NoteBooks/k-Nearest-Neighbors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  k-Nearest Neighbors\n",
    "Using an algorithm in Sklearn, we generate a k-Nearest-Neighbors as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we should import the required modules as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.datasets as datasets\n",
    "from sklearn import metrics, model_selection, preprocessing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import set_option\n",
    "from mlxtend.plotting import plot_learning_curves\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "%matplotlib inline \n",
    "import metric_learn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Iris Data and Explore it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data as DataFrame\n",
    "df = pd.read_csv('Data/iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the shape of DataFrame\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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>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>4.6</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>5.0</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",
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      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the top five rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      "sepal_length    150 non-null float64\n",
      "sepal_width     150 non-null float64\n",
      "petal_length    150 non-null float64\n",
      "petal_width     150 non-null float64\n",
      "species         150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Show the information of DataFrame (Data Columns, Types, ...)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.84</td>\n",
       "      <td>3.06</td>\n",
       "      <td>3.76</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.77</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.10</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.80</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.40</td>\n",
       "      <td>3.30</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.90</td>\n",
       "      <td>4.40</td>\n",
       "      <td>6.90</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count        150.00       150.00        150.00       150.00\n",
       "mean           5.84         3.06          3.76         1.20\n",
       "std            0.83         0.44          1.77         0.76\n",
       "min            4.30         2.00          1.00         0.10\n",
       "25%            5.10         2.80          1.60         0.30\n",
       "50%            5.80         3.00          4.35         1.30\n",
       "75%            6.40         3.30          5.10         1.80\n",
       "max            7.90         4.40          6.90         2.50"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the decimal precision\n",
    "set_option('precision', 2)\n",
    "# Generate descriptive statistics  \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "species         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify there is no missing value in data\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of classes in target \n",
    "df.species.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-versicolor    50\n",
       "Iris-setosa        50\n",
       "Iris-virginica     50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check target for imbalanced classes\n",
    "df.species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species  \\\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa   \n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa   \n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa   \n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa   \n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa   \n",
       "\n",
       "   class_label  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the following code, one can convert the target column to Numeric\n",
    "df['class_label'] = df.species.map({'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2})\n",
    "#\n",
    "# Alternative Method:\n",
    "# df['class_label']= pd.factorize(df['species'])[0]\n",
    "# df['class_label'], _= pd.factorize(df['class'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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     "execution_count": 11,
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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plot of all numeric columns, grouped by species\n",
    "feature=[\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]\n",
    "df.boxplot(column=feature, by='species', rot=45, figsize=(8,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a17831f60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1787a9e8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1789eeb8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a178d0588>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a178f7c18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a178f7c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1794e978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a17985048>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a179aa6d8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a179d4d68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a17a06438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a17a2cac8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a17a60198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a17a88828>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a17aafeb8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a17ae0588>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter matrix of all features, colored by species\n",
    "pd.plotting.scatter_matrix(df.drop([\"species\", \"class_label\"], axis=1), c=df.class_label, figsize=(12, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop target column\n",
    "feature=[\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]\n",
    "X=df[feature]\n",
    "#\n",
    "## Alternative Method\n",
    "# X= df.drop([\"species\", \"class_label\"], axis=1)\n",
    "#\n",
    "y=df[\"class_label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           5.1          3.5           1.4          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: class_label, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.12</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.87</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.82</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_label</th>\n",
       "      <td>0.78</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width  class_label\n",
       "sepal_length          1.00        -0.12          0.87         0.82         0.78\n",
       "sepal_width          -0.12         1.00         -0.43        -0.37        -0.43\n",
       "petal_length          0.87        -0.43          1.00         0.96         0.95\n",
       "petal_width           0.82        -0.37          0.96         1.00         0.96\n",
       "class_label           0.78        -0.43          0.95         0.96         1.00"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the correlations between features\n",
    "correlations = df.corr(method='pearson')\n",
    "set_option('display.width', 150)\n",
    "correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize the Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Is it necessary to standardize the features in  k-Nearest-Neighbors? Yes!\n",
    "sc = preprocessing.StandardScaler()\n",
    "sc.fit(X)\n",
    "X= sc.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## If training set and test set are different DataFrame\n",
    "# sc = preprocessing.StandardScaler()\n",
    "# sc.fit(X_train)\n",
    "# X_train= sc.transform(X_train)\n",
    "# X_test= sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and Test Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data randomly into 70% training and 30% test\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tain the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import neighbors\n",
    "clf = neighbors.KNeighborsClassifier(3, weights = 'uniform')\n",
    "trained_model = clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, one can use this model to predict the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassified samples: 2\n"
     ]
    }
   ],
   "source": [
    "# Count the number of misclassifications\n",
    "count_misclassified = (y_test != y_pred).sum()\n",
    "print('Misclassified samples: {}'.format(count_misclassified))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.96\n"
     ]
    }
   ],
   "source": [
    "# Evaluate accuracy\n",
    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "print('Accuracy: {:.2f}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.2,\n",
       "  0.09523809523809523,\n",
       "  0.06451612903225806,\n",
       "  0.047619047619047616,\n",
       "  0.019230769230769232,\n",
       "  0.047619047619047616,\n",
       "  0.0410958904109589,\n",
       "  0.03571428571428571,\n",
       "  0.031914893617021274,\n",
       "  0.02857142857142857],\n",
       " [0.6,\n",
       "  0.4888888888888889,\n",
       "  0.06666666666666667,\n",
       "  0.06666666666666667,\n",
       "  0.06666666666666667,\n",
       "  0.06666666666666667,\n",
       "  0.022222222222222223,\n",
       "  0.022222222222222223,\n",
       "  0.022222222222222223,\n",
       "  0.022222222222222223])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create training and test scores for various training set sizes\n",
    "clf = neighbors.KNeighborsClassifier(3, weights = 'uniform')\n",
    "plot_learning_curves(X_train, y_train, X_test, y_test, clf, print_model=False, style='ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Learning Curve (Alternative Method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "# Create training and test scores for various training set sizes\n",
    "from sklearn.model_selection import learning_curve\n",
    "clf = neighbors.KNeighborsClassifier(3, weights = 'uniform')\n",
    "\n",
    "train_sizes, train_scores, test_scores = learning_curve(clf, X, y, cv=5, scoring='accuracy', \n",
    "                                                        train_sizes=np.linspace(0.3, 1.0, 5))\n",
    "\n",
    "# Create means of training set scores and test set scores\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "\n",
    "# Draw lines\n",
    "plt.plot(train_sizes, train_mean, '--', color=\"#111111\",  label=\"Training score\")\n",
    "plt.plot(train_sizes, test_mean, color=\"#111111\", label=\"Cross-validation score\")\n",
    "\n",
    "# Create plot\n",
    "plt.title(\"Learning Curve\")\n",
    "plt.xlabel(\"Training Set Size\")\n",
    "plt.ylabel(\"Accuracy Score\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metric Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.90068117,  1.01900435, -1.34022653, -1.3154443 ],\n",
       "       [-1.14301691, -0.13197948, -1.34022653, -1.3154443 ]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a183c7320>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(X[:, 2], X[:, 3], c=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Various Metric\n",
    "There are various metric learning algorithms in Python. We refer the readers to the webpage of [metric-learn](https://github.com/metric-learn/metric-learn). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Large Margin Nearest Neighbor (LMNN) Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hhaji/anaconda3/lib/python3.6/site-packages/metric_learn/lmnn.py:62: UserWarning: use_pca does nothing for the python_LMNN implementation\n",
      "  warnings.warn('use_pca does nothing for the python_LMNN implementation')\n"
     ]
    }
   ],
   "source": [
    "# setting up LMNN metric \n",
    "lmnn = metric_learn.LMNN(k=3, learn_rate=1e-6)\n",
    "\n",
    "# fit the data!\n",
    "lmnn.fit(X, y)\n",
    "\n",
    "# transform our input space\n",
    "X_new = lmnn.transform()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.31825307,  0.00259365, -0.45615509, -0.5547039 ],\n",
       "       [ 0.00259365,  1.21409024, -0.71452839, -0.06607121],\n",
       "       [-0.45615509, -0.71452839,  2.23666905,  2.03400075],\n",
       "       [-0.5547039 , -0.06607121,  2.03400075,  2.25742113]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lmnn.metric()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.99119882,  1.40627451, -2.96538435, -2.48830533],\n",
       "       [-1.34666748,  0.22418796, -2.5035749 , -2.58326972]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new[0:2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a18487d68>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(X_new[:, 2], X_new[:, 3], c=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Evaluate the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.82\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X_new, y, test_size=0.3, random_state=0)\n",
    "clf = neighbors.KNeighborsClassifier(3, weights = 'uniform')\n",
    "trained_model = clf.fit(X, y)\n",
    "y_pred = clf.predict(X_test)\n",
    "count_misclassified = (y_test != y_pred).sum()\n",
    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "print('Accuracy: {:.2f}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the Model's Decision Regions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_selected=[\"petal_length\", \"petal_width\"]\n",
    "X_2=df[feature_selected]\n",
    "y_2=df[\"class_label\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the Model Again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = neighbors.KNeighborsClassifier(3, weights = 'uniform')\n",
    "clf.fit(X_2,y_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert DataFrame to Numpy Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_2=np.array(X_2)\n",
    "y_2=np.array(y_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a18491160>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from mlxtend.plotting import plot_decision_regions \n",
    "plot_decision_regions(X_2, y_2, clf=clf, legend=2)\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('petal width')\n",
    "plt.legend(loc='lower right')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}