animal_attribute_clustering.ipynb

{
 "cells": [
  {
   "source": [
    "# Animal Attribute-Based Cluster Comparison"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "This repo relies on data from the \"Animals with Attributes\" dataset found [here](https://cvml.ist.ac.at/AwA/). Only the base package is utilized, meaning all clustering is text / attribute based. Two methods of clustering are explored, K-Means and hierarchical/agglomerative.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Environment Setup"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "A setup section similar to my other DS repos, downloads and manipulates the necessary files and then cleans up a bit afterwards."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grab files, unzip\n",
    "import requests, tarfile\n",
    "\n",
    "!rm -rf w10q02\n",
    "\n",
    "file_url = 'https://cvml.ist.ac.at/AwA/AwA-base.tar.bz2'\n",
    "fname = file_url[file_url.find('/AwA/')+5:]\n",
    "\n",
    "r = requests.get(file_url)\n",
    "open(fname, 'wb').write(r.content)\n",
    "\n",
    "!mkdir w10q02\n",
    "\n",
    "tar = tarfile.open(fname, 'r:bz2')\n",
    "tar.extractall()\n",
    "tar.close()\n",
    "\n",
    "!mv ./Animals_with_Attributes/* ./w10q02/\n",
    "!rm -r Animals_with_Attributes AwA-base.tar.bz2"
   ]
  },
  {
   "source": [
    "## Cluster Method #1: K-Means"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cluster 0: killer+whale, blue+whale, humpback+whale, seal, otter, walrus, dolphin\nCluster 1: beaver, skunk, mole, hamster, squirrel, rabbit, rat, mouse, raccoon\nCluster 2: spider+monkey, gorilla, chimpanzee, giant+panda\nCluster 3: hippopotamus, moose, elephant, ox, sheep, rhinoceros, buffalo, pig, cow\nCluster 4: tiger, leopard, lion\nCluster 5: dalmatian, persian+cat, german+shepherd, siamese+cat, chihuahua, collie\nCluster 6: antelope, horse, giraffe, zebra, deer\nCluster 7: bat\nCluster 8: fox, wolf, weasel, bobcat\nCluster 9: grizzly+bear, polar+bear\n"
     ]
    }
   ],
   "source": [
    "# import data and build kmeans model\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "pred_matrix = np.loadtxt('./w10q02/predicate-matrix-continuous.txt')\n",
    "animals = np.loadtxt('./w10q02/classes.txt', dtype = 'str')[:, 1]\n",
    "k = KMeans(n_clusters = 10)\n",
    "k.fit(pred_matrix)\n",
    "clusters = k.labels_\n",
    "for i in range(10):\n",
    "    mask = [a for a in range(len(clusters)) if clusters[a] == i]\n",
    "    print(f'Cluster {i}: {\", \".join(animals[mask])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cluster 0: antelope, horse, hippopotamus, moose, elephant, ox, sheep, rhinoceros, giraffe, buffalo, zebra, giant+panda, deer, pig, cow\nCluster 1: grizzly+bear, beaver, dalmatian, persian+cat, german+shepherd, siamese+cat, skunk, mole, tiger, leopard, spider+monkey, gorilla, fox, chimpanzee, hamster, squirrel, rabbit, bat, wolf, chihuahua, rat, weasel, bobcat, lion, mouse, polar+bear, collie, raccoon\nCluster 2: killer+whale, blue+whale, humpback+whale, seal, otter, walrus, dolphin\n"
     ]
    }
   ],
   "source": [
    "# re-running this a k = 3, for later comparison\n",
    "k = KMeans(n_clusters = 3)\n",
    "k.fit(pred_matrix)\n",
    "clusters = k.labels_\n",
    "for i in range(3):\n",
    "    mask = [a for a in range(len(clusters)) if clusters[a] == i]\n",
    "    print(f'Cluster {i}: {\", \".join(animals[mask])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "k = 10 clusters\n",
    ">Cluster 0: dalmatian, persian+cat, german+shepherd, siamese+cat, chihuahua, collie\n",
    ">\n",
    ">Cluster 1: antelope, horse, giraffe, zebra, deer\n",
    ">\n",
    ">Cluster 2: killer+whale, blue+whale, humpback+whale, seal, otter, walrus, dolphin\n",
    ">\n",
    ">Cluster 3: moose, ox, sheep, buffalo, giant+panda, pig, cow\n",
    ">\n",
    ">Cluster 4: beaver, mole, fox, bat, rat, weasel\n",
    ">\n",
    ">Cluster 5: grizzly+bear, polar+bear\n",
    ">\n",
    ">Cluster 6: spider+monkey, gorilla, chimpanzee\n",
    ">\n",
    ">Cluster 7: hippopotamus, elephant, rhinoceros\n",
    ">\n",
    ">Cluster 8: tiger, leopard, wolf, bobcat, lion\n",
    ">\n",
    ">Cluster 9: skunk, hamster, squirrel, rabbit, mouse, raccoon\n",
    "\n",
    "Since the clusters change with each run of the above cell, I copied the output above for permanence. Each of the clusters appear to have a general theme, like how cluster 2 is strictly oceanic mammals or how cluster 6 is primates. Of the ten classes above, only two have animals included that are decidedly different than their group: cluster 3 with a 'giant+panda' (in a group of ungulates) and cluster 4 with a 'bat' (in a group of smaller oblong mammals). On the whole, the clusters look to be reasonably accurate, more so than originally expected.\n",
    "\n",
    "k = 3 clusters\n",
    "> Cluster 0: antelope, horse, hippopotamus, moose, elephant, ox, sheep, rhinoceros, giraffe, buffalo, zebra, deer, pig, cow\n",
    ">\n",
    ">Cluster 1: grizzly+bear, beaver, dalmatian, persian+cat, german+shepherd, siamese+cat, skunk, mole, tiger, leopard, spider+monkey, gorilla, fox, chimpanzee, hamster, squirrel, rabbit, bat, wolf, chihuahua, rat, weasel, giant+panda, bobcat, lion, mouse, polar+bear, collie, raccoon\n",
    ">\n",
    ">Cluster 2: killer+whale, blue+whale, humpback+whale, seal, otter, walrus, dolphin\n",
    "\n",
    "Later when the hierarchical method is run, it returns 3 main clusters. In order to more easily compare the two classifiers, this K-Means classifier was also run with a k = 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# optimizing the number of clusters\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sses = []\n",
    "ks = range(1, 10)\n",
    "for k in ks:\n",
    "    kmodel = KMeans(n_clusters = k)\n",
    "    kmodel.fit(pred_matrix)\n",
    "    sses.append(kmodel.inertia_)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(ks, sses, 'rx-')\n",
    "ax.set_xlabel('k values')\n",
    "ax.set_ylabel('SSEs')\n",
    "ax.set_title('Optimizing k via SSEs')\n",
    "plt.show()\n"
   ]
  },
  {
   "source": [
    "The optimal k value looks to be right at 4. I will run through the clusters once more just to see what the final cluster created looks like."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cluster 0: antelope, horse, hippopotamus, moose, elephant, ox, sheep, rhinoceros, giraffe, buffalo, zebra, deer, pig, cow\nCluster 1: beaver, dalmatian, persian+cat, siamese+cat, skunk, mole, spider+monkey, chimpanzee, hamster, squirrel, rabbit, bat, chihuahua, rat, weasel, giant+panda, mouse, collie, raccoon\nCluster 2: killer+whale, blue+whale, humpback+whale, seal, otter, walrus, dolphin\nCluster 3: grizzly+bear, german+shepherd, tiger, leopard, gorilla, fox, wolf, bobcat, lion, polar+bear\n"
     ]
    }
   ],
   "source": [
    "# cluster at k = 4\n",
    "k = KMeans(n_clusters = 4)\n",
    "k.fit(pred_matrix)\n",
    "clusters = k.labels_\n",
    "for i in range(4):\n",
    "    mask = [a for a in range(len(clusters)) if clusters[a] == i]\n",
    "    print(f'Cluster {i}: {\", \".join(animals[mask])}')"
   ]
  },
  {
   "source": [
    "k = 4 clusters\n",
    ">Cluster 0: beaver, dalmatian, persian+cat, siamese+cat, skunk, mole, spider+monkey, chimpanzee, hamster, squirrel, rabbit, bat, chihuahua, rat, weasel, mouse, collie, raccoon\n",
    ">\n",
    ">Cluster 1: antelope, horse, hippopotamus, moose, elephant, gorilla, ox, sheep, rhinoceros, giraffe, buffalo, zebra, giant+panda, deer, pig, cow\n",
    ">\n",
    ">Cluster 2: grizzly+bear, german+shepherd, tiger, leopard, fox, wolf, bobcat, lion, polar+bear\n",
    ">\n",
    ">Cluster 3: killer+whale, blue+whale, humpback+whale, seal, otter, walrus, dolphin\n",
    "\n",
    "Cluster copied into a markdown cell to freeze output. The final cluster looks to differentiate the larger k = 3 cluster 1 into smaller and larger mammals."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Cluster Method #2: Hierarchical/Agglomerative Linkages"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1440x1080 with 1 Axes>",
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id=\"ytick_22\">\n     <g id=\"text_31\">\n      <!-- mouse -->\n      <g transform=\"translate(55.43 475.017375)scale(0.08 -0.08)\">\n       <use xlink:href=\"#DejaVuSans-6d\"/>\n       <use x=\"97.412109\" xlink:href=\"#DejaVuSans-6f\"/>\n       <use x=\"158.59375\" xlink:href=\"#DejaVuSans-75\"/>\n       <use x=\"221.972656\" xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"274.072266\" xlink:href=\"#DejaVuSans-65\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_23\">\n     <g id=\"text_32\">\n      <!-- hamster -->\n      <g transform=\"translate(48.9975 458.709375)scale(0.08 -0.08)\">\n       <use xlink:href=\"#DejaVuSans-68\"/>\n       <use x=\"63.378906\" xlink:href=\"#DejaVuSans-61\"/>\n       <use x=\"124.658203\" xlink:href=\"#DejaVuSans-6d\"/>\n       <use x=\"222.070312\" xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"274.169922\" xlink:href=\"#DejaVuSans-74\"/>\n       <use x=\"313.378906\" xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"374.902344\" 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xlink:href=\"#DejaVuSans-72\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_30\">\n     <g id=\"text_39\">\n      <!-- otter -->\n      <g transform=\"translate(62.89875 344.553375)scale(0.08 -0.08)\">\n       <use xlink:href=\"#DejaVuSans-6f\"/>\n       <use x=\"61.181641\" xlink:href=\"#DejaVuSans-74\"/>\n       <use x=\"100.390625\" xlink:href=\"#DejaVuSans-74\"/>\n       <use x=\"139.599609\" xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"201.123047\" xlink:href=\"#DejaVuSans-72\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_31\">\n     <g id=\"text_40\">\n      <!-- blue+whale -->\n      <g transform=\"translate(34.62 328.245375)scale(0.08 -0.08)\">\n       <use xlink:href=\"#DejaVuSans-62\"/>\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-6c\"/>\n       <use x=\"91.259766\" xlink:href=\"#DejaVuSans-75\"/>\n       <use x=\"154.638672\" xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"216.162109\" xlink:href=\"#DejaVuSans-2b\"/>\n       <use 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xlink:href=\"#DejaVuSans-2b\"/>\n       <use x=\"609.082031\" xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"690.869141\" xlink:href=\"#DejaVuSans-68\"/>\n       <use x=\"754.248047\" xlink:href=\"#DejaVuSans-61\"/>\n       <use x=\"815.527344\" xlink:href=\"#DejaVuSans-6c\"/>\n       <use x=\"843.310547\" xlink:href=\"#DejaVuSans-65\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_33\">\n     <g id=\"text_42\">\n      <!-- walrus -->\n      <g transform=\"translate(56.08375 295.629375)scale(0.08 -0.08)\">\n       <use xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"81.787109\" xlink:href=\"#DejaVuSans-61\"/>\n       <use x=\"143.066406\" xlink:href=\"#DejaVuSans-6c\"/>\n       <use x=\"170.849609\" xlink:href=\"#DejaVuSans-72\"/>\n       <use x=\"211.962891\" xlink:href=\"#DejaVuSans-75\"/>\n       <use x=\"275.341797\" xlink:href=\"#DejaVuSans-73\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_34\">\n     <g id=\"text_43\">\n      <!-- killer+whale -->\n      <g transform=\"translate(32.4025 279.321375)scale(0.08 -0.08)\">\n       <use xlink:href=\"#DejaVuSans-6b\"/>\n       <use x=\"57.910156\" xlink:href=\"#DejaVuSans-69\"/>\n       <use x=\"85.693359\" xlink:href=\"#DejaVuSans-6c\"/>\n       <use x=\"113.476562\" xlink:href=\"#DejaVuSans-6c\"/>\n       <use x=\"141.259766\" xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"202.783203\" xlink:href=\"#DejaVuSans-72\"/>\n       <use x=\"243.896484\" xlink:href=\"#DejaVuSans-2b\"/>\n       <use x=\"327.685547\" xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"409.472656\" xlink:href=\"#DejaVuSans-68\"/>\n       <use x=\"472.851562\" xlink:href=\"#DejaVuSans-61\"/>\n       <use x=\"534.130859\" xlink:href=\"#DejaVuSans-6c\"/>\n       <use x=\"561.914062\" xlink:href=\"#DejaVuSans-65\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_35\">\n     <g id=\"text_44\">\n      <!-- seal -->\n      <g transform=\"translate(66.0625 263.013375)scale(0.08 -0.08)\">\n       <use xlink:href=\"#DejaVuSans-73\"/>\n       <use x=\"52.099609\" xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"113.623047\" xlink:href=\"#DejaVuSans-61\"/>\n       <use x=\"174.902344\" xlink:href=\"#DejaVuSans-6c\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_36\">\n     <g id=\"text_45\">\n      <!-- dolphin -->\n      <g transform=\"translate(52.64 246.705375)scale(0.08 -0.08)\">\n       <use xlink:href=\"#DejaVuSans-64\"/>\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-6f\"/>\n       <use x=\"124.658203\" xlink:href=\"#DejaVuSans-6c\"/>\n       <use x=\"152.441406\" xlink:href=\"#DejaVuSans-70\"/>\n       <use x=\"215.917969\" xlink:href=\"#DejaVuSans-68\"/>\n       <use x=\"279.296875\" xlink:href=\"#DejaVuSans-69\"/>\n       <use x=\"307.080078\" xlink:href=\"#DejaVuSans-6e\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_37\">\n     <g id=\"text_46\">\n      <!-- elephant -->\n      <g transform=\"translate(46.9525 230.397375)scale(0.08 -0.08)\">\n       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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# build hierarchical cluster\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "l = linkage(pred_matrix, 'ward')\n",
    "rcParams['figure.figsize'] = (20, 15)\n",
    "d = dendrogram(l, orientation = 'right', labels = animals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hierarchical clustering does look to follow logically, in that in addition to clustering animals according to shared attributes it also highlights how closely related clusters are with one another. In particular, it highlights which animals of a cluster are most similar (by attribute) to animal(s) of another cluster. The dendogram visualization provides a bit more insight into how the method clustered animal groups together, something not as transparently displayed via just the group names and participants K-Means cluster output. The dendrogram shows three major clusters: \"ungulates\", \"aquatic\", and \"all other\" animals.\n",
    "\n",
    "In comparing just the k = 3 clusters, they are close though not exactly the same. The hierarchical cluster method moved \"beavers\" in the \"aquatic animals\" cluster (green), while K-Means placed it with the \"all other\" land-based mammals."
   ]
  }
 ],
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   "file_extension": ".py",
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