{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beneficial-treat",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.naive_bayes import CategoricalNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "unexpected-justice",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:/Users/student/Desktop/ipIndustija4/ballons.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "outside-cornell",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>act</th>\n",
       "      <th>age</th>\n",
       "      <th>inflated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>YELLOW</td>\n",
       "      <td>SMALL</td>\n",
       "      <td>DIP</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         color   size  act    age inflated\n",
       "count       76     76   76     76       76\n",
       "unique       2      2    2      2        2\n",
       "top     YELLOW  SMALL  DIP  ADULT        F\n",
       "freq        40     40   38     38       41"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>act</th>\n",
       "      <th>age</th>\n",
       "      <th>inflated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>YELLOW</td>\n",
       "      <td>SMALL</td>\n",
       "      <td>DIP</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         color   size  act    age inflated\n",
       "count       76     76   76     76       76\n",
       "unique       2      2    2      2        2\n",
       "top     YELLOW  SMALL  DIP  ADULT        F\n",
       "freq        40     40   38     38       41"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>color</th>\n",
       "      <th>size</th>\n",
       "      <th>act</th>\n",
       "      <th>age</th>\n",
       "      <th>inflated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>YELLOW</td>\n",
       "      <td>SMALL</td>\n",
       "      <td>DIP</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         color   size  act    age inflated\n",
       "count       76     76   76     76       76\n",
       "unique       2      2    2      2        2\n",
       "top     YELLOW  SMALL  DIP  ADULT        F\n",
       "freq        40     40   38     38       41"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "prescription-german",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "thorough-royal",
   "metadata": {},
   "outputs": [],
   "source": [
    "features=df.columns[:-1].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "constant-there",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['color', 'size', 'act', 'age']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "['color', 'size', 'act', 'age']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "['color', 'size', 'act', 'age']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "impossible-dublin",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "returning-makeup",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "quick-flash",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "incredible-collectible",
   "metadata": {},
   "outputs": [],
   "source": [
    "oe = OrdinalEncoder()\n",
    "#moze i\n",
    "#oe = OrdinalEncoder(categories=[['YELLOW', 'PURPLE'], ['LARGE', 'SMALL'],['DIP', 'STRETCH'], ['ADULT', 'CHILD']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "color-carbon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrdinalEncoder()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "OrdinalEncoder()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "OrdinalEncoder()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oe.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "allied-commissioner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['PURPLE', 'YELLOW'], dtype=object),\n",
       " array(['LARGE', 'SMALL'], dtype=object),\n",
       " array(['DIP', 'STRETCH'], dtype=object),\n",
       " array(['ADULT', 'CHILD'], dtype=object)]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[array(['PURPLE', 'YELLOW'], dtype=object),\n",
       " array(['LARGE', 'SMALL'], dtype=object),\n",
       " array(['DIP', 'STRETCH'], dtype=object),\n",
       " array(['ADULT', 'CHILD'], dtype=object)]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[array(['PURPLE', 'YELLOW'], dtype=object),\n",
       " array(['LARGE', 'SMALL'], dtype=object),\n",
       " array(['DIP', 'STRETCH'], dtype=object),\n",
       " array(['ADULT', 'CHILD'], dtype=object)]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oe.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "instant-declaration",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_transform = pd.DataFrame(oe.fit_transform(x_train), columns=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "removable-journalism",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_transform = pd.DataFrame(oe.transform(x_test), columns=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "present-tissue",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=CategoricalNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "forbidden-desktop",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalNB()"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "CategoricalNB()"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "CategoricalNB()"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_train_transform, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "standing-campaign",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "color\n",
      "   PURPLE  YELLOW\n",
      "F    16.0    12.0\n",
      "T     9.0    16.0\n",
      "\n",
      "size\n",
      "   LARGE  SMALL\n",
      "F   13.0   15.0\n",
      "T    9.0   16.0\n",
      "\n",
      "act\n",
      "    DIP  STRETCH\n",
      "F  20.0      8.0\n",
      "T   8.0     17.0\n",
      "\n",
      "age\n",
      "   ADULT  CHILD\n",
      "F    8.0   20.0\n",
      "T   18.0    7.0\n",
      "\n",
      "color\n",
      "   PURPLE  YELLOW\n",
      "F    16.0    12.0\n",
      "T     9.0    16.0\n",
      "\n",
      "size\n",
      "   LARGE  SMALL\n",
      "F   13.0   15.0\n",
      "T    9.0   16.0\n",
      "\n",
      "act\n",
      "    DIP  STRETCH\n",
      "F  20.0      8.0\n",
      "T   8.0     17.0\n",
      "\n",
      "age\n",
      "   ADULT  CHILD\n",
      "F    8.0   20.0\n",
      "T   18.0    7.0\n",
      "\n",
      "color\n",
      "   PURPLE  YELLOW\n",
      "F    16.0    12.0\n",
      "T     9.0    16.0\n",
      "\n",
      "size\n",
      "   LARGE  SMALL\n",
      "F   13.0   15.0\n",
      "T    9.0   16.0\n",
      "\n",
      "act\n",
      "    DIP  STRETCH\n",
      "F  20.0      8.0\n",
      "T   8.0     17.0\n",
      "\n",
      "age\n",
      "   ADULT  CHILD\n",
      "F    8.0   20.0\n",
      "T   18.0    7.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#izvestaj o zastupljenosti \n",
    "for i in range(len(features)):\n",
    "    print(features[i])\n",
    "    print(pd.DataFrame(clf.category_count_[i], index=clf.classes_, columns=oe.categories_[i]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "greatest-leave",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    28.0\n",
       "T    25.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "F    28.0\n",
       "T    25.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "F    28.0\n",
       "T    25.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(clf.class_count_, index=clf.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "immune-estonia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trening skup\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           F       0.79      0.79      0.79        28\n",
      "           T       0.76      0.76      0.76        25\n",
      "\n",
      "    accuracy                           0.77        53\n",
      "   macro avg       0.77      0.77      0.77        53\n",
      "weighted avg       0.77      0.77      0.77        53\n",
      "\n",
      "Trening skup\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           F       0.79      0.79      0.79        28\n",
      "           T       0.76      0.76      0.76        25\n",
      "\n",
      "    accuracy                           0.77        53\n",
      "   macro avg       0.77      0.77      0.77        53\n",
      "weighted avg       0.77      0.77      0.77        53\n",
      "\n",
      "Trening skup\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           F       0.79      0.79      0.79        28\n",
      "           T       0.76      0.76      0.76        25\n",
      "\n",
      "    accuracy                           0.77        53\n",
      "   macro avg       0.77      0.77      0.77        53\n",
      "weighted avg       0.77      0.77      0.77        53\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Trening skup')\n",
    "print(classification_report(y_train, clf.predict(x_train_transform)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sublime-image",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test skupTest skupTest skup\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           F       0.85      0.85      0.85        13\n",
      "           T       0.80      0.80      0.80        10\n",
      "\n",
      "    accuracy                           0.83        23\n",
      "   macro avg       0.82      0.82      0.82        23\n",
      "weighted avg       0.83      0.83      0.83        23\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           F       0.85      0.85      0.85        13\n",
      "           T       0.80      0.80      0.80        10\n",
      "\n",
      "    accuracy                           0.83        23\n",
      "   macro avg       0.82      0.82      0.82        23\n",
      "weighted avg       0.83      0.83      0.83        23\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           F       0.85      0.85      0.85        13\n",
      "           T       0.80      0.80      0.80        10\n",
      "\n",
      "    accuracy                           0.83        23\n",
      "   macro avg       0.82      0.82      0.82        23\n",
      "weighted avg       0.83      0.83      0.83        23\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Test skup')\n",
    "print(classification_report(y_test, clf.predict(x_test_transform)))"
   ]
  }
 ],
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   "display_name": "Python 3",
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