{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d563033c",
   "metadata": {},
   "source": [
    "# Import tools/models be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "id": "fc9e6e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from math import pi\n",
    "from sklearn.linear_model import LinearRegression     # import linear model to be used\n",
    "import joblib                                         # to store model\n",
    "import os\n",
    "import errno\n",
    "from sklearn.model_selection import train_test_split  # this is very important"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313e157e",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "bdc08ed0",
   "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>$M_{inv}$</th>\n",
       "      <th>$rapidity$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>512.889687</td>\n",
       "      <td>-1.698364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>349.604335</td>\n",
       "      <td>0.649795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102.015218</td>\n",
       "      <td>0.935912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>475.693746</td>\n",
       "      <td>1.691695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>422.624040</td>\n",
       "      <td>1.233733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8604</th>\n",
       "      <td>310.392511</td>\n",
       "      <td>1.763102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8605</th>\n",
       "      <td>322.772764</td>\n",
       "      <td>1.343101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8606</th>\n",
       "      <td>373.867518</td>\n",
       "      <td>-0.255696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8607</th>\n",
       "      <td>275.877980</td>\n",
       "      <td>0.169265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8608</th>\n",
       "      <td>440.485176</td>\n",
       "      <td>0.265116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8609 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       $M_{inv}$  $rapidity$\n",
       "0     512.889687   -1.698364\n",
       "1     349.604335    0.649795\n",
       "2     102.015218    0.935912\n",
       "3     475.693746    1.691695\n",
       "4     422.624040    1.233733\n",
       "...          ...         ...\n",
       "8604  310.392511    1.763102\n",
       "8605  322.772764    1.343101\n",
       "8606  373.867518   -0.255696\n",
       "8607  275.877980    0.169265\n",
       "8608  440.485176    0.265116\n",
       "\n",
       "[8609 rows x 2 columns]"
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directory = '../data/'\n",
    "filein    = 'checkNLO.txt'\n",
    "file      = directory+filein\n",
    "data = np.genfromtxt(file)\n",
    "\n",
    "m        = data[:,0:1]\n",
    "rap      = data[:,1:2]\n",
    "x2       = data[:,2:3]\n",
    "\n",
    "m = np.concatenate((m),axis=0) \n",
    "rap = np.concatenate((rap),axis=0) \n",
    "x2 = np.concatenate((x2),axis=0) \n",
    "\n",
    "Data_ave = np.stack((m,rap),axis=-1)\n",
    "df = pd.DataFrame(Data_ave)\n",
    "df.columns = ['$M_{inv}$','$rapidity$']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0248b96b",
   "metadata": {},
   "source": [
    "# Split data into training and testing sub-sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "id": "de4cf784",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for reproducibility\n",
    "#X_train, X_test, y_train, y_test = train_test_split(Data_ave, x2, test_size=0.4, train_size=0.6 , random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(Data_ave, x2, test_size=0.4, train_size=0.6 )\n",
    "\n",
    "# rename data for convenience\n",
    "x2_train   = y_train\n",
    "m_train    = X_train[:,0:1]\n",
    "rap_train  = X_train[:,1:2]\n",
    "\n",
    "x2_test   = y_test\n",
    "m_test    = X_test[:,0:1]\n",
    "rap_test  = X_test[:,1:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2624e86c",
   "metadata": {},
   "source": [
    "# Now let's do some training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "id": "b8ef444a",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Training and prediction with Linear Model'''\n",
    "#-----------------------------------------------------------------------------#\n",
    "# Input:                                                                      #\n",
    "# X_train  := training data set                                               #\n",
    "# y_train  := targets for training                                            #\n",
    "# X_trest  := testing data set                                                #\n",
    "# y_test   := targets for testing                                             #\n",
    "# filename := name for training storage                                       #\n",
    "# model    := name of the mode we want to use                                 #\n",
    "# ----------------------------------------------------------------------------#\n",
    "# Output:                                                                     #  \n",
    "# loaded_model := trained model                                               #\n",
    "# R2 := quality of the training on the testing set                            #\n",
    "# y_fit := predicted targets                                                  #\n",
    "# y_est := estimated targets                                                  #\n",
    "# ----------------------------------------------------------------------------#\n",
    "# Output labelling :                                                          #\n",
    "# Training(X,y,filename)[0] -> R2                                             #\n",
    "# Training(X,y,filename)[1] -> y_new                                          #\n",
    "# ----------------------------------------------------------------------------#\n",
    "\n",
    "def training (X_train, y_train, X_test, y_test,filename,model):\n",
    "\n",
    "    # train \n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # save trained model\n",
    "    joblib.dump(model, filename)\n",
    "\n",
    "    # load model \n",
    "    loaded_model = joblib.load(filename)\n",
    "\n",
    "    # R^2\n",
    "    R2   = loaded_model.score(X_test, y_test)\n",
    "    \n",
    "    # predicted targets \n",
    "    y_fit = loaded_model.predict(X_test)\n",
    "    # estimated targets\n",
    "    y_est = loaded_model.predict(X_train)\n",
    "\n",
    "    \n",
    "    return R2, y_fit,y_est, model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "06b6bed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x2_train,x2_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "ca19e5ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_train,m_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "be16d388",
   "metadata": {},
   "outputs": [],
   "source": [
    "# name of the model for storage\n",
    "filename = 'storage/x2_NLO_LinearRegression_model_not_thinking.sav'\n",
    "\n",
    "# train \n",
    "Training_machine_learning = training(X_train,y_train,X_test,y_test,filename,LinearRegression())\n",
    "\n",
    "# predict\n",
    "R2    = Training_machine_learning[0] \n",
    "y_fit = Training_machine_learning[1]\n",
    "y_est = Training_machine_learning[2]\n",
    "coefs = Training_machine_learning[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "3b225d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_est,y_train;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "1cf2c418",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_fit,y_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "49492be3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6631001458686402"
      ]
     },
     "execution_count": 533,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "93efe92e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00019207, -0.02985217])"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56253913",
   "metadata": {},
   "source": [
    "# Let's plot and see what we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "c97b86ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "ax1.scatter(m, x2, label='data')\n",
    "ax1.scatter(m_test, y_fit,label='prediction')\n",
    "ax1.scatter(m_train, y_est,label='estimated')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$x_2$',fontsize=16)\n",
    "\n",
    "ax2.scatter(rap, x2, label='data')\n",
    "ax2.scatter(rap_test, y_fit,label='prediction')\n",
    "ax2.scatter(rap_train, y_est,label='estimated')\n",
    "\n",
    "ax2.set_xlabel(xlabel='rapidity',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_NLO_Linear_not_thinking_1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "51451285",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1, 1)\n",
    "\n",
    "ax1.scatter(m_test, (y_test-y_fit)/y_test*100,label='relative % diff test')\n",
    "ax1.scatter(m_train, (y_train-y_est)/y_train*100,label='relative % diff train')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$\\% diff$',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_NLO_Linear_not_thinking_2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97025949",
   "metadata": {},
   "source": [
    "# Let's give it another go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "id": "98a7e102",
   "metadata": {},
   "outputs": [],
   "source": [
    "# name of the model\n",
    "filename = 'storage/x2_NLO_LinearRegression_model_smarter.sav'\n",
    "\n",
    "# training data \n",
    "\n",
    "kinematic = m*np.exp(-rap)\n",
    "\n",
    "kinematic_train = m_train*np.exp(-rap_train)\n",
    "kinematic_test = m_test*np.exp(-rap_test)\n",
    "\n",
    "X_train = kinematic_train\n",
    "X_test = kinematic_test\n",
    "\n",
    "# train \n",
    "Training_machine_learning = training(X_train,y_train,X_test,y_test,filename,LinearRegression())\n",
    "\n",
    "# predict\n",
    "R2    = Training_machine_learning[0] \n",
    "y_fit = Training_machine_learning[1]\n",
    "y_est = Training_machine_learning[2]\n",
    "coefs = Training_machine_learning[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "id": "c51df2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43576818938458284"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "6f0e9a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_fit,y_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "2b8f9e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_est,y_train;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "88d519bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "coefs;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "1b331f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "coefs*8160;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416020bc",
   "metadata": {},
   "source": [
    "# Let's plot again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "id": "720bd1a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1)\n",
    "\n",
    "ax1.scatter(kinematic, x2,label='data')\n",
    "ax1.scatter(kinematic_test, y_fit,label='prediction')\n",
    "ax1.scatter(kinematic_train, y_train,label='estimated')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$x_2$',fontsize=16)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('Plots/x2_NLO_Linear_smarter_1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "9e3aa712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1)\n",
    "\n",
    "plt.ylim(-1000,0)\n",
    "ax1.scatter(kinematic_test, (y_test-y_fit)/y_test*100,label='relative % diff test')\n",
    "ax1.scatter(kinematic_train, (y_train-y_est)/y_train*100,label='relative % diff train')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$\\% diff$',fontsize=16)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('Plots/x2_NLO_Linear_smarter_2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20faf9d2",
   "metadata": {},
   "source": [
    "# Let's try a different basis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "id": "e327c964",
   "metadata": {},
   "outputs": [],
   "source": [
    "# name of the model\n",
    "filename = 'storage/x2_NLO_LinearRegression_model_smarter2.sav'\n",
    "\n",
    "# training data \n",
    "\n",
    "kinematic_LO       = m*np.exp(-rap)\n",
    "kinematic_LO_train = m_train*np.exp(-rap_train)\n",
    "kinematic_LO_test  = m_test*np.exp(-rap_test)\n",
    "\n",
    "kinematic = np.stack((kinematic_LO ,kinematic_LO*rap),axis=-1)[0]\n",
    "kinematic_train = np.stack((kinematic_LO_train,kinematic_LO_train*rap_train),axis=-1)\n",
    "kinematic_test = np.stack((kinematic_LO_test,kinematic_LO_test*rap_test),axis=-1)\n",
    "n=kinematic.size\n",
    "\n",
    "# reshape to correct dimentions\n",
    "kinematic=np.reshape(kinematic,(-1,n))\n",
    "kinematic_train=np.reshape(kinematic_train,(-1,n))\n",
    "kinematic_test=np.reshape(kinematic_test,(-1,n))\n",
    "\n",
    "X_train = kinematic_train\n",
    "X_test = kinematic_test\n",
    "\n",
    "# train \n",
    "Training_machine_learning = training(X_train,y_train,X_test,y_test,filename,LinearRegression())\n",
    "\n",
    "# predict\n",
    "R2    = Training_machine_learning[0] \n",
    "y_fit = Training_machine_learning[1]\n",
    "y_est = Training_machine_learning[2]\n",
    "coefs = Training_machine_learning[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "id": "e80fd8ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9255267989605758"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "id": "da934304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.1192629 , 0.99838456])"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs*8160"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "6b3e41c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "id": "be0baa3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1)\n",
    "\n",
    "ax1.scatter(kinematic_LO, x2,label='data')\n",
    "ax1.scatter(kinematic_LO_test, y_fit,label='prediction')\n",
    "ax1.scatter(kinematic_LO_train, y_train,label='estimated')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$\\% diff$',fontsize=16)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('Plots/x2_NLO_Linear_smarter2_1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "id": "47357988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1)\n",
    "\n",
    "plt.ylim(-100,100)\n",
    "ax1.scatter(kinematic_LO_test, (y_test-y_fit)/y_test*100,label='relative % diff test')\n",
    "ax1.scatter(kinematic_LO_train, (y_train-y_est)/y_train*100,label='relative % diff train')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$\\% diff$',fontsize=16)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('Plots/x2_NLO_Linear_smarter2_2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e69974b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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