multiclassifier/graphErrors.ipynb

{
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   "id": "76ccf823-82dc-4dcc-b575-d1a8be023cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import read_csv\n",
    "import math\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4344b298-f6d2-49d8-a272-474770e8c43a",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yvalues</th>\n",
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       "      <th>33</th>\n",
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       "<p>68 rows × 1 columns</p>\n",
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      "text/plain": [
       "     yvalues\n",
       "0   0.815385\n",
       "1   0.839286\n",
       "2   0.930693\n",
       "3   0.897436\n",
       "4   0.880342\n",
       "..       ...\n",
       "29  0.943548\n",
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       "\n",
       "[68 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('PositiveYValues.out', header=None)\n",
    "df1 = df1.T\n",
    "\n",
    "df2 = pd.read_csv('NegativeYValues.out', header=None)\n",
    "df2 = df2.T\n",
    "\n",
    "df1.columns =['yvalues']\n",
    "df2.columns =['yvalues']\n",
    "\n",
    "df3 = pd.concat([df2,df1],join=\"inner\")\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5c1c5f26-6cb7-4a8e-9495-d14529f60d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_error = pd.read_csv('errorsPositive.out', header=None)\n",
    "df1_error = df1_error.T\n",
    "\n",
    "df2_error = pd.read_csv('errorsNegative.out', header=None)\n",
    "df2_error = df2_error.T\n",
    "\n",
    "df1_error.columns =['error']\n",
    "df2_error.columns =['error']\n",
    "\n",
    "df3_error = pd.concat([df2_error,df1_error],join=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c1bc70b4-c237-4aab-a48d-6db8dad36eb2",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.943548</td>\n",
       "      <td>0.020726</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.899183</td>\n",
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       "      <th>31</th>\n",
       "      <td>0.934783</td>\n",
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       "      <th>33</th>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.032572</td>\n",
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       "</table>\n",
       "<p>68 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "     yvalues     error\n",
       "0   0.815385  0.048124\n",
       "1   0.839286  0.034703\n",
       "2   0.930693  0.017870\n",
       "3   0.897436  0.015363\n",
       "4   0.880342  0.030006\n",
       "..       ...       ...\n",
       "29  0.943548  0.020726\n",
       "30  0.899183  0.015717\n",
       "31  0.934783  0.018202\n",
       "32  0.883495  0.031612\n",
       "33  0.916667  0.032572\n",
       "\n",
       "[68 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "mergeDF = pd.concat([df3,df3_error],axis=1)\n",
    "mergeDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "845d389b-2033-477c-b5c2-26651ddcc9aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "xvalues = [-4.5,-4.0,-3.5,-3.0, \n",
    "-2.9,-2.8,-2.7,-2.6,-2.5,-2.4,-2.3,-2.2,-2.1,-2.0,\n",
    "-1.9,-1.8,-1.7,-1.6,-1.5,-1.4,-1.3,-1.2,-1.1,-1.0,\n",
    "-0.9,-0.8,-0.7,-0.6,-0.5,-0.4,-0.3,-0.2, -0.1, 0,\n",
    "0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2.0,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,3.0,3.5,4.0,4.5,]"
   ]
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   "cell_type": "code",
   "execution_count": 26,
   "id": "f6715e02-3a7d-4888-8c22-67e685ab5981",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.839286</td>\n",
       "      <td>0.034703</td>\n",
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       "      <td>0.930693</td>\n",
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       "      <th>29</th>\n",
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       "      <td>2.9</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.899183</td>\n",
       "      <td>0.015717</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.934783</td>\n",
       "      <td>0.018202</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.883495</td>\n",
       "      <td>0.031612</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.032572</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     yvalues     error  xvalues\n",
       "0   0.815385  0.048124     -4.5\n",
       "1   0.839286  0.034703     -4.0\n",
       "2   0.930693  0.017870     -3.5\n",
       "3   0.897436  0.015363     -3.0\n",
       "4   0.880342  0.030006     -2.9\n",
       "..       ...       ...      ...\n",
       "29  0.943548  0.020726      2.9\n",
       "30  0.899183  0.015717      3.0\n",
       "31  0.934783  0.018202      3.5\n",
       "32  0.883495  0.031612      4.0\n",
       "33  0.916667  0.032572      4.5\n",
       "\n",
       "[68 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeDF['xvalues'] = xvalues\n",
    "mergeDF "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "144d9441-ba88-4b3c-b12d-7a7247db50e7",
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     "data": {
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       "dtype('float64')"
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     "execution_count": 55,
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   ],
   "source": [
    "xvaluesArray = mergeDF[['xvalues']].to_numpy()\n",
    "xvaluesArray\n",
    "yvaluesArray = mergeDF[['yvalues']].to_numpy()\n",
    "yvaluesArray\n",
    "\n",
    "errorArray = mergeDF[['error']].to_numpy()\n",
    "errorArray.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8082543d-9288-44d3-8cd0-a7b2c7b06bad",
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    {
     "data": {
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       "array([0.04812365, 0.03470349, 0.01786964, 0.01536268, 0.03000571,\n",
       "       0.03272038, 0.02454928, 0.02083003, 0.01890844, 0.01723235,\n",
       "       0.01419922, 0.01751775, 0.01698277, 0.01429796, 0.01315844,\n",
       "       0.01172378, 0.01026941, 0.01039151, 0.00890219, 0.00839476,\n",
       "       0.00741146, 0.00698876, 0.00622617, 0.00591374, 0.00522084,\n",
       "       0.00553299, 0.00576442, 0.00577805, 0.00641114, 0.00678998,\n",
       "       0.00745243, 0.00819913, 0.0392613 , 0.02738613, 0.01769039,\n",
       "       0.0389973 , 0.0079421 , 0.00749792, 0.00664994, 0.0060321 ,\n",
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       "       0.01820238, 0.03161226, 0.03257233])"
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   ],
   "source": [
    "xvaluesArray = np.array(mergeDF['xvalues'])\n",
    "yvaluesArray = np.array(mergeDF['yvalues'])\n",
    "errorArray = np.array(mergeDF['error'])\n",
    "errorArray"
   ]
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   "cell_type": "code",
   "execution_count": 64,
   "id": "9ea9269f-c065-4e20-b8dc-bb663e5ab222",
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    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(xvaluesArray, yvaluesArray, yerr=errorArray, fmt=\"o\")\n",
    "plt.title('Multiclassifier')\n",
    "plt.xlabel('true PT (GeV)')\n",
    "plt.ylabel('fraction classified as > abs (0.2GeV)')\n",
    "plt.savefig('multiclassifier_turnonCurve.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a63cc286-6a39-4ecf-bb62-c82d80f3716b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "py3-preamble"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}