+AVoNO4+Py5MQa32/FcIcDo5cIAtW/jlFywW9Hry8kr/2woLQ6/HZqNhQ7p0oWNHqlXzSbkhT4JQuOf773niCU/2Ui+eKkFoMjF5MiNdf6SHtJMnad2aM2eKvvIJjIZI2A3Nir6s0/H007zwAopShvArTuE9ok5XUe8RMzJYs4b163E6yc/3/G8rPBxFoUED+valffvgezoRXCQIhdsmTWL9eqyuF+fygPeDUK/n+utJSKioH7LltWoVffu6/GweDkvgBtgKYRePGgyMHMmLL2IyeXlGaUWMw9OnSUhgzx4UhYICrzVrNBIezr330r27xKFaJAiF2/LyuPNOTp701oQK7wdh5cqsXk316t5prUIaP5533y162Aw3wB/wb5hZeKhfP+bMoVIlIiPVKqaixGFODkuWsGULdvv5bmOvMxoxGhkzhg4dVGk/xEkQirJITubuuzGbvdKYl4MwIoKFC0NrrwkPWK106kRSUtFXdkEXKICvq1btu3Qp7durGIGX0mjQ6YL3ZufHH/noIwoKzo9/UVV4OFddxWOPybNDLwuJB9fCaxo25IMPiIjwdx1FRETwxhuSgqULD2fJEkymoq+0g5cKl17Tak83aeKjFAQUBbvdm52JvmKx8PrrfPgheXm+SEHAauXgQSZPlnXRvUyCUJRRu3bMnh1YWRgRwXPP0bu3v+sIEi1aMGuWi+M63VOzZvW89da0M2fuHzvWqVIfX3GcTmy2IFrG6MQJnniCffu8PquoFA4HeXm89x4ffCB7R3qNBKEou27dmDMnULLQaOSFFxg82N91BJWHH2bIkMuOREayapV21KhFCxfWjItbu37927Nn+7qqwkEmPg5gj+zdy9SpZGX56EawKKuVH39k+nRfx3BFJc8Ihaf27GHMGHJzPf69tLzPCLVawsN55x1uu82zAkJaVhZt2pCcDFC5Mt9/z1VXFS6wvnrt2jv799fr9T9u2NCxfXtfF6bRoNcH8nTDbdv473+9OHrac2FhxMXx0ku+68auqAL3p00EujZtWLmSBg38sz1FeDjVq5OYKCnooZgYPvsMg4HoaDZu5OqrL/479rr99omPP15QUDBs1KjsbNeLr6mo8JFhoN4X/vxzoKQgYLORksLUqbIibHlJEIpyqFOHlSu55x5fZ6HJRLdurFlDMxfzv4W7OnTghRdYuZLGja/Yr+r1GTPatG595OjR8f/6lx8KC9Qs/P13/u//AiUFCxUUkJbGjBl+66StGKRrVHjDzp088QTnzpXpV1NPukaNRiIieO01unf3pE5xhYIC8vJc7tr455EjbTt1ysnJWfTxx8Nd7eWkOo0GgyFwphiePs0zzwTovVdYGO3aMXGiv+sIWnJHKLyhfXs2bGDiRCIj1RpEEx6O0cjo0WzeLCnoHQ4HTmdxexdf1aTJO2+9BYwdP/6Pw4d9WxmAlxdoKR+bjddeC6x7wUvZbOzezdq1/q4jaEkQCi8xGBgzhi1beOQRoqO9uSKXyYTJxPDhbN7ME08EymjVYKcopY5yemDUqPuGDDGbzfeNGmWz2XxT15UCo8tv/nwyMwOws/YfViuLF/P33/6uIzhJ16hQQUEBa9eycCH796PXY7G4fFcpXaMRETidNGrEqFH06SP552XuTVQ4d+5cm44djyUnPzV58huvvOKDulwwGPw7iHTfPmbODNzbwYs0GmrX5q23AnnIbYCSIBRqysxk40bWrGHDhqIvlhSEHTvSpw+33Sa7KanC4cDhcHP2+s5du7p062a321cuX97HL6sWFO7a5ycFBYwfT2amv65fNkYjgwdz553+riPYSBAKn2jduuh9YUlBuGePl7c7EJcq4xour77xxnMvvhhbo8beXbtq+uVXE73eX4uRrljBl18G07z1iAjefVf+7ykbuYUWIsSU/anb008+2eO229LS0+9/8EH//Ors9v2rd1ksQZaCgMPBF1/4u4hgI0EoRIhxOssaKlqtdvEnn8TFxq5Zt27WnDkq1VUKfyys+e23AT1AxiWbjfXrycnxdx1BRYJQiFDiaZzExcZ+/MEHGo3m6eef/3nnTu8WVTpF8X0iFRTw3Xf4a7RsOa1e7e8KgooEoRChpBx9jL3vuGP8uHEFBQXD778/x/d3HG7M9/Cun38OvtvBQjYbq1cHa/F+ofd3AUIIXyl7p+gV3nz11R+3bNmTlDThiSc+/uCDK151OBy5ubnlab8UWi0Gg4rtX+6rr7y1BTWAVmvUan1XvMPBb79x3XU+u2BwkyAUImSU+x4hPDz8s8WLb7jxxk8WLepx223DLl967YeffrrtjjvKeYmKqnXrWU2bTvLZ5fLz2bhRgtBdEoRChAxvdJZdfdVVb7/55kNjxz46fnz7du2aXn31xZf0en1URRm273B4eX03rdanUyEVhT17UJTAWas1oEkQChEaFMVbMxAeHD16w6ZNCZ99Nuz++7ds3Bh2YbZ71y5dcs6c8coliuWr3tE33uCXX3xwHXUlJ9Ookb+LCAYyWEaI0OB0evHu4L9z5jRq2HDX7t0vTp/urTbd4qvZhAcP+uY6KrLb+f13fxcRJCQIhQgN3rsjBCpXrvzpokUGg+HN//xn3fffe6tZt6ifhRkZAbLWd7kUFLB/v7+LCBIShEKEBm+Ppu/Qvv0Lzz7rdDpHjB6dkprq3caL5dU4L85ff6GvEE+NkpP9XUGQkCAUIjSokB/PTpnSvVu31LS00Q895Lul19S/0KlT6s6jt9stZ85sPXduv6KoOzMyK8svK9MFnwrxa48QomSFwwe9/aGo1WoXfPRR6/btV69dO/vddyeNH+/d9l1T/6M9NVWtrlGLJXn79iFnz+4qjECdzlinzoB27T7U6yPVuJxOx7lzxMSo0XaFIneEQoQGdfKjTu3aCz76qHDptT1JSWpc4krqB2F6uirN5uQc3LDhpoyMnxs1Gn3jjcvat59fvXqXEyc+3bixa0FBlhpXLAxCUSoJQiFCgJoTyu7s1euxRx+1Wq2Dhg3zxdJr6gdhMTtJl1dS0hN5eSc7dPikXbsP6tWLb9Ro9M03r2nYcGRm5p4//5ynxhU1GlRd6qfCkCAUQpTXzNdea9a06fETJ7Zu3+7vWrzAu1PpC5nNf54+vbpmzdsbNhx18aBGo2vd+p2wsCpHjrzv/UsC6nwvFY8EoRBCqC4zczco9esPveJ4WFiV2NjbcnOPOxxy7+Y3EoRCiPJ68plnDv3xR4P69Tt36uTvWrxAjbVrLJZjQGRk46IvmUz1gby8096/qjrfS8UjQShECFBhyOhFq1av/r/33gsPD1+2ZEl0dLRKV/mH+qtnRqowhDMsrCpgt7t4/JifnwqaiIjaXr+oohAR4fVWKyAJQiFCgzr5cfLUqVFjxiiK8vqMGW1at1bjEldSPwhr1PB+m1FRTYGMjG1XHHc6C9LTN0dGNtDpvB9ZDofMnXCLBKEQIUCdO0Kn0zlqzJgzGRm9br994uOPe71919QPwrg4768sU71658jIxocPz83L+/vS43/++X95eSevvlqVKZgOB5Urq9FwRSMT6oUIDSpk4atvvPH9xo1xsbEff/CBxmf7/ah/odq1CQvz8px6rTasdev/bN1679q1bVu0mBoTc53DkX/69HeHD8+uWrVDkybjvHmxC2JiZBsmt0gQChEatFoc3lzQa8fOnS+/+qpWq1308cc14+K82HJJNBoffLTXq6fKyjJ16gzo1m3Tjh3379kzofCIVhvepMkjbdrM1mrDvX89aNhQjVYrIAlCIUJDYYR46abw3LlzQ0aMKCgoePrJJ3t27+6VNt2lfhBWq4Zer8pyo9Wr33TnnX/m558+d+638PDYypVbajRqfQgbDLRsqVLbFY0EoRChQav14m3O2AkTjiUnt7vhhpemTvVWm27xVU9f8+YqbsxrNNYyGmup1foFej0tWqh9kQpCglCI0OC9O8IPP/444bPPoqKilnzyycXt6YEft2y5s3//8rcfCBwOL6/Jct11b1x1lSoPAksgXaNukiAUImR44zHh4T//fOKpp4D35s5tevXVl75kt9vNZnM526+onE41N3ZypU0bGSnjLglCIUJGuYPQarUOHj48Jyfn/hEjhg29crWwm2+6KVuljRsKabW+XChl+nSOHPFaa1qt0WttuSEigm7dfHnB4CZBKETI0GrL2Tv61LPP7klKuqpJkzlvv130VZ1Op+7KMno9Op2K7V/urrv48EPy8nx2QW/S6WjVyt9FBA+ZUC9EKNHpPO4v+27Nmrnz5hkMhsWffOKLpdSuoNH4MgWBjh2DtWsxLIxevdDKp7vb5K9KiFDiaZakpqWNfuihwqXUOrZv792iSqfR+P5z3WCgd28uGQwUTHr18ncFQUWCUIgQU9hBWhZOp3P4/fenpqXd0bPnvyZMUKmuUnh90TM39OkTfPdVYWH06IHv79iDWrD9IwshyqnsifL6zJnrN2yIrVHjkw8/9N1SapfybafoRZGRDBhAuCqrvqhFp2PgQH8XEWwkCIWaMjNZvpxHHsHiYveZkjz6KJ9+SmqqOmWFvLI8Kdy5a9e0GTM0Gs3899/33VJql/L508FL9e2LyeSvi5eZ0cigQURF+buOYKNRVNulTISuggLWrmXhQvbvR68vLgUrHT2a43Sea9y4ksvuJ5MJh4NGjRg1ij59ZF81LysowOks9V3nzp1r07HjseTkpyZPfuOVV3xQlwsGg387KPftY+ZMrFY/luAWjYbatXnrreDrzvU7CULhVWYzCxYwfz5OJ6XNrS4lCC8q/IV86FAefpiqVb1Xa2hTFAoKSp1KMez++5d++ukNbdtu3bQpzPfjRgrHyPjj6eAV3nuPLVtUWX3Ui8LDefVV6tb1dx1BSH5zEF5SUMBHH3HTTbz3HtnZpaZgGeTmkpvL4sXccgtvvx2sE7sCjRv9jfMXLFj66adRUVFLFyzwQwoWCoAUBB54gCpVAvpOKzyc4cMlBT0UwP+wIojs3En37syejcVCfr4ql7Bayc/n44+55RbWr1flEqFGp0OrLa7L788jRyb9+9/Af4sspeYjGo0v15EpWVgYzzwTuKNmwsK44QZuv93fdQQtCUJRPjYbL77ImDGcPu2Le7X8fDIzmTyZxx/35k1nyHrjDfbsKfq7i9VqHTRsWE5Ozshhw4YXWUrNFzQa9PqAmtBeqxZPPRWIWWgwUL8+jz3m7zqCmQShKIeTJ+nXj+XLfd1dmZvLpk3ccQeHDvn0uhXMjh28/DL9+nH06BX3hU8///yepKQmjRvPnTXLD4UVpmDgdUS2aMG4cYGVhQYDsbE8/3yAdCEHq4D7URNBY88e+vXj+HG1+kJLZrVy5gzx8WzY4IerVwBZWQweTEEBOTl068bhwxf/HVevXTv73XcNBsOSBQsqVark68ICNQULderE2LGBkoVhYdSsyfTpMqS6vAL0p00Eus2bGTWKnJzyb+vjOaeTvDwmTuTzz/1WQ/AaO5bk5PN/PneOW27h55/Jzb24lNqrL7/sn6XUAjgFC914I5MnY/TpZhIuhIfTpAkzZhAZ6edKKgCZPiHKbsMGJk0qf3eou9MnShURwXPPMXhwOesJIe+/zyOPXHlQp3O+9Vavr75at2nT7T16fPf111ofB1Lh6JhAei5YghMneOUVLBYv79/rpvBwbr6Z0aP9uNJAhSJBKMpo507GjPHKQ0GvBSFgNPLmm/TuXf6qKr7ff6d9e3Jzi77yOjwDsdWrJ23bVqtePZ9W5du9Br3CYmHuXA4c8OnDAZ2OsDAefpjOnX130QovoLsgRMBJTubhhwNxJl9+PlOmsHevv+sIeFYrw4a5TMFd8CJo4COns9aRI2VeFc9jhd2hwZaCQGQkTz/Ngw8SEeGjsSrh4TRvzn/+IynoZXJHKNyWl0fv3pw6VZ6dXS/lzTvCQjExfPcd1at7p7UKafx43n236GEz3AB/wL9hZuGhfv2YM4foaBVXrixcOKYcWyQGiJwclixhyxbsdnfWrfOE0YjRyJgxdOigSvshToJQuG3SJNav9+KSi94PQr2e668nISHYP1jVsmoVffu6/D1mOCyBG2Ar/LOEjMHAyJG8+CImk5fjsKJE4KVOnyYhgT17zq9e5y1GI2FhxMfTvbs8EVSLBKFwz/r1TJ7sskvNY94PQsBk4t//ZsQIrzVYYZw8SevWnDlT9JVPYDREwm5oVvRlnY6nn+aFF1AUNJry9gdUxAi8VEYGa9awfj1OJ/n5nv9thYejKDRoQN++dOgQ4ANpg54EoXCDxcItt3DunHdbVSUIAZOJtWvxy4ZBAcvp5Pbb+f77oq/8CW0hBxbASJfnVq/O3r3Uro2i4HSe/4IyhGJh5mm1579CgNPJgQNs2cIvv2CxoNeTl1f631ZYGHo9NhsNG9KlCx07Uq2aT8oNebIagXDD7Nn+mTXvGZuNl15i3jx/1xFIXn3VZQoWwHDIgUHFpaBGw0cfUbv2+T/rdOe755xOFOV8NMKVn/GFyafRnL//K/xDKNFqadmSli0Bzp7l8GGOHuXYMVJTycnBasVuB9BoCAvDZCImhnr1aNz4/FcQjhwKbnJHKEqTkkLPnmoEoVp3hIDRyGef0aKFl5sNUjt2cNNNLh9bTYa3oTHsAddLyEycyDvvqFyfEH4WEt0UolzmzPHn8jGesVp54w1/FxEYzp1jyBCXKbgGZoEelhSXgq1a8dprKtcnhP9JEIoSZWby9df+WTyjPBSFPXs4etTfdQSAsWM5dqzo4TS4HxR4BTq5PDEykmXLZBVLEQokCEWJPvssWJ/uFBQwf76/i/C3Dz8kIaHoYQUegBS4Ff5d3LmzZ3PNNSrWJkTAkCAUJVq8OJiGyVzKbufrr7046zH4HD7ME0+4fGUmfAs1YGlxHwH33suYMWoWJ0QAkSAUxdu3L7g3v9Xp+OEHfxfhJ1YrgweTk1P0ld0wtXApNajl8tx69fjf/1SuT4gAIkEoird6dXDfUVksrFzp7yL85Kmn2LOn6GEz3Ac2+Bf0c3miXs+nn1K1qsr1CRFAJAhF8dasOT/dqdxynM45WVm+nqmjKPz4o7dWRg0m333H3LkuXxkLf0BbeLW4c6dNkxWdRaiRIBTFyM7m9GmvtLTLam37118Tz5x5z9tr05TO6eTIEV9f1L9SUxk92mX8L4TFEAlLwPUW6zffzNNPq1yfEAFHglAU49dfCXf9aek+BWZnZXX5++8/CwpahoV19ctY/KQkP1zUX5xOhg8nNbXoK0dgPADvQnOX51apwqJFsq6zCEEShKIY5d5vNM3h6Hvq1KQzZwoUZUJMzO569VqFhZV+mnfl5obWJoWvv8769UUPF8AwyIZ4uN/liRoNH39M/frqlidEQJK1RkUx9u8vzzz69bm5I9PSTtvtNXS6+bGxfSMjvVha2fz+u98u7WM7dzJtmstXnoWfoTF8UNy5jz9O//5qFSZEYJMgFMU4ftyz8+yKMiMzc/rZs064LSJiUVxcbd/s3l2clBR/Xt1nzp1j8GCXv7ushbdBD4uhsstzW7WSFelEKJMgFMVIT/fgpOSCgvtSU7fl5+s1mqlVqrxQtar/O9+zsvxdgXtOnOD33zl8mOPHOXmSrCzsdrRaIiOpWZOGDWnUiObNueYaXP5iMW5cCUupOeEVuNHldY1Gli6VpdREKJMgFMUo+x68C3NyHktPNzudDfT6pTVrdjYa1airzAoKcDgCdAzIsWN88w0rVrBzJ04nBgM2G3l5Lt6p12MyoShYrTRrRt++9OtHx47nt/ebP5+lS4uepMAYOA23wJPF1TBnDtde67XvSIggJNswiWK0auX+bPocp3NcevrinBzg3qioD2JjY9zYWalwG6bFcXH3REVFqLeiqcHAjh1ERanVvgfOnmXRIubN46+/0Gg8+J3jfC5qtQwfTp8+DBrkchGZt+BJqAFJUNtlOwMH8vnnZb66EBWLBKEoRosWbg6W2WW1Dk1J+bOgIEKjea1atYkxMW5e4bvc3AGnT9sUJUKj6W4yxUdFDYiM9P7ehGFhbNtGJdcbDfna0aO8/DLLlnmYf0Xp9f9sGX+53dAZCuBLuMvlufXqkZQki8gIIUEoinHttaVOn1BgTlbWUxkZNkVpGx6eULNm07JsrZ3tdL6fnf2F2fxzfn7hT6FRo7ndZBoYFdUvMrKKtxJRr+eXX/z/DCw1lSlTWLYMu90H21pZ4AY4BJNglst36HSsX8+tt6pdiRCBT4JQFKNDBzIzS3g9zeG4PzX1u9xcDYyPiXmzWrVwT7s3/7bbV+XmrrRYVufm2hUF0EEnozE+KmpwdHTNcj7e02jYt6/8iwN4zulkzhymTqWgwGdrt46ChXAt7ADXj2pffpmpU31TjBABToJQFKNnT5KTi3vx0mmCH8fG9vHSNMEMh+Pb3NxEs3ltbq5NUQAt3Gg0xkdF3RsVVcezaRhOJ2fP0rMnfftyzz2+fliYnMzAgRw6hMXis2sug8EQCbuKW0Sma1c2bgzQAURC+JwEoSjGiBFs3170cIGivHJhmmD3iIiF6kwTzHQ6V1osiWbzutxc64VEbBMe3jcyclh09NVl6YCloOCfOZEREXTvTnw8Awb44qnhypXcdx95eTgcql/rgqPQBrLhI3jA5TuqVCEpSRaREeIiCUJRjFdeYcGCK9ZuPmSz3Zea+ovVqtdonvPJNMFcRfk+NzfRbF5hsZgvDAlpERYWHxU1JCqquTtrtuXmcurUlQeNRnr0ID6e/v2p7HqWeXm98gqvvuqdETFus0NX2A73QmJxb1qxggEDfFmVEAFOglAUY/lyXnrp0s/xi9MEGxoMS+PibvTtNME8RVmfm5toNn9lsWRfnoj9IiNvKO4RoKKQmcnZs8W2q9PRqRPx8QwZQlycd2pVFB59lCVLfNkdWmgKvAn1YC9UcfmOxx7j3Xd9XJUQAU6CUBTjjz8YPLhwh/psp3NcevqSnBwgPirqffemCZaVAw7abC1Lu8mzKsqPeXkrc3M/zclJu9Dl2Mhg6BcZGR8V1cVovGzEjtNJSopbt2V6PbfcwsCB3H03NWt6+k2AojBqFMuX+z4FN0F30MJmcL2jYMuW7Nzp/wG0QgQYCUJRDKeT668nP3+n1XpfSsqfBQXRWu3/1agxIjpajav9ZbcPT03dZ7XuqV+/gXsPHR2wLT8/0WxelpOTciERG+j1/aOi4qOiOhuNWkBROHbM5TS7Ymm13Hgj/foxcCBXXVXm72TCBObP930KpsP1cBpegWddvsNo5Oefue46HxcmROCTIBTFUkaPnvPNN4XTBG8ID0+oWbNso1TcttxsfjAtLdPprKvXL69Vq30Zpzo4YWt+fqLZ/IXZfNJuLzxYQ6frZTLF6PXf6QAAGsZJREFUG42909M9H8zTogXx8QwdSrNmbr1/7lyeecb3KahAf1gJt8D34HowaJMmHDokI0WFKEqCULiWmpp6f+/eq/fsKZwmOLNatTAVVkHLV5QpGRlzsrKA/pGRH8XGVivfJ/V+my3RbF6ak3P4wqT1anAnxMMd4Pl2iIWJOGgQLVoU+54ffqB3bx+Pjin0NkyGKpAExQ4GjYxkzBhmz/ZlYUIEBQlC4cK6detGjhyZkpISq9d/HBt7p8mkxlV+t9mGpqT8arMZNZrXq1WbEBPjxaTdb7Ml5uQsy8o6cOEnvAr0hX7QBzz/fho3pm9f4uPp0oVLfzPIyKBZMzIyylm2B/ZBB7DCCihlR0GTiWXL6NPHN4UJESwkCMVlrFbriy++OHPmTKfT2aNHj4XVq9fatUuNCy3MyRmblparKM3Dwj6Ni7tejZVfbDZOnNgP38BK2HLhsAlug3i4BzyfXd+gAf37/5OId93F2rU+WzjmIgu0g4MwAdy614uJ4c8/qVZN7cKECCIShOIfhw4dGjp06J49e/R6/XPPPffCCy9ot29n3DjvPvQ653Q+nJa2zGwGRkRH/7dGjUgVxqCiKKSnk5198cAx+BoSYSsU/tBHQHeIhwHg+ez6evVo2ZJNm0pdmlUNo+ETaAU7wK3BoOHh3H03CQkq1yVEMJEgFOctXLjwscceM5vNDRs2TEhI6NSpE4CicNtt/P23t66yOS9veGrq33Z7Za32vdjYIeoteOZwkJyMqx/vE7ACEmEbFA4nDYeu0BeGQqxaBXlfIgwCE+yCa9w/zWRiwwY6dlSvMCGCiwShIDs7e+zYsUuXLgVGjBgxb968qEvz6ZtveP758t8U2hVlRmbmjLNnHdDRaFwaF9dYnTGoAE4nWVklzaMHIB2+g0RYDYWDTXXQCeJhENRSqzjvOApt4Rx8AA+W6UyNhjZt2L1bpcKECDoShKFux44d991335EjR6Kjo+fNmzd8+PAr3+F00rMnJ06U5yon7Pbhqak/5uVp4fGYmLeqVTOotxMvJd0OupQB30IirAUbULjYN8TDvVBHvTo9ZYebYRsMBE/21Y2KYsUKevTwemFCBCMJwtClKMqcOXOefPLJgoKCdu3aJSQkXFXc/PFt23jkEfLyPLvQCovlwbS0sw5HPb1+cVzczWqvbKIopKW53LG9VJmwEr6BVXDxFrgFxMMwuNqLRZbPM/A61IMk8GRfXY2GTp3YutXrhQkRjCQIQ1RqauqoUaPWrFmj0WjGjx8/c+bMsJLXNhs3js2bsdnKdJU8RXn6wjTBAZGRH5Z7mmDpdDquuYYnnuDbb/n8c44c8ayZXPgeEuFLuJiohYk4uEwP5FSwGbqDBjZBF49bMZnYvZvmrrdpEiKkSBCGorVr144cOTI1NTU2NvaTTz7p3bt36edkZtKjx6WDMEv1u802JCVlnzrTBIsVEcHKlTRocP4/9+8nMZFPP+XQIc/ay4P1kAhfw7kLB1tAP+gLN3mh4rJJh9ZwCqbD8+VpyGBg7FiZXy8EEoShxmq1PvXUU3PnzlUUpWfPngsWLKhVy+1BIT/8wOOPu9lBenGa4DVhYQkqTRMsymTiuecYNMjFS4WJmJjI77971rYVfoSV8CmkXTjYCPpBPHQBH8S8AgPga7gZNhS3lJr7qlThzBnUmLsiRFCRIAwhBw8eHDp0aFJSksFgePbZZ1944QVtWT8E336bTz4pOQvPOBwPpKWttFiAEdHR78XGmlQdF3OR0UjPnrz9dilvO3qUlStJTGTrVvdH01zKAdsgERLh9IWD9WEAxENnUC9YZsOkUpdSc190NOvWyTwKISQIQ8XChQvHjRtnsViaNWuWkJDQpk0bT1op3Glv69biJo9vyssbnpp60m6vrNX+LzZ2sHrTBK8QFkbTpnz2Ge5s1VsoOZmvvipPIjphKyTCF3DywsEa0AvioRd4d3bIb9AB8uDLUpdSc5PBwJQpTJ/ulcaECF4ShBVfdnb2o48+mpCQgMtpgmVltTJ8OAcOXLGc2KXTBDsZjUvj4hqpN03wCno9tWuzfLmHe82fOMHy5XzxBVu3lm3Dpkvsh0RIgD8uHPHOYt8XWKA9HIDHYW65W/tH69bs2ePF9oQIRhKEFdyOHTuGDh169OjRSpUqzZs3b9iwYV5oNDeX4cM5fPjifeEJu31YSspP+fk6eL5q1alVq/pusx+DgVq1WLbMC+tnnjnDqlUkJrJmDRc2ryirwkRcBgcuHLm42PedEOlpafugF1SDHWD0tBEXwsKwWHBvA0ghKioJQv+w2Th3DouF3Fzy8rDZzt+KGAyEhxMZiclEdDRRUXj8fO3SaYLt27dfunRpsdMEPZCfz8MPk5REXt7F3QR9NE3wUkYjDRuyeLGH94LFOXuWb74hMZG1a8s6Y+SiEhb7vhs82N34DGSB9/4JAYiOZvv2kvaWEiIESBD6jtXK6dOcPk1GBnY7Oh2KwoV9ZC+j1Z5/1ekkOppatahZkypVyhCKf//99/Dhwzdv3uzuNEEPOBx5zz//9IcfzjlzBrg7MvLD2Niqvtz31WSic2dmzcLozXuky2RlsW4dK1eyYgVms2dt/AHL4XO4uKaZCXrDPRDv7eeIZVa5MvPnc889/q1CCP+SIFSd3c7Jkxw5Qk4OGo3r5CtZYS4C9erR+P/bu/eoqMq9D+DfPRcuMwgotxREseUlEaVSMrMCU2tJ2FtmJ1QsUbFjJpqWrczS0qw8KZCeLEtN65hBnVyGdrKEksxbXkGSRMKD6MtVVBguM7PfPyBfLyQzw95zcX8/f8Vmz36eJa39nf3s5/c8PeDd1kYJW7ZsmTx5cmVlZVBQ0Pr16x966CFb+t2WvLy8J598Mjc310MQ3vL3T5b2mezGBAEeHkhOxuTJdmrRYMD33yM9HVu2WFVMeaVrFvv2B84C/kAN8AMw7IozBwNaYBcAoDPQD9hx9aXCAY8rktV2bm54803MmdP+KxG5Lr4bkFF9PQoKUFRkY/5dZja3DJwWFeH0aXTogL59ERTUaov18+bNay4THDly5CeffHLLLbfY3vBfEEVxzZo1s2bNMhgMffv23bR8ef/330dRkZ02Z9fp4O+PlStxmx0XePH0RFwc4uJQX48dO7BqFb77ztq5pqFAMpAMnAH+DRgBzZ8bQk0Hjl4xp0b883jzf18/gcd8xQnt0tjYzlVkiW4CDEJZNDUhPx9FRS3Dm1JpHkqtrsbevdDpcPvtV00Qyc/Pj4+PP3LkiLu7+6JFi1544QWrywQtUFFRkZiYuHXrVgAJCQmrV6/W6XQYORLp6Vi6FEajjNvyublBrcb06Zg82WHzOzw8EBcHQcCePaipafv81gQDM64+cgJ4G1jQ/u7Z4Nw5hzRL5Dy4qIT0Tp/Gt9+iqAgmk5QpeCWjERcuICcHe/a0VDFs2LBh0KBBR44c6d279y+//DJv3jw5UjArKysyMnLr1q0+Pj6bN2/esGGDTqcDAEHAE09g1y5MmgRPT0g+X8bdHe7uePxxZGdj2jTHz3Ksq7Ot9LBV8UAf4E3gynVR7ffGwqbVyYluJo6+odxcGhqwfz+qqto1EGo5kwnnzuHrr2s2bXpmy5bPIUmZ4F8wGo2LFy9evHixyWSKjo7euHFjSEjItSd5eeH555GUhE2bsG4dDAbU1bXru4AgQK+HSoUJEzBxogQFElIxmyUMQndgDXAfMAPY3toJFcA3Vx+5aOF+9JaQ6csaketgEEqmogJ79sBotOuN5cSJvcuXx5eVFen13h988P748ePkaKW4uHj8+PE///yzRqN57bXXFixYoL7B7FAvL0ydiilTsH8/vvoKO3bAZILJZMWQqbs7tFqYzbj/fowZg6FDYc/JqJbQam2va2nNUODvwD+BdGDsdb89CsRddzBQqrYln05M5GoYhNIoKsKxY3Z6EGxmNpu2bPnHpk0LTKamnj2j5sz5V5cutzY2Sn9b+/LLL6dOnVpdXR0aGvrpp5/ee++9Fn1MEBAVhagovPUWCgqwbx/27EFeHs6dg1oNrRYq1VUPVUYjGhsRGIg+fTB4MAYNQni4864H7eUled/eArYCs4Dr5/gOATZffSRawoZ9fSW8GJErYhBK4PhxnDxp1xSsqPhvauqE48d/EgQhNnbmU0/9Q63W1tQgKwv33SfZGzqDwfDSSy+lpaUBeOyxx9asWdOpky27wKJXL/TqhQkTWn6srERFBS5ebKlV12rRoQP8/ODvL+1jloz8/SUcGm3WAXgfeLi1KTMewDXD0JJVHwoCrh/iJlIYBmF75eaisBAmk/1a3Lfv61WrJl+6VOXrG/Tcc59ERj7YfNxsRl0dsrIQEyNBFubm5sbHx+fm5np6ei5dujQ5Obm9V7zMz8+J3vbZJiTE5hVnbiAWiAdWAp5Af8mv3ipPT4RKsI8FkUtz1qEnF3HiBE6dsl8KNjXVr12b/Pbbj166VDVo0OiUlLzLKdhMFNHQgOzsdt2lRVFMTU0dOHBgbm5u37599+7dK2UK3hwCA2V6FZwK+AI2rmFjAzc33Hqr3Vojck4MQtudOYPffrPfiGhJSf68eXdlZqZptR6JiSnz5n3doUMrz1XNWbhrl4036vLy8tGjR8+aNauhoSEhIWH//v0RERHt7frNRxAQFibNla7e0TcASPnzeKsn3OCgLRoaEB4uyZWIXBeXWLPRhQvIzrZfCmZnb/jww783NNQFB/eZPXtTWFjkjc9XqxESgjvvtK6VnTt3JiQklJaW+vv7r127Ni7u+rmK9KcpU7B2reRvCu3Nxwfnzzu6E0QOxidCW5hM2L3bTiOidXU1y5c/+d57TzU01N1/f8KyZQfaTEEAJhNKSlBSYmkrRqNx4cKFI0aMKC0tjYmJOXz4MFOwDQ88ALttOyyfwYMd3QMix+NkGVscOYKGBns8DBQU7FmxYlxZWZGnp/czz6weOjTe8s+aTDh4EP7+be/NUFxcPG7cuN27d2s0mgULFrRRJkjNhg2zec9CZ6HX49FHHd0JIsfj0KjVqqqQkyP7oKjZbEpPfyMjY7HZbOrZM2r27E1BQT2svYhKhYAA3HPPjc7JyMiYOnXq+fPnQ0NDP/vss6FDh9reaaW57Tb89pujO9EOHh74/XeWTxBxaNQ6oogDB2RPwYqK/776aswXXywSRTE2duaSJTk2pCAAsxmVlSgra/23BoMhOTl57Nix58+fHzNmzOHDh5mC1nn6aelXVbWnHj2YgkRgEFqrpAQGg7xN7N377zlzIvPzd/n5hSxatDMxMVWttr1+2mjEoUOtHP/1118jIyPT0tI8PT1TUlIyMjI6duxoe6eVacIEF54so9fj2Wcd3Qkip8ChUSuIIrZvl3GXoaam+o0b52VmpgGIinpk+vSPWy2QsJZGg4ED0aVLy4+iKKalpb344ouNjY3h4eGff/55v3792t+KQg0fjp07XTIOdTqUlsKe2ykTOStOlrFCaamMg6IlJceXL48vLj6q1XokJLw1atRMQaL1xoxGHD/eEoTl5eWTJk3KzMwUBCEpKSklJcXTpQf3HO6VV7B3Ly7ZrwJeGm5uGD+eKUjUjEFohRMnZAzCzZsXFhcf7do1fPbsTd26SVzDXleHqiocOvTDxIkTm8sE161b9/DDD0vbihJFRyMsDMeOObofVlKr8corju4EkbNgEFrq0iV5dzBNSno/MDDsb39b6OYm/SNaY6PxpZcWf/zxG2azOSYmZuPGjcHBwZK3olCpqYiLQ22to/thMXd3TJjAJUaJLuM7Qkvl5eH3311yE9Oysj9SUsadOPGLRqOZP38+ywSlN2KEXdcZaqcOHXDqFPz9Hd0PImfBJ0JLlZS4ZAru3p2+enVSbe35wMBuH330WVzcDYsKyTZr1iA83DWC0MsL77zDFCS6EssnLGIwyDhZVCYGw8W0tInvvvtEbe35wYPHLFt2qGtXpqA8unfHkiXQ6x3dj7ZoNAgPx7Rpju4HkXPh0KhFTp/G4cOu8Y2/WWHhgRUrxp09+7ubm+eECUtjY5MB6HR46Prtz0kSoojoaOzZI8c+hZLx9kZeHovoia7BoVGLlJe7TAqKorhtW9qGDS8ajY2hof1mz94UGtpSJlhfD6MRGv7N5SAI+OorhIejrMxJywp1OnzxBVOQ6Hq8KVqkutrRPbBMTU3ZypWTDh7cJghCbOzMhIR3tFr3y79Vq1FT4/KbwzsvPz/s2IEhQ5yxrFCvx8KFePDBts8kUh4GoUXq6uS9fkND7R9/HNHpfIKD+6hUNk7pPHr0+7S0idXVZ729A2bMWHfnnbHXnCCKuHiRQSiniAhs2YK4ONn/j7GKXo/ERMyd6+h+EDkpBmHbmppknC9aVvbH8uVPFhYeMJtNALRaj7vu+p/p0z9yd7di5oXJZMzIWJye/oYomiMihs2cubFTpy6tneZK1W6uatgwpKdj7FhnyUK9HuPGITXV0f0gcl4MwrbV10OtliULz5z5beHC4VVVZ4YPnzJgwMj6+ks//fRZTs7nZ86cWLRop17va8lFysr+WLEivqBgj1qtGTNmwRNPvCoIrU8GFkVnuTnf5EaNwjffYPRo1NY6+H2hXo/p0/H225BouT6imxKDsG2NjXLdRtavf76q6sxzz62Pjn6q+Uh09MRVqxKzszd8++0/x4x5uc0rZGdvWLPm2fr6SwEB3WbP/lfv3kNufL7LFYG4qpgY7N6NESNQXe2weaQ6HZYvR1KSY1onch2sI2ybySTL1/pz504eOvTtgAEjL6cgAJVKPWlSipdXxx07Przxxw2Gi6mpCe+991R9/aW773783XcPt5mCAEym9nabLBURgbw83H23A+oLPTwQEIAffmAKElmCQdg2mQa3Cgt/FUXx3nvjrznu5dWxX79h5eXFDQ1/OY5ZWHhg7tw7fvrpU0/PDs8888HcuekWjqM658T+m5afH7KysGQJdDr7la3o9YiNRUEBBg+2U4tELo5B2DaVPP9IZWVFAFrdej4gIBRAdfXZ638limJmZurLL99z7tzJW2+9c9myX0eMsOJbP18V2ZsgIDkZx49j2DDo9fL+Aby8EBKCL79ERgZ8LfpiRETgO0JLyPRV3surE4D6+lbmcZ4//7+CIFw/87OmpmzlyqcPHtzeXCY4ceIyjcbNqkZZTe8Y3brhP//Bjz9i1iycPCn9JBovL2i1eP11TJsGrVbKKxMpAJ8I2+bmJsuIYpcuvQAUFPxyzXGTqSkv78eAgG7X7MdkMhnnzx968OB2X9+g+fO3JSamWpuCALgLryPdfz8OHUJmJoYPh4eHBH8MjQZeXujRAytW4OxZzJjBFCSyAYOwbZ6estRO9O49JCiox7Zt71VWllx5fPv2VVVVZ0aNeu6a89Vqzfjxb0ZEPLBs2cHbb7dlzVBBgE5ne4dJGvfdh+++w6lTeP119O0Ld3d4e1s3ZKrTQa+Hvz+SkpCVhcJCTJkCd/e2P0hEreGi2xbZuhVNTdJfdt++r5cte9zLq9PYsQu6devf1FR/8OD2zMzUnj2j3njjR63W4/qPiKIo2PqeSaPBHXdwsUknU16OnTuRlYWcHJw6BVGEm1vLe2lRhEoFsxmCgMZGNDYiOBh33IEHHkBMDPr2dXTXiW4SDEKLZGejqkqWK+fn56xc+fS5c4XNP2q17jExTycmpl65RqhUNBpER8PbW/ILk3QqKlBairIyXLjQUuzi7Q0fH4SEoHNncEdlIhkwCC2Sl4eCAhlrD6qrz54+nevjE9i1a7haLdeEFpUKjzzCiaNERFfhJEKL+Pvj1ClZRkebdezYuWPHznJd/U/WvooiIlICTpaxiJ+fy6/JolKhs+xRS0TkehiEFtFoXL5AWaVCl1Z2pCAiUjoGoaW6d3ftanSNBj4+ju4EEZHzYRBaKjhYxl0J5aZSISzM0Z0gInJKDEJLabXo3NklJ5uIIgQB3bs7uh9ERE6JQWiF3r3lWoBbVioVgoK4uBoRUetc8L7uOL6+8PV1vYdClQq33eboThAROSsGoXUGDHCxxT0EAYGBnCZDRPSXGITW8fVFUJArDZCqVOjf39GdICJyYq5zR3caAwa4TBCq1ejZE3q9o/tBROTEXOSO7kw8PNC/v2vUFHp6ok8fR3eCiMi5MQht0b07AgKc/WWhRoPBg13m4ZWIyFF4m7TRwIFwd3feGaQaDSIjueMSEVHbGIQ20moxdKiTDpCq1QgLQ2ioo/tBROQKGIS28/LCPfc43QCpWo1bbkFEhKP7QUTkIhiE7dKpE4YMcaIsVKsRGIioKEf3g4jIdXCHeglUVmL3bhiNMm5hbwmNBp07Y+BA531zSUTkhBiE0rh4ETk5aGhw2A4VajV69eJSakREVmMQSqapCfv2oaLC3nvZq1RQqxEVhaAgu7ZLRHRzYBBKrKgIx47BZLLTMKlGA19fREXBw8MezRER3XwYhNIzGHDoEMrL5X00VKuhViMyEiEhMrZCRHTTYxDKpbISR47g0iXpnw7VaggCevVCz55ONGGViMhFMQjlVVmJ/HxUVkIQYDS261KC0PIU2KsXwsKctJafiMjlMAjtob4ep0+juBi1tVCprEvE5rkwoojOnVvWOCUiIgkxCO2qoQHl5SgrQ2UlamshCC2LYotiy/Dp5TWyTSZotfDxQWAgAgLg68vqQCIiWTAIHam+HgYDGhv//xlREODmBg8P6HTcOIKIyB4YhEREpGh86CAiIkVjEBIRkaIxCImISNEYhEREpGgMQiIiUjQGIRERKRqDkIiIFI1BSEREisYgJCIiRWMQEhGRojEIiYhI0RiERESkaAxCIiJSNAYhEREpGoOQiIgUjUFIRESKxiAkIiJFYxASEZGiMQiJiEjRGIRERKRoDEIiIlI0BiERESkag5CIiBSNQUhERIrGICQiIkVjEBIRkaIxCImISNEYhEREpGgMQiIiUjQGIRERKRqDkIiIFI1BSEREisYgJCIiRWMQEhGRojEIiYhI0RiERESkaAxCIiJSNAYhEREpGoOQiIgUjUFIRESKxiAkIiJFYxASEZGiMQiJiEjRGIRERKRoDEIiIlI0BiERESkag5CIiBSNQUhERIrGICQiIkVjEBIRkaIxCImISNEYhEREpGgMQiIiUjQGIRERKRqDkIiIFO3/AJfy3QMe2Om5AAAAAElFTkSuQmCC' /><img style='width: 480px; margin: 0px; float: left; border: 0px solid black;' 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' 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7VFVdl+RjVXVWkkck+Z2e2wQAAAAmQ+8LSaa19t4k7527rKqmkvxSkn9YVT/fWvs/eucAAAAAVlb30mE+rbWvJbl6iG0DAAAAK6P3NR0AAACANUrpAAAAAHShdAAAAAC6UDoAAAAAXSgdAAAAgC6UDgAAAEAXSgcAAACgC6UDAAAA0IXSAQAAAOhC6QAAAAB0oXQAAAAAulA6AAAAAF0oHQAAAIAulA4AAABAF0oHAAAAoAulAwAAAAxg47XXDh2hO6UDAAAA0IXSAQAAAOhC6QAAAAB0oXQAAAAAulg/dIAFe8xjkuc+d+gU/Rw4sLpfX5Kctco7rt27k+3bh07R14kTQycAAABGZJX/FggAAAAMRekAAAAAdKF0AAAAALpQOgAAAABdKB0AAACALsbz6RUAAACwitx94405su99Q8dYlIvuOLKgxznSAQAAAOhC6QAAAAB0oXQAAAAAulA6AAAAAF0oHQAAAIAulA4AAABAF0oHAAAAoAulAwAAANCF0gEAAADoQukAAAAAdKF0AAAAALpQOgAAAABdKB0AAACALpQOAAAAQBdKBwAAAKALpQMAAADQhdIBAAAA6ELpAAAAAHShdAAAAAC6UDrAMjl85525augQAAAAE6R76VBVr6uqVlUXzVl2pKqe3HvbAAAAwHBW4kiHZyQ5nGRHklTVo5NsSnLXCmwbAAAAGEi11vpuoOqTSX49yetaa9ur6tlJbmyt/fACnrszyc4k2bRp09Z9+/Z1zTqko0ePZmpqKlU1dJR+Dh0aOkEX19xwQx44fjzfPn489xw7lsedd16SZOeOHbn0wgsHTrfMtm4dOkFXa2I/XOXMcPzMcNzMb/zMcPxW+wx37dqVgwcPLvXF9f0l+Ay8fsOGXLth49AxFuWiO47MvXnKmazvHyVPS/KHSd5aVedm5siHzyzkia21PUn2JMm2bdva5Zdd1i3k0M6fmspthw5ly5YtQ0fpZ/v2oRN08eCu9o6rr87hm27K3vvum1kwPT1Ypm5OnBg6QVdrYj9c5cxw/Mxw3Mxv/Mxw/MyQSdO1dKiqzUm+1lq7r6o+nuQFSZ6Z5PaqemlmTrk4J8m7W2v/sWeWSTc9PZ3NmzcPHQPWNPvh+Jnh+JnhuJnf+Jnh+Jkhk6b3NR2ekeT22a//ODMlwzOSfKa19gettdcmuTrJz3TOMfGmpqaybt26oWPAmmY/HD8zHD8zHDfzGz8zHD8zZNL0Lh2emb8rHQ4ked5Jy5LkXyV5Z+cc0N3FF1yQvUOHAAAAmCArdqRDa+3+zFzL4Tuttb+tGf82yZ+01m7rnAMAAABYYV2v6dBau/Kk2y+Zc/MNSX4syblVdUFr7aaeWQAAAICVtRKfXjGv1tqvJfm1obYPAAAA9NX79AoAAABgjVI6AAAAAF0oHQAAAIAulA4AAABAF0oHAAAAoAulAwAAANCF0gEAAADoQukAAAAAdKF0AAAAALpQOgAAAABdKB0AAACALpQOAAAAQBdKBwAAAKALpQMAAADQhdIBAAAA6GL90AEAAABgLdp47bW56Lrrho7RlSMdAAAAgC6UDgAAAEAXSgcAAACgC6UDAAAA0MV4LiR56FBy1iruSHbvTrZvHzpFXydODJ2grwMHVv9rBAAAOAOr+Ld4AAAAYEhKBwAAAKALpQMAAADQhdIBAAAA6ELpAAAAAHQxnk+vAAAAgFXk7htvzJF97xs6xilddMeRJa/DkQ4AAABAF0oHAAAAoAulAwAAANCF0gEAAADoQukAAAAAdKF0AAAAALpQOgAAAABdKB0AAACALpQOAAAAQBdKBwAAAKALpQMAAADQhdIBAAAA6ELpAAAAAHShdAAAAAC6UDoAAAAAXSgdAAAAgC6UDgAAAEAXSgcAAACgC6UDAAAA0IXSYUIcvvPOXDV0CAAAAFhGK1I6VNXLq+qWqjpcVZ+tqretxHYBAACA4XQvHarqVUnenORlrbWLk1yS5Ou9twsAAAAMq1pr/VZedU6Sv0pySWvt84t4/s4kO5Nk07nnbt331rcuc8LhXXPDDXng+PF8+/jx3HPsWB533nlJkp07duTSCy8cON0y27p16ARdHT16NFNTU6mqoaOwSGY4fmY4fmY4buY3fmY4fqt9hrt27crBgweX+uL6/RJ8hl6/YUOu3bBx6BindNEdRxb60FPOZP3yRDmllya5ZTGFQ5K01vYk2ZMk26ra5bt2LWe2ifDgCN9x9dU5fNNN2XvffTMLpqcHy9TNiRNDJ+jq/Kmp3HboULZs2TJ0FBbJDMfPDMfPDMfN/MbPDMfPDJk0vU+veHqSwycvrKqLquqmqvpAVV3TOQOsiOnp6WzevHnoGCyBGY6fGY6fGY6b+Y2fGY6fGTJpepcO9863jdbakdba1UlekeQ5nTPAipiamsq6deuGjsESmOH4meH4meG4md/4meH4mSGTpnfp8CdJXl5Vm5Kkqh5VVa+d/frFST6S5I87ZxiFiy+4IHuHDgEAAADLqGvp0Fr7VJLrknysqj6TmVMtHjd734dbay9KcmXPDAAAAMAwel9IMq219yZ579xlVXV5kp9K8qg40gEAAABWpe6lw3xaa/uT7B9i2wAAAMDK6H1NBwAAAGCNUjoAAAAAXSgdAAAAgC6UDgAAAEAXSgcAAACgC6UDAAAA0IXSAQAAAOhC6QAAAAB0oXQAAAAAulA6AAAAAF0suHSoqudW1atnv95YVU/uFwsAAAAYuwWVDlX1tiRvTvLzs4sekeR3eoUCAAAAxm+hRzr8ZJIXJ7k3SVpr/z3JY3uFAgAAAMZvoaXDd1prLUlLkqo6u18kAAAAYDVYaOnw/qr6jSTfW1WvTfKfkkz3iwUAAACM3fqFPKi1truqfjzJsSQXJvnF1trHuyYDAACAVWzjtdfmouuuGzpGVwsqHZJktmRQNAAAAAAL8rClQ1V9K7PXcZhPa+2cZU8EAAAArAoPWzq01h6bJFX19iRfTvLeJJXkyiSP754OAAAAGK2FXkjyxa21X2+tfau1dqy19n8meUnPYAAAAMC4LfSaDvdW1ZVJ9mXmdIufTXJvt1Tz2bo1ufXWFd3kijpwIDlxYugU8LDqrBo6Qle7dyfbt6/u19hOnPKMOQAAWHYLPdLhlUlekeRvknw1yctnlwEAAADMa6EfmXlXnE4BAAAAnIEFHelQVU+sqt+vqq/O/vlgVT2xdzgAAABgvBZ6esVvJflwkifM/vkPs8sAAAAA5rXQ0mFja+23Wmvfnf2zN8nGjrkAAACAkVvop1d8rar+SZLfm739s0m+1icSAAAAjMOWt3xk0c+96tHLGGRCLfRIh3+amU+v+EqSLyf56SSv7hUKAAAAGL+FfnrFXyd5cecsAAAAwCrysKVDVf3iw9zdWmtvX+Y8AAAAwCpxuiMd7p1n2dlJXpNkKonSAQAAAJjXw5YOrbVfefDrqnpskn+emWs57EvyK6d6HgAAAMBpr+lQVecneWOSK5O8J8mzW2vf6B0MAAAAGLfTXdPhl5P8VJI9SZ7RWrtnRVIBAAAAo3e6j8x8U5InJPlXSf57VR2b/fOtqjrWPx4AAAAwVqe7psPpSgkAAACAeSkVAAAAgC6UDgAAAEAXSgcAAACgC6UDAAAA0IXSAQAAAOhC6QAAAAB0oXQAAAAAulA6AAAAAF0oHQAAAIAulA4AAABAF0oHgCTJ/uzbd/3QIQAAYFXpXjpU1cuq6paq+nRVHayqF/TeJgAAADC89T1XXlWvTPKGJC9prX2lqp6S5OaquqS19oWe2wYAAACG1a10qKqzk1yf5Dmtta8kSWvtc1W1P8nzk+xdwDp2JtmZJJs2bcr+Awd6xR3c0aNH82f796eqho7CIq2FGe7ePXSC5XfDDdfk+PEHcv/99+XIkWN5whMuSJLs2LEzF1546cDplt/+1fttNMna2A9XOzMcN/MbPzMcPzNk0vQ80uGKJLfNc0TD/UnOXcgKWmt7kuxJkm3btrXLL7tseRNOkPOnpnLboUPZsmXL0FFYpLUww+3bV+Ob15HZ/+7Ptm3X5+DBjyZJpqeHS9RTO9GGjtDVWtgPVzszHDfzGz8zHD8zZNL0vKbD05N8ep7lz0pye1WdPXuNh5/omGE0pqens3nz5qFjsARmCMOzH46fGY6b+Y2fGY6fGTJpepYO30zyyLkLquqHkpyT5ECSNyd5f8ftj8rU1FTWrVs3dAyWwAxhePbD8TPDcTO/8TPD8TNDJk3P0uGPkryiqjYmSVU9Ncm7krw6yY8m+YskX+24fYAzcHmuuOItQ4cAAIBVpds1HVprB6vq7Uk+UVUtM0c+XN1au7mqfinJ2UmeluS+qvrj1tqJXlkAAACAldf1IzNba3szz6dUtNb+ZZJU1VVJjiocAAAAYPXpWjqczmwpAQAAAKxCPa/pAAAAAKxhSgcAAACgC6UDAAAA0IXSAQAAAOhC6QAAAAB0oXQAAAAAulA6AAAAAF0oHQAAAIAulA4AAABAF0oHAAAAoAulAwAAANCF0gEAAADoQukAAAAAdKF0AAAAALpQOgAAAABdrB86AAAAAIzVXdfvWPRzr7vu1mVMMpkc6QAAAAB0oXQAAAAAulA6AAAAAF0oHQAAAIAulA4AAABAF+P59IpDh5KzVnFHsnt3sn370Cn6OnFi6AQsUTvRho7Q1f4Dq/81AgDASlrFv8UDAAAAQ1I6AAAAAF0oHQAAAIAulA4AAABAF+O5kCQAAACsInffeGOO7Hvf0DFO6aI7jix5HY50AAAAALpQOgAAAABdKB0AAACALpQOAAAAQBdKBwAAAKALpQMAAADQhdIBAAAA6ELpAAAAAHShdAAAAAC6UDoAAAAAXSgdAAAAgC6UDgAAAEAXSgcAAACgC6UDAAAA0IXSAQAAAOhC6QAAAAB0oXQAAAAAulA6AAAAAF0oHQAAAIAulA4T4vCdd+aqoUMAAADAMlqR0qGqXl5Vt1TV4ar6bFW9bSW2CwAAAAyne+lQVa9K8uYkL2utXZzkkiRf771dAAAAYFjVWuu38qpzkvxVkktaa59fxPN3JtmZJJvOPXfrvre+dZkTDu+aG27IA8eP59vHj+eeY8fyuPPOS5Ls3LEjl1544cDpltnWrUMn6Oro0aOZmppKVQ0dhUUyw/Ezw/Ezw3Ezv/Ezw/Fb7TPctWtXDh48uNQX1++X4DP0+g0bcu2GjUPHOKWL7jiy0IeecibrlyfKKb00yS2LKRySpLW2J8meJNlW1S7ftWs5s02EB0f4jquvzuGbbsre++6bWTA9PVimbk6cGDpBV+dPTeW2Q4eyZcuWoaOwSGY4fmY4fmY4buY3fmY4fmbIpOl9esXTkxw+eWFVXV5VN1fVTVV1eecMsCKmp6ezefPmoWOwBGY4fmY4fmY4buY3fmY4fmbIpOldOtx7im20JPckeXSSL3bOACtiamoq69atGzoGS2CG42eG42eG42Z+42eG42eGTJrepcOfJHl5VW1Kkqp6VFW9NsnNrbUXZeYCk/975wyjcPEFF2Tv0CEAAABgGXW9pkNr7VNVdV2Sj1XVWUkekeR3WmsPntz/jSSP6pkBAAAAGEbvC0mmtfbeJO+du6yqfirJC5J8b5Ibe2cAAAAAVl730mE+rbUPJfnQENsGAAAAVkbvazoAAAAAa5TSAQAAAOhC6QAAAAB0oXQAAAAAulA6AAAAAF0oHQAAAIAulA4AAABAF0oHAAAAoAulAwAAANCF0gEAAADoQukAAAAAdKF0AAAAALpQOgAAAABdKB0AAACALpQOAAAAQBfrhw4AAAAAa9HGa6/NRdddN3SMrhzpAAAAAHShdAAAAAC6UDoAAAAAXSgdAAAAgC6UDgAAAEAX4/n0iq1bk1tvHTpFPwcOJCdODJ0CHladVUNH6Gr37mT79tX9GtuJNnQEAADWEEc6AAAAAF0oHQAAAIAulA4AAABAF0oHAAAAoIvxXEgSAAAAJsyWt3xk0c+96tHLGGRCOdIBAAAA6ELpAAAAAHShdAAAAAC6UDoAAAAAXSgdAAAAgC6UDgAAAEAXSgcAAACgC6UDAAAA0IXSAQAAAOhC6QAAAAB0oXQAAAAAulA6AAAAAF0oHQAAAIAulA4AAABAF0oHALob1A4AAAwJSURBVAAAoAulAwAAANCF0gEAAADoQukAAAAAdKF0AAAAALpQOgAkSfZn377rhw4BAACrSvfSoapeVlW3VNWnq+pgVb2g9zYBAACA4a3vufKqemWSNyR5SWvtK1X1lCQ3V9UlrbUv9Nw2AAAAMKxupUNVnZ3k+iTPaa19JUlaa5+rqv1Jnp9k7wLWsTPJziTZtGlT9h840Cvu4I4ePZo/278/VTV0FBZpLcxw9+6hEyy/G264JsePP5D7778vR44cyxOecEGSZMeOnbnwwksHTrf89q/eb6NJ1sZ+uNqZ4biZ3/iZ4fiZIZOm55EOVyS5bZ4jGu5Pcu5CVtBa25NkT5Js27atXX7ZZcubcIKcPzWV2w4dypYtW4aOwiKthRlu374a37yOzP53f7Ztuz4HD340STI9PVyintqJNnSErtbCfrjameG4md/4meH4mSGTpuc1HZ6e5NPzLH9Wktur6peq6t9X1as6ZhiN6enpbN68eegYLIEZwvDsh+NnhuNmfuNnhuNnhkyanqXDN5M8cu6CqvqhJOckOS/JE5M8kOSLHTOMxtTUVNatWzd0DJbADGF49sPxM8NxM7/xM8PxM0MmTc/S4Y+SvKKqNiZJVT01ybuSvDrJU5L8P621Nya5pmMGgAW6PFdc8ZahQwAAwKrS7ZoOrbWDVfX2JJ+oqpaZIx+ubq3dXFWbk3xn9qHHe2UAAAAAhtP1IzNba3sz/6dUfCjJv6+qH0nyyZ4ZAAAAgGF0LR1OpbX27SSvGWLbAAAAwMroeU0HAAAAYA1TOgAAAABdKB0AAACALpQOAAAAQBdKBwAAAKALpQMAAADQhdIBAAAA6ELpAAAAAHShdAAAAAC6UDoAAAAAXSgdAAAAgC6UDgAAAEAXSgcAAACgC6UDAAAA0IXSAQAAAOhi/dABAAAAYKzuun7Hop973XW3LmOSyeRIBwAAAKALpQMAAADQhdIBAAAA6ELpAAAAAHShdAAAAAC68OkVrJyzVnnHtXt3sn370Cm6aidODB2hq/0HknaiDR0DAABWjVX+WyAAAAAwFKUDAAAA0IXSAQAAAOhC6QAAAAB04UKSAAAAMIC7b7wxR/a9b+gYi3LRHUcW9DhHOgAAAABdKB0AAACALpQOAAAAQBdKBwAAAKALpQMAAADQhdIBAAAA6ELpAAAAAHShdAAAAAC6UDoAAAAAXSgdAAAAgC6UDgAAAEAXSgcAAACgC6UDAAAA0IXSAQAAAOhC6QAAAAB0oXQAAAAAulA6AAAAAF0oHQAAAIAulA4AAABAF0oHWCaH77wzVw0dAgAAYIJ0Lx2q6nVV1arqojnLjlTVk3tvGwAAABjOShzp8Iwkh5PsSJKqenSSTUnuWoFtAwAAAAOp1lrfDVR9MsmvJ3lda217VT07yY2ttR9ewHN3JtmZJJs2bdq6b9++rlmHdPTo0UxNTaWqho7Sz6FDQyfo4pobbsgDx4/n28eP555jx/K4885LkuzcsSOXXnjhwOmW2datQyfoak3sh6ucGY6fGY6b+Y2fGY7fap/hrl27cvDgwaW+uL6/BJ+B12/YkGs3bBw6xqJcdMeRuTdPOZP1/aPkaUn+MMlbq+rczBz58JmFPLG1tifJniTZtm1bu/yyy7qFHNr5U1O57dChbNmyZego/WzfPnSCLh7c1d5x9dU5fNNN2XvffTMLpqcHy9TNiRNDJ+hqTeyHq5wZjp8Zjpv5jZ8Zjp8ZMmm6lg5VtTnJ11pr91XVx5O8IMkzk9xeVT+S5MrZDE9byJEPq9n09HQ2b948dAxY0+yH42eG42eG42Z+42eG42eGTJreRzo8I8nts1//cWZKhscn+YPW2s1Jbq6qlya5tXOOiTc1NZV169YNHQPWNPvh+Jnh+JnhuJnf+Jnh+Jkhk6b3hSSfmb8rHQ4ked5Jy5LklUl+t3MO6O7iCy7I3qFDAAAATJDepcP/f6RDa+3+zFzL4Tuttb9Nkqp6UpJvtta+1TkHAAAAsMK6nl7RWrvypNsvOekhr0nyWz0zAAAAAMNYiU+vOKXW2tuG3D4AAADQT+/TKwAAAIA1SukAAAAAdKF0AAAAALpQOgAAAABdKB0AAACALpQOAAAAQBdKBwAAAKALpQMAAADQhdIBAAAA6ELpAAAAAHShdAAAAAC6UDoAAAAAXSgdAAAAgC6UDgAAAEAXSgcAAACgi/VDBwAAAIC1aOO11+ai664bOkZXjnQAAAAAulA6AAAAAF0oHQAAAIAulA4AAABAF0oHAAAAoItqrQ2dYUGq6u4kfz10jo42JDk6dAiWxAzHzwzHzwzHzwzHzfzGzwzHb7XP8H9srW1cygqq6qOZ+f80CcY8r7nZj7bWXjjfg0ZTOqx2VXWwtbZt6BwsnhmOnxmOnxmOnxmOm/mNnxmOnxmOy5jntdDsTq8AAAAAulA6AAAAAF0oHSbHnqEDsGRmOH5mOH5mOH5mOG7mN35mOH5mOC5jnteCsrumAwAAANCFIx0AAACALpQOAAAAsMKq6oVV9ZdVdWdVvWXoPAtVVb9ZVV+tqv+ykMcrHQZQVRdU1XPmWf6cqvqfhsgEa1VVPbqqnj7759FD5wEAYPWrqnVJ3pnkRUmeluRnq+ppw6ZasL1JXrjQBysdhvGOJMfmWX5s9j6gs6paX1X/LskXk7wnyW8n+UJV/buqesSw6Tidqrqkqv7BnNs/V1V/WFW/VlXnD5mNhauqJw2dAday2eL9X1TVjVX1uqpaP3QmzlxVrauqO4bOwRm7NMmdrbXPt9a+k2RfkpcMnGlBWmufTPL1hT7eN5ZhbGqt3X7ywtba7VW1ZeXjcKaq6hcf5u7WWnv7ioVhsX45yWOTPLm19q0kqapzkuye/fPPB8zG6f1Gkh9Lkqp6XpLrk7whycWZuZLyTw8XjTPwB0menSRV9cHW2ssGzsMZqKrbk5zyiuSttWeuYBwW5z1JHkhyc/7uX1u9/41Ma+347CH6T2qt/beh87Bg35fkC3NufzHJPxooS1dKh2F878Pc9z0rloKluHeeZf9Dkn+WZCqJ0mHy/USSp7Y5H+HTWjtWVdckuSN+6Jp061prDzbsP5NkT2vtg0k+WFWHB8zFmak5X3//YClYrJ9KsikP/aE5STYn+crKx2ERntZae0aSVNW7k3xq4Dws3nlJPltVn8qcn1Nbay8eLhLMUDoM42BVvba1Nj13YVX9sySHBsrEGWit/cqDX1fVYzPzC+o/zcxhUb9yqucxUVqb5zODZ/+1wGcJT751VbW+tfbdJM9PsnPOfd7bxqOd4mvG4VeT/Hxr7a/nLpw9auxXk/yvg6TiTDzw4Bette9W1cM9lsn21qEDcMa+lJmS9kFPnF226vjBbBj/IsnvV9WV+buSYVuSRyb5ycFScUZmzxt/Y5IrM3N44rNba98YNhVn4C+q6udaa789d2FV/ZPMHOnAZPu9JAeq6miS+zJzaHCq6oIk3xwyGGfkWVV1LDNHPHzP7NeZvd1aa+cMF40FcLro+D3rpP3ue+bsk/bBEWmtHXjw66rakORr8/3jChPl1iRPqaonZ6ZsuCLJK4eN1Ef5uzicqtqe5OmzNz/bWvvTIfOwcFX1y5k5rHRPkne21u4ZOBJnqKq+L8mHMvML69zy73uS/GRrbVU2zatJVf3PSR6f5D+21u6dXfbUJI9prd02aDhYA6rqc621p5zivjtbaxesdCZYa2bfC6/PzEX93p7kvUk2ZOYDA36utfbRAeNxGlX1v2TmgwTWJfnN1tovDRxpQarq95Jcnpm/a3+T5G2ttXef8vFKBzhzVXUiyf1JvpuHHhLsXwZGpqp+NMkPzt78i9baJ4bMAzAWsz90/ukpThf98dbazwyTDNaOqjqY5BeSnJuZfwx7UWvt/62qH0jye621fzhoQIjSAQCARaiqTUl+P8l3Ms/poq01F5OEzqrqcGvt4tmvj7TWLppz339WOjAJXNMBAIAz1lr7myQ/fNLpoh9xuiisqBNzvr7vpPv86zITwZEOAAAAI1RVxzPzEZmVmetSffvBu5I8urX2iKGywYOUDgAAAEAXZw0dAAAAAFidlA4AAABAF0oHAAAAoAulAwAAANCF0gEAAADo4v8DtnKo8jPaDFEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fn(762, 'bayes', 'abs_max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='width: 480px; margin: 0px; float: left; border: 0px solid black;' 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' 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' 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fn(762, 'bayes', 'fixed_val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2224a0c08e74a528d4391424e5159af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=450, description='i', max=901), Dropdown(description='saliency', options…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(plot_fn, i=(0, len(smiles) - 1, 1), \n",
    "         saliency=['vanilla', 'smooth', 'bayes'],\n",
    "        scaler=['none', 'abs_max', 'min_max', 'fixed_val'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8912913115561549, 2.161531431846e-311)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pearson's correlation coefficient between y_train and sum of saliency\n",
    "y_train = np.array([d[2][0] for d in train])\n",
    "scipy.stats.pearsonr(y_train, np.sum(saliency_agg_bayes, axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# Classification (2 class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/2050 [00:00<?, ?it/s]RDKit ERROR: [17:12:18] Explicit valence for atom # 1 N, 4, is greater than permitted\n",
      "RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit ERROR: [17:12:18] Explicit valence for atom # 6 N, 4, is greater than permitted\n",
      "RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
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      "RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
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      " 19%|█▊        | 382/2050 [00:00<00:01, 1288.06it/s]RDKit ERROR: [17:12:18] Explicit valence for atom # 6 N, 4, is greater than permitted\n",
      "RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
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      " 25%|██▌       | 514/2050 [00:00<00:01, 1293.65it/s]RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
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      "RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit ERROR: [17:12:18] Explicit valence for atom # 11 N, 4, is greater than permitted\n",
      "RDKit ERROR: [17:12:18] Explicit valence for atom # 12 N, 4, is greater than permitted\n",
      "RDKit ERROR: [17:12:18] Explicit valence for atom # 5 N, 4, is greater than permitted\n",
      "RDKit ERROR: [17:12:18] Explicit valence for atom # 5 N, 4, is greater than permitted\n",
      "RDKit ERROR: [17:12:18] Explicit valence for atom # 5 N, 4, is greater than permitted\n",
      "RDKit ERROR: [17:12:18] Explicit valence for atom # 5 N, 4, is greater than permitted\n",
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      " 32%|███▏      | 653/2050 [00:00<00:01, 1320.95it/s]RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
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      "RDKit ERROR: [17:12:18] Explicit valence for atom # 5 N, 4, is greater than permitted\n",
      "RDKit WARNING: [17:12:18] WARNING: not removing hydrogen atom without neighbors\n",
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      " 77%|███████▋  | 1587/2050 [00:01<00:00, 1099.95it/s]RDKit WARNING: [17:12:19] WARNING: not removing hydrogen atom without neighbors\n",
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      " 83%|████████▎ | 1702/2050 [00:01<00:00, 1110.50it/s]RDKit WARNING: [17:12:19] WARNING: not removing hydrogen atom without neighbors\n",
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      " 88%|████████▊ | 1814/2050 [00:01<00:00, 1036.60it/s]RDKit WARNING: [17:12:19] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:19] WARNING: not removing hydrogen atom without neighbors\n",
      " 94%|█████████▎| 1919/2050 [00:01<00:00, 959.94it/s] RDKit WARNING: [17:12:19] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:19] WARNING: not removing hydrogen atom without neighbors\n",
      "100%|██████████| 2050/2050 [00:01<00:00, 1102.54it/s]\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
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      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
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      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
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      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:20] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n",
      "RDKit WARNING: [17:12:21] WARNING: not removing hydrogen atom without neighbors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set up GGNN predictor...\n",
      "epoch       main/loss   main/accuracy  validation/main/loss  validation/main/accuracy  elapsed_time\n",
      "\u001b[J1           0.725896    0.78125                                                        3.14109       \n",
      "\u001b[J2           0.553694    0.75                                                           5.94418       \n",
      "\u001b[J3           0.483764    0.75                                                           8.73912       \n",
      "\u001b[J4           0.555882    0.6875                                                         11.8673       \n",
      "\u001b[J5           0.206467    0.9375                                                         14.5804       \n",
      "\u001b[J6           0.521157    0.75                                                           17.2683       \n",
      "\u001b[J7           0.429691    0.84375                                                        19.9826       \n",
      "\u001b[J8           0.415282    0.8125                                                         22.8634       \n",
      "\u001b[J9           0.29645     0.90625                                                        25.8285       \n",
      "\u001b[J10          0.458737    0.75                                                           29.1269       \n",
      "\u001b[J11          0.546403    0.71875                                                        32.1219       \n",
      "\u001b[J12          0.372951    0.8125                                                         35.1651       \n",
      "\u001b[J13          0.425699    0.875                                                          37.98         \n",
      "\u001b[J14          0.301747    0.90625                                                        40.8412       \n",
      "\u001b[J15          0.317776    0.90625                                                        43.6026       \n",
      "\u001b[J16          0.332253    0.90625                                                        46.3931       \n",
      "\u001b[J17          0.426464    0.8125                                                         49.4109       \n",
      "\u001b[J18          0.466184    0.78125                                                        52.6649       \n",
      "\u001b[J19          0.269933    0.90625                                                        55.5484       \n",
      "\u001b[J20          0.495539    0.71875                                                        58.3666       \n",
      "\u001b[J21          0.244894    0.96875                                                        61.1571       \n",
      "\u001b[J22          0.438079    0.84375                                                        63.9473       \n",
      "\u001b[J23          0.319733    0.90625                                                        66.7112       \n",
      "\u001b[J24          0.486976    0.75                                                           69.6039       \n",
      "\u001b[J25          0.48693     0.84375                                                        72.6632       \n",
      "\u001b[J26          0.484872    0.78125                                                        75.5975       \n",
      "\u001b[J27          0.523269    0.75                                                           78.5006       \n",
      "\u001b[J28          0.418594    0.875                                                          81.3083       \n",
      "\u001b[J29          0.332426    0.84375                                                        84.4546       \n",
      "\u001b[J30          0.600776    0.6875                                                         87.3077       \n",
      "\u001b[J31          0.539023    0.84375                                                        90.0313       \n",
      "\u001b[J32          0.412491    0.8125                                                         93.1824       \n",
      "\u001b[J33          0.410706    0.875                                                          96.3893       \n",
      "\u001b[J34          0.271813    0.9375                                                         99.2003       \n",
      "\u001b[J35          0.259641    0.90625                                                        102.193       \n",
      "\u001b[J36          0.342481    0.90625                                                        105.43        \n",
      "\u001b[J37          0.299576    0.875                                                          108.24        \n",
      "\u001b[J38          0.384189    0.84375                                                        111.282       \n",
      "\u001b[J39          0.254721    0.90625                                                        114.542       \n",
      "\u001b[J40          0.483707    0.8125                                                         117.654       \n",
      "\u001b[J41          0.36285     0.875                                                          120.595       \n",
      "\u001b[J42          0.250881    0.90625                                                        123.39        \n",
      "\u001b[J43          0.278912    0.9375                                                         126.157       \n",
      "\u001b[J44          0.505601    0.75                                                           128.902       \n",
      "\u001b[J45          0.287509    0.875                                                          131.846       \n",
      "\u001b[J46          0.372941    0.84375                                                        134.645       \n",
      "\u001b[J47          0.302535    0.90625                                                        137.715       \n",
      "\u001b[J48          0.182388    0.9375                                                         140.723       \n",
      "\u001b[J49          0.27328     0.9375                                                         144.055       \n",
      "\u001b[J50          0.62728     0.8125                                                         147.745       \n",
      "CPU times: user 9min 25s, sys: 18 s, total: 9min 43s\n",
      "Wall time: 2min 30s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "np.random.seed(20200106)\n",
    "preprocessor = GGNNPreprocessor()\n",
    "bbbp_data = get_molnet_dataset('bbbp', preprocessor, labels=None, return_smiles=True)\n",
    "bbbp_train = bbbp_data['dataset'][0]\n",
    "bbbp_smiles = bbbp_data['smiles'][0]\n",
    "\n",
    "n_unit = 32\n",
    "conv_layers = 4\n",
    "class_num = 1\n",
    "device = -1\n",
    "predictor_clf = set_up_predictor('ggnn',  n_unit=n_unit, conv_layers=conv_layers,\n",
    "    class_num=class_num, postprocess_fn=activation_relu_dropout)\n",
    "\n",
    "# https://github.com/chainer/chainer-chemistry/blob/master/chainer_chemistry/datasets/molnet/molnet_config.py\n",
    "clf = Classifier(predictor_clf, \n",
    "                            lossfun=F.sigmoid_cross_entropy,\n",
    "                            metrics_fun=F.binary_accuracy,\n",
    "                            device=device)\n",
    "fit(clf, bbbp_train, batchsize=32, epoch=50, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:23<00:00, 23.63s/it]\n",
      "100%|██████████| 5/5 [01:44<00:00, 20.96s/it]\n",
      "100%|██████████| 5/5 [01:39<00:00, 19.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before aggregation: (1, 1633, 101, 32) -> After: (1633, 101)\n",
      "Before aggregation: (5, 1633, 101, 32) -> After: (1633, 101)\n",
      "Before aggregation: (5, 1633, 101, 32) -> After: (1633, 101)\n",
      "CPU times: user 14min 35s, sys: 29 s, total: 15min 4s\n",
      "Wall time: 3min 48s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def eval_fun_clf(x, adj, t):\n",
    "    pred =  predictor_clf(x, adj)\n",
    "    pred_summed = F.sum(pred)\n",
    "    return pred_summed\n",
    "\n",
    "calculator_clf = IntegratedGradientsCalculator(\n",
    "    predictor_clf, steps=5, eval_fun=eval_fun_clf, \n",
    "    target_extractor=VariableMonitorLinkHook(predictor_clf.graph_conv.embed, timing='post'),\n",
    "    device=device\n",
    ")\n",
    "\n",
    "M = 5\n",
    "bbbp_saliency_vanilla = calculator_clf.compute(bbbp_train, M=1, converter=concat_mols)\n",
    "bbbp_saliency_smooth = calculator_clf.compute(bbbp_train, M=M, converter=concat_mols, noise_sampler=GaussianNoiseSampler())\n",
    "bbbp_saliency_bayes = calculator_clf.compute(bbbp_train, M=M, converter=concat_mols, train=True)\n",
    "\n",
    "method = 'raw'\n",
    "bbbp_saliency_agg_vanilla = calculator_clf.aggregate(bbbp_saliency_vanilla, ch_axis=3, method=method)\n",
    "print(f'Before aggregation: {bbbp_saliency_vanilla.shape} -> After: {bbbp_saliency_agg_vanilla.shape}')\n",
    "bbbp_saliency_agg_smooth = calculator_clf.aggregate(bbbp_saliency_smooth, ch_axis=3, method=method)\n",
    "print(f'Before aggregation: {bbbp_saliency_smooth.shape} -> After: {bbbp_saliency_agg_smooth.shape}')\n",
    "bbbp_saliency_agg_bayes = calculator_clf.aggregate(bbbp_saliency_bayes, ch_axis=3, method=method)\n",
    "print(f'Before aggregation: {bbbp_saliency_bayes.shape} -> After: {bbbp_saliency_agg_bayes.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fn_bbbp(i, saliency, scaler):\n",
    "    saliency_dict ={\n",
    "        'vanilla': bbbp_saliency_agg_vanilla,\n",
    "        'smooth': bbbp_saliency_agg_smooth,\n",
    "        'bayes': bbbp_saliency_agg_bayes,\n",
    "    }\n",
    "    max_val = np.max(saliency_dict[saliency])\n",
    "    scaler_dict = {\n",
    "        'none': None,\n",
    "        'abs_max': abs_max_scaler,\n",
    "        'min_max': min_max_scaler,\n",
    "        'fixed_val': fixed_val_scaler(max_val)\n",
    "    }  \n",
    "    mol = Chem.MolFromSmiles(bbbp_smiles[i])\n",
    "    plot_mol_saliency(mol, saliency=saliency_dict[saliency][i], scaler=scaler_dict[scaler], atoms=['C', 'N', 'O', 'S', 'F', 'Cl', 'P', 'Br'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bbfbab6e4f184741bb2172536e912a6f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=816, description='i', max=1632), Dropdown(description='saliency', option…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(plot_fn_bbbp, i=(0, len(bbbp_smiles) - 1, 1), \n",
    "         saliency=['vanilla', 'smooth', 'bayes'],\n",
    "        scaler=['none', 'abs_max', 'min_max', 'fixed_val'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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