iris-hierarchical-clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical Clustering Lab\n",
    "This project uses 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 project, we 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,\n",
       "       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, 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,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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,\n",
       "       2, 2, 2, 2, 2, 2, 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",
    "Below code conducts hierarchical clustering with complete linkage, store the predicted labels in the variable ```complete_pred```. It also conducts hierarchical clustering with average linkage, stores 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) for more information about the appropriate value to pass as the ```linkage``` value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hierarchical clustering using complete linkage\n",
    "# Create an instance of AgglomerativeClustering with the appropriate parameters\n",
    "complete = AgglomerativeClustering(n_clusters=3, linkage='complete')\n",
    "# Fit & predict\n",
    "# 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",
    "# Create an instance of AgglomerativeClustering with the appropriate parameters\n",
    "avg = AgglomerativeClustering(n_clusters=3, linkage='average')\n",
    "# Fit & predict\n",
    "# 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)."
   ]
  },
  {
   "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": [
    "The code cell below calculates the Adjusted Rand score of the clusters resulting from complete linkage and average linkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculated the adjusted Rand score for the complete linkage clustering labels\n",
    "complete_ar_score = adjusted_rand_score(iris.target, complete_pred)\n",
    "\n",
    "# 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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: \n",
      "Ward: 0.7311985567707745 \n",
      "Complete:  0.64225125183629 \n",
      "Average:  0.7591987071071522\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": 9,
   "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": 9,
     "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": 10,
   "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": 10,
     "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). Let's see!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: \n",
      "Ward: 0.8856970310281228 \n",
      "Complete:  0.644447235392006 \n",
      "Average:  0.5583714437541352\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": 14,
   "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": [
       "<Figure size 1584x1296 with 1 Axes>"
      ]
     },
     "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": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.clustermap(normalized_X, figsize=(12,18), 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 we can observe how they differ between the three type of flowers (we can 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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}