{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Classification Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## predict a national cuisine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <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>"
      ],
      "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]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, precision_recall_curve, precision_score\n",
    "from sklearn.svm import SVC\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    indian\n",
       "1    indian\n",
       "2    indian\n",
       "3    indian\n",
       "4    indian\n",
       "Name: cuisine, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cuisines_label_df = cuisines_df['cuisine']\n",
    "cuisines_label_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>almond</th>\n",
       "      <th>angelica</th>\n",
       "      <th>anise</th>\n",
       "      <th>anise_seed</th>\n",
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       "      <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",
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       "      <th>yeast</th>\n",
       "      <th>yogurt</th>\n",
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       "<p>5 rows × 380 columns</p>\n",
       "</div>"
      ],
      "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",
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       "3                        0     0     0    0      0       0         0  \n",
       "4                        0     0     0    0      0       1         0  \n",
       "\n",
       "[5 rows x 380 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cusisines_features_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
    "cusisines_features_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(cusisines_features_df, cuisines_label_df, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.79\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/ML-For-Beginners/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(multi_class = 'ovr', solver='liblinear')\n",
    "model = lr.fit(X_train, np.ravel(y_train))\n",
    "accuracy = model.score(X_test, y_test)\n",
    "print(f\"Logistic Regression Accuracy: {accuracy:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ingredients: Index(['fish', 'lime_juice', 'shallot'], dtype='object')\n",
      "cuisine: thai\n"
     ]
    }
   ],
   "source": [
    "print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
    "print(f'cuisine: {y_test.iloc[50]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/ML-For-Beginners/.venv/lib/python3.11/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>thai</th>\n",
       "      <td>0.839568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>japanese</th>\n",
       "      <td>0.134624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chinese</th>\n",
       "      <td>0.014531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>korean</th>\n",
       "      <td>0.008383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indian</th>\n",
       "      <td>0.002894</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                 0\n",
       "thai      0.839568\n",
       "japanese  0.134624\n",
       "chinese   0.014531\n",
       "korean    0.008383\n",
       "indian    0.002894"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test= X_test.iloc[50].values.reshape(-1, 1).T\n",
    "proba = model.predict_proba(test)\n",
    "classes = model.classes_\n",
    "resultdf = pd.DataFrame(data=proba, columns=classes)\n",
    "\n",
    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
    "topPrediction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     chinese       0.69      0.69      0.69       236\n",
      "      indian       0.91      0.91      0.91       245\n",
      "    japanese       0.73      0.72      0.73       231\n",
      "      korean       0.81      0.76      0.78       242\n",
      "        thai       0.78      0.84      0.81       245\n",
      "\n",
      "    accuracy                           0.79      1199\n",
      "   macro avg       0.79      0.78      0.78      1199\n",
      "weighted avg       0.79      0.79      0.79      1199\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
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