{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "\n",
    "X = pd.read_csv('data/X_train_6ZIKlTY.csv')\n",
    "y = pd.read_csv('data/y_train_lXj6X5y.csv')\n",
    "X_challenge = pd.read_csv('data/X_test_oiZ2ukx.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Analyse des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valeurs manquantes dans les données d'entrainement :\n",
      "Index                        0\n",
      "patient_id                   0\n",
      "cohort                       0\n",
      "sexM                         0\n",
      "gene                     17958\n",
      "age_at_diagnosis          2830\n",
      "age                          0\n",
      "ledd                     20593\n",
      "time_since_intake_on     25940\n",
      "time_since_intake_off    43828\n",
      "on                       16659\n",
      "off                      23407\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Valeurs manquantes dans les données d'entrainement :\")\n",
    "print(X.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportions de valeurs manquantes dans les données d'entrainement :\n",
      "Index                    0.000000\n",
      "patient_id               0.000000\n",
      "cohort                   0.000000\n",
      "sexM                     0.000000\n",
      "gene                     0.322968\n",
      "age_at_diagnosis         0.050897\n",
      "age                      0.000000\n",
      "ledd                     0.370358\n",
      "time_since_intake_on     0.466522\n",
      "time_since_intake_off    0.788231\n",
      "on                       0.299606\n",
      "off                      0.420966\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Proportions de valeurs manquantes dans les données d'entrainement :\")\n",
    "print(X.isna().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type des données\n",
      "Index                      int64\n",
      "patient_id                object\n",
      "cohort                    object\n",
      "sexM                       int64\n",
      "gene                      object\n",
      "age_at_diagnosis         float64\n",
      "age                      float64\n",
      "ledd                     float64\n",
      "time_since_intake_on     float64\n",
      "time_since_intake_off    float64\n",
      "on                       float64\n",
      "off                      float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"Type des données\")\n",
    "print(X.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comme mentionné dans le contexte du challenge, off et on ont beaucoup de valeurs manquantes et il faudra réfléchir à comment on comble ces valeurs. Cependant time_since_intake_xx ont aussi pas mal de valeurs manquantes notamment _off qui a quand meme 78% de valeurs manquantes donc à voir si on peut pas enlever cette feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution des données non NaN\n",
      "              Index          sexM  age_at_diagnosis           age  \\\n",
      "count  55603.000000  55603.000000      52773.000000  55603.000000   \n",
      "mean   27801.000000      0.596353         56.734648     62.516422   \n",
      "std    16051.347846      0.490633         10.823093     11.484480   \n",
      "min        0.000000      0.000000         16.500000     16.500000   \n",
      "25%    13900.500000      0.000000         49.500000     54.900000   \n",
      "50%    27801.000000      1.000000         56.900000     62.500000   \n",
      "75%    41701.500000      1.000000         64.400000     70.500000   \n",
      "max    55602.000000      1.000000         89.900000    103.300000   \n",
      "\n",
      "               ledd  time_since_intake_on  time_since_intake_off  \\\n",
      "count  35010.000000          29663.000000           11775.000000   \n",
      "mean     638.102228              1.953717              14.199448   \n",
      "std      219.443182              1.168793               2.844782   \n",
      "min       50.000000              0.000000               6.900000   \n",
      "25%      481.000000              1.100000              12.200000   \n",
      "50%      611.000000              1.600000              13.800000   \n",
      "75%      765.000000              2.600000              15.700000   \n",
      "max     1796.000000              6.300000              25.100000   \n",
      "\n",
      "                 on           off  \n",
      "count  38944.000000  32196.000000  \n",
      "mean      21.985980     26.295316  \n",
      "std       10.371291     16.534567  \n",
      "min        0.000000      0.000000  \n",
      "25%       14.000000     13.000000  \n",
      "50%       21.000000     24.000000  \n",
      "75%       28.000000     38.000000  \n",
      "max       95.000000    106.000000  \n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution des données non NaN\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"Distribution des données non NaN\")\n",
    "print(X.describe())\n",
    "X.hist(bins=50, figsize=(20,15))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution des données du challenge\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Distribution des données du challenge\")\n",
    "X_challenge.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y a des valeurs aberrantes dans time since diagnosis de X_challenge, ca peut pas etre négatif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Faut faire gaffe faut mettre le time since diagnosis dans X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['time_since_diagnosis']=X['age']-X['age_at_diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avec time since diagnosis c'est mieux\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Index'}>,\n",
       "        <Axes: title={'center': 'sexM'}>,\n",
       "        <Axes: title={'center': 'age_at_diagnosis'}>],\n",
       "       [<Axes: title={'center': 'age'}>,\n",
       "        <Axes: title={'center': 'ledd'}>,\n",
       "        <Axes: title={'center': 'time_since_intake_on'}>],\n",
       "       [<Axes: title={'center': 'time_since_intake_off'}>,\n",
       "        <Axes: title={'center': 'on'}>, <Axes: title={'center': 'off'}>],\n",
       "       [<Axes: title={'center': 'time_since_diagnosis'}>, <Axes: >,\n",
       "        <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Avec time since diagnosis c'est mieux\")\n",
    "X.hist(bins=50, figsize=(20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index                        0\n",
       "patient_id                   0\n",
       "cohort                       0\n",
       "sexM                         0\n",
       "gene                     17958\n",
       "age_at_diagnosis          2830\n",
       "age                          0\n",
       "ledd                     20593\n",
       "time_since_intake_on     25940\n",
       "time_since_intake_off    43828\n",
       "on                       16659\n",
       "off                      23407\n",
       "time_since_diagnosis      2830\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for /: 'list' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mX\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgene\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue_counts\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtolist\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'list' and 'int'"
     ]
    }
   ],
   "source": [
    "X['gene'].value_counts()/X.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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