BayesianClassifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COLUMN\tVALUE\tOUTPUT\tPROBABILITY\n",
      "Temp--->Hot--->Rainy--->0.4\n",
      "Temp--->Mild--->Rainy--->0.4\n",
      "Temp--->Cool--->Rainy--->0.2\n",
      "Humidity--->High--->Rainy--->0.6\n",
      "Humidity--->Normal--->Rainy--->0.4\n",
      "Windy--->Low--->Rainy--->0.6\n",
      "Windy--->High--->Rainy--->0.4\n",
      "Cloudy--->Yes--->Rainy--->0.6\n",
      "Cloudy--->No--->Rainy--->0.4\n",
      "Temp--->Hot--->Overcast--->0.5\n",
      "Temp--->Mild--->Overcast--->0.25\n",
      "Temp--->Cool--->Overcast--->0.25\n",
      "Humidity--->High--->Overcast--->0.5\n",
      "Humidity--->Normal--->Overcast--->0.5\n",
      "Windy--->Low--->Overcast--->0.5\n",
      "Windy--->High--->Overcast--->0.5\n",
      "Cloudy--->Yes--->Overcast--->0.5\n",
      "Cloudy--->No--->Overcast--->0.5\n",
      "Temp--->Hot--->Sunny--->0.0\n",
      "Temp--->Mild--->Sunny--->0.6\n",
      "Temp--->Cool--->Sunny--->0.4\n",
      "Humidity--->High--->Sunny--->0.4\n",
      "Humidity--->Normal--->Sunny--->0.6\n",
      "Windy--->Low--->Sunny--->0.6\n",
      "Windy--->High--->Sunny--->0.4\n",
      "Cloudy--->Yes--->Sunny--->0.2\n",
      "Cloudy--->No--->Sunny--->0.8\n",
      "{'Rainy': 5, 'Overcast': 4, 'Sunny': 5}\n",
      "{'Rainy': 0.6162624821683309, 'Overcast': 0.17831669044222537, 'Sunny': 0.20542082738944364}\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "class BayesianClassifier:\n",
    "\n",
    "    def __init__(self, path, col):\n",
    "        self.cols = {}\n",
    "        self.op_cols = {}\n",
    "        self.col = col\n",
    "        self.data = pd.read_csv(path)\n",
    "        self.dataM = self.data\n",
    "        self.op = self.data[col]\n",
    "        self.data = self.data.drop(col, axis=1)\n",
    "        self.total = len(self.dataM)\n",
    "    \n",
    "    def get_probability_table(self):\n",
    "        for i in self.op.unique():\n",
    "            self.cols[i] = {}\n",
    "            for j in self.data.columns:\n",
    "                self.cols[i][j] = {}\n",
    "                for k in self.data[j]:\n",
    "                    if k not in self.cols[i][j]:\n",
    "                        self.cols[i][j][k] = \"\"\n",
    "        dfs = []\n",
    "        for i in self.op.unique():\n",
    "            for j in self.data.columns:\n",
    "                for k in self.data[j].unique():\n",
    "                    dfs.append(self.dataM[self.dataM[self.col] == i])\n",
    "\n",
    "                    \n",
    "        print(\"COLUMN\\tVALUE\\tOUTPUT\\tPROBABILITY\")\n",
    "        for x in self.cols:\n",
    "            for y in self.cols[x]:\n",
    "                for z in self.cols[x][y]:\n",
    "                    self.op_cols[x] = len(self.dataM[self.dataM[self.col] == x])\n",
    "                    total = len(self.dataM[(self.dataM[self.col] == x) & (self.dataM[y])])\n",
    "                    p = len(self.dataM[(self.dataM[y] == z) & (self.dataM[self.col] == x)]) / total\n",
    "                    self.cols[x][y][z] = p\n",
    "                    print(\"{}--->{}--->{}--->{}\".format(y,z,x,p))\n",
    "        print(self.op_cols)\n",
    "                    \n",
    "    def classify(self, values):\n",
    "        p = {}\n",
    "        tot = 0\n",
    "        for i in self.op.unique():\n",
    "            a = 1\n",
    "            for key, value in values.items():\n",
    "                a *= self.cols[i][key][value]\n",
    "            p[i] = a*self.op_cols[i]/self.total\n",
    "            tot += a*self.op_cols[i]/self.total\n",
    "        \n",
    "        for i in p:\n",
    "            p[i] = p[i] / tot\n",
    "        \n",
    "        return p\n",
    "            \n",
    "b = BayesianClassifier(\"new.csv\", \"Weather\")\n",
    "\n",
    "b.get_probability_table()\n",
    "print(b.classify({\"Temp\": 'Mild', \"Windy\": \"Low\", \"Humidity\": \"High\", \"Cloudy\": \"Yes\"}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}