Gaussian_NaiveBayes_fromScratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a0f95b08-c2b5-4e1a-8888-b73ee7a9d84c",
   "metadata": {},
   "source": [
    "# Types of Naive Bayes algorithm\n",
    "- Gaussian Naïve Bayes\n",
    "- Multinomial Naïve Bayes\n",
    "- Bernoulli Naïve Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320e14e6-4f5d-4b0e-862d-38fc78f18099",
   "metadata": {},
   "source": [
    "## Coding from scrath\n",
    "\n",
    "Source of dataset: https://www.kaggle.com/datasets/merishnasuwal/breast-cancer-prediction-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "70a95158-3c16-4977-a1e4-3322b40b3556",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numerical library\n",
    "import numpy as np\n",
    "\n",
    "# DataScience library\n",
    "import pandas as pd\n",
    "\n",
    "# Show the chards\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create the heatmap\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "713492b2-675f-41cb-a530-225ed2653693",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6daf9c7e-1c9d-47b3-bf47-37b67bb3816b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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_perimeter</th>\n",
       "      <th>mean_area</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>122.80</td>\n",
       "      <td>1001.0</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>132.90</td>\n",
       "      <td>1326.0</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>130.00</td>\n",
       "      <td>1203.0</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>77.58</td>\n",
       "      <td>386.1</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>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "\n",
       "   diagnosis  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'C:\\Users\\usuario\\Documents\\Nicole\\Upwork\\Breast_cancer_data.csv')\n",
    "# obs: Be carefull putting the \"r\" before your file path or it could return an error.\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "34b83ed7-50f9-462e-91ac-df040ead9294",
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of features = 5 (mean_radius,\tmean_texture,\tmean_perimeter,\tmean_area,\tmean_smoothness)\n",
    "# diagnosis: Negative (0), Positive (1)\n",
    "\n",
    "# We will predict if the pacient has or not the breast cancer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "56c8551f-04c4-4036-8498-59627291641b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a heat map the see the correlation between the features and the result\n",
    "# Remember that the Naive-Bayes method it's a method where the features are independent.\n",
    "\n",
    "# \"data\" is the DataFrame containing the data we set previously, where the last columns are the classes/targets\n",
    "# \"person\" is a correlation method\n",
    "corr = data.iloc[:,:-1].corr(method = \"pearson\")\n",
    "\n",
    "# Define the color palette for the heatmap\n",
    "# The color palette will be used to represent different levels of correlation\n",
    "cmap = sns.diverging_palette(250, 354, 80, 60, center = 'dark', as_cmap = True) \n",
    "\n",
    "\n",
    "# Plot the heatmap of the correlation matrix\n",
    "sns.heatmap(corr, vmax=1, vmin=-.1, cmap=cmap, square=True, linewidth=.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ba60ae-3a3c-430a-95b9-25697c5cf6fb",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "You can see from the graph that there are correlated values, given by the pink color, and the value \"1\". And that makes sense, because \"radius\" and \"area\" really are correlated. \n",
    "However, as our dataset can't have correlated values, since it's the Naive-Bayes method, we'll have to choose one of the data points to keep and remove the others.\n",
    "Let's keep \"Mean_radius, mean_texture, mean_smoothness, diagnosis\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3ebf6463-bf9d-40e7-a258-1e95fd65c82b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "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"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[[\"mean_radius\", \"mean_texture\", \"mean_smoothness\", \"diagnosis\"]]\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7124d486-0cb5-430b-936c-3ac409723a92",
   "metadata": {},
   "source": [
    "## Normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8263991b-e398-44ae-a21a-3fabbc9b33f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=r'C:\\Users\\usuario\\Documents\\Nicole\\Upwork\\normaldistribution.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e6c73a-c992-4cc1-bb16-5af2f10809f2",
   "metadata": {},
   "source": [
    "A \"normal\" or Gaussian distribution is a graphical distribution where the graph will be symmetrical on both sides of the mean value. \n",
    "We will check whether our data is distributed in this way and whether we can apply the normal distribution method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7c1a547e-17ec-419f-8488-7efc0ab4a4fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='mean_texture', ylabel='Count'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(18,8), sharey= True)\n",
    "\n",
    "# Plotagem dos histogramas\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",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c92e0f3e-bf17-4a3f-961d-8d544bc78e18",
   "metadata": {},
   "source": [
    "# The theory with the code:\n",
    "$ X = (x_{0} , x_{1}, x_{2}) , Y \\epsilon \\{ 0,1 \\} $\n",
    "\n",
    "$ P(Y|X) = \\frac{P(X = x_{0}, x_{1}, x_{2}|Y=y) * P(Y=y)}{P(X)} $\n",
    "\n",
    "\n",
    "We have to find the value of y that maximize the $ P(Y=y|X) $\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38ce2913-f657-4094-a570-efc9f54a3fc5",
   "metadata": {},
   "source": [
    "## Calculate P(Y=y) for all possible y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6572f34c-3988-4455-bb26-188542976371",
   "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\n",
    "\n",
    "\n",
    "# len(df[df[Y]==i] is the number of samples in df[Y], divided by the total number of samples. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5072e4-77fa-441d-bee5-7ac3fea3eb8c",
   "metadata": {},
   "source": [
    "## Approach 1: Calculate P(X=x | Y=y) using Gaussian distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5521750-4114-4fc0-a931-35c0eab2aee2",
   "metadata": {},
   "source": [
    "This approach, is the likelihood, where all the x(i) are independent. \n",
    "Here, the $ P(X = (x_{0} , x_{1}, x_{2}) | Y = y) $ is given by the product of: \n",
    "$ P(X=x_{0}| Y = y) * P(X=x_{1}| Y = y) * P(X=x_{2}| Y = y) $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "26fd2bb8-4ecb-42dd-bc54-985ebe7b3aee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_likelihood_gaussin(df, feat_name, feat_val, Y, label):\n",
    "    feat = list(df.columns)\n",
    "    df = df[df[Y]==label]\n",
    "\n",
    "    #Here we set the normal distribution\n",
    "    mean, std = df[feat_name].mean(), df[feat_name].std()\n",
    "    #The formula of probability density function of normal distribution\n",
    "    p_x_given_y = (1 / (np.sqrt(2 * np.pi) * std)) * np.exp(-((feat_val-mean)**2 / (2 * std**2)))\n",
    "    return p_x_given_y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc0c800-595f-42c3-8112-07875110ace3",
   "metadata": {},
   "source": [
    "feat = list(df.columns): This gets a list of the column names of the DataFrame df.\n",
    "\n",
    "df = df[df[Y]==label]: This filters the DataFrame to keep only the rows where the Y class column has the value label. In other words, we are only considering the samples that belong to the specific class we are analyzing.\n",
    "\n",
    "mean, std = df[feat_name].mean(), df[feat_name].std(): This calculates the mean (mean) and standard deviation (std) of the resource value (feat_name) for the samples belonging to the specified class.\n",
    "\n",
    "(1 / (np.sqrt(2* np.pi) * std)) * np.exp(-((feat_val-mean)**2 / (2* std**2)): This is the formula for calculating the probability of a feat_val value given that the class is label, assuming a Gaussian distribution. The formula represents the probability density function (PDF) of a normal distribution.\n",
    "\n",
    "$\\frac{1}{\\sqrt{2 \\pi} \\cdot \\text{std}} \\cdot e^{-\\frac{(x - \\text{mean})^2}{2 \\cdot \\text{std}^2}} $\n",
    "\n",
    "return p_x_given_y: Returns the calculated probability of a feat_val resource value given that the class is label, assuming a Gaussian distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de7d7430-545b-495c-8d56-b1844c649ece",
   "metadata": {},
   "source": [
    "## Calculate P(X = x1 | Y = y)P(X = x2 | Y = y)...P(X = xn | Y = y) * P(Y=y) for all y find the maxium "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8ffcf946-b9d4-446d-9a8e-e0ffbe290e6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def naive_bayes_gaussian(df, X, Y):\n",
    "    #get features names\n",
    "    features = list(df.columns)[:-1]\n",
    "\n",
    "    # Calculate prior probabilities\n",
    "    prior = calculate_prior(df , Y)\n",
    "\n",
    "    Y_pred = []\n",
    "\n",
    "    #loop over every data sample\n",
    "    for x in X:\n",
    "        #calculate likelihood\n",
    "        labels = sorted(list(df[Y].unique())) # Get unique classes\n",
    "        likelihood = [1]*len(labels)\n",
    "        for j in range(len(labels)): # Initialize list of likelihoods for each class\n",
    "            for i in range(len(features)):\n",
    "                # Multiply likelihood of each feature for each class\n",
    "                likelihood[j] *= calculate_likelihood_gaussin(df, features[i], x[i], Y, labels[j])\n",
    "\n",
    "        #calculate posterior probability (numerator only)\n",
    "        post_prob = [1]*len(labels) # Initialize list of posterior probabilities for each class\n",
    "        for j in range(len(labels)):\n",
    "            post_prob[j]= likelihood[j] * prior[j]\n",
    "            \n",
    "        # Add the class with highest probability as the prediction for the current sample\n",
    "        Y_pred.append(np.argmax(post_prob))\n",
    "        \n",
    "    return np.array(Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364b2e27-56c0-436c-a72a-aa99a7760682",
   "metadata": {},
   "source": [
    "## Test Gaussian model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "febdebd9-22e6-4894-9bcd-e40c6e80a191",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[36  4]\n",
      " [ 0 74]]\n",
      "0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# Split the data into training and testing sets\n",
    "train, test = train_test_split(data, test_size = .2, random_state=41)\n",
    "\n",
    "# Extract features and labels from the testing set\n",
    "X_test = test.iloc[:, :-1].values # Extract features, excluding the last column (class)\n",
    "Y_test = test.iloc[:, -1].values  # Extract labels, taking only the last column (class)\n",
    "Y_pred = naive_bayes_gaussian(train, X=X_test, Y=\"diagnosis\")\n",
    "\n",
    "# Use the Gaussian Naive Bayes model to predict labels for the testing set\n",
    "from sklearn.metrics import confusion_matrix, f1_score\n",
    "\n",
    "# Print the confusion matrix to evaluate the performance of the model\n",
    "print(confusion_matrix(Y_test, Y_pred))\n",
    "# Compute the F1 score to evaluate the model's accuracy\n",
    "print(f1_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a49425b-6d09-4e4a-982a-6fd474d81b82",
   "metadata": {},
   "source": [
    "Just to understand the confusion matrix:\n",
    "[[36 4]\n",
    " [0  74]]\n",
    "\n",
    "It means we have: 36 = Correct positives, 4 = False positives, 0 = False negatives, 74 = Correct negatives.\n",
    "This is something important to look at. Because this means that 4 people would receive a positive diagnosis for cancer, even though they don't have it, so they could start treatment without having the disease. In this case, it would be important to improve the model so that this number is equal to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61a5450-17ca-4ddd-9ab0-b34a6928cd14",
   "metadata": {},
   "source": [
    "## Convert continuos features to categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "06f4fb61-3cba-457d-a07c-d0e222cd0f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>"
      ],
      "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"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create categorical features based on binning of continuous features\n",
    "# bins are the number of equally spaced categories\n",
    "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",
    "# Rearrange the columns to put the new categorical features first, followed by the diagnosis column\n",
    "data = data[[\"cat_mean_radius\", \"cat_mean_texture\", \"cat_mean_smoothness\", \"diagnosis\"]]\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ace8de1-78f4-4051-84ef-2082645c2cc5",
   "metadata": {},
   "source": [
    "## Approach 2: Calculate P(X=x | Y = y) categorically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "31175da6-9895-4046-b4c1-24b3b444a33f",
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ebd127-8d64-4cfd-ae9d-98d703c47923",
   "metadata": {},
   "source": [
    "## Calculate P(X = x1 | Y = y)P(X = x2 | Y = y)...P(X = xn | Y = y) * P(Y=y) for all y find the maxium "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0ba795bc-dacd-4caf-a5ef-4a31707b46ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def naive_bayes_gaussian(df, X, Y):\n",
    "    #get features names\n",
    "    features = list(df.columns)[:-1]\n",
    "\n",
    "    # Calculate prior probabilities\n",
    "    prior = calculate_prior(df , Y)\n",
    "\n",
    "    Y_pred = []\n",
    "\n",
    "    #loop over every data sample\n",
    "    for x in X:\n",
    "        #calculate likelihood\n",
    "        labels = sorted(list(df[Y].unique())) # Get unique classes\n",
    "        likelihood = [1]*len(labels)\n",
    "        \n",
    "        for j in range(len(labels)): # Initialize list of likelihoods for each class\n",
    "            for i in range(len(features)):\n",
    "                # Multiply likelihood of each feature for each class\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) # Initialize list of posterior probabilities for each class\n",
    "        for j in range(len(labels)):\n",
    "            post_prob[j]= likelihood[j] * prior[j]\n",
    "            \n",
    "        # Add the class with highest probability as the prediction for the current sample\n",
    "        Y_pred.append(np.argmax(post_prob))\n",
    "        \n",
    "    return np.array(Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29e9b6d8-a54f-4917-a029-3b6d2e1dc405",
   "metadata": {},
   "source": [
    "## Test categorical Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ef30ba6f-59fb-4275-9008-c5f4f59e024d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[38  2]\n",
      " [ 5 69]]\n",
      "0.9517241379310345\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# Split the data into training and testing sets\n",
    "train, test = train_test_split(data, test_size = .2, random_state=41)\n",
    "\n",
    "# Extract features and labels from the testing set\n",
    "X_test = test.iloc[:, :-1].values # Extract features, excluding the last column (class)\n",
    "Y_test = test.iloc[:, -1].values  # Extract labels, taking only the last column (class)\n",
    "Y_pred = naive_bayes_gaussian(train, X=X_test, Y=\"diagnosis\")\n",
    "\n",
    "# Use the Gaussian Naive Bayes model to predict labels for the testing set\n",
    "from sklearn.metrics import confusion_matrix, f1_score\n",
    "\n",
    "# Print the confusion matrix to evaluate the performance of the model\n",
    "print(confusion_matrix(Y_test, Y_pred))\n",
    "# Compute the F1 score to evaluate the model's accuracy\n",
    "print(f1_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0cb49e4-44c8-493b-b164-e03d80b3cc38",
   "metadata": {},
   "source": [
    "This shows us that this format is weaker than the previous one, showing that the Gaussian distribution worked better than categorization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f72fec0c-b699-4143-a1bf-ddc53c15a1f0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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