7.model_trackster_graph_net.link.prediction.multiparticle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c07744a",
   "metadata": {},
   "source": [
    "# Graph net for link prediction\n",
    "\n",
    "Question: does including the neighborhood provide a benefit over the pairwise classification?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3962d51",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import sys\n",
    "\n",
    "import torch.nn as nn\n",
    "from torch.optim import Adam\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "\n",
    "from torch.utils.data import random_split\n",
    "from torch_geometric.loader import DataLoader\n",
    "\n",
    "import torch_geometric.utils as geou\n",
    "\n",
    "import torch_geometric.nn as gnn\n",
    "\n",
    "from reco.model import EdgeConvBlock, DynamicEdgeConvBlock\n",
    "\n",
    "from reco.training import precision_recall_curve, roc_auc\n",
    "from reco.loss import QualityFocalLoss\n",
    "from reco.datasetPU import TracksterGraph\n",
    "\n",
    "\n",
    "ds_name = \"MultiParticle\"\n",
    "\n",
    "data_root = \"data\"\n",
    "raw_dir = f\"/Users/ecuba/data/{ds_name}\"\n",
    "\n",
    "data_root = \"/mnt/ceph/users/ecuba/processed\"\n",
    "raw_dir = f\"/mnt/ceph/users/ecuba/{ds_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "02f59304-76e4-4132-a379-978a46a042fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "# CUDA Setup\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ba68cd4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TracksterGraph(graphs=5000, nodes=1272368, radius=30, bigT_e_th=10, lp)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = TracksterGraph(\n",
    "    ds_name,\n",
    "    data_root,\n",
    "    raw_dir,\n",
    "    N_FILES=50,\n",
    "    pileup=False,\n",
    "    radius=30,\n",
    "    bigT_e_th=10,\n",
    "    link_prediction=True,\n",
    ")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7dd413f0-2572-4ca6-a276-8fdb79038665",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sum(ds.data.y > 0.5) / len(ds.data.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1cda13ae-04bb-4cbb-aecb-3280f3875afa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13283054"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ds.data.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "604298f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train graphs: 4750, Test graphs: 250, total: 5000\n"
     ]
    }
   ],
   "source": [
    "ds_size = len(ds)\n",
    "test_set_size = ds_size // 20\n",
    "train_set_size = ds_size - test_set_size\n",
    "train_set, test_set = random_split(ds, [train_set_size, test_set_size])\n",
    "print(f\"Train graphs: {len(train_set)}, Test graphs: {len(test_set)}, total: {ds_size}\")\n",
    "\n",
    "train_dl = DataLoader(train_set, batch_size=8, shuffle=True, drop_last=True)\n",
    "test_dl = DataLoader(test_set, batch_size=8, shuffle=True, drop_last=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3f4a0b99-bef3-4c82-a0ac-d88e1521382b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinkPredictionNet(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim=1, dropout=0.2):\n",
    "        super(LinkPredictionNet, self).__init__()\n",
    "        self.norm = gnn.LayerNorm(input_dim)\n",
    "        \n",
    "        hdim1 = 64\n",
    "        hdim2 = 64\n",
    "        hdim3 = 64\n",
    "        hdim_fc = 256\n",
    "\n",
    "        self.edgeconv1 = DynamicEdgeConvBlock(input_dim, hdim1, k=8)\n",
    "        self.edgeconv2 = DynamicEdgeConvBlock(hdim1, hdim2, k=8)\n",
    "        self.edgeconv3 = DynamicEdgeConvBlock(hdim2, hdim3, k=8)\n",
    "        \n",
    "        # Edge features from node embeddings for classification        \n",
    "        self.nodenetwork = nn.Sequential(\n",
    "            nn.Linear(2 * hdim3, hdim_fc),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(hdim_fc, output_dim),\n",
    "        )\n",
    "\n",
    "    def forward(self, X, edge_index, batch=None):\n",
    "        \n",
    "        #(prepared_edges, _) = geou.add_self_loops(edge_index)  \n",
    "        #undirected_index = geou.to_undirected(prepared_edges)\n",
    "\n",
    "        H0 = self.norm(X, batch)\n",
    "        H1 = self.edgeconv1(H0, batch=batch)\n",
    "        H2 = self.edgeconv2(H1, batch=batch)\n",
    "        H3 = self.edgeconv3(H2, batch=batch)\n",
    "        \n",
    "        src, dst = edge_index\n",
    "        edges = torch.cat([H3[src], H3[dst]], dim=-1)\n",
    "        return self.nodenetwork(edges).squeeze(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2539cf65",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_link_prediction(model, device, optimizer, loss_func, train_dl):\n",
    "    train_loss = []\n",
    "    model.train()\n",
    "\n",
    "    for data in train_dl:\n",
    "        data = data.to(device)\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        seg_pred = model(data.x, data.edge_index, batch=data.batch)        \n",
    "        loss = loss_func(seg_pred, data.y)\n",
    "        \n",
    "        loss.backward()\n",
    "        \n",
    "        optimizer.step()\n",
    "        train_loss.append(loss.item())\n",
    "\n",
    "    return np.mean(train_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "259ee8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def test_link_prediction(model, device, loss_func, test_dl):\n",
    "    test_loss = []\n",
    "    model.eval()\n",
    "    for data in test_dl:\n",
    "        data = data.to(device)\n",
    "        seg_pred = model(data.x, data.edge_index, data.batch)\n",
    "        loss = loss_func(seg_pred, data.y)\n",
    "        test_loss.append(loss.item())\n",
    "\n",
    "    return np.mean(test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b85638de",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinkPredictionNet(input_dim=ds.data.x.shape[1])\n",
    "epochs = 101\n",
    "model_path = f\"models/LinkPredictionNet.PnetL.ns.{epochs}e-{ds_name}.r{ds.RADIUS}.e{ds.bigT_e_th}.f{ds.N_FILES}.pt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2d3f1e82-74c5-4629-9b20-f05d7662aaf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0:\t train loss: 1581.485\t validation loss: 1456.502\n",
      "Epoch 5:\t train loss: 1104.280\t validation loss: 1083.859\n",
      "Epoch 10:\t train loss: 836.785\t validation loss: 815.774\n",
      "Epoch 15:\t train loss: 793.508\t validation loss: 763.746\n",
      "Epoch 20:\t train loss: 766.626\t validation loss: 734.025\n",
      "Epoch 25:\t train loss: 748.645\t validation loss: 708.586\n",
      "Epoch 30:\t train loss: 730.224\t validation loss: 688.772\n",
      "Epoch 35:\t train loss: 718.553\t validation loss: 687.950\n",
      "Epoch 40:\t train loss: 709.739\t validation loss: 668.761\n",
      "Epoch 45:\t train loss: 701.296\t validation loss: 699.374\n",
      "Epoch 50:\t train loss: 693.809\t validation loss: 660.919\n",
      "Epoch 55:\t train loss: 688.256\t validation loss: 656.794\n",
      "Epoch 60:\t train loss: 683.724\t validation loss: 648.978\n",
      "Epoch 65:\t train loss: 676.459\t validation loss: 640.425\n",
      "Epoch 70:\t train loss: 671.924\t validation loss: 636.738\n",
      "Epoch 75:\t train loss: 669.631\t validation loss: 634.486\n",
      "Epoch 80:\t train loss: 667.136\t validation loss: 635.332\n",
      "Epoch 85:\t train loss: 664.511\t validation loss: 627.984\n",
      "Epoch 90:\t train loss: 662.036\t validation loss: 628.365\n",
      "Epoch 95:\t train loss: 661.209\t validation loss: 635.511\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "models/LinkPredictionNet.PnetL.ns.101e-MultiParticle.r30.e10.f50.pt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 100:\t train loss: 660.537\t validation loss: 631.087\n"
     ]
    }
   ],
   "source": [
    "# %%script echo skipping\n",
    "# alpha - percentage of negative edges\n",
    "loss_func = QualityFocalLoss()\n",
    "\n",
    "model = model.to(device)\n",
    "optimizer = Adam(model.parameters(), lr=1e-3)\n",
    "scheduler = CosineAnnealingLR(optimizer, epochs, eta_min=1e-5)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "\n",
    "    train_loss = train_link_prediction(\n",
    "        model,\n",
    "        device,\n",
    "        optimizer,\n",
    "        loss_func,\n",
    "        train_dl\n",
    "    )\n",
    "    \n",
    "    scheduler.step()\n",
    "\n",
    "    if epoch % 5 == 0:\n",
    "        test_loss = test_link_prediction(model, device, loss_func, test_dl)\n",
    "        print(\n",
    "            f\"Epoch {epoch}:\\t train loss: {train_loss:.3f}\\t validation loss: {test_loss:.3f}\",\n",
    "            file=sys.stderr\n",
    "        )\n",
    "        \n",
    "torch.save(model.state_dict(), model_path)\n",
    "print(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0b9cbaa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skipping\n"
     ]
    }
   ],
   "source": [
    "%%script echo skipping\n",
    "model.load_state_dict(torch.load(\n",
    "    model_path,\n",
    "    map_location=device\n",
    "))\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c86c98db-0ac0-4a0c-84fd-4c50df507528",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9432493143773512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Future exception was never retrieved\n",
      "future: <Future finished exception=BrokenPipeError(32, 'Broken pipe')>\n",
      "Traceback (most recent call last):\n",
      "  File \"/mnt/sw/nix/store/z6v265ivx5w6xbajay41vdfc0la8gla5-python-3.9.12-view/lib/python3.9/asyncio/unix_events.py\", line 665, in write\n",
      "    n = os.write(self._fileno, data)\n",
      "BrokenPipeError: [Errno 32] Broken pipe\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP: 89729, TN: 505115, FP: 33638, FN: 28519\n",
      "TH: 0.36 precision: 0.727 recall: 0.759 fbeta: 0.743 b_acc: 0.848\n"
     ]
    }
   ],
   "source": [
    "#%%script echo skipping\n",
    "print(roc_auc(model, device, test_dl))\n",
    "precision_recall_curve(model, device, test_dl, focus_metric=\"fbeta\", beta=1, step=5, truth_threshold=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02ff033c-a829-4527-b0fe-fe83505cb261",
   "metadata": {},
   "source": [
    "- 51 epochs: 91.9\n",
    "- 100 epochs: 92.6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb8ce4e2",
   "metadata": {},
   "source": [
    "## Eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fb3f12d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from reco.data import get_event_data\n",
    "from reco.evaluation import model_evaluation\n",
    "from reco.dummy import DummyPleaser\n",
    "\n",
    "file_name = f\"{raw_dir}/new_ntuples_15163313_1.root\"\n",
    "cluster_data, trackster_data, simtrackster_data, assoc_data = get_event_data(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6b5ffc46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Radius threshold: 5 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.166 F: 0.494\n",
      "\treco_to_sim:\tP: 0.988 R: 0.170 F: 0.504\n",
      "\t|S| = 10 |T| = 57 |R| = 55\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.922 R: 0.174 F: 0.495\n",
      "\treco_to_sim:\tP: 0.951 R: 0.139 F: 0.438\n",
      "\t|S| = 10 |T| = 72 |R| = 91\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.990 R: 0.228 F: 0.593\n",
      "\treco_to_sim:\tP: 0.990 R: 0.224 F: 0.587\n",
      "\t|S| = 10 |T| = 27 |R| = 33\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.973 R: 0.236 F: 0.599\n",
      "\treco_to_sim:\tP: 0.973 R: 0.234 F: 0.597\n",
      "\t|S| = 10 |T| = 76 |R| = 82\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.144 F: 0.450\n",
      "\treco_to_sim:\tP: 0.957 R: 0.141 F: 0.444\n",
      "\t|S| = 10 |T| = 56 |R| = 53\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.975 R: 0.181 F: 0.520\n",
      "\treco_to_sim:\tP: 0.975 R: 0.181 F: 0.519\n",
      "\t|S| = 10 |T| = 54 |R| = 55\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.251 F: 0.619\n",
      "\treco_to_sim:\tP: 0.982 R: 0.233 F: 0.598\n",
      "\t|S| = 10 |T| = 46 |R| = 53\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.990 R: 0.191 F: 0.539\n",
      "\treco_to_sim:\tP: 0.988 R: 0.188 F: 0.533\n",
      "\t|S| = 10 |T| = 32 |R| = 31\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.965 R: 0.180 F: 0.515\n",
      "\treco_to_sim:\tP: 0.983 R: 0.164 F: 0.493\n",
      "\t|S| = 10 |T| = 47 |R| = 51\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.225 F: 0.587\n",
      "\treco_to_sim:\tP: 0.984 R: 0.221 F: 0.582\n",
      "\t|S| = 10 |T| = 71 |R| = 77\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.970 R: 0.249 F: 0.614\n",
      "\treco_to_sim:\tP: 0.950 R: 0.272 F: 0.633\n",
      "\t|S| = 10 |T| = 67 |R| = 59\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.245 F: 0.608\n",
      "\treco_to_sim:\tP: 0.963 R: 0.231 F: 0.590\n",
      "\t|S| = 10 |T| = 91 |R| = 94\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.905 R: 0.262 F: 0.608\n",
      "\treco_to_sim:\tP: 0.949 R: 0.202 F: 0.545\n",
      "\t|S| = 10 |T| = 54 |R| = 60\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.095 F: 0.341\n",
      "\treco_to_sim:\tP: 0.966 R: 0.089 F: 0.325\n",
      "\t|S| = 10 |T| = 100 |R| = 100\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.199 F: 0.540\n",
      "\treco_to_sim:\tP: 0.958 R: 0.156 F: 0.472\n",
      "\t|S| = 10 |T| = 75 |R| = 87\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.950 R: 0.313 F: 0.675\n",
      "\treco_to_sim:\tP: 0.942 R: 0.346 F: 0.701\n",
      "\t|S| = 10 |T| = 84 |R| = 78\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.188 F: 0.528\n",
      "\treco_to_sim:\tP: 0.959 R: 0.205 F: 0.552\n",
      "\t|S| = 10 |T| = 62 |R| = 60\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.146 F: 0.450\n",
      "\treco_to_sim:\tP: 0.975 R: 0.110 F: 0.379\n",
      "\t|S| = 10 |T| = 96 |R| = 112\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.977 R: 0.146 F: 0.457\n",
      "\treco_to_sim:\tP: 0.970 R: 0.149 F: 0.462\n",
      "\t|S| = 10 |T| = 101 |R| = 93\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.931 R: 0.198 F: 0.536\n",
      "\treco_to_sim:\tP: 0.966 R: 0.168 F: 0.495\n",
      "\t|S| = 10 |T| = 51 |R| = 65\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.962 R: 0.201 F: 0.538\n",
      "mean reco_to_sim:\tP: 0.968 R: 0.191 F: 0.522\n",
      "mean n_tracksters:\tP: 10.000 R: 65.950 F: 69.450\n",
      "--- Radius threshold: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.883 R: 0.214 F: 0.544\n",
      "\treco_to_sim:\tP: 0.984 R: 0.204 F: 0.558\n",
      "\t|S| = 10 |T| = 28 |R| = 30\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.564 R: 0.295 F: 0.477\n",
      "\treco_to_sim:\tP: 0.933 R: 0.186 F: 0.517\n",
      "\t|S| = 10 |T| = 23 |R| = 52\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.937 R: 0.295 F: 0.653\n",
      "\treco_to_sim:\tP: 0.989 R: 0.285 F: 0.662\n",
      "\t|S| = 10 |T| = 12 |R| = 22\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.833 R: 0.363 F: 0.661\n",
      "\treco_to_sim:\tP: 0.971 R: 0.315 F: 0.685\n",
      "\t|S| = 10 |T| = 23 |R| = 34\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.805 R: 0.228 F: 0.535\n",
      "\treco_to_sim:\tP: 0.894 R: 0.186 F: 0.507\n",
      "\t|S| = 10 |T| = 30 |R| = 35\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.859 R: 0.221 F: 0.544\n",
      "\treco_to_sim:\tP: 0.894 R: 0.230 F: 0.567\n",
      "\t|S| = 10 |T| = 27 |R| = 23\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.754 R: 0.392 F: 0.636\n",
      "\treco_to_sim:\tP: 0.978 R: 0.315 F: 0.688\n",
      "\t|S| = 10 |T| = 14 |R| = 28\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.989 R: 0.211 F: 0.569\n",
      "\treco_to_sim:\tP: 0.988 R: 0.210 F: 0.568\n",
      "\t|S| = 10 |T| = 17 |R| = 17\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.961 R: 0.255 F: 0.618\n",
      "\treco_to_sim:\tP: 0.982 R: 0.206 F: 0.560\n",
      "\t|S| = 10 |T| = 13 |R| = 26\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.982 R: 0.281 F: 0.655\n",
      "\treco_to_sim:\tP: 0.952 R: 0.285 F: 0.649\n",
      "\t|S| = 10 |T| = 31 |R| = 32\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.875 R: 0.366 F: 0.685\n",
      "\treco_to_sim:\tP: 0.887 R: 0.369 F: 0.693\n",
      "\t|S| = 10 |T| = 22 |R| = 25\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.912 R: 0.363 F: 0.700\n",
      "\treco_to_sim:\tP: 0.934 R: 0.353 F: 0.703\n",
      "\t|S| = 10 |T| = 40 |R| = 53\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.886 R: 0.315 F: 0.651\n",
      "\treco_to_sim:\tP: 0.942 R: 0.256 F: 0.613\n",
      "\t|S| = 10 |T| = 23 |R| = 44\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.900 R: 0.167 F: 0.479\n",
      "\treco_to_sim:\tP: 0.959 R: 0.135 F: 0.431\n",
      "\t|S| = 10 |T| = 52 |R| = 66\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.889 R: 0.280 F: 0.620\n",
      "\treco_to_sim:\tP: 0.927 R: 0.197 F: 0.532\n",
      "\t|S| = 10 |T| = 29 |R| = 66\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.888 R: 0.446 F: 0.741\n",
      "\treco_to_sim:\tP: 0.938 R: 0.446 F: 0.768\n",
      "\t|S| = 10 |T| = 33 |R| = 43\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.901 R: 0.266 F: 0.610\n",
      "\treco_to_sim:\tP: 0.914 R: 0.257 F: 0.605\n",
      "\t|S| = 10 |T| = 36 |R| = 43\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.898 R: 0.215 F: 0.549\n",
      "\treco_to_sim:\tP: 0.954 R: 0.166 F: 0.489\n",
      "\t|S| = 10 |T| = 44 |R| = 73\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.920 R: 0.197 F: 0.531\n",
      "\treco_to_sim:\tP: 0.959 R: 0.169 F: 0.496\n",
      "\t|S| = 10 |T| = 55 |R| = 71\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.798 R: 0.293 F: 0.594\n",
      "\treco_to_sim:\tP: 0.963 R: 0.210 F: 0.561\n",
      "\t|S| = 10 |T| = 22 |R| = 43\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.872 R: 0.283 F: 0.603\n",
      "mean reco_to_sim:\tP: 0.947 R: 0.249 F: 0.593\n",
      "mean n_tracksters:\tP: 10.000 R: 28.700 F: 41.300\n",
      "--- Radius threshold: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.881 R: 0.224 F: 0.555\n",
      "\treco_to_sim:\tP: 0.959 R: 0.223 F: 0.578\n",
      "\t|S| = 10 |T| = 20 |R| = 24\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.482 R: 0.338 F: 0.444\n",
      "\treco_to_sim:\tP: 0.909 R: 0.209 F: 0.545\n",
      "\t|S| = 10 |T| = 10 |R| = 44\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.934 R: 0.296 F: 0.653\n",
      "\treco_to_sim:\tP: 0.989 R: 0.286 F: 0.663\n",
      "\t|S| = 10 |T| = 9 |R| = 20\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.736 R: 0.371 F: 0.615\n",
      "\treco_to_sim:\tP: 0.968 R: 0.334 F: 0.701\n",
      "\t|S| = 10 |T| = 11 |R| = 26\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.779 R: 0.242 F: 0.539\n",
      "\treco_to_sim:\tP: 0.895 R: 0.196 F: 0.523\n",
      "\t|S| = 10 |T| = 21 |R| = 32\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.826 R: 0.236 F: 0.551\n",
      "\treco_to_sim:\tP: 0.900 R: 0.230 F: 0.569\n",
      "\t|S| = 10 |T| = 14 |R| = 15\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.754 R: 0.398 F: 0.639\n",
      "\treco_to_sim:\tP: 0.977 R: 0.348 F: 0.717\n",
      "\t|S| = 10 |T| = 12 |R| = 25\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.214 F: 0.573\n",
      "\treco_to_sim:\tP: 0.987 R: 0.209 F: 0.565\n",
      "\t|S| = 10 |T| = 13 |R| = 14\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.961 R: 0.256 F: 0.620\n",
      "\treco_to_sim:\tP: 0.981 R: 0.207 F: 0.561\n",
      "\t|S| = 10 |T| = 11 |R| = 23\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.702 R: 0.319 F: 0.566\n",
      "\treco_to_sim:\tP: 0.974 R: 0.278 F: 0.650\n",
      "\t|S| = 10 |T| = 14 |R| = 26\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.831 R: 0.385 F: 0.675\n",
      "\treco_to_sim:\tP: 0.885 R: 0.373 F: 0.695\n",
      "\t|S| = 10 |T| = 9 |R| = 24\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.787 R: 0.407 F: 0.663\n",
      "\treco_to_sim:\tP: 0.909 R: 0.359 F: 0.696\n",
      "\t|S| = 10 |T| = 23 |R| = 53\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.723 R: 0.376 F: 0.610\n",
      "\treco_to_sim:\tP: 0.940 R: 0.287 F: 0.646\n",
      "\t|S| = 10 |T| = 8 |R| = 40\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.835 R: 0.198 F: 0.508\n",
      "\treco_to_sim:\tP: 0.959 R: 0.143 F: 0.448\n",
      "\t|S| = 10 |T| = 25 |R| = 58\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.751 R: 0.297 F: 0.575\n",
      "\treco_to_sim:\tP: 0.925 R: 0.219 F: 0.562\n",
      "\t|S| = 10 |T| = 14 |R| = 62\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.880 R: 0.481 F: 0.755\n",
      "\treco_to_sim:\tP: 0.937 R: 0.446 F: 0.768\n",
      "\t|S| = 10 |T| = 20 |R| = 37\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.768 R: 0.322 F: 0.601\n",
      "\treco_to_sim:\tP: 0.912 R: 0.264 F: 0.612\n",
      "\t|S| = 10 |T| = 20 |R| = 35\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.637 R: 0.279 F: 0.507\n",
      "\treco_to_sim:\tP: 0.879 R: 0.184 F: 0.500\n",
      "\t|S| = 10 |T| = 17 |R| = 59\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.853 R: 0.239 F: 0.563\n",
      "\treco_to_sim:\tP: 0.916 R: 0.185 F: 0.512\n",
      "\t|S| = 10 |T| = 30 |R| = 52\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.790 R: 0.347 F: 0.629\n",
      "\treco_to_sim:\tP: 0.962 R: 0.226 F: 0.583\n",
      "\t|S| = 10 |T| = 6 |R| = 43\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.795 R: 0.311 F: 0.592\n",
      "mean reco_to_sim:\tP: 0.938 R: 0.260 F: 0.605\n",
      "mean n_tracksters:\tP: 10.000 R: 15.350 F: 35.600\n",
      "--- Radius threshold: 30 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.878 R: 0.255 F: 0.590\n",
      "\treco_to_sim:\tP: 0.959 R: 0.238 F: 0.597\n",
      "\t|S| = 10 |T| = 14 |R| = 22\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.478 R: 0.354 F: 0.447\n",
      "\treco_to_sim:\tP: 0.905 R: 0.213 F: 0.549\n",
      "\t|S| = 10 |T| = 5 |R| = 41\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.917 R: 0.297 F: 0.647\n",
      "\treco_to_sim:\tP: 0.989 R: 0.286 F: 0.663\n",
      "\t|S| = 10 |T| = 8 |R| = 20\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.735 R: 0.372 F: 0.615\n",
      "\treco_to_sim:\tP: 0.968 R: 0.336 F: 0.703\n",
      "\t|S| = 10 |T| = 9 |R| = 23\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.778 R: 0.243 F: 0.540\n",
      "\treco_to_sim:\tP: 0.894 R: 0.197 F: 0.523\n",
      "\t|S| = 10 |T| = 19 |R| = 31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.680 R: 0.242 F: 0.499\n",
      "\treco_to_sim:\tP: 0.896 R: 0.237 F: 0.576\n",
      "\t|S| = 10 |T| = 8 |R| = 13\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.753 R: 0.399 F: 0.640\n",
      "\treco_to_sim:\tP: 0.977 R: 0.349 F: 0.718\n",
      "\t|S| = 10 |T| = 10 |R| = 24\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.218 F: 0.579\n",
      "\treco_to_sim:\tP: 0.985 R: 0.213 F: 0.570\n",
      "\t|S| = 10 |T| = 12 |R| = 13\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.961 R: 0.256 F: 0.620\n",
      "\treco_to_sim:\tP: 0.981 R: 0.207 F: 0.561\n",
      "\t|S| = 10 |T| = 11 |R| = 23\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.690 R: 0.343 F: 0.574\n",
      "\treco_to_sim:\tP: 0.849 R: 0.279 F: 0.603\n",
      "\t|S| = 10 |T| = 9 |R| = 24\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.831 R: 0.385 F: 0.675\n",
      "\treco_to_sim:\tP: 0.885 R: 0.375 F: 0.696\n",
      "\t|S| = 10 |T| = 8 |R| = 23\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.557 R: 0.446 F: 0.530\n",
      "\treco_to_sim:\tP: 0.908 R: 0.364 F: 0.699\n",
      "\t|S| = 10 |T| = 16 |R| = 51\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.722 R: 0.378 F: 0.611\n",
      "\treco_to_sim:\tP: 0.938 R: 0.295 F: 0.653\n",
      "\t|S| = 10 |T| = 7 |R| = 38\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.804 R: 0.212 F: 0.515\n",
      "\treco_to_sim:\tP: 0.958 R: 0.149 F: 0.459\n",
      "\t|S| = 10 |T| = 19 |R| = 56\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.751 R: 0.297 F: 0.575\n",
      "\treco_to_sim:\tP: 0.955 R: 0.217 F: 0.568\n",
      "\t|S| = 10 |T| = 12 |R| = 65\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.877 R: 0.485 F: 0.755\n",
      "\treco_to_sim:\tP: 0.934 R: 0.463 F: 0.776\n",
      "\t|S| = 10 |T| = 16 |R| = 32\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.766 R: 0.329 F: 0.605\n",
      "\treco_to_sim:\tP: 0.912 R: 0.265 F: 0.613\n",
      "\t|S| = 10 |T| = 15 |R| = 33\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.639 R: 0.279 F: 0.508\n",
      "\treco_to_sim:\tP: 0.874 R: 0.185 F: 0.501\n",
      "\t|S| = 10 |T| = 13 |R| = 59\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.852 R: 0.241 F: 0.565\n",
      "\treco_to_sim:\tP: 0.917 R: 0.186 F: 0.513\n",
      "\t|S| = 10 |T| = 21 |R| = 52\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.656 R: 0.355 F: 0.561\n",
      "\treco_to_sim:\tP: 0.962 R: 0.226 F: 0.583\n",
      "\t|S| = 10 |T| = 5 |R| = 43\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.766 R: 0.319 F: 0.583\n",
      "mean reco_to_sim:\tP: 0.932 R: 0.264 F: 0.606\n",
      "mean n_tracksters:\tP: 10.000 R: 11.850 F: 34.300\n"
     ]
    }
   ],
   "source": [
    "r_ranges = r_ranges = [5, 10, 20, 30]\n",
    "results = []\n",
    "\n",
    "max_events = 20\n",
    "clue3D_F = []\n",
    "target_F = []\n",
    "naive_reco_F = []\n",
    "model_reco_F = []\n",
    "for r in r_ranges:\n",
    "    print(f\"--- Radius threshold: {r} ---\")\n",
    "    result = model_evaluation(\n",
    "        cluster_data,\n",
    "        trackster_data,\n",
    "        simtrackster_data,\n",
    "        assoc_data,\n",
    "        model.to(\"cpu\"),\n",
    "        decision_th=0.5,\n",
    "        radius=r,\n",
    "        max_events=max_events,\n",
    "        bigT_e_th=10,\n",
    "        pileup=False,\n",
    "        graph=True,\n",
    "        link_prediction=True,\n",
    "    )\n",
    "    clue3D_F.append(np.sum(np.array(result[\"clue3d_to_sim\"])[:,2]) / max_events)\n",
    "    target_F.append(np.sum(np.array(result[\"target_to_sim\"])[:,2]) / max_events)\n",
    "    model_reco_F.append(np.sum(np.array(result[\"reco_to_sim\"])[:,2]) / max_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9d301515-3325-477c-b21b-08b26767baa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.45468812367852296, 0.45468812367852296, 0.45468812367852296, 0.45468812367852296]\n",
      "[0.5384628263859672, 0.6025392654121396, 0.5920900464142245, 0.5825359464438431]\n",
      "[0.5223763352150408, 0.5925567316424671, 0.6047396967353035, 0.6060942759227783]\n"
     ]
    }
   ],
   "source": [
    "print(clue3D_F)\n",
    "print(target_F)\n",
    "print(model_reco_F)\n",
    "\n",
    "# XXX: the problem here is probably in the reconstruction (should allow double connections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bc9b49b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(r_ranges[:5], (np.array(target_F) - np.array(clue3D_F))[:5], '--s', label=\"target $\\Delta F_{0.5}$\", c=\"#D55E00\")\n",
    "ax.plot(r_ranges[:5], (np.array(model_reco_F) - np.array(clue3D_F))[:5], '-o', label=\"GNN LP $\\Delta F_{0.5}$\", c=\"#56B4E9\")\n",
    "# ax.plot(r_ranges[:5], (np.array(naive_reco_F) - np.array(clue3D_F))[:5], '-v', label=\"naive $\\Delta F_{0.5}$\", c=\"#E69F00\")\n",
    "\n",
    "# ax.axhline(max(np.array(naive_reco_F) - np.array(clue3D_F)), label=\"baseline\", c=\"lightgray\", linestyle=\"--\")\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"Neighborhood radius (cm)\")\n",
    "ax.set_ylabel(\"$\\Delta F_{0.5}$\")\n",
    "# ax.set_ylim((-0.1, 0.1))\n",
    "\n",
    "fig.savefig(f'export/{ds_name}-e10-graph-lp-radius-plot.png', dpi=300, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "931b57e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reco 0.15140615224425535\n",
      "Target 0.1478511417336167\n"
     ]
    }
   ],
   "source": [
    "print(\"Reco\", max(np.array(model_reco_F) - np.array(clue3D_F)))\n",
    "# print(\"Naive\", max(np.array(naive_reco_F) - np.array(clue3D_F)))\n",
    "print(\"Target\", max(np.array(target_F) - np.array(clue3D_F)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a309e4f",
   "metadata": {},
   "source": [
    "## Setup comparison:\n",
    "\n",
    "Pairwise:\n",
    "- mean clue3d_to_sim:\tP: 0.994 R: 0.301 F: 0.652\n",
    "- mean target_to_sim:\tP: 0.992 R: 0.689 F: 0.892\n",
    "- mean reco_to_sim:\t    P: 0.659 R: 0.699 F: 0.642\n",
    "\n",
    "Graph (node-pred):\n",
    "- mean clue3d_to_sim:\tP: 0.994 R: 0.301 F: 0.652\n",
    "- mean target_to_sim:\tP: 0.992 R: 0.688 F: 0.892\n",
    "- mean reco_to_sim:\t    P: 0.659 R: 0.699 F: 0.642\n",
    "\n",
    "Graph (link-pred):\n",
    "- mean clue3d_to_sim:\tP: 0.994 R: 0.301 F: 0.652\n",
    "- mean target_to_sim:\tP: 0.992 R: 0.689 F: 0.892\n",
    "- mean reco_to_sim:\tP: 0.915 R: 0.329 F: 0.637\n"
   ]
  },
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    "## Results\n",
    "\n",
    "Using alpha=balance seems to converge faster (100 epochs rather than 200)\n",
    "- maybe just sticking to 0.25? (works just fine)\n",
    "\n",
    "Hyperparams\n",
    "- (64, 128, noskip), alpha=balance, roc_auc = 0.9733\n",
    "- (64, 128, 256fc, noskip, 200e), alpha=1-balance, roc_auc = 0.9771\n",
    "- (64, 128, 256fc, noskip, 200e), alpha=balance, roc_auc = 0.981\n",
    "- (64, 128, 256fc, noskip, 100e), alpha=0.25, roc_auc = 0.9796"
   ]
  }
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