naive bayes-categorical data-from scratch-for github.ipynb

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    "### Machine Learning: Naive Bayes classifier for categorical data from scratch in Python\n",
    "$P(C_k|\\textbf{x})\\propto P(C_k) P(\\textbf{x}|C_k)$\n",
    "###### by Hamed Shah-Hosseini\n",
    "Explanation at: https://www.pinterest.com/HamedShahHosseini/\n",
    "<br>https://github.com/ostad-ai/Machine-Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "50b8a8b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "31065e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples in dataset:14\n"
     ]
    }
   ],
   "source": [
    "# Reading dataset\n",
    "weather=pd.read_csv('./datasets/weather.csv')\n",
    "print(f'Number of samples in dataset:{len(weather)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d8583f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Outlook</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Windy</th>\n",
       "      <th>Play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlook Temperature Humidity  Windy Play\n",
       "0     Sunny         Hot     High  False   No\n",
       "1     Sunny         Hot     High   True   No\n",
       "2  Overcast         Hot     High  False  Yes\n",
       "3     Rainy        Mild     High  False  Yes\n",
       "4     Rainy        Cool   Normal  False  Yes"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first five rows of dataset\n",
    "weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "37b96427",
   "metadata": {},
   "outputs": [],
   "source": [
    "# naive Bayes for categorial data\n",
    "#alpha is the smoothing parameter\n",
    "class NBCategorial:\n",
    "    def __init__(self,alpha=.5):\n",
    "        self.alpha=alpha\n",
    "    def fit(self,data_frame,class_column):\n",
    "        df=data_frame.astype(str)\n",
    "        self.groups=df.groupby(class_column)\n",
    "        self.Ncats=dict() # no. of unique categories in each feature\n",
    "        self.features=df.columns.to_list()\n",
    "        self.features.remove(class_column)\n",
    "        self.probs={key:defaultdict(dict) for key in self.features}\n",
    "        for feature in self.features:\n",
    "            self.Ncats[feature]=len(df[feature].unique())\n",
    "        self.priors=(self.groups.size()/len(df)).to_dict() #prior\n",
    "        for cName,cData in self.groups:            \n",
    "            for feature in self.features:\n",
    "                result=pd.value_counts(cData[feature]).to_dict()\n",
    "                num=len(cData[feature])\n",
    "                for value in df[feature].unique():\n",
    "                    if value in result.keys():\n",
    "                        self.probs[feature][value][cName]=\\\n",
    "                        (result[value]+self.alpha)/\\\n",
    "                       (num+self.alpha*self.Ncats[feature])\n",
    "                    else: # value (category) is not in cData\n",
    "                        self.probs[feature][value][cName]=self.alpha/\\\n",
    "                       (num+self.alpha*self.Ncats[feature])                \n",
    "    def predict(self,query,with_priors=True):\n",
    "        probs_classes=dict()        \n",
    "        for cName,cData in self.groups:\n",
    "            if with_priors:\n",
    "                prob=self.priors[cName]\n",
    "            else:\n",
    "                prob=1.\n",
    "            for q,feature in zip(query,self.features):\n",
    "                if q in self.probs[feature].keys():\n",
    "                    prob*=self.probs[feature][q][cName] \n",
    "                else: # haven't seen such a category\n",
    "                    num=len(cData[feature])\n",
    "                    prob*=self.alpha/(num+\\\n",
    "                    self.alpha*(1+self.Ncats[feature]))\n",
    "            probs_classes[cName]=prob\n",
    "        predict=max(probs_classes,key=probs_classes.get)\n",
    "        return predict,probs_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6db8fd5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:% 92.86\n"
     ]
    }
   ],
   "source": [
    "# Testing with training data, not very usual:)\n",
    "# That's because the dataset is so small\n",
    "classCol='Play'\n",
    "Ns=weather.shape[0] #number of samples\n",
    "columns=weather.columns.to_list()\n",
    "classCol_inx=columns.index(classCol)\n",
    "nbayes=NBCategorial()\n",
    "nbayes.fit(weather,classCol)\n",
    "accuracy=0.\n",
    "for n in range(Ns):\n",
    "    query=weather.astype(str).iloc[n].to_list()\n",
    "    #remove the value of class column\n",
    "    class_q=query.pop(classCol_inx) \n",
    "    accuracy+=nbayes.predict(query)[0]==class_q\n",
    "accuracy/=Ns    \n",
    "print('Accuracy:%',round(100*accuracy,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b83998be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class of ['Sunny', 'Mild', 'High', 'False']: No\n"
     ]
    }
   ],
   "source": [
    "# Example: testing with a given query\n",
    "classCol='Play'\n",
    "query=['Sunny','Mild','High','False']\n",
    "nbayes=NBCategorial()\n",
    "nbayes.fit(weather,classCol)\n",
    "predict,probs=nbayes.predict(query)\n",
    "print(f'Predicted class of {query}: {predict}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da82c766",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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