{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a22039c2",
   "metadata": {},
   "source": [
    "# Import tools/models be used\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "733e2915",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "import joblib   \n",
    "import os\n",
    "import errno\n",
    "from sklearn.model_selection import train_test_split  # this is very important"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70abfde6",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34c9c341",
   "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": 3,
     "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": "efff9012",
   "metadata": {},
   "source": [
    "# Split data into training and testing sub-sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "476866cf",
   "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",
    "\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": "4bf1d35d",
   "metadata": {},
   "source": [
    "# Now let's do some training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d93a4aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Training and prediction'''\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",
    "# Note: not all models have the same characteristics\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.coefs_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d9c2d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# name of the model\n",
    "filename = 'storage/x2_NLO_NN_MPL.sav'\n",
    "\n",
    "# training data \n",
    "\n",
    "kinematic = np.stack((m,rap),axis=-1)\n",
    "kinematic_train = np.stack((m_train,rap_train),axis=-1)\n",
    "kinematic_test = np.stack((m_test,rap_test),axis=-1)\n",
    "\n",
    "n=kinematic.shape[-1]\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,\n",
    "                                    MLPRegressor(hidden_layer_sizes=(100,100,100), tol=1e-6, max_iter=1000,activation='logistic'))\n",
    "\n",
    "# predict\n",
    "R2    = Training_machine_learning[0] \n",
    "y_fit = Training_machine_learning[1]\n",
    "y_est = Training_machine_learning[2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18f7e03d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "R2;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f69a65c",
   "metadata": {},
   "source": [
    "# Let's plot and see what we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "02c82f55",
   "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_NN_MLP_1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f63de988",
   "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",
    "plt.ylim(-100,100)\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_NN_MLP_2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d28fb9b1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
