Lab9 14Oct2021.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 368,
     "status": "ok",
     "timestamp": 1634202389154,
     "user": {
      "displayName": "Garima Chhikara",
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      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "iPNmaC0ZzXkK"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1634202389821,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "1Ao0gzBYzhIE",
    "outputId": "b37d882a-ceac-4021-fb75-e16abba08861"
   },
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"https://raw.githubusercontent.com/garimachhikara128/DTU-ML-Lab-C4-G2/master/TrainingData/Iris.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 31,
     "status": "ok",
     "timestamp": 1634202389822,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "FN1J-2Ic091T",
    "outputId": "09c219fe-8def-4dbe-eb12-db1316e0e21b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1634202389823,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "t-qXUjG71DMV",
    "outputId": "61b6f0d6-4a27-4774-ab1c-e28c2f7972e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "data = data.values\n",
    "X = data[:,1:5]\n",
    "Y = data[:,5]\n",
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LOCFunLo1F2l"
   },
   "source": [
    "## 1. Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 28,
     "status": "ok",
     "timestamp": 1634202389824,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "YT_RihX01Gll"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1634202389824,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "KrvuZvW71UeL"
   },
   "outputs": [],
   "source": [
    "X_std = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ret4YjV81fCE"
   },
   "source": [
    "## 2. Covariance Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 52,
     "status": "ok",
     "timestamp": 1634202389850,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "EAX3uwHT1fvD",
    "outputId": "6b90707d-652e-47c8-c212-7faf6ad7bf08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 4)\n"
     ]
    }
   ],
   "source": [
    "X_cov = np.cov(X_std.T)\n",
    "print(X_cov.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zUjejiKe1keS"
   },
   "source": [
    "## 3. Eigen Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 62,
     "status": "ok",
     "timestamp": 1634202389864,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "8tLqK9jM1lbB",
    "outputId": "2f9089f1-0739-41f4-d4bf-36144d0e4f20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.93035378 0.92740362 0.14834223 0.02074601]\n",
      "[[ 0.52237162 -0.37231836 -0.72101681  0.26199559]\n",
      " [-0.26335492 -0.92555649  0.24203288 -0.12413481]\n",
      " [ 0.58125401 -0.02109478  0.14089226 -0.80115427]\n",
      " [ 0.56561105 -0.06541577  0.6338014   0.52354627]]\n"
     ]
    }
   ],
   "source": [
    "evalue, evectors = np.linalg.eig(X_cov)\n",
    "\n",
    "print(evalue)\n",
    "print(evectors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 67,
     "status": "ok",
     "timestamp": 1634202389872,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "sB5ed36t9oc9",
    "outputId": "a5d09e3e-d2d6-492d-8b3e-77ec1601babc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.930353775589317, array([ 0.52237162, -0.26335492,  0.58125401,  0.56561105])]\n",
      "[0.9274036215173419, array([-0.37231836, -0.92555649, -0.02109478, -0.06541577])]\n",
      "[0.14834222648163944, array([-0.72101681,  0.24203288,  0.14089226,  0.6338014 ])]\n",
      "[0.02074601399559593, array([ 0.26199559, -0.12413481, -0.80115427,  0.52354627])]\n"
     ]
    }
   ],
   "source": [
    "# Create eigen pairs in which we have eigen vectors corresponding to eigen values\n",
    "epairs = []\n",
    "\n",
    "for i in range(len(evalue)) :\n",
    "  epairs.append( [evalue[i], evectors[:,i]] )\n",
    "\n",
    "# Sort the pairs on the basis of eigen values\n",
    "epairs.sort(reverse = True)\n",
    "\n",
    "for ep in epairs :\n",
    "  print(ep)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 64,
     "status": "ok",
     "timestamp": 1634202389873,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "_-WzUmYM9of3",
    "outputId": "0501d853-4775-4cfa-947a-a05a2a7f113f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.52237162 -0.37231836]\n",
      " [-0.26335492 -0.92555649]\n",
      " [ 0.58125401 -0.02109478]\n",
      " [ 0.56561105 -0.06541577]]\n"
     ]
    }
   ],
   "source": [
    "# Pick top k eigen vectors\n",
    "matrix = np.hstack ((epairs[0][1].reshape(4,1) , epairs[1][1].reshape(4,1)))\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 571,
     "status": "ok",
     "timestamp": 1634202657198,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "CwxZd1awC85Y",
    "outputId": "cfc4cce8-43b5-457a-ff19-72d4d1581280"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.26454173 -0.5057039 ]\n",
      " [-2.0864255   0.65540473]\n",
      " [-2.36795045  0.31847731]\n",
      " [-2.30419716  0.57536771]\n",
      " [-2.38877749 -0.6747674 ]]\n"
     ]
    }
   ],
   "source": [
    "# Project the data points onto the new axis (evectors)\n",
    "new_dp =  X_std.dot(matrix)\n",
    "\n",
    "print(new_dp[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "executionInfo": {
     "elapsed": 1289,
     "status": "ok",
     "timestamp": 1634202900989,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "8p1w-qCQDgCy",
    "outputId": "2a8cb472-f333-4172-b6a5-c266e6b035b3"
   },
   "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": [
    "# show the new data points on a graph\n",
    "for i in range(len(new_dp)) :\n",
    "\n",
    "  if Y[i] == 'Iris-setosa' :\n",
    "    plt.scatter( new_dp[i,0] , new_dp[i,1] , color = \"red\")\n",
    "\n",
    "  elif Y[i] == 'Iris-versicolor' :\n",
    "    plt.scatter( new_dp[i,0] , new_dp[i,1] , color = \"green\")\n",
    "\n",
    "  elif Y[i] == 'Iris-virginica' :\n",
    "    plt.scatter( new_dp[i,0] , new_dp[i,1] , color = \"blue\")\n",
    "\n",
    "plt.xlabel(\"PCA 1\")\n",
    "plt.ylabel(\"PCA 2\")\n",
    "plt.title(\"PCA for Iris Dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 378,
     "status": "ok",
     "timestamp": 1634202723172,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "ZnSi8M1iEYGZ",
    "outputId": "d19a85f2-8c8a-4e0f-a348-5ec516b2abbe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hv_6E1q4RwbC"
   },
   "source": [
    "# NAIVE BAYES CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "executionInfo": {
     "elapsed": 416,
     "status": "ok",
     "timestamp": 1634206193140,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "-VpZ-SXkRyBu"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w-K4kV-tSb9B"
   },
   "source": [
    "## Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 274
    },
    "executionInfo": {
     "elapsed": 489,
     "status": "ok",
     "timestamp": 1634206218179,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "HNBScbodR5Ho",
    "outputId": "5e7c2334-8884-4685-e708-97e41d8808ba"
   },
   "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>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/garimachhikara128/DTU-ML-Lab-C4-G2/master/TrainingData/mushrooms.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 453,
     "status": "ok",
     "timestamp": 1634206278361,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "SGE4Vyp7R_DU",
    "outputId": "2fde8bfd-14cb-4b51-adac-a3a21aeab411"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8124, 23)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-pEzn_ojShsB"
   },
   "source": [
    "## Convert Categorical Data to Numerical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 419,
     "status": "ok",
     "timestamp": 1634206435881,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "E2DJfG_RSoFT"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "executionInfo": {
     "elapsed": 390,
     "status": "ok",
     "timestamp": 1634206531750,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "XdfohZ8OS0YF",
    "outputId": "79eef277-9bbc-4e6e-9383-ec693911cad0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
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       "      <td>4</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>8</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   class  cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
       "0      1          5            2          4        1     6                1   \n",
       "1      0          5            2          9        1     0                1   \n",
       "2      0          0            2          8        1     3                1   \n",
       "3      1          5            3          8        1     6                1   \n",
       "4      0          5            2          3        0     5                1   \n",
       "\n",
       "   gill-spacing  gill-size  gill-color  ...  stalk-surface-below-ring  \\\n",
       "0             0          1           4  ...                         2   \n",
       "1             0          0           4  ...                         2   \n",
       "2             0          0           5  ...                         2   \n",
       "3             0          1           5  ...                         2   \n",
       "4             1          0           4  ...                         2   \n",
       "\n",
       "   stalk-color-above-ring  stalk-color-below-ring  veil-type  veil-color  \\\n",
       "0                       7                       7          0           2   \n",
       "1                       7                       7          0           2   \n",
       "2                       7                       7          0           2   \n",
       "3                       7                       7          0           2   \n",
       "4                       7                       7          0           2   \n",
       "\n",
       "   ring-number  ring-type  spore-print-color  population  habitat  \n",
       "0            1          4                  2           3        5  \n",
       "1            1          4                  3           2        1  \n",
       "2            1          4                  3           2        3  \n",
       "3            1          4                  2           3        5  \n",
       "4            1          0                  3           0        1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "nd = df.apply(le.fit_transform)\n",
    "print(type(nd))\n",
    "nd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 382,
     "status": "ok",
     "timestamp": 1634206626088,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "pkgnl4spTB3f",
    "outputId": "18e354ad-cd62-41ff-bf23-cc89269fc309"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8124, 23)\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Convert pandas dataframe to numpy array\n",
    "data = nd.values\n",
    "print(data.shape)\n",
    "print(type(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 363,
     "status": "ok",
     "timestamp": 1634206719936,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "4P_d0QQwTafJ",
    "outputId": "59729e6c-e584-4bd6-b859-93b2eb9d5d99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8124,)\n",
      "(8124, 22)\n"
     ]
    }
   ],
   "source": [
    "Y = data[:,0]\n",
    "X = data[:,1:]\n",
    "\n",
    "print(Y.shape)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Divide the data into training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 370,
     "status": "ok",
     "timestamp": 1634206914648,
     "user": {
      "displayName": "Garima Chhikara",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ggi9Hv_sAnUzQlZnP_iuiVNwRUwQ6xA0rvVuOv1Og=s64",
      "userId": "04303712092160616729"
     },
     "user_tz": -330
    },
    "id": "Xt39tUt2T5qk",
    "outputId": "72ff1511-72ce-41e5-9f6b-6438c52bfcb5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6499, 22) (6499,)\n",
      "(1625, 22) (1625,)\n"
     ]
    }
   ],
   "source": [
    "# Break the data into 2 parts : training data & testing data\n",
    "x_train, x_test, y_train, y_test = train_test_split(X,Y,test_size = 0.2)\n",
    "print(x_train.shape, y_train.shape)\n",
    "print(x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "q-jBgNfbUi8H"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True False False  True  True False False False False]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1,1,2,3,1,1,2,2,3,2])\n",
    "print(a == 1)\n",
    "sum(a == 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prior Probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prior_probability(y_train, c) :\n",
    "    \n",
    "    class_points = np.sum(y_train == c)\n",
    "    total_points = y_train.shape[0]\n",
    "    \n",
    "    return class_points / float(total_point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.array([1,2,2,3,1,1,2,2,3,2])\n",
    "prior_probability(y,3)           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conditional_probability(x_train, y_train, feature_col, feature_val, c) :\n",
    "    \n",
    "    # filter out the rows which are having y_train as c\n",
    "    x_filtered = x_train[y_train == c]\n",
    "    \n",
    "    # extract the feature_col from filtered x\n",
    "    x_feature = x_filtered[:,feature_col]\n",
    "    \n",
    "    # denom\n",
    "    denominator = x_feature.shape[0]\n",
    "    \n",
    "    # numerator\n",
    "    numerator = np.sum(x_feature == feature_val)\n",
    "    \n",
    "    return numerator / float(denominator)\n",
    "\n",
    "def predict(x_train, y_train, x_test) :\n",
    "    \n",
    "    classes = np.unique(y_train)\n",
    "    n = x_train.shape[1]\n",
    "    post_prob_list = []\n",
    "    \n",
    "    for c in classes :\n",
    "        \n",
    "        likelihood = 1.0\n",
    "        \n",
    "        for f in range(n) :\n",
    "            \n",
    "            cp = conditional_probability(x_train, y_train, f, x_test[f], c)\n",
    "            likelihood *= cp\n",
    "            \n",
    "        prior_prob = prior_probability(y_train, c)\n",
    "        \n",
    "        post_prob = likelihood * prior_prob\n",
    "        post_prob_list.append(post_prob)\n",
    "            \n",
    "    pred = np.argmax(post_prob_list)\n",
    "    return pred \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(predict(x_train, y_train, x_test[2]))\n",
    "print(y_test[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score(x_train, y_train, x_test, y_test) :\n",
    "    \n",
    "    predict_list = []\n",
    "    \n",
    "    for i in range(x_test.shape[0]) :\n",
    "        \n",
    "        predict_class = predict(x_train, y_train, x_test[i])\n",
    "        predict_list.append(predict_class)\n",
    "        \n",
    "    predict_list = np.array(predict_list)\n",
    "    \n",
    "    accuracy = np.sum(predict_list == y_test) / x_test.shape[0]\n",
    "    return accuracy\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.6923076923077\n"
     ]
    }
   ],
   "source": [
    "print(score(x_train, y_train, x_test, y_test) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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