naive bayes.ipynb

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 "cells": [
  {
   "source": [
    "## Tools"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "source": [
    "## Dataset"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   mean_radius  mean_texture  mean_perimeter  mean_area  mean_smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "5        12.45         15.70           82.57      477.1          0.12780   \n",
       "6        18.25         19.98          119.60     1040.0          0.09463   \n",
       "7        13.71         20.83           90.20      577.9          0.11890   \n",
       "8        13.00         21.82           87.50      519.8          0.12730   \n",
       "9        12.46         24.04           83.97      475.9          0.11860   \n",
       "\n",
       "   diagnosis  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  \n",
       "5          0  \n",
       "6          0  \n",
       "7          0  \n",
       "8          0  \n",
       "9          0  "
      ],
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     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "data = pd.read_csv(\"Breast_cancer_data.csv\")\n",
    "data.head(10)"
   ]
  },
  {
   "source": [
    "## Basic EDA"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     "metadata": {},
     "execution_count": 3
    },
    {
     "output_type": "display_data",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "data[\"diagnosis\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 4
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "corr = data.iloc[:,:-1].corr(method=\"pearson\")\n",
    "cmap = sns.diverging_palette(250,354,80,60,center='dark',as_cmap=True)\n",
    "sns.heatmap(corr, vmax=1, vmin=-.5, cmap=cmap, square=True, linewidths=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   mean_radius  mean_texture  mean_smoothness  diagnosis\n",
       "0        17.99         10.38          0.11840          0\n",
       "1        20.57         17.77          0.08474          0\n",
       "2        19.69         21.25          0.10960          0\n",
       "3        11.42         20.38          0.14250          0\n",
       "4        20.29         14.34          0.10030          0\n",
       "5        12.45         15.70          0.12780          0\n",
       "6        18.25         19.98          0.09463          0\n",
       "7        13.71         20.83          0.11890          0\n",
       "8        13.00         21.82          0.12730          0\n",
       "9        12.46         24.04          0.11860          0"
      ],
      "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>mean_radius</th>\n      <th>mean_texture</th>\n      <th>mean_smoothness</th>\n      <th>diagnosis</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>17.99</td>\n      <td>10.38</td>\n      <td>0.11840</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>20.57</td>\n      <td>17.77</td>\n      <td>0.08474</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>19.69</td>\n      <td>21.25</td>\n      <td>0.10960</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11.42</td>\n      <td>20.38</td>\n      <td>0.14250</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20.29</td>\n      <td>14.34</td>\n      <td>0.10030</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>12.45</td>\n      <td>15.70</td>\n      <td>0.12780</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>18.25</td>\n      <td>19.98</td>\n      <td>0.09463</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>13.71</td>\n      <td>20.83</td>\n      <td>0.11890</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>13.00</td>\n      <td>21.82</td>\n      <td>0.12730</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>12.46</td>\n      <td>24.04</td>\n      <td>0.11860</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "data = data[[\"mean_radius\", \"mean_texture\", \"mean_smoothness\", \"diagnosis\"]]\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mean_texture', ylabel='Count'>"
      ]
     },
     "metadata": {},
     "execution_count": 6
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(18, 6), sharey=True)\n",
    "sns.histplot(data, ax=axes[0], x=\"mean_radius\", kde=True, color='r')\n",
    "sns.histplot(data, ax=axes[1], x=\"mean_smoothness\", kde=True, color='b')\n",
    "sns.histplot(data, ax=axes[2], x=\"mean_texture\", kde=True)\n"
   ]
  },
  {
   "source": [
    "## Calculate P(Y=y) for all possible y"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_prior(df, Y):\n",
    "    classes = sorted(list(df[Y].unique()))\n",
    "    prior = []\n",
    "    for i in classes:\n",
    "        prior.append(len(df[df[Y]==i])/len(df))\n",
    "    return prior"
   ]
  },
  {
   "source": [
    "## Approach 1: Calculate P(X=x|Y=y) using Gaussian dist."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_likelihood_gaussian(df, feat_name, feat_val, Y, label):\n",
    "    feat = list(df.columns)\n",
    "    df = df[df[Y]==label]\n",
    "    mean, std = df[feat_name].mean(), df[feat_name].std()\n",
    "    p_x_given_y = (1 / (np.sqrt(2 * np.pi) * std)) *  np.exp(-((feat_val-mean)**2 / (2 * std**2 )))\n",
    "    return p_x_given_y"
   ]
  },
  {
   "source": [
    "## Calculate P(X=x1|Y=y)P(X=x2|Y=y)...P(X=xn|Y=y) * P(Y=y) for all y and find the maximum"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def naive_bayes_gaussian(df, X, Y):\n",
    "    # get feature names\n",
    "    features = list(df.columns)[:-1]\n",
    "\n",
    "    # calculate prior\n",
    "    prior = calculate_prior(df, Y)\n",
    "\n",
    "    Y_pred = []\n",
    "    # loop over every data sample\n",
    "    for x in X:\n",
    "        # calculate likelihood\n",
    "        labels = sorted(list(df[Y].unique()))\n",
    "        likelihood = [1]*len(labels)\n",
    "        for j in range(len(labels)):\n",
    "            for i in range(len(features)):\n",
    "                likelihood[j] *= calculate_likelihood_gaussian(df, features[i], x[i], Y, labels[j])\n",
    "\n",
    "        # calculate posterior probability (numerator only)\n",
    "        post_prob = [1]*len(labels)\n",
    "        for j in range(len(labels)):\n",
    "            post_prob[j] = likelihood[j] * prior[j]\n",
    "\n",
    "        Y_pred.append(np.argmax(post_prob))\n",
    "\n",
    "    return np.array(Y_pred) "
   ]
  },
  {
   "source": [
    "## Test Gaussian model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[36  4]\n [ 0 74]]\n0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(data, test_size=.2, random_state=41)\n",
    "\n",
    "X_test = test.iloc[:,:-1].values\n",
    "Y_test = test.iloc[:,-1].values\n",
    "Y_pred = naive_bayes_gaussian(train, X=X_test, Y=\"diagnosis\")\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, f1_score\n",
    "print(confusion_matrix(Y_test, Y_pred))\n",
    "print(f1_score(Y_test, Y_pred))"
   ]
  },
  {
   "source": [
    "## Convert continuous features to Categorical features"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  cat_mean_radius cat_mean_texture cat_mean_smoothness  diagnosis\n",
       "0               1                0                   1          0\n",
       "1               1                0                   0          0\n",
       "2               1                1                   1          0\n",
       "3               0                1                   2          0\n",
       "4               1                0                   1          0\n",
       "5               0                0                   2          0\n",
       "6               1                1                   1          0\n",
       "7               0                1                   1          0\n",
       "8               0                1                   2          0\n",
       "9               0                1                   1          0"
      ],
      "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>cat_mean_radius</th>\n      <th>cat_mean_texture</th>\n      <th>cat_mean_smoothness</th>\n      <th>diagnosis</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</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    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "data[\"cat_mean_radius\"] = pd.cut(data[\"mean_radius\"].values, bins = 3, labels = [0,1,2])\n",
    "data[\"cat_mean_texture\"] = pd.cut(data[\"mean_texture\"].values, bins = 3, labels = [0,1,2])\n",
    "data[\"cat_mean_smoothness\"] = pd.cut(data[\"mean_smoothness\"].values, bins = 3, labels = [0,1,2])\n",
    "\n",
    "data = data.drop(columns=[\"mean_radius\", \"mean_texture\", \"mean_smoothness\"])\n",
    "data = data[[\"cat_mean_radius\",\t\"cat_mean_texture\",\t\"cat_mean_smoothness\", \"diagnosis\"]]\n",
    "data.head(10)"
   ]
  },
  {
   "source": [
    "## Approach 2: Calculate P(X=x|Y=y) categorically"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_likelihood_categorical(df, feat_name, feat_val, Y, label):\n",
    "    feat = list(df.columns)\n",
    "    df = df[df[Y]==label]\n",
    "    p_x_given_y = len(df[df[feat_name]==feat_val]) / len(df)\n",
    "    return p_x_given_y"
   ]
  },
  {
   "source": [
    "## Calculate P(X=x1|Y=y)P(X=x2|Y=y)...P(X=xn|Y=y) * P(Y=y) for all y and find the maximum"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def naive_bayes_categorical(df, X, Y):\n",
    "    # get feature names\n",
    "    features = list(df.columns)[:-1]\n",
    "\n",
    "    # calculate prior\n",
    "    prior = calculate_prior(df, Y)\n",
    "\n",
    "    Y_pred = []\n",
    "    # loop over every data sample\n",
    "    for x in X:\n",
    "        # calculate likelihood\n",
    "        labels = sorted(list(df[Y].unique()))\n",
    "        likelihood = [1]*len(labels)\n",
    "        for j in range(len(labels)):\n",
    "            for i in range(len(features)):\n",
    "                likelihood[j] *= calculate_likelihood_categorical(df, features[i], x[i], Y, labels[j])\n",
    "\n",
    "        # calculate posterior probability (numerator only)\n",
    "        post_prob = [1]*len(labels)\n",
    "        for j in range(len(labels)):\n",
    "            post_prob[j] = likelihood[j] * prior[j]\n",
    "\n",
    "        Y_pred.append(np.argmax(post_prob))\n",
    "\n",
    "    return np.array(Y_pred) "
   ]
  },
  {
   "source": [
    "## Test Categorical model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[38  2]\n [ 5 69]]\n0.9517241379310345\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(data, test_size=.2, random_state=41)\n",
    "\n",
    "X_test = test.iloc[:,:-1].values\n",
    "Y_test = test.iloc[:,-1].values\n",
    "Y_pred = naive_bayes_categorical(train, X=X_test, Y=\"diagnosis\")\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, f1_score\n",
    "print(confusion_matrix(Y_test, Y_pred))\n",
    "print(f1_score(Y_test, Y_pred))"
   ]
  }
 ]
}