HiggsChallenge.ipynb

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   "source": [
    "# Higgs Challenge Example\n",
    "In this part we will look at the **[Higgs Boson ML Challenge](https://www.kaggle.com/c/Higgs-boson)** on Kaggle and attempt a solution using Boosted Decision Trees (BDTs), a popular method in experimental particle physics. BDTs are based on an ensamble of weak classifiers (decision trees), and boosting increases the weight of misclassified events. Tomorrow, I will leave it as an excercise to try to get a solution using a neural network. The data is available from **[CERN Open Data](http://opendata.cern.ch/record/328)**. More information about the data is available from the links, and in particular at **[Documentation](http://opendata.cern.ch/record/329/files/atlas-higgs-challenge-2014.pdf)**. The general idea is that we want to extract $H\\to\\tau\\tau$ signal from background. In particular, the selection requires one of the taus to decay into an electron or muon and two neutrinos, and the other into hadrons and a neutrino. The challenge is based on Monte Carlo events processed through the **[ATLAS detector](http://atlas.cern/)** simulation and reconstruction.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the usual setup: \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load training data\n",
    "df = pd.read_csv('data/atlas-higgs-challenge-2014-v2.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EventId</th>\n",
       "      <th>DER_mass_MMC</th>\n",
       "      <th>DER_mass_transverse_met_lep</th>\n",
       "      <th>DER_mass_vis</th>\n",
       "      <th>DER_pt_h</th>\n",
       "      <th>DER_deltaeta_jet_jet</th>\n",
       "      <th>DER_mass_jet_jet</th>\n",
       "      <th>DER_prodeta_jet_jet</th>\n",
       "      <th>DER_deltar_tau_lep</th>\n",
       "      <th>DER_pt_tot</th>\n",
       "      <th>...</th>\n",
       "      <th>PRI_jet_leading_eta</th>\n",
       "      <th>PRI_jet_leading_phi</th>\n",
       "      <th>PRI_jet_subleading_pt</th>\n",
       "      <th>PRI_jet_subleading_eta</th>\n",
       "      <th>PRI_jet_subleading_phi</th>\n",
       "      <th>PRI_jet_all_pt</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Label</th>\n",
       "      <th>KaggleSet</th>\n",
       "      <th>KaggleWeight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000</td>\n",
       "      <td>138.470</td>\n",
       "      <td>51.655</td>\n",
       "      <td>97.827</td>\n",
       "      <td>27.980</td>\n",
       "      <td>0.91</td>\n",
       "      <td>124.711</td>\n",
       "      <td>2.666</td>\n",
       "      <td>3.064</td>\n",
       "      <td>41.928</td>\n",
       "      <td>...</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.444</td>\n",
       "      <td>46.062</td>\n",
       "      <td>1.24</td>\n",
       "      <td>-2.475</td>\n",
       "      <td>113.497</td>\n",
       "      <td>0.000814</td>\n",
       "      <td>s</td>\n",
       "      <td>t</td>\n",
       "      <td>0.002653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100001</td>\n",
       "      <td>160.937</td>\n",
       "      <td>68.768</td>\n",
       "      <td>103.235</td>\n",
       "      <td>48.146</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.473</td>\n",
       "      <td>2.078</td>\n",
       "      <td>...</td>\n",
       "      <td>0.725</td>\n",
       "      <td>1.158</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>46.226</td>\n",
       "      <td>0.681042</td>\n",
       "      <td>b</td>\n",
       "      <td>t</td>\n",
       "      <td>2.233584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100002</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>162.172</td>\n",
       "      <td>125.953</td>\n",
       "      <td>35.635</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.148</td>\n",
       "      <td>9.336</td>\n",
       "      <td>...</td>\n",
       "      <td>2.053</td>\n",
       "      <td>-2.028</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>44.251</td>\n",
       "      <td>0.715742</td>\n",
       "      <td>b</td>\n",
       "      <td>t</td>\n",
       "      <td>2.347389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100003</td>\n",
       "      <td>143.905</td>\n",
       "      <td>81.417</td>\n",
       "      <td>80.943</td>\n",
       "      <td>0.414</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.310</td>\n",
       "      <td>0.414</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>1.660654</td>\n",
       "      <td>b</td>\n",
       "      <td>t</td>\n",
       "      <td>5.446378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100004</td>\n",
       "      <td>175.864</td>\n",
       "      <td>16.915</td>\n",
       "      <td>134.805</td>\n",
       "      <td>16.405</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.891</td>\n",
       "      <td>16.405</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.904263</td>\n",
       "      <td>b</td>\n",
       "      <td>t</td>\n",
       "      <td>6.245333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EventId  DER_mass_MMC  DER_mass_transverse_met_lep  DER_mass_vis  DER_pt_h  \\\n",
       "0   100000       138.470                       51.655        97.827    27.980   \n",
       "1   100001       160.937                       68.768       103.235    48.146   \n",
       "2   100002      -999.000                      162.172       125.953    35.635   \n",
       "3   100003       143.905                       81.417        80.943     0.414   \n",
       "4   100004       175.864                       16.915       134.805    16.405   \n",
       "\n",
       "   DER_deltaeta_jet_jet  DER_mass_jet_jet  DER_prodeta_jet_jet  \\\n",
       "0                  0.91           124.711                2.666   \n",
       "1               -999.00          -999.000             -999.000   \n",
       "2               -999.00          -999.000             -999.000   \n",
       "3               -999.00          -999.000             -999.000   \n",
       "4               -999.00          -999.000             -999.000   \n",
       "\n",
       "   DER_deltar_tau_lep  DER_pt_tot  ...  PRI_jet_leading_eta  \\\n",
       "0               3.064      41.928  ...                2.150   \n",
       "1               3.473       2.078  ...                0.725   \n",
       "2               3.148       9.336  ...                2.053   \n",
       "3               3.310       0.414  ...             -999.000   \n",
       "4               3.891      16.405  ...             -999.000   \n",
       "\n",
       "   PRI_jet_leading_phi  PRI_jet_subleading_pt  PRI_jet_subleading_eta  \\\n",
       "0                0.444                 46.062                    1.24   \n",
       "1                1.158               -999.000                 -999.00   \n",
       "2               -2.028               -999.000                 -999.00   \n",
       "3             -999.000               -999.000                 -999.00   \n",
       "4             -999.000               -999.000                 -999.00   \n",
       "\n",
       "   PRI_jet_subleading_phi  PRI_jet_all_pt    Weight  Label  KaggleSet  \\\n",
       "0                  -2.475         113.497  0.000814      s          t   \n",
       "1                -999.000          46.226  0.681042      b          t   \n",
       "2                -999.000          44.251  0.715742      b          t   \n",
       "3                -999.000          -0.000  1.660654      b          t   \n",
       "4                -999.000           0.000  1.904263      b          t   \n",
       "\n",
       "   KaggleWeight  \n",
       "0      0.002653  \n",
       "1      2.233584  \n",
       "2      2.347389  \n",
       "3      5.446378  \n",
       "4      6.245333  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:5]"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is more information about the variables in the documentation. The variables that start with **DER** are derived quantities, determined by the physicists performing the analysis as variables that discriminate signal from backround. On the other hand, those that start with **PRI** are considered to be primary variables, from which the derived variables are calculated. They themselves generally do not provide much discrimination, but one if the ideas suggested by deep networks is that they can determine the necessary features from the primary variables, potentially even finding variables that the physicists did not consider. *EventId* identifies the event but is not a \"feature.\" The *Weight* is the event weight so that the sum of weights of all signal events should produce the signal yield expected to be observed in 2012, and the sum of weights of all background events should produce the backgroudn yield. Note that the weight varies event to event, because different background and signal processes contribute to the background and signal sets. *Label* indicates if it is a signal or background event. Ignore the *Kaggle* variables--they are only used if you want to reproduce exactly what was used in the Challenge. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can use seaborn or other plotting packages to get a feel for the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x10fc0aa10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 762.25x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(df, vars=['DER_mass_MMC', 'DER_mass_transverse_met_lep', 'DER_mass_vis', 'DER_mass_jet_jet'], hue='Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map y values to integers\n",
    "df['Label'] = df['Label'].map({'b':0, 's':1})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>EventId</th>\n",
       "      <th>DER_mass_MMC</th>\n",
       "      <th>DER_mass_transverse_met_lep</th>\n",
       "      <th>DER_mass_vis</th>\n",
       "      <th>DER_pt_h</th>\n",
       "      <th>DER_deltaeta_jet_jet</th>\n",
       "      <th>DER_mass_jet_jet</th>\n",
       "      <th>DER_prodeta_jet_jet</th>\n",
       "      <th>DER_deltar_tau_lep</th>\n",
       "      <th>DER_pt_tot</th>\n",
       "      <th>...</th>\n",
       "      <th>PRI_jet_leading_eta</th>\n",
       "      <th>PRI_jet_leading_phi</th>\n",
       "      <th>PRI_jet_subleading_pt</th>\n",
       "      <th>PRI_jet_subleading_eta</th>\n",
       "      <th>PRI_jet_subleading_phi</th>\n",
       "      <th>PRI_jet_all_pt</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Label</th>\n",
       "      <th>KaggleSet</th>\n",
       "      <th>KaggleWeight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000</td>\n",
       "      <td>138.470</td>\n",
       "      <td>51.655</td>\n",
       "      <td>97.827</td>\n",
       "      <td>27.980</td>\n",
       "      <td>0.91</td>\n",
       "      <td>124.711</td>\n",
       "      <td>2.666</td>\n",
       "      <td>3.064</td>\n",
       "      <td>41.928</td>\n",
       "      <td>...</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.444</td>\n",
       "      <td>46.062</td>\n",
       "      <td>1.24</td>\n",
       "      <td>-2.475</td>\n",
       "      <td>113.497</td>\n",
       "      <td>0.000814</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>0.002653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100001</td>\n",
       "      <td>160.937</td>\n",
       "      <td>68.768</td>\n",
       "      <td>103.235</td>\n",
       "      <td>48.146</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.473</td>\n",
       "      <td>2.078</td>\n",
       "      <td>...</td>\n",
       "      <td>0.725</td>\n",
       "      <td>1.158</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>46.226</td>\n",
       "      <td>0.681042</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>2.233584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100002</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>162.172</td>\n",
       "      <td>125.953</td>\n",
       "      <td>35.635</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.148</td>\n",
       "      <td>9.336</td>\n",
       "      <td>...</td>\n",
       "      <td>2.053</td>\n",
       "      <td>-2.028</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>44.251</td>\n",
       "      <td>0.715742</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>2.347389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100003</td>\n",
       "      <td>143.905</td>\n",
       "      <td>81.417</td>\n",
       "      <td>80.943</td>\n",
       "      <td>0.414</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.310</td>\n",
       "      <td>0.414</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>1.660654</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>5.446378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100004</td>\n",
       "      <td>175.864</td>\n",
       "      <td>16.915</td>\n",
       "      <td>134.805</td>\n",
       "      <td>16.405</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.891</td>\n",
       "      <td>16.405</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.904263</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>6.245333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EventId  DER_mass_MMC  DER_mass_transverse_met_lep  DER_mass_vis  DER_pt_h  \\\n",
       "0   100000       138.470                       51.655        97.827    27.980   \n",
       "1   100001       160.937                       68.768       103.235    48.146   \n",
       "2   100002      -999.000                      162.172       125.953    35.635   \n",
       "3   100003       143.905                       81.417        80.943     0.414   \n",
       "4   100004       175.864                       16.915       134.805    16.405   \n",
       "\n",
       "   DER_deltaeta_jet_jet  DER_mass_jet_jet  DER_prodeta_jet_jet  \\\n",
       "0                  0.91           124.711                2.666   \n",
       "1               -999.00          -999.000             -999.000   \n",
       "2               -999.00          -999.000             -999.000   \n",
       "3               -999.00          -999.000             -999.000   \n",
       "4               -999.00          -999.000             -999.000   \n",
       "\n",
       "   DER_deltar_tau_lep  DER_pt_tot  ...  PRI_jet_leading_eta  \\\n",
       "0               3.064      41.928  ...                2.150   \n",
       "1               3.473       2.078  ...                0.725   \n",
       "2               3.148       9.336  ...                2.053   \n",
       "3               3.310       0.414  ...             -999.000   \n",
       "4               3.891      16.405  ...             -999.000   \n",
       "\n",
       "   PRI_jet_leading_phi  PRI_jet_subleading_pt  PRI_jet_subleading_eta  \\\n",
       "0                0.444                 46.062                    1.24   \n",
       "1                1.158               -999.000                 -999.00   \n",
       "2               -2.028               -999.000                 -999.00   \n",
       "3             -999.000               -999.000                 -999.00   \n",
       "4             -999.000               -999.000                 -999.00   \n",
       "\n",
       "   PRI_jet_subleading_phi  PRI_jet_all_pt    Weight  Label  KaggleSet  \\\n",
       "0                  -2.475         113.497  0.000814      1          t   \n",
       "1                -999.000          46.226  0.681042      0          t   \n",
       "2                -999.000          44.251  0.715742      0          t   \n",
       "3                -999.000          -0.000  1.660654      0          t   \n",
       "4                -999.000           0.000  1.904263      0          t   \n",
       "\n",
       "   KaggleWeight  \n",
       "0      0.002653  \n",
       "1      2.233584  \n",
       "2      2.347389  \n",
       "3      5.446378  \n",
       "4      6.245333  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's create separate arrays\n",
    "eventID = df['EventId']\n",
    "X = df.loc[:,'DER_mass_MMC':'PRI_jet_all_pt']\n",
    "y = df['Label']\n",
    "weight = df['Weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now split into testing and training samples\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test, eventID_train, event_ID_test, weight_train, weight_test = train_test_split(\n",
    "    X, y, eventID, weight, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now let's define the model\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbc = GradientBoostingClassifier(n_estimators=50, max_depth=10,\n",
    "                                    min_samples_leaf=200,\n",
    "                                    max_features=10, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Iter       Train Loss   Remaining Time \n",
      "         1           1.1980            4.95m\n",
      "         2           1.1296            4.86m\n",
      "         3           1.0732            4.90m\n",
      "         4           1.0253            4.76m\n",
      "         5           0.9850            4.67m\n",
      "         6           0.9503            4.54m\n",
      "         7           0.9198            4.52m\n",
      "         8           0.8937            4.46m\n",
      "         9           0.8716            4.33m\n",
      "        10           0.8519            4.22m\n",
      "        20           0.7438            3.34m\n",
      "        30           0.7039            2.34m\n",
      "        40           0.6843            1.19m\n",
      "        50           0.6717            0.00s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=10,\n",
       "                           max_features=10, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=200, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=50,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=1,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and train\n",
    "gbc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.842340724171262"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an aside, let's look at logistic regression. This is a very simple, linear model. In the exercises you can look at optimizing it a bit more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7506286594647044"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try using fewer features\n",
    "lr2 = LogisticRegression(solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr2.fit(X_train.loc[:,:'DER_pt_tot'], y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7176494987389777"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr2.score(X_test.loc[:,:'DER_pt_tot'], y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get back to the original problem using the GradientBoostingClassifier. The Kaggle competition used the approximate median segnificance (AMS), as defined below, to determine how good a solution was. The number 10, added to the background yield, is a regularization term to decrease the variance of the AMS.\n",
    "\n",
    "Note that if you do not use the full data set (i.e. you split into training and testing) you have to reweigh the inputs so that the subsample yield matches to the toal yield, which we will do below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute approximate median segnificance (AMS)\n",
    "\n",
    "def ams(s,b):\n",
    "    from math import sqrt,log\n",
    "    if b==0:\n",
    "        return 0\n",
    "\n",
    "    return sqrt(2*((s+b+10)*log(1+float(s)/(b+10))-s))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try a different probability cut, not the one given by default to predict().\n",
    "# We choose the top 15%, but can optimize\n",
    "y_train_prob = gbc.predict_proba(X_train)[:, 1]\n",
    "y_test_prob = gbc.predict_proba(X_test)[:, 1]\n",
    "pcut = np.percentile(y_train_prob,85)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the probability to the original data frame\n",
    "df['Prob']=gbc.predict_proba(X)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>EventId</th>\n",
       "      <th>DER_mass_MMC</th>\n",
       "      <th>DER_mass_transverse_met_lep</th>\n",
       "      <th>DER_mass_vis</th>\n",
       "      <th>DER_pt_h</th>\n",
       "      <th>DER_deltaeta_jet_jet</th>\n",
       "      <th>DER_mass_jet_jet</th>\n",
       "      <th>DER_prodeta_jet_jet</th>\n",
       "      <th>DER_deltar_tau_lep</th>\n",
       "      <th>DER_pt_tot</th>\n",
       "      <th>...</th>\n",
       "      <th>PRI_jet_leading_phi</th>\n",
       "      <th>PRI_jet_subleading_pt</th>\n",
       "      <th>PRI_jet_subleading_eta</th>\n",
       "      <th>PRI_jet_subleading_phi</th>\n",
       "      <th>PRI_jet_all_pt</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Label</th>\n",
       "      <th>KaggleSet</th>\n",
       "      <th>KaggleWeight</th>\n",
       "      <th>Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000</td>\n",
       "      <td>138.470</td>\n",
       "      <td>51.655</td>\n",
       "      <td>97.827</td>\n",
       "      <td>27.980</td>\n",
       "      <td>0.910</td>\n",
       "      <td>124.711</td>\n",
       "      <td>2.666</td>\n",
       "      <td>3.064</td>\n",
       "      <td>41.928</td>\n",
       "      <td>...</td>\n",
       "      <td>0.444</td>\n",
       "      <td>46.062</td>\n",
       "      <td>1.240</td>\n",
       "      <td>-2.475</td>\n",
       "      <td>113.497</td>\n",
       "      <td>0.000814</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>0.002653</td>\n",
       "      <td>0.475462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100001</td>\n",
       "      <td>160.937</td>\n",
       "      <td>68.768</td>\n",
       "      <td>103.235</td>\n",
       "      <td>48.146</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.473</td>\n",
       "      <td>2.078</td>\n",
       "      <td>...</td>\n",
       "      <td>1.158</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>46.226</td>\n",
       "      <td>0.681042</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>2.233584</td>\n",
       "      <td>0.285616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100002</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>162.172</td>\n",
       "      <td>125.953</td>\n",
       "      <td>35.635</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.148</td>\n",
       "      <td>9.336</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.028</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>44.251</td>\n",
       "      <td>0.715742</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>2.347389</td>\n",
       "      <td>0.070826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100003</td>\n",
       "      <td>143.905</td>\n",
       "      <td>81.417</td>\n",
       "      <td>80.943</td>\n",
       "      <td>0.414</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.310</td>\n",
       "      <td>0.414</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>1.660654</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>5.446378</td>\n",
       "      <td>0.091025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100004</td>\n",
       "      <td>175.864</td>\n",
       "      <td>16.915</td>\n",
       "      <td>134.805</td>\n",
       "      <td>16.405</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.891</td>\n",
       "      <td>16.405</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.904263</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>6.245333</td>\n",
       "      <td>0.029303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818233</th>\n",
       "      <td>918233</td>\n",
       "      <td>105.668</td>\n",
       "      <td>46.443</td>\n",
       "      <td>60.048</td>\n",
       "      <td>156.191</td>\n",
       "      <td>0.403</td>\n",
       "      <td>47.746</td>\n",
       "      <td>0.936</td>\n",
       "      <td>1.279</td>\n",
       "      <td>6.133</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.766</td>\n",
       "      <td>41.791</td>\n",
       "      <td>0.787</td>\n",
       "      <td>-1.090</td>\n",
       "      <td>154.056</td>\n",
       "      <td>0.005721</td>\n",
       "      <td>1</td>\n",
       "      <td>u</td>\n",
       "      <td>0.259892</td>\n",
       "      <td>0.519829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818234</th>\n",
       "      <td>918234</td>\n",
       "      <td>99.294</td>\n",
       "      <td>30.097</td>\n",
       "      <td>62.713</td>\n",
       "      <td>65.861</td>\n",
       "      <td>3.312</td>\n",
       "      <td>471.319</td>\n",
       "      <td>-2.611</td>\n",
       "      <td>2.294</td>\n",
       "      <td>2.889</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.868</td>\n",
       "      <td>70.158</td>\n",
       "      <td>-2.018</td>\n",
       "      <td>2.893</td>\n",
       "      <td>178.856</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>1</td>\n",
       "      <td>u</td>\n",
       "      <td>0.020956</td>\n",
       "      <td>0.703786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818235</th>\n",
       "      <td>918235</td>\n",
       "      <td>108.497</td>\n",
       "      <td>9.837</td>\n",
       "      <td>65.149</td>\n",
       "      <td>18.006</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>2.742</td>\n",
       "      <td>18.006</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>1.189365</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "      <td>53.284258</td>\n",
       "      <td>0.549241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818236</th>\n",
       "      <td>918236</td>\n",
       "      <td>96.711</td>\n",
       "      <td>20.006</td>\n",
       "      <td>66.942</td>\n",
       "      <td>29.761</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>2.479</td>\n",
       "      <td>2.739</td>\n",
       "      <td>...</td>\n",
       "      <td>2.637</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>30.863</td>\n",
       "      <td>0.512740</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "      <td>22.971060</td>\n",
       "      <td>0.274946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818237</th>\n",
       "      <td>918237</td>\n",
       "      <td>92.373</td>\n",
       "      <td>80.109</td>\n",
       "      <td>77.619</td>\n",
       "      <td>3.984</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>2.486</td>\n",
       "      <td>3.984</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>1.531213</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "      <td>68.599269</td>\n",
       "      <td>0.044258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>818238 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        EventId  DER_mass_MMC  DER_mass_transverse_met_lep  DER_mass_vis  \\\n",
       "0        100000       138.470                       51.655        97.827   \n",
       "1        100001       160.937                       68.768       103.235   \n",
       "2        100002      -999.000                      162.172       125.953   \n",
       "3        100003       143.905                       81.417        80.943   \n",
       "4        100004       175.864                       16.915       134.805   \n",
       "...         ...           ...                          ...           ...   \n",
       "818233   918233       105.668                       46.443        60.048   \n",
       "818234   918234        99.294                       30.097        62.713   \n",
       "818235   918235       108.497                        9.837        65.149   \n",
       "818236   918236        96.711                       20.006        66.942   \n",
       "818237   918237        92.373                       80.109        77.619   \n",
       "\n",
       "        DER_pt_h  DER_deltaeta_jet_jet  DER_mass_jet_jet  DER_prodeta_jet_jet  \\\n",
       "0         27.980                 0.910           124.711                2.666   \n",
       "1         48.146              -999.000          -999.000             -999.000   \n",
       "2         35.635              -999.000          -999.000             -999.000   \n",
       "3          0.414              -999.000          -999.000             -999.000   \n",
       "4         16.405              -999.000          -999.000             -999.000   \n",
       "...          ...                   ...               ...                  ...   \n",
       "818233   156.191                 0.403            47.746                0.936   \n",
       "818234    65.861                 3.312           471.319               -2.611   \n",
       "818235    18.006              -999.000          -999.000             -999.000   \n",
       "818236    29.761              -999.000          -999.000             -999.000   \n",
       "818237     3.984              -999.000          -999.000             -999.000   \n",
       "\n",
       "        DER_deltar_tau_lep  DER_pt_tot  ...  PRI_jet_leading_phi  \\\n",
       "0                    3.064      41.928  ...                0.444   \n",
       "1                    3.473       2.078  ...                1.158   \n",
       "2                    3.148       9.336  ...               -2.028   \n",
       "3                    3.310       0.414  ...             -999.000   \n",
       "4                    3.891      16.405  ...             -999.000   \n",
       "...                    ...         ...  ...                  ...   \n",
       "818233               1.279       6.133  ...               -0.766   \n",
       "818234               2.294       2.889  ...               -0.868   \n",
       "818235               2.742      18.006  ...             -999.000   \n",
       "818236               2.479       2.739  ...                2.637   \n",
       "818237               2.486       3.984  ...             -999.000   \n",
       "\n",
       "        PRI_jet_subleading_pt  PRI_jet_subleading_eta  PRI_jet_subleading_phi  \\\n",
       "0                      46.062                   1.240                  -2.475   \n",
       "1                    -999.000                -999.000                -999.000   \n",
       "2                    -999.000                -999.000                -999.000   \n",
       "3                    -999.000                -999.000                -999.000   \n",
       "4                    -999.000                -999.000                -999.000   \n",
       "...                       ...                     ...                     ...   \n",
       "818233                 41.791                   0.787                  -1.090   \n",
       "818234                 70.158                  -2.018                   2.893   \n",
       "818235               -999.000                -999.000                -999.000   \n",
       "818236               -999.000                -999.000                -999.000   \n",
       "818237               -999.000                -999.000                -999.000   \n",
       "\n",
       "        PRI_jet_all_pt    Weight  Label  KaggleSet  KaggleWeight      Prob  \n",
       "0              113.497  0.000814      1          t      0.002653  0.475462  \n",
       "1               46.226  0.681042      0          t      2.233584  0.285616  \n",
       "2               44.251  0.715742      0          t      2.347389  0.070826  \n",
       "3               -0.000  1.660654      0          t      5.446378  0.091025  \n",
       "4                0.000  1.904263      0          t      6.245333  0.029303  \n",
       "...                ...       ...    ...        ...           ...       ...  \n",
       "818233         154.056  0.005721      1          u      0.259892  0.519829  \n",
       "818234         178.856  0.000461      1          u      0.020956  0.703786  \n",
       "818235          -0.000  1.189365      0          u     53.284258  0.549241  \n",
       "818236          30.863  0.512740      0          u     22.971060  0.274946  \n",
       "818237          -0.000  1.531213      0          u     68.599269  0.044258  \n",
       "\n",
       "[818238 rows x 36 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x127acb110>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "kwargs = dict(histtype='stepfilled', alpha=0.3, density=True, bins=40)\n",
    "df[df.Label==0].Prob.hist(label='Background',**kwargs)\n",
    "df[df.Label==1].Prob.hist(label='Signal',**kwargs)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's calculate the total weights (yields)\n",
    "sigall = weight.dot(y)\n",
    "backall = weight.dot(y == 0)\n",
    "\n",
    "# The training weights\n",
    "sigtrain = weight_train.dot(y_train)\n",
    "backtrain = weight_train.dot(y_train == 0)\n",
    "\n",
    "# The training weights\n",
    "sigtest = weight_test.dot(y_test)\n",
    "backtest = weight_test.dot(y_test == 0)\n",
    "\n",
    "# aside:  these can also be done by looping instead of using a dot product\n",
    "#  (Usually vectorized operations are faster for interpreted code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's look at event yields that pass our selection\n",
    "sigtrain_sel = weight_train.dot(y_train & (y_train_prob > pcut))\n",
    "backtrain_sel = weight_train.dot((y_train == 0) & (y_train_prob > pcut))\n",
    "\n",
    "sigtest_sel = weight_test.dot(y_test & (y_test_prob > pcut))\n",
    "backtest_sel = weight_test.dot((y_test == 0) & (y_test_prob > pcut))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corrected selected yields in training sample, signal = 224.9449563300971, background = 3104.404910176948\n",
      "Corrected selected yields in test sample, signal = 220.6812585684483, background = 3791.8170371495853\n"
     ]
    }
   ],
   "source": [
    "# Now we need to correct the selected yields to be is if we used the full sample\n",
    "sigtrain_sel_corr = sigtrain_sel*sigall/sigtrain\n",
    "backtrain_sel_corr = backtrain_sel*backall/backtrain\n",
    "\n",
    "sigtest_sel_corr = sigtest_sel*sigall/sigtest\n",
    "backtest_sel_corr = backtest_sel*backall/backtest\n",
    "\n",
    "print(f\"Corrected selected yields in training sample, signal = {sigtrain_sel_corr}, background = {backtrain_sel_corr}\")\n",
    "print(f\"Corrected selected yields in test sample, signal = {sigtest_sel_corr}, background = {backtest_sel_corr}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMS of training sample 3.9836590001113916\n",
      "AMS of test sample 3.5452534038821906\n"
     ]
    }
   ],
   "source": [
    "print(\"AMS of training sample\", ams(sigtrain_sel_corr,backtrain_sel_corr))\n",
    "print(\"AMS of test sample\", ams(sigtest_sel_corr,backtest_sel_corr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How did we do? Not too bad. Here are the scores of real submissions.\n",
    "![Comparison with submissions](data/tr150908_davidRousseau_TMVAFuture_HiggsML.001.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is of course a bit of a simplification from a real physics analysis, where systematics often seem to take the most time. They are ignored here.\n",
    "![Comparison with real analysis](data/tr140415_davidRousseau_Rome_Higgs_MVA_HiggsML.001.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What to work on for the rest of the day:\n",
    "1. Attempt to calculate the AMS for the logistic regression cases.\n",
    "1. Do we overfit? Add plots to see.\n",
    "1. Which variables are important?\n",
    "1. Should we **[preprocess](http://scikit-learn.org/stable/modules/preprocessing.html)** the input data to be the same scale? Note that we have some -999 values that indicate the variable could not be calculated.\n",
    "1. We do not use the event weights in the training. Can they help? Note, that you don't want to just apply the weights as is since they will make background dominate over signal.\n",
    "1. The best scores in the Challenge all used cross-validation; if you have time, try to implement it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tomorrow we will continue on this example with neural networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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