{
 "cells": [
  {
   "source": [],
   "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": [
    "# Pokušajte različite klasifikatore\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**Odricanje od odgovornosti**:  \nOvaj dokument je preveden korištenjem AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati mjerodavnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane stručnjaka. Ne preuzimamo odgovornost za bilo kakve nesporazume ili pogrešne interpretacije proizašle iz korištenja ovog prijevoda.\n"
   ]
  }
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