{
 "cells": [
  {
   "source": [
    "# Erstellen Sie mehr Klassifikationsmodelle\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
       "0           0  indian       0         0      0           0      0   \n",
       "1           1  indian       1         0      0           0      0   \n",
       "2           2  indian       0         0      0           0      0   \n",
       "3           3  indian       0         0      0           0      0   \n",
       "4           4  indian       0         0      0           0      0   \n",
       "\n",
       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
       "0             0        0         0  ...        0            0           0   \n",
       "1             0        0         0  ...        0            0           0   \n",
       "2             0        0         0  ...        0            0           0   \n",
       "3             0        0         0  ...        0            0           0   \n",
       "4             0        0         0  ...        0            0           0   \n",
       "\n",
       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
       "0                        0     0     0    0      0       0         0  \n",
       "1                        0     0     0    0      0       0         0  \n",
       "2                        0     0     0    0      0       0         0  \n",
       "3                        0     0     0    0      0       0         0  \n",
       "4                        0     0     0    0      0       1         0  \n",
       "\n",
       "[5 rows x 382 columns]"
      ],
      "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>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</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>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "source": [
    "import pandas as pd\n",
    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
    "cuisines_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    indian\n",
       "1    indian\n",
       "2    indian\n",
       "3    indian\n",
       "4    indian\n",
       "Name: cuisine, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "cuisines_label_df = cuisines_df['cuisine']\n",
    "cuisines_label_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
       "0       0         0      0           0      0             0        0   \n",
       "1       1         0      0           0      0             0        0   \n",
       "2       0         0      0           0      0             0        0   \n",
       "3       0         0      0           0      0             0        0   \n",
       "4       0         0      0           0      0             0        0   \n",
       "\n",
       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
       "0         0          0          0  ...        0            0           0   \n",
       "1         0          0          0  ...        0            0           0   \n",
       "2         0          0          0  ...        0            0           0   \n",
       "3         0          0          0  ...        0            0           0   \n",
       "4         0          0          0  ...        0            0           0   \n",
       "\n",
       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
       "0                        0     0     0    0      0       0         0  \n",
       "1                        0     0     0    0      0       0         0  \n",
       "2                        0     0     0    0      0       0         0  \n",
       "3                        0     0     0    0      0       0         0  \n",
       "4                        0     0     0    0      0       1         0  \n",
       "\n",
       "[5 rows x 380 columns]"
      ],
      "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>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\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      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
    "cuisines_feature_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Probieren Sie verschiedene Klassifikatoren aus\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "C = 10\n",
    "# Create different classifiers.\n",
    "classifiers = {\n",
    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
    "    'KNN classifier': KNeighborsClassifier(C),\n",
    "    'SVC': SVC(),\n",
    "    'RFST': RandomForestClassifier(n_estimators=100),\n",
    "    'ADA': AdaBoostClassifier(n_estimators=100)\n",
    "    \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Accuracy (train) for Linear SVC: 76.4% \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     chinese       0.64      0.66      0.65       242\n",
      "      indian       0.91      0.86      0.89       236\n",
      "    japanese       0.72      0.73      0.73       245\n",
      "      korean       0.83      0.75      0.79       234\n",
      "        thai       0.75      0.82      0.78       242\n",
      "\n",
      "    accuracy                           0.76      1199\n",
      "   macro avg       0.77      0.76      0.77      1199\n",
      "weighted avg       0.77      0.76      0.77      1199\n",
      "\n",
      "Accuracy (train) for KNN classifier: 70.7% \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     chinese       0.65      0.63      0.64       242\n",
      "      indian       0.84      0.81      0.82       236\n",
      "    japanese       0.60      0.81      0.69       245\n",
      "      korean       0.89      0.53      0.67       234\n",
      "        thai       0.69      0.75      0.72       242\n",
      "\n",
      "    accuracy                           0.71      1199\n",
      "   macro avg       0.73      0.71      0.71      1199\n",
      "weighted avg       0.73      0.71      0.71      1199\n",
      "\n",
      "Accuracy (train) for SVC: 80.1% \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     chinese       0.71      0.69      0.70       242\n",
      "      indian       0.92      0.92      0.92       236\n",
      "    japanese       0.77      0.78      0.77       245\n",
      "      korean       0.87      0.77      0.82       234\n",
      "        thai       0.75      0.86      0.80       242\n",
      "\n",
      "    accuracy                           0.80      1199\n",
      "   macro avg       0.80      0.80      0.80      1199\n",
      "weighted avg       0.80      0.80      0.80      1199\n",
      "\n",
      "Accuracy (train) for RFST: 82.8% \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     chinese       0.80      0.75      0.77       242\n",
      "      indian       0.90      0.91      0.90       236\n",
      "    japanese       0.82      0.78      0.80       245\n",
      "      korean       0.85      0.82      0.83       234\n",
      "        thai       0.78      0.89      0.83       242\n",
      "\n",
      "    accuracy                           0.83      1199\n",
      "   macro avg       0.83      0.83      0.83      1199\n",
      "weighted avg       0.83      0.83      0.83      1199\n",
      "\n",
      "Accuracy (train) for ADA: 71.1% \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     chinese       0.60      0.57      0.58       242\n",
      "      indian       0.87      0.84      0.86       236\n",
      "    japanese       0.71      0.60      0.65       245\n",
      "      korean       0.68      0.78      0.72       234\n",
      "        thai       0.70      0.78      0.74       242\n",
      "\n",
      "    accuracy                           0.71      1199\n",
      "   macro avg       0.71      0.71      0.71      1199\n",
      "weighted avg       0.71      0.71      0.71      1199\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n_classifiers = len(classifiers)\n",
    "\n",
    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
    "    classifier.fit(X_train, np.ravel(y_train))\n",
    "\n",
    "    y_pred = classifier.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
    "    print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**Haftungsausschluss**:  \nDieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, weisen wir darauf hin, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner ursprünglichen Sprache sollte als maßgebliche Quelle betrachtet werden. Für kritische Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die sich aus der Nutzung dieser Übersetzung ergeben.\n"
   ]
  }
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