Code-Example-020-Gaussian-Naive-Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes (GaussianNB)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from scipy.stats import norm\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "from random import sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Some Example Data with 2 Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_feature = 2 # the number of features\n",
    "n_components = 2 # the number of clusters\n",
    "\n",
    "n = 1000 # the number of total samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.9738175 ,  -7.50934555],\n",
       "       [ -1.19505856,  -5.8378053 ],\n",
       "       [  0.37631303, -13.57342824],\n",
       "       ...,\n",
       "       [ -0.8821049 , -19.09837957],\n",
       "       [ -2.82660508, -13.01978618],\n",
       "       [ -0.92520746,  -7.54158259]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate isotropic Gaussian blobs (data from mixture Gaussian distributions)\n",
    "X, y = make_blobs(n_samples=n, # the number of total samples\n",
    "                  centers=n_components, # the number of clusters\n",
    "                  n_features=n_feature, # the number of features\n",
    "                  cluster_std = [4] * n_components, # the std of each cluster\n",
    "                  random_state=2) # 2\n",
    "\n",
    "# X = np.hstack((np.ones((m, 1)), X))\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [ 1.9738175  -7.50934555] , y = 1\n",
      "x = [-1.19505856 -5.8378053 ] , y = 0\n",
      "x = [  0.37631303 -13.57342824] , y = 0\n",
      "x = [-0.58648176 -0.37741265] , y = 1\n",
      "x = [ 5.5126432  -1.76458642] , y = 1\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print('x =', X[i, ], ', y =', y[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 1, 0, 1, 1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just to show you what is inside it. We print the first 10 elements of this numpy array. \n",
    "y[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot, data points annotated by different colors\n",
    "df = pd.DataFrame(dict(feature_1=X[:,0], feature_2=X[:,1], label=y))\n",
    "\n",
    "\n",
    "cluster_name = set(y)\n",
    "colors = dict(zip(cluster_name, cm.rainbow(np.linspace(0, 1, len(cluster_name)))))\n",
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "grouped = df.groupby('label')\n",
    "\n",
    "for key, group in grouped:\n",
    "    group.plot(ax=ax, kind='scatter', x='feature_1', y='feature_2', label=key, color=colors[key].reshape(1,-1))\n",
    "    \n",
    "pyplot.title('Original 2D Data from {} Clusters'.format(n_components))\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Create Test/Train\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes (GaussianNB).\n",
    "\n",
    "Can perform online updates to model parameters via partial_fit. \n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB\n",
    "\n",
    "\n",
    "## See also\n",
    "\n",
    "BernoulliNB\n",
    "\n",
    "Naive Bayes classifier for multivariate Bernoulli models.\n",
    "\n",
    "CategoricalNB\n",
    "\n",
    "Naive Bayes classifier for categorical features.\n",
    "\n",
    "ComplementNB\n",
    "\n",
    "Complement Naive Bayes classifier.\n",
    "\n",
    "MultinomialNB\n",
    "\n",
    "Naive Bayes classifier for multinomial models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "model = GaussianNB()\n",
    "model.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.875\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.90      0.88       104\n",
      "           1       0.89      0.84      0.87        96\n",
      "\n",
      "    accuracy                           0.88       200\n",
      "   macro avg       0.88      0.87      0.87       200\n",
      "weighted avg       0.88      0.88      0.87       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partial Fit\n",
    "\n",
    "Gaussian Naive Bayes (GaussianNB).\n",
    "\n",
    "Can perform online updates to model parameters via partial_fit. \n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GaussianNB()\n",
    "model.partial_fit(X_train, y_train, np.unique(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.875\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.90      0.88       104\n",
      "           1       0.89      0.84      0.87        96\n",
      "\n",
      "    accuracy                           0.88       200\n",
      "   macro avg       0.88      0.87      0.87       200\n",
      "weighted avg       0.88      0.88      0.87       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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