tephra_starting_kit.ipynb

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   "cell_type": "markdown",
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    "# RAMP: Predicting volcanic events from Tephrochronology of the Southern and Austral Volcanic Zones of the Andes\n",
    "*Consuelo Martinez, Chiara Marmo, Guillaume Delpech (GEOPS, UPS), Marine Le Morvan, Thomas Moreau (Inria), François Caud (DATAIA, UPS)*\n",
    "\n",
    "<div>\n",
    "<table style=\"width:100%; background-color:transparent;\">\n",
    "  <tr style=\"background-color:transparent;\">\n",
    "    <td align=\"left\"; style=\"background-color:transparent; width: 40%;\">\n",
    "        <a href=\"https://dataia.eu\">\n",
    "            <img src=\"https://github.com/ramp-kits/tephra/raw/main/img/DATAIA-h.png\" width=\"450px\"/>\n",
    "        </a>\n",
    "    </td>\n",
    "    <td align=\"right\"; style=\"background-color:transparent; width: 40%;\">\n",
    "        <a href=\"https://www.geops.universite-paris-saclay.fr/\">\n",
    "            <img src=\"https://github.com/ramp-kits/tephra/raw/main/img/LOGO-GEOPS-2020-1024x488-1.jpg\" width=\"350px\"/>\n",
    "        </a>\n",
    "    </td>\n",
    "  </tr>\n",
    " </table>\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "## Table of Contents\n",
    "* [Introduction](#introduction)\n",
    "* [The dataset](#dataset)\n",
    "* [Data exploration](#exploration)\n",
    "* [Requirements](#requirements)\n",
    "* [Base model](#base_model)\n",
    "* [Submitting on RAMP](#submitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction <a class=\"anchor\" id=\"introduction\"></a>\n",
    "Tephrochronology is the discipline of geosciences which uses the deposits of explosive volcanic eruptions\n",
    "as stratigraphic and chronological markers.\n",
    "By identifying the pyroclastic remains of specific eruptions (tephras) in different sites, tephrochronology helps reconstruct the eruptive history of volcanic centers: the magnitude of their eruptions, their recurrence in time and the dispersion of its products.\n",
    "Also, tephras can be viewed as regional stratigraphic tie points.\n",
    "If a specific tephra is identified in different paleoenvironmental archives (e.g. paleoclimatological, paleoceanographic, archaeological), their chronologies can be synchronized, which is essential when interpreting the evolution of complex systems in time, such as the climate.\n",
    "\n",
    "The Southern (SVZ, 33–46° S) and Austral (AVZ, 49–55° S) volcanic Zones of the Andes are two very active\n",
    "volcanic zones, where so far around 65 volcanic centers have been identified which have had recurrent\n",
    "explosive activity in historical times or during the last 20,000 years.\n",
    "In these regions, tephrochronology has a great potential of use, for example by helping reconstruct the eruptive history of these volcanic centers, essential in the production of volcanic hazard maps.\n",
    "In the last four decades, tephrochronology together with volcanology have importantly increased our\n",
    "understanding of the eruptive history of the SVZ and AVZ, revealing higher recurrence and explosivity of\n",
    "many volcanic centers than previously thought.\n",
    "It has also become apparent that there are still many under researched active volcanic centers in the area for\n",
    "which no eruptions have been robustly identified yet.\n",
    "Disentangling this intricate record is hampered by the very similar geochemical composition of the products of many volcanic centers, which makes it difficult to identify their source; additionally, the high uncertainties associated\n",
    "with the age estimates of the tephra deposits further hinders their correlations.\n",
    "\n",
    "The original dataset contains tephrochronological and volcanological information on 32 active volcanic centers from the\n",
    "SVZ and AVZ, collected from seventy-three scientific publications published in peer reviewed journals, six publications from [SERNAGEOMIN](https://www.sernageomin.cl/), and two doctoral theses.\n",
    "These correspond to 130 different eruptive events and 16,500 sample analyses.\n",
    "The data were obtained either directly from the publication tables, text and supplementary material, or alternatively through private requests to the authors when the data discussed in the publication was not readily available.\n",
    "\n",
    "The goal of the data challenge is to predict the volcanic eruption (event) using the geochemical composition of the tephras."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The dataset <a class=\"anchor\" id=\"dataset\"></a>\n",
    "The description of all the columns of the dataset is available from [Martinez PhD Thesis](https://theses.hal.science/tel-03543983/document#page=84)\n",
    "\n",
    "| ![dataset_structure.png](https://github.com/ramp-kits/tephra/raw/main/img/dataset_structure.png) |\n",
    "|:--:|\n",
    "| <b>Dataset structure, using information from sample LAZ-T7A (Alloway et al. 2017b) as an example. The Flag and comment attributes are not illustrated</b>|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this challenge, the data were first preprocessed and then split in order to preserve a private test set on which to evaluate the models on our servers. This leaves 6220 observations in the public train set and 839 observations in the public test set. \n",
    "Observations are grouped in samples (with a SampleID each) and we are very cautious to keep those observations from the same sample either in the train set or in the test set (both during splitting (see point 4. of preprocess) and cross-validation).\n",
    "\n",
    "Preprocessing steps before splitting the data:\n",
    "1.  We dropped rows corresponding to samples not analyzed for geochemistry, as well as outliers, samples for which the volcanic source is uncertain, and samples with Analytical Totals lower than 94 wt.%, as they might correspond to altered samples.\n",
    "\n",
    "2.  We replaced some values in the dataset: \n",
    "    - Replace element concentrations registered as \"0\" with \"below detection limit\" (bdl). Because a value equal to zero is not possible to determine with the current analytical techniques, thus bdl is more accurate.\n",
    "    -  Replace the various missing values placeholders by np.nan\n",
    "    -  Make sure major and trace elements correspond to numbers and not strings.\n",
    "\n",
    "3. Because Fe can be analyzed in different states (FeO, Fe2O3, FeOT, Fe2O3T), the columns describing Fe have many missing values but which can be filled by transforming one form of Fe into another. Because most of the samples in the BOOM dataset have been analyzed by Electron microscopy which analyzes Fe as FeOT, we calculate FeOT for all the samples and drop the other rows (Fe2O3, Fe2O3T, FeO) as they are redundant.\n",
    "\n",
    "4. We checked if there was any event with observations from only one sample (SampleID) and removed them. \n",
    "\n",
    "5. We dropped the events with too few observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Glossary of the terms used in the dataset:\n",
    "- Events: labels of the volcanic events to predict (we have 30 events to classify from in this dataset)\n",
    "- Observations: rows of the dataset (6220 observations in the train set)\n",
    "- Samples: groups of observations that correspond to samples (a sample of a tephra deposit can be described by several observations)\n",
    "- SampleID: ID for samples (used for group splitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requirements <a class=\"anchor\" id=\"requirements\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import missingno as msno\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline,make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking the data URL...Ok.\n",
      "Downloading the data...\n",
      "100%|████████████████████████████████████| 883k/883k [00:00<00:00, 11.8Mbytes/s]\n",
      "Extracting now...Ok.\n",
      "Removing the archive...Ok.\n",
      "Checking the data...Ok.\n"
     ]
    }
   ],
   "source": [
    "# Uncomment the following line to download data:\n",
    "!python download_data.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration <a class=\"anchor\" id=\"exploration\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training data \n",
    "\n",
    "Here we load the whole .csv file but we will select only the geochemical variables in order to train models for this challenge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>PCC-1</td>\n",
       "      <td>13-19-1-03</td>\n",
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       "      <td>Bariloche (BSI),  km 7.600</td>\n",
       "      <td>-41.130000</td>\n",
       "      <td>-71.401777</td>\n",
       "      <td>...</td>\n",
       "      <td>4.353741</td>\n",
       "      <td>41.363636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AllowayMay2014</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>PCC2011</td>\n",
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       "      <td>PCC-1</td>\n",
       "      <td>13-19-1-06</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Chaitén</td>\n",
       "      <td>Vcha-2008</td>\n",
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       "      <td>-42.810000</td>\n",
       "      <td>-72.690000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Chaitén</td>\n",
       "      <td>Vcha-2008</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-42.810000</td>\n",
       "      <td>-72.690000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Geochemistry_Issue</td>\n",
       "      <td>The original analytical total was not publishe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6218</th>\n",
       "      <td>Chaitén</td>\n",
       "      <td>Vcha-2008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cha 2008</td>\n",
       "      <td>G5e-8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-42.810000</td>\n",
       "      <td>-72.690000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Geochemistry_Issue</td>\n",
       "      <td>The original analytical total was not publishe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6219</th>\n",
       "      <td>Hudson</td>\n",
       "      <td>HW6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CS-4145</td>\n",
       "      <td>CS-4145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Laguna Churrasco</td>\n",
       "      <td>-45.690114</td>\n",
       "      <td>-71.821028</td>\n",
       "      <td>...</td>\n",
       "      <td>8.954923</td>\n",
       "      <td>25.812848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tephra F3 in Weller et al. (2015), who correla...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6220 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Volcano      Event  Magnitude  VEI  SampleID  \\\n",
       "0     Puyehue-Cordón Caulle    PCC2011        5.0  NaN     PCC-1   \n",
       "1     Puyehue-Cordón Caulle    PCC2011        5.0  NaN     PCC-1   \n",
       "2     Puyehue-Cordón Caulle    PCC2011        5.0  NaN     PCC-1   \n",
       "3     Puyehue-Cordón Caulle    PCC2011        5.0  NaN     PCC-1   \n",
       "4     Puyehue-Cordón Caulle    PCC2011        5.0  NaN     PCC-1   \n",
       "...                     ...        ...        ...  ...       ...   \n",
       "6215                Chaitén  Vcha-2008        NaN  NaN  Cha 2008   \n",
       "6216                Chaitén  Vcha-2008        NaN  NaN  Cha 2008   \n",
       "6217                Chaitén  Vcha-2008        NaN  NaN  Cha 2008   \n",
       "6218                Chaitén  Vcha-2008        NaN  NaN  Cha 2008   \n",
       "6219                 Hudson        HW6        NaN  NaN   CS-4145   \n",
       "\n",
       "     SampleObservationID IGSN                    Location   Latitude  \\\n",
       "0             13-19-1-01  NaN  Bariloche (BSI),  km 7.600 -41.130000   \n",
       "1             13-19-1-02  NaN  Bariloche (BSI),  km 7.600 -41.130000   \n",
       "2             13-19-1-03  NaN  Bariloche (BSI),  km 7.600 -41.130000   \n",
       "3             13-19-1-06  NaN  Bariloche (BSI),  km 7.600 -41.130000   \n",
       "4             13-19-1-07  NaN  Bariloche (BSI),  km 7.600 -41.130000   \n",
       "...                  ...  ...                         ...        ...   \n",
       "6215               G5e-5  NaN                         NaN -42.810000   \n",
       "6216               G5e-6  NaN                         NaN -42.810000   \n",
       "6217               G5e-7  NaN                         NaN -42.810000   \n",
       "6218               G5e-8  NaN                         NaN -42.810000   \n",
       "6219             CS-4145  NaN            Laguna Churrasco -45.690114   \n",
       "\n",
       "      Longitude  ...     La_Yb      Zr_Nb Sr87_Sr86 Sr87_Sr86_2SE Nd143_Nd144  \\\n",
       "0    -71.401777  ...  5.576923  38.125000       NaN           NaN         NaN   \n",
       "1    -71.401777  ...  4.892183  34.274194       NaN           NaN         NaN   \n",
       "2    -71.401777  ...  4.353741  41.363636       NaN           NaN         NaN   \n",
       "3    -71.401777  ...  5.617284  35.130435       NaN           NaN         NaN   \n",
       "4    -71.401777  ...  5.284431  37.477477       NaN           NaN         NaN   \n",
       "...         ...  ...       ...        ...       ...           ...         ...   \n",
       "6215 -72.690000  ...       NaN        NaN       NaN           NaN         NaN   \n",
       "6216 -72.690000  ...       NaN        NaN       NaN           NaN         NaN   \n",
       "6217 -72.690000  ...       NaN        NaN       NaN           NaN         NaN   \n",
       "6218 -72.690000  ...       NaN        NaN       NaN           NaN         NaN   \n",
       "6219 -71.821028  ...  8.954923  25.812848       NaN           NaN         NaN   \n",
       "\n",
       "     Nd143_Nd144_2SE  MeasurementRun  \\\n",
       "0                NaN  AllowayMay2014   \n",
       "1                NaN  AllowayMay2014   \n",
       "2                NaN  AllowayMay2014   \n",
       "3                NaN  AllowayMay2014   \n",
       "4                NaN  AllowayMay2014   \n",
       "...              ...             ...   \n",
       "6215             NaN             NaN   \n",
       "6216             NaN             NaN   \n",
       "6217             NaN             NaN   \n",
       "6218             NaN             NaN   \n",
       "6219             NaN             NaN   \n",
       "\n",
       "                                               Comments                Flag  \\\n",
       "0                                                   NaN                 NaN   \n",
       "1                                                   NaN                 NaN   \n",
       "2                                                   NaN                 NaN   \n",
       "3                                                   NaN                 NaN   \n",
       "4                                                   NaN                 NaN   \n",
       "...                                                 ...                 ...   \n",
       "6215                                                NaN  Geochemistry_Issue   \n",
       "6216                                                NaN  Geochemistry_Issue   \n",
       "6217                                                NaN  Geochemistry_Issue   \n",
       "6218                                                NaN  Geochemistry_Issue   \n",
       "6219  Tephra F3 in Weller et al. (2015), who correla...                 NaN   \n",
       "\n",
       "                                        FlagDescription  \n",
       "0                                                   NaN  \n",
       "1                                                   NaN  \n",
       "2                                                   NaN  \n",
       "3                                                   NaN  \n",
       "4                                                   NaN  \n",
       "...                                                 ...  \n",
       "6215  The original analytical total was not publishe...  \n",
       "6216  The original analytical total was not publishe...  \n",
       "6217  The original analytical total was not publishe...  \n",
       "6218  The original analytical total was not publishe...  \n",
       "6219                                                NaN  \n",
       "\n",
       "[6220 rows x 95 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('./data/train.csv')\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Volcano</th>\n",
       "      <th>Event</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>VEI</th>\n",
       "      <th>SampleID</th>\n",
       "      <th>SampleObservationID</th>\n",
       "      <th>IGSN</th>\n",
       "      <th>Location</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>La_Yb</th>\n",
       "      <th>Zr_Nb</th>\n",
       "      <th>Sr87_Sr86</th>\n",
       "      <th>Sr87_Sr86_2SE</th>\n",
       "      <th>Nd143_Nd144</th>\n",
       "      <th>Nd143_Nd144_2SE</th>\n",
       "      <th>MeasurementRun</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Flag</th>\n",
       "      <th>FlagDescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Puyehue-Cordón Caulle</td>\n",
       "      <td>PCC2011</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PCC-10 (2)</td>\n",
       "      <td>PCC-10 (2)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Villa Traful</td>\n",
       "      <td>-40.595329</td>\n",
       "      <td>-71.384650</td>\n",
       "      <td>...</td>\n",
       "      <td>5.152778</td>\n",
       "      <td>32.974773</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AllowaySOLICPMS2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SampleID_Issue</td>\n",
       "      <td>A specific SampleID is not provided in the pub...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Puyehue-Cordón Caulle</td>\n",
       "      <td>PCC2011</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PCC-19 (2)</td>\n",
       "      <td>PCC-19 (2)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>National Route 40, Villa Llanquin</td>\n",
       "      <td>-40.880955</td>\n",
       "      <td>-71.016003</td>\n",
       "      <td>...</td>\n",
       "      <td>5.313993</td>\n",
       "      <td>34.827795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AllowaySOLICPMS2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SampleID_Issue</td>\n",
       "      <td>A specific SampleID is not provided in the pub...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puyehue-Cordón Caulle</td>\n",
       "      <td>PCC2011</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PCC-20</td>\n",
       "      <td>PCC-20_10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>National Route 40, Confluencia</td>\n",
       "      <td>-40.670264</td>\n",
       "      <td>-71.062683</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25th August, 2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Geochemistry_Issue</td>\n",
       "      <td>The data is published normalized in the origin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Puyehue-Cordón Caulle</td>\n",
       "      <td>PCC2011</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PCC-20</td>\n",
       "      <td>PCC-20_11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>National Route 40, Confluencia</td>\n",
       "      <td>-40.670264</td>\n",
       "      <td>-71.062683</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25th August, 2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Geochemistry_Issue</td>\n",
       "      <td>The data is published normalized in the origin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Puyehue-Cordón Caulle</td>\n",
       "      <td>PCC2011</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PCC-20</td>\n",
       "      <td>PCC-20_12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>National Route 40, Confluencia</td>\n",
       "      <td>-40.670264</td>\n",
       "      <td>-71.062683</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25th August, 2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Geochemistry_Issue</td>\n",
       "      <td>The data is published normalized in the origin...</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>834</th>\n",
       "      <td>Reclus</td>\n",
       "      <td>R1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90-02</td>\n",
       "      <td>90-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Río Tres Brazos</td>\n",
       "      <td>-53.600000</td>\n",
       "      <td>-71.033333</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stern et al. (2008) estimate the erupted volum...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>Reclus</td>\n",
       "      <td>R1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90-05</td>\n",
       "      <td>90-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pampa Alegre (Rio Seco)</td>\n",
       "      <td>-53.066667</td>\n",
       "      <td>-70.850000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stern et al. (2008) estimate the erupted volum...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>Reclus</td>\n",
       "      <td>R1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90-06</td>\n",
       "      <td>90-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pampa Alegre (Rio Seco)</td>\n",
       "      <td>-53.066667</td>\n",
       "      <td>-70.850000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stern et al. (2008) estimate the erupted volum...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>Reclus</td>\n",
       "      <td>R1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CS-102</td>\n",
       "      <td>CS-102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cabo Boquerón</td>\n",
       "      <td>-53.466667</td>\n",
       "      <td>-70.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stern et al. (2008) estimate the erupted volum...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>Chaitén</td>\n",
       "      <td>Cha1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23-5A</td>\n",
       "      <td>23-5A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-41.695214</td>\n",
       "      <td>-72.392883</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.538269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>839 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Volcano    Event  Magnitude  VEI    SampleID  \\\n",
       "0    Puyehue-Cordón Caulle  PCC2011        5.0  NaN  PCC-10 (2)   \n",
       "1    Puyehue-Cordón Caulle  PCC2011        5.0  NaN  PCC-19 (2)   \n",
       "2    Puyehue-Cordón Caulle  PCC2011        5.0  NaN      PCC-20   \n",
       "3    Puyehue-Cordón Caulle  PCC2011        5.0  NaN      PCC-20   \n",
       "4    Puyehue-Cordón Caulle  PCC2011        5.0  NaN      PCC-20   \n",
       "..                     ...      ...        ...  ...         ...   \n",
       "834                 Reclus       R1        NaN  5.0       90-02   \n",
       "835                 Reclus       R1        NaN  5.0       90-05   \n",
       "836                 Reclus       R1        NaN  5.0       90-06   \n",
       "837                 Reclus       R1        NaN  5.0      CS-102   \n",
       "838                Chaitén     Cha1        5.5  5.0       23-5A   \n",
       "\n",
       "    SampleObservationID  IGSN                           Location   Latitude  \\\n",
       "0            PCC-10 (2)   NaN                       Villa Traful -40.595329   \n",
       "1            PCC-19 (2)   NaN  National Route 40, Villa Llanquin -40.880955   \n",
       "2             PCC-20_10   NaN     National Route 40, Confluencia -40.670264   \n",
       "3             PCC-20_11   NaN     National Route 40, Confluencia -40.670264   \n",
       "4             PCC-20_12   NaN     National Route 40, Confluencia -40.670264   \n",
       "..                  ...   ...                                ...        ...   \n",
       "834               90-02   NaN                    Río Tres Brazos -53.600000   \n",
       "835               90-05   NaN            Pampa Alegre (Rio Seco) -53.066667   \n",
       "836               90-06   NaN            Pampa Alegre (Rio Seco) -53.066667   \n",
       "837              CS-102   NaN                      Cabo Boquerón -53.466667   \n",
       "838               23-5A   NaN                                NaN -41.695214   \n",
       "\n",
       "     Longitude  ...     La_Yb      Zr_Nb Sr87_Sr86 Sr87_Sr86_2SE Nd143_Nd144  \\\n",
       "0   -71.384650  ...  5.152778  32.974773       NaN           NaN         NaN   \n",
       "1   -71.016003  ...  5.313993  34.827795       NaN           NaN         NaN   \n",
       "2   -71.062683  ...       NaN        NaN       NaN           NaN         NaN   \n",
       "3   -71.062683  ...       NaN        NaN       NaN           NaN         NaN   \n",
       "4   -71.062683  ...       NaN        NaN       NaN           NaN         NaN   \n",
       "..         ...  ...       ...        ...       ...           ...         ...   \n",
       "834 -71.033333  ...       NaN        NaN       NaN           NaN         NaN   \n",
       "835 -70.850000  ...       NaN        NaN       NaN           NaN         NaN   \n",
       "836 -70.850000  ...       NaN        NaN       NaN           NaN         NaN   \n",
       "837 -70.333333  ...       NaN        NaN       NaN           NaN         NaN   \n",
       "838 -72.392883  ...       NaN   9.538269       NaN           NaN         NaN   \n",
       "\n",
       "    Nd143_Nd144_2SE       MeasurementRun  \\\n",
       "0               NaN  AllowaySOLICPMS2015   \n",
       "1               NaN  AllowaySOLICPMS2015   \n",
       "2               NaN    25th August, 2013   \n",
       "3               NaN    25th August, 2013   \n",
       "4               NaN    25th August, 2013   \n",
       "..              ...                  ...   \n",
       "834             NaN                  NaN   \n",
       "835             NaN                  NaN   \n",
       "836             NaN                  NaN   \n",
       "837             NaN                  NaN   \n",
       "838             NaN                  NaN   \n",
       "\n",
       "                                              Comments                Flag  \\\n",
       "0                                                  NaN      SampleID_Issue   \n",
       "1                                                  NaN      SampleID_Issue   \n",
       "2                                                  NaN  Geochemistry_Issue   \n",
       "3                                                  NaN  Geochemistry_Issue   \n",
       "4                                                  NaN  Geochemistry_Issue   \n",
       "..                                                 ...                 ...   \n",
       "834  Stern et al. (2008) estimate the erupted volum...                 NaN   \n",
       "835  Stern et al. (2008) estimate the erupted volum...                 NaN   \n",
       "836  Stern et al. (2008) estimate the erupted volum...                 NaN   \n",
       "837  Stern et al. (2008) estimate the erupted volum...                 NaN   \n",
       "838                                                NaN                 NaN   \n",
       "\n",
       "                                       FlagDescription  \n",
       "0    A specific SampleID is not provided in the pub...  \n",
       "1    A specific SampleID is not provided in the pub...  \n",
       "2    The data is published normalized in the origin...  \n",
       "3    The data is published normalized in the origin...  \n",
       "4    The data is published normalized in the origin...  \n",
       "..                                                 ...  \n",
       "834                                                NaN  \n",
       "835                                                NaN  \n",
       "836                                                NaN  \n",
       "837                                                NaN  \n",
       "838                                                NaN  \n",
       "\n",
       "[839 rows x 95 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('./data/test.csv')\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Volcano', 'Event', 'Magnitude', 'VEI', 'SampleID',\n",
       "       'SampleObservationID', 'IGSN', 'Location', 'Latitude', 'Longitude',\n",
       "       'Authors', 'DOI', 'TypeOfRegister', 'TypeOfAnalysis',\n",
       "       'AnalyzedMaterial', 'AnalyticalTechnique', 'TypeOfSection', 'SectionID',\n",
       "       'SubSectionID', 'SubSection_DistanceFromTop_cm', 'HistoricalAge',\n",
       "       'RadiocarbonAge', 'RadiocarbonAgeError', 'RadiocarbonLabCode',\n",
       "       'StratigraphicPosition', 'Ar40Ar39_Age', 'Ar40Ar39_Age_Error',\n",
       "       'DepositColor', 'DepositThickness_cm', 'GrainSize_min', 'GrainSize_max',\n",
       "       'SiO2', 'TiO2', 'Al2O3', 'FeO', 'Fe2O3', 'Fe2O3T', 'FeOT', 'MnO', 'MgO',\n",
       "       'CaO', 'Na2O', 'K2O', 'P2O5', 'Cl', 'LOI', 'Total', 'SiO2_normalized',\n",
       "       'TiO2_normalized', 'Al2O3_normalized', 'FeOT_normalized',\n",
       "       'MnO_normalized', 'MgO_normalized', 'CaO_normalized', 'Na2O_normalized',\n",
       "       'K2O_normalized', 'P2O5_normalized', 'Cl_normalized',\n",
       "       'Total_normalization', 'Rb', 'Sr', 'Y', 'Zr', 'Nb', 'Cs', 'Ba', 'La',\n",
       "       'Ce', 'Pr', 'Nd', 'Sm', 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb',\n",
       "       'Lu', 'Hf', 'Ta', 'Pb', 'Th', 'U', 'La_Yb', 'Zr_Nb', 'Sr87_Sr86',\n",
       "       'Sr87_Sr86_2SE', 'Nd143_Nd144', 'Nd143_Nd144_2SE', 'MeasurementRun',\n",
       "       'Comments', 'Flag', 'FlagDescription'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Missingness\n",
    "\n",
    "msno.matrix(train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labels\n",
    "The target variable is in the column Event since we classify volcanic events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((30,),\n",
       " array(['PCC2011', 'Cha1', 'Grande', 'Vcha-2008', 'Vilcún', 'Lepué',\n",
       "        '1852 Llaima', 'HW3', 'H1', 'MB1', 'R1', 'Achen', 'Llaima Pumice',\n",
       "        'Arauco', 'Enco', 'Hua-hum', 'Neltume', 'Mil Hojas', 'PCC2',\n",
       "        'Ranco', 'Puesco', 'Quet1', 'Pucón', 'Playas Blanca-Negra',\n",
       "        'La Junta', 'HW6', 'Huilo', 'MC12', 'Pirehueico', 'Riñihue'],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.Event.unique().shape, train_df.Event.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 30 events to classify examples from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCC2011                567\n",
       "Vcha-2008              521\n",
       "Neltume                407\n",
       "Cha1                   391\n",
       "Puesco                 389\n",
       "Mil Hojas              318\n",
       "Enco                   313\n",
       "Lepué                  299\n",
       "La Junta               292\n",
       "Pucón                  227\n",
       "Arauco                 218\n",
       "Pirehueico             210\n",
       "Llaima Pumice          179\n",
       "Ranco                  177\n",
       "Huilo                  167\n",
       "H1                     158\n",
       "1852 Llaima            150\n",
       "MC12                   141\n",
       "Hua-hum                140\n",
       "Vilcún                 116\n",
       "Grande                 115\n",
       "Achen                   95\n",
       "Riñihue                 89\n",
       "MB1                     86\n",
       "R1                      81\n",
       "PCC2                    77\n",
       "Playas Blanca-Negra     77\n",
       "HW6                     77\n",
       "HW3                     76\n",
       "Quet1                   67\n",
       "Name: Event, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.Event.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_pub = train_df.Event.value_counts(normalize=False).plot(kind='bar', figsize=(20, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that we deal with an imbalanced dataset, where some classes have  almost 10 times more observations than others. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>SampleID</th>\n",
       "      <th>0201DT8, 1804 cm</th>\n",
       "      <th>020300-1</th>\n",
       "      <th>020300-2B</th>\n",
       "      <th>030114-1</th>\n",
       "      <th>030114-4</th>\n",
       "      <th>040114-2B</th>\n",
       "      <th>040114-2C</th>\n",
       "      <th>040300-1D</th>\n",
       "      <th>040300-2C</th>\n",
       "      <th>070114-8C</th>\n",
       "      <th>...</th>\n",
       "      <th>T-24</th>\n",
       "      <th>T6/98</th>\n",
       "      <th>T8/100</th>\n",
       "      <th>Trevelín3-08-05-2008</th>\n",
       "      <th>VILL1C-II-55</th>\n",
       "      <th>VILLAR1B-I-91</th>\n",
       "      <th>VILLAR1B-II-38.5</th>\n",
       "      <th>VILLAR1C-I-119</th>\n",
       "      <th>VILLAR1C-II-37</th>\n",
       "      <th>psC-5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Event</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1852 Llaima</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Achen</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arauco</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cha1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enco</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grande</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HW3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HW6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hua-hum</th>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huilo</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>La Junta</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lepué</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Llaima Pumice</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MB1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MC12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mil Hojas</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neltume</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCC2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCC2011</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pirehueico</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Playas Blanca-Negra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pucón</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puesco</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quet1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ranco</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Riñihue</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vcha-2008</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vilcún</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 455 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "SampleID             0201DT8, 1804 cm  020300-1  020300-2B  030114-1  \\\n",
       "Event                                                                  \n",
       "1852 Llaima                         0         0          0         0   \n",
       "Achen                               0         0          0         0   \n",
       "Arauco                              0         0          0         0   \n",
       "Cha1                                0         0          0         0   \n",
       "Enco                                0         0         32         0   \n",
       "Grande                              0         0          0         0   \n",
       "H1                                  0         0          0         0   \n",
       "HW3                                 0         0          0         0   \n",
       "HW6                                 0         0          0         0   \n",
       "Hua-hum                             0        25          0         0   \n",
       "Huilo                               0         0          0         0   \n",
       "La Junta                            0         0          0         0   \n",
       "Lepué                              19         0          0         0   \n",
       "Llaima Pumice                       0         0          0         0   \n",
       "MB1                                 0         0          0         0   \n",
       "MC12                                0         0          0         0   \n",
       "Mil Hojas                           0         0          0         0   \n",
       "Neltume                             0         0          0        20   \n",
       "PCC2                                0         0          0         0   \n",
       "PCC2011                             0         0          0         0   \n",
       "Pirehueico                          0         0          0         0   \n",
       "Playas Blanca-Negra                 0         0          0         0   \n",
       "Pucón                               0         0          0         0   \n",
       "Puesco                              0         0          0         0   \n",
       "Quet1                               0         0          0         0   \n",
       "R1                                  0         0          0         0   \n",
       "Ranco                               0         0          0         0   \n",
       "Riñihue                             0         0          0         0   \n",
       "Vcha-2008                           0         0          0         0   \n",
       "Vilcún                              0         0          0         0   \n",
       "\n",
       "SampleID             030114-4  040114-2B  040114-2C  040300-1D  040300-2C  \\\n",
       "Event                                                                       \n",
       "1852 Llaima                 0          0          0          0          0   \n",
       "Achen                       0          0          0          0          0   \n",
       "Arauco                      0          0          0          0         27   \n",
       "Cha1                        0          0          0          0          0   \n",
       "Enco                        0          0          0         40          0   \n",
       "Grande                      0          0          0          0          0   \n",
       "H1                          0          0          0          0          0   \n",
       "HW3                         0          0          0          0          0   \n",
       "HW6                         0          0          0          0          0   \n",
       "Hua-hum                     0          0          0          0          0   \n",
       "Huilo                       0          0          0          0          0   \n",
       "La Junta                    0          0          0          0          0   \n",
       "Lepué                       0          0          0          0          0   \n",
       "Llaima Pumice               0          0          0          0          0   \n",
       "MB1                         0          0          0          0          0   \n",
       "MC12                        0          0          0          0          0   \n",
       "Mil Hojas                   0          0          0          0          0   \n",
       "Neltume                    13         26         18          0          0   \n",
       "PCC2                        0          0          0          0          0   \n",
       "PCC2011                     0          0          0          0          0   \n",
       "Pirehueico                  0          0          0          0          0   \n",
       "Playas Blanca-Negra         0          0          0          0          0   \n",
       "Pucón                       0          0          0          0          0   \n",
       "Puesco                      0          0          0          0          0   \n",
       "Quet1                       0          0          0          0          0   \n",
       "R1                          0          0          0          0          0   \n",
       "Ranco                       0          0          0          0          0   \n",
       "Riñihue                     0          0          0          0          0   \n",
       "Vcha-2008                   0          0          0          0          0   \n",
       "Vilcún                      0          0          0          0          0   \n",
       "\n",
       "SampleID             070114-8C  ...  T-24  T6/98  T8/100  \\\n",
       "Event                           ...                        \n",
       "1852 Llaima                  0  ...     0      0       0   \n",
       "Achen                        0  ...     0      0       0   \n",
       "Arauco                      29  ...     0      0       0   \n",
       "Cha1                         0  ...     0      0       0   \n",
       "Enco                         0  ...     0      0       0   \n",
       "Grande                       0  ...     0      0       0   \n",
       "H1                           0  ...     0      0       0   \n",
       "HW3                          0  ...     0      0       0   \n",
       "HW6                          0  ...     0      0       0   \n",
       "Hua-hum                      0  ...     0      0       0   \n",
       "Huilo                        0  ...     0      0       0   \n",
       "La Junta                     0  ...     0      0       0   \n",
       "Lepué                        0  ...     0     14      47   \n",
       "Llaima Pumice                0  ...     0      0       0   \n",
       "MB1                          0  ...     0      0       0   \n",
       "MC12                         0  ...     0      0       0   \n",
       "Mil Hojas                    0  ...     0      0       0   \n",
       "Neltume                      0  ...     0      0       0   \n",
       "PCC2                         0  ...     0      0       0   \n",
       "PCC2011                      0  ...     0      0       0   \n",
       "Pirehueico                   0  ...     0      0       0   \n",
       "Playas Blanca-Negra          0  ...     0      0       0   \n",
       "Pucón                        0  ...     0      0       0   \n",
       "Puesco                       0  ...     0      0       0   \n",
       "Quet1                        0  ...     0      0       0   \n",
       "R1                           0  ...    13      0       0   \n",
       "Ranco                        0  ...     0      0       0   \n",
       "Riñihue                      0  ...     0      0       0   \n",
       "Vcha-2008                    0  ...     0      0       0   \n",
       "Vilcún                       0  ...     0      0       0   \n",
       "\n",
       "SampleID             Trevelín3-08-05-2008  VILL1C-II-55  VILLAR1B-I-91  \\\n",
       "Event                                                                    \n",
       "1852 Llaima                             0             0              0   \n",
       "Achen                                   0             0              0   \n",
       "Arauco                                  0             0              0   \n",
       "Cha1                                    0             0              0   \n",
       "Enco                                    0             0              0   \n",
       "Grande                                  0             0              0   \n",
       "H1                                      0             0              0   \n",
       "HW3                                     0             0              0   \n",
       "HW6                                     0             0              0   \n",
       "Hua-hum                                 0             0              0   \n",
       "Huilo                                   0             0              0   \n",
       "La Junta                                0             0              0   \n",
       "Lepué                                   0             0              0   \n",
       "Llaima Pumice                           0             0              0   \n",
       "MB1                                     0             0              0   \n",
       "MC12                                    0             0              0   \n",
       "Mil Hojas                               0             0              0   \n",
       "Neltume                                 0             0              0   \n",
       "PCC2                                    0             0              0   \n",
       "PCC2011                                 0             0              0   \n",
       "Pirehueico                              0             0              0   \n",
       "Playas Blanca-Negra                     0             0              0   \n",
       "Pucón                                   0            28             27   \n",
       "Puesco                                  0             0              0   \n",
       "Quet1                                   0             0              0   \n",
       "R1                                      0             0              0   \n",
       "Ranco                                   0             0              0   \n",
       "Riñihue                                 0             0              0   \n",
       "Vcha-2008                              18             0              0   \n",
       "Vilcún                                  0             0              0   \n",
       "\n",
       "SampleID             VILLAR1B-II-38.5  VILLAR1C-I-119  VILLAR1C-II-37  psC-5  \n",
       "Event                                                                         \n",
       "1852 Llaima                         0               0               0      0  \n",
       "Achen                               0               0               0      0  \n",
       "Arauco                              0               0               0      0  \n",
       "Cha1                                0               0               0      0  \n",
       "Enco                                0               0               0      0  \n",
       "Grande                              0               0               0     21  \n",
       "H1                                  0               0               0      0  \n",
       "HW3                                 0               0               0      0  \n",
       "HW6                                 0               0               0      0  \n",
       "Hua-hum                             0               0               0      0  \n",
       "Huilo                               0               0               0      0  \n",
       "La Junta                            0               0               0      0  \n",
       "Lepué                               0               0               0      0  \n",
       "Llaima Pumice                       0              17               0      0  \n",
       "MB1                                 0               0               0      0  \n",
       "MC12                                0               0               0      0  \n",
       "Mil Hojas                           0               0               0      0  \n",
       "Neltume                             0               0              12      0  \n",
       "PCC2                                0               0               0      0  \n",
       "PCC2011                             0               0               0      0  \n",
       "Pirehueico                          0               0               0      0  \n",
       "Playas Blanca-Negra                 0               0               0      0  \n",
       "Pucón                               0               0               0      0  \n",
       "Puesco                              0               0               0      0  \n",
       "Quet1                               0               0               0      0  \n",
       "R1                                  0               0               0      0  \n",
       "Ranco                              28               0               0      0  \n",
       "Riñihue                             0               0               0      0  \n",
       "Vcha-2008                           0               0               0      0  \n",
       "Vilcún                              0               0               0      0  \n",
       "\n",
       "[30 rows x 455 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribution of groups in each class\n",
    "group_class_counts = train_df.groupby(['Event', 'SampleID']).size().unstack(fill_value=0)\n",
    "group_class_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap of the distribution of groups in each class\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.heatmap(group_class_counts, cmap=\"viridis\")\n",
    "plt.title('Distribution of Groups in Each Class')\n",
    "plt.xlabel('Group')\n",
    "plt.ylabel('Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base model <a class=\"anchor\" id=\"base_model\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We only keep geochemical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve the geochemical data. FeO, Fe2O3 and FeO2O3T are dropped because\n",
    "# FeOT is a different expression of the same element (Fe).\n",
    "# P2O5 and Cl are also dropped because they are sporadically analyzed.\n",
    "majors = ['SiO2_normalized', 'TiO2_normalized', 'Al2O3_normalized',\n",
    "          'FeOT_normalized',\n",
    "          # 'FeO_normalized', 'Fe2O3_normalized', 'Fe2O3T_normalized',\n",
    "          'MnO_normalized', 'MgO_normalized', 'CaO_normalized',\n",
    "          'Na2O_normalized', 'K2O_normalized',\n",
    "          # 'P2O5_normalized','Cl_normalized'\n",
    "          ]\n",
    "traces = ['Rb', 'Sr', 'Y', 'Zr', 'Nb', 'Cs', 'Ba', 'La',\n",
    "          'Ce', 'Pr', 'Nd', 'Sm', 'Eu', 'Gd', 'Tb', 'Dy',\n",
    "          'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Ta', 'Pb',\n",
    "          'Th', 'U']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_majors_train = train_df.loc[:, majors]\n",
    "X_traces_train = train_df.loc[:, traces]\n",
    "X_train_df = pd.concat([X_majors_train, X_traces_train], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SiO2_normalized</th>\n",
       "      <th>TiO2_normalized</th>\n",
       "      <th>Al2O3_normalized</th>\n",
       "      <th>FeOT_normalized</th>\n",
       "      <th>MnO_normalized</th>\n",
       "      <th>MgO_normalized</th>\n",
       "      <th>CaO_normalized</th>\n",
       "      <th>Na2O_normalized</th>\n",
       "      <th>K2O_normalized</th>\n",
       "      <th>Rb</th>\n",
       "      <th>...</th>\n",
       "      <th>Ho</th>\n",
       "      <th>Er</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Yb</th>\n",
       "      <th>Lu</th>\n",
       "      <th>Hf</th>\n",
       "      <th>Ta</th>\n",
       "      <th>Pb</th>\n",
       "      <th>Th</th>\n",
       "      <th>U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.170000</td>\n",
       "      <td>6.86000</td>\n",
       "      <td>1.170000</td>\n",
       "      <td>6.760000</td>\n",
       "      <td>1.330000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.756</td>\n",
       "      <td>36.10000</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.440000</td>\n",
       "      <td>6.73000</td>\n",
       "      <td>0.952000</td>\n",
       "      <td>7.420000</td>\n",
       "      <td>0.971000</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>0.828</td>\n",
       "      <td>43.40000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>2.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.790000</td>\n",
       "      <td>7.84000</td>\n",
       "      <td>1.190000</td>\n",
       "      <td>8.820000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>0.691</td>\n",
       "      <td>37.00000</td>\n",
       "      <td>11.900000</td>\n",
       "      <td>2.89000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>7.67000</td>\n",
       "      <td>0.943000</td>\n",
       "      <td>6.480000</td>\n",
       "      <td>1.490000</td>\n",
       "      <td>11.900000</td>\n",
       "      <td>0.559</td>\n",
       "      <td>45.00000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>3.27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.220000</td>\n",
       "      <td>7.18000</td>\n",
       "      <td>1.420000</td>\n",
       "      <td>6.680000</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>0.725</td>\n",
       "      <td>49.00000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>2.74000</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",
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       "    <tr>\n",
       "      <th>6215</th>\n",
       "      <td>75.550683</td>\n",
       "      <td>0.124652</td>\n",
       "      <td>14.134401</td>\n",
       "      <td>1.236851</td>\n",
       "      <td>0.103793</td>\n",
       "      <td>0.272280</td>\n",
       "      <td>1.395362</td>\n",
       "      <td>4.004196</td>\n",
       "      <td>3.177781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6216</th>\n",
       "      <td>75.298120</td>\n",
       "      <td>0.163281</td>\n",
       "      <td>14.389786</td>\n",
       "      <td>1.382133</td>\n",
       "      <td>0.083662</td>\n",
       "      <td>0.256138</td>\n",
       "      <td>1.398704</td>\n",
       "      <td>3.897638</td>\n",
       "      <td>3.130538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6217</th>\n",
       "      <td>75.636943</td>\n",
       "      <td>0.104154</td>\n",
       "      <td>14.038277</td>\n",
       "      <td>1.331270</td>\n",
       "      <td>0.067500</td>\n",
       "      <td>0.220927</td>\n",
       "      <td>1.328066</td>\n",
       "      <td>4.052097</td>\n",
       "      <td>3.220767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6218</th>\n",
       "      <td>75.380973</td>\n",
       "      <td>0.164384</td>\n",
       "      <td>13.996524</td>\n",
       "      <td>1.493424</td>\n",
       "      <td>0.026216</td>\n",
       "      <td>0.388185</td>\n",
       "      <td>1.447166</td>\n",
       "      <td>3.920314</td>\n",
       "      <td>3.182813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6219</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.994783</td>\n",
       "      <td>...</td>\n",
       "      <td>1.227664</td>\n",
       "      <td>3.78919</td>\n",
       "      <td>0.443554</td>\n",
       "      <td>3.524362</td>\n",
       "      <td>0.482097</td>\n",
       "      <td>5.013576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.04442</td>\n",
       "      <td>4.265854</td>\n",
       "      <td>0.99752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6220 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SiO2_normalized  TiO2_normalized  Al2O3_normalized  FeOT_normalized  \\\n",
       "0                 NaN              NaN               NaN              NaN   \n",
       "1                 NaN              NaN               NaN              NaN   \n",
       "2                 NaN              NaN               NaN              NaN   \n",
       "3                 NaN              NaN               NaN              NaN   \n",
       "4                 NaN              NaN               NaN              NaN   \n",
       "...               ...              ...               ...              ...   \n",
       "6215        75.550683         0.124652         14.134401         1.236851   \n",
       "6216        75.298120         0.163281         14.389786         1.382133   \n",
       "6217        75.636943         0.104154         14.038277         1.331270   \n",
       "6218        75.380973         0.164384         13.996524         1.493424   \n",
       "6219              NaN              NaN               NaN              NaN   \n",
       "\n",
       "      MnO_normalized  MgO_normalized  CaO_normalized  Na2O_normalized  \\\n",
       "0                NaN             NaN             NaN              NaN   \n",
       "1                NaN             NaN             NaN              NaN   \n",
       "2                NaN             NaN             NaN              NaN   \n",
       "3                NaN             NaN             NaN              NaN   \n",
       "4                NaN             NaN             NaN              NaN   \n",
       "...              ...             ...             ...              ...   \n",
       "6215        0.103793        0.272280        1.395362         4.004196   \n",
       "6216        0.083662        0.256138        1.398704         3.897638   \n",
       "6217        0.067500        0.220927        1.328066         4.052097   \n",
       "6218        0.026216        0.388185        1.447166         3.920314   \n",
       "6219             NaN             NaN             NaN              NaN   \n",
       "\n",
       "      K2O_normalized         Rb  ...        Ho       Er        Tm        Yb  \\\n",
       "0                NaN  71.000000  ...  2.170000  6.86000  1.170000  6.760000   \n",
       "1                NaN  72.000000  ...  2.440000  6.73000  0.952000  7.420000   \n",
       "2                NaN  70.400000  ...  2.790000  7.84000  1.190000  8.820000   \n",
       "3                NaN  79.900000  ...  2.260000  7.67000  0.943000  6.480000   \n",
       "4                NaN  74.100000  ...  2.220000  7.18000  1.420000  6.680000   \n",
       "...              ...        ...  ...       ...      ...       ...       ...   \n",
       "6215        3.177781        NaN  ...       NaN      NaN       NaN       NaN   \n",
       "6216        3.130538        NaN  ...       NaN      NaN       NaN       NaN   \n",
       "6217        3.220767        NaN  ...       NaN      NaN       NaN       NaN   \n",
       "6218        3.182813        NaN  ...       NaN      NaN       NaN       NaN   \n",
       "6219             NaN  54.994783  ...  1.227664  3.78919  0.443554  3.524362   \n",
       "\n",
       "            Lu         Hf     Ta        Pb         Th        U  \n",
       "0     1.330000  12.600000  0.756  36.10000  10.900000  3.00000  \n",
       "1     0.971000  11.600000  0.828  43.40000  11.100000  2.90000  \n",
       "2     1.400000  13.700000  0.691  37.00000  11.900000  2.89000  \n",
       "3     1.490000  11.900000  0.559  45.00000  10.500000  3.27000  \n",
       "4     0.940000  10.700000  0.725  49.00000  10.100000  2.74000  \n",
       "...        ...        ...    ...       ...        ...      ...  \n",
       "6215       NaN        NaN    NaN       NaN        NaN      NaN  \n",
       "6216       NaN        NaN    NaN       NaN        NaN      NaN  \n",
       "6217       NaN        NaN    NaN       NaN        NaN      NaN  \n",
       "6218       NaN        NaN    NaN       NaN        NaN      NaN  \n",
       "6219  0.482097   5.013576    NaN   9.04442   4.265854  0.99752  \n",
       "\n",
       "[6220 rows x 35 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(X_train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to tackle missingness even on this 35 variables data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_majors_test = test_df.loc[:, majors]\n",
    "X_traces_test = test_df.loc[:, traces]\n",
    "X_test_df = pd.concat([X_majors_test, X_traces_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>SiO2_normalized</th>\n",
       "      <th>TiO2_normalized</th>\n",
       "      <th>Al2O3_normalized</th>\n",
       "      <th>FeOT_normalized</th>\n",
       "      <th>MnO_normalized</th>\n",
       "      <th>MgO_normalized</th>\n",
       "      <th>CaO_normalized</th>\n",
       "      <th>Na2O_normalized</th>\n",
       "      <th>K2O_normalized</th>\n",
       "      <th>Rb</th>\n",
       "      <th>...</th>\n",
       "      <th>Ho</th>\n",
       "      <th>Er</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Yb</th>\n",
       "      <th>Lu</th>\n",
       "      <th>Hf</th>\n",
       "      <th>Ta</th>\n",
       "      <th>Pb</th>\n",
       "      <th>Th</th>\n",
       "      <th>U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.271754</td>\n",
       "      <td>0.908540</td>\n",
       "      <td>14.445790</td>\n",
       "      <td>4.885928</td>\n",
       "      <td>0.131234</td>\n",
       "      <td>0.868161</td>\n",
       "      <td>2.543913</td>\n",
       "      <td>5.148395</td>\n",
       "      <td>2.634767</td>\n",
       "      <td>69.21</td>\n",
       "      <td>...</td>\n",
       "      <td>1.87</td>\n",
       "      <td>5.62</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.87</td>\n",
       "      <td>8.430000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>25.77</td>\n",
       "      <td>8.57</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.767911</td>\n",
       "      <td>0.706357</td>\n",
       "      <td>14.238143</td>\n",
       "      <td>4.177598</td>\n",
       "      <td>0.121090</td>\n",
       "      <td>0.655903</td>\n",
       "      <td>2.189707</td>\n",
       "      <td>5.216953</td>\n",
       "      <td>2.785066</td>\n",
       "      <td>73.31</td>\n",
       "      <td>...</td>\n",
       "      <td>1.92</td>\n",
       "      <td>5.80</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5.86</td>\n",
       "      <td>0.89</td>\n",
       "      <td>8.810000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>26.22</td>\n",
       "      <td>8.78</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71.652835</td>\n",
       "      <td>0.579942</td>\n",
       "      <td>14.408559</td>\n",
       "      <td>3.709629</td>\n",
       "      <td>0.089991</td>\n",
       "      <td>0.539946</td>\n",
       "      <td>1.629837</td>\n",
       "      <td>4.259574</td>\n",
       "      <td>2.939706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.552845</td>\n",
       "      <td>0.589941</td>\n",
       "      <td>14.538546</td>\n",
       "      <td>3.949605</td>\n",
       "      <td>0.119988</td>\n",
       "      <td>0.529947</td>\n",
       "      <td>1.669833</td>\n",
       "      <td>4.109589</td>\n",
       "      <td>2.769723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>71.780000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>14.270000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>1.630000</td>\n",
       "      <td>4.390000</td>\n",
       "      <td>2.870000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>834</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.286429</td>\n",
       "      <td>0.838537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>839 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SiO2_normalized  TiO2_normalized  Al2O3_normalized  FeOT_normalized  \\\n",
       "0          68.271754         0.908540         14.445790         4.885928   \n",
       "1          69.767911         0.706357         14.238143         4.177598   \n",
       "2          71.652835         0.579942         14.408559         3.709629   \n",
       "3          71.552845         0.589941         14.538546         3.949605   \n",
       "4          71.780000         0.560000         14.270000         3.800000   \n",
       "..               ...              ...               ...              ...   \n",
       "834              NaN              NaN               NaN              NaN   \n",
       "835              NaN              NaN               NaN              NaN   \n",
       "836              NaN              NaN               NaN              NaN   \n",
       "837              NaN              NaN               NaN              NaN   \n",
       "838              NaN              NaN               NaN              NaN   \n",
       "\n",
       "     MnO_normalized  MgO_normalized  CaO_normalized  Na2O_normalized  \\\n",
       "0          0.131234        0.868161        2.543913         5.148395   \n",
       "1          0.121090        0.655903        2.189707         5.216953   \n",
       "2          0.089991        0.539946        1.629837         4.259574   \n",
       "3          0.119988        0.529947        1.669833         4.109589   \n",
       "4          0.050000        0.510000        1.630000         4.390000   \n",
       "..              ...             ...             ...              ...   \n",
       "834             NaN             NaN             NaN              NaN   \n",
       "835             NaN             NaN             NaN              NaN   \n",
       "836             NaN             NaN             NaN              NaN   \n",
       "837             NaN             NaN             NaN              NaN   \n",
       "838             NaN             NaN             NaN              NaN   \n",
       "\n",
       "     K2O_normalized     Rb  ...    Ho    Er    Tm    Yb    Lu        Hf  \\\n",
       "0          2.634767  69.21  ...  1.87  5.62  0.86  5.76  0.87  8.430000   \n",
       "1          2.785066  73.31  ...  1.92  5.80  0.87  5.86  0.89  8.810000   \n",
       "2          2.939706    NaN  ...   NaN   NaN   NaN   NaN   NaN       NaN   \n",
       "3          2.769723    NaN  ...   NaN   NaN   NaN   NaN   NaN       NaN   \n",
       "4          2.870000    NaN  ...   NaN   NaN   NaN   NaN   NaN       NaN   \n",
       "..              ...    ...  ...   ...   ...   ...   ...   ...       ...   \n",
       "834             NaN  37.00  ...   NaN   NaN   NaN   NaN   NaN       NaN   \n",
       "835             NaN  33.00  ...   NaN   NaN   NaN   NaN   NaN       NaN   \n",
       "836             NaN  32.00  ...   NaN   NaN   NaN   NaN   NaN       NaN   \n",
       "837             NaN  29.00  ...   NaN   NaN   NaN   NaN   NaN       NaN   \n",
       "838             NaN    NaN  ...   NaN   NaN   NaN   NaN   NaN  2.286429   \n",
       "\n",
       "           Ta     Pb    Th     U  \n",
       "0    0.620000  25.77  8.57  2.28  \n",
       "1    0.570000  26.22  8.78  2.20  \n",
       "2         NaN    NaN   NaN   NaN  \n",
       "3         NaN    NaN   NaN   NaN  \n",
       "4         NaN    NaN   NaN   NaN  \n",
       "..        ...    ...   ...   ...  \n",
       "834       NaN    NaN   NaN   NaN  \n",
       "835       NaN    NaN   NaN   NaN  \n",
       "836       NaN    NaN   NaN   NaN  \n",
       "837       NaN    NaN   NaN   NaN  \n",
       "838  0.838537    NaN   NaN   NaN  \n",
       "\n",
       "[839 rows x 35 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(X_test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model will take numpy arrays as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[        nan,         nan,         nan, ..., 36.1       ,\n",
       "        10.9       ,  3.        ],\n",
       "       [        nan,         nan,         nan, ..., 43.4       ,\n",
       "        11.1       ,  2.9       ],\n",
       "       [        nan,         nan,         nan, ..., 37.        ,\n",
       "        11.9       ,  2.89      ],\n",
       "       ...,\n",
       "       [75.63694268,  0.10415415, 14.03827665, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [75.38097296,  0.16438395, 13.99652405, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,  9.04441963,\n",
       "         4.26585366,  0.99751983]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train_df.to_numpy()\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[68.27175449,  0.90854028, 14.44579043, ..., 25.77      ,\n",
       "         8.57      ,  2.28      ],\n",
       "       [69.7679112 ,  0.70635722, 14.23814329, ..., 26.22      ,\n",
       "         8.78      ,  2.2       ],\n",
       "       [71.65283472,  0.57994201, 14.40855914, ...,         nan,\n",
       "                nan,         nan],\n",
       "       ...,\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = X_test_df.to_numpy()\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Labels for the train and test sets.\n",
    "We use a dictionary to store the correspondence between categories and integer labels for this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hard code int_to_cat dict and retrieve cat_to_int with dictionary comprehension to use in all needed files\n",
    "int_to_cat = {\n",
    "    0: \"1852 Llaima\",\n",
    "    1: \"Achen\",\n",
    "    2: \"Arauco\",\n",
    "    3: \"Cha1\",\n",
    "    4: \"Enco\",\n",
    "    5: \"Grande\",\n",
    "    6: \"H1\",\n",
    "    7: \"HW3\",\n",
    "    8: \"HW6\",\n",
    "    9: \"Hua-hum\",\n",
    "    10: \"Huilo\",\n",
    "    11: \"La Junta\",\n",
    "    12: \"Lepué\",\n",
    "    13: \"Llaima Pumice\",\n",
    "    14: \"MB1\",\n",
    "    15: \"MC12\",\n",
    "    16: \"Mil Hojas\",\n",
    "    17: \"Neltume\",\n",
    "    18: \"PCC2\",\n",
    "    19: \"PCC2011\",\n",
    "    20: \"Pirehueico\",\n",
    "    21: \"Playas Blanca-Negra\",\n",
    "    22: \"Pucón\",\n",
    "    23: \"Puesco\",\n",
    "    24: \"Quet1\",\n",
    "    25: \"R1\",\n",
    "    26: \"Ranco\",\n",
    "    27: \"Riñihue\",\n",
    "    28: \"Vcha-2008\",\n",
    "    29: \"Vilcún\",\n",
    "}\n",
    "\n",
    "cat_to_int = {v: k for k, v in int_to_cat.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([19, 19, 19, ..., 28, 28,  8], dtype=int8), (6220,))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = np.array(train_df['Event'].map(cat_to_int).fillna(-1).astype('int8'))\n",
    "y_train, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,\n",
       "        19, 19,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,\n",
       "         3,  3,  3,  3,  3,  3,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,\n",
       "         5,  5,  5,  5,  5,  5,  5,  5,  5, 28, 28, 28, 28, 28, 28, 28, 28,\n",
       "        28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,\n",
       "        28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,\n",
       "        28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,\n",
       "        28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29,\n",
       "        29, 29, 29, 29, 29, 29, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
       "        12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
       "        12, 12, 12, 12, 12, 12,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,\n",
       "         6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6, 14, 14, 14, 14, 14,\n",
       "        14, 14, 14, 14, 14,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,\n",
       "         3,  3,  3,  3,  3,  3,  3,  3,  3,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "         1,  1,  1,  1,  1,  1,  1,  1, 13, 13, 13, 13, 13, 13, 13, 13,  2,\n",
       "         2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,\n",
       "         2,  2,  2,  2,  2,  2,  2, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17, 17, 17, 17, 17, 17, 16, 16, 16, 16, 16, 16, 16, 16,\n",
       "        16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,\n",
       "        16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,\n",
       "        18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,\n",
       "        18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,\n",
       "        19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,\n",
       "        19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 26, 26, 26, 26, 26,\n",
       "        26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,\n",
       "        26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 23, 23,\n",
       "        23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,\n",
       "        23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,\n",
       "        23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
       "        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 22,\n",
       "        22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22,\n",
       "        22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 21, 21, 21, 21, 21, 21, 21,\n",
       "        21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 11, 11, 11, 11, 11,\n",
       "        11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,\n",
       "        11, 11, 11, 11, 11, 11, 11, 11, 11, 11,  7,  7,  7,  7,  7,  7,  7,\n",
       "         7,  7,  7,  7,  7,  7,  7,  7,  7,  8,  8,  8,  8,  8,  8,  8,  8,\n",
       "         8,  8,  8,  8,  8,  3, 11, 19,  4,  4,  4,  4,  4,  4,  4,  4,  4,\n",
       "         4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,\n",
       "         4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  9,  9,  9,  9,  9,  9,  9,\n",
       "         9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9, 10, 10, 10, 10, 10, 10,\n",
       "        10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 15, 15,\n",
       "        15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,\n",
       "        15, 15, 15, 15, 15, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 20,\n",
       "        20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,\n",
       "        20, 20, 20, 20, 20, 20, 20, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,\n",
       "        27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27,  1,  4, 17,  0,  0,\n",
       "        13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 25, 25, 25, 25, 25,\n",
       "        25, 25, 25, 25, 25,  3], dtype=int8),\n",
       " (839,))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = np.array(test_df['Event'].map(cat_to_int).fillna(-1).astype('int8'))\n",
    "y_test, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputation and scaling of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_transformer = Pipeline(\n",
    "    steps=[(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classifier: here we take a simple model, logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = Pipeline(\n",
    "    steps=[(\"transformer\", numeric_transformer), (\"classifier\", LogisticRegression(max_iter=500))]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit the pipeline on train data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                 (&#x27;scaler&#x27;, StandardScaler())])),\n",
       "                (&#x27;classifier&#x27;, LogisticRegression(max_iter=500))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                 (&#x27;scaler&#x27;, StandardScaler())])),\n",
       "                (&#x27;classifier&#x27;, LogisticRegression(max_iter=500))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                (&#x27;scaler&#x27;, StandardScaler())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=500)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('transformer',\n",
       "                 Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),\n",
       "                                 ('scaler', StandardScaler())])),\n",
       "                ('classifier', LogisticRegression(max_iter=500))])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluation on test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(839,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7377830750893921"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.71      0.77        17\n",
      "           1       0.92      0.71      0.80        17\n",
      "           2       0.49      0.72      0.58        25\n",
      "           3       1.00      0.14      0.24        44\n",
      "           4       0.58      0.59      0.59        37\n",
      "           5       0.89      0.40      0.55        20\n",
      "           6       1.00      0.64      0.78        25\n",
      "           7       0.57      0.25      0.35        16\n",
      "           8       0.00      0.00      0.00        13\n",
      "           9       0.83      0.28      0.42        18\n",
      "          10       0.94      0.76      0.84        21\n",
      "          11       0.66      0.88      0.75        33\n",
      "          12       0.71      0.88      0.79        34\n",
      "          13       0.90      0.95      0.93        20\n",
      "          14       1.00      1.00      1.00        10\n",
      "          15       0.82      0.96      0.88        24\n",
      "          16       0.48      0.98      0.65        42\n",
      "          17       0.76      0.88      0.82        48\n",
      "          18       0.90      0.95      0.93        20\n",
      "          19       0.96      0.40      0.56        63\n",
      "          20       0.82      0.56      0.67        25\n",
      "          21       1.00      1.00      1.00        19\n",
      "          22       0.70      1.00      0.82        28\n",
      "          23       0.90      1.00      0.95        44\n",
      "          24       1.00      0.96      0.98        25\n",
      "          25       1.00      0.50      0.67        10\n",
      "          26       0.97      0.95      0.96        37\n",
      "          27       0.78      0.64      0.70        22\n",
      "          28       0.57      1.00      0.73        66\n",
      "          29       0.81      0.81      0.81        16\n",
      "\n",
      "    accuracy                           0.74       839\n",
      "   macro avg       0.80      0.72      0.72       839\n",
      "weighted avg       0.79      0.74      0.72       839\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = ConfusionMatrixDisplay.from_predictions(y_test, y_pred)\n",
    "fig = disp.figure_\n",
    "fig.set_figwidth(10)\n",
    "fig.set_figheight(10)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Balanced accuracy score will be used as the leaderboard score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7158235028382088"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the score to beat in this challenge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submitting to the online challenge: [ramp.studio](https://ramp.studio) <a class=\"anchor\" id=\"submitting\"></a>\n",
    "\n",
    "Once you found a good model, you can submit them to [ramp.studio](https://www.ramp.studio) to enter the online challenge. First, if it is your first time using the RAMP platform, [sign up](https://www.ramp.studio/sign_up), otherwise [log in](https://www.ramp.studio/login). Then sign up to the event [tephra](http://www.ramp.studio/events/tephra_datacamp2023). Both signups are controled by RAMP administrators, so there **can be a delay between asking for signup and being able to submit**.\n",
    "\n",
    "Once your signup request is accepted, you can go to your [sandbox](https://www.ramp.studio/events/tephra_datacamp2023/sandbox) and write the code for your classifier directly on the browser. You can also create a new folder `my_submission` in the `submissions` folder containing `classifier.py` and upload this file directly. You can check the starting-kit ([`classifier.py`](/edit/submissions/starting_kit/classifier.py)) for an example. The submission is trained and tested on our backend in the similar way as `ramp-test` does it locally. While your submission is waiting in the queue and being trained, you can find it in the \"New submissions (pending training)\" table in [my submissions](https://www.ramp.studio/events/tephra_datacamp2023/my_submissions). Once it is trained, your submission shows up on the [public leaderboard](https://www.ramp.studio/events/tephra_datacamp2023/leaderboard). \n",
    "If there is an error (despite having tested your submission locally with `ramp-test`), it will show up in the \"Failed submissions\" table in [my submissions](https://www.ramp.studio/events/tephra_datacamp2023/my_submissions). You can click on the error to see part of the trace.\n",
    "\n",
    "The data set we use at the backend is usually different from what you find in the starting kit, so the score may be different.\n",
    "\n",
    "The usual way to work with RAMP is to explore solutions, add feature transformations, select models, etc., _locally_, and checking them with `ramp-test`. The script prints mean cross-validation scores.\n",
    "\n",
    "The official score in this RAMP (the first score column on the [leaderboard](http://www.ramp.studio/events/tephra_datacamp2023/leaderboard)) is the balenced accuracy score (`bal_acc`). When the score is good enough, you can submit it at the RAMP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the script proposed as the starting_kit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.pipeline import make_pipeline, Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "class Classifier(BaseEstimator):\n",
    "    def __init__(self):\n",
    "        self.transformer = Pipeline(\n",
    "            steps=[\n",
    "                (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "                (\"scaler\", StandardScaler()),\n",
    "            ]\n",
    "        )\n",
    "        self.model = LogisticRegression(max_iter=500)\n",
    "        self.pipe = make_pipeline(self.transformer, self.model)\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        X = X.drop([\"groups\"], axis=1)\n",
    "        self.pipe.fit(X, y)\n",
    "\n",
    "    def predict(self, X):\n",
    "        X = X.drop([\"groups\"], axis=1)\n",
    "        return self.pipe.predict(X)\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        X = X.drop([\"groups\"], axis=1)\n",
    "        return self.pipe.predict_proba(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can test your solution locally by running the ramp-test command followed by --submission <my_submission folder>.\n",
    "Here is an example with the starting_kit submission:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;178m\u001b[1mTesting Volcanic events prediction from tephras\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mReading train and test files from ./data/ ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mReading cv ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mTraining submissions/starting_kit ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 0\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  bal_acc    acc      time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m    \u001b[38;5;10m\u001b[1m0.833\u001b[0m  \u001b[38;5;150m0.864\u001b[0m  \u001b[38;5;150m1.835487\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m    \u001b[38;5;12m\u001b[1m0.682\u001b[0m  \u001b[38;5;105m0.713\u001b[0m  \u001b[38;5;105m0.020979\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m     \u001b[38;5;1m\u001b[1m0.635\u001b[0m  \u001b[38;5;218m0.695\u001b[0m  \u001b[38;5;218m0.005458\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 1\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  bal_acc    acc      time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m    \u001b[38;5;10m\u001b[1m0.826\u001b[0m  \u001b[38;5;150m0.879\u001b[0m  \u001b[38;5;150m1.536761\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m    \u001b[38;5;12m\u001b[1m0.670\u001b[0m  \u001b[38;5;105m0.692\u001b[0m  \u001b[38;5;105m0.013410\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m     \u001b[38;5;1m\u001b[1m0.609\u001b[0m  \u001b[38;5;218m0.648\u001b[0m  \u001b[38;5;218m0.004637\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mMean CV scores\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore         bal_acc             acc        time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m  \u001b[38;5;10m\u001b[1m0.829\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m0.0035\u001b[0m  \u001b[38;5;150m0.871\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m0.0076\u001b[0m  \u001b[38;5;150m1.7\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m0.15\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m0.676\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m0.0\u001b[0m\u001b[0m056\u001b[0m  \u001b[38;5;105m0.703\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m0.0\u001b[0m\u001b[0m103\u001b[0m   \u001b[38;5;105m\u001b[38;5;105m0.0\u001b[0m\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;105m\u001b[38;5;105m0.0\u001b[0m\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m0.622\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m0.0\u001b[0m\u001b[0m129\u001b[0m  \u001b[38;5;218m0.672\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m0.0\u001b[0m\u001b[0m232\u001b[0m   \u001b[38;5;218m\u001b[38;5;218m0.0\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m0.0\u001b[0m\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mBagged scores\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  bal_acc    acc\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m    \u001b[38;5;12m\u001b[1m0.625\u001b[0m  \u001b[38;5;105m0.703\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m     \u001b[38;5;1m\u001b[1m0.646\u001b[0m  \u001b[38;5;218m0.683\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!ramp-test --submission starting_kit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More information\n",
    "\n",
    "See the [online documentation](https://paris-saclay-cds.github.io/ramp-docs/ramp-workflow/stable/using_kits.html) for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions\n",
    "\n",
    "Questions related to the starting kit should be asked on the [issue tracker](https://github.com/ramp-kits/tephra/issues)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "ramp-tephra",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
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