bnwiran/ML0101EN-Clus-Hierarchical-Cars-py-v1.ipynb

## ML0101EN-Clus-Hierarchical-Cars-py-v1.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
    "\n",
    "<h1><center>Hierarchical Clustering</center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to Lab of Hierarchical Clustering with Python using Scipy and Scikit-learn package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"#hierarchical_agglomerative\">Hierarchical Clustering - Agglomerative</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#generating_data\">Generating Random Data</a></li>\n",
    "                <li><a href=\"#agglomerative_clustering\">Agglomerative Clustering</a></li>\n",
    "                <li><a href=\"#dendrogram\">Dendrogram Associated for the Agglomerative Hierarchical Clustering</a></li>\n",
    "            </ol>            \n",
    "        <li><a href=\"#clustering_vehicle_dataset\">Clustering on the Vehicle Dataset</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#data_cleaning\">Data Cleaning</a></li>\n",
    "                <li><a href=\"#clustering_using_scipy\">Clustering Using Scipy</a></li>\n",
    "                <li><a href=\"#clustering_using_skl\">Clustering using scikit-learn</a></li>\n",
    "            </ol>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 id=\"hierarchical_agglomerative\">Hierarchical Clustering - Agglomerative</h1>\n",
    "\n",
    "We will be looking at a clustering technique, which is <b>Agglomerative Hierarchical Clustering</b>. Remember that agglomerative is the bottom up approach. <br> <br>\n",
    "In this lab, we will be looking at Agglomerative clustering, which is more popular than Divisive clustering. <br> <br>\n",
    "We will also be using Complete Linkage as the Linkage Criteria. <br>\n",
    "<b> <i> NOTE: You can also try using Average Linkage wherever Complete Linkage would be used to see the difference! </i> </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from scipy import ndimage \n",
    "from scipy.cluster import hierarchy \n",
    "from scipy.spatial import distance_matrix \n",
    "from matplotlib import pyplot as plt \n",
    "from sklearn import manifold, datasets \n",
    "from sklearn.cluster import AgglomerativeClustering \n",
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h3 id=\"generating_data\">Generating Random Data</h3>\n",
    "We will be generating a set of data using the <b>make_blobs</b> class. <br> <br>\n",
    "Input these parameters into make_blobs:\n",
    "<ul>\n",
    "    <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n",
    "    <ul> <li> Choose a number from 10-1500 </li> </ul>\n",
    "    <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n",
    "    <ul> <li> Choose arrays of x,y coordinates for generating the centers. Have 1-10 centers (ex. centers=[[1,1], [2,5]]) </li> </ul>\n",
    "    <li> <b>cluster_std</b>: The standard deviation of the clusters. The larger the number, the further apart the clusters</li>\n",
    "    <ul> <li> Choose a number between 0.5-1.5 </li> </ul>\n",
    "</ul> <br>\n",
    "Save the result to <b>X1</b> and <b>y1</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1, y1 = make_blobs(n_samples=50, centers=[[4,4], [-2, -1], [1, 1], [10,4]], cluster_std=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scatter plot of the randomly generated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa415ea67f0>"
      ]
     },
     "execution_count": 3,
     "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(X1[:, 0], X1[:, 1], marker='o') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h3 id=\"agglomerative_clustering\">Agglomerative Clustering</h3>\n",
    "We will start by clustering the random data points we just created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <b> Agglomerative Clustering </b> class will require two inputs:\n",
    "<ul>\n",
    "    <li> <b>n_clusters</b>: The number of clusters to form as well as the number of centroids to generate. </li>\n",
    "    <ul> <li> Value will be: 4 </li> </ul>\n",
    "    <li> <b>linkage</b>: Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion. </li>\n",
    "    <ul> \n",
    "        <li> Value will be: 'complete' </li> \n",
    "        <li> <b>Note</b>: It is recommended you try everything with 'average' as well </li>\n",
    "    </ul>\n",
    "</ul> <br>\n",
    "Save the result to a variable called <b> agglom </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "agglom = AgglomerativeClustering(n_clusters = 4, linkage = 'average')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model with <b> X2 </b> and <b> y2 </b> from the generated data above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
       "            connectivity=None, linkage='average', memory=None,\n",
       "            n_clusters=4, pooling_func='deprecated')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agglom.fit(X1,y1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to show the clustering! <br>\n",
    "Remember to read the code and comments to gain more understanding on how the plotting works."
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure of size 6 inches by 4 inches.\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "# These two lines of code are used to scale the data points down,\n",
    "# Or else the data points will be scattered very far apart.\n",
    "\n",
    "# Create a minimum and maximum range of X1.\n",
    "x_min, x_max = np.min(X1, axis=0), np.max(X1, axis=0)\n",
    "\n",
    "# Get the average distance for X1.\n",
    "X1 = (X1 - x_min) / (x_max - x_min)\n",
    "\n",
    "# This loop displays all of the datapoints.\n",
    "for i in range(X1.shape[0]):\n",
    "    # Replace the data points with their respective cluster value \n",
    "    # (ex. 0) and is color coded with a colormap (plt.cm.spectral)\n",
    "    plt.text(X1[i, 0], X1[i, 1], str(y1[i]),\n",
    "             color=plt.cm.nipy_spectral(agglom.labels_[i] / 10.),\n",
    "             fontdict={'weight': 'bold', 'size': 9})\n",
    "    \n",
    "# Remove the x ticks, y ticks, x and y axis\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "#plt.axis('off')\n",
    "\n",
    "\n",
    "\n",
    "# Display the plot of the original data before clustering\n",
    "plt.scatter(X1[:, 0], X1[:, 1], marker='.')\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h3 id=\"dendrogram\">Dendrogram Associated for the Agglomerative Hierarchical Clustering</h3>\n",
    "Remember that a <b>distance matrix</b> contains the <b> distance from each point to every other point of a dataset </b>. <br>\n",
    "Use the function <b> distance_matrix, </b> which requires <b>two inputs</b>. Use the Feature Matrix, <b> X2 </b> as both inputs and save the distance matrix to a variable called <b> dist_matrix </b> <br> <br>\n",
    "Remember that the distance values are symmetric, with a diagonal of 0's. This is one way of making sure your matrix is correct. <br> (print out dist_matrix to make sure it's correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.78571326 0.33733454 ... 0.76825947 0.70646205 0.36171765]\n",
      " [0.78571326 0.         0.4838935  ... 0.10462969 0.16322342 0.42403064]\n",
      " [0.33733454 0.4838935  0.         ... 0.49622928 0.37763805 0.12239701]\n",
      " ...\n",
      " [0.76825947 0.10462969 0.49622928 ... 0.         0.24604728 0.41347009]\n",
      " [0.70646205 0.16322342 0.37763805 ... 0.24604728 0.         0.35672444]\n",
      " [0.36171765 0.42403064 0.12239701 ... 0.41347009 0.35672444 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "dist_matrix = distance_matrix(X1,X1) \n",
    "print(dist_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the <b> linkage </b> class from hierarchy, pass in the parameters:\n",
    "<ul>\n",
    "    <li> The distance matrix </li>\n",
    "    <li> 'complete' for complete linkage </li>\n",
    "</ul> <br>\n",
    "Save the result to a variable called <b> Z </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Z = hierarchy.linkage(dist_matrix, 'complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Hierarchical clustering is typically visualized as a dendrogram as shown in the following cell. Each merge is represented by a horizontal line. The y-coordinate of the horizontal line is the similarity of the two clusters that were merged, where cities are viewed as singleton clusters. \n",
    "By moving up from the bottom layer to the top node, a dendrogram allows us to reconstruct the history of merges that resulted in the depicted clustering. \n",
    "\n",
    "Next, we will save the dendrogram to a variable called <b>dendro</b>. In doing this, the dendrogram will also be displayed.\n",
    "Using the <b> dendrogram </b> class from hierarchy, pass in the parameter:\n",
    "<ul> <li> Z </li> </ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "dendro = hierarchy.dendrogram(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "We used __complete__ linkage for our case, change it to __average__ linkage to see how the dendogram changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  \n"
     ]
    },
    {
     "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": [
    "# write your code here\n",
    "Z2 = hierarchy.linkage(dist_matrix, 'average')\n",
    "dendro2 = hierarchy.dendrogram(Z2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Your answer is below:\n",
    "    \n",
    "Z = hierarchy.linkage(dist_matrix, 'average')\n",
    "dendro = hierarchy.dendrogram(Z)\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h1 id=\"clustering_vehicle_dataset\">Clustering on Vehicle dataset</h1>\n",
    "\n",
    "Imagine that an automobile manufacturer has developed prototypes for a new vehicle. Before introducing the new model into its range, the manufacturer wants to determine which existing vehicles on the market are most like the prototypes--that is, how vehicles can be grouped, which group is the most similar with the model, and therefore which models they will be competing against.\n",
    "\n",
    "Our objective here, is to use clustering methods, to find the most distinctive clusters of vehicles. It will summarize the existing vehicles and help manufacturers to make decision about the supply of new models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data\n",
    "To download the data, we will use **`!wget`** to download it from IBM Object Storage.  \n",
    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-07-16 02:08:15--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cars_clus.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 17774 (17K) [text/csv]\n",
      "Saving to: ‘cars_clus.csv’\n",
      "\n",
      "cars_clus.csv       100%[===================>]  17.36K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2019-07-16 02:08:15 (828 KB/s) - ‘cars_clus.csv’ saved [17774/17774]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O cars_clus.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cars_clus.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data\n",
    "lets read dataset to see what features the manufacturer has collected about the existing models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset:  (159, 16)\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>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0.000</td>\n",
       "      <td>21.500</td>\n",
       "      <td>1.800</td>\n",
       "      <td>140.000</td>\n",
       "      <td>101.200</td>\n",
       "      <td>67.300</td>\n",
       "      <td>172.400</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.200</td>\n",
       "      <td>28.000</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0.000</td>\n",
       "      <td>28.400</td>\n",
       "      <td>3.200</td>\n",
       "      <td>225.000</td>\n",
       "      <td>108.100</td>\n",
       "      <td>70.300</td>\n",
       "      <td>192.900</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.200</td>\n",
       "      <td>25.000</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acura</td>\n",
       "      <td>CL</td>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>0.000</td>\n",
       "      <td>$null$</td>\n",
       "      <td>3.200</td>\n",
       "      <td>225.000</td>\n",
       "      <td>106.900</td>\n",
       "      <td>70.600</td>\n",
       "      <td>192.000</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.200</td>\n",
       "      <td>26.000</td>\n",
       "      <td>2.647</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0.000</td>\n",
       "      <td>42.000</td>\n",
       "      <td>3.500</td>\n",
       "      <td>210.000</td>\n",
       "      <td>114.600</td>\n",
       "      <td>71.400</td>\n",
       "      <td>196.600</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.000</td>\n",
       "      <td>22.000</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.990</td>\n",
       "      <td>1.800</td>\n",
       "      <td>150.000</td>\n",
       "      <td>102.600</td>\n",
       "      <td>68.200</td>\n",
       "      <td>178.000</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.400</td>\n",
       "      <td>27.000</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufact    model   sales  resale   type   price engine_s horsepow wheelbas  \\\n",
       "0    Acura  Integra  16.919  16.360  0.000  21.500    1.800  140.000  101.200   \n",
       "1    Acura       TL  39.384  19.875  0.000  28.400    3.200  225.000  108.100   \n",
       "2    Acura       CL  14.114  18.225  0.000  $null$    3.200  225.000  106.900   \n",
       "3    Acura       RL   8.588  29.725  0.000  42.000    3.500  210.000  114.600   \n",
       "4     Audi       A4  20.397  22.255  0.000  23.990    1.800  150.000  102.600   \n",
       "\n",
       "    width   length curb_wgt fuel_cap     mpg lnsales  partition  \n",
       "0  67.300  172.400    2.639   13.200  28.000   2.828        0.0  \n",
       "1  70.300  192.900    3.517   17.200  25.000   3.673        0.0  \n",
       "2  70.600  192.000    3.470   17.200  26.000   2.647        0.0  \n",
       "3  71.400  196.600    3.850   18.000  22.000   2.150        0.0  \n",
       "4  68.200  178.000    2.998   16.400  27.000   3.015        0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'cars_clus.csv'\n",
    "\n",
    "#Read csv\n",
    "pdf = pd.read_csv(filename)\n",
    "print (\"Shape of dataset: \", pdf.shape)\n",
    "\n",
    "pdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature sets include  price in thousands (price), engine size (engine_s), horsepower (horsepow), wheelbase (wheelbas), width (width), length (length), curb weight (curb_wgt), fuel capacity (fuel_cap) and fuel efficiency (mpg)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"data_cleaning\">Data Cleaning</h2>\n",
    "lets simply clear the dataset by dropping the rows that have null value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset before cleaning:  2544\n",
      "Shape of dataset after cleaning:  1872\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>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A6</td>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.95</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.933</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufact    model   sales  resale  type  price  engine_s  horsepow  \\\n",
       "0    Acura  Integra  16.919  16.360   0.0  21.50       1.8     140.0   \n",
       "1    Acura       TL  39.384  19.875   0.0  28.40       3.2     225.0   \n",
       "2    Acura       RL   8.588  29.725   0.0  42.00       3.5     210.0   \n",
       "3     Audi       A4  20.397  22.255   0.0  23.99       1.8     150.0   \n",
       "4     Audi       A6  18.780  23.555   0.0  33.95       2.8     200.0   \n",
       "\n",
       "   wheelbas  width  length  curb_wgt  fuel_cap   mpg  lnsales  partition  \n",
       "0     101.2   67.3   172.4     2.639      13.2  28.0    2.828        0.0  \n",
       "1     108.1   70.3   192.9     3.517      17.2  25.0    3.673        0.0  \n",
       "2     114.6   71.4   196.6     3.850      18.0  22.0    2.150        0.0  \n",
       "3     102.6   68.2   178.0     2.998      16.4  27.0    3.015        0.0  \n",
       "4     108.7   76.1   192.0     3.561      18.5  22.0    2.933        0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"Shape of dataset before cleaning: \", pdf.size)\n",
    "pdf[[ 'sales', 'resale', 'type', 'price', 'engine_s',\n",
    "       'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap',\n",
    "       'mpg', 'lnsales']] = pdf[['sales', 'resale', 'type', 'price', 'engine_s',\n",
    "       'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap',\n",
    "       'mpg', 'lnsales']].apply(pd.to_numeric, errors='coerce')\n",
    "pdf = pdf.dropna()\n",
    "pdf = pdf.reset_index(drop=True)\n",
    "print (\"Shape of dataset after cleaning: \", pdf.size)\n",
    "pdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selection\n",
    "Lets select our feature set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "featureset = pdf[['engine_s',  'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap', 'mpg']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization\n",
    "Now we can normalize the feature set. __MinMaxScaler__ transforms features by scaling each feature to a given range. It is by default (0, 1). That is, this estimator scales and translates each feature individually such that it is between zero and one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.11428571, 0.21518987, 0.18655098, 0.28143713, 0.30625832,\n",
       "        0.2310559 , 0.13364055, 0.43333333],\n",
       "       [0.31428571, 0.43037975, 0.3362256 , 0.46107784, 0.5792277 ,\n",
       "        0.50372671, 0.31797235, 0.33333333],\n",
       "       [0.35714286, 0.39240506, 0.47722343, 0.52694611, 0.62849534,\n",
       "        0.60714286, 0.35483871, 0.23333333],\n",
       "       [0.11428571, 0.24050633, 0.21691974, 0.33532934, 0.38082557,\n",
       "        0.34254658, 0.28110599, 0.4       ],\n",
       "       [0.25714286, 0.36708861, 0.34924078, 0.80838323, 0.56724368,\n",
       "        0.5173913 , 0.37788018, 0.23333333]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "x = featureset.values #returns a numpy array\n",
    "min_max_scaler = MinMaxScaler()\n",
    "feature_mtx = min_max_scaler.fit_transform(x)\n",
    "feature_mtx [0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"clustering_using_scipy\">Clustering using Scipy</h2>\n",
    "In this part we use Scipy package to cluster the dataset:  \n",
    "First, we calculate the distance matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "leng = feature_mtx.shape[0]\n",
    "D = scipy.zeros([leng,leng])\n",
    "for i in range(leng):\n",
    "    for j in range(leng):\n",
    "        D[i,j] = scipy.spatial.distance.euclidean(feature_mtx[i], feature_mtx[j])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In agglomerative clustering, at each iteration, the algorithm must update the distance matrix to reflect the distance of the newly formed cluster with the remaining clusters in the forest. \n",
    "The following methods are supported in Scipy for calculating the distance between the newly formed cluster and each:\n",
    "    - single\n",
    "    - complete\n",
    "    - average\n",
    "    - weighted\n",
    "    - centroid\n",
    "    \n",
    "    \n",
    "We use __complete__ for our case, but feel free to change it to see how the results change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "import pylab\n",
    "import scipy.cluster.hierarchy\n",
    "Z = hierarchy.linkage(D, 'complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Essentially, Hierarchical clustering does not require a pre-specified number of clusters. However, in some applications we want a partition of disjoint clusters just as in flat clustering.\n",
    "So you can use a cutting line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  5,  5,  6,  5,  4,  6,  5,  5,  5,  5,  5,  4,  4,  5,  1,  6,\n",
       "        5,  5,  5,  4,  2, 11,  6,  6,  5,  6,  5,  1,  6,  6, 10,  9,  8,\n",
       "        9,  3,  5,  1,  7,  6,  5,  3,  5,  3,  8,  7,  9,  2,  6,  6,  5,\n",
       "        4,  2,  1,  6,  5,  2,  7,  5,  5,  5,  4,  4,  3,  2,  6,  6,  5,\n",
       "        7,  4,  7,  6,  6,  5,  3,  5,  5,  6,  5,  4,  4,  1,  6,  5,  5,\n",
       "        5,  6,  4,  5,  4,  1,  6,  5,  6,  6,  5,  5,  5,  7,  7,  7,  2,\n",
       "        2,  1,  2,  6,  5,  1,  1,  1,  7,  8,  1,  1,  6,  1,  1],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "max_d = 3\n",
    "clusters = fcluster(Z, max_d, criterion='distance')\n",
    "clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, you can determine the number of clusters directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 2, 2, 3, 1, 3, 3, 3, 3, 2, 1,\n",
       "       5, 3, 3, 3, 3, 3, 1, 3, 3, 4, 4, 4, 4, 2, 3, 1, 3, 3, 3, 2, 3, 2,\n",
       "       4, 3, 4, 1, 3, 3, 3, 2, 1, 1, 3, 3, 1, 3, 3, 3, 3, 2, 2, 2, 1, 3,\n",
       "       3, 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 2, 1, 3, 3, 3, 3, 3, 2,\n",
       "       3, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 1,\n",
       "       3, 4, 1, 1, 3, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "k = 5\n",
    "clusters = fcluster(Z, k, criterion='maxclust')\n",
    "clusters\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, plot the dendrogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pylab.figure(figsize=(18,50))\n",
    "def llf(id):\n",
    "    return '[%s %s %s]' % (pdf['manufact'][id], pdf['model'][id], int(float(pdf['type'][id])) )\n",
    "    \n",
    "dendro = hierarchy.dendrogram(Z,  leaf_label_func=llf, leaf_rotation=0, leaf_font_size =12, orientation = 'right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"clustering_using_skl\">Clustering using scikit-learn</h2>\n",
    "Lets redo it again, but this time using scikit-learn package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_matrix = distance_matrix(feature_mtx,feature_mtx) \n",
    "print(dist_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can use the 'AgglomerativeClustering' function from scikit-learn library to cluster the dataset. The AgglomerativeClustering performs a hierarchical clustering using a bottom up approach. The linkage criteria determines the metric used for the merge strategy:\n",
    "\n",
    "- Ward minimizes the sum of squared differences within all clusters. It is a variance-minimizing approach and in this sense is similar to the k-means objective function but tackled with an agglomerative hierarchical approach.\n",
    "- Maximum or complete linkage minimizes the maximum distance between observations of pairs of clusters.\n",
    "- Average linkage minimizes the average of the distances between all observations of pairs of clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agglom = AgglomerativeClustering(n_clusters = 6, linkage = 'complete')\n",
    "agglom.fit(feature_mtx)\n",
    "agglom.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, we can add a new field to our dataframe to show the cluster of each row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf['cluster_'] = agglom.labels_\n",
    "pdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.cm as cm\n",
    "n_clusters = max(agglom.labels_)+1\n",
    "colors = cm.rainbow(np.linspace(0, 1, n_clusters))\n",
    "cluster_labels = list(range(0, n_clusters))\n",
    "\n",
    "# Create a figure of size 6 inches by 4 inches.\n",
    "plt.figure(figsize=(16,14))\n",
    "\n",
    "for color, label in zip(colors, cluster_labels):\n",
    "    subset = pdf[pdf.cluster_ == label]\n",
    "    for i in subset.index:\n",
    "            plt.text(subset.horsepow[i], subset.mpg[i],str(subset['model'][i]), rotation=25) \n",
    "    plt.scatter(subset.horsepow, subset.mpg, s= subset.price*10, c=color, label='cluster'+str(label),alpha=0.5)\n",
    "#    plt.scatter(subset.horsepow, subset.mpg)\n",
    "plt.legend()\n",
    "plt.title('Clusters')\n",
    "plt.xlabel('horsepow')\n",
    "plt.ylabel('mpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we are seeing the distribution of each cluster using the scatter plot, but it is not very clear where is the centroid of each cluster. Moreover, there are 2 types of vehicles in our dataset, \"truck\" (value of 1 in the type column) and \"car\" (value of 1 in the type column). So, we use them to distinguish the classes, and summarize the cluster. First we count the number of cases in each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf.groupby(['cluster_','type'])['cluster_'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can look at the characteristics of each cluster:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_cars = pdf.groupby(['cluster_','type'])['horsepow','engine_s','mpg','price'].mean()\n",
    "agg_cars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "It is obvious that we have 3 main clusters with the majority of vehicles in those.\n",
    "\n",
    "__Cars__:\n",
    "- Cluster 1: with almost high mpg, and low in horsepower.\n",
    "- Cluster 2: with good mpg and horsepower, but higher price than average.\n",
    "- Cluster 3: with low mpg, high horsepower, highest price.\n",
    "    \n",
    "    \n",
    "    \n",
    "__Trucks__:\n",
    "- Cluster 1: with almost highest mpg among trucks, and lowest in horsepower and price.\n",
    "- Cluster 2: with almost low mpg and medium horsepower, but higher price than average.\n",
    "- Cluster 3: with good mpg and horsepower, low price.\n",
    "\n",
    "\n",
    "Please notice that we did not use __type__ , and __price__ of cars in the clustering process, but Hierarchical clustering could forge the clusters and discriminate them with quite high accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "for color, label in zip(colors, cluster_labels):\n",
    "    subset = agg_cars.loc[(label,),]\n",
    "    for i in subset.index:\n",
    "        plt.text(subset.loc[i][0]+5, subset.loc[i][2], 'type='+str(int(i)) + ', price='+str(int(subset.loc[i][3]))+'k')\n",
    "    plt.scatter(subset.horsepow, subset.mpg, s=subset.price*20, c=color, label='cluster'+str(label))\n",
    "plt.legend()\n",
    "plt.title('Clusters')\n",
    "plt.xlabel('horsepow')\n",
    "plt.ylabel('mpg')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
    "\n",
    "<h3>Thanks for completing this lesson!</h3>\n",
    "\n",
    "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
    "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
    "\n",
    "<hr>\n",
    "\n",
    "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
   ]
  }
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