QMLHEP_task_ii.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xShigmfLHZvg"
   },
   "source": [
    "# Task II - Jet Tagging with ParticleNet and LorentzNet\n",
    "\n",
    "For this task, I chose to start with ParticleNet, which preserves permutation symmetry and is known to work well for jet tagging: https://arxiv.org/abs/1902.08570\n",
    "\n",
    "* First, we begin by loading the data from the energyflow package, which contains the same data from the zenodo link. Once this is done, we will see the architecture sketched out, and then implement it using PyTorch geometric. I will train the classical GNN on quarks vs gluons, and we'll see what is the corresponding performance, where we consider accuracy, AUC, and the confusion matrix.\n",
    "\n",
    "* Once this is done, I chose to test the LorentzNet for this task (https://arxiv.org/abs/2201.08187), which is known to be data-efficient and have better generalization than ParticleNet, since it is not only permutation-invariant, but also Lorentz-equivariant. Since the latter is the symmetry of space-time, the performance should be higher, as we'll see. I took the code from the official implementation, which will be used as the **starting point** for **task VI**, where I sketch out and implement a Quantum Lie-Equivariant GNN (Q-LieEGNN), which is a simple modification of LorentzNet. The proposed idea is that, given some experimental data, an appropriate method can be used to find the corresponding Lie algebra basis, like in the LieGAN approach (https://arxiv.org/abs/2302.00236), or in the oracle-preserving latent flows, from Roy T. Forestano et al (https://arxiv.org/abs/2302.00806). From the generators, then, it is possible to find the corresponding metric $J$. The details are left in notebook corresponding to the Task VIII"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "zF5bw3m9UrMy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.1\n"
     ]
    }
   ],
   "source": [
    "# Install required packages.\n",
    "import os\n",
    "import torch\n",
    "os.environ['TORCH'] = torch.__version__\n",
    "print(torch.__version__)\n",
    "# !pip install -q torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
    "# !pip install -q torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
    "# !pip install -q torch_cluster -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
    "# !pip install -q git+https://github.com/pyg-team/pytorch_geometric.git\n",
    "# !pip install energyflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "nlCu-Wh5CaDF"
   },
   "outputs": [],
   "source": [
    "# Helper function for visualization.\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# Numpy, h5py, etc\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DI3fN87dKVii"
   },
   "source": [
    "Set up a GPU for the training and check how much memory is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "Fv43DFEXHvoX"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device :  cuda:0\n",
      "Total memory in Gb: 11.77\n",
      "Reserved memory in Gb: 0.00\n",
      "Allocated memory in Gb: 0.00\n",
      "Free memory in Gb: 0.00\n"
     ]
    }
   ],
   "source": [
    "def print_device_usage(device):\n",
    "  tot_memory = torch.cuda.get_device_properties(device).total_memory/1024.0**3\n",
    "  reserved_memory = torch.cuda.memory_reserved(device)/1024.0**3\n",
    "  allocated_memory = torch.cuda.memory_allocated(device)/1024.0**3\n",
    "  free_memory = reserved_memory-allocated_memory  # free inside reserved\n",
    "  print('Total memory in Gb: %.2f'%tot_memory)\n",
    "  print('Reserved memory in Gb: %.2f'%reserved_memory)\n",
    "  print('Allocated memory in Gb: %.2f'%allocated_memory)\n",
    "  print('Free memory in Gb: %.2f'%free_memory)\n",
    "\n",
    "\n",
    "# check if a GPU is available. Otherwise run on CPU\n",
    "device = 'cpu'\n",
    "args_cuda = torch.cuda.is_available()\n",
    "if args_cuda: device = \"cuda:0\"\n",
    "print('device : ',device)\n",
    "if args_cuda: print_device_usage(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Id1c30h3KaPz"
   },
   "source": [
    "# Dataset exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uu7Zo4rcmwEW"
   },
   "source": [
    "First, let's download the jet-tagging dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mqsd_aP__RIi"
   },
   "source": [
    "Our labels are:\n",
    "*  0 for gluons\n",
    "*  1 for quarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "CSqhHCx2_T2W"
   },
   "outputs": [],
   "source": [
    "num_classes = 2 #len(np.unique(target))\n",
    "label_names= [\"gluon\", \"quark\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'px': 0,\n",
       " 'py': 1,\n",
       " 'pz': 2,\n",
       " 'e': 3,\n",
       " 'erel': 4,\n",
       " 'pt': 5,\n",
       " 'ptrel': 6,\n",
       " 'eta': 7,\n",
       " 'etarel': 8,\n",
       " 'etarot': 9,\n",
       " 'phi': 10,\n",
       " 'phirel': 11,\n",
       " 'phirot': 12,\n",
       " 'deltaR': 13,\n",
       " 'costheta': 14,\n",
       " 'costhetarel': 15}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ik-6OX0LMJW7"
   },
   "source": [
    "Now let's inspect our data. Each jet is a point cloud/graph with 100 particles/nodes, each of which has 16 features. We have a double-index dataset: (jet index, particle index). The list is cut at 100 constituents per jet. If less constituents are present in the jet/point cloud, the dataset is completed filling it with 0s (zero padding). Note : zero-padding is not using during the training, it is only used to store the ragged dataset.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "uS7gG-LjMIrd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jets shape :  (50000, 100, 16)\n",
      "Target/Labels shape :  (50000,)\n",
      "Particles/Nodes features :  ['px', 'py', 'pz', 'e', 'erel', 'pt', 'ptrel', 'eta', 'etarel', 'etarot', 'phi', 'phirel', 'phirot', 'deltaR', 'costheta', 'costhetarel']\n"
     ]
    }
   ],
   "source": [
    "print('Jets shape : ',jetList.shape)\n",
    "print('Target/Labels shape : ',target.shape)\n",
    "print('Particles/Nodes features : ',list(features_names.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the features\n",
    "\n",
    "As common in collider physics, a Cartesian coordinate system is used with the $z$ axis oriented along the beam axis, the $x$ axis on the horizontal plane, and the $y$ axis oriented upward. This way, $xy$ define defines the transverse plane, and $z$ defines the longitudinal direction. This can be seen in the following illustration (https://arxiv.org/pdf/2106.11535.pdf):\n",
    "\n",
    "<img src=\"figures/particle_cloud_mpgan.png\" width=65% style=\"margin-left:auto; margin-right:auto\">\n",
    "\n",
    "The features available in the zenodo link are the transverse momentum $p_{T}$, the projection of the particle momentum in the $xy$ plane; the pseudorapidity $\\eta = -log(tan(\\theta / 2))$; and finally, the azimuthal angle $\\phi$, computed with respect to the $x$-axis; and the particle id, that identifies each particle. The energyflow dataset provides this same dataset for us, and for this very first experiment I chose to work with just the 3 first features, for simplicity. In the next architecture, we convert these features into 4-momenta to exploit the instrinsic Lorentz symmetry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the challenge dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import energyflow\n",
    "X, y = energyflow.qg_jets.load(num_data=100000, pad=True, ncol=4, generator='pythia',\n",
    "                        with_bc=False, cache_dir='datasets/QMLHEP/quark_gluons')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_names = {'pt': 0, \n",
    "                  'rapidity': 1, \n",
    "                  'azimuthal angle': 2,\n",
    "                  'pdgid': 3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_consider = 'pt,rapidity,azimuthal angle'.split(',')\n",
    "features_idx = [features_names[name] for name in features_to_consider]\n",
    "jetList = X[:,:,features_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X has shape: (100000, 139, 4)\n",
      "y has shape: (100000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X has shape: {}\\ny has shape: {}\".format(X.shape, y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.26876914,  0.35690317,  4.74138734],\n",
       "        [ 0.16007638, -0.25560953,  4.5502291 ],\n",
       "        [ 1.14868731, -0.06243802,  4.50385377],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ]],\n",
       "\n",
       "       [[ 1.21266321, -0.11285309,  3.04708757],\n",
       "        [ 0.24089398, -0.01671749,  2.82705667],\n",
       "        [ 0.10277845, -0.08587205,  3.04180579],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.21682942, -0.99705702,  0.53256891],\n",
       "        [ 0.2313594 , -1.59192211,  0.20290659],\n",
       "        [ 0.3415725 , -1.34588077,  0.17991001],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 0.56230598,  1.30600679,  4.45584187],\n",
       "        [ 0.53372085,  1.27847224,  4.63907136],\n",
       "        [ 0.82217339,  0.71264558,  4.7073644 ],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.23112434, -0.06347617,  3.95621834],\n",
       "        [ 1.28517426, -0.07184956,  3.79668946],\n",
       "        [ 2.69184416,  0.02798251,  4.10518218],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.30414014,  0.72162897,  1.25271426],\n",
       "        [ 0.90064906,  1.19311927,  1.03353543],\n",
       "        [ 0.15484698,  1.37730434,  1.24606922],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ]]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jetList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_consider = 'pt,rapidity,azimuthal angle,pdgid'.split(',')\n",
    "features_idx = [features_names[name] for name in features_to_consider]\n",
    "jetList = X[:,:,features_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hOmMwAOBRfPI"
   },
   "source": [
    "### Particle distributions\n",
    "Now let's plot the distributions of the jets consituents (particle/node features)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "B2Mre28ScNWz"
   },
   "outputs": [],
   "source": [
    "#Let's define a helper function to plot the distibutions\n",
    "def plot_features(feat_idx, X_features, y, features_to_consider):\n",
    "    plt.subplots(figsize=(5,5))\n",
    "    for jet_type_idx in range(len(label_names)):\n",
    "        data_feature = X_features[:,:,feat_idx]\n",
    "        data_feature = data_feature[y == jet_type_idx]\n",
    "        # then plot the right quantity for the reduced array (merge all constituents together)\n",
    "        plt.hist(data_feature[:,feat_idx].flatten(), bins=100, density=True, histtype='step', fill=False, linewidth=1.5)\n",
    "    #plt.yscale('log', nonposy='clip')    \n",
    "    plt.legend(label_names, fontsize=12, frameon=False)  \n",
    "    plt.xlabel(features_to_consider[feat_idx], fontsize=15)\n",
    "    plt.ylabel('Prob. Density (a.u.)', fontsize=15)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "TQwS0EgedHW_"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdkAAAHGCAYAAAA4x/FMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABjvklEQVR4nO3dfVyT9f4/8NcYG2zeBQiaoaZ5ApUbbzITJVPxeNfpeHM0TbxLy2PmMdOfpplHzKJMLdGOpajhzbeyFMrE7FDqsSOZmiapqQdLBe8QNJQNx8b1+2NsbGyDbWzs2ng9Hw8fbtfd3vsw9ub63EoEQRBARERELufn6QCIiIh8FZMsERGRmzDJEhERuQmTLBERkZswyRIREbkJkywREZGbMMkSERG5CZMsERGRm/h7OgBvUl5eDq1WCz8/P0gkEk+HQ0REHiIIAsrLy+Hv7w8/P9v3q0yyDtBqtcjJyfF0GEREJBLR0dGQy+U29zPJOsDw10p0dDSkUqnLr6/T6ZCTk+O269cnLEvXYnm6DsvSdTxZlobXru4uFmCSdYihilgqlbr1B+ru69cnLEvXYnm6DsvSdTxZljU1HbLjExERkZswyRIREbkJkywREZGbMMkSERG5CZMsERGRmzDJEhERuQmTLBERkZswyRIREbkJkywREZGbiDrJbtu2DX379kV0dDRGjhyJkydP2nXe7t27ERERgRdeeMFsuyAIWLVqFXr16oWYmBhMnDgRv//+uxsiJyIiEnGSzczMRHJyMqZPn4709HRERkZi8uTJKCwsrPa8vLw8vP3223jkkUcs9q1fvx5btmzB4sWLsX37digUCkyePBn37t1z19sgIqJ6TLRJdtOmTRg1ahRGjBiBdu3aISkpCYGBgdixY4fNc3Q6HebMmYMZM2agZcuWZvsEQcDmzZsxbdo0JCQkIDIyEsuWLcONGzeQlZXl7rdDRET1kCgXCNBoNDh16hSmTp1q3Obn54e4uDgcP37c5nnvv/8+QkJCMHLkSBw7dsxsX15eHgoKChAXF2fc1qhRI8TGxuL48eMYMmSI3fHpdDoH3o39DNd11/XrE5ala7E8Xce0LAVBgLpMPGWqkEndvlZ2QkICHn30Ubz55pu1vpYnP5f2vqYok+ytW7eg0+kQEhJitj0kJAQXLlywes7Ro0fx+eefIyMjw+r+goIC4zWqXvPmzZsOxefuNWW5Zq3rsCxdy6w8BQF+ulKrx5VLAwE3f1l7u5ycHJRqyzE2/YanQzHaNiwMgf7ureDUaDQoKirCiRMnXHZNMf+eizLJOuru3buYO3cuXn/9dQQHB7v99bierPixLF3LanlqSiB9u6X14+ddBuQN6jBC72Falvd0ApAunuaqmJgYKOXuTQtyuRzBwcHo1KlTra8lhvVkayLKJBsUFASpVGrRyamwsBBNmza1OP7y5cvIz8/HtGnTjNvKy8sBAB06dMDXX3+N0NBQ4zXCwsLMrhkZGelQfFxP1nuwLF3LrDyrKVepVFrtfqooSwjG50cXJkApr/syU2l0eGRpVmVMbv65SSQSSCQSl76OmH/PRdnxSS6Xo2PHjsjOzjZuKy8vR3Z2Njp37mxxfNu2bbFr1y5kZGQY//Xt2xfdu3dHRkYGmjdvjvDwcISGhppd8+7du/j555+tXpPI6wkCoCmx/k8Qaj7fEXP+p/9noFG577V8lFIuhVLu74F/rktOhw8fxvDhwxEdHY2EhAR88sknWL16NSIiImyeY2v/zp07ERERgby8PLPt27Ztw5AhQxAVFYXevXtj06ZNKC4uNjtm3LhxePLJJ/G///0P48aNQ2xsLOLj47F+/XrXvFEHiPJOFgAmTZqEefPmISoqCjExMUhLS4Narcbw4cMBAHPnzkWzZs0we/ZsBAQE4OGHHzY7v3HjxgBgtn38+PFYu3YtWrdujfDwcKxatQphYWFISEiouzdGVFfKVMCbLazvW3DFtdW5cqX58+Xt3PdaJEqnT5/GlClTEBoaihkzZqC8vBzvv/++S5vwVq9ejTVr1iAuLg5jxoxBbm4uPvnkE1y7dg0ff/wxZDKZ8dg//vgDU6ZMQf/+/TFo0CDs3bsXy5cvx8MPP4zevXu7LKaaiDbJDh48GEVFRUhJSUFBQQHat2+P1NRUY3Xx1atX4efn2I34c889B7VajUWLFqG4uBhdu3ZFamoqAgIC3PEWiIjqjZSUFEilUnz88cdo1qwZAGDQoEEYPHiwS65fVFSEDz/8EL169cL69evh5+cHnU4HuVyOjz76CF9++SVGjBhhPP7GjRt4++23MXToUADA3/72N/Tt2xc7duxgkjVITExEYmKi1X1btmyp9ty33nrLYptEIsHMmTMxc+ZMl8RH5DUMVbmmd5iuJlPq71oBfXWx6WsJgv7O2to57IXs9XQ6HbKzs5GQkGBMsADQunVrxMfHY9++fbV+jUOHDqGsrAzjx483u8Hq27cvPv/8cxw4cMAsySqVSvz1r381PpfL5YiOjsbly5drHYsjRJ1kichFqlbnuoNEYrta2FbVNauSfUJhYSFKS0vRunVri33WtjnjyhX9H3Bt27Y12+7v74/w8HDk5+ebbW/evLnFmN8mTZrg7NmzLonHXqLs+ERERPWDrckvajvBhFh6GzPJEnmz6noQa6xUz4rBzJ89HQG5WEhICAICAnDx4kWLfda2mTJ0Uq3aQ9hw52rQooW+JqTqhERarRb5+fl44IEHHI67LrC6mMibVdeDWKxkrB72NVKpFHFxcfj2229x/fp1Y7vsxYsXcfDgwWrPbdWqFQDgyJEj6NevHwBApVJZzN4XFxcHmUyGLVu24PHHHzfeAe/btw937typ085MjmCSJXI1dvIx5447altlDHhtOas0npnD2FWv++KLL+L777/HmDFjMGbMGJSXl2Pr1q3405/+hDNnztg8r2fPnmjRogVeffVVXLhwAVKpFDt27EBQUJDZ3WxwcDCmTp2KNWvWYMqUKejbty9yc3Px8ccfIzo6Gk899ZRL3oerMckSuZqnOvnM+Z/tDk4ype2k5G7u6NFcl2OA64hh1iVvFRUVhfXr12PZsmVYtWoV7r//fvzjH//AhQsXbM45DwAymQxr1qxBUlISVq1ahdDQUEyYMAGNGzfG/PnzzY6dMWMGgoODsXXrViQnJ6Nx48bo27cvli5dajZGVkyYZIl8hVzplcmFfEePHj2Qnp5utu2FF15A8+bNjc+/++47i/M6duyI7du3W2w3TD5kauzYsRg7diwAfeeoEydOGNt1DWwN8bQ2tNPdmGSJ3Gnmz8CqWE9HUfdMx8xW5Y4pHQH3jgF2E4VMitNLBng6DCOFrHY9cktLSxEYGGh8/vvvv+M///mPcUKI+ohJlsidfK2Tj6E3M1B9W2t1Y2YN57tKXYwBdhOJROL2VW/qUkJCAoYNG4aWLVsiPz8fn3zyCWQyGaZMmeLp0DzGd366ROR+ZSrAxvJ2RPHx8di9ezcKCgogl8vRqVMnvPzyy3jwwQc9HZrHMMkSiV11PWm5wg2JSHJysqdDEB0mWSKxq64nrenycu5gSPA6Hfy0avNkb9qbWea9VbZE7sQkS+QpYhlPW9OdcvIDkALoDAB7TPa5ojdz1XZdLx3jSmQLkyyRpzgzntYdPWk9eadc9X146RhXIluYZIlEThAEGO7tVJADADxROfvzn3cgqvOjlROvu6GK2PS9EvkCJlkiMahmPK26TGdMql0rZgU6E2j10Nqr5k65XBqov8us7eomMiVUcy4Z34sS93AscBqAivcaULvLE4kJkyyRGIhlPK2bxpwKggB1WeUcuSoEQo0a/lJgr2ryAUyyRF7k37N6o/+7BzwdhsPUZTp0WLTX6r5/z+oNrLWyw5NtxUQuwiRL5EUCZVWWgC5z8exJVbn7+rDynuqT6u7WPcHHenevXr0aa9asQXZ2NoKDgz0SA5MskTdz97zIbrj+0YUJUMor23WFe3Ykci+en7haYlsPmL27XY5JloicUrWd1ZRCJjUuql2VUi41m69XpbHjxbx4fmKq35hkibyMGgFoX7oRxxYmmE8u76ohNVZW0NHpdCg/dc48jmraWU8lDTCrdazTBclNJ7jwpurP6tYDdieNyudqCFQqFZRKcfxhxiRL5HUk+p658gaAO1ZwsbaCjk7nULJSl+k8twi5acLwpupPH1gP+OjRo0hOTsa5c+fQrFkzTJkyBQUFBVizZg3Onj2LvLw89OvXD8nJyRZrxUZERODFF1/EjBkzAAD5+flYv349srOzcfXqVSgUCnTv3h1z585FeHi48bwDBw7gmWeewZYtW5CZmYm9e/dCq9XiyJEjVmPMz8/HxIkTERAQgI8++ghNmzZ1X4GASZbIa1W9O6yuitbdji5MAADPJVbyuLNnz2Ly5MkIDg7GjBkzoNVqsXr1aoSEhDh1vZycHBw/fhxDhgxB8+bNkZ+fj48//hjjx4/H7t27oVAozI5PSkpCcHAwpk+fDpXKemeyS5cuYcKECWjSpAk2btxYJ52hmGSJvFTVhHZ6yQCXr01qaHfV6XQo1ZZDpdFCKhUq9lUeZ9qRqaqqHZ1quzB4VSqNVl8tPOeSfkOZCspVkS59DapZSkoKBEHAtm3b0KKFvjPXgAED8Je//MWp6z3xxBMYOHCg2bY+ffrg6aefxt69ey0Wgm/SpAk++uijyhnJqsjNzcXEiRPRrFkzbNiwAU2aNHEqLkcxyRKRTRbtrumVid1w91qTqh2dXK3r0iyziS0UKHXfjFhklU6nw/fff4+EhARjggWAhx56CL169cKBA46P7Q4MrPwhlpWV4e7du2jVqhUaN26M06dPWyTZUaNG2Uyw58+fx6xZs9CqVSukpqaiYcOGDsfjLCZZIi+ikElxeskA43OVxoNtn0QVioqKUFpaitatW1vsa9OmjVNJtrS0FB9++CF27tyJ69evQzCpOrlz547F8abttFX9/e9/R9OmTbFhwwY0aFC37d5MskRiZDpJgclkBRIJbN4VVteDVyGTQq3RGudAVmm0ALT6Jybbq5ugf+NfQtExKgrdk/fZ/TYcJwCainGzVZfBqwbbhL2DrT4DOp3lZ/f111/Hzp07MWHCBHTq1AmNGjWCRCLBrFmzzBKuQUCA7UmvBwwYgPT0dOzatQujR492/g04gUmWSIxMJimwdyBCdQnm6MIExC/NMlajPrI0C4avKf0E/frH1U3QH+Avqbbt1RXUJXehXN7R4fPcHRdVLzg4GIGBgbh48aLFvt9++8342NAOWlxcbHbMlSvmQ8YAGNtdX3nlFeO2e/fuWb2LrcncuXMhlUqRlJSEBg0aON1O7AwmWaJ6SGGy8o2Y/Pnd/xgTPnkPqVSKXr16ISsrC1euXDG2y+bm5uL77783HtewYUMEBQXh6NGjmDhxonH7//3f/1m9ZlVbtmyxetdrj9dffx0lJSV45ZVXoFQq0a9fP6eu4ygmWSKRU838FV3fPgQAOFZlwomqbbRm51XTXntwXh9glWvjdLWupWtxcOEQffW4TCmuOX7dwYHqcTG+7owZM3Dw4EGMHTsWY8aMgU6nw9atW9GuXTucPXvWeNzIkSOxbt06vPrqq4iKisLRo0fN7nYNnnjiCXzxxRdo2LAh2rVrhxMnTuDQoUO47777nIrPz88P77zzDqZPn46XXnoJ69atQ48ePZx9u3ZjkiUSO5mysvdslTYtiUTiVM9dpcxkWsNqkriz1A7M8GQ6pMc0+asQ4L4JN8TIy2ddioyMxIYNG5CcnIyUlBQ0b94cM2bMQEFBgVmSnT59OoqKirB3717s2bMHjz/+OFJTUy0S3quvvgo/Pz/s2rUL9+7dQ5cuXbBp0yZMmTLF6RhlMhlSUlLw3HPP4YUXXsBHH32E2Fj3zv9dTz69RF6kru9oqknizopfZn/nKNPOMKbJn7xPt27dsHPnTrNtq1evNnseGBiIN954A2+88YbZdtNEDACNGzdGcnKyxWt89913Zs979+6NmTNnWq1enjFjhnEGKdPX37JlS81vxkX4iSYSGy+/o6mVKkvrmfaYVlTT89lrWZkn2qNcNf81GTHJEtUVwWR4CuC5NjjALevE2mofdmiGpypL65m2KZ9e2MvuntZew9o80eRTRJ1kt23bhg0bNqCgoACRkZF47bXXEBMTY/XYb775Bh988AEuXboErVaL1q1bY9KkSWazgrzyyitIT083O69Xr17YsGGDO98GkV6Z2vZdqkxh+45GkLs+FjesE+ts+zCRLxPtb0RmZiaSk5ORlJSE2NhYpKWlYfLkyfj666+tTjjdpEkTTJs2DW3btoVMJsO+ffuwYMEChISEID4+3nhcfHy8WT2/XO6GLzAiR1V3R6PR1vryjnREqnNVqkwN8yUfkymhKit37wQTppN+VI3JW5bIEzlr7aL1iWiT7KZNmzBq1CiMGDECgH6Fhf3792PHjh14/vnnLY7v3r272fMJEyYgIyMDx44dM0uycrkcoaGh7g2eqCZV1w51c1uYviOSjXVo3XGn7Igqf2BIgMoJMSS1/wOjWiaTfpjxpiXySNREmWQ1Gg1OnTqFqVOnGrf5+fkhLi4Ox48fr/F8QRDwww8/4LfffsOcOXPM9v3444/o0aMHGjdujMceewwvvfQSgoKCHIrP2cHQ9l7XXdevTzxaljodDK2QunIbj6UBgNRk1oXycuNDw52cgWnnH51OB53Ovjssy/euX4dWJw2ETlr5q68zuVOuev2q19Dpyq3ucyQuR1R9DeNjk7K0FqfZ9uo+Azrz69h9Xi3w99x1PFmW9r6mKJPsrVu3oNPpLKqFQ0JCcOHCBZvn3blzB48//jg0Gg38/Pzwz3/+Ez179jTuj4+PR//+/REeHo7Lly9j5cqVeO655/Dpp5/aXL3BmpycHMfflAPcff36xBNl6adVo3PF419++QWG1s/TPx9FdMXjkydPotxfYeVsoFRbjrHpN6zuO3nyJAL9/eyKQxAEbBsWZrH97Kkcs2EzpdrKxFn1+qb7AODUqV8qH//yi83zXMX09X/55Rf0MHls2qp88uRJq+dXV86A+c8qp982RH871q7zXIG/564j5rIUZZJ1VoMGDZCRkQGVSoXs7Gy89dZbaNmypbEqeciQIcZjIyIiEBERgYSEBOPdrb2io6MdSsr20ul0yMnJcdv16xOPlqWmBNijfxgVFQV8o39s+AIHoO/AZ6M6UqXRmi0pZyomJsblnYtMX69dZEezeYAFAWaxdOwYBaTrxyl2jIoCdu1zW1xVY4uKigL+rd8e9XBbY7kaXh+ARbnFxMTYni3KsL3iZ9Uh9hHgW5Pz3FRdzN9z1/FkWRpeuyaiTLJBQUGQSqUoLCw0215YWIimTZvaPM/Pz8+41FL79u2Rm5uLdevWWbTXGrRs2RJBQUG4ePGiQ0lWKpW69Qfq7uvXJx4pS5PXk/pZf22pVGp2nPm+yhVGrC14bmslE2eZvl7VFXYOzu1T5djKu1XTvlTuKmfT2O7pgEaG7Ws6VznORlnq7gG6e9Z7dS+4YvNnVd3Px1X4e+46Yi5LUSZZuVyOjh07Ijs7GwkJ+iWsysvLkZ2djcTERLuvU15eDo1GY3P/tWvXcPv2bXaEIvcxGZpj1tYqyI29hqtLnO5e8Lwm1c3c5MisTq4Qv2yf44ux1+eJPUgURJlkAWDSpEmYN28eoqKiEBMTg7S0NKjVagwfPhyAfumiZs2aYfbs2QCADz/8EFFRUWjVqhU0Gg0OHDiAL7/8EosXLwYAlJSUYM2aNRgwYACaNm2Ky5cv45133kHr1q3Neh8TuZRJz1m1RosOS/daHHJ6yQCPJlJvWQheXdE72uDg3D6IX7YPatheR9TCzJ/dMkaYyBbRJtnBgwejqKgIKSkpKCgoQPv27ZGammqsLr569Sr8/CqrrlQqFZKSknDt2jUEBgaibdu2eOeddzB48GAA+uqEc+fOISMjA3fu3EFYWBh69uyJmTNncqws1WtVJ5GwNnOTTqfD2VM5rpnVyQHV/gEgb1A557IJNQKgmnPJ+h8uVhb7rk7Vnt6mcbm62p58k2iTLAAkJibarB6uOsHzrFmzMGvWLJvXCgwM5MxOJBqGuzAxsjZzk04ngUQiqdhXd21fzs0iJYEKgUDF4ByzhGgyraWqorreMEJZVaa1mLZRXaZDh0Xiq30g78FPCZEHKOowUdVHZnMe20iIXSuOMbTzxr+9jwvGk8u5fmAbUX1jmPjf+M/HFxcXibqeKrJqT2sie/BOlshBFu10mhIol7dy+nqqKsnCwWbDeqtqdbtp+629nbcOzu0DpNj3eqx9IGcwyRI5qGo7nQKljg8tMVE1GfCOyTnOtN/WZfsy1U9MskSu5IKJ/8XaIUoMXNm7WYl71meCAkyaALRQoFS/TVMCBUodGzJE9R6TLFEtHJzbB39etqdyg1xp13R83jI2VWxcuWbtscBpwCobOyvW/lWismMUUvSPTcfqEtWESZaoFpxtp5NIUOPYVMN2qj1jO7rGcpgOkTsxyRI5RYAC9/SdnnCv1ldz5R0aWapsRxegQOWd6LGF+mlbuy7Ngq2pJbqWrtUfGzjNzVGSL+JvNZEtgmC9zU6QQ4F7OBP4LJACjq30KhLzWaIMU14isLLttQoV22CpFphkiWwpUwFvtrDcPudS3cdCLmO6spFCJrU6bSKRqzDJEtVS19K1UCEAxxYmQGnSm7jqeNqq42HJMzy9shHVL/ykEdnDdPWWMpVZO6wKAfoqSHkDfY+mCrbmvSWi+oNJlsgessphOcpVkWyHJSK7MMkSVWFtuIe1FVocYdoOCHBoTl1iNT15EpMsURWGal7T6RIfefsQBGw0G/IB6CejUC87VOM12Q7oOZzkgzyJv/VEdhCAynZXw2Og4jkX7yYi65hkicjn2JpBy7CPqK4wyRKRz+EMWiQWXLSdiKgKVZnW0yGQj2CSJSKqIv5tLjdIrsH6FCIDw1zFFWuIumLifyKq35hkiQwq5io2W0OUfJ6xk5SmBFju6WjI1zDJElG9VtlJil+H5Hr8VBFZYTrpPwColx606zzTRQE40xARMckSWWE26T8Aeyec4KIARGSKvYuJiKpQSNjpjVzDqTvZgoICHDx4EEeOHMGZM2dQVFSEO3fuoFGjRggODkb79u3RrVs3xMfHIzQ01NUxE7mcIAhQa2q3CEBVVRcHJ+/xfcBLng6BfIRDSfbgwYP4+OOP8Z///Ac6nQ6CIJjtV6vVuHHjBn799VdkZGRAKpWid+/eGDNmDHr16uXSwInsZhiaU5VMaVz/VV2mQ9elWS7tVcxFAYjIrm+AnJwcLFu2DEePHgUAdO3aFY8++ihiYmLQtm1bNGnSBA0bNsSdO3dQXFyM3NxcnDx5EocPH8Z3332H7777Do888gjmzZuHqKgot74hIgsVQ3MsLLiiT7QVY2PtGRfLzkw+TKaEas4l4wpLpp3eFBwzTU6yK8mOHDkSTZs2xaxZs/DUU0+hefPmVo+77777cN9996FVq1bo06cPAODq1av48ssvsWXLFowcORJnzpxxXfREtWUyNtZ0IfZjCxMAeQMoZFJjb2GAy6b5NIkEkDeossISwFWWqDbsSrLz58/H6NGjERAQ4PAL3H///Zg6dSomTpyITz75xOHziVxq5s/AqtgaD1PK/QFW9ZKdTIduVaWQSSGRMFHXV3Z9i0yYMKHWLxQQEOCS6xDViqyBzV2mY2OVssouUNUtm1alWwL5EFtNA0rc088OZfL1qdZo0XVpFtQIQNU739NLBrBtvh7jT558iuGOQqfToVRbDpVGC6nOvl7DZmNjTe48qls2TaXhai2+ylbTwLHAaRbTLxqm4mxfurGyupkITLLkY/STQXxd2VEl/RKUuGfW3krkTkcrOkyx/Z4ANybZrKws3L17FwAwdOhQd70MkQUF7uFM4LM1H6ixMqyH6jWbTQOCgBLNJTzyRmXPY2PthkYFLG9nPNQwNpoIcGOSXbFiBX7//XcAzifZbdu2YcOGDSgoKEBkZCRee+01xMTEWD32m2++wQcffIBLly5Bq9WidevWmDRpktlrC4KAlJQUfPbZZyguLkaXLl2wePFiPPjgg07FR17O5IuRCKihaUBSpecx21nJDm77lERHR6Np06ZOn5+ZmYnk5GQkJSUhNjYWaWlpmDx5Mr7++muEhIRYHN+kSRNMmzYNbdu2hUwmw759+7BgwQKEhIQgPj4eALB+/Xps2bIFb731FsLDw7Fq1SpMnjwZmZmZTvWcJvEz68wk92dPJSKqU25LssuWLavV+Zs2bcKoUaMwYsQIAEBSUhL279+PHTt24Pnnn7c4vnv37mbPJ0yYgIyMDBw7dgzx8fEQBAGbN2/GtGnTkJCQYIwxLi4OWVlZGDJkSK3iJXEy68xkSLILrlgep9HavdIOEWAybMfF03GSbxFlfYdGo8GpU6cwdepU4zY/Pz/ExcXh+PHjNZ4vCAJ++OEH/Pbbb5gzZw4AIC8vDwUFBYiLizMe16hRI8TGxuL48eMOJVmdzj2z/hiu667r1we2yk6n00Gnq+gxLLXsBaWTamEYemF2rAOvV/W1HbmO2PGzafmzvqvTITopCwqUmk3HWdPngGXpOp4sS3tfU5RJ9tatW9DpdBbVwiEhIbhw4YLN8+7cuYPHH38cGo0Gfn5++Oc//4mePXsC0C9qYLhG1WvevHnTofhycnIcOt5R7r6+LyvVllvdfvLkSQT62150yvS8mo6t7jxHXtMb1efPZnU/a1NV9x356WcE+OuTbIBUYpyYoj6XpauJuSydSrJXrlhWt1WnRQsr88a6QYMGDZCRkQGVSoXs7Gy89dZbaNmypUVVcm1FR0dDKnV9D0KdToecnBy3Xb8+UGm0QPoli+0xMTHVTgigPy/LrmNtndcusmPF1hsOX0fs+Nm0/IwAMD43+HF+HygaNNZXI1fse3ZXgXF/zj8TECCV1PuydBVPfi4Nr10Tp74B+vbta/c0YRKJBKdPn3bo+kFBQZBKpSgsLDTbXlhYWG1nKj8/P7Ru3RoA0L59e+Tm5mLdunXo3r27ccm9wsJChIWFmV0zMjLSofikUqlbf6Duvr7PsLK6jlSntTrTbE1lKpUKdh9r67zuyfscek1v5IvvyV5VPyPWNFIEAP7+kFqvUKkoP4nJ4/pZlq4m5rJ0Ksl269bN6nZBEHD16lVcvXoV5eXl6NSpE2QymcPXl8vl6NixI7Kzs42dlMrLy5GdnY3ExES7r1NeXg6NRgMACA8PR2hoKLKzs9G+fXsAwN27d/Hzzz9jzJgxDsdIImBldR0lAAXWeiYeogqm421VGh0npqjHnEqyW7ZsqXb/b7/9hoULF0IQBKSmpjoV2KRJk4xL48XExCAtLQ1qtRrDhw8HAMydOxfNmjXD7NmzAQAffvghoqKi0KpVK2g0Ghw4cABffvklFi9eDEB/Rz1+/HisXbsWrVu3Ng7hCQsLMyZyIkdVN68xF2qvv4zjbQUB0JRAgVL9Dk0JIJVwKFk94pYGozZt2mD16tUYNGgQUlJS8P/+3/9z+BqDBw9GUVERUlJSUFBQgPbt2yM1NdVYXXz16lX4+VV2KlGpVEhKSsK1a9cQGBiItm3b4p133sHgwYONxzz33HNQq9VYtGgRiouL0bVrV6SmpnKMrC+wc3UdV6tu8gLyXXavK1ymgnJ5q8rexxVzHvsN2u2WuEh83PbtEBwcjJiYGGRmZjqVZAEgMTHRZvVw1bvpWbNmYdasWdVeTyKRYObMmZg5c6ZT8ZCIVbO6DpGrsfqX7OXW8QWCIDg8PIaIyBf1uveep0MgD3Dbnezp06dx5MiROhu+Q0TkTjbb3zUllUvfmS46ITOfB0otVDZLGZZhbBjoxwXdfZxTSXbNmjU296lUKvz+++/4z3/+A51Oh9GjRzsdHJG7GKfEq2B3GxvVW7bb3022mS46YWX6ToNndxVAvSuLC7rXA04nWYlEAqGaHnIKhQJTp07FpEmTnA6OyF30687u9XQYROTjnEqyycnJNvfJZDKEhoYiOjoaSiWnzSYiHydTVt61Vllb1tTBeX2AVXUYF4mCU0l22LBhro6DyEzV6lwDhUzq8jasowsTzBba5vhWcohEol/lqQZKGauF6yP+1EmUbFXnuqMNSymXsl2M6pQS9/QPNCUA/PV3w+wA5ZP4zUJEVMeOBU7TPzD0Sp6fbzvJMgF7Nbcl2XfffRc3btyARCLBm2++6a6XoXrg4Nw+iF+2r+YDibxVmdpmWy4WXLGrOprEyW1J9ptvvsFvv/3GJEu1ppCzjZR8gEwBLLiCO+p7eDR5H5S4Z7yjVZVpYaubqEqjhUImcDytl3Jbkk1MTMStW7fcdXkih1QdB8txsVTnDB2kdP5QI9BsV/zb+3CsYlPXUv0qUoYE3HVpFo4t+Sv7DXgpt/3Uxo4d665LE1VLIblnsY1zzZK3UIELlvgS/mlEPuf7gJc8HQKRTQqZFNuGhSEm8iHgXU9HQ+7GJEs+7cf5fSBVNK72GI6LpbokkUgQ6O/H6t96otY/5fPnz+PixYu4e/euzWOGDh1a25chql5FpxKVRouuFVXDG/8SikcbNIbUn19m5AGmiwXUUtU+BO6YlIXcw+lvn0OHDiEpKQmXLl2yeYwg6HvEMcmS2xln3dEaO5WU+ys4vpA8x9aQHCdU7VPAhQW8h1M/pZycHDz//POQSCR48sknce7cOZw7dw7PP/88Ll26hEOHDqG4uBjDhw/nUndERFRvOZVk161bB51Oh9TUVPTs2RPz58/HuXPnMGvWLABAcXExFi1ahP3792PHjh0uDZiISLRMFwuwts8G08UDji1M0D9YbvJc3gAqjY695L2QnzMnHT9+HO3bt0fPnj2t7m/cuDGWLVsGPz8/vPfee7WJj8iCIAhQabRQabTGbSqNttqlF4nqhKHZwtq/apouTBcPUMr9zaqCDc+VnJTFKzmVZG/fvo0HH3zQ+FwmkwHQL9huIJfL0bVrV/z3v/+tXYREVRgWD+hq8ld916VZVlftISLyJKeSbHBwsFlv4qCgIADA5cuXzY67d+9etb2OiYiIfJlTSbZ169bIy8szPo+JiYEgCPj000+N2y5evIgffvgBLVu2rH2URBVUGh2nRCQir+FUx6fHH38cK1euRG5uLh566CHEx8ejRYsW+Pjjj5GTk4PmzZvjhx9+wL179zBixAhXx0z1GDt+EJE3cSrJDh06FI0aNUJ5eTkAffvr2rVr8dJLLyEnJwc5OTnw8/PDyJEjMWHCBJcGTD5OEIAyFaDRQoFS/TZNCRQohRoBADjulYi8h1NJNjQ0FKNHjzbbFhERgT179iA3NxfFxcVo3bo1goODXRIk1SNlKuDNFlACOGNYqCRF/1g155L5upqakspFr4mIRMjlU4Y89NBDrr4kEQD9UAaYzXLDGW+ISNyc6vhEVBe6lq41rq1JROSN7Eqy77zzDm7fvl2rFyoqKsKyZctqdQ2qX1QI4NqaROTV7EqyaWlp6Nu3L5KTk3H69GmHXuCXX37B0qVL0a9fP2zZssWpIImIfFpZiXPnCYK+b0LVf5z9TDTsatTavXs3VqxYgbS0NGzevBmtW7fGo48+iujoaLRp0wZNmjRBgwYNUFJSgj/++AO//fYbcnJycPjwYVy6dAmCIGDgwIGYPXu2u98PEZH3WRXr3HkVHQUtLLhi3kmQPMauJNu6dWukpKTg1KlT+Pjjj5GZmYnt27fjs88+s3mOIAhQKpUYOXIknnnmGURGRrosaCIiIm/gUPfMjh07YunSpViwYAGOHDmCo0eP4uzZsygsLMTdu3fRsGFDhISEIDIyEl27dkW3bt2gVNpeeYLIIYaqMQMXLopNVOdsrdgjU+rvUB0182fn74jJbZwaA6FUKtG7d2/07t3b1fFQPSUIgn6Cf40WNv8sK1O7dCFsIo8yrNjjKjJWD4sRBxqSKBhW1lGgtHISiqqc7RxCROQhTLLkPUyrwub8D5ArodJo9cvcIYALBxCR6Ig6yW7btg0bNmxAQUEBIiMj8dprryEmJsbqsdu3b0dGRgbOnz8PQN9+/PLLL5sd/8orryA9Pd3svF69emHDhg3uexPktGMLE/QPrE2dKFdWVLVpoYb+1peLB5BPMvQ9qDKfN2SNPRcT2U20STYzMxPJyclISkpCbGws0tLSMHnyZHz99dcICQmxOP7w4cMYMmQIunTpArlcjtTUVDz77LPYvXs3mjVrZjwuPj4eycnJxudyubxO3g85Tin3r75zCFF9UNEPwWw+7+Ww/ntBoiPaJLtp0yaMGjXKuFReUlIS9u/fjx07duD555+3OH7FihVmz5cuXYq9e/ciOzsbQ4cONW6Xy+UIDQ11a+zkQjV0DlHIpDi9ZIDFdp1Oh7OnctwZGRFRjUSZZDUaDU6dOoWpU6cat/n5+SEuLg7Hjx+36xpqtRparRZNmjQx2/7jjz+iR48eaNy4MR577DG89NJLCAoKcig+nc49bX+G67rr+mJm7T3rdDrAjrIIkFouf6eDBBKJpF6WpTvU58+mq9lVln4BwLzLZptUGi2eSP4axwKnAQDuqO8BABpV7L9beg8NTV+jHvysPPm5tPc1RZlkb926BZ1OZ1EtHBISggsXLth1jeXLlyMsLAxxcXHGbfHx8ejfvz/Cw8Nx+fJlrFy5Es899xw+/fRTSKVSu+PLyXHvHZK7ry9Gpdpyi20nT55Eub+iVtetj2XpTixP13G0LEu15WZzeT+avA9AZRVy7+X/wbGKx6743fEmYv5cijLJ1ta6deuQmZmJzZs3IyCg8kM5ZMgQ4+OIiAhEREQgISHBeHdrr+joaIeSsr10Oh1ycnLcdn0xU2m0QLp5x6WYmBinxxHW57J0B5an6zhblvrfkUt2HdsusoPxd0chk0Iisazt8QWe/FwaXrsmTiXZhIQEjBo1CsOGDXNL+2ZQUBCkUikKCwvNthcWFqJp06bVnrthwwasW7cOmzZtqnEqx5YtWyIoKAgXL150KMlKpVK3/kDdfX1PMU44YYWfn+X7lUqlQC3LwVfL0lNYnq7jaFlKpeaT/itxz+axjybvM/a6P71kAJRy3/6Ziflz6VSSzc/Px7vvvouUlBQ88cQTGDVqFOLj413215JcLkfHjh2RnZ2NhAT9MI7y8nJkZ2cjMTHR5nnr16/HBx98gA0bNiA6OrrG17l27Rpu377NjlB1xDDhhDVHDcN1iMguhrZZEjenFm3ft28fpk+fjtDQUGRlZWHq1Kno27cv1qxZg2vXrrkksEmTJmH79u1IT09Hbm4uFi9eDLVajeHDhwMA5s6da9ajeN26dVi1ahXefPNNPPDAAygoKEBBQQFKSvSzBJWUlODtt9/GiRMnkJeXh+zsbLzwwgto3bo14uPjXRIzEZGnHJzXp/Lx3D7VHEl1yak72ebNm+PFF1/E9OnTcfDgQXz22WfYt28f1qxZg7Vr1yI+Ph4jR45Enz594OfnVB7H4MGDUVRUhJSUFBQUFKB9+/ZITU01VhdfvXrV7NqffPIJysrK8I9//MPsOi+++CJmzJgBqVSKc+fOISMjA3fu3EFYWBh69uyJmTNncqysBxjuXDmBBJH91AhA+9KNZtuOLUyAUu4Ppckasr5ePexNatXxSSKR4PHHH8fjjz+OwsJC7Ny5E59//jn279+PAwcOoGnTphgxYgT+9re/ITw83OHrJyYm2qwerroA/HfffVfttQIDAzmzk4jwS4DIMfox4QOtbodEYr5CFYmGc7eZVoSEhOC5555DZmYmJk6cCEEQUFBQgA8++AB//vOfMW3aNJw5c8ZVL0dEVK9IJBL9HWuVf77ac9hXuGwIT35+Pj7//HPs3LkTN27cAAB07twZjz32GPbs2YN9+/bh4MGDePfdd9G/f39XvSwREZFo1SrJarVafPvtt9i+fTuys7NRXl6Ohg0bYsyYMRg9ejQefvhhAMDMmTOxZ88ezJs3DykpKUyyRERULziVZH///Xd89tlnyMjIQFFREQRBQIcOHTBmzBg8+eSTUCgsZxoZNGgQ9uzZU2PbKRERka9wKskOHDgQEokEgYGBGD58OEaPHm3XuNRGjRpBq9U685JERERex6kk265dO4wePRpDhw5Fw4YNaz6hwhtvvIE33njDmZckIiLyOk4l2a+++srVcRAREfkcp4bw9OvXD++8806Nx61YscI4LSIREVF941SSzc/PR1FRUY3H3bp1C/n5+c68BBERkddz2WQU1qjVavj7++RqekRERDVySwYsLy/Hb7/9hsOHD6NFixbueAkiIiLRszvJtm/f3ux5RkYGMjIyqj1HEASMGjXKqcCIiIi8nd1J9v777zc+vnr1KgIDAxEUFGT1WJlMhrCwMPTr1w/jxo2rfZREREReyO4kazpTU2RkJAYOHIjk5GS3BEW+QxAEqMt0AACVRufhaIiI6pZTbbKbN282rutKVB11mQ4dFu2t4SgB0JRAgVIoca9O4iIiqgtOJdlHH33U1XFQPabAPTRNaYMzgZ6OhIjItexKskeOHAEAxMTEICAgwPjcXt26dXM8MvI5RxcmGBdrV8ikxmpkIiJfZVeSHTduHCQSCTIzM9GmTRvjc3txsXYCAKVcCqW8+o9c19K1OLhwiP44mbKOIiMicg+7kuzQoUMhkUjQqFEjs+dEtaGQSXF6yQBAUwIs1287uHAIFA0aA/x8EZEPsCvJvvXWW9U+J3KGRCKpuLOt/Bgq5f5MsETkM9w6rSIREVF95vJpFXNzc3H+/Hncf//9iI2NdfXliYioJmUqKFCqf6wpgdlXvUzJ2qI65FSSzczMxCeffILZs2ebJdK3334bH330kfF53759kZKSAqlUWutAiYjIPspVkZVD4pZX2bngCiBvUNch1VtOVRd/+eWX+PXXX83mM/7pp5+wadMmNGjQAIMHD8YDDzyA7777Drt27XJZsOTN9BNOWP+n8nRwRERu4dSd7Llz5xAREQG5XG7c9sUXX0AikeC9995Dr169cPv2bfTr1w+fffYZhg4d6qp4yUspcA/K5a08HQaR75Ip9XepAFQaLbouzQIAHFuYACU0wPJ2noyu3nLqTraoqAhhYWFm2w4fPoyQkBD06tULAHDfffehW7duuHjxYu2jJCKi6kkk+mrgin9qBEKNwIrnHHPuKU7dyQYEBKCkpMT4/MaNG/j9998xaNAgs+MaNWqE4uLi2kVIvmfO/2z/0nMCCiLyIU4l2ZYtW+Lo0aMoLi5G48aNsWvXLkgkEvTs2dPsuJs3byIkJMQlgZIPkSvZ8YKI6gWnqouHDRuGu3fvYvjw4ZgxYwbee+89KJVK9OvXz3hMWVkZfvnlFzz44IOuipWIiMirOHUnO2rUKBw+fBjffPMN8vLyoFAosGTJErNF3Pfv3487d+7gsccec1mwRERE3sSpJCuTyZCSkoK8vDwUFRWhbdu2aNiwodkx4eHhWLNmDTp16uSKOImIiLxOrWZ8Cg8PR3h4uNV97du3NxtHS0REVN9w7mIiIiI3cfpOVqPR4KuvvsKRI0dQUFAAjUZj9TiJRIK0tDSnAyQiIuepNDoAWnBwnGc4lWSvX7+OCRMm4OLFixAEodpjue5s/SIIAtRlOuNz/S84EXnKI0uzoEBp5VzGVKecSrLLli3D77//js6dO2PSpEl48MEH0aCB68c9btu2DRs2bEBBQQEiIyPx2muvISYmxuqx27dvR0ZGBs6fPw8A6NixI15++WWz4wVBQEpKCj777DMUFxejS5cuWLx4MYcZuZC6TIcOi/Z6OgwiIlFwKsl+//33aNGiBT766CMEBAS4OiYA+pV+kpOTkZSUhNjYWKSlpWHy5Mn4+uuvrU5wcfjwYQwZMgRdunSBXC5Hamoqnn32WezevRvNmjUDAKxfvx5btmzBW2+9hfDwcKxatQqTJ09GZmam295HfWB698o7VyLPU8ikOL1kAAD972T80q88HFH95VSS1Wg06NGjh1sT06ZNmzBq1CiMGDECAJCUlIT9+/djx44deP755y2OX7FihdnzpUuXYu/evcjOzsbQoUMhCAI2b96MadOmISEhAYD+jjwuLg5ZWVkYMmSI296Lr7N193p0YQKU8oplDjUllktuEZFbSCQSKOUuXy6cnODUT+Hhhx/GrVu3XB2LkUajwalTpzB16lTjNj8/P8TFxeH48eN2XUOtVkOr1aJJkyYAgLy8PBQUFCAuLs54TKNGjRAbG4vjx487lGR1OvfcrRmu667ru4uteAOkQIC0ok1eKjE/3s3v0VvLUqxYnq5T12VZ9XXq4vevrnjyc2nvazqVZJ977jnMnDkTJ0+etNlGWhu3bt2CTqezqBYOCQnBhQsX7LrG8uXLERYWZkyqBQUFxmtUvebNmzcdii8nJ8eh4x3l7uu7Wqm23Ph4419CEeCvT6hnT+UYO775adXoXHHMyZMnUe6vqJPYvK0sxY7l6Tp1VZamv59A3f7+1RUxfy6dSrIdOnTAxIkTMWHCBEyaNAlxcXFo3rw5/PysD7tt0aJFrYJ01Lp165CZmYnNmze7pUo7OjoaUqnU5dfV6XTIyclx2/XdRaXRAun6tSu7dYm1Xk2lKQH26B/GxMS4fYEAby1LsWJ5uk5dl6X+9/OS8Xld/P7VFU9+Lg2vXROnkmzfvn0hkUggCALWrl2LtWvX2jxWIpHg9OnTDl0/KCgIUqkUhYWFZtsLCwvRtGnTas/dsGED1q1bh02bNiEyMtK4PTQ01HgN07VwCwsLzY6zh1QqdesP1N3XdzWpVDB5bCN2k21SqdTsuTt5W1mKHcvTdeqqLE1/PwHgnk4AdJZDLxUyqdcOuRTz59KpJNutWzdXx2FGLpejY8eOyM7ONnZSKi8vR3Z2NhITE22et379enzwwQfYsGEDoqOjzfaFh4cjNDQU2dnZxuke7969i59//hljxoxx35shIhKRrkuz9Iu5V3F6yQB2lnIDp0p0y5Ytro7DwqRJkzBv3jxERUUhJiYGaWlpUKvVGD58OABg7ty5aNasGWbPng1AX0WckpKCFStW4IEHHjC2wSqVSjRo0AASiQTjx4/H2rVr0bp1a+MQnrCwMGMiJyIiciXR/tkyePBgFBUVISUlBQUFBWjfvj1SU1ON1cVXr141awP+5JNPUFZWhn/84x9m13nxxRcxY8YMAPoOW2q1GosWLUJxcTG6du2K1NRUjpElIp+lkElxbGGCcQjdsYUJxjZZlUaHR5ZmeTA63+eSJPv777/j1q1buO+++9CmTRtXXBIAkJiYaLN6uOrd9HfffVfj9SQSCWbOnImZM2e6JD4iIrGrOmZWKfcHWC1cZ5xehUej0WDlypXo3r07Bg0ahGeeeQbr1q0z7v/iiy8wbNgwnDlzxiWBEhEReRunkmxpaSnGjRuH9evXQyaToXfv3hYLBTz22GP49ddfsWfPHpcESkRE5G2cSrKpqan4+eefMWLECHz77bf44IMPLI5p1qwZ2rVrh0OHDtU6SCIiIm/kVJLNzMxEixYtsHjx4mo7DbVp0wbXrl1zOjgiIiJv5lSSzcvLQ1RUFPz9q288l8lk+OOPP5wKjIiIyNs5lWQDAwPtSp55eXnGCfqJiIjqG6eSbGRkJH755RcUFRXZPOby5cs4ffo0oqKinA6OvI2gn6PY6j+Vp4MjIqpzTg2WGjVqFObMmYOXX34ZK1euRHBwsNn+4uJiLFiwAFqtFk8//bRLAiXxU+AelMtbeToMIiLRcCrJPvnkk9i3bx92796NhIQEdO6sX8Tsp59+wrRp03DkyBHcvXsXQ4cORZ8+fVwaMBER1YJprZJGC0AA4J0LA3gDp6f9WL58Odq3b48NGzbgv//9LwDg4sWLuHjxIho1aoRZs2bh+eefd1mg5GXm/A+QK63vk9nYTkTut7yd8aESgAIbrS4YQK7hdJKVSCSYMmUKJk2ahFOnTiE/Px+CIKBZs2aIjo6GXC53ZZzkbeRKn1mzkojIWbWewFIqlSImJka/EDAREYmPTAksuFL5XKMyu6Ml93E6yWq1WuTn5+OPP/6ARCLBfffdhwceeMBsZRwiIhIBiYQ1Sx7icJLdt28ftmzZguPHj6O0tNRsX2BgIB555BGMHz8e8fHxLguSiIjIG9mdZMvKyrBgwQJ89dVXxsUA5HI5GjduDAD4448/oFarcfDgQXz//fcYOnQoli5dCqlU6p7IiYiIRM7uJLtixQrs2rULSqUSEyZMwJAhQ/DQQw9BItF3/RYEAefPn8fu3buxefNmZGRkIDg4GP/v//0/twVPREQkZnYl2fz8fGzevBlBQUHYtm0b2rZta3GMRCLBww8/jIcffhhPPfUUxo4di48++giJiYm4//77XR44ERGR2NnVSyk9PR2CIGDBggVWE2xVDz30EBYsWACdTof09PRaB0lEROSN7Eqyx48fR8OGDTFkyBC7LzxkyBA0bNgQx44dczo4IiIib2ZXks3NzUWHDh0cGp4jlUoRFRWF3Nxcp4MjIiLyZnZlzeLiYjRt2tThiwcHB6O4uNjh84iIiHyBXR2fVCqVU9MkBgQEQK1WO3weeQlBAMpUgEYLBUqhxD1PR0REJCq1nlaR6rEyFfBmCygBnOH84kREFuxOsr/++ivWrFnj0MXPnDnjcEBERES+wu4ke+bMGYeSpkQigSAIxskqyLd1LV0LFQJwbGEClHJ/LmdHRAQ7k+yLL77o7jjIy6kQoF+TUt4AkLMVgshbGPtSaEoAWWP9YgLkMkyyRET12LHAafoHywHVnEvG1XoUMilrIl2AtxxERAQA6Lo0S18jBeD0kgH6ph+qFS7+SkRU31Qs4q6acwldS9d6OhqfxiRLVgmCAJVGa/WfYalDIvJSFYu4Kxo0xsGFldPlHpzbx4NB+SbWBZBV6jIdOizaa3XfqaQB+r4RGi3Yh5jIe0kkErMqYaWc63+7GpMsOayoRIP4ZfugQCknoSAiqgaTLNXo6MIEAMAjS7MACPjzsj1QAJxGkciHqTQ642P2NHYekyzVyLQKSYF7OBP4rMUxxxYm6Nt4ZKxuIvIF+j+q9djT2HksNbKLQibF6SUD9APWl1vuV8r9OQkFEVEVov1W3LZtGzZs2ICCggJERkbitddeQ0xMjNVjz58/j5SUFJw6dQr5+fmYP38+Jk6caHbM6tWrLeZebtOmDb7++mt3vQWfUtlBwuQjM+d/gLyi6xOnUSTyesY/pqGvLja9myXnuC3Jvvvuu7hx4wYkEgnefPNNh87NzMxEcnIykpKSEBsbi7S0NEyePBlff/01QkJCLI5Xq9UIDw/HwIEDkZycbPO6f/rTn7Bp0ybjc6mUVZu1IlcaZ4chIu8nKVNDKTe0vWoBCADYFlsbbkuy33zzDX777TenkuymTZswatQojBgxAgCQlJSE/fv3Y8eOHXj++ectjo+JiTHe5a5YscLmdaVSKUJDQx2KhYio3ljezvhQCUCBjcYZoMg5bkuyiYmJuHXrlsPnaTQanDp1ClOnTjVu8/PzQ1xcHI4fP16rmC5evIhevXohICAAnTp1wuzZs9GiRYtaXZOIiMgWtyXZsWPHOnXerVu3oNPpLKqFQ0JCcOHCBafjiYmJQXJyMtq0aYOCggK8//77GDt2LHbt2oWGDRs6dC2dTlfzQU4wXNdd13eEaQw6nQ46ncTwBFLTY0QQqzViKktfwPJ0HdGVpV8AMO9y5fMyFaQrI8wOuaPWmMUrliE9nixLe19TtB2fXK13797Gx5GRkYiNjUWfPn2wZ88ejBw50qFr5eTkuDq8Or2+PUq15cbHJ0+eRKC/fgZOP60anU22l/srPBCd/cRQlr6E5ek6Yi1L099xg+7J+8yebxsWZvxOEAOxliXgwiT7xx9/AAAaN25cq79wgoKCIJVKUVhYaLa9sLAQTZs2rVWMpho3bowHH3wQly5dcvjc6Ohot3Sa0ul0yMnJcdv1HaHSaIF0fc/CmJiYyjFymhJgD4zbxdrxSUxl6QtYnq4j+rI0+R23xew7wYM8WZaG165JrUrp22+/xbZt23D8+HGUlpYCAAIDA9G5c2c888wzSEhIcPiacrkcHTt2RHZ2tvH88vJyZGdnIzExsTbhmikpKcHly5ed6ggllUrd+gN19/Xti0EweWwSj0lcUqnU7LkYiaEsfQnL03VEW5YmMRkmmQHMh/SILXaxxWPKqSQrCAIWLFiAjIwM44osjRs3BgAUFxfj0KFDyM7Oxl//+lckJyc7fGc7adIkzJs3D1FRUYiJiUFaWhrUajWGDx8OAJg7dy6aNWuG2bNnA9B3lsrNzTU+vn79Os6cOQOlUonWrVsDAN5++2306dMHLVq0wI0bN7B69Wr4+fnhySefdKYIiIh8HieZqT2nSi8tLQ3p6ekICwvDCy+8gCeffNLYeeju3bvYvXs33n//fXzxxReIjIy0mBiiJoMHD0ZRURFSUlJQUFCA9u3bIzU11VhdfPXqVfj5VbYH3LhxA0OHDjU+37hxIzZu3IhHH30UW7ZsAQBcu3YNL7/8Mm7fvo3g4GB07doV27dvR3BwsDNFQEREVCOnkuz27duhUCiwbds2tGzZ0mxfw4YN8fTTTyMuLg5PPfUUtm/f7nCSBfRDgGxVDxsSp0F4eDjOnj1b7fXeffddh2MgIiKqDae6h+Xl5eGxxx6zSLCmWrZsicceewx5eXlOB0dEROTNnEqywcHBkMlkNR4nk8kQFBTkzEsQERF5PaeSbEJCAg4fPmwctmPN7du38cMPPzjVw5iIiERAo9IP6dGUAIJQ8/Fkwakk+9JLLyE8PBwTJkxAdna2xf4ffvgBzz77LFq2bIlZs2bVOkgiIvKA5e2AN1vo/5WpPB2NV7Kr49P48eMttslkMpw6dQrPPvssmjRpYpwD+OrVq7h9+zYAIDY2FtOnT0daWprrIiYiorpXpoIC+vkQoCkBZI0BEUytKHZ2Jdkff/zR5j5BEHD79m1jYjV14sQJUcxvSUREdpIpgQVX9I81KuPKPMpVkThjWJBnOfTHiHTGNzGxK8l+++237o6DiIjEQCJh8nQhu5LsAw884O44iIhIbEzualUaLeKX7saxwGnG54BWNCvyiBXnyyIiIuvM7mq1UCHAuKvr0iyoEYjTSwaIYrEAsapVydy8eRM7duzA0aNHcf36dQBAs2bN0K1bNwwfPtylq+aQ+wmCAHWZfo1ElUYka10SEXkxp5Ps3r17sWDBAqhUKuMiAQBw7tw5fP/991i3bh3eeOMNDBgwwCWBkvupy3TosGiv5Q5B0PcmBPQdIYio3lHIpPpVeZZ7OhLv4lSSzcnJwezZs1FeXo7+/fvjqaeeQnh4OAAgPz8fX3zxBbKysjB79my0aNEC0dHRLg2a6liZCljeytNREJEHSSQSq9XCVWu92EZrzqkku27dOuh0OqSkpKB///5m+yIjI9GvXz/8+9//xowZM7B+/XqkpKS4JFiqO0cXJkAp16/PqBBKPRwNEYmVYY1ZA7bRmnOqJI4dO4bOnTtbJFhT/fv3R5cuXXD06FGngyPPUcqllb8oGpO/Suf8D5Ar9Y9lyroPjIjIiziVZO/cuYP777+/xuPuv/9+5OTkOPMSJFZyJcfQEZG+fbbiu0Cl0Vnc0ZKeU0k2NDQUZ86cqfG4X3/9FaGhoc68BBERiZhS7g+wWrhGTi0Q0KtXL/z2229YuXIldDrLoR6CIODdd9/FhQsX0KtXr1oHSURE5I2c+jPkhRdewDfffIP169fjq6++wqBBg4yzQl25cgVff/018vPzcd999+GFF15wacBERETewqkk27x5c6SlpWHOnDk4f/48NmzYYOyybRgz+/DDD2P58uVo3ry566IlIiLyIk5XqEdERGDXrl04fPgwjh49ihs3bgAAwsLC8Mgjj6B79+4uC5KIiMgbOZVkX3zxRYSGhuKf//wnunfvzoRKREQAzCen4MQUTibZAwcOICEhwdWxkGgYplE0jJPlVIpEZB/ToTycmMLJJBseHg61Wu3qWMiTKuYnVqAUStyDktMoEhHVmlNJdsiQIdi4cSMKCgo4DtZXlKmgXN4KZwI9HQgReRuFTIrTS/SLwXBiCnNOJdmpU6fi5MmTSExMxJw5c/DEE09AJpO5OjYSA9NpFAFOpUhEFmwtHkBOJtmBAwdCEARcvXoV//jHPyCRSBAcHIyAgACLYyUSCbKy+FeNN+lauhYHFw7R/9LIlPqFm4mIyGFOJdn8/Hyz54Ig4ObNmy4JiDxPhQD9nKT8y5SIqFac+hb99ddfXR0HERGRz3Fq7mIiIiKqmUN3sgcOHEBWVhauXr0KuVyOiIgIDB8+HC1btnRXfERERF7L7iQ7e/ZsZGZmAqicn3jfvn3YuHEjVq5ciX79+rknQiIiIi9lV5L97LPPsHv3bvj7++Opp55Chw4dUFJSgn379uHEiROYN28e9u3bh0aNGrk7XiIiIq9hV5LNyMiAn58f1q9fjx49ehi3T506FfPnz0dGRga++eYbjBgxwm2BkusJggB1WcU8oxotOAKWiOxmOt0qh/rZZFeSPXfuHGJjY80SrMHUqVORnp6Oc+fOuTw4ci91mQ4dFu0FAChQytmeiMh+y9tVPl5wRT/sjyzY1bv47t27aNXK+ly2hu137951XVREREQ+wK4kKwgC/PysH2rYXl5e7rqoAGzbtg19+/ZFdHQ0Ro4ciZMnT9o89vz585gxYwb69u2LiIgIfPTRR7W+Zn1zcG4f4+NjCxOgkEk9GA0RiZJMqb9rXXBFP+VqDVQaHVQarfGfodNsfSLKcbKZmZlITk7G9OnTkZ6ejsjISEyePBmFhYVWj1er1QgPD8fs2bNtLljg6DXrG6VcavLYv96vAUlEVkgkFbPBNTCf09yGR5ZmocOivcZ/xj4g9YjdQ3gyMjKQkZFhdZ9EIrG5XyKR4PTp0w4FtWnTJowaNcrYkSopKQn79+/Hjh078Pzzz1scHxMTg5iYGADAihUrXHJNIiKi2rI7yTp7m+/oeRqNBqdOncLUqVON2/z8/BAXF4fjx487FYM7rklERJZMl70DuPSdXUm2LucqvnXrFnQ6HUJCQsy2h4SE4MKFC6K4pk7nnioPw3XddX1br2f1cR3F4C51XZa+juXpOj5TljodpMaH5t8ZAdLK5iad1PQUHXQ61zVFebIs7X1NLrPihJycHK+5viAIuKezXptguvWXX36BYYDWyZMnUe6vcFkMnuTun1V9w/J0HW8vSz+tGp0rHlf3nVGqrewUe/LkSQT6u74rkJjLUnRJNigoCFKp1KJDUmFhIZo2bSqKa0ZHR0MqdX3vW51Oh5ycHJdeX6XRIjrJelXN4fl9ANwAAERFRQH/1m+PiYnx+jFv7ijL+ozl6To+U5aaEmCP/mF13xkqjRZIzzIe58rF3T1ZlobXronokqxcLkfHjh2RnZ2NhIQEAPrhQdnZ2UhMTBTFNaVSqVt/oK68vlRqu03c9DUsHnvzL78Jd/+s6huWp+t4fVna+Z1h+h3krvcs5rIUXZIFgEmTJmHevHmIiopCTEwM0tLSoFarMXz4cADA3Llz0axZM8yePRuAvmNTbm6u8fH169dx5swZKJVKtG7d2q5r1gdHF+r/wNB3QhAATQkUKNXvLFPZPpGIiJwiyiQ7ePBgFBUVISUlBQUFBWjfvj1SU1ONVbtXr141mxzjxo0bGDp0qPH5xo0bsXHjRjz66KPYsmWLXdesD0zHwipwD01T2lROpbjKMzERUf2h0lR2FlLIpPViPL4okywAJCYm2qzKNSROg/DwcJw9e7ZW1/QlphP/m36oiYg8yXQoz+klA1zaPitWvv8O6yHTif/NCAJQpoICpVDinnFz19K1OLhwSOUHXsb1eIiIXIFJtj4pU0G5vJXFajsqBFRMk8aPAxE5oZpl70wnp6iPE1PwW9XHHV2YYGyLVQilVo/hggBEVCvVLHsnkUjqRbWwLfX3ndcTSrm08gOuMelkMOd/xgm+lVxwmYhcRVNlpEI9/35hkq2v5Eqvn3CCiDzIsOwdoE+shrtZ07taoN4v6M4kS0REjjMse0fVYpIlIqLaMb2rBczvbE2rjwV53cYlAkyyRERUO9Xd1ZpWH8+5VDfxiIjrl0MgIiIiALyTJSIiV7PVKaoeYpIlIiLXYqcoI1YXExERuQmTLBERkZuwutgHmK66A3DlHSISv6rfU7669B2TrBeyllTr26TbROTdqn5n+erSd773juoBm0vZERGRqDDJ+hjTVXcAcHUdIhIN02XvgPpRC8ck6+Uskqq/HyRadeUBZRX/cyF2IvKw+rjsXf16tz7IbCk7ANCUAG+2sDzQdF5RIiKqE0yy9UXVNR6JiETEtLexL/U0ZpL1ZTN/BlbF6h/X42nNiEj8TNtmfamnMSej8GUyTmtGRORJvvGnAlknU9hui2VHKCLyMNPexr7a05hJ1pdxkm4iErH60NvYt98dERF5HV+acpFJloiIRMWXplxkxyciIiI38c4/DYiIyKf46pSLTLJERORxvtoJitXFREREbsIkS0RE5Ca+d29ORETiVHUOdZlSP57fhzHJeglBEKAu048dqzqGjIjIK1SdQ33BFZ+fMIdJ1kuoy3TosGiv5Q5B0C9vZ8DVdoiIRINJ1tuVqYDlrTwdBRGRdTKl+RzqGlW9WhVM1El227Zt2LBhAwoKChAZGYnXXnsNMTExNo/fs2cPVq1ahfz8fDz44IOYM2cOevfubdz/yiuvID093eycXr16YcOGDW57D+5wdGEClHIpAEAhlHo4GiKialQ3h7ppzVs17bPevNasaJNsZmYmkpOTkZSUhNjYWKSlpWHy5Mn4+uuvERISYnH8Tz/9hNmzZ+Pll19Gnz59sGvXLkyfPh07d+7Eww8/bDwuPj4eycnJxudyubxO3o8rKWV+UOKe/kmZunLHnP8BcpPVdbjSDhGJmekdbTXts9681qxoh/Bs2rQJo0aNwogRI9CuXTskJSUhMDAQO3bssHr85s2bER8fjylTpuChhx7CSy+9hA4dOmDr1q1mx8nlcoSGhhr/NWnSpC7ejmuVqYA3W+j/mX5I5Ur9h9Twz4v+2iMi8kWi/HNAo9Hg1KlTmDp1qnGbn58f4uLicPz4cavnnDhxAhMnTjTb1qtXL2RlmU/L9eOPP6JHjx5o3LgxHnvsMbz00ksICgpy+XsgIiIrTNtoq2mf9ZW1ZkWZZG/dugWdTmdRLRwSEoILFy5YPefmzZto2rSpxfE3b940Po+Pj0f//v0RHh6Oy5cvY+XKlXjuuefw6aefQiqV2h2fTueeITSG61q7vuk2s8cvn62sFvYLANwUm7eprizJcSxP12FZApAGVvyvg+GbV6fTWXx/BUj1tXE6k69nnU4HnU5SeQ48U5b2vqYok6y7DBkyxPg4IiICERERSEhIMN7d2isnJ8cd4VV7/VJtufHxL7/8AkO0J8/8D+X+CrfG483c/bOqb1iersOyBPy0anSueHzy5Emb32Wm338nT55EoL95S6eYy1KUSTYoKAhSqRSFhYVm2wsLCy3uVg2aNm1qdtda0/EA0LJlSwQFBeHixYsOJdno6GiH7nztpdPpkJOTY/X6Ko0WSNdXl0RFRQH/1m+PiYnx+cHczqiuLMlxLE/XYVma0JQAe/QPY9q3M++sadLb2PT7LyYmxtjxyZNlaXjtmogyycrlcnTs2BHZ2dlISEgAAJSXlyM7OxuJiYlWz+nUqRN++OEHs3bZQ4cOoVOnTjZf59q1a7h9+zZCQ0Mdik8qlbr1B2rt+lKpYLbf7HF9/0Wthrt/VvUNy9N1WJYw++6Sroww32fS27jq95/l96N4y1K0vYsnTZqE7du3Iz09Hbm5uVi8eDHUajWGDx8OAJg7dy5WrFhhPH78+PE4ePAgNm7ciNzcXKxevRq//PKLMSmXlJTg7bffxokTJ5CXl4fs7Gy88MILaN26NeLj4z3yHomIyLeJ8k4WAAYPHoyioiKkpKSgoKAA7du3R2pqqrH69+rVq/Dzq/wboUuXLli+fDnee+89rFy5Eg8++CDef/994xhZqVSKc+fOISMjA3fu3EFYWBh69uyJmTNneuVYWSIir1fdbFCmE1UI3vsdLdokCwCJiYk2q4e3bNlisW3QoEEYNGiQ1eMDAwO9bmYnIiKfVt1sUKZDe+Zcqpt43EDUSbY+M111B+DKO0RE3ohJVqRsrrpDROSr7JyowpswyRIRkThUV33spZhkvYDpqjsAV94hIvIWTLJeQCmXmq86oeHE/0RE3kC042SJiIi8He9kRUQQBJRqy6HSaHGPnYmJiCyYjrTQ6XQQBKGaoz2PSVZE1GU6jE2/YZyj00gQ9HN8GpgO0iYi8nVlKiig74sSv/QrqBEAQN9stm1YmAcDqxmTrDcoUwHLW3k6CiIij1CuisSZwMrn7Us3Qo1A2yeICJOsSJn2KGZvYiKiSgfn9sEjy7I9HYZdmGRFyqxHsWlv4jn/A+RVloMiIvI11cxrbDqkUeyYZMXKtB3WtA1WrvS5wdpERBZ8ZGIKJlmxYjssEZHXY5IlIiLvYtLb+F6ZftijVCpAIZNCIhHXZD1Msh5k90o7pu2wbIMlonrOtLdx+682Qv3VTQDA6SUDzGfHEwFxRVPP2L3SDtthiYi8EqdVJCIi8TP0Nl5wBcKc88bNawc39WBQNeOdrEgcXZiAAClw8uRJxMTEQOFX5umQiIjEw6S3sWmra4BUXG2wVTHJioRSLkWAVIJAfz8o5f6Q6LSeDomIiGqJSVZMBAF+WrV+fKzunqejISKiWmKSFZMyFTrvGQLs8XQgRETkCuz4RERE5Ca8kxUrjo0lIvJ6TLKeJAjGWUugKQHK1ZX7ODaWiKhGUl2p+feorLG+J7JIMMl6UpkKZwKf1T9e7tlQiIi80aPfjaxca3Y59GNpRXSDwjZZIiIiN+GdrEioZv6KgMAGxskopGyHJSKyrmL2J51Ohx9/OoEXd+XhWOA0AIBKowWgn2dADAsGMMnWMdNFAVQaHYypVKZvgy33V+irOkTUpkBEJCqG2Z90OpT7K6BCgHFX16VZUENffyyGBQOYZOuY6aIACpRWtiUQEZHPYZssERF5rQCpBD/O72N8fnBun2qOrntMsh5k+mFQyKQejISIyDtJJBKzKmGlXFzfpawu9iDTD4OnG+eJiHxCmapy3KwgeDYWMMkSEZEPUa6KNPZ1UZVdAgKaeDQeVhcTERG5Ce9kiYjIu1WMmwUAVUkxlKsiPRxQJSbZumY6X3GZyrOxEBH5AsO4WQDQaD0bSxWiri7etm0b+vbti+joaIwcORInT56s9vg9e/Zg4MCBiI6Oxl/+8hccOHDAbL8gCFi1ahV69eqFmJgYTJw4Eb///rsb34EVFfMVnwl8VlR/bRER+RqVRgeVRguVRgvBQ52gRJtkMzMzkZycjOnTpyM9PR2RkZGYPHkyCgsLrR7/008/Yfbs2fjb3/6GjIwM9OvXD9OnT8e5c+eMx6xfvx5btmzB4sWLsX37digUCkyePBn37t2rq7dFRER1JH7ZPnRYtBcdFu1FYYnGmHDrMumKNslu2rQJo0aNwogRI9CuXTskJSUhMDAQO3bssHr85s2bER8fjylTpuChhx7CSy+9hA4dOmDr1q0A9HexmzdvxrRp05CQkIDIyEgsW7YMN27cQFZWVl2+NSPVzF/17QgLrnDNWCIiN3pkaZYx4XZYtNc4va27ibJNVqPR4NSpU5g6dapxm5+fH+Li4nD8+HGr55w4cQITJ04029arVy9jAs3Ly0NBQQHi4uKM+xs1aoTY2FgcP34cQ4YMqTEuw18+Go0GUqlzA541ZWUI8NcnVA1k8IdMv6OsDDqdrtbXJz2WpWuxPF2HZek61spSKpRDV/Ed+93L8eibctTquRqNBv4or/Vr13RHLMoke+vWLeh0OoSEhJhtDwkJwYULF6yec/PmTTRt2tTi+Js3bwIACgoKjNtsHVOT8nL9D+T06dN2HW/ToK/0/1+4aHV3ra9PRixL12J5ug7L0nUsytLwHXv5MrYOa2b1nNyzZ1zy2oa8YIsok6xY+fv7Izo6Gn5+fpyhiYioHhMEAeXl5fD3rz6NijLJBgUFQSqVWnRyKiwstLhbNWjatKnFHanp8aGhocZtYWFhZsdERtrXy9fPzw9yudzu90FERPWbKDs+yeVydOzYEdnZ2cZt5eXlyM7ORufOna2e06lTJ/zwww9m2w4dOoROnToBAMLDwxEaGmp2zbt37+Lnn3+2eU0iIqLaEGWSBYBJkyZh+/btSE9PR25uLhYvXgy1Wo3hw4cDAObOnYsVK1YYjx8/fjwOHjyIjRs3Ijc3F6tXr8Yvv/yCxMREAPoJ+MePH4+1a9fi22+/xdmzZzF37lyEhYUhISHBI++RiIh8myiriwFg8ODBKCoqQkpKCgoKCtC+fXukpqYaq3+vXr0KP7/KvxG6dOmC5cuX47333sPKlSvx4IMP4v3338fDDz9sPOa5556DWq3GokWLUFxcjK5duyI1NRUBAQF1/v6IiMj3SQRPTYNBRETk40RbXUxEROTtmGSJiIjchEmWiIjITZhkiYiI3IRJVoTy8vKwYMEC9O3bFzExMUhISEBKSgo0Go2nQ/NKa9euxejRoxEbG4tHHnnE0+F4HUeXnCTrjhw5gr///e/o1asXIiIiPLYwiS/48MMPMWLECHTu3Bk9evTACy+8YHPKXU9jkhWhCxcuQBAELFmyBLt378b8+fPxySef4N133/V0aF6prKwMAwcOxJgxYzwditdxdMlJsk2lUiEiIgL//Oc/PR2K1/vxxx8xduxYbN++HZs2bYJWq8XkyZOhUqk8HZoFDuHxEqmpqfj444/x7bffejoUr7Vz5068+eabOHrU+qocZGnkyJGIjo7GokWLAOhnXuvduzfGjRuH559/3sPRea+IiAi8//77nAjHRYqKitCjRw9s3boV3bp183Q4Zngn6yXu3LmDJk2aeDoMqkcMS06aLg9Z05KTRJ5w584dABDldySTrBe4ePEitm7ditGjR3s6FKpHqlty0t7lIYncrby8HG+++Sa6dOliNsOfWIh2WkVftHz5cqxfv77aYzIzM/HQQw8Zn1+/fh1TpkzBwIEDMWrUKHeH6DWcKUsi8j1JSUk4f/48/u///s/ToVjFJFuHnn32WQwbNqzaY1q2bGl8fP36dYwfPx6dO3fG66+/7u7wvIqjZUmOc2bJSaK6tGTJEuzfvx9bt25F8+bNPR2OVUyydSg4OBjBwcF2HWtIsB07dkRycrLZYgjkWFmSc0yXnDR00DEsOWlY3YrIEwRBwOuvv45///vf2LJli6j/oGaSFaHr169j3LhxaNGiBebNm4eioiLjPsPi82S/K1eu4I8//sCVK1eg0+lw5swZAECrVq3QoEEDD0cnbpMmTcK8efMQFRWFmJgYpKWlmS05SfYrKSnBpUuXjM/z8vJw5swZNGnSBC1atPBgZN4nKSkJX331Ff71r3+hQYMGKCgoAAA0atQIgYGBHo7OHIfwiNDOnTsxf/58q/vOnj1bx9F4v1deeQXp6ekW2zdv3ozu3bt7ICLvsnXrVmzYsMG45OTChQsRGxvr6bC8zuHDhzF+/HiL7cOGDcNbb73lgYi8V0REhNXtycnJovsDkEmWiIjITdjQR0RE5CZMskRERG7CJEtEROQmTLJERERuwiRLRETkJkyyREREbsIkS0RE5CZMskRUo507dyIiIgKrV6926LyIiAj07dvXbNvhw4cRERGBV155xZUhEokSkywRicLq1asRERGBnTt3ejoUIpfh3MVEVKP+/fsjNjYWQUFBtb5WTEwMMjMz0ahRIxdERiRuTLJEVKNGjRq5LCkqFAqu80v1BquLiUQsLy8PERERGDduHO7evYvk5GT07dsXHTt2xBtvvIHi4mJs2bIFkydPRp8+fRAVFYXu3btj8uTJ+O9//2v1muPGjUNERATy8vLwxRdfYPjw4YiNjUWPHj0wb948XL9+3eKc6tpkb9++jSVLlqBXr16Ijo7G4MGDkZaWBlvToltrk+3bty/WrFkDAJg/fz4iIiKM/w4fPowNGzYgIiICK1eutFlWzz77LCIiIvDDDz9UW6ZEdYlJlsgLlJaWIjExEenp6Wjfvj369u2LJk2a4MSJE1i6dCl+++03tGnTBv3790ebNm3w3//+F5MnT8bnn39u85obN27EvHnzoFQq0a9fPygUCmRkZGDUqFG4du2aXXH98ccfeOaZZ7Bt2zYAQL9+/dCsWTMsW7YMS5cutfv9DRgwAJGRkQCALl26YNiwYcZ/TZs2xbBhwyCXy7Fz505otVqL8y9fvoxDhw7hwQcfxGOPPWb36xK5G6uLibzAyZMn0blzZ2RlZaFx48bG7ZcvX8ann36KTp06mR1/+vRpTJgwAcnJyRg0aJDVdXM//fRTfPjhh+jduzcAoKysDPPnz8euXbuwZMkS/Otf/6oxrpUrVyI3Nxfx8fFYvXo1FAqFMd4JEybY/f7mzZuH1atX49dff8XIkSOtLlf25z//GV999RX2799vXETe4PPPP4cgCPjb3/5m92sS1QXeyRJ5iVdffdUswQJAy5YtLRIsAHTo0AHPPPMM7t69i8OHD1u93sCBA40JFgBkMhleffVVKBQKfPfdd7h69Wq18ahUKmRkZMDPzw+LFi0yJlhA37lp7NixDry7mo0ZMwYAsH37drPtOp0O6enpkMlkoltLlIh3skReIDQ0FNHR0Vb36XQ6ZGdn4/jx4ygoKIBGowEA/P777wCAixcvWj1vyJAhFtuCgoLQs2dPZGVl4dixY3jyySdtxnTq1CmUlpYiJiYGrVq1stj/5JNPYv369TW9Nbs98sgj+NOf/oSDBw/i6tWruP/++wEABw4cwPXr1zFgwACEhIS47PWIXIFJlsgLtGjRwur2a9euYerUqfj1119tnltSUuLQNR944AEAwI0bN6qNybC/puu40tNPP42lS5dix44dePHFFwFU3tmOGjXK5a9HVFusLibyAgEBAVa3v/rqq/j1118xYMAAfPbZZzh69CjOnDmDs2fPYsmSJQBgs5evNxo6dCgUCgV27NiB8vJyXL9+Hf/5z3/wwAMPoGfPnp4Oj8gC72SJvJRKpcKhQ4fQtGlTvPvuu5BKpWb7L1++XO35V65cMfborbodAMLCwqo9PzQ01Oz4qvLz86s93xmNGjXC4MGDsWPHDhw8eBCnT5+GTqfDyJEjIZFIXP56RLXFO1kiL3Xnzh2Ul5cjNDTUIsGWlZXh3//+d7Xn79mzx2Lb7du38d///hcSiQRdunSp9vyOHTsiMDAQp06dsprQMzMz7XgXlWQyGQB9G3N1Ro8eDUDfO/rzzz+HVCplhycSLSZZIi8VEhKCRo0a4fz58zh27Jhxu06nw/Lly40dn2zZs2cPDh48aHyu1WqRnJwMlUqFJ554wmZbq0GDBg3w17/+FTqdDq+//jpKS0uN+3JycrB161aH3o/hzvnChQvVHhcTE4OOHTvi22+/RV5eHnr37o1mzZo59FpEdYXVxUReyt/fH1OmTMG7776LcePG4bHHHkOTJk3w888/o7CwEGPHjjVOEmHNqFGj8Nxzz6Fbt24IDQ3Fzz//jLy8PISFhWHRokV2xfDyyy/jxx9/xIEDB5CQkIBu3bqhuLgYP/zwA55++ulqX7+qnj17IiAgAGlpaTh//jzCwsIgkUgwefJktG3b1uzY0aNH47XXXgOg7wxFJFa8kyXyYn//+9/x9ttvIyIiAj/99BMOHTqEyMhIfPrpp4iKiqr23GeffRZvvvkm7ty5g6ysLNy9exd//etf8dlnn9V4F2tw33334eOPPzaOYc3KysKVK1cwe/ZsYxK0V7NmzfCvf/0LsbGxOHbsGHbs2IHPP/8cBQUFFscaZnVq3rw54uPjHXodorokEXyp6yER1WjcuHH48ccf8e233yI8PNzT4Tjlww8/xMqVK/Hiiy9ixowZng6HyCbeyRKRV7l79y62bNkCmUzGsbEkemyTJSKvsGPHDhw5cgRHjhxBQUEBJkyYwA5PJHq8kyUir3DkyBGkp6dDpVJh7NixmDNnjqdDIqoR22SJiIjchHeyREREbsIkS0RE5CZMskRERG7CJEtEROQmTLJERERuwiRLRETkJkyyREREbsIkS0RE5CZMskRERG7y/wFzj26CF5Vt1QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we now plot all the features\n",
    "for i in range(len(features_to_consider)):\n",
    "    plot_features(i, jetList, y, features_to_consider)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AjsawpbUmioJ"
   },
   "source": [
    "# Starting with ParticleNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "soejowXJjYMJ"
   },
   "source": [
    "ParticleNet relies on [Dynamic Graph Convolutional Neural Networks](https://dl.acm.org/doi/10.1145/3326362) (DGCNN), which are a special case of the GCN where the graph is re-built in the new ${h_i^{(k)}}$ latent space created by the node feature update. The DGCNN uses the EdgeConv operation to create a new latent graph where the original points are projected into a higher dimensional space. For this, for each point, its k-nearest neighbors are found and chosen to be connected to the given point. This can be thought as an analogy to the traditional convolution on images, where $3\\times3$ kernels learn correlations for the $8$ nearest neighbors for each pixel on the image, and then they produce a new grid after the convolution operation. This is based on the principle of locality, that says that images are made of local patterns, and so CNNs exploit this property, and analogously, the EdgeConv has the same mechanism. This is the architecture of ParticleNet:\n",
    "\n",
    "<div>\n",
    "<img src=\"https://raw.githubusercontent.com/jngadiub/ML_course_Pavia_23/main/neural-networks/particle-net-arch.png\" width=\"800\"/>\n",
    "</div>\n",
    "\n",
    "The advantage of such architecture is the natural invariance to permutations. In contrast to other NN flavours, like LSTMs, the ordering here doesn't matter. If we consider the example of text data, before the Transformers and LLMs wave, Recurrent Neural Networks (RNN) were a gold standard for natural language; but the ordering of each token plays a significant context in determining the semantic meaning of sentences. In the case of jets, this is not a good inductive bias, so GNNs are a better option.\n",
    "Here, I use the built in [EdgeConv](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.EdgeConv.html) module with the [`knn_graph`](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.knn_graph.html#torch_geometric.nn.pool.knn_graph) method implemented in PyG, with $k=16$ neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "4i43nLs2zB_f"
   },
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "from torch_geometric.nn import EdgeConv, global_mean_pool\n",
    "from torch_cluster import knn_graph\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class ParticleNetEdgeNet(nn.Module):\n",
    "    def __init__(self, in_size, layer_size):\n",
    "        super(ParticleNetEdgeNet, self).__init__()\n",
    "\n",
    "        layers = []\n",
    "\n",
    "        layers.append(nn.Linear(in_size * 2, layer_size)) #undirected graph\n",
    "        layers.append(nn.BatchNorm1d(layer_size))\n",
    "        layers.append(nn.ReLU())\n",
    "\n",
    "        for i in range(2):\n",
    "            layers.append(nn.Linear(layer_size, layer_size))\n",
    "            layers.append(nn.BatchNorm1d(layer_size))\n",
    "            layers.append(nn.ReLU())\n",
    "\n",
    "        self.model = nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def __repr__(self):\n",
    "        return \"{}(nn={})\".format(self.__class__.__name__, self.model)\n",
    "\n",
    "\n",
    "class ParticleNet(nn.Module):\n",
    "    def __init__(self, node_feat_size, num_classes=5):\n",
    "        super(ParticleNet, self).__init__()\n",
    "        self.node_feat_size = node_feat_size\n",
    "        self.num_classes = num_classes\n",
    "\n",
    "        self.k = 16 #for kNN algo\n",
    "        self.num_edge_convs = 3 #number of stacked EdgeConv blocks\n",
    "        self.kernel_sizes = [64, 128, 256] #number of units in the three linear transformation layers in each EdgeConv block\n",
    "        self.fc_size = 256 #units of the first FC layer after EdgeConv blocks\n",
    "        self.dropout = 0.1\n",
    "\n",
    "        self.edge_nets = nn.ModuleList()\n",
    "        self.edge_convs = nn.ModuleList()\n",
    "\n",
    "        self.kernel_sizes.insert(0, self.node_feat_size)\n",
    "        self.output_sizes = np.cumsum(self.kernel_sizes) #inputs include also previous layer output\n",
    "\n",
    "        #the first EdgeConv block\n",
    "        self.edge_nets.append(ParticleNetEdgeNet(self.node_feat_size, self.kernel_sizes[1]))\n",
    "        self.edge_convs.append(EdgeConv(self.edge_nets[-1], aggr=\"mean\"))\n",
    "\n",
    "        #stack other two EdgeConv blocks\n",
    "        for i in range(1, self.num_edge_convs):\n",
    "            print(\"self.num_edge_convs\",i)\n",
    "            # adding kernel sizes because of skip connections\n",
    "            self.edge_nets.append(\n",
    "                ParticleNetEdgeNet(self.output_sizes[i], self.kernel_sizes[i + 1])\n",
    "            )\n",
    "            self.edge_convs.append(EdgeConv(self.edge_nets[-1], aggr=\"mean\"))\n",
    "\n",
    "        self.fc1 = nn.Sequential(nn.Linear(self.output_sizes[-1], self.fc_size))\n",
    "\n",
    "        self.dropout_layer = nn.Dropout(p=self.dropout)\n",
    "\n",
    "        self.fc2 = nn.Linear(self.fc_size, self.num_classes)\n",
    "\n",
    "    def forward(self, data):\n",
    "        # input data is an instance of PyG Data() \n",
    "        x = data.x\n",
    "        batch = data.batch\n",
    "\n",
    "        for i in range(self.num_edge_convs):\n",
    "            # using only angular coords eta and phi for knn in the first edgeconv block, computing edge_index.\n",
    "            edge_index = (\n",
    "                knn_graph(x[:, :2], self.k, batch) if i == 0 else knn_graph(x, self.k, batch)\n",
    "            )\n",
    "            x = torch.cat(\n",
    "                (self.edge_convs[i](x, edge_index), x), dim=1\n",
    "            )  # concatenating with original features i.e. skip connection\n",
    "\n",
    "        x = global_mean_pool(x, batch) # this runs scatter under the hood\n",
    "        x = self.fc1(x)\n",
    "\n",
    "        return self.fc2(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "78vgy877zCBJ"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_177098/1953283684.py:13: DeprecationWarning: an integer is required (got type numpy.float64).  Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n",
      "  y=torch.tensor(y[i_jet], dtype=torch.long),\n"
     ]
    }
   ],
   "source": [
    "from torch_geometric.data import Data\n",
    "\n",
    "num_jets = len(jetList) #np.minimum(50000, len(jetList)) #number of training+validation samples. We can start with 10000.\n",
    "jet_dataset = []\n",
    "for i_jet in range(num_jets):\n",
    "    # remove the zero padded particles\n",
    "    mask_real = np.where(jetList[i_jet,:, 0]>=0) # keep only particles where energy is > 0. e.g. real particles\n",
    "    \n",
    "    # mask_real = np.where(jetList[i_jet,:,features_to_consider.index('e')]>0) # keep only particles where energy is > 0. e.g. real particles\n",
    "    \n",
    "    num_nodes = len(mask_real[0])\n",
    "    d = Data(x=torch.tensor(jetList[i_jet,mask_real,:].squeeze(), dtype=torch.float), \n",
    "             y=torch.tensor(y[i_jet], dtype=torch.long),\n",
    "             num_nodes = num_nodes)\n",
    "    # typically we do no need to set the num_nodes explicitely and it is inferred automatically from the edge_index. \n",
    "    # however, in this case we do not pass edge_index, since we will build the graph dynamically inside the network, so we need to pass num_nodes.\n",
    "    jet_dataset.append(d)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "jBvZXDaN4sVR"
   },
   "outputs": [],
   "source": [
    "from torch_geometric.loader import DataLoader\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# split the resulting dataset in training and validation datasets\n",
    "dataset_size = len(jet_dataset)\n",
    "val_split = 0.1\n",
    "val_size = int(val_split * dataset_size)\n",
    "train_size = dataset_size - val_size\n",
    "jet_dataset_train, jet_dataset_val = train_test_split(jet_dataset,test_size = val_size,train_size = train_size, shuffle=True)\n",
    "\n",
    "# create dataloaders\n",
    "batch_size = 128\n",
    "jets_loader_train = DataLoader(jet_dataset_train, batch_size=batch_size,shuffle=True)\n",
    "jets_loader_val = DataLoader(jet_dataset_val, batch_size=batch_size,shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tEC27H3A8aOA"
   },
   "source": [
    "Let's have a look at what is yielded by the DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "XuoYdg2sKc1B"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataBatch(x=[6533, 7], y=[128], num_nodes=6533, batch=[6533], ptr=[129])\n",
      "tensor([  0,   0,   0,  ..., 127, 127, 127])\n"
     ]
    }
   ],
   "source": [
    "for data in jets_loader_val:\n",
    "  print(data)\n",
    "  print(data.batch)\n",
    "  break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finally, let's instantiate our model and look at the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "dSr265wc8fuz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.num_edge_convs 1\n",
      "self.num_edge_convs 2\n"
     ]
    }
   ],
   "source": [
    "#Instantiate the model and move it to the device, define the optimizer and the loss function\n",
    "import torch.optim as optim\n",
    "\n",
    "gnn = ParticleNet(node_feat_size=len(features_to_consider), num_classes=num_classes)\n",
    "#print(gnn)\n",
    "gnn.to(device)\n",
    "\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(gnn.parameters(), lr = 0.002)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EKjxkqLAmCi9"
   },
   "source": [
    "Let's add a helper function that will count how many trainable parameters are in our graph neural network "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "id": "TJXoSlzy5mQ6"
   },
   "outputs": [],
   "source": [
    "def model_summary(model):\n",
    "    model_params_list = list(model.named_parameters())\n",
    "    print(\"----------------------------------------------------------------\")\n",
    "    line_new = \"{:>20}  {:>25} {:>15}\".format(\"Layer.Parameter\", \"Param Tensor Shape\", \"Param #\")\n",
    "    print(line_new)\n",
    "    print(\"----------------------------------------------------------------\")\n",
    "    for elem in model_params_list:\n",
    "        p_name = elem[0] \n",
    "        p_shape = list(elem[1].size())\n",
    "        p_count = torch.tensor(elem[1].size()).prod().item()\n",
    "        line_new = \"{:>20}  {:>25} {:>15}\".format(p_name, str(p_shape), str(p_count))\n",
    "        print(line_new)\n",
    "    print(\"----------------------------------------------------------------\")\n",
    "    total_params = sum([param.nelement() for param in model.parameters()])\n",
    "    print(\"Total params:\", total_params)\n",
    "    num_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "    print(\"Trainable params:\", num_trainable_params)\n",
    "    print(\"Non-trainable params:\", total_params - num_trainable_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "     Layer.Parameter         Param Tensor Shape         Param #\n",
      "----------------------------------------------------------------\n",
      "edge_nets.0.model.0.weight                    [64, 6]             384\n",
      "edge_nets.0.model.0.bias                       [64]              64\n",
      "edge_nets.0.model.1.weight                       [64]              64\n",
      "edge_nets.0.model.1.bias                       [64]              64\n",
      "edge_nets.0.model.3.weight                   [64, 64]            4096\n",
      "edge_nets.0.model.3.bias                       [64]              64\n",
      "edge_nets.0.model.4.weight                       [64]              64\n",
      "edge_nets.0.model.4.bias                       [64]              64\n",
      "edge_nets.0.model.6.weight                   [64, 64]            4096\n",
      "edge_nets.0.model.6.bias                       [64]              64\n",
      "edge_nets.0.model.7.weight                       [64]              64\n",
      "edge_nets.0.model.7.bias                       [64]              64\n",
      "edge_nets.1.model.0.weight                 [128, 134]           17152\n",
      "edge_nets.1.model.0.bias                      [128]             128\n",
      "edge_nets.1.model.1.weight                      [128]             128\n",
      "edge_nets.1.model.1.bias                      [128]             128\n",
      "edge_nets.1.model.3.weight                 [128, 128]           16384\n",
      "edge_nets.1.model.3.bias                      [128]             128\n",
      "edge_nets.1.model.4.weight                      [128]             128\n",
      "edge_nets.1.model.4.bias                      [128]             128\n",
      "edge_nets.1.model.6.weight                 [128, 128]           16384\n",
      "edge_nets.1.model.6.bias                      [128]             128\n",
      "edge_nets.1.model.7.weight                      [128]             128\n",
      "edge_nets.1.model.7.bias                      [128]             128\n",
      "edge_nets.2.model.0.weight                 [256, 390]           99840\n",
      "edge_nets.2.model.0.bias                      [256]             256\n",
      "edge_nets.2.model.1.weight                      [256]             256\n",
      "edge_nets.2.model.1.bias                      [256]             256\n",
      "edge_nets.2.model.3.weight                 [256, 256]           65536\n",
      "edge_nets.2.model.3.bias                      [256]             256\n",
      "edge_nets.2.model.4.weight                      [256]             256\n",
      "edge_nets.2.model.4.bias                      [256]             256\n",
      "edge_nets.2.model.6.weight                 [256, 256]           65536\n",
      "edge_nets.2.model.6.bias                      [256]             256\n",
      "edge_nets.2.model.7.weight                      [256]             256\n",
      "edge_nets.2.model.7.bias                      [256]             256\n",
      "        fc1.0.weight                 [256, 451]          115456\n",
      "          fc1.0.bias                      [256]             256\n",
      "          fc2.weight                   [2, 256]             512\n",
      "            fc2.bias                        [2]               2\n",
      "----------------------------------------------------------------\n",
      "Total params: 409666\n",
      "Trainable params: 409666\n",
      "Non-trainable params: 0\n"
     ]
    }
   ],
   "source": [
    "model_summary(gnn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ylhqxDyhAG9I"
   },
   "source": [
    "Not the biggest model... will serve as a starting point for us. Now let's define the training and testing functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "x_wO_hPPCgAz"
   },
   "outputs": [],
   "source": [
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "wvZbwgQO8fzj"
   },
   "outputs": [],
   "source": [
    "def train_test_one_epoch(model,jets_loader, mode='train'):\n",
    "    if mode=='train':\n",
    "        model.train(True)\n",
    "    else:\n",
    "        model.train(False)\n",
    "\n",
    "    running_loss, running_correct = 0.,0.\n",
    "    tot_iter = len(jets_loader)\n",
    "    t = tqdm.tqdm(enumerate(jets_loader),total=tot_iter)\n",
    "\n",
    "    if mode == 'train':\n",
    "        for i, data in t:\n",
    "            inputs = data # For the inputs we are passing the whole Data() object\n",
    "            labels = data.y\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "          \n",
    "            optimizer.zero_grad() # Clear gradients\n",
    "            outputs = gnn(inputs) # Perform a single forward pass\n",
    "            loss = loss_fn(outputs, labels) # Compute the loss\n",
    "            if mode=='train':\n",
    "                loss.backward()  # Derive gradients\n",
    "                optimizer.step() # Update parameters based on gradients.\n",
    "    \n",
    "            running_loss += loss.item() #.item() returns average loss over the batch \n",
    "            del loss\n",
    "            _, predicted = torch.max(F.softmax(outputs,dim=1).data, dim=1) # Adding softmax since there is none in the model, and taking the index of the highest probability.\n",
    "            running_correct += float(torch.sum(predicted == labels.data))\n",
    "    else:\n",
    "        with torch.no_grad():\n",
    "            for i, data in t:\n",
    "                inputs = data # For the inputs we are passing the whole Data() object\n",
    "                labels = data.y\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "              \n",
    "                optimizer.zero_grad() # Clear gradients\n",
    "                outputs = gnn(inputs) # Perform a single forward pass\n",
    "                loss = loss_fn(outputs, labels) # Compute the loss\n",
    "                if mode=='train':\n",
    "                    loss.backward()  # Derive gradients\n",
    "                    optimizer.step() # Update parameters based on gradients.\n",
    "        \n",
    "                running_loss += loss.item() #.item() returns average loss over the batch \n",
    "                del loss\n",
    "                _, predicted = torch.max(F.softmax(outputs,dim=1).data, dim=1) # Adding softmax since there is none in the model, and taking the index of the highest probability.\n",
    "                running_correct += float(torch.sum(predicted == labels.data))\n",
    "    epoch_loss = running_loss / tot_iter\n",
    "    epoch_acc = running_correct / (tot_iter*batch_size) \n",
    "\n",
    "    return epoch_loss,epoch_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "LGxbzq2J8f26"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH 1:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.564, valid 0.504\n",
      "ACCURACY train 0.753, valid 0.753\n",
      "EPOCH 2:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.87it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.494, valid 0.487\n",
      "ACCURACY train 0.768, valid 0.762\n",
      "EPOCH 3:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.87it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.487, valid 0.484\n",
      "ACCURACY train 0.771, valid 0.764\n",
      "EPOCH 4:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.484, valid 0.483\n",
      "ACCURACY train 0.774, valid 0.760\n",
      "EPOCH 5:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.483, valid 0.482\n",
      "ACCURACY train 0.774, valid 0.764\n",
      "EPOCH 6:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:08<00:00,  2.83it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.483, valid 0.482\n",
      "ACCURACY train 0.774, valid 0.763\n",
      "EPOCH 7:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.84it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.482, valid 0.489\n",
      "ACCURACY train 0.775, valid 0.759\n",
      "EPOCH 8:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.482, valid 0.479\n",
      "ACCURACY train 0.774, valid 0.764\n",
      "EPOCH 9:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:04<00:00,  2.88it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.482, valid 0.478\n",
      "ACCURACY train 0.775, valid 0.765\n",
      "EPOCH 10:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.482, valid 0.479\n",
      "ACCURACY train 0.774, valid 0.766\n",
      "EPOCH 11:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.87it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.481, valid 0.481\n",
      "ACCURACY train 0.775, valid 0.765\n",
      "EPOCH 12:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:09<00:00,  2.83it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.481, valid 0.480\n",
      "ACCURACY train 0.775, valid 0.765\n",
      "EPOCH 13:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:08<00:00,  2.83it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.481, valid 0.480\n",
      "ACCURACY train 0.775, valid 0.765\n",
      "EPOCH 14:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.481, valid 0.478\n",
      "ACCURACY train 0.776, valid 0.764\n",
      "EPOCH 15:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.481, valid 0.480\n",
      "ACCURACY train 0.775, valid 0.766\n",
      "EPOCH 16:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.481, valid 0.480\n",
      "ACCURACY train 0.776, valid 0.766\n",
      "EPOCH 17:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.480\n",
      "ACCURACY train 0.776, valid 0.767\n",
      "EPOCH 18:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.84it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.479\n",
      "ACCURACY train 0.775, valid 0.766\n",
      "EPOCH 19:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.479\n",
      "ACCURACY train 0.777, valid 0.767\n",
      "EPOCH 20:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.479\n",
      "ACCURACY train 0.776, valid 0.767\n",
      "EPOCH 21:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:08<00:00,  2.84it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.482\n",
      "ACCURACY train 0.776, valid 0.763\n",
      "EPOCH 22:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.84it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.481\n",
      "ACCURACY train 0.776, valid 0.763\n",
      "EPOCH 23:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.87it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.478\n",
      "ACCURACY train 0.776, valid 0.766\n",
      "EPOCH 24:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.481\n",
      "ACCURACY train 0.776, valid 0.768\n",
      "EPOCH 25:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.479\n",
      "ACCURACY train 0.775, valid 0.766\n",
      "EPOCH 26:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.482\n",
      "ACCURACY train 0.776, valid 0.766\n",
      "EPOCH 27:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.84it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.483\n",
      "ACCURACY train 0.777, valid 0.763\n",
      "EPOCH 28:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.87it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.477\n",
      "ACCURACY train 0.776, valid 0.767\n",
      "EPOCH 29:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.480\n",
      "ACCURACY train 0.776, valid 0.768\n",
      "EPOCH 30:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.479\n",
      "ACCURACY train 0.776, valid 0.765\n",
      "EPOCH 31:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.480, valid 0.481\n",
      "ACCURACY train 0.776, valid 0.767\n",
      "EPOCH 32:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.479\n",
      "ACCURACY train 0.776, valid 0.768\n",
      "EPOCH 33:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.479\n",
      "ACCURACY train 0.776, valid 0.768\n",
      "EPOCH 34:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:09<00:00,  2.83it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.480\n",
      "ACCURACY train 0.776, valid 0.768\n",
      "EPOCH 35:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.480\n",
      "ACCURACY train 0.776, valid 0.767\n",
      "EPOCH 36:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.481\n",
      "ACCURACY train 0.776, valid 0.764\n",
      "EPOCH 37:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.87it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.479\n",
      "ACCURACY train 0.776, valid 0.767\n",
      "EPOCH 38:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.480\n",
      "ACCURACY train 0.777, valid 0.764\n",
      "EPOCH 39:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.482\n",
      "ACCURACY train 0.777, valid 0.765\n",
      "EPOCH 40:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.478\n",
      "ACCURACY train 0.777, valid 0.768\n",
      "EPOCH 41:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.479\n",
      "ACCURACY train 0.777, valid 0.768\n",
      "EPOCH 42:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:08<00:00,  2.84it/s]\n",
      "100%|██████████| 79/79 [00:17<00:00,  4.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.478\n",
      "ACCURACY train 0.776, valid 0.766\n",
      "EPOCH 43:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.477\n",
      "ACCURACY train 0.777, valid 0.767\n",
      "EPOCH 44:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:07<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.479, valid 0.477\n",
      "ACCURACY train 0.776, valid 0.767\n",
      "EPOCH 45:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.480\n",
      "ACCURACY train 0.777, valid 0.766\n",
      "EPOCH 46:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:03<00:00,  2.89it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.480\n",
      "ACCURACY train 0.777, valid 0.767\n",
      "EPOCH 47:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:17<00:00,  4.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.481\n",
      "ACCURACY train 0.777, valid 0.768\n",
      "EPOCH 48:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.483\n",
      "ACCURACY train 0.777, valid 0.763\n",
      "EPOCH 49:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.480\n",
      "ACCURACY train 0.777, valid 0.764\n",
      "EPOCH 50:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:08<00:00,  2.84it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.477\n",
      "ACCURACY train 0.778, valid 0.766\n",
      "EPOCH 51:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:03<00:00,  2.89it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.481\n",
      "ACCURACY train 0.777, valid 0.766\n",
      "EPOCH 52:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:17<00:00,  4.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.479\n",
      "ACCURACY train 0.777, valid 0.764\n",
      "EPOCH 53:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.477\n",
      "ACCURACY train 0.777, valid 0.766\n",
      "EPOCH 54:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.86it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.480\n",
      "ACCURACY train 0.777, valid 0.768\n",
      "EPOCH 55:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:04<00:00,  2.88it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.480\n",
      "ACCURACY train 0.777, valid 0.767\n",
      "EPOCH 56:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:04<00:00,  2.87it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.480\n",
      "ACCURACY train 0.778, valid 0.766\n",
      "EPOCH 57:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:06<00:00,  2.85it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.479\n",
      "ACCURACY train 0.777, valid 0.768\n",
      "EPOCH 58:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:04<00:00,  2.88it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.481\n",
      "ACCURACY train 0.777, valid 0.766\n",
      "EPOCH 59:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:04<00:00,  2.88it/s]\n",
      "100%|██████████| 79/79 [00:16<00:00,  4.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.477, valid 0.477\n",
      "ACCURACY train 0.777, valid 0.768\n",
      "EPOCH 60:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 704/704 [04:05<00:00,  2.87it/s]\n",
      "100%|██████████| 79/79 [00:15<00:00,  4.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS train 0.478, valid 0.479\n",
      "ACCURACY train 0.777, valid 0.763\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 60\n",
    "\n",
    "loss_train, loss_val = [],[]\n",
    "acc_train, acc_val = [],[]\n",
    "for epoch in range(EPOCHS):\n",
    "    print('EPOCH {}:'.format(epoch + 1))\n",
    "\n",
    "    loss_train_epoch,acc_train_epoch = train_test_one_epoch(gnn,jets_loader_train,mode = 'train')\n",
    "    loss_val_epoch,acc_val_epoch = train_test_one_epoch(gnn,jets_loader_val,mode = 'test')\n",
    "\n",
    "    print('LOSS train {:.3f}, valid {:.3f}'.format(loss_train_epoch, loss_val_epoch))\n",
    "    print('ACCURACY train {:.3f}, valid {:.3f}'.format(acc_train_epoch, acc_val_epoch))\n",
    "\n",
    "    loss_train.append(loss_train_epoch)\n",
    "    loss_val.append(loss_val_epoch)\n",
    "    acc_train.append(acc_train_epoch)\n",
    "    acc_val.append(acc_val_epoch)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dXS4sgGJYGZu"
   },
   "source": [
    "Let's plot evaluation of training and validation loss and accuracy with epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "zJama0QcYGA-"
   },
   "outputs": [],
   "source": [
    "#First we define a helper function\n",
    "def draw_metric_evolution():\n",
    "  epoch_number = list(range(EPOCHS))\n",
    "  fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(16,6), tight_layout=True)\n",
    "  axes.flat[0].plot(epoch_number, loss_train,label='Training ')\n",
    "  axes.flat[0].plot(epoch_number, loss_val,label='Validation ')\n",
    "  axes.flat[0].set_ylabel(\"Loss\",fontsize=15)\n",
    "  axes.flat[0].legend(loc='upper right',fontsize=15,frameon=False)\n",
    "\n",
    "  axes.flat[1].plot(epoch_number, acc_train,label='Training ')\n",
    "  axes.flat[1].plot(epoch_number, acc_val,label='Validation ')\n",
    "  axes.flat[1].set_ylabel(\"Accuracy\",fontsize=15)\n",
    "  axes.flat[1].legend(loc='upper left',fontsize=15,frameon=False)\n",
    "\n",
    "  for i in [0,1]:\n",
    "    axes.flat[i].set_xlabel(\"Epoch\",fontsize=15)\n",
    "    axes.flat[i].grid(True)\n",
    "\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "sQ9DcwFNZr0f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_metric_evolution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_nQ4oI7VSPZU"
   },
   "source": [
    "\n",
    "\n",
    "Now let's evaluate how our model performs on the validation dataset by calculating accuracy for each jet type and building the ROC curve.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "s1r9A8JG8f6a"
   },
   "outputs": [],
   "source": [
    "pred_val = []\n",
    "pred_label = []\n",
    "true_label = []\n",
    "for i, data in enumerate(jets_loader_val):\n",
    "    inputs, labels = data, data.y\n",
    "    inputs = inputs.to(device)\n",
    "    # there is no softmax in the output layer. We have to put it. \n",
    "    pred_val.append(F.softmax(gnn(inputs.to(device)), dim=1).cpu().detach().numpy())\n",
    "    pred_label.append(np.argmax(pred_val[i],axis=1))\n",
    "    true_label.append(labels.numpy())\n",
    "pred_val = np.concatenate(pred_val, axis=0)\n",
    "pred_label = np.concatenate(pred_label, axis=0)\n",
    "true_label = np.concatenate(true_label,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "VEjiIFDrDo2F"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc, accuracy_score\n",
    "\n",
    "#### get the ROC curves\n",
    "fpr = {}\n",
    "tpr = {}\n",
    "auc_value = {}\n",
    "plt.figure(figsize=(12,8))\n",
    "for i, label in enumerate(label_names):\n",
    "    fpr[label], tpr[label], threshold = roc_curve((true_label== i), pred_val[:,i])\n",
    "    auc_value[label] = auc(fpr[label], tpr[label])\n",
    "    plt.plot(tpr[label],fpr[label],label='%s tagger, AUC = %.1f%%'%(label,auc_value[label]*100.))\n",
    "#computing accuracy now\n",
    "acc_score = accuracy_score(true_label, pred_label)\n",
    "acc_manual = np.sum(pred_label == true_label)\n",
    "plt.title('Accuracy : {:.3f}'.format(acc_score), fontsize=15)\n",
    "plt.semilogy()\n",
    "plt.xlabel(\"sig. efficiency\",fontsize=15)\n",
    "plt.ylabel(\"bkg. mistag rate\",fontsize=15)\n",
    "plt.ylim(0.001,1)\n",
    "plt.grid(True)\n",
    "plt.legend(loc='lower right',fontsize=15, frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SIHrki24VhTp"
   },
   "source": [
    "Now let's evaluate confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "id": "2_Y-hMwTThVH"
   },
   "outputs": [],
   "source": [
    "# First we define a helper function\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "def plot_matrix(cm, classes, title):\n",
    "  plt.figure(figsize = (12,12))  \n",
    "  ax = sns.heatmap(cm, cmap=\"Blues\", annot=True, xticklabels=classes, yticklabels=classes, cbar=False)\n",
    "  ax.set(title=title, xlabel=\"Predicted label\", ylabel=\"True label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "rd8QaxnNT_4R"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Evaluate confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(true_label, pred_label,normalize = 'true')\n",
    "plot_matrix(cm, label_names, title = 'Normalized Confusion Matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gF1ksFJ9XC2Q"
   },
   "source": [
    "We can see that overall performance is not bad!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZUiLf8zBQ0Pz"
   },
   "source": [
    "## LorentzNet\n",
    "\n",
    "Now, let's look at the performance of LorentzNet on our dataset. Before, I projected only the first three features to the point-cloud, as a simpler test, but given the pt, rapidity, azimuthal_angle and the particle id, we can retrieve a more complete information, like its four-momentum, and also associated scalars, like its mass and charge, or particle id.\n",
    "\n",
    "Here, each jet is denoted as a graph consisting of an unordered set of nodes (particles), where each node is considered a point-cloud living in $\\mathbb{R}^{4}$. Each jet can have different number of constituent particles, and the LorentzNet is built upon the universal approximation theorem for Lorentz-equivariant functions. This is the sketched out architecture:\n",
    "\n",
    "<img src=\"figures/LorentzNet.png\" width=65% style=\"margin-left:auto; margin-right:auto\">\n",
    "\n",
    "The **input layer** consists of four-momentum vectors of particles from a collision event, and the input is a set of vectors $f_i = v_i \\mathop{\\bigoplus} s_i$, where $f_i = \\{E_i, p_{x_i}, p_{y_i}, p_{z_i}\\}$ is the four-momentum vector for particle $i$, and $s_i$ is a set of scalars for particle $i$, like mass, charge, particle id. \n",
    "\n",
    "Now, the **Lorentz Group Equivariant Block** (LGEB) constitutes of $\\phi_{e}(x), \\phi_{h}(x), \\phi_{x}(x)$, which are continuous functions modeled by neural networks, where $\\phi_{e}(x)$ is responsible for computing the edge message between particles $i$ and $j$ for the $l$-th layer via:\n",
    "\n",
    "\\begin{equation}\n",
    "m_{ij}^{l} = \\phi_{e}(h_i, h_j, \\psi(||x_{i}^{l} - x_{j}^{l}||^2), \\psi(\\langle x_{i}, x_{j}\\rangle)),\n",
    "\\end{equation}\n",
    "\n",
    "and $\\psi(\\cdot) = sgn(\\cdot)log(|\\cdot| + 1)$ is just a function to normalize large numbers from different distributions, making optimization easier. Now, $\\phi_{x}(x)$ is the responsible for the Minkowski dot product attention, which is proposed in the paper as:\n",
    "\n",
    "\\begin{equation}\n",
    "x_{i}^{l+1} = x_{i}^{l} + c\\sum_{j\\in\\mathcal{N}(i)}\\phi_{x}(m_{ij}^{j})\\cdot x_{j}^{l},\n",
    "\\end{equation}\n",
    "\n",
    "so the feature for each particle in the next layer is the feature for the current layer and a weighted feature sum of neighboring particles \\mathcal{N}(i), where the weights depend on each edge message calculated before and $c$, which is just a scalar to control the scale again. Finally, $\\phi_{h}(x)$ updates the scalar features for particle $i$ as:\n",
    "\n",
    "\\begin{equation}\n",
    "h_{i}^{l+1} = h_{i}^{l} + \\phi_{h}(h_{i}^{l}, \\sum_{j\\in\\mathcal{N}(i)}w_{ij}m_{ij}),\n",
    "\\end{equation}\n",
    "\n",
    "where $w_{ij}$ learns the edge significance between particle $i$ and $j$ through again another neural network $\\phi_{m}(m_{ij}^{l}) \\in [0, 1]$ . It's important to note that the initial scalar features $h_{i}^{0}$ are embedded into deeper representations through the layers, and since the **edge message $m_{ij}$** contains information about **both** **$x_{i}$** and **$x_{j}$**, in the very end, if you look into the diagram, you'll see that only **$h^{l}$** is returned, a clever trick to avoid redundancy.\n",
    "\n",
    "\n",
    "Lastly, given the transversal momentum **$p_{T}$**, pseudo-rapidity **$\\eta$**, and azimuthal angle **$\\phi$** and the particle id, it is possible to retrieve the 4-momenta for each particle. The energyflow package facilitates this, so, in the code, this is the first thing done to retrieve our dataset.\n",
    "\n",
    "\n",
    "I am going to use the official implementation from the Author's GitHub here: https://github.com/sdogsq/LorentzNet-release/tree/maine/tree/main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "kdil7P-NaMfZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32]) torch.Size([32, 139, 4]) torch.Size([32, 139, 8]) torch.Size([32, 139]) torch.Size([32, 139, 139]) torch.Size([64608]) torch.Size([64608])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import energyflow\n",
    "from scipy.sparse import coo_matrix\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from torch.utils.data.distributed import DistributedSampler\n",
    "\n",
    "def get_adj_matrix(n_nodes, batch_size, edge_mask):\n",
    "    rows, cols = [], []\n",
    "    for batch_idx in range(batch_size):\n",
    "        nn = batch_idx*n_nodes\n",
    "        x = coo_matrix(edge_mask[batch_idx])\n",
    "        rows.append(nn + x.row)\n",
    "        cols.append(nn + x.col)\n",
    "    rows = np.concatenate(rows)\n",
    "    cols = np.concatenate(cols)\n",
    "\n",
    "    edges = [torch.LongTensor(rows), torch.LongTensor(cols)]\n",
    "    return edges\n",
    "\n",
    "def collate_fn(data):\n",
    "    data = list(zip(*data)) # label p4s nodes atom_mask\n",
    "    data = [torch.stack(item) for item in data]\n",
    "    batch_size, n_nodes, _ = data[1].size()\n",
    "    atom_mask = data[-1]\n",
    "    edge_mask = atom_mask.unsqueeze(1) * atom_mask.unsqueeze(2)\n",
    "    diag_mask = ~torch.eye(edge_mask.size(1), dtype=torch.bool).unsqueeze(0)\n",
    "    edge_mask *= diag_mask\n",
    "    edges = get_adj_matrix(n_nodes, batch_size, edge_mask)\n",
    "    return data + [edge_mask, edges]\n",
    "\n",
    "def retrieve_dataloaders(batch_size, num_data = -1, use_one_hot = True, cache_dir = './data', num_workers=4):\n",
    "    raw = energyflow.qg_jets.load(num_data=num_data, pad=True, ncol=4, generator='pythia',\n",
    "                            with_bc=False, cache_dir=cache_dir)\n",
    "    splits = ['train', 'val', 'test']\n",
    "    data = {type:{'raw':None,'label':None} for type in splits}\n",
    "    (data['train']['raw'],  data['val']['raw'],   data['test']['raw'],\n",
    "    data['train']['label'], data['val']['label'], data['test']['label']) = \\\n",
    "        energyflow.utils.data_split(*raw, train=0.8, val=0.1, test=0.1, shuffle = False)\n",
    "\n",
    "    enc = OneHotEncoder(handle_unknown='ignore').fit([[11],[13],[22],[130],[211],[321],[2112],[2212]])\n",
    "    \n",
    "    for split, value in data.items():\n",
    "        pid = torch.from_numpy(np.abs(np.asarray(value['raw'][...,3], dtype=int))).unsqueeze(-1)\n",
    "        p4s = torch.from_numpy(energyflow.p4s_from_ptyphipids(value['raw'],error_on_unknown=True))\n",
    "        one_hot = enc.transform(pid.reshape(-1,1)).toarray().reshape(pid.shape[:2]+(-1,))\n",
    "        one_hot = torch.from_numpy(one_hot)\n",
    "        mass = torch.from_numpy(energyflow.ms_from_p4s(p4s)).unsqueeze(-1)\n",
    "        charge = torch.from_numpy(energyflow.pids2chrgs(pid))\n",
    "        if use_one_hot:\n",
    "            nodes = one_hot\n",
    "        else:\n",
    "            nodes = torch.cat((mass,charge),dim=-1)\n",
    "            nodes = torch.sign(nodes) * torch.log(torch.abs(nodes) + 1)\n",
    "        atom_mask = (pid[...,0] != 0)\n",
    "        value['p4s'] = p4s\n",
    "        value['nodes'] = nodes\n",
    "        value['label'] = torch.from_numpy(value['label'])\n",
    "        value['atom_mask'] = atom_mask.to(torch.bool)\n",
    "\n",
    "    datasets = {split: TensorDataset(value['label'], value['p4s'],\n",
    "                                     value['nodes'], value['atom_mask'])\n",
    "                for split, value in data.items()}\n",
    "\n",
    "    # distributed training\n",
    "    # train_sampler = DistributedSampler(datasets['train'], shuffle=True)\n",
    "    # Construct PyTorch dataloaders from datasets\n",
    "    dataloaders = {split: DataLoader(dataset,\n",
    "                                     batch_size=batch_size,\n",
    "                                     # sampler=train_sampler if (split == 'train') else DistributedSampler(dataset, shuffle=False),\n",
    "                                     pin_memory=True,\n",
    "                                     # persistent_workers=True,\n",
    "                                     drop_last=True if (split == 'train') else False,\n",
    "                                     num_workers=num_workers,\n",
    "                                     collate_fn=collate_fn)\n",
    "                        for split, dataset in datasets.items()}\n",
    "\n",
    "    return dataloaders #train_sampler, dataloaders\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # train_sampler, dataloaders = retrieve_dataloaders(32, 100)\n",
    "    dataloaders = retrieve_dataloaders(32, 100)\n",
    "    for (label, p4s, nodes, atom_mask, edge_mask, edges) in dataloaders['train']:\n",
    "        print(label.shape, p4s.shape, nodes.shape, atom_mask.shape,\n",
    "              edge_mask.shape, edges[0].shape, edges[1].shape)\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's now define our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import numpy as np\n",
    "\n",
    "class LGEB(nn.Module):\n",
    "    def __init__(self, n_input, n_output, n_hidden, n_node_attr=0,\n",
    "                 dropout = 0., c_weight=1.0, last_layer=False, A=None, include_x=False):\n",
    "        super(LGEB, self).__init__()\n",
    "        self.c_weight = c_weight\n",
    "        n_edge_attr = 2 if not include_x else 10 # dims for Minkowski norm & inner product\n",
    "\n",
    "        self.include_x = include_x\n",
    "        self.phi_e = nn.Sequential(\n",
    "            nn.Linear(n_input * 2 + n_edge_attr, n_hidden, bias=False),\n",
    "            nn.BatchNorm1d(n_hidden),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(n_hidden, n_hidden),\n",
    "            nn.ReLU())\n",
    "\n",
    "        self.phi_h = nn.Sequential(\n",
    "            nn.Linear(n_hidden + n_input + n_node_attr, n_hidden),\n",
    "            nn.BatchNorm1d(n_hidden),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(n_hidden, n_output))\n",
    "\n",
    "        layer = nn.Linear(n_hidden, 1, bias=False)\n",
    "        torch.nn.init.xavier_uniform_(layer.weight, gain=0.001)\n",
    "\n",
    "        self.phi_x = nn.Sequential(\n",
    "            nn.Linear(n_hidden, n_hidden),\n",
    "            nn.ReLU(),\n",
    "            layer)\n",
    "\n",
    "        self.phi_m = nn.Sequential(\n",
    "            nn.Linear(n_hidden, 1),\n",
    "            nn.Sigmoid())\n",
    "        \n",
    "        self.last_layer = last_layer\n",
    "        if last_layer:\n",
    "            del self.phi_x\n",
    "\n",
    "        self.A = A\n",
    "        self.norm_fn = normA_fn(A) if A is not None else normsq4\n",
    "        self.dot_fn = dotA_fn(A) if A is not None else dotsq4\n",
    "\n",
    "    def m_model(self, hi, hj, norms, dots):\n",
    "        out = torch.cat([hi, hj, norms, dots], dim=1)\n",
    "        out = self.phi_e(out)\n",
    "        w = self.phi_m(out)\n",
    "        out = out * w\n",
    "        return out\n",
    "\n",
    "    def m_model_extended(self, hi, hj, norms, dots, xi, xj):\n",
    "        out = torch.cat([hi, hj, norms, dots, xi, xj], dim=1)\n",
    "        out = self.phi_e(out)\n",
    "        w = self.phi_m(out)\n",
    "        out = out * w\n",
    "        return out\n",
    "\n",
    "    def h_model(self, h, edges, m, node_attr):\n",
    "        i, j = edges\n",
    "        agg = unsorted_segment_sum(m, i, num_segments=h.size(0))\n",
    "        agg = torch.cat([h, agg, node_attr], dim=1)\n",
    "        out = h + self.phi_h(agg)\n",
    "        return out\n",
    "\n",
    "    def x_model(self, x, edges, x_diff, m):\n",
    "        i, j = edges\n",
    "        trans = x_diff * self.phi_x(m)\n",
    "        # From https://github.com/vgsatorras/egnn\n",
    "        # This is never activated but just in case it explosed it may save the train\n",
    "        trans = torch.clamp(trans, min=-100, max=100)\n",
    "        agg = unsorted_segment_mean(trans, i, num_segments=x.size(0))\n",
    "        x = x + agg * self.c_weight\n",
    "        return x\n",
    "\n",
    "    def minkowski_feats(self, edges, x):\n",
    "        i, j = edges\n",
    "        x_diff = x[i] - x[j]\n",
    "        norms = self.norm_fn(x_diff).unsqueeze(1)\n",
    "        dots = self.dot_fn(x[i], x[j]).unsqueeze(1)\n",
    "        norms, dots = psi(norms), psi(dots)\n",
    "        return norms, dots, x_diff\n",
    "\n",
    "    def forward(self, h, x, edges, node_attr=None):\n",
    "        i, j = edges\n",
    "        norms, dots, x_diff = self.minkowski_feats(edges, x)\n",
    "\n",
    "        if self.include_x:\n",
    "            m = self.m_model_extended(h[i], h[j], norms, dots, x[i], x[j])\n",
    "        else:\n",
    "            m = self.m_model(h[i], h[j], norms, dots) # [B*N, hidden]\n",
    "        if not self.last_layer:\n",
    "            x = self.x_model(x, edges, x_diff, m)\n",
    "        h = self.h_model(h, edges, m, node_attr)\n",
    "        return h, x, m\n",
    "\n",
    "class LorentzNet(nn.Module):\n",
    "    r''' Implementation of LorentzNet.\n",
    "\n",
    "    Args:\n",
    "        - `n_scalar` (int): number of input scalars.\n",
    "        - `n_hidden` (int): dimension of latent space.\n",
    "        - `n_class`  (int): number of output classes.\n",
    "        - `n_layers` (int): number of LGEB layers.\n",
    "        - `c_weight` (float): weight c in the x_model.\n",
    "        - `dropout`  (float): dropout rate.\n",
    "    '''\n",
    "    def __init__(self, n_scalar, n_hidden, n_class = 2, n_layers = 6, c_weight = 1e-3, dropout = 0., A=None, include_x=False):\n",
    "        super(LorentzNet, self).__init__()\n",
    "        self.n_hidden = n_hidden\n",
    "        self.n_layers = n_layers\n",
    "        self.embedding = nn.Linear(n_scalar, n_hidden)\n",
    "        self.LGEBs = nn.ModuleList([LGEB(self.n_hidden, self.n_hidden, self.n_hidden, \n",
    "                                    n_node_attr=n_scalar, dropout=dropout,\n",
    "                                    c_weight=c_weight, last_layer=(i==n_layers-1), A=A, include_x=include_x)\n",
    "                                    for i in range(n_layers)])\n",
    "        self.graph_dec = nn.Sequential(nn.Linear(self.n_hidden, self.n_hidden),\n",
    "                                       nn.ReLU(),\n",
    "                                       nn.Dropout(dropout),\n",
    "                                       nn.Linear(self.n_hidden, n_class)) # classification\n",
    "\n",
    "    def forward(self, scalars, x, edges, node_mask, edge_mask, n_nodes):\n",
    "        h = self.embedding(scalars)\n",
    "\n",
    "        for i in range(self.n_layers):\n",
    "            # print(i)\n",
    "            h, x, _ = self.LGEBs[i](h, x, edges, node_attr=scalars)\n",
    "\n",
    "        h = h * node_mask\n",
    "        h = h.view(-1, n_nodes, self.n_hidden)\n",
    "        h = torch.mean(h, dim=1)\n",
    "        pred = self.graph_dec(h)\n",
    "        return pred.squeeze(1)\n",
    "\n",
    "\n",
    "def unsorted_segment_sum(data, segment_ids, num_segments):\n",
    "    r'''Custom PyTorch op to replicate TensorFlow's `unsorted_segment_sum`.\n",
    "    Adapted from https://github.com/vgsatorras/egnn.\n",
    "    '''\n",
    "    result = data.new_zeros((num_segments, data.size(1)))\n",
    "    result.index_add_(0, segment_ids, data)\n",
    "    return result\n",
    "\n",
    "def unsorted_segment_mean(data, segment_ids, num_segments):\n",
    "    r'''Custom PyTorch op to replicate TensorFlow's `unsorted_segment_mean`.\n",
    "    Adapted from https://github.com/vgsatorras/egnn.\n",
    "    '''\n",
    "    result = data.new_zeros((num_segments, data.size(1)))\n",
    "    count = data.new_zeros((num_segments, data.size(1)))\n",
    "    result.index_add_(0, segment_ids, data)\n",
    "    count.index_add_(0, segment_ids, torch.ones_like(data))\n",
    "    return result / count.clamp(min=1)\n",
    "\n",
    "def normsq4(p):\n",
    "    r''' Minkowski square norm\n",
    "         `\\|p\\|^2 = p[0]^2-p[1]^2-p[2]^2-p[3]^2`\n",
    "    ''' \n",
    "    psq = torch.pow(p, 2)\n",
    "    return 2 * psq[..., 0] - psq.sum(dim=-1)\n",
    "    \n",
    "def dotsq4(p,q):\n",
    "    r''' Minkowski inner product\n",
    "         `<p,q> = p[0]q[0]-p[1]q[1]-p[2]q[2]-p[3]q[3]`\n",
    "    '''\n",
    "    psq = p*q\n",
    "    return 2 * psq[..., 0] - psq.sum(dim=-1)\n",
    "\n",
    "def normA_fn(A):\n",
    "    return lambda p: torch.einsum('...i, ij, ...j->...', p, A, p)\n",
    "\n",
    "def dotA_fn(A):\n",
    "    return lambda p, q: torch.einsum('...i, ij, ...j->...', p, A, q)\n",
    "    \n",
    "def psi(p):\n",
    "    ''' `\\psi(p) = Sgn(p) \\cdot \\log(|p| + 1)`\n",
    "    '''\n",
    "    return torch.sign(p) * torch.log(torch.abs(p) + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "     Layer.Parameter         Param Tensor Shape         Param #\n",
      "----------------------------------------------------------------\n",
      "    embedding.weight                    [72, 8]             576\n",
      "      embedding.bias                       [72]              72\n",
      "LGEBs.0.phi_e.0.weight                  [72, 146]           10512\n",
      "LGEBs.0.phi_e.1.weight                       [72]              72\n",
      "LGEBs.0.phi_e.1.bias                       [72]              72\n",
      "LGEBs.0.phi_e.3.weight                   [72, 72]            5184\n",
      "LGEBs.0.phi_e.3.bias                       [72]              72\n",
      "LGEBs.0.phi_h.0.weight                  [72, 152]           10944\n",
      "LGEBs.0.phi_h.0.bias                       [72]              72\n",
      "LGEBs.0.phi_h.1.weight                       [72]              72\n",
      "LGEBs.0.phi_h.1.bias                       [72]              72\n",
      "LGEBs.0.phi_h.3.weight                   [72, 72]            5184\n",
      "LGEBs.0.phi_h.3.bias                       [72]              72\n",
      "LGEBs.0.phi_x.0.weight                   [72, 72]            5184\n",
      "LGEBs.0.phi_x.0.bias                       [72]              72\n",
      "LGEBs.0.phi_x.2.weight                    [1, 72]              72\n",
      "LGEBs.0.phi_m.0.weight                    [1, 72]              72\n",
      "LGEBs.0.phi_m.0.bias                        [1]               1\n",
      "LGEBs.1.phi_e.0.weight                  [72, 146]           10512\n",
      "LGEBs.1.phi_e.1.weight                       [72]              72\n",
      "LGEBs.1.phi_e.1.bias                       [72]              72\n",
      "LGEBs.1.phi_e.3.weight                   [72, 72]            5184\n",
      "LGEBs.1.phi_e.3.bias                       [72]              72\n",
      "LGEBs.1.phi_h.0.weight                  [72, 152]           10944\n",
      "LGEBs.1.phi_h.0.bias                       [72]              72\n",
      "LGEBs.1.phi_h.1.weight                       [72]              72\n",
      "LGEBs.1.phi_h.1.bias                       [72]              72\n",
      "LGEBs.1.phi_h.3.weight                   [72, 72]            5184\n",
      "LGEBs.1.phi_h.3.bias                       [72]              72\n",
      "LGEBs.1.phi_x.0.weight                   [72, 72]            5184\n",
      "LGEBs.1.phi_x.0.bias                       [72]              72\n",
      "LGEBs.1.phi_x.2.weight                    [1, 72]              72\n",
      "LGEBs.1.phi_m.0.weight                    [1, 72]              72\n",
      "LGEBs.1.phi_m.0.bias                        [1]               1\n",
      "LGEBs.2.phi_e.0.weight                  [72, 146]           10512\n",
      "LGEBs.2.phi_e.1.weight                       [72]              72\n",
      "LGEBs.2.phi_e.1.bias                       [72]              72\n",
      "LGEBs.2.phi_e.3.weight                   [72, 72]            5184\n",
      "LGEBs.2.phi_e.3.bias                       [72]              72\n",
      "LGEBs.2.phi_h.0.weight                  [72, 152]           10944\n",
      "LGEBs.2.phi_h.0.bias                       [72]              72\n",
      "LGEBs.2.phi_h.1.weight                       [72]              72\n",
      "LGEBs.2.phi_h.1.bias                       [72]              72\n",
      "LGEBs.2.phi_h.3.weight                   [72, 72]            5184\n",
      "LGEBs.2.phi_h.3.bias                       [72]              72\n",
      "LGEBs.2.phi_x.0.weight                   [72, 72]            5184\n",
      "LGEBs.2.phi_x.0.bias                       [72]              72\n",
      "LGEBs.2.phi_x.2.weight                    [1, 72]              72\n",
      "LGEBs.2.phi_m.0.weight                    [1, 72]              72\n",
      "LGEBs.2.phi_m.0.bias                        [1]               1\n",
      "LGEBs.3.phi_e.0.weight                  [72, 146]           10512\n",
      "LGEBs.3.phi_e.1.weight                       [72]              72\n",
      "LGEBs.3.phi_e.1.bias                       [72]              72\n",
      "LGEBs.3.phi_e.3.weight                   [72, 72]            5184\n",
      "LGEBs.3.phi_e.3.bias                       [72]              72\n",
      "LGEBs.3.phi_h.0.weight                  [72, 152]           10944\n",
      "LGEBs.3.phi_h.0.bias                       [72]              72\n",
      "LGEBs.3.phi_h.1.weight                       [72]              72\n",
      "LGEBs.3.phi_h.1.bias                       [72]              72\n",
      "LGEBs.3.phi_h.3.weight                   [72, 72]            5184\n",
      "LGEBs.3.phi_h.3.bias                       [72]              72\n",
      "LGEBs.3.phi_x.0.weight                   [72, 72]            5184\n",
      "LGEBs.3.phi_x.0.bias                       [72]              72\n",
      "LGEBs.3.phi_x.2.weight                    [1, 72]              72\n",
      "LGEBs.3.phi_m.0.weight                    [1, 72]              72\n",
      "LGEBs.3.phi_m.0.bias                        [1]               1\n",
      "LGEBs.4.phi_e.0.weight                  [72, 146]           10512\n",
      "LGEBs.4.phi_e.1.weight                       [72]              72\n",
      "LGEBs.4.phi_e.1.bias                       [72]              72\n",
      "LGEBs.4.phi_e.3.weight                   [72, 72]            5184\n",
      "LGEBs.4.phi_e.3.bias                       [72]              72\n",
      "LGEBs.4.phi_h.0.weight                  [72, 152]           10944\n",
      "LGEBs.4.phi_h.0.bias                       [72]              72\n",
      "LGEBs.4.phi_h.1.weight                       [72]              72\n",
      "LGEBs.4.phi_h.1.bias                       [72]              72\n",
      "LGEBs.4.phi_h.3.weight                   [72, 72]            5184\n",
      "LGEBs.4.phi_h.3.bias                       [72]              72\n",
      "LGEBs.4.phi_x.0.weight                   [72, 72]            5184\n",
      "LGEBs.4.phi_x.0.bias                       [72]              72\n",
      "LGEBs.4.phi_x.2.weight                    [1, 72]              72\n",
      "LGEBs.4.phi_m.0.weight                    [1, 72]              72\n",
      "LGEBs.4.phi_m.0.bias                        [1]               1\n",
      "LGEBs.5.phi_e.0.weight                  [72, 146]           10512\n",
      "LGEBs.5.phi_e.1.weight                       [72]              72\n",
      "LGEBs.5.phi_e.1.bias                       [72]              72\n",
      "LGEBs.5.phi_e.3.weight                   [72, 72]            5184\n",
      "LGEBs.5.phi_e.3.bias                       [72]              72\n",
      "LGEBs.5.phi_h.0.weight                  [72, 152]           10944\n",
      "LGEBs.5.phi_h.0.bias                       [72]              72\n",
      "LGEBs.5.phi_h.1.weight                       [72]              72\n",
      "LGEBs.5.phi_h.1.bias                       [72]              72\n",
      "LGEBs.5.phi_h.3.weight                   [72, 72]            5184\n",
      "LGEBs.5.phi_h.3.bias                       [72]              72\n",
      "LGEBs.5.phi_m.0.weight                    [1, 72]              72\n",
      "LGEBs.5.phi_m.0.bias                        [1]               1\n",
      "  graph_dec.0.weight                   [72, 72]            5184\n",
      "    graph_dec.0.bias                       [72]              72\n",
      "  graph_dec.3.weight                    [2, 72]             144\n",
      "    graph_dec.3.bias                        [2]               2\n",
      "----------------------------------------------------------------\n",
      "Total params: 227096\n",
      "Trainable params: 227096\n",
      "Non-trainable params: 0\n"
     ]
    }
   ],
   "source": [
    "model_summary(LorentzNet(n_scalar = 8, n_hidden = 72, n_class = 2,\\\n",
    "                       dropout = 0.2, n_layers = 6,\\\n",
    "                       c_weight = 1e-3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ParticleNet had 409666 total params...\n",
    "\n",
    "...And now we have a model half the size of the first. Of course, the features used will be different now, since we have a specific inductive bias, but let's see what happens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## and our training function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jogi/anaconda3/envs/ml4sci/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n",
      "  warnings.warn(\"The verbose parameter is deprecated. Please use get_last_lr() \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Size: 227096\n",
      " train samples: 80000\n",
      " val samples: 10000\n",
      " test samples: 10000\n",
      "Training...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:09,  4.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 1/60 \t Batch 624/625 \t Loss 0.4700 \t Running Acc 0.785 \t Total Acc 0.785 \t Avg Batch Time 0.2066\n",
      "Time: train: 129.12 \t Train loss 0.4700 \t Train acc: 0.7852\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:08,  9.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4668 \t Running Acc 6.202 \t Total Acc 0.784 \t Avg Batch Time 0.0133\n",
      "New best validation model, saving...\n",
      "Epoch 0/60 finished.\n",
      "Train time: 129.12 \t Val time 8.31\n",
      "Train loss 0.4700 \t Train acc: 0.7852\n",
      "Val loss: 0.4672 \t Val acc: 0.7839\n",
      "Best val acc: 0.7839 at epoch 0.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:09,  4.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 2/60 \t Batch 624/625 \t Loss 0.4564 \t Running Acc 0.792 \t Total Acc 0.792 \t Avg Batch Time 0.2074\n",
      "Time: train: 129.62 \t Train loss 0.4564 \t Train acc: 0.7916\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:08,  9.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4550 \t Running Acc 6.265 \t Total Acc 0.792 \t Avg Batch Time 0.0133\n",
      "New best validation model, saving...\n",
      "Epoch 1/60 finished.\n",
      "Train time: 129.62 \t Val time 8.29\n",
      "Train loss 0.4564 \t Train acc: 0.7916\n",
      "Val loss: 0.4558 \t Val acc: 0.7919\n",
      "Best val acc: 0.7919 at epoch 1.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:18,  4.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 3/60 \t Batch 624/625 \t Loss 0.4512 \t Running Acc 0.795 \t Total Acc 0.795 \t Avg Batch Time 0.2209\n",
      "Time: train: 138.09 \t Train loss 0.4512 \t Train acc: 0.7948\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4516 \t Running Acc 6.283 \t Total Acc 0.794 \t Avg Batch Time 0.0147\n",
      "New best validation model, saving...\n",
      "Epoch 2/60 finished.\n",
      "Train time: 138.09 \t Val time 9.18\n",
      "Train loss 0.4512 \t Train acc: 0.7948\n",
      "Val loss: 0.4523 \t Val acc: 0.7942\n",
      "Best val acc: 0.7942 at epoch 2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 4/60 \t Batch 624/625 \t Loss 0.4484 \t Running Acc 0.796 \t Total Acc 0.796 \t Avg Batch Time 0.2275\n",
      "Time: train: 142.17 \t Train loss 0.4484 \t Train acc: 0.7961\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4556 \t Running Acc 6.277 \t Total Acc 0.793 \t Avg Batch Time 0.0145\n",
      "Epoch 3/60 finished.\n",
      "Train time: 142.17 \t Val time 9.04\n",
      "Train loss 0.4484 \t Train acc: 0.7961\n",
      "Val loss: 0.4565 \t Val acc: 0.7934\n",
      "Best val acc: 0.7942 at epoch 2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 5/60 \t Batch 624/625 \t Loss 0.4478 \t Running Acc 0.797 \t Total Acc 0.797 \t Avg Batch Time 0.2273\n",
      "Time: train: 142.09 \t Train loss 0.4478 \t Train acc: 0.7967\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4558 \t Running Acc 6.250 \t Total Acc 0.790 \t Avg Batch Time 0.0146\n",
      "Epoch 4/60 finished.\n",
      "Train time: 142.09 \t Val time 9.12\n",
      "Train loss 0.4478 \t Train acc: 0.7967\n",
      "Val loss: 0.4565 \t Val acc: 0.7900\n",
      "Best val acc: 0.7942 at epoch 2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 6/60 \t Batch 624/625 \t Loss 0.4439 \t Running Acc 0.798 \t Total Acc 0.798 \t Avg Batch Time 0.2275\n",
      "Time: train: 142.20 \t Train loss 0.4439 \t Train acc: 0.7984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4474 \t Running Acc 6.286 \t Total Acc 0.795 \t Avg Batch Time 0.0145\n",
      "New best validation model, saving...\n",
      "Epoch 5/60 finished.\n",
      "Train time: 142.20 \t Val time 9.06\n",
      "Train loss 0.4439 \t Train acc: 0.7984\n",
      "Val loss: 0.4482 \t Val acc: 0.7946\n",
      "Best val acc: 0.7946 at epoch 5.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 7/60 \t Batch 624/625 \t Loss 0.4393 \t Running Acc 0.801 \t Total Acc 0.801 \t Avg Batch Time 0.2281\n",
      "Time: train: 142.57 \t Train loss 0.4393 \t Train acc: 0.8009\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4481 \t Running Acc 6.290 \t Total Acc 0.795 \t Avg Batch Time 0.0149\n",
      "New best validation model, saving...\n",
      "Epoch 6/60 finished.\n",
      "Train time: 142.57 \t Val time 9.34\n",
      "Train loss 0.4393 \t Train acc: 0.8009\n",
      "Val loss: 0.4489 \t Val acc: 0.7950\n",
      "Best val acc: 0.7950 at epoch 6.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 8/60 \t Batch 624/625 \t Loss 0.4350 \t Running Acc 0.803 \t Total Acc 0.803 \t Avg Batch Time 0.2280\n",
      "Time: train: 142.50 \t Train loss 0.4350 \t Train acc: 0.8028\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4424 \t Running Acc 6.318 \t Total Acc 0.799 \t Avg Batch Time 0.0146\n",
      "New best validation model, saving...\n",
      "Epoch 7/60 finished.\n",
      "Train time: 142.50 \t Val time 9.12\n",
      "Train loss 0.4350 \t Train acc: 0.8028\n",
      "Val loss: 0.4434 \t Val acc: 0.7986\n",
      "Best val acc: 0.7986 at epoch 7.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 9/60 \t Batch 624/625 \t Loss 0.4436 \t Running Acc 0.799 \t Total Acc 0.799 \t Avg Batch Time 0.2277\n",
      "Time: train: 142.31 \t Train loss 0.4436 \t Train acc: 0.7993\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4493 \t Running Acc 6.289 \t Total Acc 0.795 \t Avg Batch Time 0.0148\n",
      "Epoch 8/60 finished.\n",
      "Train time: 142.31 \t Val time 9.23\n",
      "Train loss 0.4436 \t Train acc: 0.7993\n",
      "Val loss: 0.4502 \t Val acc: 0.7949\n",
      "Best val acc: 0.7986 at epoch 7.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 10/60 \t Batch 624/625 \t Loss 0.4409 \t Running Acc 0.801 \t Total Acc 0.801 \t Avg Batch Time 0.2279\n",
      "Time: train: 142.44 \t Train loss 0.4409 \t Train acc: 0.8011\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4479 \t Running Acc 6.289 \t Total Acc 0.795 \t Avg Batch Time 0.0145\n",
      "Epoch 9/60 finished.\n",
      "Train time: 142.44 \t Val time 9.08\n",
      "Train loss 0.4409 \t Train acc: 0.8011\n",
      "Val loss: 0.4487 \t Val acc: 0.7949\n",
      "Best val acc: 0.7986 at epoch 7.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 11/60 \t Batch 624/625 \t Loss 0.4373 \t Running Acc 0.802 \t Total Acc 0.802 \t Avg Batch Time 0.2275\n",
      "Time: train: 142.20 \t Train loss 0.4373 \t Train acc: 0.8023\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4456 \t Running Acc 6.298 \t Total Acc 0.796 \t Avg Batch Time 0.0146\n",
      "Epoch 10/60 finished.\n",
      "Train time: 142.20 \t Val time 9.12\n",
      "Train loss 0.4373 \t Train acc: 0.8023\n",
      "Val loss: 0.4463 \t Val acc: 0.7961\n",
      "Best val acc: 0.7986 at epoch 7.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 12/60 \t Batch 624/625 \t Loss 0.4343 \t Running Acc 0.805 \t Total Acc 0.805 \t Avg Batch Time 0.2281\n",
      "Time: train: 142.54 \t Train loss 0.4343 \t Train acc: 0.8046\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4451 \t Running Acc 6.290 \t Total Acc 0.795 \t Avg Batch Time 0.0147\n",
      "Epoch 11/60 finished.\n",
      "Train time: 142.54 \t Val time 9.18\n",
      "Train loss 0.4343 \t Train acc: 0.8046\n",
      "Val loss: 0.4457 \t Val acc: 0.7950\n",
      "Best val acc: 0.7986 at epoch 7.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 13/60 \t Batch 624/625 \t Loss 0.4305 \t Running Acc 0.806 \t Total Acc 0.806 \t Avg Batch Time 0.2272\n",
      "Time: train: 142.00 \t Train loss 0.4305 \t Train acc: 0.8059\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4406 \t Running Acc 6.326 \t Total Acc 0.800 \t Avg Batch Time 0.0145\n",
      "New best validation model, saving...\n",
      "Epoch 12/60 finished.\n",
      "Train time: 142.00 \t Val time 9.07\n",
      "Train loss 0.4305 \t Train acc: 0.8059\n",
      "Val loss: 0.4411 \t Val acc: 0.7996\n",
      "Best val acc: 0.7996 at epoch 12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:21,  4.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 14/60 \t Batch 624/625 \t Loss 0.4265 \t Running Acc 0.809 \t Total Acc 0.809 \t Avg Batch Time 0.2268\n",
      "Time: train: 141.76 \t Train loss 0.4265 \t Train acc: 0.8092\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4355 \t Running Acc 6.363 \t Total Acc 0.804 \t Avg Batch Time 0.0146\n",
      "New best validation model, saving...\n",
      "Epoch 13/60 finished.\n",
      "Train time: 141.76 \t Val time 9.10\n",
      "Train loss 0.4265 \t Train acc: 0.8092\n",
      "Val loss: 0.4364 \t Val acc: 0.8043\n",
      "Best val acc: 0.8043 at epoch 13.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 15/60 \t Batch 624/625 \t Loss 0.4222 \t Running Acc 0.811 \t Total Acc 0.811 \t Avg Batch Time 0.2275\n",
      "Time: train: 142.21 \t Train loss 0.4222 \t Train acc: 0.8106\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4335 \t Running Acc 6.370 \t Total Acc 0.805 \t Avg Batch Time 0.0145\n",
      "New best validation model, saving...\n",
      "Epoch 14/60 finished.\n",
      "Train time: 142.21 \t Val time 9.09\n",
      "Train loss 0.4222 \t Train acc: 0.8106\n",
      "Val loss: 0.4346 \t Val acc: 0.8052\n",
      "Best val acc: 0.8052 at epoch 14.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 16/60 \t Batch 624/625 \t Loss 0.4188 \t Running Acc 0.813 \t Total Acc 0.813 \t Avg Batch Time 0.2284\n",
      "Time: train: 142.76 \t Train loss 0.4188 \t Train acc: 0.8132\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4288 \t Running Acc 6.371 \t Total Acc 0.805 \t Avg Batch Time 0.0145\n",
      "New best validation model, saving...\n",
      "Epoch 15/60 finished.\n",
      "Train time: 142.76 \t Val time 9.09\n",
      "Train loss 0.4188 \t Train acc: 0.8132\n",
      "Val loss: 0.4302 \t Val acc: 0.8053\n",
      "Best val acc: 0.8053 at epoch 15.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:21,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 17/60 \t Batch 624/625 \t Loss 0.4310 \t Running Acc 0.807 \t Total Acc 0.807 \t Avg Batch Time 0.2269\n",
      "Time: train: 141.84 \t Train loss 0.4310 \t Train acc: 0.8070\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4390 \t Running Acc 6.332 \t Total Acc 0.800 \t Avg Batch Time 0.0146\n",
      "Epoch 16/60 finished.\n",
      "Train time: 141.84 \t Val time 9.13\n",
      "Train loss 0.4310 \t Train acc: 0.8070\n",
      "Val loss: 0.4399 \t Val acc: 0.8004\n",
      "Best val acc: 0.8053 at epoch 15.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 18/60 \t Batch 624/625 \t Loss 0.4299 \t Running Acc 0.808 \t Total Acc 0.808 \t Avg Batch Time 0.2272\n",
      "Time: train: 142.01 \t Train loss 0.4299 \t Train acc: 0.8075\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4364 \t Running Acc 6.327 \t Total Acc 0.800 \t Avg Batch Time 0.0145\n",
      "Epoch 17/60 finished.\n",
      "Train time: 142.01 \t Val time 9.04\n",
      "Train loss 0.4299 \t Train acc: 0.8075\n",
      "Val loss: 0.4374 \t Val acc: 0.7997\n",
      "Best val acc: 0.8053 at epoch 15.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 19/60 \t Batch 624/625 \t Loss 0.4286 \t Running Acc 0.809 \t Total Acc 0.809 \t Avg Batch Time 0.2274\n",
      "Time: train: 142.14 \t Train loss 0.4286 \t Train acc: 0.8085\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4362 \t Running Acc 6.362 \t Total Acc 0.804 \t Avg Batch Time 0.0144\n",
      "Epoch 18/60 finished.\n",
      "Train time: 142.14 \t Val time 9.02\n",
      "Train loss 0.4286 \t Train acc: 0.8085\n",
      "Val loss: 0.4372 \t Val acc: 0.8041\n",
      "Best val acc: 0.8053 at epoch 15.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 20/60 \t Batch 624/625 \t Loss 0.4264 \t Running Acc 0.810 \t Total Acc 0.810 \t Avg Batch Time 0.2271\n",
      "Time: train: 141.95 \t Train loss 0.4264 \t Train acc: 0.8099\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.75it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4348 \t Running Acc 6.366 \t Total Acc 0.805 \t Avg Batch Time 0.0145\n",
      "Epoch 19/60 finished.\n",
      "Train time: 141.95 \t Val time 9.03\n",
      "Train loss 0.4264 \t Train acc: 0.8099\n",
      "Val loss: 0.4359 \t Val acc: 0.8046\n",
      "Best val acc: 0.8053 at epoch 15.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 21/60 \t Batch 624/625 \t Loss 0.4245 \t Running Acc 0.811 \t Total Acc 0.811 \t Avg Batch Time 0.2271\n",
      "Time: train: 141.93 \t Train loss 0.4245 \t Train acc: 0.8107\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4368 \t Running Acc 6.335 \t Total Acc 0.801 \t Avg Batch Time 0.0146\n",
      "Epoch 20/60 finished.\n",
      "Train time: 141.93 \t Val time 9.10\n",
      "Train loss 0.4245 \t Train acc: 0.8107\n",
      "Val loss: 0.4379 \t Val acc: 0.8008\n",
      "Best val acc: 0.8053 at epoch 15.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 22/60 \t Batch 624/625 \t Loss 0.4227 \t Running Acc 0.812 \t Total Acc 0.812 \t Avg Batch Time 0.2273\n",
      "Time: train: 142.09 \t Train loss 0.4227 \t Train acc: 0.8122\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4360 \t Running Acc 6.391 \t Total Acc 0.808 \t Avg Batch Time 0.0146\n",
      "New best validation model, saving...\n",
      "Epoch 21/60 finished.\n",
      "Train time: 142.09 \t Val time 9.10\n",
      "Train loss 0.4227 \t Train acc: 0.8122\n",
      "Val loss: 0.4370 \t Val acc: 0.8078\n",
      "Best val acc: 0.8078 at epoch 21.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 23/60 \t Batch 624/625 \t Loss 0.4197 \t Running Acc 0.813 \t Total Acc 0.813 \t Avg Batch Time 0.2273\n",
      "Time: train: 142.05 \t Train loss 0.4197 \t Train acc: 0.8133\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4350 \t Running Acc 6.392 \t Total Acc 0.808 \t Avg Batch Time 0.0146\n",
      "New best validation model, saving...\n",
      "Epoch 22/60 finished.\n",
      "Train time: 142.05 \t Val time 9.12\n",
      "Train loss 0.4197 \t Train acc: 0.8133\n",
      "Val loss: 0.4360 \t Val acc: 0.8079\n",
      "Best val acc: 0.8079 at epoch 22.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 24/60 \t Batch 624/625 \t Loss 0.4165 \t Running Acc 0.815 \t Total Acc 0.815 \t Avg Batch Time 0.2282\n",
      "Time: train: 142.64 \t Train loss 0.4165 \t Train acc: 0.8146\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4330 \t Running Acc 6.393 \t Total Acc 0.808 \t Avg Batch Time 0.0145\n",
      "New best validation model, saving...\n",
      "Epoch 23/60 finished.\n",
      "Train time: 142.64 \t Val time 9.05\n",
      "Train loss 0.4165 \t Train acc: 0.8146\n",
      "Val loss: 0.4341 \t Val acc: 0.8081\n",
      "Best val acc: 0.8081 at epoch 23.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 26/60 \t Batch 624/625 \t Loss 0.4105 \t Running Acc 0.818 \t Total Acc 0.818 \t Avg Batch Time 0.2272\n",
      "Time: train: 142.02 \t Train loss 0.4105 \t Train acc: 0.8178\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4304 \t Running Acc 6.410 \t Total Acc 0.810 \t Avg Batch Time 0.0144\n",
      "New best validation model, saving...\n",
      "Epoch 25/60 finished.\n",
      "Train time: 142.02 \t Val time 9.00\n",
      "Train loss 0.4105 \t Train acc: 0.8178\n",
      "Val loss: 0.4318 \t Val acc: 0.8102\n",
      "Best val acc: 0.8102 at epoch 25.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 27/60 \t Batch 624/625 \t Loss 0.4078 \t Running Acc 0.820 \t Total Acc 0.820 \t Avg Batch Time 0.2278\n",
      "Time: train: 142.39 \t Train loss 0.4078 \t Train acc: 0.8197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4302 \t Running Acc 6.393 \t Total Acc 0.808 \t Avg Batch Time 0.0145\n",
      "Epoch 26/60 finished.\n",
      "Train time: 142.39 \t Val time 9.08\n",
      "Train loss 0.4078 \t Train acc: 0.8197\n",
      "Val loss: 0.4317 \t Val acc: 0.8081\n",
      "Best val acc: 0.8102 at epoch 25.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 28/60 \t Batch 624/625 \t Loss 0.4041 \t Running Acc 0.822 \t Total Acc 0.822 \t Avg Batch Time 0.2272\n",
      "Time: train: 142.00 \t Train loss 0.4041 \t Train acc: 0.8224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4306 \t Running Acc 6.386 \t Total Acc 0.807 \t Avg Batch Time 0.0145\n",
      "Epoch 27/60 finished.\n",
      "Train time: 142.00 \t Val time 9.04\n",
      "Train loss 0.4041 \t Train acc: 0.8224\n",
      "Val loss: 0.4322 \t Val acc: 0.8072\n",
      "Best val acc: 0.8102 at epoch 25.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 29/60 \t Batch 624/625 \t Loss 0.4008 \t Running Acc 0.824 \t Total Acc 0.824 \t Avg Batch Time 0.2273\n",
      "Time: train: 142.06 \t Train loss 0.4008 \t Train acc: 0.8240\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4285 \t Running Acc 6.405 \t Total Acc 0.810 \t Avg Batch Time 0.0145\n",
      "Epoch 28/60 finished.\n",
      "Train time: 142.06 \t Val time 9.06\n",
      "Train loss 0.4008 \t Train acc: 0.8240\n",
      "Val loss: 0.4301 \t Val acc: 0.8096\n",
      "Best val acc: 0.8102 at epoch 25.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "264it [01:00,  4.35it/s]IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n",
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 39/60 \t Batch 624/625 \t Loss 0.3937 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2276\n",
      "Time: train: 142.25 \t Train loss 0.3937 \t Train acc: 0.8288\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0146\n",
      "Epoch 38/60 finished.\n",
      "Train time: 142.25 \t Val time 9.10\n",
      "Train loss 0.3937 \t Train acc: 0.8288\n",
      "Val loss: 0.4254 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 40/60 \t Batch 624/625 \t Loss 0.3934 \t Running Acc 0.828 \t Total Acc 0.828 \t Avg Batch Time 0.2279\n",
      "Time: train: 142.42 \t Train loss 0.3934 \t Train acc: 0.8283\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0146\n",
      "Epoch 39/60 finished.\n",
      "Train time: 142.42 \t Val time 9.11\n",
      "Train loss 0.3934 \t Train acc: 0.8283\n",
      "Val loss: 0.4254 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:23,  4.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 41/60 \t Batch 624/625 \t Loss 0.3932 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2290\n",
      "Time: train: 143.12 \t Train loss 0.3932 \t Train acc: 0.8288\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0147\n",
      "Epoch 40/60 finished.\n",
      "Train time: 143.12 \t Val time 9.18\n",
      "Train loss 0.3932 \t Train acc: 0.8288\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 42/60 \t Batch 624/625 \t Loss 0.3935 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2278\n",
      "Time: train: 142.39 \t Train loss 0.3935 \t Train acc: 0.8289\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0145\n",
      "Epoch 41/60 finished.\n",
      "Train time: 142.39 \t Val time 9.05\n",
      "Train loss 0.3935 \t Train acc: 0.8289\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 43/60 \t Batch 624/625 \t Loss 0.3936 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2276\n",
      "Time: train: 142.25 \t Train loss 0.3936 \t Train acc: 0.8290\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0146\n",
      "Epoch 42/60 finished.\n",
      "Train time: 142.25 \t Val time 9.11\n",
      "Train loss 0.3936 \t Train acc: 0.8290\n",
      "Val loss: 0.4254 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 44/60 \t Batch 624/625 \t Loss 0.3935 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2277\n",
      "Time: train: 142.30 \t Train loss 0.3935 \t Train acc: 0.8286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0145\n",
      "Epoch 43/60 finished.\n",
      "Train time: 142.30 \t Val time 9.08\n",
      "Train loss 0.3935 \t Train acc: 0.8286\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 45/60 \t Batch 624/625 \t Loss 0.3936 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2272\n",
      "Time: train: 141.99 \t Train loss 0.3936 \t Train acc: 0.8286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0145\n",
      "Epoch 44/60 finished.\n",
      "Train time: 141.99 \t Val time 9.06\n",
      "Train loss 0.3936 \t Train acc: 0.8286\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 46/60 \t Batch 624/625 \t Loss 0.3933 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2275\n",
      "Time: train: 142.19 \t Train loss 0.3933 \t Train acc: 0.8290\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0144\n",
      "Epoch 45/60 finished.\n",
      "Train time: 142.19 \t Val time 9.02\n",
      "Train loss 0.3933 \t Train acc: 0.8290\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 47/60 \t Batch 624/625 \t Loss 0.3932 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2272\n",
      "Time: train: 141.97 \t Train loss 0.3932 \t Train acc: 0.8286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0145\n",
      "Epoch 46/60 finished.\n",
      "Train time: 141.97 \t Val time 9.07\n",
      "Train loss 0.3932 \t Train acc: 0.8286\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 48/60 \t Batch 624/625 \t Loss 0.3936 \t Running Acc 0.828 \t Total Acc 0.828 \t Avg Batch Time 0.2278\n",
      "Time: train: 142.36 \t Train loss 0.3936 \t Train acc: 0.8283\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0145\n",
      "Epoch 47/60 finished.\n",
      "Train time: 142.36 \t Val time 9.08\n",
      "Train loss 0.3936 \t Train acc: 0.8283\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 49/60 \t Batch 624/625 \t Loss 0.3932 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2272\n",
      "Time: train: 142.02 \t Train loss 0.3932 \t Train acc: 0.8287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0145\n",
      "Epoch 48/60 finished.\n",
      "Train time: 142.02 \t Val time 9.06\n",
      "Train loss 0.3932 \t Train acc: 0.8287\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:23,  4.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 50/60 \t Batch 624/625 \t Loss 0.3935 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2291\n",
      "Time: train: 143.17 \t Train loss 0.3935 \t Train acc: 0.8289\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0145\n",
      "Epoch 49/60 finished.\n",
      "Train time: 143.17 \t Val time 9.08\n",
      "Train loss 0.3935 \t Train acc: 0.8289\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 51/60 \t Batch 624/625 \t Loss 0.3936 \t Running Acc 0.828 \t Total Acc 0.828 \t Avg Batch Time 0.2277\n",
      "Time: train: 142.28 \t Train loss 0.3936 \t Train acc: 0.8284\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0145\n",
      "Epoch 50/60 finished.\n",
      "Train time: 142.28 \t Val time 9.05\n",
      "Train loss 0.3936 \t Train acc: 0.8284\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 52/60 \t Batch 624/625 \t Loss 0.3935 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2275\n",
      "Time: train: 142.18 \t Train loss 0.3935 \t Train acc: 0.8287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0147\n",
      "Epoch 51/60 finished.\n",
      "Train time: 142.18 \t Val time 9.18\n",
      "Train loss 0.3935 \t Train acc: 0.8287\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 53/60 \t Batch 624/625 \t Loss 0.3935 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2282\n",
      "Time: train: 142.64 \t Train loss 0.3935 \t Train acc: 0.8289\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0146\n",
      "Epoch 52/60 finished.\n",
      "Train time: 142.64 \t Val time 9.15\n",
      "Train loss 0.3935 \t Train acc: 0.8289\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 54/60 \t Batch 624/625 \t Loss 0.3936 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2282\n",
      "Time: train: 142.60 \t Train loss 0.3936 \t Train acc: 0.8287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0146\n",
      "Epoch 53/60 finished.\n",
      "Train time: 142.60 \t Val time 9.15\n",
      "Train loss 0.3936 \t Train acc: 0.8287\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 55/60 \t Batch 624/625 \t Loss 0.3933 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2286\n",
      "Time: train: 142.85 \t Train loss 0.3933 \t Train acc: 0.8291\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0146\n",
      "Epoch 54/60 finished.\n",
      "Train time: 142.85 \t Val time 9.10\n",
      "Train loss 0.3933 \t Train acc: 0.8291\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 56/60 \t Batch 624/625 \t Loss 0.3935 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2272\n",
      "Time: train: 142.03 \t Train loss 0.3935 \t Train acc: 0.8286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0145\n",
      "Epoch 55/60 finished.\n",
      "Train time: 142.03 \t Val time 9.08\n",
      "Train loss 0.3935 \t Train acc: 0.8286\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 57/60 \t Batch 624/625 \t Loss 0.3935 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2278\n",
      "Time: train: 142.36 \t Train loss 0.3935 \t Train acc: 0.8290\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0146\n",
      "Epoch 56/60 finished.\n",
      "Train time: 142.36 \t Val time 9.12\n",
      "Train loss 0.3935 \t Train acc: 0.8290\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 58/60 \t Batch 624/625 \t Loss 0.3936 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2287\n",
      "Time: train: 142.91 \t Train loss 0.3936 \t Train acc: 0.8289\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0147\n",
      "Epoch 57/60 finished.\n",
      "Train time: 142.91 \t Val time 9.16\n",
      "Train loss 0.3936 \t Train acc: 0.8289\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 59/60 \t Batch 624/625 \t Loss 0.3934 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2280\n",
      "Time: train: 142.49 \t Train loss 0.3934 \t Train acc: 0.8288\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0146\n",
      "Epoch 58/60 finished.\n",
      "Train time: 142.49 \t Val time 9.09\n",
      "Train loss 0.3934 \t Train acc: 0.8288\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "625it [02:22,  4.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> train \t Epoch 60/60 \t Batch 624/625 \t Loss 0.3936 \t Running Acc 0.829 \t Total Acc 0.829 \t Avg Batch Time 0.2278\n",
      "Time: train: 142.35 \t Train loss 0.3936 \t Train acc: 0.8290\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> val \t Loss 0.4239 \t Running Acc 6.421 \t Total Acc 0.812 \t Avg Batch Time 0.0146\n",
      "Epoch 59/60 finished.\n",
      "Train time: 142.35 \t Val time 9.10\n",
      "Train loss 0.3936 \t Train acc: 0.8290\n",
      "Val loss: 0.4255 \t Val acc: 0.8116\n",
      "Best val acc: 0.8117 at epoch 30.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [00:09,  8.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> test \t Loss 0.4286 \t Running Acc 6.430 \t Total Acc 0.813 \t Avg Batch Time 0.0148\n",
      "Final  tensor([[0.0000, 0.6743, 0.3257],\n",
      "        [1.0000, 0.1711, 0.8289],\n",
      "        [1.0000, 0.9159, 0.0841],\n",
      "        ...,\n",
      "        [0.0000, 0.9256, 0.0744],\n",
      "        [1.0000, 0.1765, 0.8235],\n",
      "        [0.0000, 0.0441, 0.9559]], device='cuda:0')\n",
      "Test: Loss 0.4238 \t Acc 0.8128 \t AUC: 0.8880 \t 1/eB 0.3: 79.0781 \t 1/eB 0.5: 27.2097\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn, optim\n",
    "import json, time\n",
    "import utils_lorentz\n",
    "import numpy as np\n",
    "import torch.distributed as dist\n",
    "from torch.nn.parallel import DistributedDataParallel\n",
    "from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "def run(epoch, loader, partition, N_EPOCHS=None):\n",
    "    if partition == 'train':\n",
    "        model.train()\n",
    "    else:\n",
    "        model.eval()\n",
    "\n",
    "    res = {'time':0, 'correct':0, 'loss': 0, 'counter': 0, 'acc': 0,\n",
    "           'loss_arr':[], 'correct_arr':[],'label':[],'score':[]}\n",
    "\n",
    "    tik = time.time()\n",
    "    loader_length = len(loader)\n",
    "\n",
    "    for i, (label, p4s, nodes, atom_mask, edge_mask, edges) in tqdm(enumerate(loader)):\n",
    "        if partition == 'train':\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "        batch_size, n_nodes, _ = p4s.size()\n",
    "        atom_positions = p4s.view(batch_size * n_nodes, -1).to(device, dtype)\n",
    "        atom_mask = atom_mask.view(batch_size * n_nodes, -1).to(device)\n",
    "        edge_mask = edge_mask.reshape(batch_size * n_nodes * n_nodes, -1).to(device)\n",
    "        nodes = nodes.view(batch_size * n_nodes, -1).to(device,dtype)\n",
    "        edges = [a.to(device) for a in edges]\n",
    "        label = label.to(device, dtype).long()\n",
    "\n",
    "        pred = model(scalars=nodes, x=atom_positions, edges=edges, node_mask=atom_mask,\n",
    "                         edge_mask=edge_mask, n_nodes=n_nodes)\n",
    "        \n",
    "        predict = pred.max(1).indices\n",
    "        correct = torch.sum(predict == label).item()\n",
    "        loss = loss_fn(pred, label)\n",
    "        \n",
    "        if partition == 'train':\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "        elif partition == 'test':\n",
    "            # save labels and probilities for ROC / AUC\n",
    "            # print(\"Preds \", pred)\n",
    "            score = torch.nn.functional.softmax(pred, dim = -1)\n",
    "            # print(\"Score test \", score)\n",
    "            # raise\n",
    "            res['label'].append(label)\n",
    "            res['score'].append(score)\n",
    "\n",
    "        res['time'] = time.time() - tik\n",
    "        res['correct'] += correct\n",
    "        res['loss'] += loss.item() * batch_size\n",
    "        res['counter'] += batch_size\n",
    "        res['loss_arr'].append(loss.item())\n",
    "        res['correct_arr'].append(correct)\n",
    "\n",
    "        # if i != 0 and i % args.log_interval == 0:\n",
    "        \n",
    "    running_loss = sum(res['loss_arr'])/len(res['loss_arr'])\n",
    "    running_acc = sum(res['correct_arr'])/(len(res['correct_arr'])*batch_size)\n",
    "    avg_time = res['time']/res['counter'] * batch_size\n",
    "    tmp_counter = res['counter']\n",
    "    tmp_loss = res['loss'] / tmp_counter\n",
    "    tmp_acc = res['correct'] / tmp_counter\n",
    "\n",
    "    if N_EPOCHS:\n",
    "        print(\">> %s \\t Epoch %d/%d \\t Batch %d/%d \\t Loss %.4f \\t Running Acc %.3f \\t Total Acc %.3f \\t Avg Batch Time %.4f\" %\n",
    "             (partition, epoch + 1, N_EPOCHS, i, loader_length, running_loss, running_acc, tmp_acc, avg_time))\n",
    "    else:\n",
    "        print(\">> %s \\t Loss %.4f \\t Running Acc %.3f \\t Total Acc %.3f \\t Avg Batch Time %.4f\" %\n",
    "             (partition, running_loss, running_acc, tmp_acc, avg_time))\n",
    "        \n",
    "    torch.cuda.empty_cache()\n",
    "    # ---------- reduce -----------\n",
    "    if partition == 'test':\n",
    "        res['label'] = torch.cat(res['label']).unsqueeze(-1)\n",
    "        res['score'] = torch.cat(res['score'])\n",
    "        res['score'] = torch.cat((res['label'],res['score']),dim=-1)\n",
    "    res['counter'] = res['counter']\n",
    "    res['loss'] = res['loss'] / res['counter']\n",
    "    res['acc'] = res['correct'] / res['counter']\n",
    "    return res\n",
    "\n",
    "def train(res, N_EPOCHS):\n",
    "    ### training and validation\n",
    "    for epoch in range(N_EPOCHS):\n",
    "        train_res = run(epoch, dataloaders['train'], partition='train', N_EPOCHS = N_EPOCHS)\n",
    "        print(\"Time: train: %.2f \\t Train loss %.4f \\t Train acc: %.4f\" % (train_res['time'],train_res['loss'],train_res['acc']))\n",
    "        # if epoch % args.val_interval == 0:\n",
    "            \n",
    "        # if (args.local_rank == 0):\n",
    "        torch.save(model.state_dict(), \"models/CMS_task_iii_g/checkpoint-epoch-{}.pt\".format(epoch))\n",
    "        with torch.no_grad():\n",
    "            val_res = run(epoch, dataloaders['val'], partition='val')\n",
    "            \n",
    "        # if (args.local_rank == 0): # only master process save\n",
    "        res['lr'].append(optimizer.param_groups[0]['lr'])\n",
    "        res['train_time'].append(train_res['time'])\n",
    "        res['val_time'].append(val_res['time'])\n",
    "        res['train_loss'].append(train_res['loss'])\n",
    "        res['train_acc'].append(train_res['acc'])\n",
    "        res['val_loss'].append(val_res['loss'])\n",
    "        res['val_acc'].append(val_res['acc'])\n",
    "        res['epochs'].append(epoch)\n",
    "\n",
    "        ## save best model\n",
    "        if val_res['acc'] > res['best_val']:\n",
    "            print(\"New best validation model, saving...\")\n",
    "            torch.save(model.state_dict(), \"models/CMS_task_iii_g/best-val-model.pt\")\n",
    "            res['best_val'] = val_res['acc']\n",
    "            res['best_epoch'] = epoch\n",
    "\n",
    "        print(\"Epoch %d/%d finished.\" % (epoch, N_EPOCHS))\n",
    "        print(\"Train time: %.2f \\t Val time %.2f\" % (train_res['time'], val_res['time']))\n",
    "        print(\"Train loss %.4f \\t Train acc: %.4f\" % (train_res['loss'], train_res['acc']))\n",
    "        print(\"Val loss: %.4f \\t Val acc: %.4f\" % (val_res['loss'], val_res['acc']))\n",
    "        print(\"Best val acc: %.4f at epoch %d.\" % (res['best_val'],  res['best_epoch']))\n",
    "\n",
    "        json_object = json.dumps(res, indent=4)\n",
    "        with open(\"logs/CMS_task_iii_g/train-result-epoch{}.json\".format(epoch), \"w\") as outfile:\n",
    "            outfile.write(json_object)\n",
    "\n",
    "        ## adjust learning rate\n",
    "        if (epoch < 31):\n",
    "            lr_scheduler.step(metrics=val_res['acc'])\n",
    "        else:\n",
    "            for g in optimizer.param_groups:\n",
    "                g['lr'] = g['lr']*0.5\n",
    "\n",
    "\n",
    "def test(res):\n",
    "    ### test on best model\n",
    "    best_model = torch.load(\"models/CMS_task_iii_g/best-val-model.pt\", map_location=device)\n",
    "    model.load_state_dict(best_model)\n",
    "    with torch.no_grad():\n",
    "        test_res = run(0, dataloaders['test'], partition='test')\n",
    "\n",
    "    print(\"Final \", test_res['score'])\n",
    "    pred = test_res['score'].cpu()\n",
    "\n",
    "    np.save(\"logs/CMS_task_iii_g/score.npy\",pred)\n",
    "    fpr, tpr, thres, eB, eS  = utils_lorentz.buildROC(pred[...,0], pred[...,2])\n",
    "    auc = utils_lorentz.roc_auc_score(pred[...,0], pred[...,2])\n",
    "\n",
    "    metric = {'test_loss': test_res['loss'], 'test_acc': test_res['acc'],\n",
    "              'test_auc': auc, 'test_1/eB_0.3':1./eB[0],'test_1/eB_0.5':1./eB[1]}\n",
    "    res.update(metric)\n",
    "    print(\"Test: Loss %.4f \\t Acc %.4f \\t AUC: %.4f \\t 1/eB 0.3: %.4f \\t 1/eB 0.5: %.4f\"\\\n",
    "           % (test_res['loss'], test_res['acc'], auc, 1./eB[0], 1./eB[1]))\n",
    "    json_object = json.dumps(res, indent=4)\n",
    "    with open(\"logs/CMS_task_iii_g/test-result.json\", \"w\") as outfile:\n",
    "        outfile.write(json_object)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    \n",
    "    N_EPOCHS = 60\n",
    "\n",
    "    # utils_lorentz.args_init(args)\n",
    "\n",
    "    ### set random seed\n",
    "    torch.manual_seed(42)\n",
    "    np.random.seed(42)\n",
    "\n",
    "    ### initialize cuda\n",
    "    # dist.init_process_group(backend='nccl')\n",
    "    device = torch.device(\"cuda\")\n",
    "    dtype = torch.float32\n",
    "\n",
    "    ### load data\n",
    "    dataloaders = retrieve_dataloaders( batch_size,\n",
    "                                        num_data=100000, # use all data\n",
    "                                        cache_dir=\"datasets/QMLHEP/quark_gluons/\",\n",
    "                                        num_workers=0,\n",
    "                                        use_one_hot=True)\n",
    "\n",
    "    ### create parallel model\n",
    "    model = LorentzNet(n_scalar = 8, n_hidden = 72, n_class = 2,\\\n",
    "                       dropout = 0.2, n_layers = 6,\\\n",
    "                       c_weight = 1e-3)\n",
    "    \n",
    "    model = model.to(device)\n",
    "\n",
    "    ### print model and dataset information\n",
    "    # if (args.local_rank == 0):\n",
    "    pytorch_total_params = sum(p.numel() for p in model.parameters())\n",
    "    print(\"Model Size:\", pytorch_total_params)\n",
    "    for (split, dataloader) in dataloaders.items():\n",
    "        print(f\" {split} samples: {len(dataloader.dataset)}\")\n",
    "\n",
    "    ### optimizer\n",
    "    optimizer = optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-2)\n",
    "\n",
    "    ### lr scheduler\n",
    "    base_scheduler = CosineAnnealingWarmRestarts(optimizer, 4, 2, verbose = False)\n",
    "    lr_scheduler = utils_lorentz.GradualWarmupScheduler(optimizer, multiplier=1,\\\n",
    "                                                warmup_epoch=5,\\\n",
    "                                                after_scheduler=base_scheduler) ## warmup\n",
    "\n",
    "    ### loss function\n",
    "    loss_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "    ### initialize logs\n",
    "    res = {'epochs': [], 'lr' : [],\\\n",
    "           'train_time': [], 'val_time': [],  'train_loss': [], 'val_loss': [],\\\n",
    "           'train_acc': [], 'val_acc': [], 'best_val': 0, 'best_epoch': 0}\n",
    "\n",
    "    ### training and testing\n",
    "    print(\"Training...\")\n",
    "    train(res, N_EPOCHS)\n",
    "    test(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's understand the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_train = res['train_loss']\n",
    "loss_val = res['val_loss']\n",
    "\n",
    "acc_train = res['train_acc']\n",
    "acc_val = res['val_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_metric_evolution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['epochs', 'lr', 'train_time', 'val_time', 'train_loss', 'val_loss', 'train_acc', 'val_acc', 'best_val', 'best_epoch', 'test_loss', 'test_acc', 'test_auc', 'test_1/eB_0.3', 'test_1/eB_0.5'])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.keys()#['test_auc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LorentzNet(\n",
       "  (embedding): Linear(in_features=8, out_features=72, bias=True)\n",
       "  (LGEBs): ModuleList(\n",
       "    (0-4): 5 x LGEB(\n",
       "      (phi_e): Sequential(\n",
       "        (0): Linear(in_features=146, out_features=72, bias=False)\n",
       "        (1): BatchNorm1d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU()\n",
       "        (3): Linear(in_features=72, out_features=72, bias=True)\n",
       "        (4): ReLU()\n",
       "      )\n",
       "      (phi_h): Sequential(\n",
       "        (0): Linear(in_features=152, out_features=72, bias=True)\n",
       "        (1): BatchNorm1d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU()\n",
       "        (3): Linear(in_features=72, out_features=72, bias=True)\n",
       "      )\n",
       "      (phi_x): Sequential(\n",
       "        (0): Linear(in_features=72, out_features=72, bias=True)\n",
       "        (1): ReLU()\n",
       "        (2): Linear(in_features=72, out_features=1, bias=False)\n",
       "      )\n",
       "      (phi_m): Sequential(\n",
       "        (0): Linear(in_features=72, out_features=1, bias=True)\n",
       "        (1): Sigmoid()\n",
       "      )\n",
       "    )\n",
       "    (5): LGEB(\n",
       "      (phi_e): Sequential(\n",
       "        (0): Linear(in_features=146, out_features=72, bias=False)\n",
       "        (1): BatchNorm1d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU()\n",
       "        (3): Linear(in_features=72, out_features=72, bias=True)\n",
       "        (4): ReLU()\n",
       "      )\n",
       "      (phi_h): Sequential(\n",
       "        (0): Linear(in_features=152, out_features=72, bias=True)\n",
       "        (1): BatchNorm1d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU()\n",
       "        (3): Linear(in_features=72, out_features=72, bias=True)\n",
       "      )\n",
       "      (phi_m): Sequential(\n",
       "        (0): Linear(in_features=72, out_features=1, bias=True)\n",
       "        (1): Sigmoid()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (graph_dec): Sequential(\n",
       "    (0): Linear(in_features=72, out_features=72, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Dropout(p=0.2, inplace=False)\n",
       "    (3): Linear(in_features=72, out_features=2, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "device = 'cpu'\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "79it [01:33,  1.19s/it]\n"
     ]
    }
   ],
   "source": [
    "pred_val = []\n",
    "pred_label = []\n",
    "true_label = []\n",
    "\n",
    "for i, (label, p4s, nodes, atom_mask, edge_mask, edges) in tqdm(enumerate(dataloaders['test'])):\n",
    "\n",
    "    batch_size, n_nodes, _ = p4s.size()\n",
    "    atom_positions = p4s.view(batch_size * n_nodes, -1).to(device, dtype)\n",
    "    atom_mask = atom_mask.view(batch_size * n_nodes, -1).to(device)\n",
    "    edge_mask = edge_mask.reshape(batch_size * n_nodes * n_nodes, -1).to(device)\n",
    "    nodes = nodes.view(batch_size * n_nodes, -1).to(device,dtype)\n",
    "    edges = [a.to(device) for a in edges]\n",
    "    label = label.to(device, dtype).long()\n",
    "\n",
    "    pred = model(scalars=nodes, x=atom_positions, edges=edges, node_mask=atom_mask,\n",
    "                     edge_mask=edge_mask, n_nodes=n_nodes)\n",
    "\n",
    "    # print(F.softmax(pred, dim=1).detach())\n",
    "    pred_val.append(F.softmax(pred, dim=1).cpu().detach().numpy())\n",
    "    predict = pred.max(1).indices\n",
    "    pred_label.append(predict)\n",
    "    true_label.extend([label])#*len(predict))\n",
    "\n",
    "    correct = torch.sum(predict == label).item()\n",
    "\n",
    "\n",
    "pred_val = np.concatenate(pred_val, axis=0)\n",
    "pred_label = np.concatenate(pred_label, axis=0)\n",
    "true_label = np.concatenate(true_label,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc, accuracy_score\n",
    "\n",
    "#### get the ROC curves\n",
    "fpr = {}\n",
    "tpr = {}\n",
    "auc_value = {}\n",
    "plt.figure(figsize=(12,8))\n",
    "for i, label in enumerate(label_names):\n",
    "    fpr[label], tpr[label], threshold = roc_curve((true_label== i), pred_val[:,i])\n",
    "    auc_value[label] = auc(fpr[label], tpr[label])\n",
    "    plt.plot(tpr[label],fpr[label],label='%s tagger, AUC = %.1f%%'%(label,auc_value[label]*100.))\n",
    "#computing accuracy now\n",
    "acc_score = accuracy_score(true_label, pred_label)\n",
    "acc_manual = np.sum(pred_label == true_label)\n",
    "plt.title('Accuracy : {:.3f}'.format(acc_score), fontsize=15)\n",
    "plt.semilogy()\n",
    "plt.xlabel(\"sig. efficiency\",fontsize=15)\n",
    "plt.ylabel(\"bkg. mistag rate\",fontsize=15)\n",
    "plt.ylim(0.001,1)\n",
    "plt.grid(True)\n",
    "plt.legend(loc='lower right',fontsize=15, frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The AUC is ~ 3% higher when we incorporate the corresponding symmetry-preserving bias!\n",
    "\n",
    "Of course, there is a lot of room to change the architecture, dimension of hidden layers, hyperparameter tuning, etc. But, even without all of this, this showcases how symmetry-preserving behavior is powerful when compared to other standard models, like ParticleNet (which is permutation invariant, so does preserve symmetry to some degree, but not as specific as the LorentzNet)."
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "include_colab_link": true,
   "provenance": []
  },
  "kernelspec": {
   "display_name": "ml4sci",
   "language": "python",
   "name": "ml4sci"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}