demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering Text \n",
    "\n",
    "In the following notebook, we try to extract sense from text data using clustering. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import import_data, preprocess\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.cluster import KMeans\n",
    "import re\n",
    "from vect_utils import c_distance, simple_tokenizer\n",
    "from scipy.sparse import csr_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from cluster import K_Means\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Preprocessing\n",
    "\n",
    "We first get our data, which is present in the data.txt file. \n",
    "\n",
    "we then preprocess it, using our preprocessing function, written in the `utils.py` file. \n",
    "\n",
    "pre-processing includes \n",
    "- converting it to lower case\n",
    "- stemming\n",
    "- removing stopwords\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2172\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "filepath = 'data.txt'\n",
    "\n",
    "data_as_lines, data_as_words = import_data(filepath)\n",
    "l = preprocess(data_as_words)\n",
    "\n",
    "\n",
    "vectorizer = TfidfVectorizer(\n",
    "    use_idf=True, tokenizer=simple_tokenizer, \n",
    "    max_features=100,\n",
    "    stop_words='english')\n",
    "\n",
    "X = vectorizer.fit_transform(l)\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=50)\n",
    "reduced = pca.fit_transform(X.todense())\n",
    "\n",
    "\n",
    "\n",
    "from itertools import cycle\n",
    "cycol = cycle('bgrcmk')\n",
    "\n",
    "\n",
    "t = reduced.transpose()\n",
    "print len(t[0])\n",
    "for k in xrange(t.shape[1]):\n",
    "    plt.scatter(t[3], t[4], c=np.random.rand(3,))\n",
    "plt.xlim(-0.04, -0.01)\n",
    "plt.ylim(-0.1, 0.05)\n",
    "plt.show()\n",
    "# fig = plt.figure()\n",
    "# ax = fig.add_subplot(111, projection='3d')\n",
    "# \n",
    "#     ax.scatter(t[0][k], t[1][k], t[2][k], c=cycol.next())\n",
    "# plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting it into a vector Space\n",
    "\n",
    "After that, we pass the pre-processed data into our vectorizer, or word2vec model. Here we use NLTKs in built vectorizer function. \n",
    "\n",
    "The following converts each word, using context words, into a vector. Thus our entire data, can now be expressed as vectors and has essentially been converted into a vector space.\n",
    "\n",
    "We attempt to picturise the data, by reducing it's dimnesions into 2 and 3 dimensions and plotting a graph for it.\n",
    "\n",
    "Although in the 2D ir 3D form, it may not give an accurate picture of how co-related each word, however, we can roughly assume, that words which are present close to each can be thought as similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "for k in xrange(t.shape[1]):\n",
    "    ax.scatter(t[0][k], t[1][k], t[2][k], c=cycol.next())\n",
    "plt.xlim(-0.04, -0.01)\n",
    "plt.ylim(-0.1, 0.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have our vectorized data, we now feed it to the K_means clustering algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_clusters = 5\n",
    "km = KMeans(n_clusters=number_of_clusters)\n",
    "\n",
    "km_own = K_Means(k=5)\n",
    "km_own.fit(X.todense())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working\n",
    "\n",
    "Given our vectors, the K-Means with the number of clusters, K-means, tries to find, global clusters, for each word belongs too, in a way, it triews to find, the most common and used parts of the graph.\n",
    "\n",
    "The algorithm for K-Means, is written in the `cluster.py` file\n",
    "\n",
    "The process for the algotihm is\n",
    "- randomly initialising centroids\n",
    "- at each step, the distance from each point in space to each to centorid is calculated.\n",
    "- The centroid which is closest for each point is the centroid which is associated with the point. Using these points, a new centroid is calculated which is the average of all the points associated with it.\n",
    "- The points new-distances too are calculated and they shift position towards their centroids\n",
    "- it thens repeats the process till a fixed number of iterations, or till the distance moved by the centroid after an iteration becomes negligble. \n",
    "\n",
    "\n",
    "After the process, we print the centroids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: nan, 1: nan, 2: array([[ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "         0.01342282,  0.01342282,  0.        ,  0.01006711,  0.01677852,\n",
      "         0.01006711,  0.        ,  0.02348993,  0.03355705,  0.        ,\n",
      "         0.        ,  0.        ,  0.03020134,  0.02018439,  0.01006711,\n",
      "         0.        ,  0.03020134,  0.01006711,  0.        ,  0.        ,\n",
      "         0.02684564,  0.        ,  0.        ,  0.        ,  0.        ,\n",
      "         0.        ,  0.        ,  0.01006711,  0.03020134,  0.02013423,\n",
      "         0.        ,  0.04697987,  0.        ,  0.01342282,  0.01006711,\n",
      "         0.        ,  0.02684564,  0.        ,  0.        ,  0.        ,\n",
      "         0.01006711,  0.01006711,  0.        ,  0.01677852,  0.        ,\n",
      "         0.01006711,  0.        ,  0.01006711,  0.        ,  0.        ,\n",
      "         0.02013423,  0.04362416,  0.        ,  0.02348993,  0.        ,\n",
      "         0.02604003,  0.0704698 ,  0.        ,  0.01342282,  0.        ,\n",
      "         0.02348993,  0.02013423,  0.03020134,  0.00662251,  0.        ,\n",
      "         0.        ,  0.01006711,  0.02013423,  0.01006711,  0.        ,\n",
      "         0.        ,  0.03783391,  0.        ,  0.        ,  0.01006711,\n",
      "         0.        ,  0.08389262,  0.01677852,  0.03187734,  0.        ,\n",
      "         0.02348993,  0.02013423,  0.        ,  0.        ,  0.        ,\n",
      "         0.        ,  0.        ,  0.        ,  0.        ,  0.01006711]]), 3: nan, 4: array([[ 0.00213447,  0.00160085,  0.00160085,  0.00266809,  0.00480256,\n",
      "         0.01120598,  0.00640342,  0.00213447,  0.00213447,  0.00213447,\n",
      "         0.        ,  0.        ,  0.00426894,  0.        ,  0.        ,\n",
      "         0.        ,  0.00426894,  0.        ,  0.        ,  0.00266809,\n",
      "         0.00213447,  0.00205778,  0.        ,  0.        ,  0.        ,\n",
      "         0.00160085,  0.        ,  0.        ,  0.00373533,  0.00266809,\n",
      "         0.        ,  0.00320171,  0.00426894,  0.00213447,  0.00213447,\n",
      "         0.00213447,  0.00213447,  0.        ,  0.        ,  0.        ,\n",
      "         0.00213447,  0.        ,  0.00213447,  0.        ,  0.        ,\n",
      "         0.00320171,  0.        ,  0.00640342,  0.00373533,  0.00213447,\n",
      "         0.        ,  0.        ,  0.00480256,  0.        ,  0.00160085,\n",
      "         0.        ,  0.00160085,  0.        ,  0.00213447,  0.00160085,\n",
      "         0.        ,  0.        ,  0.00160085,  0.        ,  0.00320171,\n",
      "         0.        ,  0.        ,  0.00213447,  0.        ,  0.00266809,\n",
      "         0.        ,  0.        ,  0.        ,  0.01013874,  0.00373533,\n",
      "         0.00213447,  0.        ,  0.        ,  0.        ,  0.00160085,\n",
      "         0.00160085,  0.00169831,  0.00426894,  0.00320171,  0.        ,\n",
      "         0.00160085,  0.        ,  0.        ,  0.        ,  0.00426894,\n",
      "         0.        ,  0.        ,  0.00320171,  0.00373533,  0.00320171,\n",
      "         0.00213447,  0.00426894,  0.00426894,  0.00160085,  0.        ]])}\n"
     ]
    }
   ],
   "source": [
    "print km_own.get_clusters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then pick the top words, associated with each centroid. \n",
    "\n",
    "Thus since we know the cenroids are places, which we have a greater number of words, picking up the top words associated with a centroid, indirectly gives us an important `topic` of the document.\n",
    "\n",
    "Such as the ones displayed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0: time phone t mrs like attention house\n",
      "Cluster 1: apps years honest experience experiment facebook feel\n",
      "Cluster 2: s trump people years going easy email\n",
      "Cluster 3: iphone years grateful email experience experiment facebook\n",
      "Cluster 4: delete years grateful experience experiment facebook feel\n"
     ]
    }
   ],
   "source": [
    "km.fit(X)\n",
    "order_centroids = km.cluster_centers_.argsort()[:, ::-1]\n",
    "terms = vectorizer.get_feature_names()\n",
    "\n",
    "centroids = []\n",
    "for i in range(number_of_clusters):\n",
    "    top_words = [terms[ind] for ind in order_centroids[i, :7]]\n",
    "    centroids.append(' '.join(top_words))\n",
    "    print \"Cluster {}: {}\".format(i, ' '.join(top_words)) "
   ]
  }
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