Hierarchical Clustering Lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical Clustering Lab\n",
    "In this notebook, we will be using sklearn to conduct hierarchical clustering on the [Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris) which contains 4 dimensions/attributes and 150 samples. Each sample is labeled as one of the three type of Iris flowers.\n",
    "\n",
    "In this exercise, we'll ignore the labeling and cluster based on the attributes, then we'll compare the results of different hierarchical clustering techniques with the original labels to see which one does a better job in this scenario. We'll then proceed to visualize the resulting cluster hierarchies.\n",
    "\n",
    "## 1. Importing the Iris dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A look at the first 10 samples in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 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]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```iris.target``` contains the labels that indicate which type of Iris flower each sample is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 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,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 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,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Clustering\n",
    "Let's now use sklearn's [```AgglomerativeClustering```](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html) to conduct the heirarchical clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "# Hierarchical clustering\n",
    "# Ward is the default linkage algorithm, so we'll start with that\n",
    "ward = AgglomerativeClustering(n_clusters=3)\n",
    "ward_pred = ward.fit_predict(iris.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also try complete and average linkages\n",
    "\n",
    "**Exercise**:\n",
    "* Conduct hierarchical clustering with complete linkage, store the predicted labels in the variable ```complete_pred```\n",
    "* Conduct hierarchical clustering with average linkage, store the predicted labels in the variable ```avg_pred```\n",
    "\n",
    "Note: look at the documentation of [```AgglomerativeClustering```](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html) to find the appropriate value to pass as the ```linkage``` value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hierarchical clustering using complete linkage\n",
    "# TODO: Create an instance of AgglomerativeClustering with the appropriate parameters\n",
    "complete = AgglomerativeClustering(n_clusters=3, linkage=\"complete\")\n",
    "# Fit & predict\n",
    "# TODO: Make AgglomerativeClustering fit the dataset and predict the cluster labels\n",
    "complete_pred = complete.fit_predict(iris.data)\n",
    "\n",
    "# Hierarchical clustering using average linkage\n",
    "# TODO: Create an instance of AgglomerativeClustering with the appropriate parameters\n",
    "avg = AgglomerativeClustering(n_clusters=3, linkage=\"average\")\n",
    "# Fit & predict\n",
    "# TODO: Make AgglomerativeClustering fit the dataset and predict the cluster labels\n",
    "avg_pred = avg.fit_predict(iris.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine which clustering result better matches the original labels of the samples, we can use ```adjusted_rand_score``` which is an *external cluster validation index* which results in a score between -1 and 1, where 1 means two clusterings are identical of how they grouped the samples in a dataset (regardless of what label is assigned to each cluster).\n",
    "\n",
    "Cluster validation indices are discussed later in the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import adjusted_rand_score\n",
    "\n",
    "ward_ar_score = adjusted_rand_score(iris.target, ward_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**:\n",
    "* Calculate the Adjusted Rand score of the clusters resulting from complete linkage and average linkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Calculated the adjusted Rand score for the complete linkage clustering labels\n",
    "complete_ar_score = adjusted_rand_score(iris.target, complete_pred)\n",
    "\n",
    "# TODO: Calculated the adjusted Rand score for the average linkage clustering labels\n",
    "avg_ar_score = adjusted_rand_score(iris.target, avg_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which algorithm results in the higher Adjusted Rand Score?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: \n",
      "Ward: 0.731198556771 \n",
      "Complete:  0.642251251836 \n",
      "Average:  0.759198707107\n"
     ]
    }
   ],
   "source": [
    "print( \"Scores: \\nWard:\", ward_ar_score,\"\\nComplete: \", complete_ar_score, \"\\nAverage: \", avg_ar_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. The Effect of Normalization on Clustering\n",
    "\n",
    "Can we improve on this clustering result?\n",
    "\n",
    "Let's take another look at the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 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",
       "       [ 5.4,  3.7,  1.5,  0.2],\n",
       "       [ 4.8,  3.4,  1.6,  0.2],\n",
       "       [ 4.8,  3. ,  1.4,  0.1],\n",
       "       [ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.8,  4. ,  1.2,  0.2]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at this, we can see that the forth column has smaller values than the rest of the columns, and so its variance counts for less in the clustering process (since clustering is based on distance). Let us [normalize](https://en.wikipedia.org/wiki/Feature_scaling) the dataset so that each dimension lies between 0 and 1, so they have equal weight in the clustering process.\n",
    "\n",
    "This is done by subtracting the minimum from each column then dividing the difference by the range.\n",
    "\n",
    "sklearn provides us with a useful utility called ```preprocessing.normalize()``` that can do that for us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.80377277,  0.55160877,  0.22064351,  0.0315205 ],\n",
       "       [ 0.82813287,  0.50702013,  0.23660939,  0.03380134],\n",
       "       [ 0.80533308,  0.54831188,  0.2227517 ,  0.03426949],\n",
       "       [ 0.80003025,  0.53915082,  0.26087943,  0.03478392],\n",
       "       [ 0.790965  ,  0.5694948 ,  0.2214702 ,  0.0316386 ],\n",
       "       [ 0.78417499,  0.5663486 ,  0.2468699 ,  0.05808704],\n",
       "       [ 0.78010936,  0.57660257,  0.23742459,  0.0508767 ],\n",
       "       [ 0.80218492,  0.54548574,  0.24065548,  0.0320874 ],\n",
       "       [ 0.80642366,  0.5315065 ,  0.25658935,  0.03665562],\n",
       "       [ 0.81803119,  0.51752994,  0.25041771,  0.01669451]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "normalized_X = preprocessing.normalize(iris.data)\n",
    "normalized_X[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now all the columns are in the range between 0 and 1. Would clustering the dataset after this transformation lead to a better clustering? (one that better matches the original labels of the samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: \n",
      "Ward: 0.885697031028 \n",
      "Complete:  0.644447235392 \n",
      "Average:  0.558371443754\n"
     ]
    }
   ],
   "source": [
    "ward = AgglomerativeClustering(n_clusters=3)\n",
    "ward_pred = ward.fit_predict(normalized_X)\n",
    "\n",
    "complete = AgglomerativeClustering(n_clusters=3, linkage=\"complete\")\n",
    "complete_pred = complete.fit_predict(normalized_X)\n",
    "\n",
    "avg = AgglomerativeClustering(n_clusters=3, linkage=\"average\")\n",
    "avg_pred = avg.fit_predict(normalized_X)\n",
    "\n",
    "\n",
    "ward_ar_score = adjusted_rand_score(iris.target, ward_pred)\n",
    "complete_ar_score = adjusted_rand_score(iris.target, complete_pred)\n",
    "avg_ar_score = adjusted_rand_score(iris.target, avg_pred)\n",
    "\n",
    "print( \"Scores: \\nWard:\", ward_ar_score,\"\\nComplete: \", complete_ar_score, \"\\nAverage: \", avg_ar_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Dendrogram visualization with scipy\n",
    "\n",
    "Let's visualize the highest scoring clustering result. \n",
    "\n",
    "To do that, we'll need to use Scipy's [```linkage```](https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html) function to perform the clusteirng again so we can obtain the linkage matrix it will later use to visualize the hierarchy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import scipy's linkage function to conduct the clustering\n",
    "from scipy.cluster.hierarchy import linkage\n",
    "\n",
    "# Specify the linkage type. Scipy accepts 'ward', 'complete', 'average', as well as other values\n",
    "# Pick the one that resulted in the highest Adjusted Rand Score\n",
    "linkage_type = 'ward'\n",
    "\n",
    "linkage_matrix = linkage(normalized_X, linkage_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot using scipy's [dendrogram](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.cluster.hierarchy.dendrogram.html) function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f612804bef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import dendrogram\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(22,18))\n",
    "\n",
    "# plot using 'dendrogram()'\n",
    "dendrogram(linkage_matrix)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Visualization with Seaborn's ```clustermap``` \n",
    "\n",
    "The [seaborn](http://seaborn.pydata.org/index.html) plotting library for python can plot a [clustermap](http://seaborn.pydata.org/generated/seaborn.clustermap.html), which is a detailed dendrogram which also visualizes the dataset in more detail. It conducts the clustering as well, so we only need to pass it the dataset and the linkage type we want, and it will use scipy internally to conduct the clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6120a49e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.clustermap(normalized_X, figsize=(18, 50), method=linkage_type, cmap='viridis')\n",
    "\n",
    "# Expand figsize to a value like (18, 50) if you want the sample labels to be readable\n",
    "# Draw back is that you'll need more scrolling to observe the dendrogram\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the colors of the dimensions can you observe how they differ between the three type of flowers? You should at least be able to notice how one is vastly different from the two others (in the top third of the image)."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}