data_analysis_0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    " (This is a messy notebook) \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and load stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kurma\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "from pathlib import Path\n",
    "from pprint import pprint\n",
    "from tqdm import tqdm\n",
    "import featuretools as ft\n",
    "from joblib import dump, load\n",
    "from imblearn.combine import SMOTETomek\n",
    "\n",
    "from sklearn.preprocessing import minmax_scale, StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression,LogisticRegressionCV, RidgeClassifierCV, RidgeClassifier\n",
    "from sklearn.metrics import make_scorer, log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler\n",
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "from  sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "from lightgbm import LGBMClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "RANDOM_SEED = 777\n",
    "np.random.seed(RANDOM_SEED)\n",
    "import warnings\n",
    "warnings.filterwarnings(\n",
    "    action='ignore',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = pd.read_csv('data/metadata.csv')\n",
    "train_labels = pd.read_csv('data/train_labels.csv', index_col=\"sample_id\")\n",
    "submission_format = pd.read_csv('data/submission_format.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "split  instrument_type\n",
       "test   commercial         447\n",
       "       sam_testbed         64\n",
       "train  commercial         754\n",
       "       sam_testbed         12\n",
       "val    commercial         293\n",
       "Name: instrument_type, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.groupby('split')['instrument_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "m/z               0                                                 \\\n",
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       "sample_id                                                            \n",
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       "\n",
       "m/z                                                                     ...  \\\n",
       "temp_bin  (1200, 1300] (1300, 1400] (1400, 1500] (200, 300] (300, 400]  ...   \n",
       "sample_id                                                               ...   \n",
       "S0000              0.0          0.0          0.0   0.001394   0.000188  ...   \n",
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       "\n",
       "m/z                 99                                                   \\\n",
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       "sample_id                                                                 \n",
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       "\n",
       "m/z                                                                   \n",
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       "sample_id                                                             \n",
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       "[5 rows x 1600 columns]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_df = pd.read_csv('data/train_df.csv',index_col=0,header=[0,1])\n",
    "train_df.sort_index(inplace=True,axis=1)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               0                                                 \\\n",
       "temp_bin  (-100, 0]  (0, 100] (100, 200] (1000, 1100] (1100, 1200]   \n",
       "sample_id                                                            \n",
       "S0766           0.0  0.410100   0.593334     0.206508     0.000000   \n",
       "S0767           0.0  0.058563   0.073878     0.088571     0.097939   \n",
       "S0768           0.0  0.029256   0.040753     0.013179     0.000000   \n",
       "S0769           0.0  0.000261   0.000842     0.000000     0.000000   \n",
       "S0770           0.0  0.003460   0.003929     0.000467     0.000000   \n",
       "\n",
       "m/z                                                                     ...  \\\n",
       "temp_bin  (1200, 1300] (1300, 1400] (1400, 1500] (200, 300] (300, 400]  ...   \n",
       "sample_id                                                               ...   \n",
       "S0766         0.000000          0.0          0.0   0.606578   0.678006  ...   \n",
       "S0767         0.063057          0.0          0.0   0.081069   0.083869  ...   \n",
       "S0768         0.000000          0.0          0.0   0.037022   0.041347  ...   \n",
       "S0769         0.000000          0.0          0.0   0.001313   0.001544  ...   \n",
       "S0770         0.000000          0.0          0.0   0.003286   0.002893  ...   \n",
       "\n",
       "m/z                 99                                                \\\n",
       "temp_bin  (1300, 1400] (1400, 1500] (200, 300] (300, 400] (400, 500]   \n",
       "sample_id                                                              \n",
       "S0766              0.0          0.0   0.001801   0.001626   0.001498   \n",
       "S0767              0.0          0.0   0.000153   0.000187   0.000173   \n",
       "S0768              0.0          0.0   0.000205   0.000122   0.000152   \n",
       "S0769              0.0          0.0   0.000010   0.000010   0.000008   \n",
       "S0770              0.0          0.0   0.000002   0.000002   0.000002   \n",
       "\n",
       "m/z                                                                \n",
       "temp_bin  (500, 600] (600, 700] (700, 800] (800, 900] (900, 1000]  \n",
       "sample_id                                                          \n",
       "S0766       0.001380   0.001495   0.001755   0.001762    0.001607  \n",
       "S0767       0.000150   0.000183   0.000162   0.000146    0.000133  \n",
       "S0768       0.000161   0.000158   0.000182   0.000248    0.000144  \n",
       "S0769       0.000009   0.000012   0.000011   0.000012    0.000000  \n",
       "S0770       0.000002   0.000001   0.000003   0.000002    0.000002  \n",
       "\n",
       "[5 rows x 1600 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('data/test_df.csv',index_col=0,header=[0,1])\n",
    "test_df.sort_index(inplace=True,axis=1)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  24,  164,  202,  493,   31,  419,  136,  349,  430,  633,  111,\n",
       "             360,  351,  338,  194,  207,    8,  635,  627,  173,  161,  425,\n",
       "             174,  171,  426,  346,  172,  427,  170,  205,  168,  153,  991,\n",
       "             348,  636,  162,  424,  145,  637,  163,  417,  428,  490,  347,\n",
       "             698,  345,  369,  632,   20,  206, 1229,  337,  431,  420,  638,\n",
       "             156,  429,  382,  220,  418,  175,  988,  634,  139,  160,  691,\n",
       "             625,  193,  981,  491,  530,  191,   10,  702,  378,  120,  350,\n",
       "             340,  165,    1,   99,  381],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_fts_dict = load('l1_imp_fts_dict.joblib')\n",
    "imp_fts_dict['phyllosilicate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples:  766\n",
      "Number of validation samples:  293\n",
      "Number of testing samples:  511\n"
     ]
    }
   ],
   "source": [
    "train_files = metadata[metadata[\"split\"] == \"train\"][\"features_path\"].to_dict()\n",
    "val_files = metadata[metadata[\"split\"] == \"val\"][\"features_path\"].to_dict()\n",
    "test_files = metadata[metadata[\"split\"] == \"test\"][\"features_path\"].to_dict()\n",
    "\n",
    "print(\"Number of training samples: \", len(train_files))\n",
    "print(\"Number of validation samples: \", len(val_files))\n",
    "print(\"Number of testing samples: \", len(test_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- multi indexing is almost similar to 2d indexing (except you use tuples for keys not headers)\n",
    "- don't forget sort_index for multi index data frame (the problem arises when it is unsorted and you try to slice'em )\n",
    "- for crazy things use the indexslice method.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's Explore the Data Now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_frac_and_He(df):\n",
    "    \"\"\"\n",
    "    Drops fractional m/z values, m/z values > 100, and carrier gas m/z\n",
    "\n",
    "    Args:\n",
    "        df: a dataframe representing a single sample, containing m/z values\n",
    "\n",
    "    Returns:\n",
    "        The dataframe without fractional an carrier gas m/z\n",
    "    \"\"\"\n",
    "\n",
    "    # drop fractional m/z values\n",
    "    df = df[df[\"m/z\"].transform(round) == df[\"m/z\"]]\n",
    "    assert df[\"m/z\"].apply(float.is_integer).all(),   \"not all m/z are integers\"\n",
    "\n",
    "    # drop m/z values greater than 99\n",
    "    # df = df[df[\"m/z\"] < 100]\n",
    "\n",
    "    # drop carrier gas\n",
    "    df = df[df[\"m/z\"] != 4]\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_background_abundance(df):\n",
    "    \"\"\"\n",
    "    Subtracts minimum abundance value\n",
    "\n",
    "    Args:\n",
    "        df: dataframe with 'm/z' and 'abundance' columns\n",
    "\n",
    "    Returns:\n",
    "        dataframe with minimum abundance subtracted for all observations\n",
    "    \"\"\"\n",
    "\n",
    "    df[\"abundance_minsub\"] = df.groupby([\"m/z\"])[\"abundance\"].transform(\n",
    "        lambda x: (x - x.min())\n",
    "    )\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_abun(df):\n",
    "    \"\"\"\n",
    "    Scale abundance from 0-100 according to the min and max values across entire sample\n",
    "\n",
    "    Args:\n",
    "        df: dataframe containing abundances and m/z\n",
    "\n",
    "    Returns:\n",
    "        dataframe with additional column of scaled abundances\n",
    "    \"\"\"\n",
    "\n",
    "    df[\"abun_minsub_scaled\"] = minmax_scale(df[\"abundance_minsub\"].astype(float))\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess function\n",
    "def preprocess_sample(df):\n",
    "    df = drop_frac_and_He(df)\n",
    "    df = remove_background_abundance(df)\n",
    "    df = scale_abun(df)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>temp_bin</th>\n",
       "      <th>m/z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-100, 100]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(-100, 100]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(-100, 100]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(-100, 100]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(-100, 100]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      temp_bin  m/z\n",
       "0  (-100, 100]    0\n",
       "1  (-100, 100]    1\n",
       "2  (-100, 100]    2\n",
       "3  (-100, 100]    3\n",
       "4  (-100, 100]    4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 2)\n"
     ]
    }
   ],
   "source": [
    "# Create a series of temperature bins\n",
    "temprange = pd.interval_range(start=-100, end=2000, freq=200)\n",
    "temprange\n",
    "\n",
    "# Make dataframe with rows that are combinations of all temperature bins\n",
    "# and all m/z values\n",
    "allcombs = list(itertools.product(temprange, [*range(0, 500)]))\n",
    "\n",
    "allcombs_df = pd.DataFrame(allcombs, columns=[\"temp_bin\", \"m/z\"])\n",
    "display(allcombs_df.head())\n",
    "print(allcombs_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def abun_per_tempbin(df):\n",
    "\n",
    "    \"\"\"\n",
    "    Transforms dataset to take the preprocessed max abundance for each\n",
    "    temperature range for each m/z value\n",
    "\n",
    "    Args:\n",
    "        df: dataframe to transform\n",
    "\n",
    "    Returns:\n",
    "        transformed dataframe\n",
    "    \"\"\"\n",
    "\n",
    "    # Bin temperatures\n",
    "    df[\"temp_bin\"] = pd.cut(df[\"temp\"], bins=temprange)\n",
    "\n",
    "    # Combine with a list of all temp bin-m/z value combinations\n",
    "    df = pd.merge(allcombs_df, df, on=[\"temp_bin\", \"m/z\"], how=\"left\")\n",
    "\n",
    "    # Aggregate to temperature bin level to find max\n",
    "    df = df.groupby([\"temp_bin\", \"m/z\"]).max(\"abun_minsub_scaled\").reset_index()\n",
    "\n",
    "    # Fill in 0 for abundance values without information\n",
    "    df = df.replace(np.nan, 0)\n",
    "\n",
    "    # Reshape so each row is a single sample\n",
    "    df = df.pivot_table(columns=[\"m/z\", \"temp_bin\"], values=[\"abun_minsub_scaled\"])\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/766 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of train files:  766\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 766/766 [02:52<00:00,  4.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 53s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Assembling preprocessed and transformed training set\n",
    "\n",
    "train_features_dict = {}\n",
    "print(\"Total number of train files: \", len(train_files))\n",
    "\n",
    "for i, (sample_id, filepath) in enumerate(tqdm(train_files.items())):\n",
    "\n",
    "    # Load training sample\n",
    "    temp = pd.read_csv('data/' + filepath)\n",
    "\n",
    "    # Preprocessing training sample\n",
    "    train_sample_pp = preprocess_sample(temp)\n",
    "\n",
    "    # Feature engineering\n",
    "    train_sample_fe = abun_per_tempbin(train_sample_pp).reset_index(drop=True)\n",
    "    train_features_dict[sample_id] = train_sample_fe\n",
    "\n",
    "\n",
    "train_features_all_mz = pd.concat(\n",
    "    train_features_dict, names=[\"sample_id\", \"dummy_index\"]\n",
    ").reset_index(level=\"dummy_index\", drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_features.shape ,train_features_binned.shape,  train_features_200_binned.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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 tr th {\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th colspan=\"10\" halign=\"left\">0</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">499</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_bin</th>\n",
       "      <th>(-100, 100]</th>\n",
       "      <th>(100, 300]</th>\n",
       "      <th>(300, 500]</th>\n",
       "      <th>(500, 700]</th>\n",
       "      <th>(700, 900]</th>\n",
       "      <th>(900, 1100]</th>\n",
       "      <th>(1100, 1300]</th>\n",
       "      <th>(1300, 1500]</th>\n",
       "      <th>(1500, 1700]</th>\n",
       "      <th>(1700, 1900]</th>\n",
       "      <th>...</th>\n",
       "      <th>(-100, 100]</th>\n",
       "      <th>(100, 300]</th>\n",
       "      <th>(300, 500]</th>\n",
       "      <th>(500, 700]</th>\n",
       "      <th>(700, 900]</th>\n",
       "      <th>(900, 1100]</th>\n",
       "      <th>(1100, 1300]</th>\n",
       "      <th>(1300, 1500]</th>\n",
       "      <th>(1500, 1700]</th>\n",
       "      <th>(1700, 1900]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</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>0</th>\n",
       "      <td>0.004085</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002270</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>0.000819</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001336</td>\n",
       "      <td>0.002464</td>\n",
       "      <td>0.000992</td>\n",
       "      <td>0.000892</td>\n",
       "      <td>0.000631</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <th>764</th>\n",
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       "    <tr>\n",
       "      <th>765</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>766 rows × 5000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               0                                                            \\\n",
       "temp_bin  (-100, 100] (100, 300] (300, 500] (500, 700] (700, 900] (900, 1100]   \n",
       "sample_id                                                                       \n",
       "0            0.004085   0.004641   0.000188   0.000000   0.000000    0.000000   \n",
       "1            0.000000   0.000000   0.000000   0.000000   0.000000    0.000000   \n",
       "2            0.002270   0.002495   0.000636   0.000819   0.000235    0.000086   \n",
       "3            0.001336   0.002464   0.000992   0.000892   0.000631    0.000306   \n",
       "4            0.005993   0.012429   0.006921   0.001966   0.000880    0.000903   \n",
       "...               ...        ...        ...        ...        ...         ...   \n",
       "761          0.000000   0.000000   0.000000   0.000000   0.000000    0.000000   \n",
       "762          0.000000   0.000000   0.000000   0.000000   0.000000    0.000000   \n",
       "763          0.000000   0.000000   0.000000   0.000000   0.000000    0.000000   \n",
       "764          0.000000   0.000000   0.000000   0.000000   0.000000    0.000000   \n",
       "765          0.000000   0.000000   0.000000   0.000000   0.000000    0.000000   \n",
       "\n",
       "m/z                                                            ...  \\\n",
       "temp_bin  (1100, 1300] (1300, 1500] (1500, 1700] (1700, 1900]  ...   \n",
       "sample_id                                                      ...   \n",
       "0                  0.0          0.0          0.0          0.0  ...   \n",
       "1                  0.0          0.0          0.0          0.0  ...   \n",
       "2                  0.0          0.0          0.0          0.0  ...   \n",
       "3                  0.0          0.0          0.0          0.0  ...   \n",
       "4                  0.0          0.0          0.0          0.0  ...   \n",
       "...                ...          ...          ...          ...  ...   \n",
       "761                0.0          0.0          0.0          0.0  ...   \n",
       "762                0.0          0.0          0.0          0.0  ...   \n",
       "763                0.0          0.0          0.0          0.0  ...   \n",
       "764                0.0          0.0          0.0          0.0  ...   \n",
       "765                0.0          0.0          0.0          0.0  ...   \n",
       "\n",
       "m/z               499                                                          \\\n",
       "temp_bin  (-100, 100] (100, 300] (300, 500] (500, 700] (700, 900] (900, 1100]   \n",
       "sample_id                                                                       \n",
       "0                 0.0        0.0        0.0        0.0        0.0         0.0   \n",
       "1                 0.0        0.0        0.0        0.0        0.0         0.0   \n",
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       "3                 0.0        0.0        0.0        0.0        0.0         0.0   \n",
       "4                 0.0        0.0        0.0        0.0        0.0         0.0   \n",
       "...               ...        ...        ...        ...        ...         ...   \n",
       "761               0.0        0.0        0.0        0.0        0.0         0.0   \n",
       "762               0.0        0.0        0.0        0.0        0.0         0.0   \n",
       "763               0.0        0.0        0.0        0.0        0.0         0.0   \n",
       "764               0.0        0.0        0.0        0.0        0.0         0.0   \n",
       "765               0.0        0.0        0.0        0.0        0.0         0.0   \n",
       "\n",
       "m/z                                                            \n",
       "temp_bin  (1100, 1300] (1300, 1500] (1500, 1700] (1700, 1900]  \n",
       "sample_id                                                      \n",
       "0                  0.0          0.0          0.0          0.0  \n",
       "1                  0.0          0.0          0.0          0.0  \n",
       "2                  0.0          0.0          0.0          0.0  \n",
       "3                  0.0          0.0          0.0          0.0  \n",
       "4                  0.0          0.0          0.0          0.0  \n",
       "...                ...          ...          ...          ...  \n",
       "761                0.0          0.0          0.0          0.0  \n",
       "762                0.0          0.0          0.0          0.0  \n",
       "763                0.0          0.0          0.0          0.0  \n",
       "764                0.0          0.0          0.0          0.0  \n",
       "765                0.0          0.0          0.0          0.0  \n",
       "\n",
       "[766 rows x 5000 columns]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features_all_mz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what if no tempbins ??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>temp</th>\n",
       "      <th>m/z</th>\n",
       "      <th>abundance</th>\n",
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       "      <td>2483.74</td>\n",
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       "    <tr>\n",
       "      <th>89417</th>\n",
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      "text/plain": [
       "          time      temp    m/z  abundance\n",
       "0         0.00     0.000  132.0       58.8\n",
       "1         0.02     0.000  133.0        0.0\n",
       "2         0.04     0.000  134.0        0.0\n",
       "3         0.06     0.000  135.0        0.0\n",
       "4         0.08     0.000  136.0        0.0\n",
       "...        ...       ...    ...        ...\n",
       "89415  2483.74  1101.676   46.0     1823.5\n",
       "89416  2483.76  1101.688   47.0     2352.9\n",
       "89417  2483.78  1101.700   48.0    21117.6\n",
       "89418  2483.80  1101.712   49.0      176.5\n",
       "89419  2483.82  1101.724   50.0     1764.7\n",
       "\n",
       "[89420 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_757 = pd.read_csv('data/train_features/S0757.csv')\n",
    "sample_757"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    89420.000000\n",
       "mean       105.235160\n",
       "std         78.058538\n",
       "min          5.000000\n",
       "25%         50.000000\n",
       "50%         95.000000\n",
       "75%        140.000000\n",
       "max        433.000000\n",
       "Name: m/z, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_757['m/z'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132.0    0.005569\n",
       "162.0    0.005569\n",
       "164.0    0.005569\n",
       "165.0    0.005569\n",
       "166.0    0.005569\n",
       "           ...   \n",
       "338.0    0.000313\n",
       "337.0    0.000313\n",
       "336.0    0.000313\n",
       "335.0    0.000313\n",
       "407.0    0.000313\n",
       "Name: m/z, Length: 429, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "sample_757['m/z'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([ 0.0,  1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0, 10.0,\n",
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       "              22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0,\n",
       "              33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0,\n",
       "              44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0,\n",
       "              55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0,\n",
       "              66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0,\n",
       "              77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0,\n",
       "              88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0,\n",
       "              99.0],\n",
       "             dtype='float64', name='m/z')"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "sample_707.groupby(['m/z'])['abundance'].agg(['sum']).T.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765], dtype='int64')"
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     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "metadata[(metadata[\"instrument_type\"] == \"sam_testbed\") & (metadata['split'] == \"train\")].index"
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  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_no_tempbins = pd.DataFrame()\n",
    "train_no_tempbins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 754/754 [00:16<00:00, 44.80it/s]\n"
     ]
    }
   ],
   "source": [
    "for path in tqdm(metadata.loc[:753,'features_path'].values):\n",
    "    sample = pd.read_csv('data/' + path)\n",
    "    sample = sample.groupby(['m/z'])['abundance'].agg(['sum']).T\n",
    "    train_no_tempbins = pd.concat([train_no_tempbins,sample])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "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>m/z</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>...</th>\n",
       "      <th>190.0</th>\n",
       "      <th>191.0</th>\n",
       "      <th>192.0</th>\n",
       "      <th>193.0</th>\n",
       "      <th>194.0</th>\n",
       "      <th>195.0</th>\n",
       "      <th>196.0</th>\n",
       "      <th>197.0</th>\n",
       "      <th>198.0</th>\n",
       "      <th>199.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</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>S0000</th>\n",
       "      <td>1.432085e-08</td>\n",
       "      <td>1.353788e-08</td>\n",
       "      <td>4.391670e-09</td>\n",
       "      <td>7.651478e-09</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>3.222145e-10</td>\n",
       "      <td>6.461331e-11</td>\n",
       "      <td>4.139626e-11</td>\n",
       "      <td>3.591410e-11</td>\n",
       "      <td>2.551507e-11</td>\n",
       "      <td>...</td>\n",
       "      <td>3.952151e-12</td>\n",
       "      <td>3.982610e-12</td>\n",
       "      <td>4.041622e-12</td>\n",
       "      <td>4.175955e-12</td>\n",
       "      <td>3.839502e-12</td>\n",
       "      <td>4.067773e-12</td>\n",
       "      <td>3.997490e-12</td>\n",
       "      <td>3.571595e-12</td>\n",
       "      <td>3.597848e-12</td>\n",
       "      <td>3.935653e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.743000e-08</td>\n",
       "      <td>5.562600e-06</td>\n",
       "      <td>5.688000e-07</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>2.324100e-09</td>\n",
       "      <td>4.050414e-10</td>\n",
       "      <td>2.920650e-10</td>\n",
       "      <td>2.488114e-10</td>\n",
       "      <td>2.096032e-10</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>S0002</th>\n",
       "      <td>8.104750e-08</td>\n",
       "      <td>1.307087e-07</td>\n",
       "      <td>3.606093e-08</td>\n",
       "      <td>6.670900e-08</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>2.004619e-09</td>\n",
       "      <td>2.621438e-10</td>\n",
       "      <td>1.782477e-10</td>\n",
       "      <td>2.033837e-10</td>\n",
       "      <td>1.242948e-10</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>S0003</th>\n",
       "      <td>3.522288e-08</td>\n",
       "      <td>5.544620e-08</td>\n",
       "      <td>1.806705e-08</td>\n",
       "      <td>3.548065e-08</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>1.183452e-09</td>\n",
       "      <td>2.490216e-10</td>\n",
       "      <td>1.702126e-10</td>\n",
       "      <td>1.849164e-10</td>\n",
       "      <td>1.174324e-10</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>S0004</th>\n",
       "      <td>1.548523e-07</td>\n",
       "      <td>2.208056e-07</td>\n",
       "      <td>7.360477e-08</td>\n",
       "      <td>1.064549e-07</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>6.461538e-09</td>\n",
       "      <td>2.860305e-10</td>\n",
       "      <td>1.527094e-10</td>\n",
       "      <td>1.660491e-10</td>\n",
       "      <td>1.021598e-10</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>S0749</th>\n",
       "      <td>4.140822e-08</td>\n",
       "      <td>6.558328e-08</td>\n",
       "      <td>1.940404e-08</td>\n",
       "      <td>4.216064e-08</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>1.406316e-09</td>\n",
       "      <td>2.465464e-10</td>\n",
       "      <td>1.668072e-10</td>\n",
       "      <td>1.827446e-10</td>\n",
       "      <td>1.133916e-10</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>S0750</th>\n",
       "      <td>2.855834e-07</td>\n",
       "      <td>4.378780e-07</td>\n",
       "      <td>1.624656e-07</td>\n",
       "      <td>2.400648e-07</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>1.665118e-08</td>\n",
       "      <td>3.847777e-10</td>\n",
       "      <td>1.609611e-10</td>\n",
       "      <td>1.583387e-10</td>\n",
       "      <td>1.047151e-10</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>S0751</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.545300e-08</td>\n",
       "      <td>1.033290e-05</td>\n",
       "      <td>5.303400e-07</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>1.769660e-09</td>\n",
       "      <td>3.300390e-10</td>\n",
       "      <td>2.290306e-10</td>\n",
       "      <td>2.643312e-10</td>\n",
       "      <td>1.869956e-10</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>S0752</th>\n",
       "      <td>2.941677e-07</td>\n",
       "      <td>4.505705e-07</td>\n",
       "      <td>1.637689e-07</td>\n",
       "      <td>2.445816e-07</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>1.770325e-08</td>\n",
       "      <td>3.951538e-10</td>\n",
       "      <td>1.622814e-10</td>\n",
       "      <td>1.573079e-10</td>\n",
       "      <td>1.054419e-10</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>S0753</th>\n",
       "      <td>6.608486e-08</td>\n",
       "      <td>1.061519e-07</td>\n",
       "      <td>2.964564e-08</td>\n",
       "      <td>6.178702e-08</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>1.815406e-09</td>\n",
       "      <td>2.529221e-10</td>\n",
       "      <td>1.734669e-10</td>\n",
       "      <td>1.945058e-10</td>\n",
       "      <td>1.186170e-10</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               0.0           1.0           2.0           3.0       4.0    \\\n",
       "sample_id                                                                     \n",
       "S0000      1.432085e-08  1.353788e-08  4.391670e-09  7.651478e-09  0.000003   \n",
       "S0001               NaN  8.743000e-08  5.562600e-06  5.688000e-07  0.000147   \n",
       "S0002      8.104750e-08  1.307087e-07  3.606093e-08  6.670900e-08  0.000045   \n",
       "S0003      3.522288e-08  5.544620e-08  1.806705e-08  3.548065e-08  0.000038   \n",
       "S0004      1.548523e-07  2.208056e-07  7.360477e-08  1.064549e-07  0.000017   \n",
       "...                 ...           ...           ...           ...       ...   \n",
       "S0749      4.140822e-08  6.558328e-08  1.940404e-08  4.216064e-08  0.000039   \n",
       "S0750      2.855834e-07  4.378780e-07  1.624656e-07  2.400648e-07  0.000029   \n",
       "S0751               NaN  6.545300e-08  1.033290e-05  5.303400e-07  0.000116   \n",
       "S0752      2.941677e-07  4.505705e-07  1.637689e-07  2.445816e-07  0.000029   \n",
       "S0753      6.608486e-08  1.061519e-07  2.964564e-08  6.178702e-08  0.000045   \n",
       "\n",
       "m/z               5.0           6.0           7.0           8.0    \\\n",
       "sample_id                                                           \n",
       "S0000      3.222145e-10  6.461331e-11  4.139626e-11  3.591410e-11   \n",
       "S0001      2.324100e-09  4.050414e-10  2.920650e-10  2.488114e-10   \n",
       "S0002      2.004619e-09  2.621438e-10  1.782477e-10  2.033837e-10   \n",
       "S0003      1.183452e-09  2.490216e-10  1.702126e-10  1.849164e-10   \n",
       "S0004      6.461538e-09  2.860305e-10  1.527094e-10  1.660491e-10   \n",
       "...                 ...           ...           ...           ...   \n",
       "S0749      1.406316e-09  2.465464e-10  1.668072e-10  1.827446e-10   \n",
       "S0750      1.665118e-08  3.847777e-10  1.609611e-10  1.583387e-10   \n",
       "S0751      1.769660e-09  3.300390e-10  2.290306e-10  2.643312e-10   \n",
       "S0752      1.770325e-08  3.951538e-10  1.622814e-10  1.573079e-10   \n",
       "S0753      1.815406e-09  2.529221e-10  1.734669e-10  1.945058e-10   \n",
       "\n",
       "m/z               9.0    ...         190.0         191.0         192.0  \\\n",
       "sample_id                ...                                             \n",
       "S0000      2.551507e-11  ...  3.952151e-12  3.982610e-12  4.041622e-12   \n",
       "S0001      2.096032e-10  ...           NaN           NaN           NaN   \n",
       "S0002      1.242948e-10  ...           NaN           NaN           NaN   \n",
       "S0003      1.174324e-10  ...           NaN           NaN           NaN   \n",
       "S0004      1.021598e-10  ...           NaN           NaN           NaN   \n",
       "...                 ...  ...           ...           ...           ...   \n",
       "S0749      1.133916e-10  ...           NaN           NaN           NaN   \n",
       "S0750      1.047151e-10  ...           NaN           NaN           NaN   \n",
       "S0751      1.869956e-10  ...           NaN           NaN           NaN   \n",
       "S0752      1.054419e-10  ...           NaN           NaN           NaN   \n",
       "S0753      1.186170e-10  ...           NaN           NaN           NaN   \n",
       "\n",
       "m/z               193.0         194.0         195.0         196.0  \\\n",
       "sample_id                                                           \n",
       "S0000      4.175955e-12  3.839502e-12  4.067773e-12  3.997490e-12   \n",
       "S0001               NaN           NaN           NaN           NaN   \n",
       "S0002               NaN           NaN           NaN           NaN   \n",
       "S0003               NaN           NaN           NaN           NaN   \n",
       "S0004               NaN           NaN           NaN           NaN   \n",
       "...                 ...           ...           ...           ...   \n",
       "S0749               NaN           NaN           NaN           NaN   \n",
       "S0750               NaN           NaN           NaN           NaN   \n",
       "S0751               NaN           NaN           NaN           NaN   \n",
       "S0752               NaN           NaN           NaN           NaN   \n",
       "S0753               NaN           NaN           NaN           NaN   \n",
       "\n",
       "m/z               197.0         198.0         199.0  \n",
       "sample_id                                            \n",
       "S0000      3.571595e-12  3.597848e-12  3.935653e-12  \n",
       "S0001               NaN           NaN           NaN  \n",
       "S0002               NaN           NaN           NaN  \n",
       "S0003               NaN           NaN           NaN  \n",
       "S0004               NaN           NaN           NaN  \n",
       "...                 ...           ...           ...  \n",
       "S0749               NaN           NaN           NaN  \n",
       "S0750               NaN           NaN           NaN  \n",
       "S0751               NaN           NaN           NaN  \n",
       "S0752               NaN           NaN           NaN  \n",
       "S0753               NaN           NaN           NaN  \n",
       "\n",
       "[754 rows x 200 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_no_tempbins.set_index(train_df.index[:754],inplace=True)\n",
    "train_no_tempbins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "basalt   basalt            1.000000\n",
       "         carbonate         0.451269\n",
       "         chloride         -0.124137\n",
       "         iron_oxide        0.252238\n",
       "         oxalate          -0.074129\n",
       "                             ...   \n",
       "sulfide  oxychlorine       0.036961\n",
       "         phyllosilicate   -0.124862\n",
       "         silicate         -0.015986\n",
       "         sulfate          -0.046793\n",
       "         sulfide           1.000000\n",
       "Length: 100, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_train_labels = train_labels.corr().unstack().sort_values(ascending=False)\n",
    "sorted_train_labels.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "split  instrument_type\n",
       "test   commercial         447\n",
       "       sam_testbed         64\n",
       "train  commercial         754\n",
       "       sam_testbed         12\n",
       "val    commercial         293\n",
       "Name: instrument_type, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.groupby('split')['instrument_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basalt  : 0.11879895561357702\n",
      "carbonate  : 0.1279373368146214\n",
      "chloride  : 0.12140992167101827\n",
      "iron_oxide  : 0.2206266318537859\n",
      "oxalate  : 0.0391644908616188\n",
      "oxychlorine  : 0.2349869451697128\n",
      "phyllosilicate  : 0.31723237597911225\n",
      "silicate  : 0.13577023498694518\n",
      "sulfate  : 0.19451697127937337\n",
      "sulfide  : 0.04699738903394256\n"
     ]
    }
   ],
   "source": [
    "for cols in train_labels.columns:\n",
    "    print(cols,\" :\", train_labels[cols].sum()/train_labels.shape[0])\n",
    "\n",
    "# sulfide and oxalate are present just 4% of the samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['basalt', 'carbonate', 'chloride', 'iron_oxide', 'oxalate',\n",
       "       'oxychlorine', 'phyllosilicate', 'silicate', 'sulfate', 'sulfide'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>basalt</th>\n",
       "      <th>carbonate</th>\n",
       "      <th>chloride</th>\n",
       "      <th>iron_oxide</th>\n",
       "      <th>oxalate</th>\n",
       "      <th>oxychlorine</th>\n",
       "      <th>phyllosilicate</th>\n",
       "      <th>silicate</th>\n",
       "      <th>sulfate</th>\n",
       "      <th>sulfide</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>760 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           basalt  carbonate  chloride  iron_oxide  oxalate  oxychlorine  \\\n",
       "sample_id                                                                  \n",
       "S0000           0          0         0           0        0            0   \n",
       "S0001           0          1         0           0        0            0   \n",
       "S0002           0          0         0           0        0            1   \n",
       "S0003           0          1         0           1        0            0   \n",
       "S0004           0          0         0           1        0            1   \n",
       "...           ...        ...       ...         ...      ...          ...   \n",
       "S0761           0          0         0           0        0            0   \n",
       "S0762           0          0         0           0        0            0   \n",
       "S0763           0          0         0           0        0            0   \n",
       "S0764           0          0         0           0        0            0   \n",
       "S0765           0          0         0           0        0            0   \n",
       "\n",
       "           phyllosilicate  silicate  sulfate  sulfide  \n",
       "sample_id                                              \n",
       "S0000                   0         0        1        0  \n",
       "S0001                   0         0        0        0  \n",
       "S0002                   0         0        0        0  \n",
       "S0003                   0         0        1        0  \n",
       "S0004                   1         0        0        0  \n",
       "...                   ...       ...      ...      ...  \n",
       "S0761                   0         0        0        0  \n",
       "S0762                   1         0        0        0  \n",
       "S0763                   0         0        0        0  \n",
       "S0764                   0         0        0        0  \n",
       "S0765                   0         0        0        0  \n",
       "\n",
       "[760 rows x 10 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_labels = train_labels.duplicated(keep=False) == True\n",
    "train_labels[dup_labels]\n",
    "\n",
    "#  look into the duplicated labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>basalt</th>\n",
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       "      <th>iron_oxide</th>\n",
       "      <th>oxalate</th>\n",
       "      <th>oxychlorine</th>\n",
       "      <th>phyllosilicate</th>\n",
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       "    <tr>\n",
       "      <th>S0755</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           basalt  carbonate  chloride  iron_oxide  oxalate  oxychlorine  \\\n",
       "sample_id                                                                  \n",
       "S0091           1          1         0           0        0            1   \n",
       "S0159           1          0         1           0        0            0   \n",
       "S0250           0          0         1           1        0            0   \n",
       "S0563           0          0         0           0        0            0   \n",
       "S0610           0          0         1           0        0            0   \n",
       "S0755           0          1         0           0        0            0   \n",
       "\n",
       "           phyllosilicate  silicate  sulfate  sulfide  \n",
       "sample_id                                              \n",
       "S0091                   0         1        1        0  \n",
       "S0159                   0         0        0        0  \n",
       "S0250                   0         1        0        0  \n",
       "S0563                   0         0        1        1  \n",
       "S0610                   0         0        0        1  \n",
       "S0755                   0         0        1        0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[~dup_labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['basalt',\n",
      " 'carbonate',\n",
      " 'chloride',\n",
      " 'iron_oxide',\n",
      " 'oxalate',\n",
      " 'oxychlorine',\n",
      " 'phyllosilicate',\n",
      " 'silicate',\n",
      " 'sulfate',\n",
      " 'sulfide']\n"
     ]
    }
   ],
   "source": [
    "mol_mass = {\n",
    "    # 'basalt':,\n",
    "    'carbonate':61.0168 ,\n",
    "    'chloride':35.453,\n",
    "    'iron_oxide':159.688,\n",
    "    'oxalate':88.019,\n",
    "    # 'oxychlorine': ,\n",
    "    'phyllosilicate':504.190 ,\n",
    "    'silicate':76.083,\n",
    "    'sulfate':96.06 ,\n",
    "    'sulfide':32.06 \n",
    "}\n",
    "\n",
    "# charge = {\n",
    "#     'basalt':0,\n",
    "#     'carbonate':0,\n",
    "#     'chloride':0,\n",
    "#     'iron_oxide':0,\n",
    "#     'oxalate':0,\n",
    "#     'oxychlorine':0,\n",
    "#     'phyllosilicate':0,\n",
    "#     'silicate':0,\n",
    "#     'sulfate':0,\n",
    "#     'sulfide':0\n",
    "# }\n",
    "\n",
    "targets = train_labels.columns.tolist()\n",
    "pprint(targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for mz in range(1,100):\n",
    "#     for tar in targets:\n",
    "#         if tar in mol_mass.keys():\n",
    "#             train_df[tar+'_z'] = 1/(mz*(1/mol_mass[tar]))\n",
    "# i am a stupid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013734312592763202"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[ ('99',  '(900, 1000]')].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>time</th>\n",
       "      <th>temp</th>\n",
       "      <th>m/z</th>\n",
       "      <th>abundance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31900.000000</td>\n",
       "      <td>31900.000000</td>\n",
       "      <td>31900.000000</td>\n",
       "      <td>3.190000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>828.239091</td>\n",
       "      <td>506.411047</td>\n",
       "      <td>49.500000</td>\n",
       "      <td>1.290167e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>479.767682</td>\n",
       "      <td>287.877305</td>\n",
       "      <td>28.866523</td>\n",
       "      <td>1.251767e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.805000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.280560e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>411.411000</td>\n",
       "      <td>255.303000</td>\n",
       "      <td>24.750000</td>\n",
       "      <td>1.979989e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>828.045000</td>\n",
       "      <td>508.451000</td>\n",
       "      <td>49.500000</td>\n",
       "      <td>2.631780e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1245.019000</td>\n",
       "      <td>757.120000</td>\n",
       "      <td>74.250000</td>\n",
       "      <td>1.419591e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1656.756000</td>\n",
       "      <td>1000.255000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.273301e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               time          temp           m/z     abundance\n",
       "count  31900.000000  31900.000000  31900.000000  3.190000e+04\n",
       "mean     828.239091    506.411047     49.500000  1.290167e-09\n",
       "std      479.767682    287.877305     28.866523  1.251767e-08\n",
       "min        0.000000     29.805000      0.000000  1.280560e-13\n",
       "25%      411.411000    255.303000     24.750000  1.979989e-13\n",
       "50%      828.045000    508.451000     49.500000  2.631780e-13\n",
       "75%     1245.019000    757.120000     74.250000  1.419591e-12\n",
       "max     1656.756000   1000.255000     99.000000  1.273301e-07"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_707 = pd.read_csv('data/train_features/S0707.csv')\n",
    "sample_456 = pd.read_csv('data/train_features/S0456.csv')\n",
    "sample_320 = pd.read_csv('data/train_features/S0320.csv')\n",
    "sample_279 = pd.read_csv('data/train_features/S0279.csv')\n",
    "sample_707.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.190000e+04\n",
       "mean     1.290167e-09\n",
       "std      1.251767e-08\n",
       "min      1.280560e-13\n",
       "25%      1.979989e-13\n",
       "50%      2.631780e-13\n",
       "75%      1.419591e-12\n",
       "max      1.273301e-07\n",
       "Name: abundance, dtype: float64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_707['abundance'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 0, 1, 1, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.loc['S0707'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ft**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entityset: train_set\n",
       "  DataFrames:\n",
       "  Relationships:\n",
       "    No relationships"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es = ft.EntitySet(id=\"train_set\")\n",
    "es"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>time</th>\n",
       "      <th>temp</th>\n",
       "      <th>m/z</th>\n",
       "      <th>abundance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.189122e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.805</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.838735e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.805</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.178675e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.805</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.172222e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.805</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.261140e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time    temp  m/z     abundance\n",
       "index                                 \n",
       "0       0.0  29.805  0.0  1.189122e-10\n",
       "1       0.0  29.805  1.0  1.838735e-10\n",
       "2       0.0  29.805  2.0  5.178675e-11\n",
       "3       0.0  29.805  3.0  1.172222e-10\n",
       "4       0.0  29.805  4.0  1.261140e-07"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_707.index.name = 'index'\n",
    "sample_707.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entityset: train_set\n",
       "  DataFrames:\n",
       "    sample_707 [Rows: 31900, Columns: 5]\n",
       "    sample_320 [Rows: 31900, Columns: 5]\n",
       "    sample_456 [Rows: 37200, Columns: 5]\n",
       "    sample_279 [Rows: 31900, Columns: 5]\n",
       "  Relationships:\n",
       "    No relationships"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es.add_dataframe(sample_707, dataframe_name=\"sample_707\",index='index',time_index='time')\n",
    "es.add_dataframe(sample_320, dataframe_name=\"sample_320\",index='index',time_index='time')\n",
    "es.add_dataframe(sample_456, dataframe_name=\"sample_456\",index='index',time_index='time')\n",
    "es.add_dataframe(sample_279, dataframe_name=\"sample_279\",index='index',time_index='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entityset: train_set\n",
       "  DataFrames:\n",
       "    sample_707 [Rows: 31900, Columns: 5]\n",
       "    sample_320 [Rows: 31900, Columns: 5]\n",
       "    sample_456 [Rows: 37200, Columns: 5]\n",
       "    sample_279 [Rows: 31900, Columns: 5]\n",
       "    train_labels [Rows: 766, Columns: 11]\n",
       "  Relationships:\n",
       "    No relationships"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es.add_dataframe(train_labels, dataframe_name=\"train_labels\",index='sample_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entityset: train_set\n",
       "  DataFrames:\n",
       "    sample_707 [Rows: 31900, Columns: 5]\n",
       "    sample_320 [Rows: 31900, Columns: 5]\n",
       "    sample_456 [Rows: 37200, Columns: 5]\n",
       "    sample_279 [Rows: 31900, Columns: 5]\n",
       "  Relationships:\n",
       "    No relationships"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es.add_relationships()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>time</th>\n",
       "      <th>temp</th>\n",
       "      <th>m/z</th>\n",
       "      <th>abundance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>63.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.903510e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000</td>\n",
       "      <td>63.516</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.522626e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000</td>\n",
       "      <td>63.516</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.501926e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000</td>\n",
       "      <td>63.516</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.104998e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000</td>\n",
       "      <td>63.516</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.022021e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37195</th>\n",
       "      <td>1900.024</td>\n",
       "      <td>861.296</td>\n",
       "      <td>195.0</td>\n",
       "      <td>6.753765e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37196</th>\n",
       "      <td>1900.024</td>\n",
       "      <td>861.296</td>\n",
       "      <td>196.0</td>\n",
       "      <td>7.024509e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37197</th>\n",
       "      <td>1900.024</td>\n",
       "      <td>861.296</td>\n",
       "      <td>197.0</td>\n",
       "      <td>4.767129e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37198</th>\n",
       "      <td>1900.024</td>\n",
       "      <td>861.296</td>\n",
       "      <td>198.0</td>\n",
       "      <td>6.534014e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37199</th>\n",
       "      <td>1900.024</td>\n",
       "      <td>861.296</td>\n",
       "      <td>199.0</td>\n",
       "      <td>6.254654e-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           time     temp    m/z     abundance\n",
       "index                                        \n",
       "0         0.000   63.516    0.0  2.903510e-11\n",
       "1         0.000   63.516    1.0  3.522626e-11\n",
       "2         0.000   63.516    2.0  1.501926e-11\n",
       "3         0.000   63.516    3.0  3.104998e-11\n",
       "4         0.000   63.516    4.0  1.022021e-08\n",
       "...         ...      ...    ...           ...\n",
       "37195  1900.024  861.296  195.0  6.753765e-14\n",
       "37196  1900.024  861.296  196.0  7.024509e-14\n",
       "37197  1900.024  861.296  197.0  4.767129e-14\n",
       "37198  1900.024  861.296  198.0  6.534014e-14\n",
       "37199  1900.024  861.296  199.0  6.254654e-14\n",
       "\n",
       "[37200 rows x 4 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name=\"sample_456\")\n",
    "feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Feature: time>, <Feature: temp>, <Feature: m/z>, <Feature: abundance>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_defs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "750/60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**how many mzs and time intervals**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1570/1570 [00:47<00:00, 32.92it/s]\n"
     ]
    }
   ],
   "source": [
    "mz_list = []\n",
    "count = 0\n",
    "for path in tqdm(metadata['features_path'].values):\n",
    "    mz = pd.read_csv('data/'+ path)['m/z'].max()   # this is max mz ratio observed. \n",
    "    mz_list.append(mz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(mz_list[:753])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "537.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(mz_list).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f6d9b7aac0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mz_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f6d9b07460>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.sort(mz_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1570/1570 [00:38<00:00, 41.07it/s]\n"
     ]
    }
   ],
   "source": [
    "sizes = []\n",
    "count = 0\n",
    "for path in tqdm(metadata['features_path'].values):\n",
    "    size = pd.read_csv('data/'+ path).shape[0]   # len of each sample csv file\n",
    "    sizes.append(size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f6d9c4c6d0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.sort(sizes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f6d9cd2940>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hLmKOD0NzGjQ1SCr4uNlQaZCsxO/PYr+OEkn24Nf5OyoNklWIX3uSidZmjJ4iDcHP7YZKgxAXqe/sPlduxPf4dV6MSoNkJX4eiQH+dS2Q7MDPc2FUGoS4iNnXuYsIaQhK+XfgQaVBSBrw/dwL8T1+bUFUGiSr8GtHMjHdCiFLwztRSBbj53ZDpUGIi0Su0yAkVfxqrVJpkKzE73MFZuSLnyc0iX/xc7Oh0iDERUIT4f4cJJIswq9NiEqDZCV8KJNcxs9b/1NpkKzEr+Y75zSIa/i0DVFpkOzCpx0pEnMS06/KjfgbP7cbKg2SlfjVfI8MuSUkVfzahqg0CEkDAdPS8KlyIyRVqDRIVuLXzdxMOKdBGoJSius0CHETv47gufcUcQuf6gwqDZJd+N3CCJElYhJ/4uexBpUGyUr8OoI3LaDQinAvhSFZjV/HHVQahLgIV4QTN/DroAig0iBZit8fypzTIA2FE+GEuIhfH8b1K8L92eFJduDXQA+ASoNkGdnyLK5/n4Z/Oz/xN35t6lQahLgI5zSIG/jVkgaoNAhxlVD0FPeeIg1Awb8DjwYpDREpE5FVIrJcREp0WisRmSUiG/X/RTpdRORJESkVkZUi0s9Szhidf6OIjLGk99fll+pzfVqNhITDlkoajj8bkRuWxmCl1LlKqQH6+zgAc5RSPQHM0d8BYCSAnvrvFgCTAUPJAJgA4EIAFwCYYCoanedmy3kjXJCXkLQRuSKckFTws4WaDvfUNQBe0J9fAPBtS/qLymAhgJYi0hHAcACzlFL7lFL7AcwCMEIfa66UWqiMrUNftJRFTlKy5WFMo5g0FL82oYYqDQXgIxFZKiK36LT2Sqkd+vNOAO3151MBbLOcW67T4qWX26RHISK3iEiJiJRUVFQ05PeQLMHv796uX6fhbzmJX/Fvu2mo0hiklOoHw/V0m4hcZj2oLYS0/3ql1DNKqQFKqQFt27Z1rdy6OoUnZm/E/qNVrpVJcpvQ+zR8OkrMNqZ8tgV/nrXBazHSytHKGvzpo/WoqqkLS/drE2qQ0lBKbdf/7wbwNow5iV3atQT9/26dfTuALpbTO+u0eOmdbdIzxryNFfjL7A148N0vM3lZ4gCr+2f3oRN4aeFWD6WJJrT3VIYHjB+u2oE1Xx/K7EXTyG+mrcGTczZ6LUZaeWLORjz1cSneXFbvWPGzgZqy0hCRpiLSzPwMYBiALwG8B8CMgBoD4F39+T0AN+ooqoEADmo31kwAw0SkSE+ADwMwUx87JCIDddTUjZayMkJ1rXHnjlfVZvKytryxtBzl+495LUaD2br3KLbta/jvWFK2D4/NWAcAuOWlpXjwnS8TlltTW4fPN+1t8LXj0dB1GvM3VmDp1v0pX//nLy/DqCfnh77vPnwCryz6KuXyshGlFJ77zxYcPFYdll5xuBJ3v77cF/3ZyrGqGgCItjR8amo0xNJoD+AzEVkBYDGA6UqpGQAmAbhKRDYCGKq/A8AHADYDKAXwDwC3AoBSah+ARwAs0X+/0WnQef6pz9kE4MMGyJs0frlnNbV1+H//XoHrnv7ca1EazOV/+ASX/n5uyuebHemT9RWY/MkmAMCBY4b7sLq2LtZpAIC/zN6AG/6xEEu37oubz02SHTD+8NnF+O7kBa5d/2cvLcX9b6/KiQGHU5Z9tR8Pv78G97+9Kiz9sRnr8NYX2/H+iq89kiyaf87fjG37jgMIVxJ+tjTyUj1RKbUZwDk26XsBXGmTrgDcFqOsKQCm2KSXADgrVRkbil/uW02dIcnuw5UeS+I/ausUAgGjt9UluGEbdx0BAFQcTt8cVf3eU2m7RFLsOWL81tpElZNDVFYbg4e9R2P0F5/cm4PHqvHb6WtjHvfru2O4IjwLMJWGXx5EXhLZkapq6urfx+2D4ZkpQsBnK8L9Ikci6upUw++jbiJ+15O1cX6nn/cso9KIg/l4mrNuN/Ye8W6UX6PdLk5HHm8tK8c7X2zH0D9/ip++VBJ1/P63V+Hc33zkqoxWJn24Dj3u/yBunp+8EC2XEyI7U2VNLbShEbcTZpp4d6p43HQ8Pju1iKAT1bUY8NtZmL1mV8K8kz/ZhK9cmD/KJN3v/wC/eqdhgSemwvbxcxdA/EGOUv4dJFJpxMF6SzfuPpIw//P/2YKFm92faDUtDafW6t1TV+Cu15ejdPcRzFwd/XB5ZdFXOBAxSegmT3+6KaE7ZPbacLlOVNeiTp+jlMJjM9Zhy56jCa9ltTTq4k9pWEjf0yRq76mIa5kPisdnJ44I+sPMdRj35sqwtK8PHMeeI1V49INwt4ZSCieqwyd4zUCBVKiurYuamE0HT87ZiOJx01FVUxeqm5cbMHF/45TFobmu6LpPXc50kGiQ41OdQaVhUlunGtxJHnp/Da5/ZqFLEtVTU+uz1u4ySin0enAGxr9lTFyW7z+OyZ9swtjnl0TljbS2KmvqQg/oOh89FWJ1+GTmFv46dxNeW7ItLM08P6jNqy17juKrvcfw51kb0OvBGTHLSrZurvm//+DMB+PHnew5UonicdOxqAEDpWfmbQYAHK+urR8cxbjW6q8PhgYWsZi3oQKfbjAW+Fqzrt95GLsPnwjL+8VX+3HoRPoGT4mI1xb805KjSXkiPNeY8N6X+GDVTvz9h/3x75JtmFpSjnO7tAwdN5XBw9/qi3U7D+PT9btxpLIG5xe3wo0XF+PyM8IXFb61rBx3T10R+v72rRfjvNOKUDxuOu4a2hN3DT0jdGzu+t246bkl+PkVPXD5GW1x/TMLcesVPbC54ihuuqQY/62vXVVTh+NVtWhcEMTfPilFba1CZU0dipoWYOygbhj95HystonR/+Gzi3DX0J5onJ+HX7y+PJR+4aOzMf/eISjIC+DuqctREAxg0nfPBmC4fcY+X4JxI3vhzA7NUFVTh6aN6pvLmq8P4Y5Xl+HFsRdiwrur0b1tU/z3+V3w6fr6FfkjHp+HdTsP46eXdcf4Ub2j5KqqqcOPn1+CyhpjhPx6yTa8XrINs+++XMtgKPE7X/sC7y7/Gr//3tlRZVgjsa5+6jN0LmqMeb8cHJocX1K2D7/89wqU7a130/zsX8sAAL8a3RvX9u+CSTPW4u0vtuPqszvhj9eegx89txhndmiG8SN746WFW/Gvz7fi1sE98NLnW1GydT8euaYvvn9hVxyprMFri7/C7z5chwnf7IObLukWFXL7z/lb8ONB3TB/wx7c9soyXNS9dUiOG55ZiJd/ciGG/vlTbN5zFGMu6ho6Zn3AFY+bjg/+91L06dQ8NDo9VlWLbzw0E4dPGOGabZs1CquX4nHTw77X1im8vGgrHni73vXzyDV9sWjLPsxdtxsrHxqOE9W1aJQXQF4wgDU7jHb00eqdGNa3Q+ic6St34I8frcesX1yGJVuMKLQp/9mC84tb4UhVDZoX5uOVRV9hyn+24I4hp+PzTXtDbSqSP8/agCOVhvzfnbwA/xp7Ydhxa6jwpY/NxXFtSU27YxBEgOue/hwf/78rcMMzC3HLZd2jXHFWRTn88Xmhz/e+sRJndWqB7/xtAS4oboWpP7sIK7YdwPf/sRCf/HIw2jZrhG37juHS38/F7797Nkaf3RF9J8zEeae1RMvG+Zi7PnzXCfPef7n9IK5+6rPQvTpRXYvvTl6A1V8fwsy7LsOZHZoBABZv2YefvLAEf7ru3LBynl9QhlcWfYUZdxlrpP26FQ2VhiYgAqUUrrWEtS7fdiAq34T3Vod9n7NuN+as243bBvcIS49cxXrfmysx406jMTw+eyOuPrsjfvfBOvTt1BxPflwKwPBBm6b13/T/+yJWo/f+9Qysfng4fj9jfVj62EHdbBUGAMzfuAfzN+5B/65FWL/rcCh916FK3PbKMgzr0x5vLTPWTfY7rQhtmhWgZZMCfFa6Bw+8vQrNG+dj/sY9KJs0OnTuu8u3Y1PFUbzzxXbD1bTWWEtilXfdTuNaf5+3GRf1aI15G/aEyWWEv0avSVirH1hmp393uREiee8bK3HHkNNtf6NJ+f7jePazLdh/rApjB3ULu5+R/Hb6WryxtDwk5xtLy/HHa8/BJ+sr8Mn6Cowf2RsPav/6na8tD533xJxSzF67OzSiBYCH31+Dypo6PKEXopkd/khlDc5+qH7+6HPLqPzzzXux92gVNms33Auf1y9QvGDinDBZRz05HyW/GhqyOrcfOB52vCJBZN2q7QfDFAYAPPhufVs+WmXIOaJvBzz9w/6h9FteWoqnbjgPCsCWiqP4i56LOXyiBtV6pJwXDOAPH63H5E82YeVDw0Khrmad/erqPnjsw3UYN7IXNuw6jM8378WtV5wetmivdPeRsFDYP85cj/+bWxr6ftzielu38zAWbt6Lo1W1uPBRo57GvRUeXgsAX3x1AE/N2YhdERYGgNBalsVl+1BxuBL/mL8ZR6tqsWDTHlxz7qm4SVu59765Eve9tTJUnh0Pv78GtXUqpMBnrt6JPp2ao/evZ4QGEcMfn4e+nZrj9sGn4+cvG4OWm18Mn9fbXFHvjvWR0RwFlYYmINKgaIu/zt0U9j1yzUB+MIAqS9qNzy7G1wdPYM663WjfvBF2HbLv9Hbms93q50RmOwDbB/SsNbswyzKpeq/2ob9168WhtPkb6x/2h05Uo3T3kZD5nBeoHw2Zi5Ts+NFz0a6mWIvYavTkhJ1LxUlnmqj9/abijYepMJJBBGEKw2TSh8nPIUTOQ8Tj0elrMebi4qSvASDM6rXDdM3OWL0z6tgdr34Rnb+2LhSgkR8QvLnUWM18rDL690z5bAteWrgVbZs1Cg2mbr0iWvlXWyalrAqjIfzJwRYkP3mxBMWtmwCob3OHjtf3Oydt7rfT1+KXw88EUN/3I89b/fWhkMKIh9/DozmnoRFx1ydeWROtNMz48cjjHVo0jlmOOXqxYteo0raNhsVEVkrhZy8txX/9bQGOardCXrC+CeUH3WlO5gMswVq9nMB0zTmhqrYurt+/Idg97ONRWV0XsnrygoHQgzIvGO1SMVOs1neNzc11Onfndmh1+b5jCOp2boqVyhXy9W9v6NxoVU0dQ26zAcM95U5ZD77zZdTDviAYiPmAqIwz2jx43NlE3aaKxNFdyWDXMatrVcg6MKOv8i0PiUZ57jSnE9XmSC1aBp+6eVPmSJIP63SNQp22M5PKmtqQZZAflNCD0m7gFQhE37TjNm0+0Yr++nzK9cii+gWiqdevGcXn9HfEwnxO+LWt0z2lCbhoadiN+vPzJMr6MLHrQCbmRGEi3A6PtJP1hEXp7ddbd+QF3Lc0zN/sp2iodHEoyYd1urYDORjmjklc7yeslkYgENqnzWnYs73ScHa/k7HOnGLqNdPN68TdG4nZXqsaGO14orrO1+FT4odVtG4yYMAAVVKS/MKxB95e1aD48JOBZoV5tu6ydHJ25xZYWX4wo9d0g7JJo6MimLKF/l2LGrRpYrbTvDAPhxrYzk9t2TgqWCFZOrUoxILxUTsypQ0RWWp5A2tM6J7SOFlIli46tSj07NqJ+MapLdChuSFfl6ImofRubZqm9ZomrZoWpFxOLx3i6BVnndrc0+sng1lX+UGJ66pqVph+58Q5nVskzpQmmhYEG6wwmhQEHSmMRL/z8jPdezeQm1Bp2NCsMA9bfjcq9L1s0mhsfnQUfjiwa1i+OfdcjrJJo1E2aTReGntBVDn/vHEANj06KirdpEXjfJRNGo0F468MlWP+Lbq/foRxZvvYD7+n/6cfhvZuF5Xet1P4A2vid87C5+OHxCzHjrdvvRjv3zEIC+835Pvgzkux5XejUDZpNN742UVheeOFwjYtCIZ9t4buRhIQ4P07BoW+P39TdL2a9O9aFFaW9Z4tGDcEM+66DOsecfZa+bJJo8PW5Vi5oLiV7RqRyPMjmXbHpSibNDpMLjt+OfxMDOlVfw9Hn90x9Pmeq4z1PHk28wImk3/QD3frfLcPDr8Pz44ZgNdvGRj3+gAw467LUDZpNDZOHIXZd18e1dYBYNQ3OmDVQ8PRv2tRwvJMFowbgsb5Qdtjl/ZsAwAY0qsd5t87OJT+7u2DUDZpNF78cfS976gHWH/9fj/b8sz+Y9bDbYN7hPUr85qmbCt+PSysjJUPDUePtsaA6NWb7ett6a+Ghsqz442fXYwr9f382w/6ReX98E6jXbx7+yDcekUP2zIA4L4RvWIe8xIqDU2kly5yYU0gIFETvQFLnqBNpw4GBHH6etyJLqeTYIYMiTPXqXB5nWD3m8x6iTwWbxI8mes2ZJ7Xes+aaEXl1jxLQ3zMyS7SsquveCP8eOUHA2Ib0ZQp8gISsy1b741dnniK0u43NbEMTsyJ7XhtLyACiWge1nYdq94StWfrYbu8dv3K9joO82UaKo0kiLz/QUuCXeMIBCTlVZ3WsuMVEYzTKa0opRLma9Yo/MEUr3NE/q6COErDiyiQxvoBkky/S6ecs+++LHEmTbLP+GBAolaim+QFAggG3O3myYgXCMTeZtMaeRdr0BWLApvBgNWiMU+NLEHC+mz8Nh7r+omURiDiGsmeH7q+T8OnqDSSoGe7cDeR9Z7a3d5Ybd7O7I4+11mDEXHWievqlG2Z1tHZ7HsudyxDtKVh74IAUhsxzb93MD76hfMHrYnpOjEfKm5txdDQuPnT2zmfX0neMol/LN6IPbYMSZ9iS16cgZPV0rBra/EsJOu5V2t3nrWdOdkROhCQuA/mWPUWaZ1ElWt9Ltj9Lof3w6lFkmmoNDROHgrXDuiM5hY3gbWR2j0YYzXIU4tiL+azKy/eQ0QgjhRMLPdU++aFtp+B+I028lBhvjvuKZMurZrgjDhzObF4/qbzMfOuy1zftyedQYaREYx2ose7fNz6ldQePm7VXjCupRFfacT7XVYrxRzMJfvSooDEt9JTtTQStb1gWN+OL58fodLQOHkoiAi+YYl4CCYyQ2M0OicjDbfdKnVKJVVmIhkiG3RcSyODbb9ZYX5oY7hk8Uv0ufXh58j1qP8Z50aTmqUR+5xkqikYiG0KWx+eybpx8sIUjvG/k7qyZknknsqL4dZLVJ0Jj1syxGtztDR8jtOOYN3qIPyeRt/gWLfcSWMIG43EyaeQeK4C0C91sSkp3jqdeG6ldE2Epxu7qCAnpFufuFZDMebWvCKepWEVy66NxGs21jmNVH9fIEGgSqxjycxp2OF0rsKnOoNKI1mse/9YR2PJPBedKI1AimXHolaphL7YSOI17siOEW8i3E9K45wYYbVA/HpOtxWSqPh410/klvGy/vMCgZhWS5ilkeQT0trezOKdlBAZ2RQ/2MM+PZnoKTucuqf8ujU6lUaSWDdaCzevnd/gZJXGTy7tFjevE1/u+cWtbGVM1Vce+RPy4oS2WvP+KMVdWr0mnRvIDe4Vvs6mIC+5h4WC8o1rLZKAxH4wRkYyJYN1kjyZvmfNGUwwpxGLxO6p+F4Ca/8fbnlXSbZApWHisNNVx3BPJdP2nJin1rK/c15ndG8bZwV2guJuvrQb+nctSn5OI07riBwF5VsK3zhxZEQ5xrE+HZvjoW/1TU6IHEbEWBTYt1OL0C3s3qYpxo2of2FVrFDamGW6K2KDEXHmnkrWf28X1ppsAIFIaqP5ZCwNu+tb+//ZnVuibNLo0KLFbIBKw4Z4TaLGsiNbqu4pJyOjyDzx3RPxMaNUbC0NlybirHkjF9R57Z6a/r+Dwt6YF4uYdSGZc089MLo3WjTJT/l8t8jELUu0zikd50oDrllfRvzjCec0bEKJ/Wop2kGlobG6H+LdP+ucRiL3VEN8ksn4eBNdR4XyJSlDEifEc0+ZxXjVL/p2ahF6eZF1XyvHKO83HU31odKtTVO0apL6/l1uYLYjc0uUUHqS7l3r4r2wNVL6S7JRZ7EGRT+93NjaI9Z7bhL1t4TuKVs3sdctzDlUGhq7TmnXNmJFT7WLeEezUaZ9QzBXKycTyfPf53eJecyuYdrJbhs9FdFYB51evzdPMkoj36WtKr59bqeYx6z7EwFIah3HiLM6YOPEkaFwXGu0V892pwBo+Oi6IZsrfvMcY4Fa5G9yKtO5XQxleE6XeqW4ceJIdGzRGC2a5GPFhGH47bfPcixPsmse4jHiLMNv/+NB4XNziULWO1hcNo9c0xfLHrwKw/q0BwA0b1xvjSUVnm75HHnef513KgDgugFdUDZpNE5p5HxzxhYx5LF7Ati5fX3+sr4wqDTisOqh4Vj1UPiGZtYXrFgfqu2aF+La/p0dlVuYH8SmR0eFNplzwk8v6x5z80NbX65NI3TSuV4ae0FoAWOi/MsevCr0OZ4ryzyW6PK/Gt0bf7ru3JjHu7RqEvqtz910PiZ8s0+CEsMx3WYrHxqGZQ9ehRW/HobPxw/Be7cPCsv33E3nh20YCSCqQiO3XAGAz+4bHJXmlO+c1xmlE0eiS6smiTPbMKRXeywcfyWG9GofSrO6CVs0zscPLjzNcXmpKNCZd9mv4H/4W32x5IGhaNooD5seHYUBetW+1dKwaz/tmhWGlETjgjw0Lgjiwav7YPEDV4Y9pM0zw+YSHK67stIozgLVWDxx/bkAgEtObx0aOCVc3GdzvKEvbsokVBoauzZ2SqM8NCsM9y9b3VORI/GiiJGmtfH07hi+62wwyX2pRMS2Y0VubRIPJ3MaIuJoszcgfGTd1sbSMsm3GVpd2K1VVNqVvdsnnEcxZRrYrTUKY+yemojmhflo2igPLZrko2OLxiHLz3o8cnV8ZPvoYDNx2aQg/sj01JbxdwKI5+Jzgp1MVuzam1vbtxcEAzEXVeYFA6H2EQwIvtPPGNF3bV2vIGP1BXO0bx7NCwbQrln47zTbq6PFfS7P1VgXADYvjJ6Lsl7upkuKAdgrSKevuvUDVBo2xJ0ID7M0Io/Z3/gVE4bh7VsvdkEyoywTc2Tq2D2VxsnNyI5sxa6TvHrzwDDLqXTiSEfv6Ehm9a9r2EyEp7J7bqR7zQ+8d9sgWwvWWr0PjOoddTyS2iQmXb5/wWkonTgS7eO0mWSwUzix20dqDSfK8rSVI/7xX1/dB6UTR9rKG2lpJNqK30v4uleN0zcYhs9phN/8mhjvurSa0g3FWpY5MrXbxdR+jsabqA27+Y7IiX6no2zjN3g/KstP4X3oftzq2olM7ZrHtiJNknk1r4jz7dqdlBp/2tkdkpmvijWpHe9310RMalw3IPYcptfQ0kiSaotiiOxw5hoO0w1hF3vtljvA+sKgZBeDRRK5N9ElejI83irvZHBzDx2zpEyHKPaJeKlVKvs5RZJMCb07ZvYthJErpxORrvthDubirtZPorxUV/3HqoNwBVGfx+yfHVs6s6Zqs2gmnJZGksTzPdZqhXLHkNMxtE97tDklfIQ2/97BUfMeqbBiwrCwXWXtNlZz6r7p1LIQs+6+LKzD/Onac3DPVWegaRLRI/FoqK/eitl5Mx2ieH5xKyx5YChGPD4Pe49WZWwzOdM6PO+0Ijx1Qz+cP3F2Rq5r8vMreqRtnY2jPdOSyBsWhqv/T8YCikeyt/u2wadjWN8OjjfPvLBbKyzasi8FyTIPLQ2NNdR07KDuMfNdafNqVZOWOh6+ZZP8KIUBGNE/yYTxAbB9lWuLxvlhu8rambxWn3snmwnY/zrvVDx1w3l4+n/6o0lBXpiCKMwPonvbU5KSEwCKmuSHXpVpnRhPtLCue4K5jLMtOwsP72tE01gf2n06umO9mT72yG3ezfc1tG3WKDTZbHdfnDDA8qrUvp0Srxmxvp89VrBBh+bh97dds0ZxN5B0wqU9jfdTD+3dDq1PMdp119bGfTqnc8uo/EN7G/fFei8vKI4OdrDSWveR8yxWc+Rrils3NfKc0ii2i9e8zgjLlhxmkMSJ6nCXcZOC2METx6pqo9LM97PEmqi3hiabYdNNC/IQCEhSuy0/+6Pzk3pRl6copXLqr3///ioVamrr1LZ9RxPmO15Voz5et0u9uGCL7bGXF25VdXV1KckQSWV1raqpTVzWxOlrVNf7pqk7X12m1u44qFaVH1Db9h1Vj05fo2av2alqLWWs3XFQfbX3qKquqXVFxq73TVNd75umlFJq35FKdfB4lVJKqb1HKtWGnYfU3iOValX5AdX1vmlq5OPzos6vOHxCHa2sjnuNY5U1ateh40opo052HDgedvxEdY06fCJ+GU44cKxKTV3yVej7ym0H1NY9R8Pu565Dx9W7y7eruro6tfdIZdjvV0qpj9ftUqW7D8e8xvEq47cs3rJX7T9aGTNfSdk+taXiiKqtrVMfrd6pjlXWKKWM+/e7D9aq9TsPqdXbD6p1Ow5FnVtdU6uqYtzfj9fuUn+du1Ft339MrSo/ELsytKwmc9ftCpV5rLJGLSjdoz5eu0u9vaxcLSjdE8prXnvXoeNh58diVfmBUBvfuudo1H2M16ci697Ki5+Xqa73TVPj3lwZlr7/aKX6+b9K1DtflEeVs3TrvqhyjlXWqJ0Hj8e83rQVX6uu901TP/9XiaqprVMHjlYl/M3xiPeb0g2AEuXgGUv3lCYYEHQuShwjX5gfxOAz7UeZhflBfD+JWPhEOJ1TMP3rp7c7Bb061I/UxttEvViPu43V9daqaUFo8vDrA8djnmNnkUXSuCAYCostyAtEhZY2ygvCDU9ai8b5uNYyAWl9d4pJu2aF+NY5xgJEu8nRWG3DpDA/iML8YNxoM6B+hAsAV/WpX3vRq0NzjBsZ/x7GcwcO7tUutEGinQUaKavJFZbf1bggiIt62FuP5rUT/T6Tsywr9E9rHd3/Uu1TTbTsx6tqwtJbNinA337Q3/acfqcVRaVZ214iggFJaQuYbIPuqRzg8jMMV8KFDvZXcptXbx6Id267JG6ebm2aolFeAPcMc76YMVtoWhBEl1aJ38RI0sOIGLvE9tKBA/0TuMiS4e6rzojahubC7q0QDAjGRqx2z2VEZdNOWQ4YMGCAKikp8VqMjFNTW+fqhDNxRp2OevFjOG2uU1unIIhd9zsOHkeH5oUJF9EWj5sOwNhx2Gtue2UZpq/c4YksIrJUKTUgYT4qDULIyczUkm3o1qYpznfRKkkVY97Am0GIU6XBOQ1CyEmNnxbSSYovhsokvvdniMgIEVkvIqUiMs5reQgh5GTG10pDRIIA/gpgJIA+AG4QkeS2NiWEEOIavlYaAC4AUKqU2qyUqgLwGoBrPJaJEEJOWvyuNE4FsM3yvVynhSEit4hIiYiUVFRUZEw4Qgg52fC70nCEUuoZpdQApdSAtm3bei0OIYTkLH5XGtsBWEMbOus0QgghHuB3pbEEQE8R6SYiBQCuB/CexzIRQshJi6/XaSilakTkdgAzAQQBTFFKrfZYLEIIOWnJuRXhIlIBYGuKp7cBsMdFcdyEsqWGX2Xzq1wAZUuVbJetq1Iq4aRwzimNhiAiJU6W0XsBZUsNv8rmV7kAypYqJ4tsfp/TIIQQ4iOoNAghhDiGSiOcZ7wWIA6ULTX8Kptf5QIoW6qcFLJxToMQQohjaGkQQghxDJUGIYQQx1BpaLx8b4eIdBGRuSKyRkRWi8idOr2ViMwSkY36/yKdLiLypJZ1pYj0y4CMQRH5QkSm6e/dRGSRluF1vWIfItJIfy/Vx4vTLFdLEXlDRNaJyFoRucgv9SYiv9D380sReVVECr2qNxGZIiK7ReRLS1rS9SQiY3T+jSIyJk1y/UHfz5Ui8raItLQcG6/lWi8iwy3prvdfO9ksx+4RESUibfT3jNVZPNlE5A5dd6tF5PeWdPfqzXi94Mn9B2O1+SYA3QEUAFgBoE8Gr98RQD/9uRmADTDeH/J7AON0+jgAj+nPowB8CEAADASwKAMy3g3gFQDT9PepAK7Xn58G8HP9+VYAT+vP1wN4Pc1yvQDgJ/pzAYCWfqg3GLsxbwHQ2FJfP/Kq3gBcBqAfgC8taUnVE4BWADbr/4v056I0yDUMQJ7+/JhFrj66bzYC0E332WC6+q+dbDq9C4xdKrYCaJPpOotTb4MBzAbQSH9vl456S1tnzqY/ABcBmGn5Ph7AeA/leRfAVQDWA+io0zoCWK8//x3ADZb8oXxpkqczgDkAhgCYpjvGHkvHDtWf7kwX6c95Op+kSa4WMB7MEpHueb2hflv/VroepgEY7mW9ASiOeMgkVU8AbgDwd0t6WD635Io49h0AL+vPYf3SrLN09l872QC8AeAcAGWoVxoZrbMY93MqgKE2+VytN7qnDBy9tyMTaLfEeQAWAWivlNqhD+0E0F5/zrS8jwO4F0Cd/t4awAGlVI3N9UOy6eMHdf500A1ABYDntOvsnyLSFD6oN6XUdgB/BPAVgB0w6mEp/FFvJsnWkxf95McwRvC+kEtErgGwXSm1IuKQ57IBOAPApdq9+amInJ8O2ag0fISInALgTQB3KaUOWY8pYyiQ8fhoEbkawG6l1NJMX9sBeTBM9MlKqfMAHIXhZgnhYb0VwXjLZDcAnQA0BTAi03I4xat6ioeIPACgBsDLXssCACLSBMD9AH7ttSwxyINh2Q4E8EsAU0VE3L4IlYaB5+/tEJF8GArjZaXUWzp5l4h01Mc7Atit0zMp7yUAviUiZTBetzsEwBMAWoqIuUuy9foh2fTxFgD2pkm2cgDlSqlF+vsbMJSIH+ptKIAtSqkKpVQ1gLdg1KUf6s0k2XrKWP2JyI8AXA3gB1qh+UGuHjAGASt0f+gMYJmIdPCBbIDRH95SBotheAbauC0blYaBp+/t0KOBZwGsVUr92XLoPQBmtMUYGHMdZvqNOmJjIICDFjeDqyilxiulOiulimHUy8dKqR8AmAvgezFkM2X+ns6flhGsUmongG0icqZOuhLAGvig3mC4pQaKSBN9f03ZPK83C8nW00wAw0SkSFtSw3Saq4jICBju0G8ppY5FyHu9GJFm3QD0BLAYGeq/SqlVSql2Sqli3R/KYQSw7ITHdaZ5B8ZkOETkDBiT23vgdr25MSGTC38woh82wIgmeCDD1x4EwzWwEsBy/TcKhk97DoCNMKIiWun8AuCvWtZVAAZkSM4rUB891V03vFIA/0Z9xEah/l6qj3dPs0znAijRdfcOjAgVX9QbgIcBrAPwJYCXYESveFJvAF6FMbdSDeNhNzaVeoIxx1Cq/25Kk1ylMHztZl942pL/AS3XegAjLemu91872SKOl6F+IjxjdRan3goA/Eu3t2UAhqSj3riNCCGEEMfQPUUIIcQxVBqEEEIcQ6VBCCHEMVQahBBCHEOlQQghxDFUGoQQQhxDpUEIIcQx/x+Q0frQLCRpEgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e010dba2e0>]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sizes[:765])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 158., 1332.,    3.,   10.,    4.,   41.,   10.,    3.,    5.,\n",
       "           4.]),\n",
       " array([  5306. ,  26410.3,  47514.6,  68618.9,  89723.2, 110827.5,\n",
       "        131931.8, 153036.1, 174140.4, 195244.7, 216349. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sizes, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mz_list =  []\n",
    "temp_list = []\n",
    "for i in train_imp_fts.columns:\n",
    "    mz_list.append(i[0])\n",
    "    temp_list.append(i[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sample_id'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_labels.agg(['sum'],axis=1).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**analyze the train test data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th colspan=\"10\" halign=\"left\">0</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_bin</th>\n",
       "      <th>(-100, 0]</th>\n",
       "      <th>(0, 100]</th>\n",
       "      <th>(100, 200]</th>\n",
       "      <th>(1000, 1100]</th>\n",
       "      <th>(1100, 1200]</th>\n",
       "      <th>(1200, 1300]</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>...</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>(400, 500]</th>\n",
       "      <th>(500, 600]</th>\n",
       "      <th>(600, 700]</th>\n",
       "      <th>(700, 800]</th>\n",
       "      <th>(800, 900]</th>\n",
       "      <th>(900, 1000]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</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>S0000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004085</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.898509e-06</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002270</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001688</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.257968e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>9.397170e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001336</td>\n",
       "      <td>0.002464</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.000992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.379099e-06</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>4.693477e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0004</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005993</td>\n",
       "      <td>0.012429</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009380</td>\n",
       "      <td>0.005099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.904920e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>3.174421e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</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>S1565</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1566</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.922441e-05</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>4.922441e-05</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1567</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.200556e-04</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>1.000520e-04</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1568</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.444685e-04</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>2.444685e-04</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.000244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1569</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1570 rows × 1600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               0                                                 \\\n",
       "temp_bin  (-100, 0]  (0, 100] (100, 200] (1000, 1100] (1100, 1200]   \n",
       "sample_id                                                            \n",
       "S0000           0.0  0.004085   0.004641     0.000000          0.0   \n",
       "S0001           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S0002           0.0  0.002270   0.002495     0.000000          0.0   \n",
       "S0003           0.0  0.001336   0.002464     0.000306          0.0   \n",
       "S0004           0.0  0.005993   0.012429     0.000593          0.0   \n",
       "...             ...       ...        ...          ...          ...   \n",
       "S1565           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S1566           0.0  0.000049   0.000000     0.000000          0.0   \n",
       "S1567           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S1568           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S1569           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "\n",
       "m/z                                                                     ...  \\\n",
       "temp_bin  (1200, 1300] (1300, 1400] (1400, 1500] (200, 300] (300, 400]  ...   \n",
       "sample_id                                                               ...   \n",
       "S0000              0.0          0.0          0.0   0.001394   0.000188  ...   \n",
       "S0001              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S0002              0.0          0.0          0.0   0.001688   0.000636  ...   \n",
       "S0003              0.0          0.0          0.0   0.001099   0.000992  ...   \n",
       "S0004              0.0          0.0          0.0   0.009380   0.005099  ...   \n",
       "...                ...          ...          ...        ...        ...  ...   \n",
       "S1565              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S1566              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S1567              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S1568              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S1569              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "\n",
       "m/z                 99                                                   \\\n",
       "temp_bin  (1300, 1400] (1400, 1500]    (200, 300] (300, 400] (400, 500]   \n",
       "sample_id                                                                 \n",
       "S0000              0.0          0.0  2.898509e-06   0.000003   0.000000   \n",
       "S0001              0.0          0.0  0.000000e+00   0.000000   0.000000   \n",
       "S0002              0.0          0.0  9.257968e-07   0.000001   0.000001   \n",
       "S0003              0.0          0.0  4.379099e-06   0.000006   0.000005   \n",
       "S0004              0.0          0.0  3.904920e-06   0.000004   0.000004   \n",
       "...                ...          ...           ...        ...        ...   \n",
       "S1565              0.0          0.0  0.000000e+00   0.000000   0.000000   \n",
       "S1566              0.0          0.0  4.922441e-05   0.000049   0.000000   \n",
       "S1567              0.0          0.0  1.200556e-04   0.000100   0.000100   \n",
       "S1568              0.0          0.0  2.444685e-04   0.000175   0.000140   \n",
       "S1569              0.0          0.0  0.000000e+00   0.000000   0.000000   \n",
       "\n",
       "m/z                                                                   \n",
       "temp_bin  (500, 600]    (600, 700] (700, 800] (800, 900] (900, 1000]  \n",
       "sample_id                                                             \n",
       "S0000       0.000000  0.000000e+00   0.000000   0.000000    0.000000  \n",
       "S0001       0.000000  0.000000e+00   0.000000   0.000000    0.000000  \n",
       "S0002       0.000001  9.397170e-07   0.000001   0.000001    0.000001  \n",
       "S0003       0.000005  4.693477e-06   0.000004   0.000004    0.000004  \n",
       "S0004       0.000005  3.174421e-06   0.000004   0.000004    0.000003  \n",
       "...              ...           ...        ...        ...         ...  \n",
       "S1565       0.000000  0.000000e+00   0.000000   0.000000    0.000000  \n",
       "S1566       0.000049  4.922441e-05   0.000098   0.000049    0.000049  \n",
       "S1567       0.000120  1.000520e-04   0.000100   0.000120    0.000080  \n",
       "S1568       0.000244  2.444685e-04   0.000279   0.000210    0.000244  \n",
       "S1569       0.000000  0.000000e+00   0.000000   0.000000    0.000000  \n",
       "\n",
       "[1570 rows x 1600 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([train_df,test_df],axis=0)\n",
    "df"
   ]
  },
  {
   "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></th>\n",
       "      <th>basalt</th>\n",
       "      <th>carbonate</th>\n",
       "      <th>chloride</th>\n",
       "      <th>iron_oxide</th>\n",
       "      <th>oxalate</th>\n",
       "      <th>oxychlorine</th>\n",
       "      <th>phyllosilicate</th>\n",
       "      <th>silicate</th>\n",
       "      <th>sulfate</th>\n",
       "      <th>sulfide</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</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>S0000</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>S0002</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0004</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           basalt  carbonate  chloride  iron_oxide  oxalate  oxychlorine  \\\n",
       "sample_id                                                                  \n",
       "S0000           0          0         0           0        0            0   \n",
       "S0001           0          1         0           0        0            0   \n",
       "S0002           0          0         0           0        0            1   \n",
       "S0003           0          1         0           1        0            0   \n",
       "S0004           0          0         0           1        0            1   \n",
       "\n",
       "           phyllosilicate  silicate  sulfate  sulfide  \n",
       "sample_id                                              \n",
       "S0000                   0         0        1        0  \n",
       "S0001                   0         0        0        0  \n",
       "S0002                   0         0        0        0  \n",
       "S0003                   0         0        1        0  \n",
       "S0004                   1         0        0        0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = train_labels.columns\n",
    "train_labels['target_vec'] =  [''.join(str(x)) for x in train_labels[cols].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.target_vec.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0 0 0 0 0 0 1 0 0 0]    109\n",
       "[0 0 0 0 0 0 0 0 0 0]    102\n",
       "[0 0 0 0 0 0 0 0 1 0]     54\n",
       "[0 0 0 0 0 0 0 1 0 0]     44\n",
       "[1 1 0 1 0 1 1 1 1 0]     43\n",
       "[0 1 0 0 0 0 0 0 0 0]     39\n",
       "[0 0 0 0 0 1 0 0 0 0]     39\n",
       "[0 0 1 0 0 0 0 0 0 0]     37\n",
       "[1 0 0 0 0 0 0 0 0 0]     34\n",
       "[0 0 0 0 1 0 0 0 0 0]     30\n",
       "[0 0 0 1 0 1 0 0 0 0]     30\n",
       "[0 0 0 0 0 1 1 0 0 0]     29\n",
       "[0 0 0 1 0 0 0 0 0 0]     22\n",
       "[0 0 1 1 0 0 1 0 0 0]     20\n",
       "[0 0 0 1 0 1 1 0 0 0]     17\n",
       "[0 0 1 0 0 0 0 0 1 0]     17\n",
       "[0 0 0 1 0 0 0 0 1 0]     13\n",
       "[0 0 1 0 0 0 1 0 0 0]     13\n",
       "[0 0 0 0 0 1 0 0 0 1]     11\n",
       "[0 0 0 0 0 0 0 0 0 1]     10\n",
       "[0 1 0 1 0 0 0 0 0 0]      5\n",
       "[1 0 0 0 0 1 0 0 0 0]      4\n",
       "[0 0 0 0 0 0 0 1 0 1]      4\n",
       "[1 0 0 0 0 0 1 1 0 0]      3\n",
       "[0 0 1 1 0 0 0 0 0 0]      3\n",
       "[1 1 0 1 0 0 0 1 1 0]      3\n",
       "[0 0 0 1 0 0 1 0 0 0]      3\n",
       "[0 0 0 0 0 0 0 1 1 0]      3\n",
       "[0 0 0 1 0 0 0 0 1 1]      3\n",
       "[0 0 0 0 0 1 0 0 1 0]      2\n",
       "[0 0 0 0 0 0 1 0 0 1]      2\n",
       "[0 1 0 0 0 0 0 0 0 1]      2\n",
       "[0 0 0 0 0 0 1 0 1 0]      2\n",
       "[1 1 0 0 0 1 1 1 1 0]      2\n",
       "[0 0 0 1 0 1 0 0 1 0]      2\n",
       "[0 0 0 1 0 0 0 0 0 1]      2\n",
       "[0 1 0 1 0 0 0 0 1 0]      2\n",
       "[1 0 1 0 0 0 0 0 0 0]      1\n",
       "[0 0 1 1 0 0 0 1 0 0]      1\n",
       "[1 1 0 0 0 1 0 1 1 0]      1\n",
       "[0 0 0 0 0 0 0 0 1 1]      1\n",
       "[0 0 1 0 0 0 0 0 0 1]      1\n",
       "[0 1 0 0 0 0 0 0 1 0]      1\n",
       "Name: target_vec, dtype: int64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.target_vec.value_counts()\n",
    "# may be these percentages can be used in meta modelling and prob calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  samples with only phyllosilicate are 14 % of the samples \n",
    "#  samples with none of the targets are 13 % of the samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**how are phyllosilicate samples different from the rest ??**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "phys_present = train_labels['phyllosilicate'] == 1\n",
    "all_phys_idx = train_labels.loc[phys_present,'phyllosilicate'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['S0004', 'S0006', 'S0011', 'S0012', 'S0014', 'S0016', 'S0018', 'S0023',\n",
       "       'S0026', 'S0029',\n",
       "       ...\n",
       "       'S0728', 'S0729', 'S0731', 'S0732', 'S0733', 'S0736', 'S0743', 'S0744',\n",
       "       'S0750', 'S0762'],\n",
       "      dtype='object', name='sample_id', length=243)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_phys_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_phys = train_labels['target_vec'] == '[0 0 0 0 0 0 1 0 0 0]'\n",
    "only_phys_idx = train_labels[only_phys].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th colspan=\"10\" halign=\"left\">0</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_bin</th>\n",
       "      <th>(-100, 0]</th>\n",
       "      <th>(0, 100]</th>\n",
       "      <th>(100, 200]</th>\n",
       "      <th>(1000, 1100]</th>\n",
       "      <th>(1100, 1200]</th>\n",
       "      <th>(1200, 1300]</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>...</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>(400, 500]</th>\n",
       "      <th>(500, 600]</th>\n",
       "      <th>(600, 700]</th>\n",
       "      <th>(700, 800]</th>\n",
       "      <th>(800, 900]</th>\n",
       "      <th>(900, 1000]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</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>S0004</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005993</td>\n",
       "      <td>0.012429</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009380</td>\n",
       "      <td>0.005099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0006</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.135941</td>\n",
       "      <td>0.159668</td>\n",
       "      <td>0.045263</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.163932</td>\n",
       "      <td>0.183625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000865</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>0.000998</td>\n",
       "      <td>0.000908</td>\n",
       "      <td>0.001142</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>0.000816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0011</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007582</td>\n",
       "      <td>0.007328</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007384</td>\n",
       "      <td>0.008399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0012</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012434</td>\n",
       "      <td>0.014042</td>\n",
       "      <td>0.001771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018895</td>\n",
       "      <td>0.009129</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0014</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006278</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.001368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008283</td>\n",
       "      <td>0.013992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</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>S0736</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013795</td>\n",
       "      <td>0.023513</td>\n",
       "      <td>0.011800</td>\n",
       "      <td>0.010587</td>\n",
       "      <td>0.009323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017304</td>\n",
       "      <td>0.014810</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0743</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>0.003582</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004720</td>\n",
       "      <td>0.005613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0744</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>0.016427</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021401</td>\n",
       "      <td>0.022305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0750</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005939</td>\n",
       "      <td>0.007804</td>\n",
       "      <td>0.012092</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006980</td>\n",
       "      <td>0.009419</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0762</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 1600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               0                                                 \\\n",
       "temp_bin  (-100, 0]  (0, 100] (100, 200] (1000, 1100] (1100, 1200]   \n",
       "sample_id                                                            \n",
       "S0004           0.0  0.005993   0.012429     0.000593     0.000000   \n",
       "S0006           0.0  0.135941   0.159668     0.045263     0.000000   \n",
       "S0011           0.0  0.007582   0.007328     0.000000     0.000000   \n",
       "S0012           0.0  0.012434   0.014042     0.001771     0.000000   \n",
       "S0014           0.0  0.006278   0.007812     0.001368     0.000000   \n",
       "...             ...       ...        ...          ...          ...   \n",
       "S0736           0.0  0.013795   0.023513     0.011800     0.010587   \n",
       "S0743           0.0  0.002268   0.003582     0.001352     0.000000   \n",
       "S0744           0.0  0.013294   0.016427     0.001396     0.000000   \n",
       "S0750           0.0  0.005939   0.007804     0.012092     0.000000   \n",
       "S0762           0.0  0.000000   0.000000     0.000000     0.000000   \n",
       "\n",
       "m/z                                                                     ...  \\\n",
       "temp_bin  (1200, 1300] (1300, 1400] (1400, 1500] (200, 300] (300, 400]  ...   \n",
       "sample_id                                                               ...   \n",
       "S0004         0.000000          0.0          0.0   0.009380   0.005099  ...   \n",
       "S0006         0.000000          0.0          0.0   0.163932   0.183625  ...   \n",
       "S0011         0.000000          0.0          0.0   0.007384   0.008399  ...   \n",
       "S0012         0.000000          0.0          0.0   0.018895   0.009129  ...   \n",
       "S0014         0.000000          0.0          0.0   0.008283   0.013992  ...   \n",
       "...                ...          ...          ...        ...        ...  ...   \n",
       "S0736         0.009323          0.0          0.0   0.017304   0.014810  ...   \n",
       "S0743         0.000000          0.0          0.0   0.004720   0.005613  ...   \n",
       "S0744         0.000000          0.0          0.0   0.021401   0.022305  ...   \n",
       "S0750         0.000000          0.0          0.0   0.006980   0.009419  ...   \n",
       "S0762         0.000000          0.0          0.0   0.000000   0.000000  ...   \n",
       "\n",
       "m/z                 99                                                \\\n",
       "temp_bin  (1300, 1400] (1400, 1500] (200, 300] (300, 400] (400, 500]   \n",
       "sample_id                                                              \n",
       "S0004              0.0          0.0   0.000004   0.000004   0.000004   \n",
       "S0006              0.0          0.0   0.000865   0.001036   0.001130   \n",
       "S0011              0.0          0.0   0.000076   0.000111   0.000085   \n",
       "S0012              0.0          0.0   0.000017   0.000010   0.000008   \n",
       "S0014              0.0          0.0   0.000004   0.000003   0.000003   \n",
       "...                ...          ...        ...        ...        ...   \n",
       "S0736              0.0          0.0   0.000023   0.000017   0.000025   \n",
       "S0743              0.0          0.0   0.000061   0.000033   0.000024   \n",
       "S0744              0.0          0.0   0.000010   0.000011   0.000013   \n",
       "S0750              0.0          0.0   0.000026   0.000017   0.000022   \n",
       "S0762              0.0          0.0   0.000195   0.000130   0.000260   \n",
       "\n",
       "m/z                                                                \n",
       "temp_bin  (500, 600] (600, 700] (700, 800] (800, 900] (900, 1000]  \n",
       "sample_id                                                          \n",
       "S0004       0.000005   0.000003   0.000004   0.000004    0.000003  \n",
       "S0006       0.000998   0.000908   0.001142   0.000895    0.000816  \n",
       "S0011       0.000094   0.000152   0.000101   0.000123    0.000104  \n",
       "S0012       0.000006   0.000009   0.000006   0.000008    0.000008  \n",
       "S0014       0.000003   0.000004   0.000004   0.000004    0.000005  \n",
       "...              ...        ...        ...        ...         ...  \n",
       "S0736       0.000023   0.000017   0.000026   0.000016    0.000024  \n",
       "S0743       0.000019   0.000021   0.000022   0.000023    0.000021  \n",
       "S0744       0.000011   0.000010   0.000011   0.000009    0.000010  \n",
       "S0750       0.000034   0.000024   0.000016   0.000019    0.000022  \n",
       "S0762       0.000162   0.000519   0.000097   0.000195    0.000162  \n",
       "\n",
       "[243 rows x 1600 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_phys_df = train_df.loc[all_phys_idx]\n",
    "all_phys_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th colspan=\"10\" halign=\"left\">0</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_bin</th>\n",
       "      <th>(-100, 0]</th>\n",
       "      <th>(0, 100]</th>\n",
       "      <th>(100, 200]</th>\n",
       "      <th>(1000, 1100]</th>\n",
       "      <th>(1100, 1200]</th>\n",
       "      <th>(1200, 1300]</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>...</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>(400, 500]</th>\n",
       "      <th>(500, 600]</th>\n",
       "      <th>(600, 700]</th>\n",
       "      <th>(700, 800]</th>\n",
       "      <th>(800, 900]</th>\n",
       "      <th>(900, 1000]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</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>S0011</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007582</td>\n",
       "      <td>0.007328</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007384</td>\n",
       "      <td>0.008399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0018</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003273</td>\n",
       "      <td>0.002890</td>\n",
       "      <td>0.002229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002621</td>\n",
       "      <td>0.003164</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0038</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031529</td>\n",
       "      <td>0.038164</td>\n",
       "      <td>0.006856</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.035636</td>\n",
       "      <td>0.037290</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0047</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001686</td>\n",
       "      <td>0.002281</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002433</td>\n",
       "      <td>0.003509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0048</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004212</td>\n",
       "      <td>0.005543</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003609</td>\n",
       "      <td>0.002095</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</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>S0733</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005423</td>\n",
       "      <td>0.006198</td>\n",
       "      <td>0.001256</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006183</td>\n",
       "      <td>0.005751</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0736</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013795</td>\n",
       "      <td>0.023513</td>\n",
       "      <td>0.011800</td>\n",
       "      <td>0.010587</td>\n",
       "      <td>0.009323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017304</td>\n",
       "      <td>0.014810</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0743</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>0.003582</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004720</td>\n",
       "      <td>0.005613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0750</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005939</td>\n",
       "      <td>0.007804</td>\n",
       "      <td>0.012092</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006980</td>\n",
       "      <td>0.009419</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0762</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 1600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               0                                                 \\\n",
       "temp_bin  (-100, 0]  (0, 100] (100, 200] (1000, 1100] (1100, 1200]   \n",
       "sample_id                                                            \n",
       "S0011           0.0  0.007582   0.007328     0.000000     0.000000   \n",
       "S0018           0.0  0.003273   0.002890     0.002229     0.000000   \n",
       "S0038           0.0  0.031529   0.038164     0.006856     0.000000   \n",
       "S0047           0.0  0.001686   0.002281     0.000000     0.000000   \n",
       "S0048           0.0  0.004212   0.005543     0.000000     0.000000   \n",
       "...             ...       ...        ...          ...          ...   \n",
       "S0733           0.0  0.005423   0.006198     0.001256     0.000000   \n",
       "S0736           0.0  0.013795   0.023513     0.011800     0.010587   \n",
       "S0743           0.0  0.002268   0.003582     0.001352     0.000000   \n",
       "S0750           0.0  0.005939   0.007804     0.012092     0.000000   \n",
       "S0762           0.0  0.000000   0.000000     0.000000     0.000000   \n",
       "\n",
       "m/z                                                                     ...  \\\n",
       "temp_bin  (1200, 1300] (1300, 1400] (1400, 1500] (200, 300] (300, 400]  ...   \n",
       "sample_id                                                               ...   \n",
       "S0011         0.000000          0.0          0.0   0.007384   0.008399  ...   \n",
       "S0018         0.000000          0.0          0.0   0.002621   0.003164  ...   \n",
       "S0038         0.000000          0.0          0.0   0.035636   0.037290  ...   \n",
       "S0047         0.000000          0.0          0.0   0.002433   0.003509  ...   \n",
       "S0048         0.000000          0.0          0.0   0.003609   0.002095  ...   \n",
       "...                ...          ...          ...        ...        ...  ...   \n",
       "S0733         0.000000          0.0          0.0   0.006183   0.005751  ...   \n",
       "S0736         0.009323          0.0          0.0   0.017304   0.014810  ...   \n",
       "S0743         0.000000          0.0          0.0   0.004720   0.005613  ...   \n",
       "S0750         0.000000          0.0          0.0   0.006980   0.009419  ...   \n",
       "S0762         0.000000          0.0          0.0   0.000000   0.000000  ...   \n",
       "\n",
       "m/z                 99                                                \\\n",
       "temp_bin  (1300, 1400] (1400, 1500] (200, 300] (300, 400] (400, 500]   \n",
       "sample_id                                                              \n",
       "S0011              0.0          0.0   0.000076   0.000111   0.000085   \n",
       "S0018              0.0          0.0   0.000136   0.000108   0.000076   \n",
       "S0038              0.0          0.0   0.000034   0.000039   0.000036   \n",
       "S0047              0.0          0.0   0.000031   0.000036   0.000048   \n",
       "S0048              0.0          0.0   0.000017   0.000011   0.000013   \n",
       "...                ...          ...        ...        ...        ...   \n",
       "S0733              0.0          0.0   0.000018   0.000012   0.000013   \n",
       "S0736              0.0          0.0   0.000023   0.000017   0.000025   \n",
       "S0743              0.0          0.0   0.000061   0.000033   0.000024   \n",
       "S0750              0.0          0.0   0.000026   0.000017   0.000022   \n",
       "S0762              0.0          0.0   0.000195   0.000130   0.000260   \n",
       "\n",
       "m/z                                                                \n",
       "temp_bin  (500, 600] (600, 700] (700, 800] (800, 900] (900, 1000]  \n",
       "sample_id                                                          \n",
       "S0011       0.000094   0.000152   0.000101   0.000123    0.000104  \n",
       "S0018       0.000072   0.000130   0.000080   0.000086    0.000083  \n",
       "S0038       0.000047   0.000038   0.000044   0.000043    0.000033  \n",
       "S0047       0.000025   0.000024   0.000028   0.000025    0.000022  \n",
       "S0048       0.000028   0.000009   0.000012   0.000010    0.000000  \n",
       "...              ...        ...        ...        ...         ...  \n",
       "S0733       0.000012   0.000017   0.000011   0.000016    0.000014  \n",
       "S0736       0.000023   0.000017   0.000026   0.000016    0.000024  \n",
       "S0743       0.000019   0.000021   0.000022   0.000023    0.000021  \n",
       "S0750       0.000034   0.000024   0.000016   0.000019    0.000022  \n",
       "S0762       0.000162   0.000519   0.000097   0.000195    0.000162  \n",
       "\n",
       "[109 rows x 1600 columns]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phys_df = train_df.loc[only_phys_idx]\n",
    "phys_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # gets list of tempbins\n",
    "# def get_tempbins(mz):\n",
    "#     col_list = []\n",
    "#     for col in train_df.columns:\n",
    "#         if col[0] == str(mz):\n",
    "#             col_list.append(col)\n",
    "#     return col_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mz(mz):\n",
    "    cols_list = []\n",
    "    for col in train_df.columns:\n",
    "        if col[0] == str(mz):\n",
    "            cols_list.append(col)\n",
    "    return cols_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "tot_abund_df = pd.DataFrame()\n",
    "tot_abund_df['sample_id'] = phys_df.index\n",
    "tot_abund_df.set_index('sample_id',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(100):\n",
    "    mz_abund = phys_df.loc[:,get_mz(i)].sum(axis=1)\n",
    "    tot_abund_df[str(i)] = mz_abund"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tot_abund_df.iloc[1,].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rand = np.random.randint(0,108)\n",
    "print(rand)\n",
    "tot_abund_df.iloc[rand,].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new feature if tot_abundance > something in mz around 20 (at peak) \n",
    "# or simple tot_abundance for a particular mz ratio will be a good feature i think."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_phys_df_tot_abund = pd.DataFrame()\n",
    "all_phys_df_tot_abund['sample_id'] = all_phys_df.index\n",
    "all_phys_df_tot_abund.set_index('sample_id',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(100):\n",
    "    mz_abund = all_phys_df.loc[:,get_mz(i)].sum(axis=1)  # sum row-wise across each column\n",
    "    all_phys_df_tot_abund[str(i)] = mz_abund"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - OMG don't confuse summing along axis=1 is summation of all features in a row (vertical summation) right in this case.\n",
    " - summationg along axis is summing all sample values of same feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rand = np.random.randint(0,242)\n",
    "print(rand)\n",
    "all_phys_df_tot_abund.iloc[rand].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList([['0', '1', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '2', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '3', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '4', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '5', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '6', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '7', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '8', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '9', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99'], ['(-100, 0]', '(0, 100]', '(100, 200]', '(1000, 1100]', '(1100, 1200]', '(1200, 1300]', '(1300, 1400]', '(1400, 1500]', '(200, 300]', '(300, 400]', '(400, 500]', '(500, 600]', '(600, 700]', '(700, 800]', '(800, 900]', '(900, 1000]']])"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns.levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "754     S0754\n",
       "755     S0755\n",
       "756     S0756\n",
       "757     S0757\n",
       "758     S0758\n",
       "        ...  \n",
       "1565    S1565\n",
       "1566    S1566\n",
       "1567    S1567\n",
       "1568    S1568\n",
       "1569    S1569\n",
       "Name: sample_id, Length: 76, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata[metadata.instrument_type == 'sam_testbed']['sample_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th colspan=\"10\" halign=\"left\">0</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"9\" halign=\"left\">99</th>\n",
       "      <th>is_sam_testbed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_bin</th>\n",
       "      <th>(-100, 0]</th>\n",
       "      <th>(0, 100]</th>\n",
       "      <th>(100, 200]</th>\n",
       "      <th>(1000, 1100]</th>\n",
       "      <th>(1100, 1200]</th>\n",
       "      <th>(1200, 1300]</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>...</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>(400, 500]</th>\n",
       "      <th>(500, 600]</th>\n",
       "      <th>(600, 700]</th>\n",
       "      <th>(700, 800]</th>\n",
       "      <th>(800, 900]</th>\n",
       "      <th>(900, 1000]</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</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>S0000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004085</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.898509e-06</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002270</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001688</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.257968e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>9.397170e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001336</td>\n",
       "      <td>0.002464</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.000992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.379099e-06</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>4.693477e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0004</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005993</td>\n",
       "      <td>0.012429</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009380</td>\n",
       "      <td>0.005099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.904920e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>3.174421e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0</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>S0761</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.563089e-05</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>7.136312e-05</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0762</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.947193e-04</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>5.193249e-04</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0763</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.838281e-04</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>2.757422e-04</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0764</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.167604e-04</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>0.000625</td>\n",
       "      <td>6.254950e-04</td>\n",
       "      <td>0.000625</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0765</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.860411e-05</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>6.860411e-05</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>766 rows × 1601 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               0                                                 \\\n",
       "temp_bin  (-100, 0]  (0, 100] (100, 200] (1000, 1100] (1100, 1200]   \n",
       "sample_id                                                            \n",
       "S0000           0.0  0.004085   0.004641     0.000000          0.0   \n",
       "S0001           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S0002           0.0  0.002270   0.002495     0.000000          0.0   \n",
       "S0003           0.0  0.001336   0.002464     0.000306          0.0   \n",
       "S0004           0.0  0.005993   0.012429     0.000593          0.0   \n",
       "...             ...       ...        ...          ...          ...   \n",
       "S0761           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S0762           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S0763           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S0764           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S0765           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "\n",
       "m/z                                                                     ...  \\\n",
       "temp_bin  (1200, 1300] (1300, 1400] (1400, 1500] (200, 300] (300, 400]  ...   \n",
       "sample_id                                                               ...   \n",
       "S0000              0.0          0.0          0.0   0.001394   0.000188  ...   \n",
       "S0001              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S0002              0.0          0.0          0.0   0.001688   0.000636  ...   \n",
       "S0003              0.0          0.0          0.0   0.001099   0.000992  ...   \n",
       "S0004              0.0          0.0          0.0   0.009380   0.005099  ...   \n",
       "...                ...          ...          ...        ...        ...  ...   \n",
       "S0761              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S0762              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S0763              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S0764              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S0765              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "\n",
       "m/z                 99                                                 \\\n",
       "temp_bin  (1400, 1500]    (200, 300] (300, 400] (400, 500] (500, 600]   \n",
       "sample_id                                                               \n",
       "S0000              0.0  2.898509e-06   0.000003   0.000000   0.000000   \n",
       "S0001              0.0  0.000000e+00   0.000000   0.000000   0.000000   \n",
       "S0002              0.0  9.257968e-07   0.000001   0.000001   0.000001   \n",
       "S0003              0.0  4.379099e-06   0.000006   0.000005   0.000005   \n",
       "S0004              0.0  3.904920e-06   0.000004   0.000004   0.000005   \n",
       "...                ...           ...        ...        ...        ...   \n",
       "S0761              0.0  8.563089e-05   0.000071   0.000071   0.000086   \n",
       "S0762              0.0  1.947193e-04   0.000130   0.000260   0.000162   \n",
       "S0763              0.0  1.838281e-04   0.000207   0.000184   0.000184   \n",
       "S0764              0.0  4.167604e-04   0.000834   0.000834   0.000625   \n",
       "S0765              0.0  6.860411e-05   0.000051   0.000086   0.000069   \n",
       "\n",
       "m/z                                                       is_sam_testbed  \n",
       "temp_bin     (600, 700] (700, 800] (800, 900] (900, 1000]                 \n",
       "sample_id                                                                 \n",
       "S0000      0.000000e+00   0.000000   0.000000    0.000000              0  \n",
       "S0001      0.000000e+00   0.000000   0.000000    0.000000              0  \n",
       "S0002      9.397170e-07   0.000001   0.000001    0.000001              0  \n",
       "S0003      4.693477e-06   0.000004   0.000004    0.000004              0  \n",
       "S0004      3.174421e-06   0.000004   0.000004    0.000003              0  \n",
       "...                 ...        ...        ...         ...            ...  \n",
       "S0761      7.136312e-05   0.000100   0.000086    0.000071              1  \n",
       "S0762      5.193249e-04   0.000097   0.000195    0.000162              1  \n",
       "S0763      2.757422e-04   0.000184   0.000184    0.000253              1  \n",
       "S0764      6.254950e-04   0.000625   0.000834    0.000834              1  \n",
       "S0765      6.860411e-05   0.000051   0.000086    0.000069              1  \n",
       "\n",
       "[766 rows x 1601 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['is_sam_testbed'] = 0\n",
    "train_df.loc[754:,'is_sam_testbed'] = 1\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    754\n",
       "1     12\n",
       "Name: is_sam_testbed, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.is_sam_testbed.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating new features -- tot_abund"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mz(mz):\n",
    "    cols_list = []\n",
    "    for col in train_df.columns:\n",
    "        if col[0] == str(mz):\n",
    "            cols_list.append(col)\n",
    "    return cols_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('3', '(-100, 0]'),\n",
       " ('3', '(0, 100]'),\n",
       " ('3', '(100, 200]'),\n",
       " ('3', '(1000, 1100]'),\n",
       " ('3', '(1100, 1200]'),\n",
       " ('3', '(1200, 1300]'),\n",
       " ('3', '(1300, 1400]'),\n",
       " ('3', '(1400, 1500]'),\n",
       " ('3', '(200, 300]'),\n",
       " ('3', '(300, 400]'),\n",
       " ('3', '(400, 500]'),\n",
       " ('3', '(500, 600]'),\n",
       " ('3', '(600, 700]'),\n",
       " ('3', '(700, 800]'),\n",
       " ('3', '(800, 900]'),\n",
       " ('3', '(900, 1000]')]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_mz(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th colspan=\"10\" halign=\"left\">0</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"9\" halign=\"left\">99</th>\n",
       "      <th>tot_abund</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_bin</th>\n",
       "      <th>(-100, 0]</th>\n",
       "      <th>(0, 100]</th>\n",
       "      <th>(100, 200]</th>\n",
       "      <th>(1000, 1100]</th>\n",
       "      <th>(1100, 1200]</th>\n",
       "      <th>(1200, 1300]</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>...</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>(400, 500]</th>\n",
       "      <th>(500, 600]</th>\n",
       "      <th>(600, 700]</th>\n",
       "      <th>(700, 800]</th>\n",
       "      <th>(800, 900]</th>\n",
       "      <th>(900, 1000]</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</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>S0000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004085</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.898509e-06</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.014152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.760583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002270</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001688</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.257968e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>9.397170e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>4.175911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001336</td>\n",
       "      <td>0.002464</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.000992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.379099e-06</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>4.693477e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>4.712937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0004</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005993</td>\n",
       "      <td>0.012429</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009380</td>\n",
       "      <td>0.005099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.904920e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>3.174421e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>4.288637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1601 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               0                                                 \\\n",
       "temp_bin  (-100, 0]  (0, 100] (100, 200] (1000, 1100] (1100, 1200]   \n",
       "sample_id                                                            \n",
       "S0000           0.0  0.004085   0.004641     0.000000          0.0   \n",
       "S0001           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S0002           0.0  0.002270   0.002495     0.000000          0.0   \n",
       "S0003           0.0  0.001336   0.002464     0.000306          0.0   \n",
       "S0004           0.0  0.005993   0.012429     0.000593          0.0   \n",
       "\n",
       "m/z                                                                     ...  \\\n",
       "temp_bin  (1200, 1300] (1300, 1400] (1400, 1500] (200, 300] (300, 400]  ...   \n",
       "sample_id                                                               ...   \n",
       "S0000              0.0          0.0          0.0   0.001394   0.000188  ...   \n",
       "S0001              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S0002              0.0          0.0          0.0   0.001688   0.000636  ...   \n",
       "S0003              0.0          0.0          0.0   0.001099   0.000992  ...   \n",
       "S0004              0.0          0.0          0.0   0.009380   0.005099  ...   \n",
       "\n",
       "m/z                 99                                                 \\\n",
       "temp_bin  (1400, 1500]    (200, 300] (300, 400] (400, 500] (500, 600]   \n",
       "sample_id                                                               \n",
       "S0000              0.0  2.898509e-06   0.000003   0.000000   0.000000   \n",
       "S0001              0.0  0.000000e+00   0.000000   0.000000   0.000000   \n",
       "S0002              0.0  9.257968e-07   0.000001   0.000001   0.000001   \n",
       "S0003              0.0  4.379099e-06   0.000006   0.000005   0.000005   \n",
       "S0004              0.0  3.904920e-06   0.000004   0.000004   0.000005   \n",
       "\n",
       "m/z                                                       tot_abund  \n",
       "temp_bin     (600, 700] (700, 800] (800, 900] (900, 1000]            \n",
       "sample_id                                                            \n",
       "S0000      0.000000e+00   0.000000   0.000000    0.000000  3.014152  \n",
       "S0001      0.000000e+00   0.000000   0.000000    0.000000  1.760583  \n",
       "S0002      9.397170e-07   0.000001   0.000001    0.000001  4.175911  \n",
       "S0003      4.693477e-06   0.000004   0.000004    0.000004  4.712937  \n",
       "S0004      3.174421e-06   0.000004   0.000004    0.000003  4.288637  \n",
       "\n",
       "[5 rows x 1601 columns]"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['tot_abund'] = train_df.sum(axis=1)   # do for test\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 60.23it/s]\n"
     ]
    }
   ],
   "source": [
    "for mz in tqdm(range(0,100)):\n",
    "    train_df[f\"tot_abund_{mz}\"] = train_df[get_mz(mz)].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th colspan=\"10\" halign=\"left\">0</th>\n",
       "      <th>...</th>\n",
       "      <th>tot_abund_90</th>\n",
       "      <th>tot_abund_91</th>\n",
       "      <th>tot_abund_92</th>\n",
       "      <th>tot_abund_93</th>\n",
       "      <th>tot_abund_94</th>\n",
       "      <th>tot_abund_95</th>\n",
       "      <th>tot_abund_96</th>\n",
       "      <th>tot_abund_97</th>\n",
       "      <th>tot_abund_98</th>\n",
       "      <th>tot_abund_99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_bin</th>\n",
       "      <th>(-100, 0]</th>\n",
       "      <th>(0, 100]</th>\n",
       "      <th>(100, 200]</th>\n",
       "      <th>(1000, 1100]</th>\n",
       "      <th>(1100, 1200]</th>\n",
       "      <th>(1200, 1300]</th>\n",
       "      <th>(1300, 1400]</th>\n",
       "      <th>(1400, 1500]</th>\n",
       "      <th>(200, 300]</th>\n",
       "      <th>(300, 400]</th>\n",
       "      <th>...</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</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>S0000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004085</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002270</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001688</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001336</td>\n",
       "      <td>0.002464</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.000992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0004</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005993</td>\n",
       "      <td>0.012429</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009380</td>\n",
       "      <td>0.005099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1701 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               0                                                 \\\n",
       "temp_bin  (-100, 0]  (0, 100] (100, 200] (1000, 1100] (1100, 1200]   \n",
       "sample_id                                                            \n",
       "S0000           0.0  0.004085   0.004641     0.000000          0.0   \n",
       "S0001           0.0  0.000000   0.000000     0.000000          0.0   \n",
       "S0002           0.0  0.002270   0.002495     0.000000          0.0   \n",
       "S0003           0.0  0.001336   0.002464     0.000306          0.0   \n",
       "S0004           0.0  0.005993   0.012429     0.000593          0.0   \n",
       "\n",
       "m/z                                                                     ...  \\\n",
       "temp_bin  (1200, 1300] (1300, 1400] (1400, 1500] (200, 300] (300, 400]  ...   \n",
       "sample_id                                                               ...   \n",
       "S0000              0.0          0.0          0.0   0.001394   0.000188  ...   \n",
       "S0001              0.0          0.0          0.0   0.000000   0.000000  ...   \n",
       "S0002              0.0          0.0          0.0   0.001688   0.000636  ...   \n",
       "S0003              0.0          0.0          0.0   0.001099   0.000992  ...   \n",
       "S0004              0.0          0.0          0.0   0.009380   0.005099  ...   \n",
       "\n",
       "m/z       tot_abund_90 tot_abund_91 tot_abund_92 tot_abund_93 tot_abund_94  \\\n",
       "temp_bin                                                                     \n",
       "sample_id                                                                    \n",
       "S0000         0.000013     0.000016     0.000011     0.000012     0.000015   \n",
       "S0001         0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "S0002         0.000011     0.000010     0.000011     0.000011     0.000010   \n",
       "S0003         0.000049     0.000052     0.000053     0.000049     0.000050   \n",
       "S0004         0.000048     0.000043     0.000047     0.000038     0.000048   \n",
       "\n",
       "m/z       tot_abund_95 tot_abund_96 tot_abund_97 tot_abund_98 tot_abund_99  \n",
       "temp_bin                                                                    \n",
       "sample_id                                                                   \n",
       "S0000         0.000012     0.000017     0.000013     0.000013     0.000013  \n",
       "S0001         0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "S0002         0.000010     0.000011     0.000011     0.000011     0.000011  \n",
       "S0003         0.000060     0.000046     0.000051     0.000045     0.000050  \n",
       "S0004         0.000043     0.000050     0.000048     0.000048     0.000045  \n",
       "\n",
       "[5 rows x 1701 columns]"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(           '0',    '(-100, 0]'),\n",
       "            (           '0',     '(0, 100]'),\n",
       "            (           '0',   '(100, 200]'),\n",
       "            (           '0', '(1000, 1100]'),\n",
       "            (           '0', '(1100, 1200]'),\n",
       "            (           '0', '(1200, 1300]'),\n",
       "            (           '0', '(1300, 1400]'),\n",
       "            (           '0', '(1400, 1500]'),\n",
       "            (           '0',   '(200, 300]'),\n",
       "            (           '0',   '(300, 400]'),\n",
       "            ...\n",
       "            ('tot_abund_90',             ''),\n",
       "            ('tot_abund_91',             ''),\n",
       "            ('tot_abund_92',             ''),\n",
       "            ('tot_abund_93',             ''),\n",
       "            ('tot_abund_94',             ''),\n",
       "            ('tot_abund_95',             ''),\n",
       "            ('tot_abund_96',             ''),\n",
       "            ('tot_abund_97',             ''),\n",
       "            ('tot_abund_98',             ''),\n",
       "            ('tot_abund_99',             '')],\n",
       "           names=['m/z', 'temp_bin'], length=1701)"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15890641649399584"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [0.18475842430743405,0.11231571226358872  ,0.1754628230604976 ,0.21491243907014437,0.009548251850182602,0.16578234261252128 ,0.2620440660158778 ,0.20974574459102496 ,0.17454675013777962 ,0.07994761103090736]\n",
    "np.array(a).mean()\n",
    "# loss without upsampling and just adding tot-abund-cols "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08148062266333495"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = [ 0.07577663346216641  , 0.0419898434986083 ,0.08308371644092631  ,0.11951283999875953,0.004385721055955345  ,0.09847862638100537 ,0.17212437697804472  ,0.09514497198085957,0.10254725158905001  ,0.02176224524797388]\n",
    "np.array(b).mean()\n",
    "# upsampled and added tot abund cols (10 fold cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08708404817015437"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = [0.07878742295536179  ,0.04523074524583713 ,0.08872348469960467  ,0.13319963021715753,0.0026662788143396194  ,0.09376803477151378  ,0.17536110938476185  ,0.11670749644665927 ,0.11547137276659762,0.02092490639971044      ]\n",
    "np.array(c).mean()\n",
    "# upsampled 5 fold cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08047141396323097"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = [0.07075227207382759 ,0.03791623993423403  ,0.08137873663688354,0.12012665794269177 ,0.002320419148247345,0.0906572427852579,0.1700037641738595 ,0.11063544289313132 ,0.10142540985325714 ,0.019497954190919566]\n",
    "np.array(d).mean()\n",
    "# upsampled 10 fold cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07721361653905755"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = [0.0665537138951669  ,0.03590359989212326,0.0779183204577051,0.11950424118183323 ,0.0021553265735045294 ,0.08634003283763375 ,0.1630858534297697,0.10384433599973009 ,0.09687003948068018 ,0.019960701642428788]\n",
    "np.array(e).mean()\n",
    "# upsampled 20 fold cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross validate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define stratified k-fold validation\n",
    "skf = StratifiedKFold(n_splits=5, random_state=RANDOM_SEED, shuffle=True)\n",
    "# Define log loss\n",
    "log_loss_scorer = make_scorer(log_loss,needs_proba=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check log loss score for baseline dummy model\n",
    "def logloss_cross_val(clf, X, y):\n",
    "    \n",
    "    # Generate a score for each label class\n",
    "    log_loss_cv = {}\n",
    "    for col in y.columns:\n",
    "        y_col = y[col]  # take one label at a time\n",
    "        log_loss_cv[col] = np.mean(cross_val_score(clf, X.values, y_col, cv=skf, scoring=log_loss_scorer))\n",
    "    avg_log_loss = np.mean(list(log_loss_cv.values()))\n",
    "\n",
    "    return log_loss_cv, avg_log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model log-loss:\n",
      "\n",
      "{'basalt': 0.30278901129298175,\n",
      " 'carbonate': 0.38291100989666,\n",
      " 'chloride': 0.328716583226917,\n",
      " 'iron_oxide': 0.37132220735417304,\n",
      " 'oxalate': 0.00402137727603233,\n",
      " 'oxychlorine': 0.2943876431466222,\n",
      " 'phyllosilicate': 0.4347718787208382,\n",
      " 'silicate': 0.3608636581799133,\n",
      " 'sulfate': 0.3976859449729116,\n",
      " 'sulfide': 0.16774882527417953}\n",
      "Average log-loss\n",
      "0.3045218139341229\n"
     ]
    }
   ],
   "source": [
    "# Baseline model\n",
    "# Define logistic regression model\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"l1\", solver=\"liblinear\", C=10, random_state=RANDOM_SEED\n",
    ")\n",
    "print(\"Logistic regression model log-loss:\\n\")\n",
    "#  this time cving with 1701 features (added tot abunds )\n",
    "logreg_logloss = logloss_cross_val(logreg_clf, train_df, train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "pprint(logreg_logloss[1])\n",
    "# this might not perform that well, coz some features are not scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th>tot_abund</th>\n",
       "      <th>tot_abund_0</th>\n",
       "      <th>tot_abund_1</th>\n",
       "      <th>tot_abund_2</th>\n",
       "      <th>tot_abund_3</th>\n",
       "      <th>tot_abund_4</th>\n",
       "      <th>tot_abund_5</th>\n",
       "      <th>tot_abund_6</th>\n",
       "      <th>tot_abund_7</th>\n",
       "      <th>tot_abund_8</th>\n",
       "      <th>...</th>\n",
       "      <th>tot_abund_90</th>\n",
       "      <th>tot_abund_91</th>\n",
       "      <th>tot_abund_92</th>\n",
       "      <th>tot_abund_93</th>\n",
       "      <th>tot_abund_94</th>\n",
       "      <th>tot_abund_95</th>\n",
       "      <th>tot_abund_96</th>\n",
       "      <th>tot_abund_97</th>\n",
       "      <th>tot_abund_98</th>\n",
       "      <th>tot_abund_99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_bin</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",
       "    <tr>\n",
       "      <th>sample_id</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>S0000</th>\n",
       "      <td>3.014152</td>\n",
       "      <td>0.010308</td>\n",
       "      <td>0.009034</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>0.000730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>1.760583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010812</td>\n",
       "      <td>0.120071</td>\n",
       "      <td>0.009249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0002</th>\n",
       "      <td>4.175911</td>\n",
       "      <td>0.009209</td>\n",
       "      <td>0.014417</td>\n",
       "      <td>0.003485</td>\n",
       "      <td>0.003207</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>4.712937</td>\n",
       "      <td>0.009939</td>\n",
       "      <td>0.014468</td>\n",
       "      <td>0.008389</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000606</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0004</th>\n",
       "      <td>4.288637</td>\n",
       "      <td>0.045431</td>\n",
       "      <td>0.063083</td>\n",
       "      <td>0.022971</td>\n",
       "      <td>0.020564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002381</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000045</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>S0761</th>\n",
       "      <td>6.610120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000871</td>\n",
       "      <td>0.002940</td>\n",
       "      <td>0.001898</td>\n",
       "      <td>0.001584</td>\n",
       "      <td>0.001556</td>\n",
       "      <td>0.001299</td>\n",
       "      <td>0.002612</td>\n",
       "      <td>0.002084</td>\n",
       "      <td>0.001442</td>\n",
       "      <td>0.000842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0762</th>\n",
       "      <td>10.166442</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.922882</td>\n",
       "      <td>0.001785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001850</td>\n",
       "      <td>0.019280</td>\n",
       "      <td>0.007984</td>\n",
       "      <td>0.006264</td>\n",
       "      <td>0.007984</td>\n",
       "      <td>0.007498</td>\n",
       "      <td>0.006297</td>\n",
       "      <td>0.009153</td>\n",
       "      <td>0.007076</td>\n",
       "      <td>0.002239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0763</th>\n",
       "      <td>12.480884</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.229343</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002505</td>\n",
       "      <td>0.007744</td>\n",
       "      <td>0.004527</td>\n",
       "      <td>0.004320</td>\n",
       "      <td>0.006319</td>\n",
       "      <td>0.004320</td>\n",
       "      <td>0.005768</td>\n",
       "      <td>0.006595</td>\n",
       "      <td>0.004090</td>\n",
       "      <td>0.002367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0764</th>\n",
       "      <td>20.241183</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.344381</td>\n",
       "      <td>0.008130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008131</td>\n",
       "      <td>0.027101</td>\n",
       "      <td>0.017303</td>\n",
       "      <td>0.020013</td>\n",
       "      <td>0.018345</td>\n",
       "      <td>0.012925</td>\n",
       "      <td>0.017511</td>\n",
       "      <td>0.024391</td>\n",
       "      <td>0.013132</td>\n",
       "      <td>0.008339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0765</th>\n",
       "      <td>4.543433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>0.003104</td>\n",
       "      <td>0.001801</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>0.001904</td>\n",
       "      <td>0.001321</td>\n",
       "      <td>0.000823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>766 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z        tot_abund tot_abund_0 tot_abund_1 tot_abund_2 tot_abund_3  \\\n",
       "temp_bin                                                               \n",
       "sample_id                                                              \n",
       "S0000       3.014152    0.010308    0.009034    0.001326    0.000730   \n",
       "S0001       1.760583    0.000000    0.010812    0.120071    0.009249   \n",
       "S0002       4.175911    0.009209    0.014417    0.003485    0.003207   \n",
       "S0003       4.712937    0.009939    0.014468    0.008389    0.007700   \n",
       "S0004       4.288637    0.045431    0.063083    0.022971    0.020564   \n",
       "...              ...         ...         ...         ...         ...   \n",
       "S0761       6.610120    0.000000    0.000000    0.000000    0.000000   \n",
       "S0762      10.166442    0.000000    0.000000    1.922882    0.001785   \n",
       "S0763      12.480884    0.000000    0.000000    3.229343    0.001700   \n",
       "S0764      20.241183    0.000000    0.000000    4.344381    0.008130   \n",
       "S0765       4.543433    0.000000    0.000000    0.000000    0.000000   \n",
       "\n",
       "m/z       tot_abund_4 tot_abund_5 tot_abund_6 tot_abund_7 tot_abund_8  ...  \\\n",
       "temp_bin                                                               ...   \n",
       "sample_id                                                              ...   \n",
       "S0000             0.0    0.000105    0.000024    0.000019    0.000025  ...   \n",
       "S0001             0.0    0.000202    0.000175    0.000130    0.000132  ...   \n",
       "S0002             0.0    0.000234    0.000034    0.000024    0.000041  ...   \n",
       "S0003             0.0    0.000606    0.000137    0.000108    0.000165  ...   \n",
       "S0004             0.0    0.002381    0.000208    0.000089    0.000144  ...   \n",
       "...               ...         ...         ...         ...         ...  ...   \n",
       "S0761             0.0    0.000143    0.000300    0.000371    0.000000  ...   \n",
       "S0762             0.0    0.000000    0.000000    0.000000    0.000000  ...   \n",
       "S0763             0.0    0.000000    0.000000    0.000000    0.000000  ...   \n",
       "S0764             0.0    0.000000    0.000000    0.000000    0.000000  ...   \n",
       "S0765             0.0    0.000137    0.000274    0.000343    0.000000  ...   \n",
       "\n",
       "m/z       tot_abund_90 tot_abund_91 tot_abund_92 tot_abund_93 tot_abund_94  \\\n",
       "temp_bin                                                                     \n",
       "sample_id                                                                    \n",
       "S0000         0.000013     0.000016     0.000011     0.000012     0.000015   \n",
       "S0001         0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "S0002         0.000011     0.000010     0.000011     0.000011     0.000010   \n",
       "S0003         0.000049     0.000052     0.000053     0.000049     0.000050   \n",
       "S0004         0.000048     0.000043     0.000047     0.000038     0.000048   \n",
       "...                ...          ...          ...          ...          ...   \n",
       "S0761         0.000871     0.002940     0.001898     0.001584     0.001556   \n",
       "S0762         0.001850     0.019280     0.007984     0.006264     0.007984   \n",
       "S0763         0.002505     0.007744     0.004527     0.004320     0.006319   \n",
       "S0764         0.008131     0.027101     0.017303     0.020013     0.018345   \n",
       "S0765         0.000703     0.003104     0.001801     0.001681     0.001681   \n",
       "\n",
       "m/z       tot_abund_95 tot_abund_96 tot_abund_97 tot_abund_98 tot_abund_99  \n",
       "temp_bin                                                                    \n",
       "sample_id                                                                   \n",
       "S0000         0.000012     0.000017     0.000013     0.000013     0.000013  \n",
       "S0001         0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "S0002         0.000010     0.000011     0.000011     0.000011     0.000011  \n",
       "S0003         0.000060     0.000046     0.000051     0.000045     0.000050  \n",
       "S0004         0.000043     0.000050     0.000048     0.000048     0.000045  \n",
       "...                ...          ...          ...          ...          ...  \n",
       "S0761         0.001299     0.002612     0.002084     0.001442     0.000842  \n",
       "S0762         0.007498     0.006297     0.009153     0.007076     0.002239  \n",
       "S0763         0.004320     0.005768     0.006595     0.004090     0.002367  \n",
       "S0764         0.012925     0.017511     0.024391     0.013132     0.008339  \n",
       "S0765         0.001338     0.002435     0.001904     0.001321     0.000823  \n",
       "\n",
       "[766 rows x 101 columns]"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot_abund_cols = [cols for cols in train_df.columns if cols[0].startswith('tot_abund')]\n",
    "train_df[tot_abund_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th>41</th>\n",
       "      <th>28</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th>64</th>\n",
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       "      <th>44</th>\n",
       "      <th colspan=\"3\" halign=\"left\">32</th>\n",
       "      <th>...</th>\n",
       "      <th>tot_abund_90</th>\n",
       "      <th>tot_abund_91</th>\n",
       "      <th>tot_abund_92</th>\n",
       "      <th>tot_abund_93</th>\n",
       "      <th>tot_abund_94</th>\n",
       "      <th>tot_abund_95</th>\n",
       "      <th>tot_abund_96</th>\n",
       "      <th>tot_abund_97</th>\n",
       "      <th>tot_abund_98</th>\n",
       "      <th>tot_abund_99</th>\n",
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       "      <th>temp_bin</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>sample_id</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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S0000</th>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>S0002</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.002742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.001688</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>0.294738</td>\n",
       "      <td>0.060377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.010228</td>\n",
       "      <td>0.001792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006309</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.485557</td>\n",
       "      <td>0.004301</td>\n",
       "      <td>0.002945</td>\n",
       "      <td>0.018220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000050</td>\n",
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       "      <th>S0004</th>\n",
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       "      <td>0.012847</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.009380</td>\n",
       "      <td>0.004096</td>\n",
       "      <td>0.108907</td>\n",
       "      <td>0.003335</td>\n",
       "      <td>0.004313</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               41          28          1                       64  \\\n",
       "temp_bin  (100, 200] (900, 1000] (200, 300] (1100, 1200] (900, 1000]   \n",
       "sample_id                                                              \n",
       "S0000       0.000031    0.000000   0.001254          0.0    0.000000   \n",
       "S0001       0.000023    0.000000   0.001677          0.0    0.000000   \n",
       "S0002       0.000003    0.001073   0.002742          0.0    0.000027   \n",
       "S0003       0.000008    0.010228   0.001792          0.0    0.006309   \n",
       "S0004       0.000045    0.001369   0.012847          0.0    0.000164   \n",
       "\n",
       "m/z                0         44         32                          ...  \\\n",
       "temp_bin  (200, 300] (500, 600] (500, 600] (700, 800] (1000, 1100]  ...   \n",
       "sample_id                                                           ...   \n",
       "S0000       0.001394   0.000000   0.000000   0.000000     0.000000  ...   \n",
       "S0001       0.000000   0.000000   0.000000   0.000000     0.000000  ...   \n",
       "S0002       0.001688   0.000438   0.294738   0.060377     0.000000  ...   \n",
       "S0003       0.001099   0.485557   0.004301   0.002945     0.018220  ...   \n",
       "S0004       0.009380   0.004096   0.108907   0.003335     0.004313  ...   \n",
       "\n",
       "m/z       tot_abund_90 tot_abund_91 tot_abund_92 tot_abund_93 tot_abund_94  \\\n",
       "temp_bin                                                                     \n",
       "sample_id                                                                    \n",
       "S0000         0.000013     0.000016     0.000011     0.000012     0.000015   \n",
       "S0001         0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "S0002         0.000011     0.000010     0.000011     0.000011     0.000010   \n",
       "S0003         0.000049     0.000052     0.000053     0.000049     0.000050   \n",
       "S0004         0.000048     0.000043     0.000047     0.000038     0.000048   \n",
       "\n",
       "m/z       tot_abund_95 tot_abund_96 tot_abund_97 tot_abund_98 tot_abund_99  \n",
       "temp_bin                                                                    \n",
       "sample_id                                                                   \n",
       "S0000         0.000012     0.000017     0.000013     0.000013     0.000013  \n",
       "S0001         0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "S0002         0.000010     0.000011     0.000011     0.000011     0.000011  \n",
       "S0003         0.000060     0.000046     0.000051     0.000045     0.000050  \n",
       "S0004         0.000043     0.000050     0.000048     0.000048     0.000045  \n",
       "\n",
       "[5 rows x 169 columns]"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([train_df.iloc[:,imp_fts_dict['basalt']],train_df[tot_abund_cols]],axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target : basalt\n",
      "catboost model log-loss:\n",
      "[0.1671251  0.1464561  0.29325769 0.13781548 0.13678636 0.21097609\n",
      " 0.19161536 0.22520733 0.18055115 0.15779358]\n",
      "mean logloss -->  0.18475842430743408  loss std -->  0.04601125220082015\n",
      "\n",
      "\n",
      "Target : carbonate\n",
      "catboost model log-loss:\n",
      "[0.06681248 0.06405699 0.09043566 0.04748098 0.11972385 0.22757573\n",
      " 0.14205762 0.08219958 0.12335774 0.15945649]\n",
      "mean logloss -->  0.11231571226358869  loss std -->  0.051533699564458677\n",
      "\n",
      "\n",
      "Target : chloride\n",
      "catboost model log-loss:\n",
      "[0.17498892 0.17582352 0.14100976 0.2080621  0.26265016 0.18720684\n",
      " 0.09532291 0.12451996 0.22261485 0.16242922]\n",
      "mean logloss -->  0.1754628230604976  loss std -->  0.04626057214495595\n",
      "\n",
      "\n",
      "Target : iron_oxide\n",
      "catboost model log-loss:\n",
      "[0.2820195  0.19029699 0.24339917 0.1682181  0.1515006  0.15859459\n",
      " 0.26898294 0.30010423 0.19925993 0.18674835]\n",
      "mean logloss -->  0.21491243907014437  loss std -->  0.05149113711135235\n",
      "\n",
      "\n",
      "Target : oxalate\n",
      "catboost model log-loss:\n",
      "[0.00073213 0.00444142 0.00496716 0.03318249 0.0067831  0.01185893\n",
      " 0.00807312 0.00093911 0.02028371 0.00422136]\n",
      "mean logloss -->  0.0095482518501826  loss std -->  0.009572208884063122\n",
      "\n",
      "\n",
      "Target : oxychlorine\n",
      "catboost model log-loss:\n",
      "[0.1032658  0.15081215 0.13694005 0.2129827  0.12524571 0.28036694\n",
      " 0.22549052 0.14769342 0.15251717 0.12250894]\n",
      "mean logloss -->  0.16578234261252128  loss std -->  0.052825990543289174\n",
      "\n",
      "\n",
      "Target : phyllosilicate\n",
      "catboost model log-loss:\n",
      "[0.30907411 0.30255524 0.29281482 0.23383938 0.23788398 0.18690077\n",
      " 0.2172285  0.2173925  0.26770498 0.35504638]\n",
      "mean logloss -->  0.2620440660158778  loss std -->  0.049506789444808505\n",
      "\n",
      "\n",
      "Target : silicate\n",
      "catboost model log-loss:\n",
      "[0.16210678 0.18822747 0.2136012  0.20649773 0.14926553 0.32119636\n",
      " 0.15164407 0.24966411 0.24839221 0.20686199]\n",
      "mean logloss -->  0.20974574459102496  loss std -->  0.050307219886905945\n",
      "\n",
      "\n",
      "Target : sulfate\n",
      "catboost model log-loss:\n",
      "[0.14087743 0.18158339 0.1156941  0.13045646 0.21195497 0.12588767\n",
      " 0.17676433 0.19173098 0.2578945  0.21262368]\n",
      "mean logloss -->  0.17454675013777962  loss std -->  0.043651715318473006\n",
      "\n",
      "\n",
      "Target : sulfide\n",
      "catboost model log-loss:\n",
      "[0.01555099 0.20683027 0.05784149 0.12535604 0.02378951 0.06735671\n",
      " 0.05081512 0.09955308 0.07527449 0.07710842]\n",
      "mean logloss -->  0.07994761103090735  loss std -->  0.052318236433057684\n",
      "\n",
      "\n",
      "Target : target_vec\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'target_vec'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_16220\\818794671.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;31m# sampler = SMOTETomek(random_state=RANDOM_SEED)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;31m# sampled_train_col = sampler.fit_resample(train_df.iloc[:,imp_fts_dict[col]], train_labels[col])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mtrain_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mimp_fts_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtot_abund_cols\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mcb_clf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCatBoostClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mRANDOM_SEED\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0meval_metric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Logloss'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mcb_logloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcb_clf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_set\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_labels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog_loss_scorer\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'target_vec'"
     ]
    }
   ],
   "source": [
    "skf = StratifiedKFold(n_splits=10, random_state=RANDOM_SEED, shuffle=True)\n",
    "for col in train_labels.columns:\n",
    "    print(\"Target :\",col)\n",
    "    # sampler = SMOTETomek(random_state=RANDOM_SEED)\n",
    "    # sampled_train_col = sampler.fit_resample(train_df.iloc[:,imp_fts_dict[col]], train_labels[col])\n",
    "    train_set = pd.concat([train_df.iloc[:,imp_fts_dict[col]],train_df[tot_abund_cols]],axis=1)\n",
    "    cb_clf = CatBoostClassifier(random_state=RANDOM_SEED,verbose=False,eval_metric='Logloss')\n",
    "    cb_logloss = cross_val_score(cb_clf, train_set,train_labels[col],scoring=log_loss_scorer,cv=skf)\n",
    "    print(\"catboost model log-loss:\")\n",
    "    print(cb_logloss)\n",
    "    print(\"mean logloss --> \",cb_logloss.mean(),\" loss std --> \",cb_logloss.std())\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15890641649399584"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_loss = [0.18475842430743408,0.11231571226358869,0.1754628230604976,0.21491243907014437,0.0095482518501826,0.16578234261252128,0.2620440660158778,0.20974574459102496,0.17454675013777962,0.07994761103090735]\n",
    "np.array(cv_loss).mean()\n",
    "# this improved a bit compared to the same clf without tot_abund features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**cv new_fts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target : basalt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Custom logger is already specified. Specify more than one logger at same time is not thread safe."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "catboost model log-loss:\n",
      "[0.09685657 0.06567548 0.07193705 0.0849902  0.08272553]\n",
      "mean logloss -->  0.08043696482241103  loss std -->  0.010823336176414585\n",
      "----------------------------------------------------------------------\n",
      "Target : carbonate\n",
      "catboost model log-loss:\n",
      "[0.03742015 0.07264863 0.03212216 0.04128072 0.03564184]\n",
      "mean logloss -->  0.043822699692063885  loss std -->  0.014712021037697302\n",
      "----------------------------------------------------------------------\n",
      "Target : chloride\n",
      "catboost model log-loss:\n",
      "[0.07644755 0.08070176 0.06231223 0.11133726 0.11304885]\n",
      "mean logloss -->  0.08876952936411811  loss std -->  0.02007833343661151\n",
      "----------------------------------------------------------------------\n",
      "Target : iron_oxide\n",
      "catboost model log-loss:\n",
      "[0.12596331 0.10845737 0.18102365 0.1461984  0.09605524]\n",
      "mean logloss -->  0.13153959486213923  loss std -->  0.02995521704511196\n",
      "----------------------------------------------------------------------\n",
      "Target : oxalate\n",
      "catboost model log-loss:\n",
      "[0.00273512 0.00119985 0.00107374 0.00330372 0.0051961 ]\n",
      "mean logloss -->  0.002701705676290964  loss std -->  0.0015160076847209362\n",
      "----------------------------------------------------------------------\n",
      "Target : oxychlorine\n",
      "catboost model log-loss:\n",
      "[0.08945432 0.14810414 0.08653503 0.07978076 0.06768585]\n",
      "mean logloss -->  0.09431201956532367  loss std -->  0.02792053377136816\n",
      "----------------------------------------------------------------------\n",
      "Target : phyllosilicate\n",
      "catboost model log-loss:\n",
      "[0.15746436 0.16131812 0.21709377 0.15092014 0.18494062]\n",
      "mean logloss -->  0.1743474032676313  loss std -->  0.02426151004157232\n",
      "----------------------------------------------------------------------\n",
      "Target : silicate\n",
      "catboost model log-loss:\n",
      "[0.10922017 0.12145252 0.09326267 0.12180304 0.1449711 ]\n",
      "mean logloss -->  0.11814189722369103  loss std -->  0.01698672018350851\n",
      "----------------------------------------------------------------------\n",
      "Target : sulfate\n",
      "catboost model log-loss:\n",
      "[0.12739365 0.09234648 0.11370556 0.0783979  0.14438959]\n",
      "mean logloss -->  0.11124663756622413  loss std -->  0.023670586753775646\n",
      "----------------------------------------------------------------------\n",
      "Target : sulfide\n",
      "catboost model log-loss:\n",
      "[0.01140021 0.04037165 0.02325044 0.01401244 0.01826998]\n",
      "mean logloss -->  0.021460946280929358  loss std -->  0.010273246412406005\n",
      "----------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "skf = StratifiedKFold(n_splits=5, random_state=RANDOM_SEED, shuffle=True)\n",
    "for col in train_labels.columns:\n",
    "    print(\"Target :\",col)\n",
    "    train_set = pd.concat([train_df.iloc[:,imp_fts_dict[col]],train_df['is_sam_testbed']],axis=1)\n",
    "    sampler = SMOTETomek(random_state=RANDOM_SEED)\n",
    "    sampled_train_col = sampler.fit_resample(train_set, train_labels[col])\n",
    "    cb_clf = CatBoostClassifier(random_state=RANDOM_SEED,verbose=False,eval_metric='Logloss')\n",
    "    cb_logloss = cross_val_score(cb_clf, sampled_train_col[0],sampled_train_col[1],scoring=log_loss_scorer,cv=skf)\n",
    "    print(\"catboost model log-loss:\")\n",
    "    print(cb_logloss)\n",
    "    print(\"mean logloss --> \",cb_logloss.mean(),\" loss std --> \",cb_logloss.std())\n",
    "    print(\"----------------------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08667793983208226"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_sam_cv = [0.08043696482241103  ,0.043822699692063885  ,0.08876952936411811  ,0.13153959486213923  ,0.002701705676290964,0.09431201956532367,0.1743474032676313 ,0.11814189722369103,0.11124663756622413,0.021460946280929358]\n",
    "np.array(is_sam_cv).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[tot_abund_cols].to_csv('train_df_tot_abund.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>m/z</th>\n",
       "      <th>tot_abund</th>\n",
       "      <th>tot_abund_0</th>\n",
       "      <th>tot_abund_1</th>\n",
       "      <th>tot_abund_2</th>\n",
       "      <th>tot_abund_3</th>\n",
       "      <th>tot_abund_4</th>\n",
       "      <th>tot_abund_5</th>\n",
       "      <th>tot_abund_6</th>\n",
       "      <th>tot_abund_7</th>\n",
       "      <th>tot_abund_8</th>\n",
       "      <th>...</th>\n",
       "      <th>tot_abund_90</th>\n",
       "      <th>tot_abund_91</th>\n",
       "      <th>tot_abund_92</th>\n",
       "      <th>tot_abund_93</th>\n",
       "      <th>tot_abund_94</th>\n",
       "      <th>tot_abund_95</th>\n",
       "      <th>tot_abund_96</th>\n",
       "      <th>tot_abund_97</th>\n",
       "      <th>tot_abund_98</th>\n",
       "      <th>tot_abund_99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_bin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>...</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</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>S0000</th>\n",
       "      <td>3.014152</td>\n",
       "      <td>0.010308</td>\n",
       "      <td>0.009034</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>0.000730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>1.760583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010812</td>\n",
       "      <td>0.120071</td>\n",
       "      <td>0.009249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0002</th>\n",
       "      <td>4.175911</td>\n",
       "      <td>0.009209</td>\n",
       "      <td>0.014417</td>\n",
       "      <td>0.003485</td>\n",
       "      <td>0.003207</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>4.712937</td>\n",
       "      <td>0.009939</td>\n",
       "      <td>0.014468</td>\n",
       "      <td>0.008389</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000606</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0004</th>\n",
       "      <td>4.288637</td>\n",
       "      <td>0.045431</td>\n",
       "      <td>0.063083</td>\n",
       "      <td>0.022971</td>\n",
       "      <td>0.020564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002381</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000045</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>S0761</th>\n",
       "      <td>6.610120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000871</td>\n",
       "      <td>0.002940</td>\n",
       "      <td>0.001898</td>\n",
       "      <td>0.001584</td>\n",
       "      <td>0.001556</td>\n",
       "      <td>0.001299</td>\n",
       "      <td>0.002612</td>\n",
       "      <td>0.002084</td>\n",
       "      <td>0.001442</td>\n",
       "      <td>0.000842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0762</th>\n",
       "      <td>10.166442</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.922882</td>\n",
       "      <td>0.001785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001850</td>\n",
       "      <td>0.019280</td>\n",
       "      <td>0.007984</td>\n",
       "      <td>0.006264</td>\n",
       "      <td>0.007984</td>\n",
       "      <td>0.007498</td>\n",
       "      <td>0.006297</td>\n",
       "      <td>0.009153</td>\n",
       "      <td>0.007076</td>\n",
       "      <td>0.002239</td>\n",
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       "    <tr>\n",
       "      <th>S0763</th>\n",
       "      <td>12.480884</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.229343</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002505</td>\n",
       "      <td>0.007744</td>\n",
       "      <td>0.004527</td>\n",
       "      <td>0.004320</td>\n",
       "      <td>0.006319</td>\n",
       "      <td>0.004320</td>\n",
       "      <td>0.005768</td>\n",
       "      <td>0.006595</td>\n",
       "      <td>0.004090</td>\n",
       "      <td>0.002367</td>\n",
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       "    <tr>\n",
       "      <th>S0764</th>\n",
       "      <td>20.241183</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.344381</td>\n",
       "      <td>0.008130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008131</td>\n",
       "      <td>0.027101</td>\n",
       "      <td>0.017303</td>\n",
       "      <td>0.020013</td>\n",
       "      <td>0.018345</td>\n",
       "      <td>0.012925</td>\n",
       "      <td>0.017511</td>\n",
       "      <td>0.024391</td>\n",
       "      <td>0.013132</td>\n",
       "      <td>0.008339</td>\n",
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       "    <tr>\n",
       "      <th>S0765</th>\n",
       "      <td>4.543433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>0.003104</td>\n",
       "      <td>0.001801</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>0.001904</td>\n",
       "      <td>0.001321</td>\n",
       "      <td>0.000823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>766 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z        tot_abund tot_abund_0 tot_abund_1 tot_abund_2 tot_abund_3  \\\n",
       "temp_bin                                                               \n",
       "sample_id                                                              \n",
       "S0000       3.014152    0.010308    0.009034    0.001326    0.000730   \n",
       "S0001       1.760583    0.000000    0.010812    0.120071    0.009249   \n",
       "S0002       4.175911    0.009209    0.014417    0.003485    0.003207   \n",
       "S0003       4.712937    0.009939    0.014468    0.008389    0.007700   \n",
       "S0004       4.288637    0.045431    0.063083    0.022971    0.020564   \n",
       "...              ...         ...         ...         ...         ...   \n",
       "S0761       6.610120    0.000000    0.000000    0.000000    0.000000   \n",
       "S0762      10.166442    0.000000    0.000000    1.922882    0.001785   \n",
       "S0763      12.480884    0.000000    0.000000    3.229343    0.001700   \n",
       "S0764      20.241183    0.000000    0.000000    4.344381    0.008130   \n",
       "S0765       4.543433    0.000000    0.000000    0.000000    0.000000   \n",
       "\n",
       "m/z       tot_abund_4 tot_abund_5 tot_abund_6 tot_abund_7 tot_abund_8  ...  \\\n",
       "temp_bin                                                               ...   \n",
       "sample_id                                                              ...   \n",
       "S0000             0.0    0.000105    0.000024    0.000019    0.000025  ...   \n",
       "S0001             0.0    0.000202    0.000175    0.000130    0.000132  ...   \n",
       "S0002             0.0    0.000234    0.000034    0.000024    0.000041  ...   \n",
       "S0003             0.0    0.000606    0.000137    0.000108    0.000165  ...   \n",
       "S0004             0.0    0.002381    0.000208    0.000089    0.000144  ...   \n",
       "...               ...         ...         ...         ...         ...  ...   \n",
       "S0761             0.0    0.000143    0.000300    0.000371    0.000000  ...   \n",
       "S0762             0.0    0.000000    0.000000    0.000000    0.000000  ...   \n",
       "S0763             0.0    0.000000    0.000000    0.000000    0.000000  ...   \n",
       "S0764             0.0    0.000000    0.000000    0.000000    0.000000  ...   \n",
       "S0765             0.0    0.000137    0.000274    0.000343    0.000000  ...   \n",
       "\n",
       "m/z       tot_abund_90 tot_abund_91 tot_abund_92 tot_abund_93 tot_abund_94  \\\n",
       "temp_bin                                                                     \n",
       "sample_id                                                                    \n",
       "S0000         0.000013     0.000016     0.000011     0.000012     0.000015   \n",
       "S0001         0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "S0002         0.000011     0.000010     0.000011     0.000011     0.000010   \n",
       "S0003         0.000049     0.000052     0.000053     0.000049     0.000050   \n",
       "S0004         0.000048     0.000043     0.000047     0.000038     0.000048   \n",
       "...                ...          ...          ...          ...          ...   \n",
       "S0761         0.000871     0.002940     0.001898     0.001584     0.001556   \n",
       "S0762         0.001850     0.019280     0.007984     0.006264     0.007984   \n",
       "S0763         0.002505     0.007744     0.004527     0.004320     0.006319   \n",
       "S0764         0.008131     0.027101     0.017303     0.020013     0.018345   \n",
       "S0765         0.000703     0.003104     0.001801     0.001681     0.001681   \n",
       "\n",
       "m/z       tot_abund_95 tot_abund_96 tot_abund_97 tot_abund_98 tot_abund_99  \n",
       "temp_bin                                                                    \n",
       "sample_id                                                                   \n",
       "S0000         0.000012     0.000017     0.000013     0.000013     0.000013  \n",
       "S0001         0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "S0002         0.000010     0.000011     0.000011     0.000011     0.000011  \n",
       "S0003         0.000060     0.000046     0.000051     0.000045     0.000050  \n",
       "S0004         0.000043     0.000050     0.000048     0.000048     0.000045  \n",
       "...                ...          ...          ...          ...          ...  \n",
       "S0761         0.001299     0.002612     0.002084     0.001442     0.000842  \n",
       "S0762         0.007498     0.006297     0.009153     0.007076     0.002239  \n",
       "S0763         0.004320     0.005768     0.006595     0.004090     0.002367  \n",
       "S0764         0.012925     0.017511     0.024391     0.013132     0.008339  \n",
       "S0765         0.001338     0.002435     0.001904     0.001321     0.000823  \n",
       "\n",
       "[766 rows x 101 columns]"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[tot_abund_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train_df_tot_abund.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>m/z</th>\n",
       "      <th>tot_abund</th>\n",
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       "      <td>0.000024</td>\n",
       "      <td>0.000019</td>\n",
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       "      <td>0.000013</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000013</td>\n",
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       "      <td>0.120071</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.014417</td>\n",
       "      <td>0.003485</td>\n",
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       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000011</td>\n",
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       "      <td>0.002612</td>\n",
       "      <td>0.002084</td>\n",
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       "      <td>0.005768</td>\n",
       "      <td>0.006595</td>\n",
       "      <td>0.004090</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>0.003104</td>\n",
       "      <td>0.001801</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>0.001904</td>\n",
       "      <td>0.001321</td>\n",
       "      <td>0.000823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           m/z  tot_abund  tot_abund_0  tot_abund_1  tot_abund_2  tot_abund_3  \\\n",
       "0     temp_bin        NaN          NaN          NaN          NaN          NaN   \n",
       "1    sample_id        NaN          NaN          NaN          NaN          NaN   \n",
       "2        S0000   3.014152     0.010308     0.009034     0.001326     0.000730   \n",
       "3        S0001   1.760583     0.000000     0.010812     0.120071     0.009249   \n",
       "4        S0002   4.175911     0.009209     0.014417     0.003485     0.003207   \n",
       "..         ...        ...          ...          ...          ...          ...   \n",
       "763      S0761   6.610120     0.000000     0.000000     0.000000     0.000000   \n",
       "764      S0762  10.166442     0.000000     0.000000     1.922882     0.001785   \n",
       "765      S0763  12.480884     0.000000     0.000000     3.229343     0.001700   \n",
       "766      S0764  20.241183     0.000000     0.000000     4.344381     0.008130   \n",
       "767      S0765   4.543433     0.000000     0.000000     0.000000     0.000000   \n",
       "\n",
       "     tot_abund_4  tot_abund_5  tot_abund_6  tot_abund_7  ...  tot_abund_90  \\\n",
       "0            NaN          NaN          NaN          NaN  ...           NaN   \n",
       "1            NaN          NaN          NaN          NaN  ...           NaN   \n",
       "2            0.0     0.000105     0.000024     0.000019  ...      0.000013   \n",
       "3            0.0     0.000202     0.000175     0.000130  ...      0.000000   \n",
       "4            0.0     0.000234     0.000034     0.000024  ...      0.000011   \n",
       "..           ...          ...          ...          ...  ...           ...   \n",
       "763          0.0     0.000143     0.000300     0.000371  ...      0.000871   \n",
       "764          0.0     0.000000     0.000000     0.000000  ...      0.001850   \n",
       "765          0.0     0.000000     0.000000     0.000000  ...      0.002505   \n",
       "766          0.0     0.000000     0.000000     0.000000  ...      0.008131   \n",
       "767          0.0     0.000137     0.000274     0.000343  ...      0.000703   \n",
       "\n",
       "     tot_abund_91  tot_abund_92  tot_abund_93  tot_abund_94  tot_abund_95  \\\n",
       "0             NaN           NaN           NaN           NaN           NaN   \n",
       "1             NaN           NaN           NaN           NaN           NaN   \n",
       "2        0.000016      0.000011      0.000012      0.000015      0.000012   \n",
       "3        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "4        0.000010      0.000011      0.000011      0.000010      0.000010   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "763      0.002940      0.001898      0.001584      0.001556      0.001299   \n",
       "764      0.019280      0.007984      0.006264      0.007984      0.007498   \n",
       "765      0.007744      0.004527      0.004320      0.006319      0.004320   \n",
       "766      0.027101      0.017303      0.020013      0.018345      0.012925   \n",
       "767      0.003104      0.001801      0.001681      0.001681      0.001338   \n",
       "\n",
       "     tot_abund_96  tot_abund_97  tot_abund_98  tot_abund_99  \n",
       "0             NaN           NaN           NaN           NaN  \n",
       "1             NaN           NaN           NaN           NaN  \n",
       "2        0.000017      0.000013      0.000013      0.000013  \n",
       "3        0.000000      0.000000      0.000000      0.000000  \n",
       "4        0.000011      0.000011      0.000011      0.000011  \n",
       "..            ...           ...           ...           ...  \n",
       "763      0.002612      0.002084      0.001442      0.000842  \n",
       "764      0.006297      0.009153      0.007076      0.002239  \n",
       "765      0.005768      0.006595      0.004090      0.002367  \n",
       "766      0.017511      0.024391      0.013132      0.008339  \n",
       "767      0.002435      0.001904      0.001321      0.000823  \n",
       "\n",
       "[768 rows x 102 columns]"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### baseline cv models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# le = LabelEncoder()\n",
    "# train_df['sample_id_encoded'] = le.fit_transform(train_df.index)\n",
    "# # train_df.reset_index(drop=False,inplace=True)\n",
    "# train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model log-loss:\n",
      "\n",
      "{'basalt': 0.26932847698554074,\n",
      " 'carbonate': 0.2681950438175522,\n",
      " 'chloride': 0.29349127950815757,\n",
      " 'iron_oxide': 0.3771894477748686,\n",
      " 'oxalate': 0.0033836488468845295,\n",
      " 'oxychlorine': 0.3138217161967988,\n",
      " 'phyllosilicate': 0.4103668405117268,\n",
      " 'silicate': 0.3579271099724828,\n",
      " 'sulfate': 0.3320723601694199,\n",
      " 'sulfide': 0.1543584773679239}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.2780134401151356"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Baseline model\n",
    "# Define logistic regression model\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"l1\", solver=\"liblinear\", C=10, random_state=RANDOM_SEED\n",
    ")\n",
    "print(\"Logistic regression model log-loss:\\n\")\n",
    "logreg_logloss = logloss_cross_val(logreg_clf, train_df, train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "pprint(logreg_logloss[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'oxalate': 0.0033836488468845295,\n",
       " 'sulfide': 0.1543584773679239,\n",
       " 'carbonate': 0.2681950438175522,\n",
       " 'basalt': 0.26932847698554074,\n",
       " 'chloride': 0.29349127950815757,\n",
       " 'oxychlorine': 0.3138217161967988,\n",
       " 'sulfate': 0.3320723601694199,\n",
       " 'silicate': 0.3579271099724828,\n",
       " 'iron_oxide': 0.3771894477748686,\n",
       " 'phyllosilicate': 0.4103668405117268}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{k: v for k, v in sorted(logreg_logloss[0].items(), key=lambda item: item[1])}\n",
    "# oxalate and sulfide has lowest loss  (or were they overfitting due to small samples ??)\n",
    "# phyllosilicate  has highest error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model log-loss:\n",
      "\n",
      "{'basalt': 0.28280686948408174,\n",
      " 'carbonate': 0.26242989412407325,\n",
      " 'chloride': 0.298586407750464,\n",
      " 'iron_oxide': 0.4057830226997957,\n",
      " 'oxalate': 0.006159962372216957,\n",
      " 'oxychlorine': 0.3010389338337914,\n",
      " 'phyllosilicate': 0.4396312659446268,\n",
      " 'silicate': 0.35904431735948367,\n",
      " 'sulfate': 0.3093371287053941,\n",
      " 'sulfide': 0.15439436322793781}\n",
      "Average log-loss\n",
      "0.28192121655018654\n"
     ]
    }
   ],
   "source": [
    "# Baseline model\n",
    "# Define logistic regression model\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"l1\", solver=\"liblinear\", C=10, random_state=RANDOM_SEED\n",
    ")\n",
    "print(\"Logistic regression model log-loss:\\n\")\n",
    "logreg_logloss = logloss_cross_val(logreg_clf, train_features_all_mz, train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "pprint(logreg_logloss[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def something(col):\n",
    "    logreg_basalt = LogisticRegression(\n",
    "        penalty=\"l2\", solver=\"liblinear\", C=20, random_state=RANDOM_SEED, max_iter = 100, verbose = 0\n",
    "    )\n",
    "    basalt_cv = cross_val_score(logreg_basalt, train_df, train_labels[col], cv=skf,scoring=log_loss_scorer,)\n",
    "    print(basalt_cv.mean(),basalt_cv.std())\n",
    "    plt.plot(logreg_basalt.coef_.ravel(),)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24429625803412858 0.07391112304293858\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "something('carbonate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2743873181257042 0.02980642714983802\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "something('basalt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12341035204584197 0.03665600165108732\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "something('sulfide')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4382702008555678 0.07553018541054406\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "something('phyllosilicate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0056017046841835075 0.002425247557981054\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "something('oxalate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3349472074714527 0.04016880231011908\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "something('sulfate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.36325732092108226 0.0333402395505509\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "something('iron_oxide')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=20, penalty='l1', random_state=777, solver='liblinear')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg_basalt = LogisticRegression(penalty='l1', solver='liblinear', C=20, random_state=RANDOM_SEED, verbose=0)\n",
    "logreg_basalt.fit(train_df, train_labels['basalt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    0.0\n",
       "2    0.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs = pd.Series(logreg_basalt.coef_.ravel())\n",
    "coefs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1600.000000\n",
       "mean       -0.101289\n",
       "std         3.526030\n",
       "min       -77.549717\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         0.000000\n",
       "max        24.719710\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([578, 351,  24,  20, 991,   8, 635, 427, 429, 419, 990, 167,  19,\n",
       "            111, 346, 174, 420, 633, 417, 339, 171, 424, 425, 169, 164, 637,\n",
       "            636, 173, 337, 627, 989, 161, 162, 168, 163, 170, 370, 345, 428,\n",
       "            166, 700, 172, 418, 638, 156, 175, 344, 205, 426, 158,  31, 634,\n",
       "            699, 431, 194, 348, 347, 981,  27, 430, 691, 341, 491, 632, 639,\n",
       "             28, 139, 490],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_fts_index = coefs.where(coefs != 0.0).dropna().sort_values(ascending=False).index\n",
    "imp_fts_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>temp_bin</th>\n",
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       "      <th>(900, 1000]</th>\n",
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       "      <th>(500, 600]</th>\n",
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       "      <th>(1000, 1100]</th>\n",
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       "      <th>(800, 900]</th>\n",
       "      <th>(1000, 1100]</th>\n",
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       "      <th>(500, 600]</th>\n",
       "      <th>(200, 300]</th>\n",
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       "      <th>(500, 600]</th>\n",
       "      <th>(400, 500]</th>\n",
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       "    <tr>\n",
       "      <th>sample_id</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",
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       "    <tr>\n",
       "      <th>S0000</th>\n",
       "      <td>0.000031</td>\n",
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       "      <td>0.001254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0001</th>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001406</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000022</td>\n",
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       "    <tr>\n",
       "      <th>S0002</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.002742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.001688</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>0.294738</td>\n",
       "      <td>0.060377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001067</td>\n",
       "      <td>0.061803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001210</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.040446</td>\n",
       "      <td>0.000380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0003</th>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.010228</td>\n",
       "      <td>0.001792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006309</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.485557</td>\n",
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       "      <td>0.002945</td>\n",
       "      <td>0.018220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>0.000614</td>\n",
       "      <td>0.005024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>0.001176</td>\n",
       "      <td>0.001050</td>\n",
       "      <td>0.083077</td>\n",
       "      <td>0.000038</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.108907</td>\n",
       "      <td>0.003335</td>\n",
       "      <td>0.004313</td>\n",
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       "      <td>0.002220</td>\n",
       "      <td>0.003211</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008528</td>\n",
       "      <td>0.017594</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.017527</td>\n",
       "      <td>0.011106</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.001456</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000899</td>\n",
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       "      <td>0.000714</td>\n",
       "      <td>0.114730</td>\n",
       "      <td>0.012347</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008193</td>\n",
       "      <td>0.000657</td>\n",
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       "      <td>0.006491</td>\n",
       "      <td>0.249107</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003505</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012593</td>\n",
       "      <td>0.013859</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018338</td>\n",
       "      <td>0.001883</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033918</td>\n",
       "      <td>0.005355</td>\n",
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       "    <tr>\n",
       "      <th>S0763</th>\n",
       "      <td>0.004917</td>\n",
       "      <td>0.041521</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.146048</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071209</td>\n",
       "      <td>0.029963</td>\n",
       "      <td>0.024334</td>\n",
       "      <td>0.016659</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021415</td>\n",
       "      <td>0.044416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004458</td>\n",
       "      <td>0.089775</td>\n",
       "      <td>0.047633</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025161</td>\n",
       "      <td>0.004412</td>\n",
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       "    <tr>\n",
       "      <th>S0764</th>\n",
       "      <td>0.012925</td>\n",
       "      <td>0.132166</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.032312</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250573</td>\n",
       "      <td>0.081301</td>\n",
       "      <td>0.060663</td>\n",
       "      <td>0.049614</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066500</td>\n",
       "      <td>0.022722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008338</td>\n",
       "      <td>0.180113</td>\n",
       "      <td>0.131541</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071086</td>\n",
       "      <td>0.007088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S0765</th>\n",
       "      <td>0.003859</td>\n",
       "      <td>0.006226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001389</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012983</td>\n",
       "      <td>0.001269</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001132</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.018728</td>\n",
       "      <td>0.008455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004665</td>\n",
       "      <td>0.000892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>766 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "m/z               41          28          1                       64  \\\n",
       "temp_bin  (100, 200] (900, 1000] (200, 300] (1100, 1200] (900, 1000]   \n",
       "sample_id                                                              \n",
       "S0000       0.000031    0.000000   0.001254          0.0    0.000000   \n",
       "S0001       0.000023    0.000000   0.001677          0.0    0.000000   \n",
       "S0002       0.000003    0.001073   0.002742          0.0    0.000027   \n",
       "S0003       0.000008    0.010228   0.001792          0.0    0.006309   \n",
       "S0004       0.000045    0.001369   0.012847          0.0    0.000164   \n",
       "...              ...         ...        ...          ...         ...   \n",
       "S0761       0.005196    0.012318   0.000000          0.0    0.001213   \n",
       "S0762       0.006491    0.249107   0.000000          0.0    0.003505   \n",
       "S0763       0.004917    0.041521   0.000000          0.0    0.146048   \n",
       "S0764       0.012925    0.132166   0.000000          0.0    0.032312   \n",
       "S0765       0.003859    0.006226   0.000000          0.0    0.001389   \n",
       "\n",
       "m/z                0         44         32                          ...  \\\n",
       "temp_bin  (200, 300] (500, 600] (500, 600] (700, 800] (1000, 1100]  ...   \n",
       "sample_id                                                           ...   \n",
       "S0000       0.001394   0.000000   0.000000   0.000000     0.000000  ...   \n",
       "S0001       0.000000   0.000000   0.000000   0.000000     0.000000  ...   \n",
       "S0002       0.001688   0.000438   0.294738   0.060377     0.000000  ...   \n",
       "S0003       0.001099   0.485557   0.004301   0.002945     0.018220  ...   \n",
       "S0004       0.009380   0.004096   0.108907   0.003335     0.004313  ...   \n",
       "...              ...        ...        ...        ...          ...  ...   \n",
       "S0761       0.000000   0.026249   0.001470   0.001456     0.000999  ...   \n",
       "S0762       0.000000   0.000000   0.012593   0.013859     0.012463  ...   \n",
       "S0763       0.000000   0.071209   0.029963   0.024334     0.016659  ...   \n",
       "S0764       0.000000   0.250573   0.081301   0.060663     0.049614  ...   \n",
       "S0765       0.000000   0.012983   0.001269   0.001235     0.001303  ...   \n",
       "\n",
       "m/z                1         32           48           28         36  \\\n",
       "temp_bin  (500, 600] (800, 900] (1000, 1100] (1200, 1300] (500, 600]   \n",
       "sample_id                                                              \n",
       "S0000       0.000000   0.000000     0.000000          0.0   0.000000   \n",
       "S0001       0.000000   0.000000     0.000000          0.0   0.000000   \n",
       "S0002       0.001067   0.061803     0.000000          0.0   0.001210   \n",
       "S0003       0.000932   0.000614     0.005024          0.0   0.000060   \n",
       "S0004       0.002220   0.003211     0.000142          0.0   0.008528   \n",
       "...              ...        ...          ...          ...        ...   \n",
       "S0761       0.000000   0.001270     0.000899          0.0   0.000714   \n",
       "S0762       0.000000   0.018338     0.001883          0.0   0.005388   \n",
       "S0763       0.000000   0.021415     0.044416          0.0   0.004458   \n",
       "S0764       0.000000   0.066500     0.022722          0.0   0.008338   \n",
       "S0765       0.000000   0.001132     0.000978          0.0   0.000995   \n",
       "\n",
       "m/z               44                      1         16         36  \n",
       "temp_bin  (200, 300] (900, 1000] (600, 700] (500, 600] (400, 500]  \n",
       "sample_id                                                          \n",
       "S0000       0.000458    0.000000   0.000000   0.000000   0.000000  \n",
       "S0001       0.001406    0.000000   0.000000   0.000000   0.000022  \n",
       "S0002       0.000259    0.000354   0.000195   0.040446   0.000380  \n",
       "S0003       0.001415    0.001176   0.001050   0.083077   0.000038  \n",
       "S0004       0.017594    0.000936   0.000697   0.017527   0.011106  \n",
       "...              ...         ...        ...        ...        ...  \n",
       "S0761       0.114730    0.012347   0.000000   0.008193   0.000657  \n",
       "S0762       0.000000    0.000000   0.000000   0.033918   0.005355  \n",
       "S0763       0.089775    0.047633   0.000000   0.025161   0.004412  \n",
       "S0764       0.180113    0.131541   0.000000   0.071086   0.007088  \n",
       "S0765       0.018728    0.008455   0.000000   0.004665   0.000892  \n",
       "\n",
       "[766 rows x 68 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_imp_fts = train_df.iloc[:,imp_fts_index]\n",
    "train_imp_fts \n",
    "# these are features selected from l2 regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.26688889041325586 0.050806806573264605\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "something('basalt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "will dts fail on train_imp_fts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgb  model log-loss:\n",
      "\n",
      "{'basalt': 0.2080851145363473,\n",
      " 'carbonate': 0.11750166153581039,\n",
      " 'chloride': 0.22771708844156952,\n",
      " 'iron_oxide': 0.25699716268351874,\n",
      " 'oxalate': 0.020319269674338686,\n",
      " 'oxychlorine': 0.19966904400419477,\n",
      " 'phyllosilicate': 0.3176549768775613,\n",
      " 'silicate': 0.2708447665212389,\n",
      " 'sulfate': 0.2161924702844312,\n",
      " 'sulfide': 0.08082980322894696}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.19158113577879576"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.pipeline import make_pipeline, Pipeline\n",
    "# Define xgb  model\n",
    "xgb_clf = XGBClassifier(random_state=RANDOM_SEED,eval_metric='logloss')\n",
    "xgb_pipe = make_pipeline(MinMaxScaler(), xgb_clf)\n",
    "print(\"xgb  model log-loss:\\n\")\n",
    "xgb_logloss = logloss_cross_val(xgb_pipe, train_imp_fts, train_labels)\n",
    "pprint(xgb_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "xgb_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# xgb  model log-loss with fts l1 regularization:\n",
    "# {'basalt': 0.2293633249345853,\n",
    "#  'carbonate': 0.13192588179496223,\n",
    "#  'chloride': 0.24021780412687083,\n",
    "#  'iron_oxide': 0.22391952244359387,\n",
    "#  'oxalate': 0.023528164902552415,\n",
    "#  'oxychlorine': 0.20813601904867327,\n",
    "#  'phyllosilicate': 0.29677587597469673,\n",
    "#  'silicate': 0.2786606076195703,\n",
    "#  'sulfate': 0.206011868818505,\n",
    "#  'sulfide': 0.09018349784237223}\n",
    "# Average log-loss\n",
    "# 0.19287225675063824\n",
    "\n",
    "# lgb  model log-loss:\n",
    "# {'basalt': 0.3094959034706338,\n",
    "#  'carbonate': 0.1577495039665277,\n",
    "#  'chloride': 0.2874017974066329,\n",
    "#  'iron_oxide': 0.23562215162750838,\n",
    "#  'oxalate': 0.02381998197555435,\n",
    "#  'oxychlorine': 0.21436703073915853,\n",
    "#  'phyllosilicate': 0.30243121575368054,\n",
    "#  'silicate': 0.3403485557162565,\n",
    "#  'sulfate': 0.2363180162325643,\n",
    "#  'sulfide': 0.14514945661919337}\n",
    "# Average log-loss\n",
    "# 0.22527036135077108\n",
    "\n",
    "\n",
    "# catlog loss is around .16 on local machine and .15 on lb (best so far)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lgb  model log-loss:\n",
      "\n",
      "{'basalt': 0.2669544433759584,\n",
      " 'carbonate': 0.17227224642721034,\n",
      " 'chloride': 0.31245690721261526,\n",
      " 'iron_oxide': 0.285607610940349,\n",
      " 'oxalate': 0.08667258390628632,\n",
      " 'oxychlorine': 0.24321370139222473,\n",
      " 'phyllosilicate': 0.33513331033385596,\n",
      " 'silicate': 0.3505016897097727,\n",
      " 'sulfate': 0.27739271409568084,\n",
      " 'sulfide': 0.12431145213988053}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.2454516659533834"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "# Define lgb  model\n",
    "lgb_clf = LGBMClassifier(random_state=RANDOM_SEED)\n",
    "print(\"lgb  model log-loss:\\n\")\n",
    "lgb_logloss = logloss_cross_val(lgb_clf, train_imp_fts, train_labels)\n",
    "pprint(lgb_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "lgb_logloss[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upsampling works ??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.combine import SMOTETomek       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "find imp features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target : basalt\n",
      "[0.28145629 0.20222666 0.36910234 0.22798772 0.21757914 0.29544072\n",
      " 0.20097045 0.29409363 0.28466385 0.2953681 ]\n",
      "cv mean:  0.26688889041325586 cv std:  0.050806806573264605\n",
      "Target : carbonate\n",
      "[0.19482698 0.41037536 0.19710714 0.17112708 0.2341675  0.64381442\n",
      " 0.27073409 0.23091639 0.16539812 0.57081833]\n",
      "cv mean:  0.30892854070010684 cv std:  0.16409558473395217\n",
      "Target : chloride\n",
      "[0.82290436 0.40821751 0.20691256 0.25194617 0.42625937 0.26595152\n",
      " 0.15623535 0.23460981 0.46128416 0.27724636]\n",
      "cv mean:  0.3511567163236281 cv std:  0.18398900294459541\n",
      "Target : iron_oxide\n",
      "[0.3648099  0.30963721 0.53064393 0.26110411 0.35804746 0.58574187\n",
      " 0.39385339 0.47832248 0.44775951 0.25645293]\n",
      "cv mean:  0.3986372799761864 cv std:  0.10538695097778113\n",
      "Target : oxalate\n",
      "[0.00043246 0.00117575 0.00161927 0.00470163 0.00081504 0.00119717\n",
      " 0.0015451  0.00128765 0.00474454 0.00154794]\n",
      "cv mean:  0.001906653191792414 cv std:  0.001448900402760595\n",
      "Target : oxychlorine\n",
      "[0.14260448 0.36150856 0.23878873 0.3143201  0.23947054 0.2990744\n",
      " 0.47461102 0.40202199 0.4412648  0.28943223]\n",
      "cv mean:  0.32030968431274254 cv std:  0.09644340002869166\n",
      "Target : phyllosilicate\n",
      "[0.52229731 0.52156728 0.36410197 0.38315554 0.50902175 0.27909754\n",
      " 0.34029791 0.40327693 0.4489649  0.38559995]\n",
      "cv mean:  0.41573810920943516 cv std:  0.07843080173301654\n",
      "Target : silicate\n",
      "[0.67607275 0.22380803 0.25072161 0.4303042  0.21489853 0.53311494\n",
      " 0.22146998 0.41230126 0.42965277 0.58780669]\n",
      "cv mean:  0.3980150767810053 cv std:  0.15829713419837416\n",
      "Target : sulfate\n",
      "[0.25428857 0.51785727 0.31619247 0.27004178 0.30694919 0.39254191\n",
      " 0.22536172 0.45988377 0.54257683 0.41660285]\n",
      "cv mean:  0.37022963574533263 cv std:  0.10657144757378245\n",
      "Target : sulfide\n",
      "[0.06695149 0.29938002 0.09962825 0.29808231 0.10090687 0.1206861\n",
      " 0.08377121 0.17494039 0.28113219 0.46041315]\n",
      "cv mean:  0.1985891980362468 cv std:  0.12324679164771461\n"
     ]
    }
   ],
   "source": [
    "imp_fts_dict = {}\n",
    "for col in train_labels.columns:\n",
    "    print(\"Target :\",col)\n",
    "    # Define stratified k-fold validation\n",
    "    skf = StratifiedKFold(n_splits=10, random_state=RANDOM_SEED, shuffle=True)\n",
    "    # Define log loss\n",
    "    log_loss_scorer = make_scorer(log_loss,needs_proba=True)\n",
    "\n",
    "    logreg_clf = LogisticRegression(penalty='l1',random_state=RANDOM_SEED,solver='liblinear',C=20)\n",
    "    # check cv score for each target\n",
    "    cv = cross_val_score(logreg_clf, train_df, train_labels[col], cv=skf,scoring=log_loss_scorer)\n",
    "    print(cv)\n",
    "    print(\"cv mean: \", cv.mean(), \"cv std: \", cv.std())\n",
    "\n",
    "    # collect non zero coeffs and store in imp_fts_dict\n",
    "    logreg_clf.fit(train_df,train_labels[col])\n",
    "    coefs = logreg_clf.coef_\n",
    "    coefs = pd.Series(coefs.ravel())\n",
    "    coefs = coefs.where(coefs != 0.0).dropna().sort_values(ascending=False).index\n",
    "    imp_fts_dict[col] = coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['basalt', 'carbonate', 'chloride', 'iron_oxide', 'oxalate',\n",
       "       'oxychlorine', 'phyllosilicate', 'silicate', 'sulfate', 'sulfide'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target : basalt\n",
      "catboost model log-loss:\n",
      "\n",
      "[0.07088796 0.08822142 0.0713588  0.05793266 0.07604916 0.05716042\n",
      " 0.06217011 0.08733612 0.07407128 0.06233481]\n",
      "mean logloss -->  0.07075227207382756  loss std -->  0.010551030470167408\n",
      "Target : carbonate\n",
      "catboost model log-loss:\n",
      "\n",
      "[0.03273008 0.03951086 0.07289039 0.04542207 0.03378555 0.02080151\n",
      " 0.03031126 0.04369321 0.04568152 0.01433596]\n",
      "mean logloss -->  0.03791623993423403  loss std -->  0.01525281192673572\n",
      "Target : chloride\n",
      "catboost model log-loss:\n",
      "\n",
      "[0.06638775 0.07119081 0.06559982 0.08708901 0.06604391 0.0540795\n",
      " 0.11639293 0.07652059 0.09041229 0.12007076]\n",
      "mean logloss -->  0.08137873663688357  loss std -->  0.02102090247381767\n",
      "Target : iron_oxide\n",
      "catboost model log-loss:\n",
      "\n",
      "[0.13322833 0.11088207 0.11328563 0.09050394 0.14810936 0.19218322\n",
      " 0.14914858 0.08171027 0.07875944 0.10345573]\n",
      "mean logloss -->  0.12012665794269177  loss std -->  0.03385832164344897\n",
      "Target : oxalate\n",
      "catboost model log-loss:\n",
      "\n",
      "[0.00092422 0.00468864 0.0011804  0.00093201 0.00115555 0.00116634\n",
      " 0.0049537  0.00091807 0.0020319  0.00525336]\n",
      "mean logloss -->  0.0023204191482473456  loss std -->  0.0017623883414961108\n",
      "Target : oxychlorine\n",
      "catboost model log-loss:\n",
      "\n",
      "[0.05486589 0.11839446 0.21034143 0.09843729 0.09125898 0.07111371\n",
      " 0.06001515 0.08391574 0.07581769 0.04241209]\n",
      "mean logloss -->  0.09065724278525789  loss std -->  0.04511065590719578\n",
      "Target : phyllosilicate\n",
      "catboost model log-loss:\n",
      "\n",
      "[0.15728025 0.14113886 0.13278985 0.16203998 0.21050824 0.21025988\n",
      " 0.15760332 0.1520215  0.22912783 0.14726794]\n",
      "mean logloss -->  0.1700037641738595  loss std -->  0.031928421030263616\n",
      "Target : silicate\n",
      "catboost model log-loss:\n",
      "\n",
      "[0.08351591 0.10067301 0.08032926 0.15930126 0.08215519 0.09593865\n",
      " 0.07555398 0.15536579 0.12256322 0.15095815]\n",
      "mean logloss -->  0.11063544289313132  loss std -->  0.031856508502191766\n",
      "Target : sulfate\n",
      "catboost model log-loss:\n",
      "\n",
      "[0.11050512 0.09256457 0.08762121 0.06326262 0.13976541 0.08561692\n",
      " 0.08258764 0.08176518 0.10513229 0.16543315]\n",
      "mean logloss -->  0.10142540985325714  loss std -->  0.028948043191784303\n",
      "Target : sulfide\n",
      "catboost model log-loss:\n",
      "\n",
      "[0.01292243 0.00688236 0.06037316 0.01007809 0.02369301 0.01801362\n",
      " 0.01971628 0.00430492 0.03329016 0.0057055 ]\n",
      "mean logloss -->  0.019497954190919566  loss std -->  0.016102924062390375\n"
     ]
    }
   ],
   "source": [
    "skf = StratifiedKFold(n_splits=10, random_state=RANDOM_SEED, shuffle=True)\n",
    "for col in train_labels.columns:\n",
    "    print(\"Target :\",col)\n",
    "    sampler = SMOTETomek(random_state=RANDOM_SEED)\n",
    "    sampled_train_col = sampler.fit_resample(train_df.iloc[:,imp_fts_dict[col]], train_labels[col])\n",
    "    cb_clf = CatBoostClassifier(random_state=RANDOM_SEED,verbose=False,eval_metric='Logloss')\n",
    "    cb_logloss = cross_val_score(cb_clf, sampled_train_col[0],sampled_train_col[1],scoring=log_loss_scorer,cv=skf)\n",
    "    print(\"catboost model log-loss:\")\n",
    "    print(cb_logloss)\n",
    "    print(\"mean logloss --> \",cb_logloss.mean(),\" loss std --> \",cb_logloss.std())\n",
    "    print(\"\\n\")\n",
    "\n",
    "#  is this on gpu?? i think not\n",
    "# note: training on gpu is losing precision leading to high error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  omg it seems like it's too good to be true... but lets see\n",
    "sampled_cvs = [0.07075227207382756, 0.03791623993423403,0.08137873663688357,0.12012665794269177 ,0.0023204191482473456 ,0.09065724278525789 , 0.1700037641738595,0.11063544289313132,0.10142540985325714 ,0.019497954190919566]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08047141396323096"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_cvs = np.array(sampled_cvs)\n",
    "sampled_cvs.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean logloss:  0.07075227207382756 loss std:  0.010551030470167408\n"
     ]
    }
   ],
   "source": [
    "print(\"mean logloss: \",cb_logloss.mean(),\"loss std: \",cb_logloss.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "# Define cb  model\n",
    "cb_clf = CatBoostClassifier(random_state=RANDOM_SEED,verbose=999,eval_metric='Logloss')\n",
    "print(\"cb  model log-loss:\\n\")\n",
    "cb_logloss = logloss_cross_val(cb_clf, train_imp_fts, train_labels)\n",
    "pprint(cb_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "cb_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seems like gbdts are performing well on train_imp_fts \n",
    "# but how to improve further?? shall observe how imp_fts are different from other features\n",
    "\n",
    "# i want the golden feature that should provide the biggest boost in the score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cross validate on train_no_tempbins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_no_tempbins.fillna(0,inplace=True)\n",
    "minmax_sclr = MinMaxScaler()\n",
    "train_no_tempbins = minmax_sclr.fit_transform(train_no_tempbins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model log-loss:\n",
      "\n",
      "{'basalt': 0.3367539214575674,\n",
      " 'carbonate': 0.3441323894694964,\n",
      " 'chloride': 0.2990165301062223,\n",
      " 'iron_oxide': 0.5215518879141426,\n",
      " 'oxalate': 0.13786822034861448,\n",
      " 'oxychlorine': 0.4310924300522857,\n",
      " 'phyllosilicate': 0.5623299947816673,\n",
      " 'silicate': 0.3665243236230667,\n",
      " 'sulfate': 0.3808912685053789,\n",
      " 'sulfide': 0.16941994111313757}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.35495809073715795"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# log reg with l2 regularization\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"l2\", solver=\"liblinear\", C=10, random_state=RANDOM_SEED\n",
    ")\n",
    "print(\"Logistic regression model log-loss:\\n\")\n",
    "# train features include sample_id\n",
    "logreg_logloss = logloss_cross_val(logreg_clf, train_no_tempbins, train_labels[:754])\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "logreg_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model log-loss:\n",
      "\n",
      "{'basalt': 3.2461339631858515,\n",
      " 'carbonate': 2.3443064429466167,\n",
      " 'chloride': 3.6498495645867686,\n",
      " 'iron_oxide': 5.858417326432781,\n",
      " 'oxalate': 0.04485555375962525,\n",
      " 'oxychlorine': 3.29453912069794,\n",
      " 'phyllosilicate': 6.0909663088939,\n",
      " 'silicate': 4.240053221871429,\n",
      " 'sulfate': 4.5546343933836315,\n",
      " 'sulfide': 1.2164170129208696}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.4540172908679416"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# log reg with l2 regularization\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"l2\", solver=\"liblinear\", C=10, random_state=RANDOM_SEED\n",
    ")\n",
    "print(\"Logistic regression model log-loss:\\n\")\n",
    "# train features include sample_id\n",
    "logreg_logloss = logloss_cross_val(logreg_clf, train_df, train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "logreg_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model log-loss:\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'basalt': 0.2722376836957933,\n",
      " 'carbonate': 0.2334777800038636,\n",
      " 'chloride': 0.27966941989266764,\n",
      " 'iron_oxide': 0.359782798347852,\n",
      " 'oxalate': 0.005002436476973758,\n",
      " 'oxychlorine': 0.28914523986590485,\n",
      " 'phyllosilicate': 0.4199491793975046,\n",
      " 'silicate': 0.3331575705962414,\n",
      " 'sulfate': 0.32691185201988354,\n",
      " 'sulfide': 0.14065204039307266}\n",
      "Average log-loss\n",
      "0.26599860006897574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s finished\n"
     ]
    }
   ],
   "source": [
    "# log reg with l2 regularization\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"l2\", solver=\"lbfgs\", C=20, random_state=RANDOM_SEED, max_iter = 100_0, verbose = False\n",
    ")\n",
    "print(\"Logistic regression model log-loss:\\n\")\n",
    "# train features include sample_id\n",
    "logreg_logloss = logloss_cross_val(logreg_clf, train_df, train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "pprint(logreg_logloss[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convergence after 3533 epochs took 60 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.0min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convergence after 3742 epochs took 136 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.3min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convergence after 4114 epochs took 147 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.4min finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convergence after 1965 epochs took 42 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   41.8s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convergence after 4119 epochs took 139 seconds\n",
      "Wall time: 8min 44s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.3min finished\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# logistic regression with elasticnet regularization\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"elasticnet\", solver=\"saga\", C=20, random_state=RANDOM_SEED,l1_ratio=0.75, max_iter = 10_000, verbose = 0\n",
    ")\n",
    "\n",
    "# logreg_clf.fit(train_df,train_labels['basalt'])\n",
    "stratify = StratifiedKFold(n_splits=5, random_state=RANDOM_SEED, shuffle=True)\n",
    "score = cross_val_score(logreg_clf,train_df.values,train_labels['basalt'],scoring='neg_log_loss',cv= stratify)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.27768909784585993"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0.2974150175597393\n",
    "# 0.27768909784585993 elastic net\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(766, 1000)\n",
      "Logistic regression model log-loss:\n",
      "\n",
      "{'basalt': 0.2827991825395489,\n",
      " 'carbonate': 0.2622501704518315,\n",
      " 'chloride': 0.29862403676626886,\n",
      " 'iron_oxide': 0.4057920658573229,\n",
      " 'oxalate': 0.006167013809661587,\n",
      " 'oxychlorine': 0.30100544470106266,\n",
      " 'phyllosilicate': 0.4395374964249781,\n",
      " 'silicate': 0.3590782050906852,\n",
      " 'sulfate': 0.3094363871687566,\n",
      " 'sulfide': 0.15437548677535956}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.2819065489585476"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logistic regression model on 200_binned data\n",
    "print(train_features_200_binned.shape)\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"l1\", solver=\"liblinear\", C=10, random_state=RANDOM_SEED\n",
    ")\n",
    "print(\"Logistic regression model log-loss:\\n\")\n",
    "# train features include sample_id\n",
    "logreg_logloss = logloss_cross_val(logreg_clf, train_features_200_binned, train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "logreg_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(766, 4000)\n",
      "Logistic regression model log-loss:\n",
      "\n",
      "{'basalt': 0.2958038284105077,\n",
      " 'carbonate': 0.27446724818289936,\n",
      " 'chloride': 0.26734018488851097,\n",
      " 'iron_oxide': 0.38555513423006343,\n",
      " 'oxalate': 0.01176901112530861,\n",
      " 'oxychlorine': 0.3312958234722928,\n",
      " 'phyllosilicate': 0.42069440756802995,\n",
      " 'silicate': 0.3432853487264241,\n",
      " 'sulfate': 0.30733841409701673,\n",
      " 'sulfide': 0.13703382647947718}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.27745832271805304"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logistic regression model on binned data\n",
    "print(train_features.shape)\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"l1\", solver=\"liblinear\", C=10, random_state=RANDOM_SEED\n",
    ")\n",
    "print(\"Logistic regression model log-loss:\\n\")\n",
    "# train features include sample_id\n",
    "logreg_logloss = logloss_cross_val(logreg_clf, train_features, train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "logreg_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model log-loss:\n",
      "\n",
      "{'basalt': 0.2743116193392846,\n",
      " 'carbonate': 0.2739140455310597,\n",
      " 'chloride': 0.27769880425833227,\n",
      " 'iron_oxide': 0.3934946425270659,\n",
      " 'oxalate': 0.01728277768743889,\n",
      " 'oxychlorine': 0.3739454088784326,\n",
      " 'phyllosilicate': 0.4399374876587756,\n",
      " 'silicate': 0.32840845017734777,\n",
      " 'sulfate': 0.35488659494472097,\n",
      " 'sulfide': 0.1868519587699174}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.2920731789772376"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logistic regression model on more binned data\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"l1\", solver=\"liblinear\", C=10, random_state=RANDOM_SEED\n",
    ")\n",
    "print(\"Logistic regression model log-loss:\\n\")\n",
    "# train features include sample_id\n",
    "logreg_logloss = logloss_cross_val(logreg_clf, train_features_binned, train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "logreg_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kurma\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\utils\\validation.py:1688: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['tuple']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=750,random_state=RANDOM_SEED)\n",
    "pca_trained = pca.fit_transform(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kurma\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "C:\\Users\\kurma\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "tsne = TSNE(n_components=3, random_state=RANDOM_SEED)\n",
    "tsne_trained = tsne.fit_transform(train_df.drop('sample_id_encoded',axis=1).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((766, 700), (766, 3))"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_trained.shape,tsne_trained.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model log-loss:\n",
      "\n",
      "{'basalt': 0.3617159846432222,\n",
      " 'carbonate': 0.3728534894648779,\n",
      " 'chloride': 0.3513597859309511,\n",
      " 'iron_oxide': 0.4834823907787708,\n",
      " 'oxalate': 0.06403883425572789,\n",
      " 'oxychlorine': 0.47733667135201135,\n",
      " 'phyllosilicate': 0.6268637113287563,\n",
      " 'silicate': 0.3945945952284848,\n",
      " 'sulfate': 0.4424766409531397,\n",
      " 'sulfide': 0.19046191883348018}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.3765184022769422"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logistic regression on tsne transformed data\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"l1\", solver=\"liblinear\", C=10, random_state=RANDOM_SEED\n",
    ")\n",
    "print(\"Logistic regression model log-loss:\\n\")\n",
    "logreg_logloss = logloss_cross_val(logreg_clf,tsne_trained, train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "logreg_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression model log-loss:\n",
      "\n",
      "{'basalt': 0.3008332137913802,\n",
      " 'carbonate': 0.28562709910916095,\n",
      " 'chloride': 0.2633569661891945,\n",
      " 'iron_oxide': 0.3762556667261534,\n",
      " 'oxalate': 0.004042972629294361,\n",
      " 'oxychlorine': 0.310686701910554,\n",
      " 'phyllosilicate': 0.46191910411308834,\n",
      " 'silicate': 0.35950347483842243,\n",
      " 'sulfate': 0.35841150394510957,\n",
      " 'sulfide': 0.14336145561713312}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.28639981588694907"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logistic regression on pca transformed data\n",
    "logreg_clf = LogisticRegression(\n",
    "    penalty=\"l1\", solver=\"liblinear\", C=10, random_state=RANDOM_SEED\n",
    ")\n",
    "print(\"Logistic regression model log-loss:\\n\")\n",
    "logreg_logloss = logloss_cross_val(logreg_clf,pca_trained, train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "logreg_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ridge classification model log-loss:\n",
      "\n",
      "{'basalt': 3.8316106953959257,\n",
      " 'carbonate': 2.974646052638505,\n",
      " 'chloride': 3.5164528872441663,\n",
      " 'iron_oxide': 5.545605785101194,\n",
      " 'oxalate': 0.1357481226902194,\n",
      " 'oxychlorine': 3.971501769135324,\n",
      " 'phyllosilicate': 6.134049144485565,\n",
      " 'silicate': 4.960682666262222,\n",
      " 'sulfate': 5.141288446915828,\n",
      " 'sulfide': 1.5776222752804523}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.7789207845149404"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ridge classification\n",
    "ridge_clf = RidgeClassifier(0.4, random_state=RANDOM_SEED)\n",
    "print(\"ridge classification model log-loss:\\n\")\n",
    "ridge_logloss = logloss_cross_val(ridge_clf,train_df, train_labels)\n",
    "pprint(ridge_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "ridge_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### knn at 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier model log-loss:\n",
      "\n",
      "{'basalt': 0.32259223288324046,\n",
      " 'carbonate': 0.23867501700895902,\n",
      " 'chloride': 0.2896943695192836,\n",
      " 'iron_oxide': 0.3889438108550601,\n",
      " 'oxalate': 0.04698203123877648,\n",
      " 'oxychlorine': 0.3334901330100087,\n",
      " 'phyllosilicate': 0.49917826600887977,\n",
      " 'silicate': 0.3515587994293642,\n",
      " 'sulfate': 0.3621776194441326,\n",
      " 'sulfide': 0.23630683308735584}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.3069599112485061"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# Define KNeighborsClassifier model\n",
    "knn_clf = KNeighborsClassifier(n_neighbors=33)\n",
    "print(\"KNeighborsClassifier model log-loss:\\n\")\n",
    "logreg_logloss = logloss_cross_val(knn_clf, pd.DataFrame(pca_trained), train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "logreg_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def knee_locator(r):\n",
    "    l = []\n",
    "    k_s = []\n",
    "    for i in tqdm(r):\n",
    "        knn_clf = KNeighborsClassifier(n_neighbors=int(i))\n",
    "        loss = logloss_cross_val(knn_clf, train_df, train_labels)\n",
    "        l.append(loss[1])\n",
    "        k_s.append(i)\n",
    "    print( k_s[np.argmin(l)])\n",
    "    plt.plot(k_s, l,label=['k'])\n",
    "    plt.xlabel('kneighbors')\n",
    "    plt.ylabel('logloss')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 96/96 [03:34<00:00,  2.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knee_locator(range(3,99))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier model log-loss:\n",
      "\n",
      "{'basalt': 0.3204824528914433,\n",
      " 'carbonate': 0.23357263908763276,\n",
      " 'chloride': 0.2876880157114259,\n",
      " 'iron_oxide': 0.3835538119003091,\n",
      " 'oxalate': 0.04204802950555841,\n",
      " 'oxychlorine': 0.32659359066854254,\n",
      " 'phyllosilicate': 0.5037799054198369,\n",
      " 'silicate': 0.31056081603179786,\n",
      " 'sulfate': 0.36121492967049795,\n",
      " 'sulfide': 0.24082025796558176}\n",
      "Average log-loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.3010314448852626"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# Define KNeighborsClassifier model\n",
    "knn_clf = KNeighborsClassifier(n_neighbors=33)\n",
    "print(\"KNeighborsClassifier model log-loss:\\n\")\n",
    "logreg_logloss = logloss_cross_val(knn_clf,train_imp_fts, train_labels)\n",
    "pprint(logreg_logloss[0])\n",
    "print(\"Average log-loss\")\n",
    "logreg_logloss[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    523\n",
       "1    243\n",
       "Name: phyllosilicate, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels['phyllosilicate']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Submissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total test files:  804\n"
     ]
    }
   ],
   "source": [
    "all_test_files = val_files.copy()\n",
    "all_test_files.update(test_files)\n",
    "print(\"Total test files: \", len(all_test_files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import submission format\n",
    "submission_template_df = submission_format.copy(deep=True)\n",
    "compounds_order = submission_template_df.columns\n",
    "sample_order = submission_template_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_frac_and_He_test(df):\n",
    "    \"\"\"\n",
    "    Drops fractional m/z values, m/z values > 100, and carrier gas m/z\n",
    "\n",
    "    Args:\n",
    "        df: a dataframe representing a single sample, containing m/z values\n",
    "\n",
    "    Returns:\n",
    "        The dataframe without fractional an carrier gas m/z\n",
    "    \"\"\"\n",
    "\n",
    "    # drop fractional m/z values\n",
    "    df = df[df[\"m/z\"].transform(round) == df[\"m/z\"]]\n",
    "    assert df[\"m/z\"].apply(float.is_integer).all(), \"not all m/z are integers\"\n",
    "\n",
    "    # drop m/z values greater than 99\n",
    "    df = df[df[\"m/z\"] < 100]\n",
    "\n",
    "    # drop carrier gas\n",
    "    df = df[df[\"m/z\"] != 4]\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_background_abundance_test(df):\n",
    "    \"\"\"\n",
    "    Subtracts minimum abundance value\n",
    "\n",
    "    Args:\n",
    "        df: dataframe with 'm/z' and 'abundance' columns\n",
    "\n",
    "    Returns:\n",
    "        dataframe with minimum abundance subtracted for all observations\n",
    "    \"\"\"\n",
    "\n",
    "    df[\"abundance_minsub\"] = df.groupby([\"m/z\"])[\"abundance\"].transform(\n",
    "        lambda x: (x - x.min())\n",
    "    )\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_abun_test(df):\n",
    "    \"\"\"\n",
    "    Scale abundance from 0-100 according to the min and max values across entire sample\n",
    "\n",
    "    Args:\n",
    "        df: dataframe containing abundances and m/z\n",
    "\n",
    "    Returns:\n",
    "        dataframe with additional column of scaled abundances\n",
    "    \"\"\"\n",
    "\n",
    "    df[\"abun_minsub_scaled\"] = minmax_scale(df[\"abundance_minsub\"].astype(float))\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess function\n",
    "def preprocess_sample_test(df):\n",
    "    df = drop_frac_and_He_test(df)\n",
    "    df = remove_background_abundance_test(df)\n",
    "    df = scale_abun_test(df)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>temp_bin</th>\n",
       "      <th>m/z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-100, 100]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(-100, 100]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(-100, 100]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(-100, 100]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(-100, 100]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      temp_bin  m/z\n",
       "0  (-100, 100]    0\n",
       "1  (-100, 100]    1\n",
       "2  (-100, 100]    2\n",
       "3  (-100, 100]    3\n",
       "4  (-100, 100]    4"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series of temperature bins\n",
    "temprange_test = pd.interval_range(start=-100, end=1500, freq=100)\n",
    "temprange_test\n",
    "\n",
    "# Make dataframe with rows that are combinations of all temperature bins\n",
    "# and all m/z values\n",
    "allcombs_test = list(itertools.product(temprange, [*range(0, 100)]))\n",
    "\n",
    "allcombs_df_test = pd.DataFrame(allcombs, columns=[\"temp_bin\", \"m/z\"])\n",
    "allcombs_df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def abun_per_tempbin_test(df):\n",
    "\n",
    "    \"\"\"\n",
    "    Transforms dataset to take the preprocessed max abundance for each\n",
    "    temperature range for each m/z value\n",
    "\n",
    "    Args:\n",
    "        df: dataframe to transform\n",
    "\n",
    "    Returns:\n",
    "        transformed dataframe\n",
    "    \"\"\"\n",
    "\n",
    "    # Bin temperatures\n",
    "    df[\"temp_bin\"] = pd.cut(df[\"temp\"], bins=temprange_test)\n",
    "\n",
    "    # Combine with a list of all temp bin-m/z value combinations\n",
    "    df = pd.merge(allcombs_df, df, on=[\"temp_bin\", \"m/z\"], how=\"left\")\n",
    "\n",
    "    # Aggregate to temperature bin level to find max\n",
    "    df = df.groupby([\"temp_bin\", \"m/z\"]).max(\"abun_minsub_scaled\").reset_index()\n",
    "\n",
    "    # Fill in 0 for abundance values without information\n",
    "    df = df.replace(np.nan, 0)\n",
    "\n",
    "    # Reshape so each row is a single sample\n",
    "    df = df.pivot_table(columns=[\"m/z\", \"temp_bin\"], values=[\"abun_minsub_scaled\"])\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/804 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of test files:  804\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 804/804 [03:02<00:00,  4.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Assembling preprocessed and transformed testing set\n",
    "\n",
    "test_feat_dict = {}\n",
    "print(\"Total number of test files: \", len(all_test_files))\n",
    "\n",
    "for i, (sample_id, filepath) in enumerate(tqdm(all_test_files.items())):\n",
    "\n",
    "    # Load testing sample\n",
    "    temp = pd.read_csv('data/' + filepath)\n",
    "\n",
    "    # Preprocessing testing sample\n",
    "    test_sample_pp = preprocess_sample_test(temp)\n",
    "\n",
    "    # Feature engineering\n",
    "    test_sample_fe = abun_per_tempbin_test(test_sample_pp).reset_index(drop=True)\n",
    "    test_feat_dict[sample_id] = test_sample_fe\n",
    "\n",
    "\n",
    "test_features = pd.concat(\n",
    "    test_feat_dict, names=[\"sample_id\", \"dummy_index\"]\n",
    ").reset_index(level=\"dummy_index\", drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imp_fts_index = [578, 351,  24,  20, 991,   8, 635, 427, 429, 419, 990, 167,  19,\n",
    "#             111, 346, 174, 420, 633, 417, 339, 171, 424, 425, 169, 164, 637,\n",
    "#             636, 173, 337, 627, 989, 161, 162, 168, 163, 170, 370, 345, 428,\n",
    "#             166, 700, 172, 418, 638, 156, 175, 344, 205, 426, 158,  31, 634,\n",
    "#             699, 431, 194, 348, 347, 981,  27, 430, 691, 341, 491, 632, 639,\n",
    "#              28, 139, 490]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_fts_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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