Supervised_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c5c922c3",
   "metadata": {},
   "source": [
    "# Supervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d9b222",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c83545d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import neighbors\n",
    "from matplotlib.colors import ListedColormap\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bc8ef9f",
   "metadata": {},
   "source": [
    "## Data\n",
    "[Iris dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#iris-plants-dataset) from the sklearn library. \n",
    "- Consists of 50 samples from each of 3 species of Iris (Iris sentosa, Iris virginica and Iris versicolor)\n",
    "- 4 features were measured from each sample: the length and width of sepals and petals (in cm)\n",
    "\n",
    "<img src = \"src/iris.png\"  width= \"500\" align=\"left\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7dbd6c11",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5690e97b",
   "metadata": {},
   "source": [
    "## Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c1cdfa9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target names:\n",
      " ['setosa' 'versicolor' 'virginica']\n",
      "\n",
      "First 10 rows of data:\n",
      " [[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]]\n",
      "\n",
      "target labels: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "data = iris.data\n",
    "labels = iris.target\n",
    "target_names = iris.target_names\n",
    "print('target names:\\n', iris.target_names)\n",
    "print('\\nFirst 10 rows of data:\\n', data[:10])\n",
    "print('\\ntarget labels:', labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03f064ae",
   "metadata": {},
   "source": [
    "## Visualizing the data (using PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5927fc8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained variance ratio (first two components): [0.92461872 0.05306648]\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(data)\n",
    "\n",
    "print(f'explained variance ratio (first two components): {str(pca.explained_variance_ratio_)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd6ad018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PCA of IRIS dataset')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the PCA\n",
    "plt.figure()\n",
    "colors = ['navy', 'turquoise', 'darkorange']\n",
    "lw = 2\n",
    "\n",
    "for color, i, target_name in zip(colors, [0, 1, 2], target_names):\n",
    "    plt.scatter(X_pca[labels == i, 0], X_pca[labels == i, 1], color=color, alpha=.8, lw=lw, label=target_name)\n",
    "    \n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('PCA of IRIS dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0c4460",
   "metadata": {},
   "source": [
    "## Training and testing set\n",
    "Use *train_test_split()*, a function from the sklearn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b8e4d218",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.6 3.  4.4 1.4]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [5.1 3.8 1.6 0.2]\n",
      " [5.1 3.5 1.4 0.3]\n",
      " [6.5 3.  5.8 2.2]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [6.1 2.8 4.  1.3]\n",
      " [5.5 2.4 3.8 1.1]\n",
      " [6.5 3.2 5.1 2. ]\n",
      " [6.5 3.  5.2 2. ]] \n",
      "\n",
      "[[6.3 2.3 4.4 1.3]\n",
      " [6.4 2.7 5.3 1.9]\n",
      " [5.4 3.7 1.5 0.2]\n",
      " [6.1 3.  4.6 1.4]\n",
      " [5.  3.3 1.4 0.2]\n",
      " [5.  2.  3.5 1. ]\n",
      " [6.3 2.5 4.9 1.5]\n",
      " [5.8 2.7 4.1 1. ]\n",
      " [5.1 3.4 1.5 0.2]\n",
      " [5.7 2.8 4.5 1.3]] \n",
      "\n",
      "[1 0 0 0 2 2 1 1 2 2] \n",
      "\n",
      "[1 2 0 1 0 1 1 1 0 1] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Create training petal_data, training labels, test petal_data, test lables using train_test_split()\n",
    "# Test size set to 60%, so more data is being used for testing than training\n",
    "data_train, data_test, label_train, label_test = train_test_split(data, labels, test_size=0.6, random_state=10)\n",
    "\n",
    "print(data_train[:10], '\\n')\n",
    "print(data_test[:10], '\\n')\n",
    "\n",
    "print(label_train[:10], '\\n')\n",
    "print(label_test[:10], '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb2bfbf3",
   "metadata": {},
   "source": [
    "## Classifying the data using different models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feb9e24f",
   "metadata": {},
   "source": [
    "### 1) Gaussian Naive Bayes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b5a7ec9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true: [1 2 0 1 0 1 1 1 0 1 1 2 1 0 0 2 1 0 0 0 2 2 2 0 1 0 1 1 1 2 1 1 2 2 2 0 2\n",
      " 2 2 2 0 0 1 0 1 0 1 1 2 2 1 2 1 1 1 0 0 1 0 2 0 0 2 1 2 0 2 0 1 1 0 2 2 2\n",
      " 2 2 0 1 2 1 0 2 1 1 0 0 0 1 2 2]\n",
      "pred: [1 2 0 1 0 1 1 1 0 1 1 2 1 0 0 2 2 0 0 0 2 2 2 0 1 0 1 1 1 2 1 1 2 2 2 0 2\n",
      " 2 2 2 0 0 1 0 2 0 2 2 2 2 1 2 1 1 1 0 0 1 0 2 0 0 1 1 2 0 2 0 1 2 0 2 2 2\n",
      " 2 2 0 1 2 1 0 2 1 1 0 0 0 1 2 2]\n",
      "Number of mislabeled points out of a total 90 points : 6\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(data_train, label_train)\n",
    "\n",
    "# Assign the predicted labels to the variable pred_NB\n",
    "pred_NB = gnb.predict(data_test)\n",
    "\n",
    "print('true:', label_test)\n",
    "print('pred:', pred_NB )\n",
    "\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\" % (data_test.shape[0], (label_test != pred_NB).sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc3c3a0b",
   "metadata": {},
   "source": [
    "### 2) K Nearest Neighbours "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff262fda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true: [1 2 0 1 0 1 1 1 0 1 1 2 1 0 0 2 1 0 0 0 2 2 2 0 1 0 1 1 1 2 1 1 2 2 2 0 2\n",
      " 2 2 2 0 0 1 0 1 0 1 1 2 2 1 2 1 1 1 0 0 1 0 2 0 0 2 1 2 0 2 0 1 1 0 2 2 2\n",
      " 2 2 0 1 2 1 0 2 1 1 0 0 0 1 2 2]\n",
      "pred: [1 2 0 1 0 1 2 1 0 1 1 2 1 0 0 2 1 0 0 0 2 2 2 0 1 0 1 1 2 2 1 1 2 2 2 0 2\n",
      " 2 2 2 0 0 1 0 2 0 2 2 2 2 1 2 1 1 1 0 0 1 0 2 0 0 1 1 2 0 2 0 1 2 0 2 2 2\n",
      " 2 2 0 1 2 1 0 2 1 1 0 0 0 1 2 2]\n",
      "Number of mislabeled points out of a total 90 points : 7\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") #for suppresson of FutureWarning\n",
    "\n",
    "n = 15\n",
    "clf = KNeighborsClassifier(n_neighbors=n, weights='uniform')\n",
    "clf.fit(data_train, label_train)\n",
    "\n",
    "# Assign the predicted labels to the variable pred_KNN\n",
    "pred_KNN = clf.predict(data_test)\n",
    "\n",
    "print('true:', label_test)\n",
    "print('pred:', pred_KNN)\n",
    "print(\"Number of mislabeled points out of a total {} points : {}\".format(data_test.shape[0], (label_test != pred_KNN).sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f01e1967",
   "metadata": {},
   "source": [
    "### 3) Support Vector Machine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4090991c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true: [1 2 0 1 0 1 1 1 0 1 1 2 1 0 0 2 1 0 0 0 2 2 2 0 1 0 1 1 1 2 1 1 2 2 2 0 2\n",
      " 2 2 2 0 0 1 0 1 0 1 1 2 2 1 2 1 1 1 0 0 1 0 2 0 0 2 1 2 0 2 0 1 1 0 2 2 2\n",
      " 2 2 0 1 2 1 0 2 1 1 0 0 0 1 2 2]\n",
      "pred: [1 2 0 2 0 1 2 1 0 1 1 1 1 0 0 2 2 0 0 0 2 2 2 0 2 0 1 1 2 2 2 2 2 1 2 0 2\n",
      " 2 2 2 0 0 1 1 2 0 2 2 1 2 1 2 1 2 2 0 0 2 0 2 0 0 1 1 2 0 2 0 1 1 0 2 2 2\n",
      " 2 2 0 1 2 1 0 2 2 1 0 0 0 0 2 2]\n",
      "Number of mislabeled points out of a total 90 points : 20\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "clf = svm.SVC()\n",
    "clf.fit(data_train[:,[0,1]], label_train)\n",
    "\n",
    "# Assign the predicted labels to the variable pred_SVM\n",
    "pred_SVM = clf.predict(data_test[:,[0,1]])\n",
    "\n",
    "print('true:', label_test)\n",
    "print('pred:', pred_SVM)\n",
    "\n",
    "print(\"Number of mislabeled points out of a total {} points : {}\".format(data_test.shape[0], (label_test != pred_SVM).sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c40a815c",
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   "source": [
    "## Assess the performance of the models\n",
    "Use *classification_report()*, a function from sklearn library which builds a text report showing the main classification metrics.\n",
    "\n",
    "**Precision:** When it predicts the positive result, how often is it correct? \n",
    "(i.e., proportion of predicted positives that truly are positives)\n",
    "\n",
    "**Recall:** When it is actually the positive result, how often does it predict correctly? (i.e., true positive rate)\n",
    "\n",
    "**F1-Score:** Harmonic mean of precision and recall, penalising large discrepancies between the two.\n",
    "\n",
    "**Accuracy:** How often is the classifier correct? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef566efa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3c2ad6",
   "metadata": {},
   "source": [
    "### 1) Gaussian Naive Bayes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d86448be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        28\n",
      "  versicolor       0.96      0.84      0.90        32\n",
      "   virginica       0.85      0.97      0.91        30\n",
      "\n",
      "    accuracy                           0.93        90\n",
      "   macro avg       0.94      0.94      0.94        90\n",
      "weighted avg       0.94      0.93      0.93        90\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(label_test, pred_NB, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d5ca37a",
   "metadata": {},
   "source": [
    "### 2) K Nearest Neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fbadfce6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        28\n",
      "  versicolor       0.96      0.81      0.88        32\n",
      "   virginica       0.83      0.97      0.89        30\n",
      "\n",
      "    accuracy                           0.92        90\n",
      "   macro avg       0.93      0.93      0.92        90\n",
      "weighted avg       0.93      0.92      0.92        90\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(label_test, pred_KNN, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e07f3004",
   "metadata": {},
   "source": [
    "### 3) Support Vector Machine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "36719947",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       0.96      0.96      0.96        28\n",
      "  versicolor       0.77      0.53      0.63        32\n",
      "   virginica       0.65      0.87      0.74        30\n",
      "\n",
      "    accuracy                           0.78        90\n",
      "   macro avg       0.80      0.79      0.78        90\n",
      "weighted avg       0.79      0.78      0.77        90\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(label_test, pred_SVM, target_names=target_names))"
   ]
  }
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