twitter_Standard_NCPD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading packages and functions\n",
    "import torch\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "\n",
    "import sys\n",
    "sys.path.insert(1,\"./src\")\n",
    "\n",
    "from NNCPD import outer_product_np\n",
    "\n",
    "import tensorly as tl\n",
    "from tensorly import unfold as tl_unfold\n",
    "from tensorly.decomposition import parafac, non_negative_parafac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.set_default_tensor_type(torch.DoubleTensor)\n",
    "\n",
    "words = np.load(\"./data/words_100.npy\", allow_pickle=True)\n",
    "X = np.load(\"./data/tweets_bag_100.npy\", allow_pickle=True)\n",
    "\n",
    "X = X.reshape((8, 10, -1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 4 NCPD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reconstruction loss: 7.952349009822536\n",
      "Relative reconstruction loss: 0.8890996483383169\n",
      "\n"
     ]
    }
   ],
   "source": [
    "r=4\n",
    "factors_tl = non_negative_parafac(X, r, init=\"random\", random_state=13)[1]\n",
    "\n",
    "approx = outer_product_np(factors_tl[0], factors_tl[1], factors_tl[2])\n",
    "\n",
    "print(\"Reconstruction loss:\", np.linalg.norm(np.ndarray.flatten(X-approx), 2))\n",
    "print(\"Relative reconstruction loss:\", np.linalg.norm(np.ndarray.flatten(X-approx), 2)  / np.linalg.norm(np.ndarray.flatten(X), 2))\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rank 4 Keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RANK 4 KEYWORDS:\n",
      "------------------\n",
      "Topic 1     Topic 2        Topic 3     Topic 4     \n",
      "-------     -------        -------     -------     \n",
      "trump       berniesanders  crooked     tedcruz     \n",
      "hillary     people         hillary     cruz        \n",
      "johnkasich  bernie         thank       ted         \n",
      "ohio        must           marcorubio  internet    \n",
      "kasich      vote           great       choosecruz  \n"
     ]
    }
   ],
   "source": [
    "C = factors_tl[2]\n",
    "\n",
    "keywords = np.empty((7,4), dtype=object)\n",
    "\n",
    "for i in range(keywords.shape[1]):\n",
    "    keywords[0,i] = \"Topic \" + str(i+1)\n",
    "    keywords[1,i] = \"-------\"\n",
    "\n",
    "for i in range(C.shape[1]):\n",
    "    col = C[:,i]\n",
    "    top = col.argsort()\n",
    "    top = top[-5:][::-1]\n",
    "\n",
    "    keywords[2:,i] = words[top]\n",
    "\n",
    "print(\"\")\n",
    "print(\"RANK 4 KEYWORDS:\")  \n",
    "print(\"------------------\")\n",
    "\n",
    "col_widths = [max([len(keywords[i][j]) for i in range(keywords.shape[0])])+2 for j in range(keywords.shape[1])]\n",
    "for row in keywords:\n",
    "    print(\"\".join(row[i].ljust(col_widths[i]) for i in range(len(row))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rank 4 Factor Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x374.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, constrained_layout=True, figsize=(7,5.2))\n",
    "\n",
    "#color = \"binary\"\n",
    "color = \"viridis\"\n",
    "plt.sca(axs[0])\n",
    "plt.yticks(range(8), ['Clinton', 'Kaine', 'O\\'Malley', 'Sanders', 'Cruz', 'Kasich', 'Rubio', 'Trump'], fontsize=22)\n",
    "plt.xticks(range(r), range(1,r+1), fontsize=22)\n",
    "plt.sca(axs[1])\n",
    "plt.xticks(range(r), range(1,r+1), fontsize=22)\n",
    "plt.yticks(range(10), ['Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov'], fontsize=22)\n",
    "\n",
    "axs[0].imshow(factors_tl[0], cmap=plt.get_cmap(color))\n",
    "axs[1].imshow(factors_tl[1], cmap=plt.get_cmap(color))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank 2 NCPD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reconstruction loss: 8.216059894201868\n",
      "Relative reconstruction loss: 0.9185834215322556\n",
      "\n"
     ]
    }
   ],
   "source": [
    "r=2\n",
    "factors_tl = non_negative_parafac(X, r, init=\"random\", random_state=6)[1]\n",
    "\n",
    "approx = outer_product_np(factors_tl[0], factors_tl[1], factors_tl[2])\n",
    "\n",
    "print(\"Reconstruction loss:\", np.linalg.norm(np.ndarray.flatten(X-approx), 2))\n",
    "print(\"Relative reconstruction loss:\", np.linalg.norm(np.ndarray.flatten(X-approx), 2)  / np.linalg.norm(np.ndarray.flatten(X), 2))\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rank 2 Keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RANK 2 KEYWORDS:\n",
      "------------------\n",
      "Topic 1  Topic 2     \n",
      "-------  -------     \n",
      "trump    tedcruz     \n",
      "hillary  cruz        \n",
      "vote     ted         \n",
      "people   johnkasich  \n",
      "donald   kasich      \n"
     ]
    }
   ],
   "source": [
    "C = factors_tl[2]\n",
    "\n",
    "keywords = np.empty((7,2), dtype=object)\n",
    "\n",
    "for i in range(keywords.shape[1]):\n",
    "    keywords[0,i] = \"Topic \" + str(i+1)\n",
    "    keywords[1,i] = \"-------\"\n",
    "\n",
    "for i in range(C.shape[1]):\n",
    "    col = C[:,i]\n",
    "    top = col.argsort()\n",
    "    top = top[-5:][::-1]\n",
    "\n",
    "    keywords[2:,i] = words[top]\n",
    "\n",
    "print(\"\")\n",
    "print(\"RANK 2 KEYWORDS:\")  \n",
    "print(\"------------------\")\n",
    "\n",
    "col_widths = [max([len(keywords[i][j]) for i in range(keywords.shape[0])])+2 for j in range(keywords.shape[1])]\n",
    "for row in keywords:\n",
    "    print(\"\".join(row[i].ljust(col_widths[i]) for i in range(len(row))))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rank 2 Factor Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x374.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, constrained_layout=True, figsize=(7,5.2))\n",
    "\n",
    "#color = \"binary\"\n",
    "color = \"viridis\"\n",
    "plt.sca(axs[0])\n",
    "plt.yticks(range(8), ['Clinton', 'Kaine', 'O\\'Malley', 'Sanders', 'Cruz', 'Kasich', 'Rubio', 'Trump'], fontsize=22)\n",
    "plt.xticks(range(r), range(1,r+1), fontsize=22)\n",
    "plt.sca(axs[1])\n",
    "plt.xticks(range(r), range(1,r+1), fontsize=22)\n",
    "plt.yticks(range(10), ['Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov'], fontsize=22)\n",
    "\n",
    "axs[0].imshow(factors_tl[0], cmap=plt.get_cmap(color))\n",
    "axs[1].imshow(factors_tl[1], cmap=plt.get_cmap(color))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}