From 63f931936019783cc0f71d9e0ebb3d8831ba9f36 Mon Sep 17 00:00:00 2001
From: atebboun <amel.tebboune@etu.ec-lyon.fr>
Date: Wed, 26 Mar 2025 11:36:35 +0100
Subject: [PATCH] pzfunzapf

---
 .../__pycache__/preprocess.cpython-310.pyc    | Bin 0 -> 2110 bytes
 .../__pycache__/preprocess4.cpython-310.pyc   | Bin 0 -> 2628 bytes
 preprocess/analysedonnees.ipynb               |  86 ++++++-
 preprocess/preprocess4.py                     |  69 +++++
 preprocess/remplissageoff.ipynb               |  22 ++
 preprocess/valeursoff.ipynb                   | 235 ++++++++++++++++++
 6 files changed, 403 insertions(+), 9 deletions(-)
 create mode 100644 preprocess/__pycache__/preprocess.cpython-310.pyc
 create mode 100644 preprocess/__pycache__/preprocess4.cpython-310.pyc
 create mode 100644 preprocess/preprocess4.py
 create mode 100644 preprocess/valeursoff.ipynb

diff --git a/preprocess/__pycache__/preprocess.cpython-310.pyc b/preprocess/__pycache__/preprocess.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e73903ceb4ff965f4e6dc61e917918940e62b406
GIT binary patch
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diff --git a/preprocess/__pycache__/preprocess4.cpython-310.pyc b/preprocess/__pycache__/preprocess4.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1f8db05ce3b312eec7248569d0e514d5e63154d7
GIT binary patch
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diff --git a/preprocess/analysedonnees.ipynb b/preprocess/analysedonnees.ipynb
index 5f37c1d..7b5e57e 100644
--- a/preprocess/analysedonnees.ipynb
+++ b/preprocess/analysedonnees.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -216,6 +216,13 @@
     "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": {},
@@ -225,16 +232,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_challenge['time_since_diagnosis']=X_challenge['age']-X_challenge['age_at_diagnosis']"
+    "X['time_since_diagnosis']=X['age']-X['age_at_diagnosis']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -259,13 +266,13 @@
        "        <Axes: >]], dtype=object)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 2000x1500 with 12 Axes>"
       ]
@@ -276,7 +283,68 @@
    ],
    "source": [
     "print(\"Avec time since diagnosis c'est mieux\")\n",
-    "X_challenge.hist(bins=50, figsize=(20,15))"
+    "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]"
    ]
   },
   {
diff --git a/preprocess/preprocess4.py b/preprocess/preprocess4.py
new file mode 100644
index 0000000..c94f954
--- /dev/null
+++ b/preprocess/preprocess4.py
@@ -0,0 +1,69 @@
+import pandas as pd
+import numpy as np
+'''
+fichier pour centraliser toutes les transformations de données
+
+'''
+class PreprocessData:
+    def __init__(self,path_X,path_y):
+        self.X = pd.read_csv(path_X)
+        self.y = pd.read_csv(path_y)
+        print("coucou")
+        self.virer_patient()
+        print("coucou 2")
+        self.remplir_gene()
+        print("Coucou 3")
+        
+        
+    
+    def valeurs_off(self):
+        pass
+
+    def remplir_gene(self):
+        pass
+
+    def virer_patient(self):
+        self.X.drop('patient_id',axis=1,inplace=True)
+        
+    def remplir_gene(self):
+        X_list=self.X['gene'].tolist()
+        def f_l(x):
+            if x=='Inconnu':
+                return 0.4705
+            elif x=='LRRK2+':
+                return 1
+            else :
+                return 0
+        def f_g(x):
+            if x=='GBA+':
+                return 1
+            elif x=='Inconnu':
+                return 0.4080
+            else :
+                return 0
+        def f_o(x):
+            if x=='OTHER+':
+                return 1
+            elif x=='Inconnu':
+                return 0.1211
+            else :
+                return 0
+        for i in range(len(X_list)):
+            x=X_list[i]
+            
+            if type(x)==float:
+  
+                X_list[i]='Inconnu'
+        self.X['gene']=X_list
+        valeurs=['LRRK2+','No Mutation','GBA+','OTHER+','Inconnu']
+        self.X['est_LRRK2+']=self.X['gene'].apply(lambda x: f_l(x))
+        self.X['est_GBA+']=self.X['gene'].apply(lambda x: f_g(x))
+        self.X['est_OTHER+']=self.X['gene'].apply(lambda x: f_o(x))
+        self.X.drop('gene',axis=1,inplace=True)
+        return self.X
+    def get_X(self):
+        return self.X
+
+        
+preprocess4 = PreprocessData('data/X_train_6ZIKlTY.csv', 'data/y_train_lXj6X5y.csv')
+print(preprocess4.get_X().head(30))
\ No newline at end of file
diff --git a/preprocess/remplissageoff.ipynb b/preprocess/remplissageoff.ipynb
index e69de29..7ad2046 100644
--- a/preprocess/remplissageoff.ipynb
+++ b/preprocess/remplissageoff.ipynb
@@ -0,0 +1,22 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/preprocess/valeursoff.ipynb b/preprocess/valeursoff.ipynb
new file mode 100644
index 0000000..6858993
--- /dev/null
+++ b/preprocess/valeursoff.ipynb
@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "X = pd.read_csv('data/X_train_6ZIKlTY.csv')\n",
+    "y = pd.read_csv('data/y_train_lXj6X5y.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Index</th>\n",
+       "      <th>patient_id</th>\n",
+       "      <th>cohort</th>\n",
+       "      <th>sexM</th>\n",
+       "      <th>gene</th>\n",
+       "      <th>age_at_diagnosis</th>\n",
+       "      <th>age</th>\n",
+       "      <th>ledd</th>\n",
+       "      <th>time_since_intake_on</th>\n",
+       "      <th>time_since_intake_off</th>\n",
+       "      <th>on</th>\n",
+       "      <th>off</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>IPLP5212</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LRRK2+</td>\n",
+       "      <td>48.5</td>\n",
+       "      <td>52.1</td>\n",
+       "      <td>607.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>IPLP5212</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LRRK2+</td>\n",
+       "      <td>48.5</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>666.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>17.6</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>44.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>IPLP5212</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LRRK2+</td>\n",
+       "      <td>48.5</td>\n",
+       "      <td>53.9</td>\n",
+       "      <td>717.0</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>IPLP5212</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LRRK2+</td>\n",
+       "      <td>48.5</td>\n",
+       "      <td>54.8</td>\n",
+       "      <td>770.0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>IPLP5212</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>LRRK2+</td>\n",
+       "      <td>48.5</td>\n",
+       "      <td>56.9</td>\n",
+       "      <td>885.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Index patient_id cohort  sexM    gene  age_at_diagnosis   age   ledd  \\\n",
+       "0      0   IPLP5212      A     0  LRRK2+              48.5  52.1  607.0   \n",
+       "1      1   IPLP5212      A     0  LRRK2+              48.5  53.0  666.0   \n",
+       "2      2   IPLP5212      A     0  LRRK2+              48.5  53.9  717.0   \n",
+       "3      3   IPLP5212      A     0  LRRK2+              48.5  54.8  770.0   \n",
+       "4      4   IPLP5212      A     0  LRRK2+              48.5  56.9  885.0   \n",
+       "\n",
+       "   time_since_intake_on  time_since_intake_off    on   off  \n",
+       "0                   1.9                    NaN   7.0   NaN  \n",
+       "1                   1.9                   17.6  12.0  44.0  \n",
+       "2                   1.2                    NaN   6.0   NaN  \n",
+       "3                   1.5                    NaN  11.0   NaN  \n",
+       "4                   0.3                    NaN  24.0   NaN  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23407"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X['off'].isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from preprocess import PreprocessData\n",
+    "\n",
+    "preprocess4 = PreprocessData('data/X_train_6ZIKlTY.csv', 'data/y_train_lXj6X5y.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'PreprocessData' object has no attribute 'get_X'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpreprocess4\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_X\u001b[49m()\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'PreprocessData' object has no attribute 'get_X'"
+     ]
+    }
+   ],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
GitLab