breast-cancer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# breast cancer detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### par asmae karmouchi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Importer les bibliothèques nécessaires\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I pretraitement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Charger l'ensemble de données\n",
    "data = pd.read_csv('breast-cancer.csv')  # Assurez-vous de remplacer 'nom_du_fichier.csv' par le nom de votre fichier CSV\n",
    "\n",
    "# Afficher les premières lignes de l'ensemble de données\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taille de l'ensemble de données (lignes, colonnes) : (569, 33)\n",
      "Noms des colonnes : Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
      "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
      "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
      "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
      "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
      "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
      "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
      "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
      "       'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Afficher la taille (nombre de lignes et de colonnes) du DataFrame\n",
    "print(\"Taille de l'ensemble de données (lignes, colonnes) :\", data.shape)\n",
    "\n",
    "# Afficher les noms des colonnes\n",
    "print(\"Noms des colonnes :\", data.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      " 32  Unnamed: 32              0 non-null      float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns={'Unnamed: 32'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datas_id = data['id']\n",
    "data.drop(columns={'id'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valeurs uniques de la colonne 'diagnosis' : ['M' 'B']\n"
     ]
    }
   ],
   "source": [
    "# Afficher les valeurs uniques de la colonne 'diagnosis'\n",
    "unique_diagnosis = data['diagnosis'].unique()\n",
    "print(\"Valeurs uniques de la colonne 'diagnosis' :\", unique_diagnosis)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conversion des données de diagnostic en version numérique\n",
    "diagnosis={'M':1, 'B':0}\n",
    "data['diagnosis']=[diagnosis[x] for x in data['diagnosis']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compter les occurrences de chaque valeur dans la colonne 'diagnosis'\n",
    "diagnosis_counts = data['diagnosis'].value_counts()\n",
    "\n",
    "# Obtenir les étiquettes (Malignant et Benign) et les valeurs correspondantes\n",
    "labels = diagnosis_counts.index\n",
    "sizes = diagnosis_counts.values\n",
    "\n",
    "# Créer un diagramme circulaire\n",
    "plt.figure(figsize=(4, 4))\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "plt.title(\"Répartition des diagnostics\")\n",
    "\n",
    "# Afficher le diagramme\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Après la vérification, il y a une différence entre les deux classes mais il y a une probabilité qu'elle soit encore utile pour créer de bons modèles d'ai sans aucun changement, parce que la différence n'est pas trop grande."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Diviser les données en fonctionnalités (X) et en étiquettes (y)\n",
    "X = data.drop('diagnosis', axis=1)\n",
    "y = data['diagnosis']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Diviser les données en ensembles d'entraînement et de test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normaliser les fonctionnalités (échelle)\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l Accuracy  de k-NN : 0.9473684210526315\n"
     ]
    }
   ],
   "source": [
    "# Appliquer l'algorithme k-NN\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)\n",
    "knn_predictions = knn.predict(X_test)\n",
    "knn_accuracy = accuracy_score(y_test, knn_predictions)\n",
    "print(f'l Accuracy  de k-NN : {knn_accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "naive_bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l Accuracy de Naïve Bayes : 0.9649122807017544\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# Appliquer l'algorithme Naïve Bayes\n",
    "naive_bayes = GaussianNB()\n",
    "naive_bayes.fit(X_train, y_train)\n",
    "naive_bayes_predictions = naive_bayes.predict(X_test)\n",
    "naive_bayes_accuracy = accuracy_score(y_test, naive_bayes_predictions)\n",
    "print(f'l Accuracy de Naïve Bayes : {naive_bayes_accuracy}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l Accuracy de SVM : 0.9824561403508771\n"
     ]
    }
   ],
   "source": [
    "# Appliquer l'algorithme SVM\n",
    "from sklearn.svm import SVC\n",
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "svm_predictions = svm.predict(X_test)\n",
    "svm_accuracy = accuracy_score(y_test, svm_predictions)\n",
    "print(f'l Accuracy de SVM : {svm_accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RL MABLANCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "# Appliquer la régression linéaire\n",
    "#linear_reg = LinearRegression()\n",
    "#linear_reg.fit(X_train, y_train)\n",
    "##linear_reg_predictions = linear_reg.predict(X_test)\n",
    "#linear_reg_mse = mean_squared_error(y_test, linear_reg_predictions)\n",
    "#print(f'Erreur quadratique moyenne de la régression linéaire : {linear_reg_mse}')\n",
    "#linear_reg_r2 = r2_score(y_test, linear_reg_predictions)\n",
    "#print(f\"R² de la régression linéaire : {linear_reg_r2}\")\n",
    "#linear_reg_accuracy = linear_reg_r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R LOG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l Accuracy  de la régression logistique : 0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "# Appliquer la régression logistique\n",
    "logistic_reg = LogisticRegression()\n",
    "logistic_reg.fit(X_train, y_train)\n",
    "logistic_reg_predictions = logistic_reg.predict(X_test)\n",
    "logistic_reg_accuracy = accuracy_score(y_test, logistic_reg_predictions)\n",
    "print(f'l Accuracy  de la régression logistique : {logistic_reg_accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Précisions des 4 algorithmes\n",
    "accuracies = [knn_accuracy, svm_accuracy,  logistic_reg_accuracy , naive_bayes_accuracy] \n",
    "algorithms = ['k-NN', 'SVM', 'Régression Logistique' , 'Naïve Bayes']\n",
    "\n",
    "# Créer un graphique à barres pour comparer les précisions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(algorithms, accuracies, color=['blue', 'green', 'red', 'purple' ])\n",
    "plt.xlabel('Algorithmes')\n",
    "plt.ylabel('Précision')\n",
    "plt.title('Comparaison des précisions des 4 algorithmes')\n",
    "plt.ylim(0.9, 1.0)  # Ajuster la plage de l'axe y si nécessaire\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['scaler.pkl']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Après avoir entraîné le modèle SVM\n",
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# Sauvegarder le modèle dans un fichier\n",
    "joblib.dump(svm, 'svm_model.pkl')\n",
    "joblib.dump(scaler, 'scaler.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Matrice de confusion pour k-NN:\n",
      "[[68  3]\n",
      " [ 3 40]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Matrice de confusion pour SVM:\n",
      "[[71  0]\n",
      " [ 2 41]]\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "predict_proba is not available when  probability=False",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\user\\Desktop\\MONDE AI\\BreastCancerApp\\breast-cancer.ipynb Cell 29\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/MONDE%20AI/BreastCancerApp/breast-cancer.ipynb#X40sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39mMatrice de confusion pour \u001b[39m\u001b[39m{\u001b[39;00mname\u001b[39m}\u001b[39;00m\u001b[39m:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{\u001b[39;00mcm\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/MONDE%20AI/BreastCancerApp/breast-cancer.ipynb#X40sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m \u001b[39m# Afficher la courbe ROC et calculer l'AUC\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/MONDE%20AI/BreastCancerApp/breast-cancer.ipynb#X40sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m fpr, tpr, thresholds \u001b[39m=\u001b[39m roc_curve(y_test, algo\u001b[39m.\u001b[39;49mpredict_proba(X_test)[:, \u001b[39m1\u001b[39m])\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/MONDE%20AI/BreastCancerApp/breast-cancer.ipynb#X40sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m roc_auc \u001b[39m=\u001b[39m auc(fpr, tpr)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/MONDE%20AI/BreastCancerApp/breast-cancer.ipynb#X40sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m \u001b[39m# Tracer la courbe ROC\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\_available_if.py:32\u001b[0m, in \u001b[0;36m_AvailableIfDescriptor.__get__\u001b[1;34m(self, obj, owner)\u001b[0m\n\u001b[0;32m     26\u001b[0m attr_err \u001b[39m=\u001b[39m \u001b[39mAttributeError\u001b[39;00m(\n\u001b[0;32m     27\u001b[0m     \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mThis \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mrepr\u001b[39m(owner\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m)\u001b[39m}\u001b[39;00m\u001b[39m has no attribute \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mrepr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mattribute_name)\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m     28\u001b[0m )\n\u001b[0;32m     29\u001b[0m \u001b[39mif\u001b[39;00m obj \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     30\u001b[0m     \u001b[39m# delegate only on instances, not the classes.\u001b[39;00m\n\u001b[0;32m     31\u001b[0m     \u001b[39m# this is to allow access to the docstrings.\u001b[39;00m\n\u001b[1;32m---> 32\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcheck(obj):\n\u001b[0;32m     33\u001b[0m         \u001b[39mraise\u001b[39;00m attr_err\n\u001b[0;32m     34\u001b[0m     out \u001b[39m=\u001b[39m MethodType(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfn, obj)\n",
      "File \u001b[1;32mc:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:829\u001b[0m, in \u001b[0;36mBaseSVC._check_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    827\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_check_proba\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m    828\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprobability:\n\u001b[1;32m--> 829\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\n\u001b[0;32m    830\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mpredict_proba is not available when  probability=False\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    831\u001b[0m         )\n\u001b[0;32m    832\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_impl \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m (\u001b[39m\"\u001b[39m\u001b[39mc_svc\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnu_svc\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m    833\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mpredict_proba only implemented for SVC and NuSVC\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: predict_proba is not available when  probability=False"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, roc_curve, auc\n",
    "\n",
    "# ...\n",
    "\n",
    "# Mesurer et afficher la matrice de confusion pour chaque algorithme\n",
    "algorithms = [knn, svm,  logistic_reg, naive_bayes]\n",
    "algorithm_names = ['k-NN', 'SVM',  'Régression Logistique', 'Naïve Bayes']\n",
    "\n",
    "for algo, name in zip(algorithms, algorithm_names):\n",
    "    predictions = algo.predict(X_test)\n",
    "    cm = confusion_matrix(y_test, predictions)\n",
    "    print(f\"\\nMatrice de confusion pour {name}:\\n{cm}\")\n",
    "\n",
    "    # Afficher la courbe ROC et calculer l'AUC\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, algo.predict_proba(X_test)[:, 1])\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    # Tracer la courbe ROC\n",
    "    plt.figure(figsize=(2, 2))\n",
    "    plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'Courbe ROC ({name}, AUC = {roc_auc:.2f})')\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.xlabel('Taux de faux positifs')\n",
    "    plt.ylabel('Taux de vrais positifs')\n",
    "    plt.title(f'Courbe ROC - {name}')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n"
   ]
  }
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