clustering_module_solutions/4.solution_hierarchical_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Produce dendrograms for the heart disease dataset using three different linkage methods: `complete`, `average` and `ward`. Which linkage method do you think produces more reasonable result?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn import metrics\n",
    "from sqlalchemy import create_engine\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "postgres_user = 'dsbc_student'\n",
    "postgres_pw = '7*.8G9QH21'\n",
    "postgres_host = '142.93.121.174'\n",
    "postgres_port = '5432'\n",
    "postgres_db = 'heartdisease'\n",
    "\n",
    "engine = create_engine('postgresql://{}:{}@{}:{}/{}'.format(\n",
    "    postgres_user, postgres_pw, postgres_host, postgres_port, postgres_db))\n",
    "heartdisease_df = pd.read_sql_query('select * from heartdisease',con=engine)\n",
    "\n",
    "# no need for an open connection, as we're only doing a single query\n",
    "engine.dispose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the features and the outcome\n",
    "X = heartdisease_df.iloc[:, :13]\n",
    "y = heartdisease_df.iloc[:, 13]\n",
    "\n",
    "# Replace missing values (marked by ?) with a 0\n",
    "X = X.replace(to_replace='?', value=0)\n",
    "\n",
    "# Binarize y so that 1 means heart disease diagnosis and 0 means no diagnosis.\n",
    "y = np.where(y > 0, 0, 1)\n",
    "\n",
    "# Standardize the data.\n",
    "scaler = StandardScaler()\n",
    "X_std = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Dendrogram with linkage method: complete\")\n",
    "dendrogram(linkage(X_std, method='complete'))\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Dendrogram with linkage method: average\")\n",
    "dendrogram(linkage(X_std, method='average'))\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Dendrogram with linkage method: Ward\")\n",
    "dendrogram(linkage(X_std, method='ward'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The more reasonable result is that of the `ward` method's. The `complete` and `average` methods produced results that are hard to reconcile with the facts as their two cluster solutions has too few observations for one of the clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Apply agglomerative clustering to the heart disease data by setting `n_clusters=2`. Try the three linkage methods above and get ARI and silhouette scores for each of your solutions. Compare the results with each other and with that of the k-means solution that you implemented in the assignment of the previous checkpoint. Which algorithm and setting does perform better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARI score of linkage method average: 0.2940490133353465\n",
      "Silhouette score of linkage method average: 0.14837359969689895\n",
      "------------------------------------------------\n",
      "ARI score of linkage method complete: 0.21394030618551016\n",
      "Silhouette score of linkage method complete: 0.11730765444448985\n",
      "------------------------------------------------\n",
      "ARI score of linkage method ward: 0.146129913123814\n",
      "Silhouette score of linkage method ward: 0.1387197366557222\n"
     ]
    }
   ],
   "source": [
    "linkage_methods = [\"ward\", \"average\", \"complete\"]\n",
    "\n",
    "# Defining the agglomerative clustering\n",
    "agg_cluster = AgglomerativeClustering(linkage=\"average\", \n",
    "                                      affinity='cosine',\n",
    "                                      n_clusters=2)\n",
    "\n",
    "# Fit model\n",
    "clusters = agg_cluster.fit_predict(X_std)\n",
    "\n",
    "print(\"ARI score of linkage method average: {}\".format(\n",
    "    metrics.adjusted_rand_score(y, clusters)))\n",
    "\n",
    "print(\"Silhouette score of linkage method average: {}\".format(\n",
    "    metrics.silhouette_score(X_std, clusters, metric='euclidean')))\n",
    "print(\"------------------------------------------------\")\n",
    "\n",
    "# Defining the agglomerative clustering\n",
    "agg_cluster = AgglomerativeClustering(linkage=\"complete\", \n",
    "                                      affinity='cosine',\n",
    "                                      n_clusters=2)\n",
    "\n",
    "# Fit model\n",
    "clusters = agg_cluster.fit_predict(X_std)\n",
    "\n",
    "print(\"ARI score of linkage method complete: {}\".format(\n",
    "    metrics.adjusted_rand_score(y, clusters)))\n",
    "\n",
    "print(\"Silhouette score of linkage method complete: {}\".format(\n",
    "    metrics.silhouette_score(X_std, clusters, metric='euclidean')))\n",
    "print(\"------------------------------------------------\")\n",
    "\n",
    "# Defining the agglomerative clustering\n",
    "agg_cluster = AgglomerativeClustering(linkage=\"ward\", \n",
    "                                      affinity='euclidean',\n",
    "                                      n_clusters=2)\n",
    "\n",
    "# Fit model\n",
    "clusters = agg_cluster.fit_predict(X_std)\n",
    "\n",
    "print(\"ARI score of linkage method ward: {}\".format(\n",
    "    metrics.adjusted_rand_score(y, clusters)))\n",
    "\n",
    "print(\"Silhouette score of linkage method ward: {}\".format(\n",
    "    metrics.silhouette_score(X_std, clusters, metric='euclidean')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the results, the best scores are achieved by the linkage methode `average`. The ARI and silhouette scores of the two cluster k-means solution was 0.29 and 0.15 respectively. So, in terms of ARI the best model seems to be k-means, however in terms of silhouette score the best one is agglomerative clustering."
   ]
  }
 ],
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