clustering/K-means.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### kmeans on embeddings with elbow curve and silhouette analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import pickle \n",
    "import matplotlib.pyplot as plt\n",
    "import ast\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6425\n"
     ]
    }
   ],
   "source": [
    "# load embeddings and their labels \n",
    "\n",
    "with open('data/vb_obj_ftext_ner.pkl', 'rb') as f:\n",
    "    vo_rep  = pickle.load(f)\n",
    "\n",
    "with open('data/vb_obj_ner_ftextLabels.pkl', 'rb') as f:\n",
    "    lab_vec_map = pickle.load(f)\n",
    "\n",
    "label = []\n",
    "for vec in vo_rep:\n",
    "    label.append(lab_vec_map[str(vec)])\n",
    "\n",
    "print(len(vo_rep))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6425, 50)\n"
     ]
    }
   ],
   "source": [
    "# reduce dimensions and standardize values\n",
    "X = np.array(vo_rep) \n",
    "rX = PCA(n_components=50).fit_transform(X)\n",
    "\n",
    "\n",
    "X = StandardScaler().fit_transform(rX)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### plot elbow curve for finding n_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sse = {}\n",
    "\n",
    "for k in range(2, 100):\n",
    "    kmeans = KMeans(n_clusters=k, n_jobs=-1, verbose=1).fit(X)\n",
    "    labels = kmeans.labels_\n",
    "    #print(data[\"clusters\"])\n",
    "    sse[k] = kmeans.inertia_ # Inertia: Sum of distances of samples to their closest cluster center \n",
    "\n",
    "    \n",
    "plt.figure()\n",
    "plt.plot(list(sse.keys()), list(sse.values()))\n",
    "plt.xlabel(\"Number of cluster\")\n",
    "plt.ylabel(\"SSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  0  0 ... 25 34  6]\n",
      "For n_clusters = 52 The average silhouette_score is : 0.46571034\n",
      "[39  2  2 ...  0 13 26]\n",
      "For n_clusters = 53 The average silhouette_score is : 0.47843617\n",
      "[49  0  0 ... 50 28 50]\n",
      "For n_clusters = 54 The average silhouette_score is : 0.4891542\n",
      "[50  1  1 ... 37 11 37]\n",
      "For n_clusters = 55 The average silhouette_score is : 0.4928847\n",
      "[43  1  1 ... 44 29 42]\n",
      "For n_clusters = 56 The average silhouette_score is : 0.49054626\n",
      "[50  1  1 ... 53 31 32]\n",
      "For n_clusters = 57 The average silhouette_score is : 0.48307347\n",
      "[48  4  4 ...  0 29  0]\n",
      "For n_clusters = 58 The average silhouette_score is : 0.48680505\n",
      "[55  0  0 ... 24  9 53]\n",
      "For n_clusters = 59 The average silhouette_score is : 0.49755704\n",
      "[43  4  4 ... 53 10 51]\n",
      "For n_clusters = 60 The average silhouette_score is : 0.491469\n",
      "[ 0  1  1 ... 39 30 39]\n",
      "For n_clusters = 61 The average silhouette_score is : 0.5007934\n",
      "[ 1  1  1 ... 56 13 39]\n",
      "For n_clusters = 62 The average silhouette_score is : 0.5067101\n",
      "[56  2  2 ... 55 28 54]\n",
      "For n_clusters = 63 The average silhouette_score is : 0.52085143\n",
      "[60  1  1 ... 45 17 45]\n",
      "For n_clusters = 64 The average silhouette_score is : 0.50442827\n",
      "[55  0  0 ... 43 22 43]\n",
      "For n_clusters = 65 The average silhouette_score is : 0.5298432\n",
      "[60  1  1 ... 42 13 50]\n",
      "For n_clusters = 66 The average silhouette_score is : 0.52321637\n",
      "[ 9 12 12 ... 51 35 52]\n",
      "For n_clusters = 67 The average silhouette_score is : 0.5343192\n",
      "[57  3  3 ... 29  9 61]\n",
      "For n_clusters = 68 The average silhouette_score is : 0.52730525\n",
      "[18  2  2 ... 68 44  1]\n",
      "For n_clusters = 69 The average silhouette_score is : 0.52553725\n",
      "[62  0  0 ... 60 19 29]\n",
      "For n_clusters = 70 The average silhouette_score is : 0.5321308\n",
      "[53  1  1 ... 43 37 60]\n",
      "For n_clusters = 75 The average silhouette_score is : 0.5481871\n",
      "[28  1  1 ... 56 16 36]\n",
      "For n_clusters = 80 The average silhouette_score is : 0.557217\n"
     ]
    }
   ],
   "source": [
    "### silhouette analysis - n with higher score around the elbow value is good\n",
    "\n",
    "range_n_clusters = [40,42,43,44,45,46,52,53,54,55,56,57,58,59,60,61,62,63]\n",
    "figures = []\n",
    "\n",
    "\n",
    "for n_clusters in range_n_clusters:\n",
    "    # Create a subplot with 1 row and 2 columns\n",
    "    fig = tools.make_subplots(rows=1, cols=2,\n",
    "                              print_grid=False,\n",
    "                              subplot_titles=('The silhouette plot for the various clusters.',\n",
    "                                              'The visualization of the clustered data.'))\n",
    "\n",
    "    # The 1st subplot is the silhouette plot\n",
    "    # The silhouette coefficient can range from -1, 1\n",
    "    fig['layout']['xaxis1'].update(title='The silhouette coefficient values',\n",
    "                                   range=[-1, 1])\n",
    "   \n",
    "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    fig['layout']['yaxis1'].update(title='Cluster label',\n",
    "                                   showticklabels=False,\n",
    "                                   range=[0, len(X) + (n_clusters + 1) * 10])\n",
    "\n",
    "    # Initialize the clusterer with n_clusters value and a random generator\n",
    "    # seed of 10 for reproducibility.\n",
    "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
    "    cluster_labels = clusterer.fit_predict(X)\n",
    "    \n",
    "    print(cluster_labels)\n",
    "\n",
    "    # The silhouette_score gives the average value for all the samples.\n",
    "    # This gives a perspective into the density and separation of the formed\n",
    "    # clusters\n",
    "    silhouette_avg = silhouette_score(X, cluster_labels)\n",
    "    print(\"For n_clusters =\", n_clusters,\n",
    "          \"The average silhouette_score is :\", silhouette_avg)\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(X, cluster_labels)\n",
    "    y_lower = 10\n",
    "    \n",
    "    for i in range(n_clusters):\n",
    "        # Aggregate the silhouette scores for samples belonging to\n",
    "        # cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = \\\n",
    "            sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "#         colors = cm.spectral(cluster_labels.astype(float) / n_clusters)\n",
    "        cmap = cm.get_cmap(\"Spectral\")\n",
    "        colors = cmap(cluster_labels / n_clusters)\n",
    "        \n",
    "        filled_area = go.Scatter(y=np.arange(y_lower, y_upper),\n",
    "                                 x=ith_cluster_silhouette_values,\n",
    "                                 mode='lines',\n",
    "                                 showlegend=False,\n",
    "                                 line=dict(width=0.5,\n",
    "                                          color=colors),\n",
    "                                 fill='tozerox'\n",
    "                                )\n",
    "        fig.append_trace(filled_area, 1, 1)\n",
    "        \n",
    "        # Compute the new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "        \n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    axis_line = go.Scatter(x=[silhouette_avg],\n",
    "                           y=[0, len(X) + (n_clusters + 1) * 10],\n",
    "                           showlegend=False,\n",
    "                           mode='lines',\n",
    "                           line=dict(color=\"red\", dash='dash',\n",
    "                                     width =1) )\n",
    "\n",
    "    fig.append_trace(axis_line, 1, 1)\n",
    "    \n",
    "    # 2nd Plot showing the actual clusters formed\n",
    "    \n",
    "    colors = matplotlib.colors.colorConverter.to_rgb(cmap(float(i) / n_clusters))\n",
    "    colors = 'rgb'+str(colors)\n",
    "    clusters = go.Scatter(x=X[:, 0], \n",
    "                          y=X[:, 1], \n",
    "                          showlegend=False,\n",
    "                          mode='markers',\n",
    "                          marker=dict(color=colors,\n",
    "                                     size=4)\n",
    "                         )\n",
    "    fig.append_trace(clusters, 1, 2)\n",
    "    \n",
    "    # Labeling the clusters\n",
    "    centers_ = clusterer.cluster_centers_\n",
    "    # Draw white circles at cluster centers\n",
    "    centers = go.Scatter(x=centers_[:, 0], \n",
    "                         y=centers_[:, 1],\n",
    "                         showlegend=False,\n",
    "                         mode='markers',\n",
    "                         marker=dict(color='green', size=10,\n",
    "                                     line=dict(color='black',\n",
    "                                                             width=1))\n",
    "                        )\n",
    "\n",
    "    fig.append_trace(centers, 1, 2)\n",
    "    \n",
    "    fig['layout']['xaxis2'].update(title='Feature space for the 1st feature',\n",
    "                                   zeroline=False)\n",
    "    fig['layout']['yaxis2'].update(title='Feature space for the 2nd feature',\n",
    "                                  zeroline=False)\n",
    "                              \n",
    "\n",
    "    fig['layout'].update(title=\"Silhouette analysis for KMeans clustering on sample data \"\n",
    "                         \"with n_clusters = %d\" % n_clusters)\n",
    "    \n",
    "    figures.append(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file:///home/rrichajalota/work/DBpedia Chatlog Analysis/temp-plot.html'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from  plotly.offline import plot\n",
    "plot(figures[-6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file:///home/rrichajalota/work/DBpedia Chatlog Analysis/temp-plot.html'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(figures[12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file:///home/rrichajalota/work/DBpedia Chatlog Analysis/temp-plot.html'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(figures[11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file:///home/rrichajalota/work/DBpedia Chatlog Analysis/temp-plot.html'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(figures[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'file:///home/rrichajalota/work/DBpedia Chatlog Analysis/temp-plot.html'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(figures[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2492928358>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the clusters\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Scaling the data to normalize\n",
    "model = KMeans(n_clusters=67).fit(X)\n",
    "\n",
    "# Visualize it:\n",
    "plt.figure(figsize=(16, 16))\n",
    "plt.scatter(X[:,0], X[:,1], c=model.labels_.astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67\n",
      "Counter({2: 1817, 7: 430, 49: 335, 1: 305, 38: 297, 4: 283, 0: 267, 9: 223, 3: 186, 63: 182, 37: 177, 41: 171, 64: 118, 17: 111, 23: 103, 10: 84, 65: 72, 47: 71, 12: 71, 45: 64, 14: 61, 46: 59, 39: 56, 40: 54, 25: 50, 43: 49, 60: 48, 29: 40, 26: 38, 31: 36, 59: 36, 24: 34, 32: 33, 51: 33, 55: 32, 56: 28, 54: 28, 48: 27, 35: 23, 34: 23, 20: 20, 33: 18, 53: 17, 61: 17, 27: 16, 42: 16, 15: 16, 66: 14, 18: 14, 8: 12, 16: 12, 6: 11, 11: 10, 62: 10, 57: 9, 36: 9, 52: 7, 30: 7, 58: 6, 21: 5, 19: 5, 13: 5, 5: 4, 50: 4, 44: 3, 28: 2, 22: 1})\n"
     ]
    }
   ],
   "source": [
    "# find the no. of points in each cluster\n",
    "from collections import Counter, defaultdict\n",
    "print(Counter(model.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "centroids  = kmeans.cluster_centers_\n",
    "centroid_labels = [centroids[i] for i in labels]\n",
    "print(centroid_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top terms per cluster:\n",
      "Cluster 0:\n",
      " who church\n",
      " what organization\n",
      " swife？ who\n",
      " person who\n",
      " get city\n",
      " person who\n",
      " learn person\n",
      " city germany\n",
      " person who\n",
      " get city\n",
      "Cluster 1:\n",
      " get city\n",
      " what organization\n",
      " person who\n",
      " person who\n",
      " get city\n",
      " person who\n",
      " what organization\n",
      " person who\n",
      " name cat\n",
      " person who\n",
      "Cluster 2:\n",
      " swife？ who\n",
      " person who\n",
      " get city\n",
      " birthdate person\n",
      " person who\n",
      " learn county\n",
      " what place\n",
      " population country\n",
      " what organization\n",
      " learning tower\n",
      "Cluster 3:\n",
      " place country\n",
      " place lithuania\n",
      " learn county\n",
      " get city\n",
      " mayor place\n",
      " what organization\n",
      " get city\n",
      " person who\n",
      " person who\n",
      " tower place\n",
      "Cluster 4:\n",
      " person who\n",
      " person who\n",
      " get city\n",
      " get city\n",
      " directed creativework\n",
      " place country\n",
      " what organization\n",
      " load person\n",
      " what place\n",
      " place lithuania\n",
      "Cluster 5:\n",
      " load person\n",
      " get city\n",
      " person who\n",
      " learn person\n",
      " person who\n",
      " get city\n",
      " load person\n",
      " get city\n",
      " get city\n",
      " place lithuania\n",
      "Cluster 6:\n",
      " mountain who\n",
      " get city\n",
      " what country\n",
      " person who\n",
      " get city\n",
      " tower place\n",
      " swife？ who\n",
      " get city\n",
      " learn person\n",
      " person who\n",
      "Cluster 7:\n",
      " city country\n",
      " person who\n",
      " get city\n",
      " learn person\n",
      " get city\n",
      " population country\n",
      " capital country\n",
      " place lithuania\n",
      " place country\n",
      " learn person\n",
      "Cluster 8:\n",
      " mayor place\n",
      " what country\n",
      " tower place\n",
      " learn person\n",
      " person who\n",
      " get city\n",
      " get city\n",
      " get city\n",
      " person who\n",
      " get city\n",
      "Cluster 9:\n",
      " what organization\n",
      " get city\n",
      " get city\n",
      " place lithuania\n",
      " what organization\n",
      " what place\n",
      " population country\n",
      " city germany\n",
      " person who\n",
      " what organization\n",
      "Cluster 10:\n",
      " person who\n",
      " get city\n",
      " person who\n",
      " swife？ who\n",
      " get city\n",
      " person who\n",
      " person who\n",
      " person who\n",
      " get city\n",
      " tower place\n",
      "Cluster 11:\n",
      " person who\n",
      " get city\n",
      " place lithuania\n",
      " what place\n",
      " person who\n",
      " get city\n",
      " mountain who\n",
      " members musicgroup\n",
      " get city\n",
      " directed creativework\n",
      "Cluster 12:\n",
      " what organization\n",
      " get city\n",
      " learn person\n",
      " load person\n",
      " get city\n",
      " swife？ who\n",
      " get city\n",
      " learn county\n",
      " person who\n",
      " place lithuania\n",
      "Cluster 13:\n",
      " members musicgroup\n",
      " city country\n",
      " get city\n",
      " city germany\n",
      " has members\n",
      " place country\n",
      " get city\n",
      " person who\n",
      " birthdate person\n",
      " get city\n",
      "Cluster 14:\n",
      " what organization\n",
      " name cat\n",
      " members musicgroup\n",
      " get city\n",
      " learn person\n",
      " population country\n",
      " what country\n",
      " directed creativework\n",
      " place lithuania\n",
      " learn person\n",
      "Cluster 15:\n",
      " learn person\n",
      " directed creativework\n",
      " person who\n",
      " mountain who\n",
      " what organization\n",
      " what country\n",
      " birthdate person\n",
      " capital country\n",
      " person who\n",
      " city country\n",
      "Cluster 16:\n",
      " get city\n",
      " learn county\n",
      " what place\n",
      " get city\n",
      " load person\n",
      " members musicgroup\n",
      " learn person\n",
      " load person\n",
      " person who\n",
      " get city\n",
      "Cluster 17:\n",
      " person who\n",
      " person who\n",
      " get city\n",
      " members musicgroup\n",
      " birthdate person\n",
      " place lithuania\n",
      " get city\n",
      " get city\n",
      " has members\n",
      " mountain who\n",
      "Cluster 18:\n",
      " person who\n",
      " learn person\n",
      " get city\n",
      " has members\n",
      " learning tower\n",
      " mayor place\n",
      " load person\n",
      " get city\n",
      " name cat\n",
      " get city\n",
      "Cluster 19:\n",
      " person who\n",
      " birthdate person\n",
      " directed creativework\n",
      " person who\n",
      " load person\n",
      " what organization\n",
      " get city\n",
      " mayor place\n",
      " person who\n",
      " swife？ who\n",
      "Cluster 20:\n",
      " city country\n",
      " name cat\n",
      " person who\n",
      " get city\n",
      " person who\n",
      " learn person\n",
      " learn person\n",
      " person who\n",
      " learn person\n",
      " directed creativework\n",
      "Cluster 21:\n",
      " learn person\n",
      " get city\n",
      " learn county\n",
      " get city\n",
      " person who\n",
      " place lithuania\n",
      " get city\n",
      " directed creativework\n",
      " load person\n",
      " get city\n",
      "Cluster 22:\n",
      " get city\n",
      " what organization\n",
      " learn person\n",
      " get city\n",
      " capital country\n",
      " what place\n",
      " load person\n",
      " mayor place\n",
      " person who\n",
      " load person\n",
      "Cluster 23:\n",
      " get city\n",
      " get city\n",
      " get city\n",
      " learn person\n",
      " get city\n",
      " person who\n",
      " get city\n",
      " city germany\n",
      " directed creativework\n",
      " mayor place\n",
      "Cluster 24:\n",
      " get city\n",
      " name cat\n",
      " capital country\n",
      " members musicgroup\n",
      " load person\n",
      " get city\n",
      " person who\n",
      " learn person\n",
      " members musicgroup\n",
      " what organization\n",
      "Cluster 25:\n",
      " get city\n",
      " what organization\n",
      " get city\n",
      " learning tower\n",
      " mayor place\n",
      " get city\n",
      " get city\n",
      " person who\n",
      " tower place\n",
      " mountain who\n",
      "Cluster 26:\n",
      " person who\n",
      " what organization\n",
      " get city\n",
      " get city\n",
      " learn person\n",
      " swife？ who\n",
      " person who\n",
      " load person\n",
      " birthdate person\n",
      " person who\n",
      "Cluster 27:\n",
      " person who\n",
      " load person\n",
      " learning tower\n",
      " get city\n",
      " get city\n",
      " place lithuania\n",
      " learn person\n",
      " directed creativework\n",
      " person who\n",
      " swife？ who\n",
      "Cluster 28:\n",
      " has members\n",
      " person who\n",
      " mountain who\n",
      " members musicgroup\n",
      " learning tower\n",
      " load person\n",
      " what country\n",
      " what organization\n",
      " person who\n",
      " swife？ who\n",
      "Cluster 29:\n",
      " population country\n",
      " capital country\n",
      " person who\n",
      " tower place\n",
      " has members\n",
      " load person\n",
      " person who\n",
      " get city\n",
      " get city\n",
      " directed creativework\n",
      "Cluster 30:\n",
      " learning tower\n",
      " get city\n",
      " population country\n",
      " get city\n",
      " get city\n",
      " members musicgroup\n",
      " load person\n",
      " learn person\n",
      " what organization\n",
      " swife？ who\n",
      "Cluster 31:\n",
      " get city\n",
      " load person\n",
      " place country\n",
      " person who\n",
      " city germany\n",
      " person who\n",
      " get city\n",
      " mountain who\n",
      " get city\n",
      " learn person\n",
      "Cluster 32:\n",
      " city germany\n",
      " city country\n",
      " learn person\n",
      " person who\n",
      " get city\n",
      " person who\n",
      " capital country\n",
      " person who\n",
      " what organization\n",
      " get city\n",
      "Cluster 33:\n",
      " what country\n",
      " get city\n",
      " members musicgroup\n",
      " person who\n",
      " capital country\n",
      " get city\n",
      " what organization\n",
      " what organization\n",
      " get city\n",
      " person who\n",
      "Cluster 34:\n",
      " person who\n",
      " learn county\n",
      " mountain who\n",
      " person who\n",
      " get city\n",
      " what organization\n",
      " learn person\n",
      " load person\n",
      " load person\n",
      " person who\n",
      "Cluster 35:\n",
      " get city\n",
      " who church\n",
      " load person\n",
      " person who\n",
      " learn county\n",
      " place country\n",
      " person who\n",
      " get city\n",
      " what organization\n",
      " city country\n",
      "Cluster 36:\n",
      " get city\n",
      " learn person\n",
      " person who\n",
      " get city\n",
      " person who\n",
      " birthdate person\n",
      " get city\n",
      " get city\n",
      " person who\n",
      " city germany\n",
      "Cluster 37:\n",
      " person who\n",
      " person who\n",
      " learn person\n",
      " swife？ who\n",
      " birthdate person\n",
      " city germany\n",
      " get city\n",
      " person who\n",
      " what organization\n",
      " learn person\n",
      "Cluster 38:\n",
      " has members\n",
      " population country\n",
      " get city\n",
      " birthdate person\n",
      " what organization\n",
      " city country\n",
      " get city\n",
      " get city\n",
      " what place\n",
      " person who\n",
      "Cluster 39:\n",
      " get city\n",
      " what country\n",
      " person who\n",
      " learning tower\n",
      " place country\n",
      " who church\n",
      " get city\n",
      " person who\n",
      " person who\n",
      " person who\n",
      "Cluster 40:\n",
      " birthdate person\n",
      " name cat\n",
      " place country\n",
      " get city\n",
      " person who\n",
      " mountain who\n",
      " tower place\n",
      " person who\n",
      " get city\n",
      " learn person\n",
      "Cluster 41:\n",
      " city germany\n",
      " person who\n",
      " person who\n",
      " get city\n",
      " birthdate person\n",
      " learn person\n",
      " get city\n",
      " person who\n",
      " mayor place\n",
      " learn county\n",
      "Cluster 42:\n",
      " place country\n",
      " get city\n",
      " load person\n",
      " person who\n",
      " members musicgroup\n",
      " city germany\n",
      " get city\n",
      " person who\n",
      " learn person\n",
      " mayor place\n",
      "Cluster 43:\n",
      " person who\n",
      " person who\n",
      " get city\n",
      " birthdate person\n",
      " what organization\n",
      " get city\n",
      " city germany\n",
      " what organization\n",
      " has members\n",
      " load person\n",
      "Cluster 44:\n",
      " person who\n",
      " what organization\n",
      " mountain who\n",
      " what place\n",
      " person who\n",
      " tower place\n",
      " get city\n",
      " get city\n",
      " learn person\n",
      " has members\n",
      "Cluster 45:\n",
      " get city\n",
      " person who\n",
      " get city\n",
      " learning tower\n",
      " place country\n",
      " mountain who\n",
      " has members\n",
      " person who\n",
      " person who\n",
      " capital country\n",
      "Cluster 46:\n",
      " members musicgroup\n",
      " person who\n",
      " person who\n",
      " get city\n",
      " what organization\n",
      " get city\n",
      " directed creativework\n",
      " learn county\n",
      " learning tower\n",
      " learn person\n",
      "Cluster 47:\n",
      " what organization\n",
      " what place\n",
      " get city\n",
      " learn person\n",
      " mayor place\n",
      " get city\n",
      " name cat\n",
      " has members\n",
      " capital country\n",
      " person who\n",
      "Cluster 48:\n",
      " get city\n",
      " load person\n",
      " person who\n",
      " name cat\n",
      " directed creativework\n",
      " what place\n",
      " load person\n",
      " learn person\n",
      " swife？ who\n",
      " place lithuania\n",
      "Cluster 49:\n",
      " person who\n",
      " what organization\n",
      " get city\n",
      " city country\n",
      " tower place\n",
      " what organization\n",
      " get city\n",
      " get city\n",
      " city germany\n",
      " get city\n",
      "Cluster 50:\n",
      " load person\n",
      " person who\n",
      " person who\n",
      " members musicgroup\n",
      " what place\n",
      " what country\n",
      " city country\n",
      " learn person\n",
      " swife？ who\n",
      " learn county\n",
      "Cluster 51:\n",
      " birthdate person\n",
      " place country\n",
      " person who\n",
      " name cat\n",
      " get city\n",
      " person who\n",
      " what place\n",
      " load person\n",
      " get city\n",
      " members musicgroup\n",
      "Cluster 52:\n",
      " population country\n",
      " learn county\n",
      " person who\n",
      " get city\n",
      " person who\n",
      " what organization\n",
      " place country\n",
      " person who\n",
      " place lithuania\n",
      " birthdate person\n",
      "Cluster 53:\n",
      " birthdate person\n",
      " get city\n",
      " learn person\n",
      " what organization\n",
      " get city\n",
      " mayor place\n",
      " get city\n",
      " tower place\n",
      " get city\n",
      " get city\n",
      "Cluster 54:\n",
      " swife？ who\n",
      " what place\n",
      " person who\n",
      " what organization\n",
      " city country\n",
      " learning tower\n",
      " get city\n",
      " learn county\n",
      " birthdate person\n",
      " person who\n",
      "Cluster 55:\n",
      " name cat\n",
      " load person\n",
      " mayor place\n",
      " swife？ who\n",
      " population country\n",
      " get city\n",
      " city country\n",
      " get city\n",
      " load person\n",
      " place country\n",
      "Cluster 56:\n",
      " person who\n",
      " person who\n",
      " get city\n",
      " load person\n",
      " swife？ who\n",
      " person who\n",
      " name cat\n",
      " city germany\n",
      " learn county\n",
      " city country\n",
      "Cluster 57:\n",
      " get city\n",
      " person who\n",
      " person who\n",
      " learn person\n",
      " learning tower\n",
      " get city\n",
      " person who\n",
      " members musicgroup\n",
      " what place\n",
      " get city\n",
      "Cluster 58:\n",
      " person who\n",
      " person who\n",
      " person who\n",
      " learning tower\n",
      " what country\n",
      " get city\n",
      " mountain who\n",
      " city country\n",
      " get city\n",
      " get city\n",
      "Cluster 59:\n",
      " what place\n",
      " city germany\n",
      " person who\n",
      " mayor place\n",
      " person who\n",
      " get city\n",
      " capital country\n",
      " members musicgroup\n",
      " load person\n",
      " person who\n",
      "Cluster 60:\n",
      " capital country\n",
      " person who\n",
      " get city\n",
      " person who\n",
      " mayor place\n",
      " mountain who\n",
      " person who\n",
      " population country\n",
      " learn person\n",
      " get city\n",
      "Cluster 61:\n",
      " population country\n",
      " tower place\n",
      " get city\n",
      " learn person\n",
      " directed creativework\n",
      " learn person\n",
      " mayor place\n",
      " what place\n",
      " place lithuania\n",
      " city germany\n",
      "Cluster 62:\n",
      " person who\n",
      " get city\n",
      " tower place\n",
      " get city\n",
      " get city\n",
      " load person\n",
      " what organization\n",
      " get city\n",
      " city country\n",
      " get city\n",
      "Cluster 63:\n",
      " learn county\n",
      " tower place\n",
      " what organization\n",
      " person who\n",
      " get city\n",
      " load person\n",
      " mayor place\n",
      " person who\n",
      " learning tower\n",
      " place country\n",
      "Cluster 64:\n",
      " person who\n",
      " learning tower\n",
      " get city\n",
      " person who\n",
      " get city\n",
      " learn person\n",
      " directed creativework\n",
      " get city\n",
      " capital country\n",
      " person who\n",
      "Cluster 65:\n",
      " get city\n",
      " get city\n",
      " person who\n",
      " place country\n",
      " capital country\n",
      " person who\n",
      " city germany\n",
      " learning tower\n",
      " members musicgroup\n",
      " tower place\n",
      "Cluster 66:\n",
      " learn person\n",
      " load person\n",
      " person who\n",
      " directed creativework\n",
      " mayor place\n",
      " birthdate person\n",
      " has members\n",
      " what organization\n",
      " swife？ who\n",
      " get city\n"
     ]
    }
   ],
   "source": [
    "print(\"Top terms per cluster:\")\n",
    "order_centroids = kmeans.cluster_centers_.argsort()[:, ::-1]\n",
    "\n",
    "for i in range(67):\n",
    "    print(\"Cluster %d:\" % i)\n",
    "    for ind in order_centroids[i, :10]:\n",
    "        print(' %s' % label[ind])\n",
    "    print\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rrichajalota/work/DBpedia Chatlog Analysis/venv/lib/python3.6/site-packages/numba/compiler.py:602: NumbaPerformanceWarning:\n",
      "\n",
      "\u001b[1m\n",
      "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n",
      "\n",
      "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n",
      "\u001b[1m\n",
      "File \"venv/lib/python3.6/site-packages/umap/nndescent.py\", line 47:\u001b[0m\n",
      "\u001b[1m    @numba.njit(parallel=True)\n",
      "\u001b[1m    def nn_descent(\n",
      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "\n",
      "/home/rrichajalota/work/DBpedia Chatlog Analysis/venv/lib/python3.6/site-packages/umap/spectral.py:229: UserWarning:\n",
      "\n",
      "Embedding a total of 23 separate connected components using meta-embedding (experimental)\n",
      "\n",
      "/home/rrichajalota/work/DBpedia Chatlog Analysis/venv/lib/python3.6/site-packages/sklearn/manifold/spectral_embedding_.py:235: UserWarning:\n",
      "\n",
      "Graph is not fully connected, spectral embedding may not work as expected.\n",
      "\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "tuple index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-d87c8ffdbeaa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mstandard_embedding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mumap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUMAP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstandard_embedding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstandard_embedding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Spectral'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/work/DBpedia Chatlog Analysis/venv/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m   2845\u001b[0m         \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0medgecolors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2846\u001b[0m         plotnonfinite=plotnonfinite, **({\"data\": data} if data is not\n\u001b[0;32m-> 2847\u001b[0;31m         None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2848\u001b[0m     \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2849\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/work/DBpedia Chatlog Analysis/venv/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1599\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1601\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1603\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/work/DBpedia Chatlog Analysis/venv/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4452\u001b[0m             self._parse_scatter_color_args(\n\u001b[1;32m   4453\u001b[0m                 \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myshape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4454\u001b[0;31m                 get_next_color_func=self._get_patches_for_fill.get_next_color)\n\u001b[0m\u001b[1;32m   4455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4456\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mplotnonfinite\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcolors\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/work/DBpedia Chatlog Analysis/venv/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36m_parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xshape, yshape, get_next_color_func)\u001b[0m\n\u001b[1;32m   4263\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# First, does 'c' look suitable for value-mapping?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4264\u001b[0m                 \u001b[0mc_array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4265\u001b[0;31m                 \u001b[0mn_elem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4266\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mc_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mxshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myshape\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4267\u001b[0m                     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # using umap\n",
    "# import umap\n",
    "# from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score\n",
    "\n",
    "# standard_embedding = umap.UMAP(random_state=42).fit_transform(X)\n",
    "# plt.scatter(standard_embedding[:, 0], standard_embedding[:, 1], c=len(label), s=0.1, cmap='Spectral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #reduce the features to 2D\n",
    "# from sklearn.decomposition import PCA\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# pca = PCA(n_components=2, random_state=42)\n",
    "# reduced_features = pca.fit_transform(vo_pair)\n",
    "\n",
    "# # reduce the cluster centers to 2D\n",
    "# reduced_cluster_centers = pca.transform(kmeans.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.scatter(reduced_features[:,0], reduced_features[:,1], c=kmeans.predict(vo_pair))\n",
    "# plt.scatter(reduced_cluster_centers[:, 0], reduced_cluster_centers[:,1], marker='x', s=150, c='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import collections\n",
    "# clustering = collections.defaultdict(list)\n",
    " \n",
    "# for idx, label in enumerate(kmeans.labels_):\n",
    "#     clustering[label].append(idx)\n",
    "\n",
    "# pprint.pprint(clustering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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