Scoring.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from trackml.dataset import load_event\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "\n",
    "# Path to events data\n",
    "path2data = '/home/alepfu/Desktop/dataset/train_100_events/'\n",
    "\n",
    "# Event filename prefix\n",
    "event_id = 'event000001000'\n",
    "\n",
    "# Load dataset\n",
    "hits, cells, particles, truth = load_event(path2data + event_id)\n",
    "\n",
    "# Merge hits, particles and truth\n",
    "data = pd.merge(pd.merge(truth, pd.DataFrame(hits), on = ['hit_id']), pd.DataFrame(particles), on = ['particle_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.177746296880315"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from trackml.randomize import shuffle_hits\n",
    "from trackml.score import score_event\n",
    "\n",
    "shuffled = shuffle_hits(truth, 0.5) # 50% probability to reassign a hit\n",
    "score = score_event(truth, shuffled)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb7970c2c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bins:  [0.         0.0001342  0.00026839 0.00040259]\n",
      "Bin means:  [7.48550487e-05 1.98850295e-04 3.21430907e-04]\n",
      "Bin labels:  [1 2 3]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Get score for each track\n",
    "particle_score = data.groupby('particle_id').agg({'weight':'sum'}).reset_index()\n",
    "particle_score.rename(columns={'weight': 'score'}, inplace=True)\n",
    "\n",
    "# Plot histogram for a low number of bins\n",
    "N_bins = 3\n",
    "ax = sns.distplot(particle_score.score.values, bins=N_bins, kde=False);\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('Track score')\n",
    "ax.set_title('Distribution of tracks scores (%d bins)' % (N_bins))\n",
    "plt.show()\n",
    "\n",
    "# Get bin ranges and means\n",
    "from scipy.stats import binned_statistic\n",
    "bin_means, bin_edges, binnumber = binned_statistic(particle_score.score.values, particle_score.score.values, bins=N_bins, statistic='mean')\n",
    "print(\"Bins: \", bin_edges)\n",
    "print(\"Bin means: \", bin_means)\n",
    "print(\"Bin labels: \", np.unique(binnumber))\n",
    "# Append the bin label to the dataframe\n",
    "particle_score['score_bin'] = binnumber\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hit_id</th>\n",
       "      <th>particle_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>score</th>\n",
       "      <th>score_bin</th>\n",
       "      <th>nhits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>22525763437723648</td>\n",
       "      <td>-55.336102</td>\n",
       "      <td>0.635342</td>\n",
       "      <td>-1502.5</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1420</td>\n",
       "      <td>22525763437723648</td>\n",
       "      <td>-55.162201</td>\n",
       "      <td>0.604841</td>\n",
       "      <td>-1497.5</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1460</td>\n",
       "      <td>22525763437723648</td>\n",
       "      <td>-47.975498</td>\n",
       "      <td>0.311848</td>\n",
       "      <td>-1302.5</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3170</td>\n",
       "      <td>22525763437723648</td>\n",
       "      <td>-47.812302</td>\n",
       "      <td>0.327322</td>\n",
       "      <td>-1297.5</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3234</td>\n",
       "      <td>22525763437723648</td>\n",
       "      <td>-40.622799</td>\n",
       "      <td>0.088046</td>\n",
       "      <td>-1102.5</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hit_id        particle_id          x         y       z    score  score_bin  \\\n",
       "0       2  22525763437723648 -55.336102  0.635342 -1502.5  0.00009          1   \n",
       "1    1420  22525763437723648 -55.162201  0.604841 -1497.5  0.00009          1   \n",
       "2    1460  22525763437723648 -47.975498  0.311848 -1302.5  0.00009          1   \n",
       "3    3170  22525763437723648 -47.812302  0.327322 -1297.5  0.00009          1   \n",
       "4    3234  22525763437723648 -40.622799  0.088046 -1102.5  0.00009          1   \n",
       "\n",
       "   nhits  \n",
       "0     10  \n",
       "1     10  \n",
       "2     10  \n",
       "3     10  \n",
       "4     10  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_score = pd.merge(data, particle_score, on = ['particle_id'])\n",
    "data_score = data_score.loc[:, [\"hit_id\", \"particle_id\", \"x\", \"y\", \"z\", \"score\", \"score_bin\", \"nhits\"]]\n",
    "data_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb78cc98b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot some tracks for every score bin\n",
    "N_viz = 10\n",
    "bin_1 = data_score[data_score.score_bin == 1].sample(N_viz)\n",
    "bin_2 = data_score[data_score.score_bin == 2].sample(N_viz)\n",
    "bin_3 = data_score[data_score.score_bin == 3].sample(N_viz)\n",
    "subset = pd.concat([bin_1, bin_2, bin_3])\n",
    "\n",
    "fig = plt.figure(figsize=(30,30))\n",
    "colors = [\"#e74c3c\", \"#34495e\", \"#2ecc71\"]\n",
    "\n",
    "plt.subplot(3,3,1)\n",
    "t1 = pd.DataFrame()\n",
    "for particle_id in bin_1.particle_id.unique():\n",
    "    t1 = pd.concat([t1, data_score[data_score.particle_id == particle_id]])\n",
    "plt.plot(t1.x, t1.y, \"o\", label=\"1\", color=colors[0])\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.legend()\n",
    "plt.ylim((-1000,1000))\n",
    "plt.xlim((-1000,1000))\n",
    "\n",
    "plt.subplot(3,3,2)\n",
    "t2 = pd.DataFrame()\n",
    "for particle_id in bin_2.particle_id.unique():\n",
    "    t2 = pd.concat([t2, data_score[data_score.particle_id == particle_id]])\n",
    "plt.plot(t2.x, t2.y, \"o\", label=\"2\", color=colors[1])\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.legend()\n",
    "plt.ylim((-1000,1000))\n",
    "plt.xlim((-1000,1000))\n",
    "\n",
    "plt.subplot(3,3,3)\n",
    "t3 = pd.DataFrame()\n",
    "for particle_id in bin_3.particle_id.unique():\n",
    "    t3 = pd.concat([t3, data_score[data_score.particle_id == particle_id]])\n",
    "plt.plot(t3.x, t3.y, \"o\", label=\"3\", color=colors[2])\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.legend()\n",
    "plt.ylim((-1000,1000))\n",
    "plt.xlim((-1000,1000))\n",
    "\n",
    "plt.subplot(3,3,4)\n",
    "t1 = pd.DataFrame()\n",
    "for particle_id in bin_1.particle_id.unique():\n",
    "    t1 = pd.concat([t1, data_score[data_score.particle_id == particle_id]])\n",
    "plt.plot(t1.x, t1.z, \"o\", label=\"1\", color=colors[0])\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Z\")\n",
    "plt.legend()\n",
    "plt.ylim((-1000,1000))\n",
    "plt.xlim((-1000,1000))\n",
    "\n",
    "plt.subplot(3,3,5)\n",
    "t2 = pd.DataFrame()\n",
    "for particle_id in bin_2.particle_id.unique():\n",
    "    t2 = pd.concat([t2, data_score[data_score.particle_id == particle_id]])\n",
    "plt.plot(t2.x, t2.z, \"o\", label=\"2\", color=colors[1])\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Z\")\n",
    "plt.legend()\n",
    "plt.ylim((-1000,1000))\n",
    "plt.xlim((-1000,1000))\n",
    "\n",
    "plt.subplot(3,3,6)\n",
    "t3 = pd.DataFrame()\n",
    "for particle_id in bin_3.particle_id.unique():\n",
    "    t3 = pd.concat([t3, data_score[data_score.particle_id == particle_id]])\n",
    "plt.plot(t3.x, t3.z, \"o\", label=\"3\", color=colors[2])\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Z\")\n",
    "plt.legend()\n",
    "plt.ylim((-1000,1000))\n",
    "plt.xlim((-1000,1000))\n",
    "\n",
    "plt.subplot(3,3,7)\n",
    "t1 = pd.DataFrame()\n",
    "for particle_id in bin_1.particle_id.unique():\n",
    "    t1 = pd.concat([t1, data_score[data_score.particle_id == particle_id]])\n",
    "plt.plot(t1.y, t1.z, \"o\", label=\"1\", color=colors[0])\n",
    "plt.xlabel(\"Y\")\n",
    "plt.ylabel(\"Z\")\n",
    "plt.legend()\n",
    "plt.ylim((-1000,1000))\n",
    "plt.xlim((-1000,1000))\n",
    "\n",
    "plt.subplot(3,3,8)\n",
    "t2 = pd.DataFrame()\n",
    "for particle_id in bin_2.particle_id.unique():\n",
    "    t2 = pd.concat([t2, data_score[data_score.particle_id == particle_id]])\n",
    "plt.plot(t2.y, t2.z, \"o\", label=\"2\", color=colors[1])\n",
    "plt.xlabel(\"Y\")\n",
    "plt.ylabel(\"Z\")\n",
    "plt.legend()\n",
    "plt.ylim((-1000,1000))\n",
    "plt.xlim((-1000,1000))\n",
    "\n",
    "plt.subplot(3,3,9)\n",
    "t3 = pd.DataFrame()\n",
    "for particle_id in bin_3.particle_id.unique():\n",
    "    t3 = pd.concat([t3, data_score[data_score.particle_id == particle_id]])\n",
    "plt.plot(t3.y, t3.z, \"o\", label=\"3\", color=colors[2])\n",
    "plt.xlabel(\"Y\")\n",
    "plt.ylabel(\"Z\")\n",
    "plt.legend()\n",
    "plt.ylim((-1000,1000))\n",
    "plt.xlim((-1000,1000))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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