demo/run_queries/Queries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Code Curator - Queries\n",
    "\n",
    "## Prerequisites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please check our [readme](./) for the requirements file and other prerequisites."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specify Server\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update below address if you use another server-port\n",
    "server_address = \"http://localhost:8890/sparql\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from SPARQLWrapper import SPARQLWrapper, JSON\n",
    "from IPython.display import display\n",
    "import pandas as pd\n",
    "import json\n",
    "import numpy as np\n",
    "from pandas.io.json import json_normalize\n",
    "import matplotlib.pyplot as plt\n",
    "pd.options.display.max_colwidth = 100\n",
    "pd.options.display.max_rows = 999\n",
    "\n",
    "\n",
    "def execute_query(sparqlQuery):\n",
    "    sparql = SPARQLWrapper(server_address)\n",
    "    sparql.setQuery(sparqlQuery)\n",
    "    sparql.setReturnFormat(JSON)\n",
    "    results = sparql.query().convert()\n",
    "    results_df = json_normalize(results[\"results\"][\"bindings\"])\n",
    "    # results_df = results_df[results_df['o.type'] == \"uri\"]\n",
    "    # results_df = results_df.drop(['o.datatype','o.type', 'o.xml:lang','p.type'],axis=1)\n",
    "    # results_df = results_df[['p.value','o.value','countS.value']]\n",
    "    # results_df['countS.value'] = results_df['countS.value'].astype(int)\n",
    "    # results_df = results_df[results_df['countS.value'] > 1]\n",
    "    return results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query 1: Number of publications by year across all conferences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Publication Counts'), Text(0.5, 0, 'Years')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e8rY6veYFeX8IMzPrGnmSwoL00yER8RC1xwkATq2xfwAXd/Q6ZmbWdfK8ZGe2pH2BI9OiVRGxvdiwzMysDHlmH00muZ9gDclv/iMlTe/IlFQzM+sZ8nQffQs4PSJWAUg6ErgTmFBkYGZmVn95Zh/1a0kIABHxFJ2YjWRmZt1fnpbCYkm3Av+Rrn8SWFJcSGZmVpY8SeGzJLOCLiUZU3gQ+G6RQZmZWTnyzD7aBnw7/ZiZ2V6s1aQgaW5ETJX0ODWeQRQR4wqNzMzM6q6tlsJl6ffZ9QjEzMzK1+rso6rnDn0uIp6r/gCfq094ZmZWT3mmpJ5Wo+zMrg7EzMzK19aYwmdJWgTvlLSsatNA4NdFB2ZmZvXX1pjCD4GfAddReY8ywNaI2FxoVGZmVopWk0JEbAG2AOcDpK/N7A8MkDQgIp6vT4hmZlYv7Y4pSPqQpKeBZ4FfkjwY72cFx2VmZiXIM9D8NeBE4KmIOJzkXQgeUzAz2wvlSQrbI2ITsI+kfSLiAaCh4LjMzKwEeZ599CdJA0ieeXSHpA3AjmLDMjOzMuRpKUwheWfy54GfA78HPlRkUGZmVo48LYVDgHUR8TowW9L+wFBgU6GRmZlZ3eVpKfwIeKNqfWda1iZJ35e0QdLyqrJrJP1BUlP6Oatq25WSVktaJemDHfkhzMysa+RJCn0j4i8tK+nyvjmOuw04o0b5jRHRkH5+CiBpDDANODo95ruS+uS4hpmZdaE8SWGjpA+3rEiaAvyxvYMi4kEg753PU4C7ImJbRDwLrAYm5jzWzMy6SJ6kcBFwlaTnJb0AfAn4zB5c8xJJy9LupYPTsuHAC1X7NKdlZmZWR+0mhYj4fUScCIwBxkTEpIhY3cnr3QQcQXKfwzrgW2m5al261gkkzZC0WNLijRs3djIMMzOrpa2npP5NRNwu6Qu7lAMQER1+PWdErK86zy3AvelqMzCyatcRwNpWzjELmAXQ2NhYM3GYmVnntNVSeGv6PbCVT4dJGla1ei7QMjNpATBN0n6SDgdGAw935hpmZtZ5bT0l9eb0e2ZnTizpTmAyMFhSM3A1MFlSA0nX0BrSsYmIWCFpLvAEyd3SF0fEzs5c18zMOk8RtXtgJH2nrQMj4tJCIuqAxsbGWLx48R6dQ7VGM+qslT+CunNdmPUOkpZERGOtbW3d0bykoHjMzKybaqv7aHb1uqQDkuLYWnhUZmZWijwv2WmU9DiwDFgu6TFJE4oPzczM6i3PA/G+D3wuIn4FIOm9wL8D44oMzMzM6i/PHc1bWxICQEQ8BLgLycxsL9TWzWvHpYsPS7oZuJNkKunHgUXFh2ZmZvXWVvfRt3ZZv7pq2RMHzcz2Qm3NPjq5noGYmVn52h1olvTVWuURcW3Xh2NmZmXKM/vo1arl/sDZwMpiwjEzszK1mxQi4k1jC5JuIHmAnZmZ7WXyTEnd1VuAd3Z1IGZmVr48YwqPU5lt1AcYAng8wcxsL5RnTOHsquUdwPqI2FFQPGZmVqK2bl7rT/J+5ncBjwO3OhmYme3d2hpTmA00kiSEM9n9ZjYzM9vLtNV9NCYixgJIuhW/HtPMbK/XVkthe8uCu43MzHqHtloKx0p6OV0WsH+6LpKX7RxQeHRmZlZXbT37qE89AzEzs/J15ua1XCR9X9IGScuryt4m6T5JT6ffB6flkvQdSaslLat6bLeZmdVRYUkBuA04Y5eyK4CFETEaWJiuQzK7aXT6mQHcVGBcZmbWisKSQkQ8CGzepXgKyVRX0u9zqsp/EInfAgdJGlZUbGZmVluRLYVahkbEOoD0+5C0fDjwQtV+zWmZmZnVUb2TQmtUo6zm290kzZC0WNLijRs3FhyWmVnvUu+ksL6lWyj93pCWNwMjq/YbAaytdYKImBURjRHROGTIkEKDNTPrbeqdFBYA09Pl6cA9VeWfSmchnQhsaelmMjOz+snzlNROkXQnMBkYLKkZuBr4BjBX0oXA88B56e4/Bc4CVgN/Bi4oKi4zM2tdYUkhIs5vZdOpNfYN4OKiYjEzs3y6y0CzmZl1A04KZmaWcVIwM7OMk4KZmWWcFMzMLOOkYGZmGScFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBzMwyTgpmZpZxUjAzs4yTgpmZZfqWcVFJa4CtwE5gR0Q0SnobMAcYBawBpkbES2XEZ2bWW5XZUjg5IhoiojFdvwJYGBGjgYXpupmZ1VF36j6aAsxOl2cD55QYi5lZr1RWUgjgvyQtkTQjLRsaEesA0u9DSorNzKzXKmVMATgpItZKOgS4T9KTeQ9Mk8gMgEMPPbSo+MzMeqVSWgoRsTb93gDMByYC6yUNA0i/N7Ry7KyIaIyIxiFDhtQrZDOzXqHuSUHSWyUNbFkGTgeWAwuA6elu04F76h2bmVlvV0b30VBgvqSW6/8wIn4u6RFgrqQLgeeB80qIzcysV6t7UoiIZ4Bja5RvAk6tdzxmZlbRnaakmplZyZwUzMws46RgZmYZJwUzM8s4KZiZWcZJwczMMk4KZmaWcVIwM7OMk4KZmWXKekqqWbeWPIWlXBFlR2C9kVsKZmaWcVIwM7OMk4KZmWWcFMzMLOOBZjNrkwfdexe3FMzMLOOkYGZmGScFMzPLOCmYmVnGScHMzDLdLilIOkPSKkmrJV1RdjxmZr1Jt0oKkvoA/xc4ExgDnC9pTLlRmZn1Ht0qKQATgdUR8UxE/AW4C5hSckxmZr1Gd7t5bTjwQtV6M3BC9Q6SZgAz0tVXJK2qU2xtGQz8sbMHd4ebg7qQ66LCdVHhuqjYo7roIoe1tqG7JYVaf/RvupcxImYBs+oTTj6SFkdEY9lxdAeuiwrXRYXroqK710V36z5qBkZWrY8A1pYUi5lZr9PdksIjwGhJh0vaF5gGLCg5JjOzXqNbdR9FxA5JlwD/D+gDfD8iVpQcVh7dqjurZK6LCtdFheuiolvXhcKPHzQzs1R36z4yM7MSOSmYmVnGScHMzDJOCmZmlnFS6ABJ+0r6lKQPpOufkPR/JF0sqV/Z8dWTpEsljWx/z72fpBMkHZAu7y9ppqT/lPRNSQeWHV+ZJL1X0hcknV52LN2BpB+UHUN7PPuoAyTdQTKN9y3An4ABwN3AqSR1Ob3E8OpK0hbgVeD3wJ3AjyJiY7lRlUPSCuDYdEr1LODPwDySvxfHRsRHSg2wjiQ9HBET0+VPAxcD84HTgf+MiG+UGV89Sdr1HisBJwO/AIiID9c9qBycFDpA0rKIGCepL/AH4B0RsVOSgMciYlzJIdaNpEeBCcAHgI8DHwaWkCSIuyNia4nh1ZWklRFxVLq8NCKOq9rWFBEN5UVXX5IejYjx6fIjwFkRsVHSW4HfRsTYciOsH0lLgSeAfyN5XI9I/n1MA4iIX5YXXevcfdQx+6R3Wg8kaS20dA3sB/Sq7iMgIuKNiPiviLgQeAfwXeAM4JlyQ6u75ZIuSJcfk9QIIOlIYHt5YZViH0kHSxpE8kvnRoCIeBXYUW5odddI8ovSPwJbImIR8FpE/LK7JgToZnc09wC3Ak+S3G39j8CPJD0DnEjymO/e5E0PL4yI7SSPJFkgaf9yQirN3wH/KunLJE+//G9JL5A88ffvSo2s/g4k+Y9QQEh6e0S8KGkAtR94udeKiDeAGyX9KP1eTw/4P9fdRx0k6R0AEbFW0kEk3SfPR8TD5UZWX5KOjIinyo6jO5E0EHgnyT/85ohYX3JI3YaktwBDI+LZsmMpi6S/Bk6KiKvKjqUtTgpdRNKAiHil7Di6A9dFheuiwnVR0Z3rwmMKXeeJsgPoRlwXFa6LCtdFRbeti27fv9WdSPpCa5tIpqf2Gq6LCtdFheuioqfWhVsKHfPPwMEks4+qPwPofXXpuqhwXVS4Lip6ZF24pdAxS4GfRMSSXTdI6m2zTFwXFa6LCtdFRY+sCw80d4CkdwOba925K2lob5pt4rqocF1UuC4qempdOCmYmVmm2/ZrdUeSDpT0DUlPStqUflamZQeVHV89uS4qXBcVrouKnloXTgodMxd4CZgcEYMiYhDJA65eAn5UamT157qocF1UuC4qemRduPuoAyStioh3d3Tb3sh1UeG6qHBdVPTUunBLoWOek/RFSUNbCiQNlfQlkufc9CauiwrXRYXroqJH1oWTQsd8HBgE/FLSS5I2A4uAtwFTywysBK6LCtdFheuiokfWhbuPOkjSe4ARJM+Gf6Wq/IyI+Hl5kdWf66LCdVHhuqjoiXXhlkIHSLoUuAe4hOQZ+lOqNv9zOVGVw3VR4bqocF1U9NS68B3NHfNpYEJEvCJpFDBP0qiI+Fd62bPicV1Uc11UuC4qemRdOCl0TJ+WJmBErJE0meQP+jC68R9yQVwXFa6LCtdFRY+sC3cfdcyLkrL37aZ/4GcDg4Fe8+7ZlOuiwnVR4bqo6JF14YHmDpA0AtgRES/W2HZSRPy6hLBK4bqocF1UuC4qempdOCmYmVnG3UdmZpZxUjAzs4yTglk7lHhI0plVZVMldcubj8z2hMcUzHKQdAzJky3HA32AJuCMiPj9Hpyzb0Ts6KIQzbqEk4JZTpKuB14F3gpsjYh/kjQduBjYF/gNcElEvCFpFnAcsD8wJyKuTc/RDNwMnAH8C8kjED4NbAcej4i/qfOPZfYmvnnNLL+ZJO/d/QvQmLYezgUmRcSONBFMA34IXBERmyX1BR6QNC8inkjP82pEnAQgaR1wWET8pTu/eMV6DycFs5wi4lVJc4BXImKbpA8AxwOLJUHSKmh5JPL5ki4k+Tf2DmAM0JIU5lSddgVwu6R7gJ/U4ccwa5OTglnHvJF+IHlUwfcj4ivVO0gaDVwGTIyIP0m6HehftcurVcsfBN4PTAG+LOmYiNhZWPRm7fDsI7POux+YKmkwgKRBkg4FDgC2Ai9LGkbyH/9uJPUBRkTEL4DLgSHAW+oSuVkr3FIw66SIeFzSTOB+SfuQDBZfBCwm6SpaDjwDtPY4g77ADyUNJPkF7ZsRsbX4yM1a59lHZmaWcfeRmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBzMwyTgpmZpZxUjAzs8z/B2Zu5hiFlyhSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearCount = \"\"\"\n",
    "Select count(distinct ?s) as ?Count ?year FROM <https://github.com/deepcurator/DCC/>\n",
    "\n",
    "where {\n",
    "\n",
    "?s a <https://github.com/deepcurator/DCC/Publication> .\n",
    "?s <https://github.com/deepcurator/DCC/conferenceSeries> ?o .\n",
    "?s <https://github.com/deepcurator/DCC/yearOfPublication> ?year .\n",
    "\n",
    "}Group by ?year ORDER by DESC(?year)\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "results_df = execute_query(yearCount)\n",
    "\n",
    "results_df = results_df.drop(['Count.datatype', 'Count.type',  'year.datatype',\n",
    "       'year.type'],axis=1)\n",
    "\n",
    "results_df.columns\n",
    "\n",
    "results_df.head()\n",
    "# results_df['Count.value'].hist(by='year.value')\n",
    "# results_df['Count.value'].plot.bar()\n",
    "# print(results_df)\n",
    "\n",
    "results_df = results_df.astype(int)\n",
    "# plt.figure()\n",
    "\n",
    "ax = results_df.plot(kind='bar',x='year.value',y='Count.value',color='blue')\n",
    "\n",
    "ax.set(xlabel = \"Years\", ylabel = \"Publication Counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query 2: Number of publications by conference series and by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "conferenceyear = \"\"\"\n",
    "\n",
    "Select count(distinct ?s) as ?Count ?conference ?year FROM <https://github.com/deepcurator/DCC/>\n",
    "\n",
    "where {\n",
    "\n",
    "\n",
    "?s a <https://github.com/deepcurator/DCC/Publication> .\n",
    "?s <https://github.com/deepcurator/DCC/conferenceSeries> ?conference .\n",
    "?s <https://github.com/deepcurator/DCC/yearOfPublication> ?year .\n",
    "\n",
    "}\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "results_df = execute_query(conferenceyear)\n",
    "results_df.columns\n",
    "\n",
    "results_df = results_df.drop(['Count.datatype', 'Count.type',  'conference.type','year.datatype',\n",
    "       'year.type'],axis=1)\n",
    "\n",
    "results_df.head()\n",
    "results_df[\"Count.value\"] = pd.to_numeric(results_df[\"Count.value\"])\n",
    "results_df[\"year.value\"] = pd.to_numeric(results_df[\"year.value\"])\n",
    "results_df.groupby(['conference.value','year.value']).size().unstack().plot(kind='bar',stacked='True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query 3: PyTorch and Tensorflow Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Platform'), Text(0.5, 0, 'Years')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#### Trends in year \n",
    "platformtrends = \"\"\"\n",
    "\n",
    "Select count(?platform) as ?pytorch ?platform ?year FROM <https://github.com/deepcurator/DCC/>\n",
    "\n",
    "where {\n",
    "\n",
    "\n",
    "?s a <https://github.com/deepcurator/DCC/Publication> .\n",
    "?s <https://github.com/deepcurator/DCC/conferenceSeries> ?conference .\n",
    "?s <https://github.com/deepcurator/DCC/yearOfPublication> ?year .\n",
    "?s <https://github.com/deepcurator/DCC/platform> ?platform .\n",
    "\n",
    "FILTER(STR(?platform) =\"pytorch\")\n",
    "} \n",
    "group by  ?platform ?year order by DESC(?year)\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "results_df = execute_query(platformtrends)\n",
    "results_df.columns\n",
    "results_df.head()\n",
    "\n",
    "results_df = results_df.drop(['pytorch.datatype', 'platform.type','pytorch.type',  'platform.type','year.datatype',\n",
    "       'year.type'],axis=1)\n",
    "\n",
    "results_df.head()\n",
    "results_df[\"pytorch.value\"] = pd.to_numeric(results_df[\"pytorch.value\"])\n",
    "\n",
    "results_df[\"year.value\"] = pd.to_numeric(results_df[\"year.value\"])\n",
    "# results_df.groupby(['platform.value','year.value']).size().unstack().plot(kind='bar',stacked='True')\n",
    "\n",
    "ax = results_df.plot(kind='bar',x='year.value',y='pytorch.value',color='blue')\n",
    "\n",
    "ax.set(xlabel = \"Years\", ylabel = \"Platform\")\n",
    "# results_df[\"Count.value\"] = pd.to_numeric(results_df[\"Count.value\"])\n",
    "# results_df[\"year.value\"] = pd.to_numeric(results_df[\"year.value\"])\n",
    "# plt.show()\n",
    "\n",
    "platformtrends = \"\"\"\n",
    "\n",
    "Select count(?platform) as ?tensorflow ?platform ?year FROM <https://github.com/deepcurator/DCC/>\n",
    "\n",
    "where {\n",
    "\n",
    "\n",
    "?s a <https://github.com/deepcurator/DCC/Publication> .\n",
    "?s <https://github.com/deepcurator/DCC/conferenceSeries> ?conference .\n",
    "?s <https://github.com/deepcurator/DCC/yearOfPublication> ?year .\n",
    "?s <https://github.com/deepcurator/DCC/platform> ?platform .\n",
    "\n",
    "FILTER(STR(?platform) =\"tensorflow\")\n",
    "} \n",
    "group by  ?platform ?year order by DESC(?year)\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "results_df = execute_query(platformtrends)\n",
    "results_df.columns\n",
    "results_df.head()\n",
    "\n",
    "results_df = results_df.drop(['tensorflow.datatype', 'platform.type','tensorflow.type',  'platform.type','year.datatype',\n",
    "       'year.type'],axis=1)\n",
    "\n",
    "results_df.head()\n",
    "results_df[\"tensorflow.value\"] = pd.to_numeric(results_df[\"tensorflow.value\"])\n",
    "\n",
    "results_df[\"year.value\"] = pd.to_numeric(results_df[\"year.value\"])\n",
    "# results_df.groupby(['platform.value','year.value']).size().unstack().plot(kind='bar',stacked='True')\n",
    "\n",
    "ax = results_df.plot(kind='bar',x='year.value',y='tensorflow.value',color='blue')\n",
    "\n",
    "ax.set(xlabel = \"Years\", ylabel = \"Platform\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query 4: Function Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     counttype.value                                   type.value  \\\n",
      "0              16557                                 get_variable   \n",
      "1              14146                                       device   \n",
      "2               7533                                        range   \n",
      "3               7341                               variable_scope   \n",
      "4               4967                                      reshape   \n",
      "5               4849                            add_to_collection   \n",
      "6               4793                            zeros_initializer   \n",
      "7               4524                      keras/layers/Activation   \n",
      "8               3931                                        range   \n",
      "9               2751                                  nn/bias_add   \n",
      "10              2659                                   nn/moments   \n",
      "11              2656                       nn/batch_normalization   \n",
      "12              2230                                        zeros   \n",
      "13              2138                                     math/abs   \n",
      "14              1909                               variable_scope   \n",
      "15              1894                                      reshape   \n",
      "16              1862                                layers/conv2d   \n",
      "17              1722                              math/reduce_sum   \n",
      "18              1579                                   name_scope   \n",
      "19              1561                                        zeros   \n",
      "20              1360                                    math/sqrt   \n",
      "21              1345                                        shape   \n",
      "22              1164                                        slice   \n",
      "23              1164                                     Variable   \n",
      "24              1151                                 get_variable   \n",
      "25              1050                                linalg/matmul   \n",
      "26              1032                                         ones   \n",
      "27              1032                                    math/sqrt   \n",
      "28              1011                                       concat   \n",
      "29              1000                                  math/square   \n",
      "30               951                                    nn/conv2d   \n",
      "31               892                                       concat   \n",
      "32               868                                      app/run   \n",
      "33               853                                        split   \n",
      "34               847                                      nn/relu   \n",
      "35               840                                     math/log   \n",
      "36               814                                     math/abs   \n",
      "37               770                                    transpose   \n",
      "38               738                                  dtypes/cast   \n",
      "39               701                             math/reduce_mean   \n",
      "40               685                            keras/backend/min   \n",
      "41               673                                     constant   \n",
      "42               664                                          pad   \n",
      "43               646                                  placeholder   \n",
      "44               641                                     math/exp   \n",
      "45               626                                     constant   \n",
      "46               620                                      reverse   \n",
      "47               611                                        stack   \n",
      "48               598                                        split   \n",
      "49               593                                 metrics/mean   \n",
      "50               592                                   name_scope   \n",
      "51               589                                  expand_dims   \n",
      "52               582                                     Variable   \n",
      "53               571                                    nn/conv2d   \n",
      "54               559                                      nn/relu   \n",
      "55               544                             math/reduce_mean   \n",
      "56               534                                    transpose   \n",
      "57               533                                  placeholder   \n",
      "58               532                                 metrics/mean   \n",
      "59               521                                    math/ceil   \n",
      "60               520                               summary/scalar   \n",
      "61               518                                      app/run   \n",
      "62               512                                    math/ceil   \n",
      "63               509                               get_collection   \n",
      "64               504                            keras/backend/max   \n",
      "65               499                                  expand_dims   \n",
      "66               477                              math/reduce_sum   \n",
      "67               456                                         tile   \n",
      "68               452                                        shape   \n",
      "69               451                                  dtypes/cast   \n",
      "70               428                                math/multiply   \n",
      "71               420                                    math/real   \n",
      "72               413                                linalg/matmul   \n",
      "73               393                                      squeeze   \n",
      "74               393                                   math/floor   \n",
      "75               384                                  nn/bias_add   \n",
      "76               381                                        stack   \n",
      "77               377                                 math/maximum   \n",
      "78               366                                    math/acos   \n",
      "79               366                                nn/max_pool2d   \n",
      "80               361                                  math/square   \n",
      "81               346                             saved_model/save   \n",
      "82               335                                     identity   \n",
      "83               333                                math/multiply   \n",
      "84               331                                       device   \n",
      "85               326                    random_normal_initializer   \n",
      "86               324                                   math/round   \n",
      "87               321                               random/shuffle   \n",
      "88               319                             saved_model/load   \n",
      "89               315                                        where   \n",
      "90               309                                        slice   \n",
      "91               305                               get_collection   \n",
      "92               305                          nn/embedding_lookup   \n",
      "93               295                            keras/backend/min   \n",
      "94               293                                         ones   \n",
      "95               286                                nn/max_pool2d   \n",
      "96               284                                     to_float   \n",
      "97               279                 keras/backend/random_uniform   \n",
      "98               279                                         cond   \n",
      "99               277                             math/logical_and   \n",
      "100              276                                      squeeze   \n",
      "101              267                               random/uniform   \n",
      "102              264                                layers/conv2d   \n",
      "103              250                 keras/backend/random_uniform   \n",
      "104              244                                          eye   \n",
      "105              244                                          pad   \n",
      "106              238                                         tile   \n",
      "107              237                    random_normal_initializer   \n",
      "108              231                             saved_model/load   \n",
      "109              229                                   zeros_like   \n",
      "110              218                                       assign   \n",
      "111              214                                 math/maximum   \n",
      "112              208                                        tuple   \n",
      "113              207                                   math/add_n   \n",
      "114              203                initializers/truncated_normal   \n",
      "115              195                                      Session   \n",
      "116              194                                     linspace   \n",
      "117              188                               random/shuffle   \n",
      "118              186                                     math/add   \n",
      "119              185                            keras/losses/Loss   \n",
      "120              185                                  train/Saver   \n",
      "121              185                                        group   \n",
      "122              185                             keras/Sequential   \n",
      "123              184                             keras/Sequential   \n",
      "124              182                                         norm   \n",
      "125              176                                     math/cos   \n",
      "126              171                                 math/minimum   \n",
      "127              169                feature_column/numeric_column   \n",
      "128              166                                   nn/dropout   \n",
      "129              165                                      Session   \n",
      "130              165                                   nn/softmax   \n",
      "131              163                                clip_by_value   \n",
      "132              163                                     to_float   \n",
      "133              163                                     math/exp   \n",
      "134              162                               layers/dropout   \n",
      "135              161                                   zeros_like   \n",
      "136              157                                  nn/avg_pool   \n",
      "137              156                                   nn/moments   \n",
      "138              151                                       gather   \n",
      "139              150                  keras/backend/random_normal   \n",
      "140              150                                     math/sin   \n",
      "141              148                            add_to_collection   \n",
      "142              148                                        where   \n",
      "143              147                                       unique   \n",
      "144              145                               summary/scalar   \n",
      "145              144                                  ConfigProto   \n",
      "146              144                              math/reduce_max   \n",
      "147              142                                        group   \n",
      "148              135                            keras/backend/max   \n",
      "149              133                             saved_model/save   \n",
      "150              128                                     math/sin   \n",
      "151              127                       nn/batch_normalization   \n",
      "152              127                                         size   \n",
      "153              126                                random/normal   \n",
      "154              123                                     math/add   \n",
      "155              122                               layers/flatten   \n",
      "156              119                                    ones_like   \n",
      "157              117                            summary/histogram   \n",
      "158              114                          nn/conv2d_transpose   \n",
      "159              113                  keras/backend/random_normal   \n",
      "160              111                                         fill   \n",
      "161              109                                   math/add_n   \n",
      "162              107                                  math/cumsum   \n",
      "163              106                                math/bincount   \n",
      "164              106                                  math/argmax   \n",
      "165              105                              linalg/cholesky   \n",
      "166              101                                  math/argmax   \n",
      "167              100                            keras/backend/sum   \n",
      "168               97                                         sort   \n",
      "169               97               train/ExponentialMovingAverage   \n",
      "170               97                                 math/minimum   \n",
      "171               95                          trainable_variables   \n",
      "172               94                                clip_by_value   \n",
      "173               93                                    ones_like   \n",
      "174               91                                math/subtract   \n",
      "175               90                                   assign_add   \n",
      "176               89         nn/sigmoid_cross_entropy_with_logits   \n",
      "177               89                                   nn/l2_loss   \n",
      "178               88                                         sort   \n",
      "179               86                                      argsort   \n",
      "180               85                                   nn/dropout   \n",
      "181               85                                       assign   \n",
      "182               81                        image/resize_bilinear   \n",
      "183               80                                stop_gradient   \n",
      "184               79                                         cond   \n",
      "185               79                                     math/pow   \n",
      "186               78                                     math/log   \n",
      "187               78                                   math/atan2   \n",
      "188               77         nn/sigmoid_cross_entropy_with_logits   \n",
      "189               76                                   nn/softmax   \n",
      "190               76                                   linalg/svd   \n",
      "191               76                                       gather   \n",
      "192               76                                   nn/l2_loss   \n",
      "193               75                                  math/divide   \n",
      "194               75                image/resize_nearest_neighbor   \n",
      "195               74                                          eye   \n",
      "196               72                                     to_int32   \n",
      "197               68                                          div   \n",
      "198               67                                   math/equal   \n",
      "199               65                                    math/tanh   \n",
      "200               65                         control_dependencies   \n",
      "201               64                                  keras/Input   \n",
      "202               63                                summary/merge   \n",
      "203               62                         control_dependencies   \n",
      "204               62                                       map_fn   \n",
      "205               62                                         size   \n",
      "206               61                           summary/FileWriter   \n",
      "207               60                          train/AdamOptimizer   \n",
      "208               60                                    gather_nd   \n",
      "209               59                          trainable_variables   \n",
      "210               59                          train/AdamOptimizer   \n",
      "211               59                                 nn/xw_plus_b   \n",
      "212               59                   random_uniform_initializer   \n",
      "213               58                                    gradients   \n",
      "214               57                                 layers/Dense   \n",
      "215               56                                train/Example   \n",
      "216               56                                   math/round   \n",
      "217               55                               train/Features   \n",
      "218               55                      train/latest_checkpoint   \n",
      "219               55                                summary/image   \n",
      "220               54                            train/Coordinator   \n",
      "221               53                                 clip_by_norm   \n",
      "222               53                                   math/floor   \n",
      "223               52                                 math/sigmoid   \n",
      "224               50                                  train/Saver   \n",
      "225               49                                    gradients   \n",
      "226               49                               layers/dropout   \n",
      "227               48                      train/import_meta_graph   \n",
      "228               48                                  ConfigProto   \n",
      "229               48                                    math/less   \n",
      "230               47                   layers/batch_normalization   \n",
      "231               47                   image/crop_to_bounding_box   \n",
      "232               47               train/ExponentialMovingAverage   \n",
      "233               45                      keras/layers/Activation   \n",
      "234               44                                     math/cos   \n",
      "235               44                           get_variable_scope   \n",
      "236               44                                  keras/Model   \n",
      "237               44                            get_default_graph   \n",
      "238               43                                 math/greater   \n",
      "239               43                                  nn/max_pool   \n",
      "240               43                                    math/sign   \n",
      "241               43                                     identity   \n",
      "242               42                                nn/avg_pool3d   \n",
      "243               42                                    nn/conv3d   \n",
      "244               42                               train/Features   \n",
      "245               41                                        no_op   \n",
      "246               41                                   GPUOptions   \n",
      "247               41                      layers/conv2d_transpose   \n",
      "248               41                                train/Example   \n",
      "249               41                                         rank   \n",
      "250               40                     keras/activations/linear   \n",
      "251               40                            keras/backend/any   \n",
      "252               39                                layers/conv3d   \n",
      "253               39                          image/resize_images   \n",
      "254               39                        math/reduce_logsumexp   \n",
      "255               39                                  train/batch   \n",
      "256               39                                  linalg/diag   \n",
      "257               39                      train/exponential_decay   \n",
      "258               38                    image/convert_image_dtype   \n",
      "259               38                                math/subtract   \n",
      "260               37                                      unstack   \n",
      "261               36                          nn/conv2d_transpose   \n",
      "262               36                            dynamic_partition   \n",
      "263               36                            summary/histogram   \n",
      "264               36                                     op_scope   \n",
      "265               36                                        tuple   \n",
      "266               35                                    math/tanh   \n",
      "267               35                initializers/truncated_normal   \n",
      "268               35                    image/pad_to_bounding_box   \n",
      "269               34                                    nn/conv1d   \n",
      "270               34                                 layers/dense   \n",
      "271               34                                     to_int32   \n",
      "272               34                                  keras/Input   \n",
      "273               34                      layers/separable_conv2d   \n",
      "274               33                               layers/flatten   \n",
      "275               33                       random/set_random_seed   \n",
      "276               32                            convert_to_tensor   \n",
      "277               32               train/GradientDescentOptimizer   \n",
      "278               32                                   math/equal   \n",
      "279               32                    image/convert_image_dtype   \n",
      "280               32                                         norm   \n",
      "281               32                      train/MomentumOptimizer   \n",
      "282               32                                   image/ssim   \n",
      "283               31                                      unstack   \n",
      "284               31                                  nn/max_pool   \n",
      "285               31                            zeros_initializer   \n",
      "286               31                  train/string_input_producer   \n",
      "287               30                            sets/intersection   \n",
      "288               30                                   math/atanh   \n",
      "289               30                            get_default_graph   \n",
      "290               29                                    gather_nd   \n",
      "291               28                           summary/FileWriter   \n",
      "292               28                                 math/greater   \n",
      "293               28                   train/get_checkpoint_state   \n",
      "294               27                             layers/InputSpec   \n",
      "295               27                       keras/layers/LeakyReLU   \n",
      "296               27                       train/RMSPropOptimizer   \n",
      "297               27                              math/reduce_max   \n",
      "298               26                  image/extract_image_patches   \n",
      "299               26                       random/set_random_seed   \n",
      "300               26                     train/create_global_step   \n",
      "301               26            compat/v2/io/parse_single_example   \n",
      "302               25                             global_variables   \n",
      "303               25                                 boolean_mask   \n",
      "304               25                            convert_to_tensor   \n",
      "305               24                                layers/conv1d   \n",
      "306               24                             train/Supervisor   \n",
      "307               24                                   math/rsqrt   \n",
      "308               23                                 layers/Layer   \n",
      "309               23                            keras/backend/all   \n",
      "310               23                        image/crop_and_resize   \n",
      "311               23                      train/AdadeltaOptimizer   \n",
      "312               23                                     math/pow   \n",
      "313               23                       train/polynomial_decay   \n",
      "314               22         nn/softmax_cross_entropy_with_logits   \n",
      "315               22                                   linalg/svd   \n",
      "316               22                                 math/sigmoid   \n",
      "317               22                          train/FtrlOptimizer   \n",
      "318               22                             math/logical_and   \n",
      "319               22                                 nn/xw_plus_b   \n",
      "320               22                                          div   \n",
      "321               22                            summary/merge_all   \n",
      "322               22                       train/AdagradOptimizer   \n",
      "323               22                                nn/leaky_relu   \n",
      "324               21                                      nn/pool   \n",
      "325               21                                     meshgrid   \n",
      "326               21                  train/SyncReplicasOptimizer   \n",
      "327               21                                random/normal   \n",
      "328               20                                     linspace   \n",
      "329               20                                       Tensor   \n",
      "330               20                                     meshgrid   \n",
      "331               20                        keras/optimizers/Adam   \n",
      "332               20                              math/reduce_min   \n",
      "333               20                                        Print   \n",
      "334               19                   feature_column/input_layer   \n",
      "335               19                                        Graph   \n",
      "336               19                                 summary/text   \n",
      "337               19                                 sparse/merge   \n",
      "338               19                           get_variable_scope   \n",
      "339               19                                   scatter_nd   \n",
      "340               19                          reset_default_graph   \n",
      "341               19                                  keras/Model   \n",
      "342               18                                  math/divide   \n",
      "343               18                           math/count_nonzero   \n",
      "344               18                         distributions/Normal   \n",
      "345               18                           random/multinomial   \n",
      "346               18                      math/squared_difference   \n",
      "347               18                            summary/merge_all   \n",
      "348               18                                        Print   \n",
      "349               18                          get_default_session   \n",
      "350               18                                      argsort   \n",
      "351               18                image/resize_nearest_neighbor   \n",
      "352               18                               dynamic_stitch   \n",
      "353               18                                        Graph   \n",
      "354               17                          estimator/RunConfig   \n",
      "355               17                                summary/image   \n",
      "356               17                                 sparse/merge   \n",
      "357               17                             debugging/Assert   \n",
      "358               17                             global_variables   \n",
      "359               17                          train/shuffle_batch   \n",
      "360               16                               TFRecordReader   \n",
      "361               16                      math/squared_difference   \n",
      "362               16                                   linalg/inv   \n",
      "363               16                             linalg/diag_part   \n",
      "364               16                random/experimental/Generator   \n",
      "365               16  nn/sparse_softmax_cross_entropy_with_logits   \n",
      "366               15                            test/get_temp_dir   \n",
      "367               15                           math/greater_equal   \n",
      "368               15                       compat/v2/image/resize   \n",
      "369               15                                    math/rint   \n",
      "370               14                                         scan   \n",
      "371               14                                summary/merge   \n",
      "372               14                                strided_slice   \n",
      "373               14                            keras/backend/all   \n",
      "374               13                          nn/embedding_lookup   \n",
      "375               13                                    nn/conv1d   \n",
      "376               12                                    math/asin   \n",
      "377               12                               batch_to_space   \n",
      "378               12                              math/logical_or   \n",
      "379               12               train/GradientDescentOptimizer   \n",
      "380               12                      train/MomentumOptimizer   \n",
      "381               12                  train/string_input_producer   \n",
      "382               11                                   linalg/inv   \n",
      "383               11                          reset_default_graph   \n",
      "384               11  nn/sparse_softmax_cross_entropy_with_logits   \n",
      "385               11                                         fill   \n",
      "386               11                           linalg/global_norm   \n",
      "387               11              train/get_or_create_global_step   \n",
      "388               11                               layers/Dropout   \n",
      "389               11                               compat/v1/test   \n",
      "390               11                                      reverse   \n",
      "391               11                           InteractiveSession   \n",
      "392               11                                  TensorShape   \n",
      "393               11                                   decode_raw   \n",
      "394               11                    distributions/Categorical   \n",
      "395               11                                   GPUOptions   \n",
      "396               11                               random/uniform   \n",
      "397               11                          train/shuffle_batch   \n",
      "\n",
      "    conference.value  \n",
      "0               NIPS  \n",
      "1               NIPS  \n",
      "2               NIPS  \n",
      "3               NIPS  \n",
      "4               NIPS  \n",
      "5               NIPS  \n",
      "6               NIPS  \n",
      "7               NIPS  \n",
      "8               CVPR  \n",
      "9               NIPS  \n",
      "10              NIPS  \n",
      "11              NIPS  \n",
      "12              NIPS  \n",
      "13              NIPS  \n",
      "14              CVPR  \n",
      "15              CVPR  \n",
      "16              CVPR  \n",
      "17              NIPS  \n",
      "18              NIPS  \n",
      "19              CVPR  \n",
      "20              NIPS  \n",
      "21              NIPS  \n",
      "22              NIPS  \n",
      "23              NIPS  \n",
      "24              CVPR  \n",
      "25              NIPS  \n",
      "26              NIPS  \n",
      "27              CVPR  \n",
      "28              NIPS  \n",
      "29              NIPS  \n",
      "30              CVPR  \n",
      "31              CVPR  \n",
      "32              NIPS  \n",
      "33              NIPS  \n",
      "34              CVPR  \n",
      "35              NIPS  \n",
      "36              CVPR  \n",
      "37              NIPS  \n",
      "38              CVPR  \n",
      "39              CVPR  \n",
      "40              CVPR  \n",
      "41              NIPS  \n",
      "42              CVPR  \n",
      "43              NIPS  \n",
      "44              NIPS  \n",
      "45              CVPR  \n",
      "46              NIPS  \n",
      "47              NIPS  \n",
      "48              CVPR  \n",
      "49              CVPR  \n",
      "50              CVPR  \n",
      "51              CVPR  \n",
      "52              CVPR  \n",
      "53              NIPS  \n",
      "54              NIPS  \n",
      "55              NIPS  \n",
      "56              CVPR  \n",
      "57              CVPR  \n",
      "58              NIPS  \n",
      "59              NIPS  \n",
      "60              CVPR  \n",
      "61              CVPR  \n",
      "62              CVPR  \n",
      "63              NIPS  \n",
      "64              CVPR  \n",
      "65              NIPS  \n",
      "66              CVPR  \n",
      "67              CVPR  \n",
      "68              CVPR  \n",
      "69              NIPS  \n",
      "70              NIPS  \n",
      "71              NIPS  \n",
      "72              CVPR  \n",
      "73              CVPR  \n",
      "74              CVPR  \n",
      "75              CVPR  \n",
      "76              CVPR  \n",
      "77              NIPS  \n",
      "78              NIPS  \n",
      "79              CVPR  \n",
      "80              CVPR  \n",
      "81              CVPR  \n",
      "82              NIPS  \n",
      "83              CVPR  \n",
      "84              CVPR  \n",
      "85              NIPS  \n",
      "86              CVPR  \n",
      "87              NIPS  \n",
      "88              NIPS  \n",
      "89              CVPR  \n",
      "90              CVPR  \n",
      "91              CVPR  \n",
      "92              NIPS  \n",
      "93              NIPS  \n",
      "94              CVPR  \n",
      "95              NIPS  \n",
      "96              CVPR  \n",
      "97              CVPR  \n",
      "98              CVPR  \n",
      "99              CVPR  \n",
      "100             NIPS  \n",
      "101             NIPS  \n",
      "102             NIPS  \n",
      "103             NIPS  \n",
      "104             NIPS  \n",
      "105             NIPS  \n",
      "106             NIPS  \n",
      "107             CVPR  \n",
      "108             CVPR  \n",
      "109             CVPR  \n",
      "110             NIPS  \n",
      "111             CVPR  \n",
      "112             NIPS  \n",
      "113             CVPR  \n",
      "114             NIPS  \n",
      "115             CVPR  \n",
      "116             NIPS  \n",
      "117             CVPR  \n",
      "118             CVPR  \n",
      "119             NIPS  \n",
      "120             CVPR  \n",
      "121             CVPR  \n",
      "122             CVPR  \n",
      "123             NIPS  \n",
      "124             NIPS  \n",
      "125             CVPR  \n",
      "126             NIPS  \n",
      "127             NIPS  \n",
      "128             NIPS  \n",
      "129             NIPS  \n",
      "130             NIPS  \n",
      "131             CVPR  \n",
      "132             NIPS  \n",
      "133             CVPR  \n",
      "134             CVPR  \n",
      "135             NIPS  \n",
      "136             CVPR  \n",
      "137             CVPR  \n",
      "138             NIPS  \n",
      "139             NIPS  \n",
      "140             CVPR  \n",
      "141             CVPR  \n",
      "142             NIPS  \n",
      "143             NIPS  \n",
      "144             NIPS  \n",
      "145             CVPR  \n",
      "146             NIPS  \n",
      "147             NIPS  \n",
      "148             NIPS  \n",
      "149             NIPS  \n",
      "150             NIPS  \n",
      "151             CVPR  \n",
      "152             CVPR  \n",
      "153             NIPS  \n",
      "154             NIPS  \n",
      "155             NIPS  \n",
      "156             NIPS  \n",
      "157             NIPS  \n",
      "158             NIPS  \n",
      "159             CVPR  \n",
      "160             CVPR  \n",
      "161             NIPS  \n",
      "162             NIPS  \n",
      "163             NIPS  \n",
      "164             NIPS  \n",
      "165             NIPS  \n",
      "166             CVPR  \n",
      "167             NIPS  \n",
      "168             CVPR  \n",
      "169             CVPR  \n",
      "170             CVPR  \n",
      "171             CVPR  \n",
      "172             NIPS  \n",
      "173             CVPR  \n",
      "174             CVPR  \n",
      "175             NIPS  \n",
      "176             NIPS  \n",
      "177             CVPR  \n",
      "178             NIPS  \n",
      "179             NIPS  \n",
      "180             CVPR  \n",
      "181             CVPR  \n",
      "182             CVPR  \n",
      "183             NIPS  \n",
      "184             NIPS  \n",
      "185             NIPS  \n",
      "186             CVPR  \n",
      "187             CVPR  \n",
      "188             CVPR  \n",
      "189             CVPR  \n",
      "190             NIPS  \n",
      "191             CVPR  \n",
      "192             NIPS  \n",
      "193             CVPR  \n",
      "194             CVPR  \n",
      "195             CVPR  \n",
      "196             CVPR  \n",
      "197             CVPR  \n",
      "198             NIPS  \n",
      "199             NIPS  \n",
      "200             NIPS  \n",
      "201             NIPS  \n",
      "202             CVPR  \n",
      "203             CVPR  \n",
      "204             NIPS  \n",
      "205             NIPS  \n",
      "206             CVPR  \n",
      "207             CVPR  \n",
      "208             CVPR  \n",
      "209             NIPS  \n",
      "210             NIPS  \n",
      "211             NIPS  \n",
      "212             CVPR  \n",
      "213             CVPR  \n",
      "214             NIPS  \n",
      "215             CVPR  \n",
      "216             NIPS  \n",
      "217             CVPR  \n",
      "218             CVPR  \n",
      "219             CVPR  \n",
      "220             NIPS  \n",
      "221             NIPS  \n",
      "222             NIPS  \n",
      "223             NIPS  \n",
      "224             NIPS  \n",
      "225             NIPS  \n",
      "226             NIPS  \n",
      "227             NIPS  \n",
      "228             NIPS  \n",
      "229             CVPR  \n",
      "230             CVPR  \n",
      "231             CVPR  \n",
      "232             NIPS  \n",
      "233             CVPR  \n",
      "234             NIPS  \n",
      "235             CVPR  \n",
      "236             NIPS  \n",
      "237             CVPR  \n",
      "238             NIPS  \n",
      "239             NIPS  \n",
      "240             CVPR  \n",
      "241             CVPR  \n",
      "242             NIPS  \n",
      "243             NIPS  \n",
      "244             NIPS  \n",
      "245             CVPR  \n",
      "246             CVPR  \n",
      "247             CVPR  \n",
      "248             NIPS  \n",
      "249             NIPS  \n",
      "250             NIPS  \n",
      "251             CVPR  \n",
      "252             CVPR  \n",
      "253             CVPR  \n",
      "254             NIPS  \n",
      "255             CVPR  \n",
      "256             NIPS  \n",
      "257             CVPR  \n",
      "258             NIPS  \n",
      "259             NIPS  \n",
      "260             NIPS  \n",
      "261             CVPR  \n",
      "262             NIPS  \n",
      "263             CVPR  \n",
      "264             NIPS  \n",
      "265             CVPR  \n",
      "266             CVPR  \n",
      "267             CVPR  \n",
      "268             CVPR  \n",
      "269             NIPS  \n",
      "270             CVPR  \n",
      "271             NIPS  \n",
      "272             CVPR  \n",
      "273             CVPR  \n",
      "274             CVPR  \n",
      "275             NIPS  \n",
      "276             CVPR  \n",
      "277             CVPR  \n",
      "278             CVPR  \n",
      "279             CVPR  \n",
      "280             CVPR  \n",
      "281             CVPR  \n",
      "282             CVPR  \n",
      "283             CVPR  \n",
      "284             CVPR  \n",
      "285             CVPR  \n",
      "286             CVPR  \n",
      "287             NIPS  \n",
      "288             NIPS  \n",
      "289             NIPS  \n",
      "290             NIPS  \n",
      "291             NIPS  \n",
      "292             CVPR  \n",
      "293             CVPR  \n",
      "294             CVPR  \n",
      "295             NIPS  \n",
      "296             CVPR  \n",
      "297             CVPR  \n",
      "298             NIPS  \n",
      "299             CVPR  \n",
      "300             CVPR  \n",
      "301             CVPR  \n",
      "302             NIPS  \n",
      "303             CVPR  \n",
      "304             NIPS  \n",
      "305             CVPR  \n",
      "306             CVPR  \n",
      "307             CVPR  \n",
      "308             NIPS  \n",
      "309             NIPS  \n",
      "310             CVPR  \n",
      "311             CVPR  \n",
      "312             CVPR  \n",
      "313             CVPR  \n",
      "314             NIPS  \n",
      "315             CVPR  \n",
      "316             CVPR  \n",
      "317             CVPR  \n",
      "318             NIPS  \n",
      "319             CVPR  \n",
      "320             NIPS  \n",
      "321             CVPR  \n",
      "322             CVPR  \n",
      "323             CVPR  \n",
      "324             NIPS  \n",
      "325             CVPR  \n",
      "326             CVPR  \n",
      "327             CVPR  \n",
      "328             CVPR  \n",
      "329             CVPR  \n",
      "330             NIPS  \n",
      "331             NIPS  \n",
      "332             NIPS  \n",
      "333             NIPS  \n",
      "334             NIPS  \n",
      "335             CVPR  \n",
      "336             NIPS  \n",
      "337             NIPS  \n",
      "338             NIPS  \n",
      "339             CVPR  \n",
      "340             CVPR  \n",
      "341             CVPR  \n",
      "342             NIPS  \n",
      "343             CVPR  \n",
      "344             NIPS  \n",
      "345             NIPS  \n",
      "346             NIPS  \n",
      "347             NIPS  \n",
      "348             CVPR  \n",
      "349             NIPS  \n",
      "350             CVPR  \n",
      "351             NIPS  \n",
      "352             NIPS  \n",
      "353             NIPS  \n",
      "354             NIPS  \n",
      "355             NIPS  \n",
      "356             CVPR  \n",
      "357             CVPR  \n",
      "358             CVPR  \n",
      "359             CVPR  \n",
      "360             CVPR  \n",
      "361             CVPR  \n",
      "362             NIPS  \n",
      "363             NIPS  \n",
      "364             NIPS  \n",
      "365             CVPR  \n",
      "366             NIPS  \n",
      "367             CVPR  \n",
      "368             CVPR  \n",
      "369             NIPS  \n",
      "370             NIPS  \n",
      "371             NIPS  \n",
      "372             NIPS  \n",
      "373             CVPR  \n",
      "374             CVPR  \n",
      "375             CVPR  \n",
      "376             CVPR  \n",
      "377             CVPR  \n",
      "378             NIPS  \n",
      "379             NIPS  \n",
      "380             NIPS  \n",
      "381             NIPS  \n",
      "382             CVPR  \n",
      "383             NIPS  \n",
      "384             NIPS  \n",
      "385             NIPS  \n",
      "386             NIPS  \n",
      "387             CVPR  \n",
      "388             NIPS  \n",
      "389             NIPS  \n",
      "390             CVPR  \n",
      "391             NIPS  \n",
      "392             NIPS  \n",
      "393             NIPS  \n",
      "394             NIPS  \n",
      "395             NIPS  \n",
      "396             CVPR  \n",
      "397             NIPS  \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### \n",
    "\n",
    "functiontrends = \"\"\"\n",
    "\n",
    "Select count(?type) as ?counttype  ?type  ?conference where {\n",
    "\n",
    "?s <https://github.com/deepcurator/DCC/conferenceSeries> ?conference .\n",
    "?s <https://github.com/deepcurator/DCC/yearOfPublication> ?year .\n",
    "?s <https://github.com/deepcurator/DCC/hasRepository> ?repository .\n",
    "?repository <https://github.com/deepcurator/DCC/hasFunction> ?y.\n",
    "?y a ?type .\n",
    "FILTER(!(STR(?type) = \"https://github.com/deepcurator/DCC/UserDefined\")).\n",
    "\n",
    "}group by ?type ?conference ORDER by DESC(?counttype)\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "results_df = execute_query(functiontrends)\n",
    "results_df[\"type.value\"] = results_df[\"type.value\"].apply(lambda x : x.replace(\"https://github.com/deepcurator/DCC/tf/\", \"\"))\n",
    "\n",
    "results_df = results_df.drop(['counttype.datatype', 'conference.type','counttype.type',  'type.type',\n",
    "       ],axis=1)\n",
    "\n",
    "#print(results_df.head())\n",
    "results_df[\"counttype.value\"] = pd.to_numeric(results_df[\"counttype.value\"])\n",
    "\n",
    "results_df = results_df[results_df['counttype.value'] > 10]\n",
    "# results_df[\"year.value\"] = pd.to_numeric(results_df[\"year.value\"])\n",
    "\n",
    "# results_df.groupby(['type.value','counttype.value']).size().unstack().plot(kind='bar',stacked='True')\n",
    "\n",
    "# results_df.groupby(['type.value','counttype.value']).size().unstack().plot(kind='bar',stacked='True',legend='False')\n",
    "print(results_df)\n",
    "ax = results_df.plot(kind='bar',x='type.value',y='counttype.value',color='blue')\n",
    "ax.set(xlabel = \"TF Functions\", ylabel = \"Count\") \n",
    "ax.get_figure().set_size_inches(15, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 5: Publication on \"Audio\" processing using \"Attention Mechanism\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://github.com/deepcurator/DCC/Yapeng_Tian_Audio-Visual_Event_Localization_ECCV_2018_paper'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "\n",
    "prefix owl: <http://www.w3.org/2002/07/owl#>\n",
    "prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\n",
    "prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
    "prefix xml: <http://www.w3.org/XML/1998/namespace>\n",
    "prefix xsd: <http://www.w3.org/2001/XMLSchema#>\n",
    "prefix dcc: <https://github.com/deepcurator/DCC/>\n",
    "\n",
    "SELECT DISTINCT(?publication), ?figure\n",
    "WHERE {\n",
    "  ?publication dcc:hasEntity ?method .\n",
    "  ?method a dcc:Method .\n",
    "  ?method dcc:hasText \"attention_mechanism\" .\n",
    "  \n",
    "  ?publication dcc:hasEntity ?material .\n",
    "  ?material a dcc:Material .\n",
    "  ?material dcc:hasText \"audio\" .\n",
    "}\n",
    "\n",
    "\"\"\"\n",
    "results_df = execute_query(query)\n",
    "results_df = results_df.drop(['publication.type'], axis=1)\n",
    "results_df[\"publication.value\"][0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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