improved_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8179ed2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import packages\n",
    "import gzip\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.sparse import coo_matrix\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99b3a49a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#open data\n",
    "with gzip.open('NCSU-DigIC-GraphData-2023-07-25/xbar/1/xbar.json.gz','rb') as f:\n",
    "    design = json.loads(f.read().decode('utf-8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "97574a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "instances = pd.DataFrame(design['instances'])\n",
    "nets = pd.DataFrame(design['nets'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0087a404",
   "metadata": {},
   "outputs": [
    {
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       "   {'name': 'dout0[59]', 'id': 478, 'dir': 1},\n",
       "   {'name': 'dout0[5]', 'id': 479, 'dir': 1},\n",
       "   {'name': 'dout0[60]', 'id': 480, 'dir': 1},\n",
       "   {'name': 'dout0[61]', 'id': 481, 'dir': 1},\n",
       "   {'name': 'dout0[62]', 'id': 482, 'dir': 1},\n",
       "   {'name': 'dout0[63]', 'id': 483, 'dir': 1},\n",
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       "   {'name': 'dout0[65]', 'id': 485, 'dir': 1},\n",
       "   {'name': 'dout0[66]', 'id': 486, 'dir': 1},\n",
       "   {'name': 'dout0[67]', 'id': 487, 'dir': 1},\n",
       "   {'name': 'dout0[68]', 'id': 488, 'dir': 1},\n",
       "   {'name': 'dout0[69]', 'id': 489, 'dir': 1},\n",
       "   {'name': 'dout0[6]', 'id': 490, 'dir': 1},\n",
       "   {'name': 'dout0[70]', 'id': 491, 'dir': 1},\n",
       "   {'name': 'dout0[71]', 'id': 492, 'dir': 1},\n",
       "   {'name': 'dout0[72]', 'id': 493, 'dir': 1},\n",
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       "   {'name': 'web0', 'id': 524, 'dir': 0}],\n",
       "  'id': 79}]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#open cell data\n",
    "with gzip.open('NCSU-DigIC-GraphData-2023-07-25/cells.json.gz','rb') as f:\n",
    "    cells = json.loads(f.read().decode('utf-8'))\n",
    "cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "525f4c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#functions for accessing GRCs\n",
    "def buildBST(array,start=0,finish=-1):\n",
    "    if finish<0:\n",
    "        finish = len(array)\n",
    "    mid = (start + finish) // 2\n",
    "    if mid-start==1:\n",
    "        ltl=start\n",
    "    else:\n",
    "        ltl=buildBST(array,start,mid)\n",
    "    \n",
    "    if finish-mid==1:\n",
    "        gtl=mid\n",
    "    else:\n",
    "        gtl=buildBST(array,mid,finish)\n",
    "        \n",
    "    return((array[mid],ltl,gtl))\n",
    "\n",
    "def getGRCIndex(x,y,xbst,ybst):\n",
    "    while (type(xbst)==tuple):\n",
    "        if x < xbst[0]:\n",
    "            xbst=xbst[1]\n",
    "        else:\n",
    "            xbst=xbst[2]\n",
    "            \n",
    "    while (type(ybst)==tuple):\n",
    "        if y < ybst[0]:\n",
    "            ybst=ybst[1]\n",
    "        else:\n",
    "            ybst=ybst[2]\n",
    "            \n",
    "    return ybst, xbst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "021e7915",
   "metadata": {},
   "outputs": [],
   "source": [
    "#open congestion data\n",
    "congestion_data = np.load('NCSU-DigIC-GraphData-2023-07-25/xbar/1/xbar_congestion.npz')\n",
    "xbst=buildBST(congestion_data['xBoundaryList'])\n",
    "ybst=buildBST(congestion_data['yBoundaryList'])\n",
    "demand = np.zeros(shape = [instances.shape[0],])\n",
    "capacity = np.zeros(shape = [instances.shape[0],])\n",
    "indices = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b08126e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract congestion data from GRCs and apply values to individual cell instances\n",
    "for k in range(instances.shape[0]):\n",
    "    #print(k)\n",
    "    xloc = instances.iloc[k]['xloc']; yloc = instances.iloc[k]['yloc']\n",
    "    i,j=getGRCIndex(xloc,yloc,xbst,ybst)\n",
    "    d = 0 \n",
    "    c = 0\n",
    "    for l in list(congestion_data['layerList']): \n",
    "        lyr=list(congestion_data['layerList']).index(l)\n",
    "        d += congestion_data['demand'][lyr][i][j]\n",
    "        c += congestion_data['capacity'][lyr][i][j]\n",
    "    demand[k] = d\n",
    "    capacity[k] = c\n",
    "    indices.append((i, j))\n",
    "        \n",
    "instances['routing_demand'] = demand\n",
    "instances['routing_capacity'] = capacity\n",
    "instances['grc_index'] = indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "417dfcdb",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>clk_gate_out_reg_1/latch</td>\n",
       "      <td>2</td>\n",
       "      <td>44160</td>\n",
       "      <td>44544</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 28)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>clk_gate_out_reg_2/latch</td>\n",
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       "      <td>44160</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>clk_gate_out_reg_3/latch</td>\n",
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       "      <td>47616</td>\n",
       "      <td>23</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>(31, 29)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3947</th>\n",
       "      <td>U4123</td>\n",
       "      <td>3947</td>\n",
       "      <td>21888</td>\n",
       "      <td>53760</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>(35, 13)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\n",
       "      <td>U4125</td>\n",
       "      <td>3948</td>\n",
       "      <td>33664</td>\n",
       "      <td>66048</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>(43, 21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>U4128</td>\n",
       "      <td>3949</td>\n",
       "      <td>23296</td>\n",
       "      <td>66048</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>(43, 14)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>ZCTSBUF_205_132</td>\n",
       "      <td>3950</td>\n",
       "      <td>40576</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 26)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>ZCTSBUF_466_133</td>\n",
       "      <td>3951</td>\n",
       "      <td>46848</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(29, 30)</td>\n",
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       "<p>3952 rows × 9 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                          name    id   xloc   yloc  cell  orient  \\\n",
       "0       clk_gate_out_reg/latch     0  41984  44544    23       0   \n",
       "1     clk_gate_out_reg_0/latch     1  41984  47616    23       6   \n",
       "2     clk_gate_out_reg_1/latch     2  44160  44544    23       0   \n",
       "3     clk_gate_out_reg_2/latch     3  44160  47616    23       0   \n",
       "4     clk_gate_out_reg_3/latch     4  46336  47616    23       0   \n",
       "...                        ...   ...    ...    ...   ...     ...   \n",
       "3947                     U4123  3947  21888  53760    42       4   \n",
       "3948                     U4125  3948  33664  66048    42       0   \n",
       "3949                     U4128  3949  23296  66048    34       0   \n",
       "3950           ZCTSBUF_205_132  3950  40576  44544    11       0   \n",
       "3951           ZCTSBUF_466_133  3951  46848  44544    11       6   \n",
       "\n",
       "      routing_demand  routing_capacity grc_index  \n",
       "0               20.0              28.0  (29, 27)  \n",
       "1               23.0              28.0  (31, 27)  \n",
       "2               23.0              33.0  (29, 28)  \n",
       "3               22.0              33.0  (31, 28)  \n",
       "4               21.0              23.0  (31, 29)  \n",
       "...              ...               ...       ...  \n",
       "3947            31.0              31.0  (35, 13)  \n",
       "3948            30.0              30.0  (43, 21)  \n",
       "3949            27.0              29.0  (43, 14)  \n",
       "3950            28.0              33.0  (29, 26)  \n",
       "3951            23.0              36.0  (29, 30)  \n",
       "\n",
       "[3952 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "4df3e33c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>...</th>\n",
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       "      <th>bottom_left_corner_demand</th>\n",
       "      <th>top_right_corner_demand</th>\n",
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       "      <th>grc_pin_count</th>\n",
       "      <th>grc_cell_count</th>\n",
       "      <th>total_demand</th>\n",
       "      <th>in_connections</th>\n",
       "      <th>out_connections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clk_gate_out_reg_0/latch</td>\n",
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       "      <td>28.0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>(29, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.25</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>89.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>clk_gate_out_reg_2/latch</td>\n",
       "      <td>3</td>\n",
       "      <td>44160</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
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       "      <td>(31, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.75</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>clk_gate_out_reg_3/latch</td>\n",
       "      <td>4</td>\n",
       "      <td>46336</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>(31, 29)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.50</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3947</th>\n",
       "      <td>U4123</td>\n",
       "      <td>3947</td>\n",
       "      <td>21888</td>\n",
       "      <td>53760</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>(35, 13)</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\n",
       "      <td>U4125</td>\n",
       "      <td>3948</td>\n",
       "      <td>33664</td>\n",
       "      <td>66048</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>(43, 21)</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>U4128</td>\n",
       "      <td>3949</td>\n",
       "      <td>23296</td>\n",
       "      <td>66048</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>(43, 14)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.50</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>ZCTSBUF_205_132</td>\n",
       "      <td>3950</td>\n",
       "      <td>40576</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 26)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.50</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>ZCTSBUF_466_133</td>\n",
       "      <td>3951</td>\n",
       "      <td>46848</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(29, 30)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.25</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>101.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3952 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name    id   xloc   yloc  cell  orient  \\\n",
       "0       clk_gate_out_reg/latch     0  41984  44544    23       0   \n",
       "1     clk_gate_out_reg_0/latch     1  41984  47616    23       6   \n",
       "2     clk_gate_out_reg_1/latch     2  44160  44544    23       0   \n",
       "3     clk_gate_out_reg_2/latch     3  44160  47616    23       0   \n",
       "4     clk_gate_out_reg_3/latch     4  46336  47616    23       0   \n",
       "...                        ...   ...    ...    ...   ...     ...   \n",
       "3947                     U4123  3947  21888  53760    42       4   \n",
       "3948                     U4125  3948  33664  66048    42       0   \n",
       "3949                     U4128  3949  23296  66048    34       0   \n",
       "3950           ZCTSBUF_205_132  3950  40576  44544    11       0   \n",
       "3951           ZCTSBUF_466_133  3951  46848  44544    11       6   \n",
       "\n",
       "      routing_demand  routing_capacity grc_index  individual_pins  ...  \\\n",
       "0               20.0              28.0  (29, 27)                4  ...   \n",
       "1               23.0              28.0  (31, 27)                4  ...   \n",
       "2               23.0              33.0  (29, 28)                4  ...   \n",
       "3               22.0              33.0  (31, 28)                4  ...   \n",
       "4               21.0              23.0  (31, 29)                4  ...   \n",
       "...              ...               ...       ...              ...  ...   \n",
       "3947            31.0              31.0  (35, 13)                3  ...   \n",
       "3948            30.0              30.0  (43, 21)                3  ...   \n",
       "3949            27.0              29.0  (43, 14)                2  ...   \n",
       "3950            28.0              33.0  (29, 26)                2  ...   \n",
       "3951            23.0              36.0  (29, 30)                2  ...   \n",
       "\n",
       "      top_left_corner_demand  bottom_right_corner_demand  \\\n",
       "0                       24.0                        23.0   \n",
       "1                       24.0                        23.0   \n",
       "2                       20.0                        20.0   \n",
       "3                       20.0                        20.0   \n",
       "4                       18.0                        27.0   \n",
       "...                      ...                         ...   \n",
       "3947                    26.0                        27.0   \n",
       "3948                    23.0                        23.0   \n",
       "3949                    24.0                        24.0   \n",
       "3950                    27.0                        28.0   \n",
       "3951                    18.0                        27.0   \n",
       "\n",
       "      bottom_left_corner_demand  top_right_corner_demand  average_demand  \\\n",
       "0                          24.0                     23.0           23.50   \n",
       "1                          27.0                     21.0           23.75   \n",
       "2                          23.0                     26.0           22.25   \n",
       "3                          21.0                     18.0           19.75   \n",
       "4                          18.0                     27.0           22.50   \n",
       "...                         ...                      ...             ...   \n",
       "3947                       28.0                     23.0           26.00   \n",
       "3948                       31.0                     31.0           27.00   \n",
       "3949                       25.0                     25.0           24.50   \n",
       "3950                       27.0                     24.0           26.50   \n",
       "3951                       26.0                     30.0           25.25   \n",
       "\n",
       "      grc_pin_count  grc_cell_count  total_demand  in_connections  \\\n",
       "0                12               3          94.0               3   \n",
       "1                 8               2          95.0               3   \n",
       "2                10               3          89.0               3   \n",
       "3                 8               2          79.0               3   \n",
       "4                 4               1          90.0               3   \n",
       "...             ...             ...           ...             ...   \n",
       "3947             12               3         104.0               2   \n",
       "3948             16               4         108.0               2   \n",
       "3949             17               6          98.0               1   \n",
       "3950             12               3         106.0               1   \n",
       "3951             11               3         101.0               1   \n",
       "\n",
       "      out_connections  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   1  \n",
       "3                   1  \n",
       "4                   1  \n",
       "...               ...  \n",
       "3947                1  \n",
       "3948                1  \n",
       "3949                1  \n",
       "3950                1  \n",
       "3951                1  \n",
       "\n",
       "[3952 rows x 26 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extract pin counts, in and out connections, and cell sizes from cells dictionary\n",
    "individual_pins = []\n",
    "cell_widths = []\n",
    "cell_heights = []\n",
    "in_connections = []\n",
    "out_connections = []\n",
    "for k in range(instances.shape[0]):\n",
    "    cell_type = instances.iloc[k]['cell']\n",
    "    cell_pins = len(cells[cell_type]['terms'])\n",
    "    cell_in_connections = 0\n",
    "    cell_out_connections = 0\n",
    "    for term in cells[cell_type]['terms']:\n",
    "        if term['dir'] == 0:\n",
    "            cell_in_connections += 1\n",
    "        if term['dir'] == 1:\n",
    "            cell_out_connections += 1\n",
    "    cell_width = cells[cell_type]['width']\n",
    "    cell_height = cells[cell_type]['height']\n",
    "    individual_pins.append(cell_pins)\n",
    "    cell_widths.append(cell_width)\n",
    "    cell_heights.append(cell_height)\n",
    "    in_connections.append(cell_in_connections)\n",
    "    out_connections.append(cell_out_connections)\n",
    "\n",
    "instances['individual_pins'] = individual_pins\n",
    "instances['width'] = cell_widths\n",
    "instances['height'] = cell_heights\n",
    "instances['in_connections'] = in_connections\n",
    "instances['out_connections'] = out_connections\n",
    "instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d71d2cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>xloc</th>\n",
       "      <th>yloc</th>\n",
       "      <th>cell</th>\n",
       "      <th>orient</th>\n",
       "      <th>routing_demand</th>\n",
       "      <th>routing_capacity</th>\n",
       "      <th>grc_index</th>\n",
       "      <th>individual_pins</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>left_bound</th>\n",
       "      <th>right_bound</th>\n",
       "      <th>bottom_bound</th>\n",
       "      <th>top_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>clk_gate_out_reg/latch</td>\n",
       "      <td>0</td>\n",
       "      <td>41984</td>\n",
       "      <td>44544</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>(29, 27)</td>\n",
       "      <td>4</td>\n",
       "      <td>2176</td>\n",
       "      <td>1536</td>\n",
       "      <td>40896</td>\n",
       "      <td>43072</td>\n",
       "      <td>43456</td>\n",
       "      <td>43072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clk_gate_out_reg_0/latch</td>\n",
       "      <td>1</td>\n",
       "      <td>41984</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>(31, 27)</td>\n",
       "      <td>4</td>\n",
       "      <td>2176</td>\n",
       "      <td>1536</td>\n",
       "      <td>40896</td>\n",
       "      <td>43072</td>\n",
       "      <td>46528</td>\n",
       "      <td>43072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>clk_gate_out_reg_1/latch</td>\n",
       "      <td>2</td>\n",
       "      <td>44160</td>\n",
       "      <td>44544</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>2176</td>\n",
       "      <td>1536</td>\n",
       "      <td>43072</td>\n",
       "      <td>45248</td>\n",
       "      <td>43456</td>\n",
       "      <td>45248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>clk_gate_out_reg_2/latch</td>\n",
       "      <td>3</td>\n",
       "      <td>44160</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(31, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>2176</td>\n",
       "      <td>1536</td>\n",
       "      <td>43072</td>\n",
       "      <td>45248</td>\n",
       "      <td>46528</td>\n",
       "      <td>45248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>clk_gate_out_reg_3/latch</td>\n",
       "      <td>4</td>\n",
       "      <td>46336</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>(31, 29)</td>\n",
       "      <td>4</td>\n",
       "      <td>2176</td>\n",
       "      <td>1536</td>\n",
       "      <td>45248</td>\n",
       "      <td>47424</td>\n",
       "      <td>46528</td>\n",
       "      <td>47424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3947</th>\n",
       "      <td>U4123</td>\n",
       "      <td>3947</td>\n",
       "      <td>21888</td>\n",
       "      <td>53760</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>(35, 13)</td>\n",
       "      <td>3</td>\n",
       "      <td>512</td>\n",
       "      <td>1536</td>\n",
       "      <td>21632</td>\n",
       "      <td>22144</td>\n",
       "      <td>53504</td>\n",
       "      <td>22144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\n",
       "      <td>U4125</td>\n",
       "      <td>3948</td>\n",
       "      <td>33664</td>\n",
       "      <td>66048</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>(43, 21)</td>\n",
       "      <td>3</td>\n",
       "      <td>512</td>\n",
       "      <td>1536</td>\n",
       "      <td>33408</td>\n",
       "      <td>33920</td>\n",
       "      <td>65792</td>\n",
       "      <td>33920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>U4128</td>\n",
       "      <td>3949</td>\n",
       "      <td>23296</td>\n",
       "      <td>66048</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>(43, 14)</td>\n",
       "      <td>2</td>\n",
       "      <td>384</td>\n",
       "      <td>1536</td>\n",
       "      <td>23104</td>\n",
       "      <td>23488</td>\n",
       "      <td>65856</td>\n",
       "      <td>23488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>ZCTSBUF_205_132</td>\n",
       "      <td>3950</td>\n",
       "      <td>40576</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 26)</td>\n",
       "      <td>2</td>\n",
       "      <td>640</td>\n",
       "      <td>1536</td>\n",
       "      <td>40256</td>\n",
       "      <td>40896</td>\n",
       "      <td>44224</td>\n",
       "      <td>40896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>ZCTSBUF_466_133</td>\n",
       "      <td>3951</td>\n",
       "      <td>46848</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(29, 30)</td>\n",
       "      <td>2</td>\n",
       "      <td>640</td>\n",
       "      <td>1536</td>\n",
       "      <td>46528</td>\n",
       "      <td>47168</td>\n",
       "      <td>44224</td>\n",
       "      <td>47168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3952 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name    id   xloc   yloc  cell  orient  \\\n",
       "0       clk_gate_out_reg/latch     0  41984  44544    23       0   \n",
       "1     clk_gate_out_reg_0/latch     1  41984  47616    23       6   \n",
       "2     clk_gate_out_reg_1/latch     2  44160  44544    23       0   \n",
       "3     clk_gate_out_reg_2/latch     3  44160  47616    23       0   \n",
       "4     clk_gate_out_reg_3/latch     4  46336  47616    23       0   \n",
       "...                        ...   ...    ...    ...   ...     ...   \n",
       "3947                     U4123  3947  21888  53760    42       4   \n",
       "3948                     U4125  3948  33664  66048    42       0   \n",
       "3949                     U4128  3949  23296  66048    34       0   \n",
       "3950           ZCTSBUF_205_132  3950  40576  44544    11       0   \n",
       "3951           ZCTSBUF_466_133  3951  46848  44544    11       6   \n",
       "\n",
       "      routing_demand  routing_capacity grc_index  individual_pins  width  \\\n",
       "0               20.0              28.0  (29, 27)                4   2176   \n",
       "1               23.0              28.0  (31, 27)                4   2176   \n",
       "2               23.0              33.0  (29, 28)                4   2176   \n",
       "3               22.0              33.0  (31, 28)                4   2176   \n",
       "4               21.0              23.0  (31, 29)                4   2176   \n",
       "...              ...               ...       ...              ...    ...   \n",
       "3947            31.0              31.0  (35, 13)                3    512   \n",
       "3948            30.0              30.0  (43, 21)                3    512   \n",
       "3949            27.0              29.0  (43, 14)                2    384   \n",
       "3950            28.0              33.0  (29, 26)                2    640   \n",
       "3951            23.0              36.0  (29, 30)                2    640   \n",
       "\n",
       "      height  left_bound  right_bound  bottom_bound  top_bound  \n",
       "0       1536       40896        43072         43456      43072  \n",
       "1       1536       40896        43072         46528      43072  \n",
       "2       1536       43072        45248         43456      45248  \n",
       "3       1536       43072        45248         46528      45248  \n",
       "4       1536       45248        47424         46528      47424  \n",
       "...      ...         ...          ...           ...        ...  \n",
       "3947    1536       21632        22144         53504      22144  \n",
       "3948    1536       33408        33920         65792      33920  \n",
       "3949    1536       23104        23488         65856      23488  \n",
       "3950    1536       40256        40896         44224      40896  \n",
       "3951    1536       46528        47168         44224      47168  \n",
       "\n",
       "[3952 rows x 16 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances['left_bound'] = instances['xloc'] - instances['width']//2\n",
    "instances['right_bound'] = instances['xloc'] + instances['width']//2\n",
    "instances['bottom_bound'] = instances['yloc'] - instances['width']//2\n",
    "instances['top_bound'] = instances['xloc'] + instances['width']//2\n",
    "instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea9d9466",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract congestion data from GRCs and apply values to individual cell instances\n",
    "for k in range(instances.shape[0]):\n",
    "    #print(k)\n",
    "    xloc = instances.iloc[k]['left_bound']; yloc = instances.iloc[k]['top_bound']\n",
    "    i,j=getGRCIndex(xloc,yloc,xbst,ybst)\n",
    "    d = 0 \n",
    "    #c = 0\n",
    "    for l in list(congestion_data['layerList']): \n",
    "        lyr=list(congestion_data['layerList']).index(l)\n",
    "        d += congestion_data['demand'][lyr][i][j]\n",
    "        #c += congestion_data['capacity'][lyr][i][j]\n",
    "    demand[k] = d\n",
    "    #capacity[k] = c\n",
    "    #indices.append((i, j))\n",
    "        \n",
    "instances['top_left_corner_demand'] = demand\n",
    "#instances['routing_capacity'] = capacity\n",
    "#instances['grc_index'] = indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "766fda89",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract congestion data from GRCs and apply values to individual cell instances\n",
    "for k in range(instances.shape[0]):\n",
    "    #print(k)\n",
    "    xloc = instances.iloc[k]['right_bound']; yloc = instances.iloc[k]['top_bound']\n",
    "    i,j=getGRCIndex(xloc,yloc,xbst,ybst)\n",
    "    d = 0 \n",
    "    #c = 0\n",
    "    for l in list(congestion_data['layerList']): \n",
    "        lyr=list(congestion_data['layerList']).index(l)\n",
    "        d += congestion_data['demand'][lyr][i][j]\n",
    "        #c += congestion_data['capacity'][lyr][i][j]\n",
    "    demand[k] = d\n",
    "    #capacity[k] = c\n",
    "    #indices.append((i, j))\n",
    "        \n",
    "instances['bottom_right_corner_demand'] = demand\n",
    "#instances['routing_capacity'] = capacity\n",
    "#instances['grc_index'] = indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa52816f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract congestion data from GRCs and apply values to individual cell instances\n",
    "for k in range(instances.shape[0]):\n",
    "    #print(k)\n",
    "    xloc = instances.iloc[k]['left_bound']; yloc = instances.iloc[k]['bottom_bound']\n",
    "    i,j=getGRCIndex(xloc,yloc,xbst,ybst)\n",
    "    d = 0 \n",
    "    #c = 0\n",
    "    for l in list(congestion_data['layerList']): \n",
    "        lyr=list(congestion_data['layerList']).index(l)\n",
    "        d += congestion_data['demand'][lyr][i][j]\n",
    "        #c += congestion_data['capacity'][lyr][i][j]\n",
    "    demand[k] = d\n",
    "    #capacity[k] = c\n",
    "    #indices.append((i, j))\n",
    "        \n",
    "instances['bottom_left_corner_demand'] = demand\n",
    "#instances['routing_capacity'] = capacity\n",
    "#instances['grc_index'] = indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f7a0b0e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract congestion data from GRCs and apply values to individual cell instances\n",
    "for k in range(instances.shape[0]):\n",
    "    #print(k)\n",
    "    xloc = instances.iloc[k]['right_bound']; yloc = instances.iloc[k]['bottom_bound']\n",
    "    i,j=getGRCIndex(xloc,yloc,xbst,ybst)\n",
    "    d = 0 \n",
    "    #c = 0\n",
    "    for l in list(congestion_data['layerList']): \n",
    "        lyr=list(congestion_data['layerList']).index(l)\n",
    "        d += congestion_data['demand'][lyr][i][j]\n",
    "        #c += congestion_data['capacity'][lyr][i][j]\n",
    "    demand[k] = d\n",
    "    #capacity[k] = c\n",
    "    #indices.append((i, j))\n",
    "        \n",
    "instances['top_right_corner_demand'] = demand\n",
    "#instances['routing_capacity'] = capacity\n",
    "#instances['grc_index'] = indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "ca9ed0e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>xloc</th>\n",
       "      <th>yloc</th>\n",
       "      <th>cell</th>\n",
       "      <th>orient</th>\n",
       "      <th>routing_demand</th>\n",
       "      <th>routing_capacity</th>\n",
       "      <th>grc_index</th>\n",
       "      <th>individual_pins</th>\n",
       "      <th>...</th>\n",
       "      <th>right_bound</th>\n",
       "      <th>bottom_bound</th>\n",
       "      <th>top_bound</th>\n",
       "      <th>top_left_corner_demand</th>\n",
       "      <th>bottom_right_corner_demand</th>\n",
       "      <th>bottom_left_corner_demand</th>\n",
       "      <th>top_right_corner_demand</th>\n",
       "      <th>average_demand</th>\n",
       "      <th>grc_pin_count</th>\n",
       "      <th>grc_cell_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>clk_gate_out_reg/latch</td>\n",
       "      <td>0</td>\n",
       "      <td>41984</td>\n",
       "      <td>44544</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>(29, 27)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>43072</td>\n",
       "      <td>43456</td>\n",
       "      <td>43072</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.50</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clk_gate_out_reg_0/latch</td>\n",
       "      <td>1</td>\n",
       "      <td>41984</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>(31, 27)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>43072</td>\n",
       "      <td>46528</td>\n",
       "      <td>43072</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.75</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>clk_gate_out_reg_1/latch</td>\n",
       "      <td>2</td>\n",
       "      <td>44160</td>\n",
       "      <td>44544</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>45248</td>\n",
       "      <td>43456</td>\n",
       "      <td>45248</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.25</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>clk_gate_out_reg_2/latch</td>\n",
       "      <td>3</td>\n",
       "      <td>44160</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(31, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>45248</td>\n",
       "      <td>46528</td>\n",
       "      <td>45248</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.75</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>clk_gate_out_reg_3/latch</td>\n",
       "      <td>4</td>\n",
       "      <td>46336</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>(31, 29)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>47424</td>\n",
       "      <td>46528</td>\n",
       "      <td>47424</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.50</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3947</th>\n",
       "      <td>U4123</td>\n",
       "      <td>3947</td>\n",
       "      <td>21888</td>\n",
       "      <td>53760</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>(35, 13)</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>22144</td>\n",
       "      <td>53504</td>\n",
       "      <td>22144</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\n",
       "      <td>U4125</td>\n",
       "      <td>3948</td>\n",
       "      <td>33664</td>\n",
       "      <td>66048</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>(43, 21)</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>33920</td>\n",
       "      <td>65792</td>\n",
       "      <td>33920</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>U4128</td>\n",
       "      <td>3949</td>\n",
       "      <td>23296</td>\n",
       "      <td>66048</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>(43, 14)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>23488</td>\n",
       "      <td>65856</td>\n",
       "      <td>23488</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.50</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>ZCTSBUF_205_132</td>\n",
       "      <td>3950</td>\n",
       "      <td>40576</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 26)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>40896</td>\n",
       "      <td>44224</td>\n",
       "      <td>40896</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.50</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>ZCTSBUF_466_133</td>\n",
       "      <td>3951</td>\n",
       "      <td>46848</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(29, 30)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>47168</td>\n",
       "      <td>44224</td>\n",
       "      <td>47168</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.25</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3952 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name    id   xloc   yloc  cell  orient  \\\n",
       "0       clk_gate_out_reg/latch     0  41984  44544    23       0   \n",
       "1     clk_gate_out_reg_0/latch     1  41984  47616    23       6   \n",
       "2     clk_gate_out_reg_1/latch     2  44160  44544    23       0   \n",
       "3     clk_gate_out_reg_2/latch     3  44160  47616    23       0   \n",
       "4     clk_gate_out_reg_3/latch     4  46336  47616    23       0   \n",
       "...                        ...   ...    ...    ...   ...     ...   \n",
       "3947                     U4123  3947  21888  53760    42       4   \n",
       "3948                     U4125  3948  33664  66048    42       0   \n",
       "3949                     U4128  3949  23296  66048    34       0   \n",
       "3950           ZCTSBUF_205_132  3950  40576  44544    11       0   \n",
       "3951           ZCTSBUF_466_133  3951  46848  44544    11       6   \n",
       "\n",
       "      routing_demand  routing_capacity grc_index  individual_pins  ...  \\\n",
       "0               20.0              28.0  (29, 27)                4  ...   \n",
       "1               23.0              28.0  (31, 27)                4  ...   \n",
       "2               23.0              33.0  (29, 28)                4  ...   \n",
       "3               22.0              33.0  (31, 28)                4  ...   \n",
       "4               21.0              23.0  (31, 29)                4  ...   \n",
       "...              ...               ...       ...              ...  ...   \n",
       "3947            31.0              31.0  (35, 13)                3  ...   \n",
       "3948            30.0              30.0  (43, 21)                3  ...   \n",
       "3949            27.0              29.0  (43, 14)                2  ...   \n",
       "3950            28.0              33.0  (29, 26)                2  ...   \n",
       "3951            23.0              36.0  (29, 30)                2  ...   \n",
       "\n",
       "      right_bound  bottom_bound  top_bound  top_left_corner_demand  \\\n",
       "0           43072         43456      43072                    24.0   \n",
       "1           43072         46528      43072                    24.0   \n",
       "2           45248         43456      45248                    20.0   \n",
       "3           45248         46528      45248                    20.0   \n",
       "4           47424         46528      47424                    18.0   \n",
       "...           ...           ...        ...                     ...   \n",
       "3947        22144         53504      22144                    26.0   \n",
       "3948        33920         65792      33920                    23.0   \n",
       "3949        23488         65856      23488                    24.0   \n",
       "3950        40896         44224      40896                    27.0   \n",
       "3951        47168         44224      47168                    18.0   \n",
       "\n",
       "      bottom_right_corner_demand  bottom_left_corner_demand  \\\n",
       "0                           23.0                       24.0   \n",
       "1                           23.0                       27.0   \n",
       "2                           20.0                       23.0   \n",
       "3                           20.0                       21.0   \n",
       "4                           27.0                       18.0   \n",
       "...                          ...                        ...   \n",
       "3947                        27.0                       28.0   \n",
       "3948                        23.0                       31.0   \n",
       "3949                        24.0                       25.0   \n",
       "3950                        28.0                       27.0   \n",
       "3951                        27.0                       26.0   \n",
       "\n",
       "      top_right_corner_demand  average_demand  grc_pin_count  grc_cell_count  \n",
       "0                        23.0           23.50             12               3  \n",
       "1                        21.0           23.75              8               2  \n",
       "2                        26.0           22.25             10               3  \n",
       "3                        18.0           19.75              8               2  \n",
       "4                        27.0           22.50              4               1  \n",
       "...                       ...             ...            ...             ...  \n",
       "3947                     23.0           26.00             12               3  \n",
       "3948                     31.0           27.00             16               4  \n",
       "3949                     25.0           24.50             17               6  \n",
       "3950                     24.0           26.50             12               3  \n",
       "3951                     30.0           25.25             11               3  \n",
       "\n",
       "[3952 rows x 23 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances['average_demand'] = (instances['top_left_corner_demand'] + instances['bottom_right_corner_demand'] + instances['bottom_left_corner_demand'] + instances['top_right_corner_demand'])/4\n",
    "instances\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "21fe54fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#instances['total_demand'] = (instances['top_left_corner_demand'] + instances['bottom_right_corner_demand'] + instances['bottom_left_corner_demand'] + instances['top_right_corner_demand'])\n",
    "#instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "84d945e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract the number of pins in each grc\n",
    "instances['grc_pin_count'] = instances['individual_pins'].groupby(instances['grc_index']).transform('sum')\n",
    "#instances['grc_pin_count'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fc1056cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grc_cell_count\n",
       "4    1644\n",
       "3    1131\n",
       "2     550\n",
       "5     430\n",
       "1     142\n",
       "6      48\n",
       "7       7\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extract the number of pins in each grc\n",
    "instances['grc_cell_count'] = instances['individual_pins'].groupby(instances['grc_index']).transform('count')\n",
    "instances['grc_cell_count'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "b731cc91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xloc</th>\n",
       "      <th>yloc</th>\n",
       "      <th>individual_pins</th>\n",
       "      <th>width</th>\n",
       "      <th>grc_pin_count</th>\n",
       "      <th>grc_cell_count</th>\n",
       "      <th>in_connections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41984</td>\n",
       "      <td>44544</td>\n",
       "      <td>4</td>\n",
       "      <td>2176</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41984</td>\n",
       "      <td>47616</td>\n",
       "      <td>4</td>\n",
       "      <td>2176</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
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       "      <td>44544</td>\n",
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       "      <td>2176</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44160</td>\n",
       "      <td>47616</td>\n",
       "      <td>4</td>\n",
       "      <td>2176</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46336</td>\n",
       "      <td>47616</td>\n",
       "      <td>4</td>\n",
       "      <td>2176</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3947</th>\n",
       "      <td>21888</td>\n",
       "      <td>53760</td>\n",
       "      <td>3</td>\n",
       "      <td>512</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\n",
       "      <td>33664</td>\n",
       "      <td>66048</td>\n",
       "      <td>3</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>23296</td>\n",
       "      <td>66048</td>\n",
       "      <td>2</td>\n",
       "      <td>384</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>40576</td>\n",
       "      <td>44544</td>\n",
       "      <td>2</td>\n",
       "      <td>640</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>46848</td>\n",
       "      <td>44544</td>\n",
       "      <td>2</td>\n",
       "      <td>640</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3952 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       xloc   yloc  individual_pins  width  grc_pin_count  grc_cell_count  \\\n",
       "0     41984  44544                4   2176             12               3   \n",
       "1     41984  47616                4   2176              8               2   \n",
       "2     44160  44544                4   2176             10               3   \n",
       "3     44160  47616                4   2176              8               2   \n",
       "4     46336  47616                4   2176              4               1   \n",
       "...     ...    ...              ...    ...            ...             ...   \n",
       "3947  21888  53760                3    512             12               3   \n",
       "3948  33664  66048                3    512             16               4   \n",
       "3949  23296  66048                2    384             17               6   \n",
       "3950  40576  44544                2    640             12               3   \n",
       "3951  46848  44544                2    640             11               3   \n",
       "\n",
       "      in_connections  \n",
       "0                  3  \n",
       "1                  3  \n",
       "2                  3  \n",
       "3                  3  \n",
       "4                  3  \n",
       "...              ...  \n",
       "3947               2  \n",
       "3948               2  \n",
       "3949               1  \n",
       "3950               1  \n",
       "3951               1  \n",
       "\n",
       "[3952 rows x 7 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create feature matrix by dropping irrelevant features\n",
    "feature_matrix = instances.drop(columns=['name', 'id', 'cell', 'orient', 'routing_demand', 'routing_capacity', 'grc_index', 'left_bound', 'right_bound', 'bottom_bound', 'top_bound', 'top_left_corner_demand', 'bottom_right_corner_demand', 'bottom_left_corner_demand', 'top_right_corner_demand', 'average_demand', 'out_connections', 'total_demand', 'height'])\n",
    "feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3b74191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       23.50\n",
       "1       23.75\n",
       "2       22.25\n",
       "3       19.75\n",
       "4       22.50\n",
       "        ...  \n",
       "3947    26.00\n",
       "3948    27.00\n",
       "3949    24.50\n",
       "3950    26.50\n",
       "3951    25.25\n",
       "Name: average_demand, Length: 3952, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create labels with demand\n",
    "labels = instances['average_demand']\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d6e1dd2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#open connectivity data\n",
    "conn=np.load('NCSU-DigIC-GraphData-2023-07-25/xbar/1/xbar_connectivity.npz')\n",
    "#A = coo_matrix((conn['data'], (conn['row'], conn['col'])), shape=conn['shape'])\n",
    "#A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a9017a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3951,  492,  941, ...,    2,    1,    0])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pd.Series(conn['data']).value_counts()\n",
    "conn['row']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e3b90789",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6e1b2e33",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create edge-index of data\n",
    "A = coo_matrix((conn['data'], (conn['row'], conn['col'])), shape=conn['shape'])\n",
    "A = A.__mul__(A.T)\n",
    "\n",
    "A_coo = A.tocoo()\n",
    "edge_index = np.array([A_coo.row, A_coo.col])\n",
    "edge_index = torch.tensor(edge_index, dtype=torch.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2044ef80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>xloc</th>\n",
       "      <th>yloc</th>\n",
       "      <th>cell</th>\n",
       "      <th>orient</th>\n",
       "      <th>routing_demand</th>\n",
       "      <th>routing_capacity</th>\n",
       "      <th>grc_index</th>\n",
       "      <th>individual_pins</th>\n",
       "      <th>...</th>\n",
       "      <th>right_bound</th>\n",
       "      <th>bottom_bound</th>\n",
       "      <th>top_bound</th>\n",
       "      <th>top_left_corner_demand</th>\n",
       "      <th>bottom_right_corner_demand</th>\n",
       "      <th>bottom_left_corner_demand</th>\n",
       "      <th>top_right_corner_demand</th>\n",
       "      <th>average_demand</th>\n",
       "      <th>grc_pin_count</th>\n",
       "      <th>grc_cell_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>clk_gate_out_reg/latch</td>\n",
       "      <td>0</td>\n",
       "      <td>41984</td>\n",
       "      <td>44544</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>(29, 27)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>43072</td>\n",
       "      <td>43456</td>\n",
       "      <td>43072</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.50</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clk_gate_out_reg_0/latch</td>\n",
       "      <td>1</td>\n",
       "      <td>41984</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>(31, 27)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>43072</td>\n",
       "      <td>46528</td>\n",
       "      <td>43072</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.75</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>clk_gate_out_reg_1/latch</td>\n",
       "      <td>2</td>\n",
       "      <td>44160</td>\n",
       "      <td>44544</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>45248</td>\n",
       "      <td>43456</td>\n",
       "      <td>45248</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.25</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>clk_gate_out_reg_2/latch</td>\n",
       "      <td>3</td>\n",
       "      <td>44160</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(31, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>45248</td>\n",
       "      <td>46528</td>\n",
       "      <td>45248</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.75</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>clk_gate_out_reg_3/latch</td>\n",
       "      <td>4</td>\n",
       "      <td>46336</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>(31, 29)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>47424</td>\n",
       "      <td>46528</td>\n",
       "      <td>47424</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.50</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3947</th>\n",
       "      <td>U4123</td>\n",
       "      <td>3947</td>\n",
       "      <td>21888</td>\n",
       "      <td>53760</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>(35, 13)</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>22144</td>\n",
       "      <td>53504</td>\n",
       "      <td>22144</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\n",
       "      <td>U4125</td>\n",
       "      <td>3948</td>\n",
       "      <td>33664</td>\n",
       "      <td>66048</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>(43, 21)</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>33920</td>\n",
       "      <td>65792</td>\n",
       "      <td>33920</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>U4128</td>\n",
       "      <td>3949</td>\n",
       "      <td>23296</td>\n",
       "      <td>66048</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>(43, 14)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>23488</td>\n",
       "      <td>65856</td>\n",
       "      <td>23488</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.50</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>ZCTSBUF_205_132</td>\n",
       "      <td>3950</td>\n",
       "      <td>40576</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 26)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>40896</td>\n",
       "      <td>44224</td>\n",
       "      <td>40896</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.50</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>ZCTSBUF_466_133</td>\n",
       "      <td>3951</td>\n",
       "      <td>46848</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(29, 30)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>47168</td>\n",
       "      <td>44224</td>\n",
       "      <td>47168</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.25</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3952 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name    id   xloc   yloc  cell  orient  \\\n",
       "0       clk_gate_out_reg/latch     0  41984  44544    23       0   \n",
       "1     clk_gate_out_reg_0/latch     1  41984  47616    23       6   \n",
       "2     clk_gate_out_reg_1/latch     2  44160  44544    23       0   \n",
       "3     clk_gate_out_reg_2/latch     3  44160  47616    23       0   \n",
       "4     clk_gate_out_reg_3/latch     4  46336  47616    23       0   \n",
       "...                        ...   ...    ...    ...   ...     ...   \n",
       "3947                     U4123  3947  21888  53760    42       4   \n",
       "3948                     U4125  3948  33664  66048    42       0   \n",
       "3949                     U4128  3949  23296  66048    34       0   \n",
       "3950           ZCTSBUF_205_132  3950  40576  44544    11       0   \n",
       "3951           ZCTSBUF_466_133  3951  46848  44544    11       6   \n",
       "\n",
       "      routing_demand  routing_capacity grc_index  individual_pins  ...  \\\n",
       "0               20.0              28.0  (29, 27)                4  ...   \n",
       "1               23.0              28.0  (31, 27)                4  ...   \n",
       "2               23.0              33.0  (29, 28)                4  ...   \n",
       "3               22.0              33.0  (31, 28)                4  ...   \n",
       "4               21.0              23.0  (31, 29)                4  ...   \n",
       "...              ...               ...       ...              ...  ...   \n",
       "3947            31.0              31.0  (35, 13)                3  ...   \n",
       "3948            30.0              30.0  (43, 21)                3  ...   \n",
       "3949            27.0              29.0  (43, 14)                2  ...   \n",
       "3950            28.0              33.0  (29, 26)                2  ...   \n",
       "3951            23.0              36.0  (29, 30)                2  ...   \n",
       "\n",
       "      right_bound  bottom_bound  top_bound  top_left_corner_demand  \\\n",
       "0           43072         43456      43072                    24.0   \n",
       "1           43072         46528      43072                    24.0   \n",
       "2           45248         43456      45248                    20.0   \n",
       "3           45248         46528      45248                    20.0   \n",
       "4           47424         46528      47424                    18.0   \n",
       "...           ...           ...        ...                     ...   \n",
       "3947        22144         53504      22144                    26.0   \n",
       "3948        33920         65792      33920                    23.0   \n",
       "3949        23488         65856      23488                    24.0   \n",
       "3950        40896         44224      40896                    27.0   \n",
       "3951        47168         44224      47168                    18.0   \n",
       "\n",
       "      bottom_right_corner_demand  bottom_left_corner_demand  \\\n",
       "0                           23.0                       24.0   \n",
       "1                           23.0                       27.0   \n",
       "2                           20.0                       23.0   \n",
       "3                           20.0                       21.0   \n",
       "4                           27.0                       18.0   \n",
       "...                          ...                        ...   \n",
       "3947                        27.0                       28.0   \n",
       "3948                        23.0                       31.0   \n",
       "3949                        24.0                       25.0   \n",
       "3950                        28.0                       27.0   \n",
       "3951                        27.0                       26.0   \n",
       "\n",
       "      top_right_corner_demand  average_demand  grc_pin_count  grc_cell_count  \n",
       "0                        23.0           23.50             12               3  \n",
       "1                        21.0           23.75              8               2  \n",
       "2                        26.0           22.25             10               3  \n",
       "3                        18.0           19.75              8               2  \n",
       "4                        27.0           22.50              4               1  \n",
       "...                       ...             ...            ...             ...  \n",
       "3947                     23.0           26.00             12               3  \n",
       "3948                     31.0           27.00             16               4  \n",
       "3949                     25.0           24.50             17               6  \n",
       "3950                     24.0           26.50             12               3  \n",
       "3951                     30.0           25.25             11               3  \n",
       "\n",
       "[3952 rows x 23 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "84101994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Demand')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(instances['xloc'], instances['average_demand'])\n",
    "plt.xlabel(\"Xloc\")\n",
    "plt.ylabel(\"Demand\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "fd2919ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Demand')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(instances['yloc'], instances['average_demand'])\n",
    "plt.xlabel(\"Yloc\")\n",
    "plt.ylabel(\"Demand\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "0b2137c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Demand')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(instances['individual_pins'], instances['average_demand'])\n",
    "plt.xlabel(\"Individual Pins\")\n",
    "plt.ylabel(\"Demand\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "d34f513a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Demand')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(instances['grc_pin_count'], instances['average_demand'])\n",
    "plt.xlabel(\"GRC Pins\")\n",
    "plt.ylabel(\"Demand\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "d858679e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Demand')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(instances['grc_cell_count'], instances['average_demand'])\n",
    "plt.xlabel(\"GRC Cells\")\n",
    "plt.ylabel(\"Demand\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "786b5fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Demand')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(instances['in_connections'], instances['average_demand'])\n",
    "plt.xlabel(\"Input Terminals\")\n",
    "plt.ylabel(\"Demand\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "e0659907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Demand')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(instances['out_connections'], instances['average_demand'])\n",
    "plt.xlabel(\"Output Connections\")\n",
    "plt.ylabel(\"Demand\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "953cad8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xloc</th>\n",
       "      <th>yloc</th>\n",
       "      <th>individual_pins</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>average_demand</th>\n",
       "      <th>grc_pin_count</th>\n",
       "      <th>grc_cell_count</th>\n",
       "      <th>in_connections</th>\n",
       "      <th>out_connections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>xloc</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.024967</td>\n",
       "      <td>0.125322</td>\n",
       "      <td>0.050211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.110322</td>\n",
       "      <td>0.100549</td>\n",
       "      <td>-0.021522</td>\n",
       "      <td>0.125322</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yloc</th>\n",
       "      <td>-0.024967</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.105851</td>\n",
       "      <td>-0.012970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.125021</td>\n",
       "      <td>-0.017230</td>\n",
       "      <td>0.062736</td>\n",
       "      <td>-0.105851</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>individual_pins</th>\n",
       "      <td>0.125322</td>\n",
       "      <td>-0.105851</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.579553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.299249</td>\n",
       "      <td>-0.011263</td>\n",
       "      <td>-0.388384</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>0.050211</td>\n",
       "      <td>-0.012970</td>\n",
       "      <td>0.579553</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.620150</td>\n",
       "      <td>-0.164704</td>\n",
       "      <td>-0.396174</td>\n",
       "      <td>0.579553</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_demand</th>\n",
       "      <td>-0.110322</td>\n",
       "      <td>0.125021</td>\n",
       "      <td>-0.299249</td>\n",
       "      <td>-0.620150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.169333</td>\n",
       "      <td>0.313930</td>\n",
       "      <td>-0.299249</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grc_pin_count</th>\n",
       "      <td>0.100549</td>\n",
       "      <td>-0.017230</td>\n",
       "      <td>-0.011263</td>\n",
       "      <td>-0.164704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.169333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836483</td>\n",
       "      <td>-0.011263</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grc_cell_count</th>\n",
       "      <td>-0.021522</td>\n",
       "      <td>0.062736</td>\n",
       "      <td>-0.388384</td>\n",
       "      <td>-0.396174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.313930</td>\n",
       "      <td>0.836483</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.388384</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>in_connections</th>\n",
       "      <td>0.125322</td>\n",
       "      <td>-0.105851</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.579553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.299249</td>\n",
       "      <td>-0.011263</td>\n",
       "      <td>-0.388384</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>out_connections</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     xloc      yloc  individual_pins     width  height  \\\n",
       "xloc             1.000000 -0.024967         0.125322  0.050211     NaN   \n",
       "yloc            -0.024967  1.000000        -0.105851 -0.012970     NaN   \n",
       "individual_pins  0.125322 -0.105851         1.000000  0.579553     NaN   \n",
       "width            0.050211 -0.012970         0.579553  1.000000     NaN   \n",
       "height                NaN       NaN              NaN       NaN     NaN   \n",
       "average_demand  -0.110322  0.125021        -0.299249 -0.620150     NaN   \n",
       "grc_pin_count    0.100549 -0.017230        -0.011263 -0.164704     NaN   \n",
       "grc_cell_count  -0.021522  0.062736        -0.388384 -0.396174     NaN   \n",
       "in_connections   0.125322 -0.105851         1.000000  0.579553     NaN   \n",
       "out_connections       NaN       NaN              NaN       NaN     NaN   \n",
       "\n",
       "                 average_demand  grc_pin_count  grc_cell_count  \\\n",
       "xloc                  -0.110322       0.100549       -0.021522   \n",
       "yloc                   0.125021      -0.017230        0.062736   \n",
       "individual_pins       -0.299249      -0.011263       -0.388384   \n",
       "width                 -0.620150      -0.164704       -0.396174   \n",
       "height                      NaN            NaN             NaN   \n",
       "average_demand         1.000000       0.169333        0.313930   \n",
       "grc_pin_count          0.169333       1.000000        0.836483   \n",
       "grc_cell_count         0.313930       0.836483        1.000000   \n",
       "in_connections        -0.299249      -0.011263       -0.388384   \n",
       "out_connections             NaN            NaN             NaN   \n",
       "\n",
       "                 in_connections  out_connections  \n",
       "xloc                   0.125322              NaN  \n",
       "yloc                  -0.105851              NaN  \n",
       "individual_pins        1.000000              NaN  \n",
       "width                  0.579553              NaN  \n",
       "height                      NaN              NaN  \n",
       "average_demand        -0.299249              NaN  \n",
       "grc_pin_count         -0.011263              NaN  \n",
       "grc_cell_count        -0.388384              NaN  \n",
       "in_connections         1.000000              NaN  \n",
       "out_connections             NaN              NaN  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create feature matrix by dropping irrelevant features\n",
    "corr_matrix = instances.drop(columns=['name', 'id', 'cell', 'orient', 'routing_demand', 'routing_capacity', 'grc_index', 'left_bound', 'right_bound', 'bottom_bound', 'top_bound', 'top_left_corner_demand', 'bottom_right_corner_demand', 'bottom_left_corner_demand', 'top_right_corner_demand', 'total_demand']).corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "657b505f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "from torch.nn import Linear, Dropout\n",
    "from torch_geometric.nn import GCNConv, GATv2Conv, GATConv\n",
    "\n",
    "class GAT(torch.nn.Module):\n",
    "    def __init__(self, dim_in, dim_h, dim_out, heads=8):\n",
    "        super().__init__()\n",
    "        self.gat1 = GATConv(dim_in, dim_h, heads=heads)\n",
    "        #arbitrarily set output of second layer to have the same number of dimensions as dim_in\n",
    "        self.gat2 = GATConv(dim_h*heads, dim_in, heads=1)\n",
    "        self.out = Linear(dim_in, dim_out)\n",
    "        self.optimizer = torch.optim.Adam(self.parameters(), lr=0.005, weight_decay=5e-4)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        h = F.dropout(x, p=0.6, training=self.training)\n",
    "        h = self.gat1(x, edge_index)\n",
    "        h = F.elu(h)\n",
    "        h = F.dropout(h, p=0.6, training=self.training)\n",
    "        h = self.gat2(h, edge_index)\n",
    "        h = F.elu(h)\n",
    "        h = self.out(h)\n",
    "        return h, F.log_softmax(h, dim=1)\n",
    "\n",
    "def accuracy(pred_y, y):\n",
    "    return ((pred_y == y).sum() / len(y)).item()\n",
    "\n",
    "def train(model, x, y, edge_index, train_mask, val_mask):\n",
    "    criterion = torch.nn.MSELoss()\n",
    "    optimizer = model.optimizer\n",
    "    epochs = 200\n",
    "\n",
    "    model.train()\n",
    "    for epoch in range(epochs+1):\n",
    "        # Training\n",
    "        optimizer.zero_grad()\n",
    "        _, out = model(x, edge_index)\n",
    "        loss = criterion(out[train_mask], y[train_mask])\n",
    "        #acc = accuracy(out[train_mask].argmax(dim=1), y[train_mask])\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Validation\n",
    "        val_loss = criterion(out[val_mask], y[val_mask])\n",
    "        val_acc = accuracy(out[val_mask].argmax(dim=1), y[val_mask])\n",
    "\n",
    "        # Print metrics every 10 epochs\n",
    "        if(epoch % 10 == 0):\n",
    "            print(f'Epoch {epoch:>3} | Train Loss: {loss:.3f} | Train Acc: '\n",
    "                  f'{acc*100:>6.2f}% | Val Loss: {val_loss:.2f} | '\n",
    "                  f'Val Acc: {val_acc*100:.2f}%')\n",
    "          \n",
    "    return model\n",
    "\n",
    "def test(model, data):\n",
    "    \"\"\"Evaluate the model on test set and print the accuracy score.\"\"\"\n",
    "    model.eval()\n",
    "    _, out = model(data.x, data.edge_index)\n",
    "    acc = accuracy(out.argmax(dim=1)[data.test_mask], data.y[data.test_mask])\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "8e788f6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       23.50\n",
       "1       23.75\n",
       "2       22.25\n",
       "3       19.75\n",
       "4       22.50\n",
       "        ...  \n",
       "3947    26.00\n",
       "3948    27.00\n",
       "3949    24.50\n",
       "3950    26.50\n",
       "3951    25.25\n",
       "Name: average_demand, Length: 3952, dtype: float64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "10f00d8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.49617152, 0.5       , 0.5       , ..., 0.39130435, 0.33333333,\n",
       "        0.5       ],\n",
       "       [0.49617152, 0.53571429, 0.5       , ..., 0.2173913 , 0.16666667,\n",
       "        0.5       ],\n",
       "       [0.52220521, 0.5       , 0.5       , ..., 0.30434783, 0.33333333,\n",
       "        0.5       ],\n",
       "       ...,\n",
       "       [0.27258806, 0.75      , 0.        , ..., 0.60869565, 0.83333333,\n",
       "        0.        ],\n",
       "       [0.47932619, 0.5       , 0.        , ..., 0.39130435, 0.33333333,\n",
       "        0.        ],\n",
       "       [0.55436447, 0.5       , 0.        , ..., 0.34782609, 0.33333333,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#normalize huge features\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "normalized_feature_matrix = scaler.fit_transform(feature_matrix[['xloc', 'yloc', 'individual_pins', 'width', 'grc_pin_count', 'grc_cell_count', 'in_connections']])\n",
    "normalized_feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "117416b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xloc</th>\n",
       "      <th>yloc</th>\n",
       "      <th>individual_pins</th>\n",
       "      <th>width</th>\n",
       "      <th>grc_pin_count</th>\n",
       "      <th>grc_cell_count</th>\n",
       "      <th>in_connections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.496172</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.496172</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.522205</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.522205</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.548239</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3947</th>\n",
       "      <td>0.255743</td>\n",
       "      <td>0.607143</td>\n",
       "      <td>3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\n",
       "      <td>0.396631</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>0.272588</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>0.479326</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>0.554364</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3952 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          xloc      yloc  individual_pins  width  grc_pin_count  \\\n",
       "0     0.496172  0.500000                4   0.50             12   \n",
       "1     0.496172  0.535714                4   0.50              8   \n",
       "2     0.522205  0.500000                4   0.50             10   \n",
       "3     0.522205  0.535714                4   0.50              8   \n",
       "4     0.548239  0.535714                4   0.50              4   \n",
       "...        ...       ...              ...    ...            ...   \n",
       "3947  0.255743  0.607143                3   0.25             12   \n",
       "3948  0.396631  0.750000                3   0.25             16   \n",
       "3949  0.272588  0.750000                2   0.00             17   \n",
       "3950  0.479326  0.500000                2   0.00             12   \n",
       "3951  0.554364  0.500000                2   0.00             11   \n",
       "\n",
       "      grc_cell_count  in_connections  \n",
       "0                  3               3  \n",
       "1                  2               3  \n",
       "2                  3               3  \n",
       "3                  2               3  \n",
       "4                  1               3  \n",
       "...              ...             ...  \n",
       "3947               3               2  \n",
       "3948               4               2  \n",
       "3949               6               1  \n",
       "3950               3               1  \n",
       "3951               3               1  \n",
       "\n",
       "[3952 rows x 7 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_matrix['xloc'] = normalized_feature_matrix[:, 0]\n",
    "feature_matrix['yloc'] = normalized_feature_matrix[:, 1]\n",
    "feature_matrix['width'] = normalized_feature_matrix[:, 2]\n",
    "feature_matrix['height'] = normalized_feature_matrix[:, 3]\n",
    "feature_matrix = feature_matrix.drop(columns=['height'])\n",
    "feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "3a7c8991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.49617152,  0.5       ,  4.        , ..., 12.        ,\n",
       "         3.        ,  3.        ],\n",
       "       [ 0.49617152,  0.53571429,  4.        , ...,  8.        ,\n",
       "         2.        ,  3.        ],\n",
       "       [ 0.52220521,  0.5       ,  4.        , ..., 10.        ,\n",
       "         3.        ,  3.        ],\n",
       "       ...,\n",
       "       [ 0.27258806,  0.75      ,  2.        , ..., 17.        ,\n",
       "         6.        ,  1.        ],\n",
       "       [ 0.47932619,  0.5       ,  2.        , ..., 12.        ,\n",
       "         3.        ,  1.        ],\n",
       "       [ 0.55436447,  0.5       ,  2.        , ..., 11.        ,\n",
       "         3.        ,  1.        ]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_matrix.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "8cf4fc74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23.5 ],\n",
       "       [23.75],\n",
       "       [22.25],\n",
       "       ...,\n",
       "       [24.5 ],\n",
       "       [26.5 ],\n",
       "       [25.25]])"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_labels = scaler.fit_transform(instances[['average_demand']])\n",
    "normalized_labels\n",
    "scaler.inverse_transform(normalized_labels)\n",
    "#instances['average_demand']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "2a343f0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([False, False, False,  ..., False, False, False])"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#move features into tensor\n",
    "#x = instances.drop(columns=['name', 'id', 'routing_demand'])\n",
    "x = torch.tensor(normalized_feature_matrix, dtype=torch.float)\n",
    "\n",
    "#move target into tensor\n",
    "y = torch.tensor(normalized_labels, dtype=torch.float)\n",
    "\n",
    "#create train and validation masks\n",
    "train_mask = (np.zeros(feature_matrix.shape[0]))\n",
    "for i in range(2000):\n",
    "    train_mask[i] += 1\n",
    "train_mask = torch.tensor(train_mask.astype(bool))\n",
    "train_mask\n",
    "\n",
    "val_mask = (np.zeros(feature_matrix.shape[0]))\n",
    "for i in range(2000, 3000):\n",
    "    val_mask[i] += 1\n",
    "val_mask = torch.tensor(val_mask.astype(bool))\n",
    "val_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "75414b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_geometric.data import Data\n",
    "\n",
    "data = Data(x=x, edge_index=edge_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "f272309a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([142., 275.,   0., 377.,   0., 411.,  86.,   0.,   8.,   1.]),\n",
       " array([1. , 1.6, 2.2, 2.8, 3.4, 4. , 4.6, 5.2, 5.8, 6.4, 7. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(instances['grc_index'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "39dd7f08",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1455709822.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[200], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    instances[instances['routing_demand'] > ]\u001b[0m\n\u001b[0m                                            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "instances[instances['routing_demand'] > ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92e48c2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "32c75b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "from torch.nn import Linear, Dropout\n",
    "from torch_geometric.nn import GCNConv, GATv2Conv\n",
    "\n",
    "class GAT(torch.nn.Module):\n",
    "    def __init__(self, dim_in, dim_h, dim_out, heads=8):\n",
    "        super().__init__()\n",
    "        self.gat1 = GATConv(dim_in, dim_h, heads=heads)\n",
    "        #arbitrarily set output of second layer to have the same number of dimensions as dim_in\n",
    "        self.gat2 = GATConv(dim_h*heads, dim_h, heads=heads)\n",
    "        #self.gat3 = GATConv(dim_h*heads, dim_h, heads=heads)\n",
    "        #self.gat4 = GATConv(dim_h*heads, dim_h, heads=heads)\n",
    "        #self.gat5 = GATConv(dim_h*heads, dim_h, heads=heads)\n",
    "        #self.gat6 = GATConv(dim_h*heads, dim_h, heads=heads)\n",
    "        #self.gat7 = GATConv(dim_h*heads, dim_h, heads=heads)\n",
    "        self.gat8 = GATConv(dim_h*heads, dim_out, heads=1)\n",
    "        #self.out = Linear(dim_in, dim_out)\n",
    "        self.optimizer = torch.optim.Adam(self.parameters(), lr=0.001, weight_decay=5e-4)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        #h = F.dropout(x, p=0.6, training=self.training)\n",
    "        h = self.gat1(x, edge_index)\n",
    "        h = F.elu(h)\n",
    "        #h = F.dropout(h, p=0.6, training=self.training)\n",
    "        h = self.gat2(h, edge_index)\n",
    "        h = F.elu(h)\n",
    "        #h = F.dropout(h, p=0.6, training=self.training)\n",
    "        #h = self.gat3(h, edge_index)\n",
    "        #h = F.elu(h)\n",
    "        #h = F.dropout(h, p=0.6, training=self.training)\n",
    "        #h = self.gat4(h, edge_index)\n",
    "        #h = F.elu(h)\n",
    "        #h = F.dropout(h, p=0.6, training=self.training)\n",
    "        #h = self.gat5(h, edge_index)\n",
    "        #h = F.elu(h)\n",
    "        #h = F.dropout(h, p=0.6, training=self.training)\n",
    "        #h = self.gat6(h, edge_index)\n",
    "        #h = F.elu(h)\n",
    "        #h = F.dropout(h, p=0.6, training=self.training)\n",
    "        #h = self.gat7(h, edge_index)\n",
    "        #h = F.elu(h)\n",
    "        h = self.gat8(h, edge_index)\n",
    "        return h\n",
    "\n",
    "#def accuracy(pred_y, y):\n",
    "#    return ((pred_y == y).sum() / len(y)).item()\n",
    "\n",
    "def train(model, x, y, edge_index):\n",
    "    criterion = torch.nn.MSELoss()\n",
    "    optimizer = model.optimizer\n",
    "    epochs = 500\n",
    "\n",
    "    model.train()\n",
    "    for epoch in range(epochs+1):\n",
    "        # Training\n",
    "        optimizer.zero_grad()\n",
    "        out = model(x, edge_index)\n",
    "        loss = criterion(out, y)\n",
    "        #acc = accuracy(out[train_mask].argmax(dim=1), y[train_mask])\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Validation\n",
    "        #val_loss = criterion(out[val_mask], y[val_mask])\n",
    "        #val_acc = accuracy(out[val_mask].argmax(dim=1), y[val_mask])\n",
    "\n",
    "        # Print metrics every 10 epochs\n",
    "        if(epoch % 100 == 0):\n",
    "            print(f'Epoch {epoch:>3} | Train Loss: {loss:.3f} | Train Acc: '\n",
    "                  )\n",
    "            \n",
    "            for name, param in model.named_parameters():\n",
    "                print(name, param.grad.norm())\n",
    "          \n",
    "    return model, out\n",
    "\n",
    "def test(model, data):\n",
    "    \"\"\"Evaluate the model on test set and print the accuracy score.\"\"\"\n",
    "    model.eval()\n",
    "    _, out = model(data.x, data.edge_index)\n",
    "    acc = accuracy(out.argmax(dim=1)[data.test_mask], data.y[data.test_mask])\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "bbc0b4bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAT(\n",
      "  (gat1): GATConv(7, 16, heads=8)\n",
      "  (gat2): GATConv(128, 16, heads=8)\n",
      "  (gat8): GATConv(128, 1, heads=1)\n",
      ")\n",
      "Epoch   0 | Train Loss: 0.052 | Train Acc: \n",
      "gat1.att_src tensor(0.0029)\n",
      "gat1.att_dst tensor(0.0005)\n",
      "gat1.bias tensor(0.4238)\n",
      "gat1.lin_src.weight tensor(0.6502)\n",
      "gat2.att_src tensor(0.0004)\n",
      "gat2.att_dst tensor(0.0003)\n",
      "gat2.bias tensor(0.4389)\n",
      "gat2.lin_src.weight tensor(0.8239)\n",
      "gat8.att_src tensor(3.0521e-05)\n",
      "gat8.att_dst tensor(9.9397e-11)\n",
      "gat8.bias tensor(0.3207)\n",
      "gat8.lin_src.weight tensor(0.5176)\n",
      "Epoch 100 | Train Loss: 0.012 | Train Acc: \n",
      "gat1.att_src tensor(6.0637e-05)\n",
      "gat1.att_dst tensor(3.7112e-05)\n",
      "gat1.bias tensor(0.0026)\n",
      "gat1.lin_src.weight tensor(0.0043)\n",
      "gat2.att_src tensor(2.0459e-05)\n",
      "gat2.att_dst tensor(1.4425e-05)\n",
      "gat2.bias tensor(0.0023)\n",
      "gat2.lin_src.weight tensor(0.0045)\n",
      "gat8.att_src tensor(1.2638e-05)\n",
      "gat8.att_dst tensor(1.6110e-11)\n",
      "gat8.bias tensor(0.0017)\n",
      "gat8.lin_src.weight tensor(0.0029)\n",
      "Epoch 200 | Train Loss: 0.012 | Train Acc: \n",
      "gat1.att_src tensor(5.3843e-05)\n",
      "gat1.att_dst tensor(3.1779e-05)\n",
      "gat1.bias tensor(0.0002)\n",
      "gat1.lin_src.weight tensor(0.0008)\n",
      "gat2.att_src tensor(1.6175e-05)\n",
      "gat2.att_dst tensor(1.0996e-05)\n",
      "gat2.bias tensor(0.0001)\n",
      "gat2.lin_src.weight tensor(0.0008)\n",
      "gat8.att_src tensor(1.2560e-05)\n",
      "gat8.att_dst tensor(4.0399e-11)\n",
      "gat8.bias tensor(3.0407e-05)\n",
      "gat8.lin_src.weight tensor(0.0006)\n",
      "Epoch 300 | Train Loss: 0.012 | Train Acc: \n",
      "gat1.att_src tensor(4.6667e-05)\n",
      "gat1.att_dst tensor(2.7760e-05)\n",
      "gat1.bias tensor(0.0002)\n",
      "gat1.lin_src.weight tensor(0.0007)\n",
      "gat2.att_src tensor(1.2987e-05)\n",
      "gat2.att_dst tensor(9.2305e-06)\n",
      "gat2.bias tensor(9.4910e-05)\n",
      "gat2.lin_src.weight tensor(0.0007)\n",
      "gat8.att_src tensor(1.2896e-05)\n",
      "gat8.att_dst tensor(5.5233e-11)\n",
      "gat8.bias tensor(4.0123e-05)\n",
      "gat8.lin_src.weight tensor(0.0005)\n",
      "Epoch 400 | Train Loss: 0.012 | Train Acc: \n",
      "gat1.att_src tensor(4.4002e-05)\n",
      "gat1.att_dst tensor(2.6956e-05)\n",
      "gat1.bias tensor(0.0002)\n",
      "gat1.lin_src.weight tensor(0.0006)\n",
      "gat2.att_src tensor(1.1288e-05)\n",
      "gat2.att_dst tensor(7.8759e-06)\n",
      "gat2.bias tensor(8.5957e-05)\n",
      "gat2.lin_src.weight tensor(0.0007)\n",
      "gat8.att_src tensor(1.3094e-05)\n",
      "gat8.att_dst tensor(4.0634e-12)\n",
      "gat8.bias tensor(4.1475e-05)\n",
      "gat8.lin_src.weight tensor(0.0004)\n",
      "Epoch 500 | Train Loss: 0.012 | Train Acc: \n",
      "gat1.att_src tensor(5.2097e-05)\n",
      "gat1.att_dst tensor(2.9621e-05)\n",
      "gat1.bias tensor(0.0001)\n",
      "gat1.lin_src.weight tensor(0.0006)\n",
      "gat2.att_src tensor(8.2312e-06)\n",
      "gat2.att_dst tensor(5.4507e-06)\n",
      "gat2.bias tensor(8.2854e-05)\n",
      "gat2.lin_src.weight tensor(0.0006)\n",
      "gat8.att_src tensor(1.3223e-05)\n",
      "gat8.att_dst tensor(1.4845e-11)\n",
      "gat8.bias tensor(4.1659e-05)\n",
      "gat8.lin_src.weight tensor(0.0004)\n",
      "CPU times: user 10min 21s, sys: 8min 51s, total: 19min 13s\n",
      "Wall time: 4min 16s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[0.3588],\n",
       "        [0.3979],\n",
       "        [0.3707],\n",
       "        ...,\n",
       "        [0.6858],\n",
       "        [0.3392],\n",
       "        [0.3938]], grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\n",
    "# Create GAT\n",
    "gat = GAT(feature_matrix.shape[1], 16, 1)\n",
    "print(gat)\n",
    "\n",
    "# Train\n",
    "model, out = train(gat, x, y, edge_index)#, train_mask, val_mask)\n",
    "out\n",
    "\n",
    "# Test\n",
    "#acc = test(gat, data)\n",
    "#print(f'GAT test accuracy: {acc*100:.2f}%\\n')\n",
    "#view raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "013f57aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "bins = np.linspace(0, 40, 40)\n",
    "\n",
    "#scaler.inverse_transform(out.detach().numpy()\n",
    "pyplot.hist(scaler.inverse_transform(out.detach().numpy()), bins, alpha = 0.3, label = 'predicted demand')\n",
    "pyplot.hist(instances['average_demand'], bins, alpha = 0.3, label = 'actual demand')\n",
    "\n",
    "pyplot.legend(loc='upper right')\n",
    "pyplot.title('Routing Demand')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "22a64eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f8094c53460>,\n",
       "  <matplotlib.axis.XTick at 0x7f8094c533d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f8094c53640>,\n",
       "  <matplotlib.axis.XTick at 0x7f80952e7730>,\n",
       "  <matplotlib.axis.XTick at 0x7f80952e77f0>,\n",
       "  <matplotlib.axis.XTick at 0x7f80952e7dc0>,\n",
       "  <matplotlib.axis.XTick at 0x7f8094c32f70>,\n",
       "  <matplotlib.axis.XTick at 0x7f80952eb340>],\n",
       " [Text(0, 0, '0'),\n",
       "  Text(5, 0, '5'),\n",
       "  Text(10, 0, '10'),\n",
       "  Text(15, 0, '15'),\n",
       "  Text(20, 0, '20'),\n",
       "  Text(25, 0, '25'),\n",
       "  Text(30, 0, '30'),\n",
       "  Text(35, 0, '35')])"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(scaler.inverse_transform(out.detach().numpy()), instances['average_demand'], )\n",
    "plt.xlabel(\"Predicted Demand\")\n",
    "plt.ylabel(\"Actual Demand\")\n",
    "plt.yticks(np.arange(0, 40, 5))\n",
    "plt.xticks(np.arange(0, 40, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "9d234dca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.536943319838056"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances['average_demand'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "186942e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[25.1517],\n",
      "        [25.2281],\n",
      "        [25.1807],\n",
      "        ...,\n",
      "        [21.6039],\n",
      "        [24.7071],\n",
      "        [24.4925]], grad_fn=<AddBackward0>)\n"
     ]
    }
   ],
   "source": [
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "c25ad151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18179"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_params = sum(p.numel() for p in model.parameters())\n",
    "total_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1044fd3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gat1.att_src tensor(0.6085)\n",
      "gat1.att_dst tensor(0.4770)\n",
      "gat1.bias tensor(0.1670)\n",
      "gat1.lin_src.weight tensor(4.4868)\n",
      "gat2.att_src tensor(0.2610)\n",
      "gat2.att_dst tensor(0.0860)\n",
      "gat2.bias tensor(0.1231)\n",
      "gat2.lin_src.weight tensor(3.2811)\n",
      "gat8.att_src tensor(1.9019)\n",
      "gat8.att_dst tensor(1.3575e-06)\n",
      "gat8.bias tensor(0.0892)\n",
      "gat8.lin_src.weight tensor(4.1896)\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    print(name, param.grad.norm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "31da53dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "print((out.detach().numpy()).tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fec7d9ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.000e+00, 5.000e+00, 2.000e+01, 1.210e+02, 4.660e+02, 2.289e+03,\n",
       "        8.900e+02, 1.400e+02, 1.100e+01, 8.000e+00]),\n",
       " array([18.77599335, 19.81291962, 20.84984779, 21.88677406, 22.92370224,\n",
       "        23.96062851, 24.99755478, 26.03448296, 27.07140923, 28.1083374 ,\n",
       "        29.14526367]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(out.detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "62f9b381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  30.,   79.,  102.,   89.,  240.,  600., 1873.,  814.,  117.,\n",
       "           8.]),\n",
       " array([ 3.75 ,  7.125, 10.5  , 13.875, 17.25 , 20.625, 24.   , 27.375,\n",
       "        30.75 , 34.125, 37.5  ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y.detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "97defd5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3fd35f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>xloc</th>\n",
       "      <th>yloc</th>\n",
       "      <th>cell</th>\n",
       "      <th>orient</th>\n",
       "      <th>routing_demand</th>\n",
       "      <th>routing_capacity</th>\n",
       "      <th>grc_index</th>\n",
       "      <th>individual_pins</th>\n",
       "      <th>...</th>\n",
       "      <th>left_bound</th>\n",
       "      <th>right_bound</th>\n",
       "      <th>bottom_bound</th>\n",
       "      <th>top_bound</th>\n",
       "      <th>top_left_corner_demand</th>\n",
       "      <th>bottom_right_corner_demand</th>\n",
       "      <th>bottom_left_corner_demand</th>\n",
       "      <th>top_right_corner_demand</th>\n",
       "      <th>average_demand</th>\n",
       "      <th>predicted_demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>clk_gate_out_reg/latch</td>\n",
       "      <td>0</td>\n",
       "      <td>41984</td>\n",
       "      <td>44544</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>(29, 27)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>40896</td>\n",
       "      <td>43072</td>\n",
       "      <td>43456</td>\n",
       "      <td>43072</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.836157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clk_gate_out_reg_0/latch</td>\n",
       "      <td>1</td>\n",
       "      <td>41984</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>(31, 27)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>40896</td>\n",
       "      <td>43072</td>\n",
       "      <td>46528</td>\n",
       "      <td>43072</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.75</td>\n",
       "      <td>23.469893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>clk_gate_out_reg_1/latch</td>\n",
       "      <td>2</td>\n",
       "      <td>44160</td>\n",
       "      <td>44544</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>43072</td>\n",
       "      <td>45248</td>\n",
       "      <td>43456</td>\n",
       "      <td>45248</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.25</td>\n",
       "      <td>24.456528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>clk_gate_out_reg_2/latch</td>\n",
       "      <td>3</td>\n",
       "      <td>44160</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(31, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>43072</td>\n",
       "      <td>45248</td>\n",
       "      <td>46528</td>\n",
       "      <td>45248</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.75</td>\n",
       "      <td>24.026892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>clk_gate_out_reg_3/latch</td>\n",
       "      <td>4</td>\n",
       "      <td>46336</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>(31, 29)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>45248</td>\n",
       "      <td>47424</td>\n",
       "      <td>46528</td>\n",
       "      <td>47424</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.50</td>\n",
       "      <td>23.513569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3947</th>\n",
       "      <td>U4123</td>\n",
       "      <td>3947</td>\n",
       "      <td>21888</td>\n",
       "      <td>53760</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>(35, 13)</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>21632</td>\n",
       "      <td>22144</td>\n",
       "      <td>53504</td>\n",
       "      <td>22144</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>24.167128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\n",
       "      <td>U4125</td>\n",
       "      <td>3948</td>\n",
       "      <td>33664</td>\n",
       "      <td>66048</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>(43, 21)</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>33408</td>\n",
       "      <td>33920</td>\n",
       "      <td>65792</td>\n",
       "      <td>33920</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>23.779009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>U4128</td>\n",
       "      <td>3949</td>\n",
       "      <td>23296</td>\n",
       "      <td>66048</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>(43, 14)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>23104</td>\n",
       "      <td>23488</td>\n",
       "      <td>65856</td>\n",
       "      <td>23488</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.50</td>\n",
       "      <td>23.896975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>ZCTSBUF_205_132</td>\n",
       "      <td>3950</td>\n",
       "      <td>40576</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 26)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>40256</td>\n",
       "      <td>40896</td>\n",
       "      <td>44224</td>\n",
       "      <td>40896</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.50</td>\n",
       "      <td>23.372410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>ZCTSBUF_466_133</td>\n",
       "      <td>3951</td>\n",
       "      <td>46848</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(29, 30)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>46528</td>\n",
       "      <td>47168</td>\n",
       "      <td>44224</td>\n",
       "      <td>47168</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.25</td>\n",
       "      <td>24.994993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3952 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name    id   xloc   yloc  cell  orient  \\\n",
       "0       clk_gate_out_reg/latch     0  41984  44544    23       0   \n",
       "1     clk_gate_out_reg_0/latch     1  41984  47616    23       6   \n",
       "2     clk_gate_out_reg_1/latch     2  44160  44544    23       0   \n",
       "3     clk_gate_out_reg_2/latch     3  44160  47616    23       0   \n",
       "4     clk_gate_out_reg_3/latch     4  46336  47616    23       0   \n",
       "...                        ...   ...    ...    ...   ...     ...   \n",
       "3947                     U4123  3947  21888  53760    42       4   \n",
       "3948                     U4125  3948  33664  66048    42       0   \n",
       "3949                     U4128  3949  23296  66048    34       0   \n",
       "3950           ZCTSBUF_205_132  3950  40576  44544    11       0   \n",
       "3951           ZCTSBUF_466_133  3951  46848  44544    11       6   \n",
       "\n",
       "      routing_demand  routing_capacity grc_index  individual_pins  ...  \\\n",
       "0               20.0              28.0  (29, 27)                4  ...   \n",
       "1               23.0              28.0  (31, 27)                4  ...   \n",
       "2               23.0              33.0  (29, 28)                4  ...   \n",
       "3               22.0              33.0  (31, 28)                4  ...   \n",
       "4               21.0              23.0  (31, 29)                4  ...   \n",
       "...              ...               ...       ...              ...  ...   \n",
       "3947            31.0              31.0  (35, 13)                3  ...   \n",
       "3948            30.0              30.0  (43, 21)                3  ...   \n",
       "3949            27.0              29.0  (43, 14)                2  ...   \n",
       "3950            28.0              33.0  (29, 26)                2  ...   \n",
       "3951            23.0              36.0  (29, 30)                2  ...   \n",
       "\n",
       "      left_bound  right_bound  bottom_bound  top_bound  \\\n",
       "0          40896        43072         43456      43072   \n",
       "1          40896        43072         46528      43072   \n",
       "2          43072        45248         43456      45248   \n",
       "3          43072        45248         46528      45248   \n",
       "4          45248        47424         46528      47424   \n",
       "...          ...          ...           ...        ...   \n",
       "3947       21632        22144         53504      22144   \n",
       "3948       33408        33920         65792      33920   \n",
       "3949       23104        23488         65856      23488   \n",
       "3950       40256        40896         44224      40896   \n",
       "3951       46528        47168         44224      47168   \n",
       "\n",
       "      top_left_corner_demand  bottom_right_corner_demand  \\\n",
       "0                       24.0                        23.0   \n",
       "1                       24.0                        23.0   \n",
       "2                       20.0                        20.0   \n",
       "3                       20.0                        20.0   \n",
       "4                       18.0                        27.0   \n",
       "...                      ...                         ...   \n",
       "3947                    26.0                        27.0   \n",
       "3948                    23.0                        23.0   \n",
       "3949                    24.0                        24.0   \n",
       "3950                    27.0                        28.0   \n",
       "3951                    18.0                        27.0   \n",
       "\n",
       "      bottom_left_corner_demand  top_right_corner_demand  average_demand  \\\n",
       "0                          24.0                     23.0           23.50   \n",
       "1                          27.0                     21.0           23.75   \n",
       "2                          23.0                     26.0           22.25   \n",
       "3                          21.0                     18.0           19.75   \n",
       "4                          18.0                     27.0           22.50   \n",
       "...                         ...                      ...             ...   \n",
       "3947                       28.0                     23.0           26.00   \n",
       "3948                       31.0                     31.0           27.00   \n",
       "3949                       25.0                     25.0           24.50   \n",
       "3950                       27.0                     24.0           26.50   \n",
       "3951                       26.0                     30.0           25.25   \n",
       "\n",
       "      predicted_demand  \n",
       "0            23.836157  \n",
       "1            23.469893  \n",
       "2            24.456528  \n",
       "3            24.026892  \n",
       "4            23.513569  \n",
       "...                ...  \n",
       "3947         24.167128  \n",
       "3948         23.779009  \n",
       "3949         23.896975  \n",
       "3950         23.372410  \n",
       "3951         24.994993  \n",
       "\n",
       "[3952 rows x 24 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances['predicted_demand'] = out.detach().numpy()\n",
    "instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "14673715",
   "metadata": {},
   "outputs": [],
   "source": [
    "instances['squared_error'] = (instances['predicted_demand'] - instances['routing_demand'])**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d1dbe277",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>xloc</th>\n",
       "      <th>yloc</th>\n",
       "      <th>cell</th>\n",
       "      <th>orient</th>\n",
       "      <th>routing_demand</th>\n",
       "      <th>routing_capacity</th>\n",
       "      <th>grc_index</th>\n",
       "      <th>individual_pins</th>\n",
       "      <th>...</th>\n",
       "      <th>right_bound</th>\n",
       "      <th>bottom_bound</th>\n",
       "      <th>top_bound</th>\n",
       "      <th>top_left_corner_demand</th>\n",
       "      <th>bottom_right_corner_demand</th>\n",
       "      <th>bottom_left_corner_demand</th>\n",
       "      <th>top_right_corner_demand</th>\n",
       "      <th>average_demand</th>\n",
       "      <th>predicted_demand</th>\n",
       "      <th>squared_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>clk_gate_out_reg/latch</td>\n",
       "      <td>0</td>\n",
       "      <td>41984</td>\n",
       "      <td>44544</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>(29, 27)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>43072</td>\n",
       "      <td>43456</td>\n",
       "      <td>43072</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.836157</td>\n",
       "      <td>14.716099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clk_gate_out_reg_0/latch</td>\n",
       "      <td>1</td>\n",
       "      <td>41984</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>(31, 27)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>43072</td>\n",
       "      <td>46528</td>\n",
       "      <td>43072</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.75</td>\n",
       "      <td>23.469893</td>\n",
       "      <td>0.220799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>clk_gate_out_reg_1/latch</td>\n",
       "      <td>2</td>\n",
       "      <td>44160</td>\n",
       "      <td>44544</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>45248</td>\n",
       "      <td>43456</td>\n",
       "      <td>45248</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.25</td>\n",
       "      <td>24.456528</td>\n",
       "      <td>2.121473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>clk_gate_out_reg_2/latch</td>\n",
       "      <td>3</td>\n",
       "      <td>44160</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(31, 28)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>45248</td>\n",
       "      <td>46528</td>\n",
       "      <td>45248</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.75</td>\n",
       "      <td>24.026892</td>\n",
       "      <td>4.108290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>clk_gate_out_reg_3/latch</td>\n",
       "      <td>4</td>\n",
       "      <td>46336</td>\n",
       "      <td>47616</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>(31, 29)</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>47424</td>\n",
       "      <td>46528</td>\n",
       "      <td>47424</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.50</td>\n",
       "      <td>23.513569</td>\n",
       "      <td>6.318029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3947</th>\n",
       "      <td>U4123</td>\n",
       "      <td>3947</td>\n",
       "      <td>21888</td>\n",
       "      <td>53760</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>(35, 13)</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>22144</td>\n",
       "      <td>53504</td>\n",
       "      <td>22144</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>24.167128</td>\n",
       "      <td>46.688145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\n",
       "      <td>U4125</td>\n",
       "      <td>3948</td>\n",
       "      <td>33664</td>\n",
       "      <td>66048</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>(43, 21)</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>33920</td>\n",
       "      <td>65792</td>\n",
       "      <td>33920</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>23.779009</td>\n",
       "      <td>38.700731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>U4128</td>\n",
       "      <td>3949</td>\n",
       "      <td>23296</td>\n",
       "      <td>66048</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>(43, 14)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>23488</td>\n",
       "      <td>65856</td>\n",
       "      <td>23488</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.50</td>\n",
       "      <td>23.896975</td>\n",
       "      <td>9.628767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>ZCTSBUF_205_132</td>\n",
       "      <td>3950</td>\n",
       "      <td>40576</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(29, 26)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>40896</td>\n",
       "      <td>44224</td>\n",
       "      <td>40896</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.50</td>\n",
       "      <td>23.372410</td>\n",
       "      <td>21.414591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>ZCTSBUF_466_133</td>\n",
       "      <td>3951</td>\n",
       "      <td>46848</td>\n",
       "      <td>44544</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(29, 30)</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>47168</td>\n",
       "      <td>44224</td>\n",
       "      <td>47168</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.25</td>\n",
       "      <td>24.994993</td>\n",
       "      <td>3.979998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3952 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name    id   xloc   yloc  cell  orient  \\\n",
       "0       clk_gate_out_reg/latch     0  41984  44544    23       0   \n",
       "1     clk_gate_out_reg_0/latch     1  41984  47616    23       6   \n",
       "2     clk_gate_out_reg_1/latch     2  44160  44544    23       0   \n",
       "3     clk_gate_out_reg_2/latch     3  44160  47616    23       0   \n",
       "4     clk_gate_out_reg_3/latch     4  46336  47616    23       0   \n",
       "...                        ...   ...    ...    ...   ...     ...   \n",
       "3947                     U4123  3947  21888  53760    42       4   \n",
       "3948                     U4125  3948  33664  66048    42       0   \n",
       "3949                     U4128  3949  23296  66048    34       0   \n",
       "3950           ZCTSBUF_205_132  3950  40576  44544    11       0   \n",
       "3951           ZCTSBUF_466_133  3951  46848  44544    11       6   \n",
       "\n",
       "      routing_demand  routing_capacity grc_index  individual_pins  ...  \\\n",
       "0               20.0              28.0  (29, 27)                4  ...   \n",
       "1               23.0              28.0  (31, 27)                4  ...   \n",
       "2               23.0              33.0  (29, 28)                4  ...   \n",
       "3               22.0              33.0  (31, 28)                4  ...   \n",
       "4               21.0              23.0  (31, 29)                4  ...   \n",
       "...              ...               ...       ...              ...  ...   \n",
       "3947            31.0              31.0  (35, 13)                3  ...   \n",
       "3948            30.0              30.0  (43, 21)                3  ...   \n",
       "3949            27.0              29.0  (43, 14)                2  ...   \n",
       "3950            28.0              33.0  (29, 26)                2  ...   \n",
       "3951            23.0              36.0  (29, 30)                2  ...   \n",
       "\n",
       "      right_bound  bottom_bound  top_bound  top_left_corner_demand  \\\n",
       "0           43072         43456      43072                    24.0   \n",
       "1           43072         46528      43072                    24.0   \n",
       "2           45248         43456      45248                    20.0   \n",
       "3           45248         46528      45248                    20.0   \n",
       "4           47424         46528      47424                    18.0   \n",
       "...           ...           ...        ...                     ...   \n",
       "3947        22144         53504      22144                    26.0   \n",
       "3948        33920         65792      33920                    23.0   \n",
       "3949        23488         65856      23488                    24.0   \n",
       "3950        40896         44224      40896                    27.0   \n",
       "3951        47168         44224      47168                    18.0   \n",
       "\n",
       "      bottom_right_corner_demand  bottom_left_corner_demand  \\\n",
       "0                           23.0                       24.0   \n",
       "1                           23.0                       27.0   \n",
       "2                           20.0                       23.0   \n",
       "3                           20.0                       21.0   \n",
       "4                           27.0                       18.0   \n",
       "...                          ...                        ...   \n",
       "3947                        27.0                       28.0   \n",
       "3948                        23.0                       31.0   \n",
       "3949                        24.0                       25.0   \n",
       "3950                        28.0                       27.0   \n",
       "3951                        27.0                       26.0   \n",
       "\n",
       "      top_right_corner_demand  average_demand  predicted_demand  squared_error  \n",
       "0                        23.0           23.50         23.836157      14.716099  \n",
       "1                        21.0           23.75         23.469893       0.220799  \n",
       "2                        26.0           22.25         24.456528       2.121473  \n",
       "3                        18.0           19.75         24.026892       4.108290  \n",
       "4                        27.0           22.50         23.513569       6.318029  \n",
       "...                       ...             ...               ...            ...  \n",
       "3947                     23.0           26.00         24.167128      46.688145  \n",
       "3948                     31.0           27.00         23.779009      38.700731  \n",
       "3949                     25.0           24.50         23.896975       9.628767  \n",
       "3950                     24.0           26.50         23.372410      21.414591  \n",
       "3951                     30.0           25.25         24.994993       3.979998  \n",
       "\n",
       "[3952 rows x 25 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ab5102aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f9a52e7cf40>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(instances['squared_error'])\n",
    "plt.axhline(instances['squared_error'].mean(), color='k', linestyle='dashed', linewidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27b79417",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "742f68da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1051"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(instances['squared_error'] > 25).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "22bd8494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.252973069921847"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances['squared_error'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e6cb6fe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "570.9110703087063"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances['squared_error'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c07fcd7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[   0,    0,    0,  ..., 3951, 3951, 3951],\n",
       "        [3951, 3950,   11,  ...,    1,    0, 3951]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dd71e1e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "routing_demand\n",
       "28.0    486\n",
       "26.0    429\n",
       "27.0    413\n",
       "29.0    401\n",
       "25.0    357\n",
       "24.0    263\n",
       "30.0    247\n",
       "23.0    221\n",
       "31.0    196\n",
       "22.0    164\n",
       "21.0    143\n",
       "32.0     92\n",
       "20.0     87\n",
       "19.0     76\n",
       "18.0     50\n",
       "33.0     47\n",
       "17.0     46\n",
       "14.0     40\n",
       "16.0     33\n",
       "34.0     29\n",
       "15.0     29\n",
       "13.0     28\n",
       "12.0     17\n",
       "35.0     15\n",
       "10.0      9\n",
       "7.0       9\n",
       "11.0      7\n",
       "8.0       5\n",
       "9.0       3\n",
       "38.0      3\n",
       "1.0       2\n",
       "4.0       2\n",
       "3.0       1\n",
       "2.0       1\n",
       "6.0       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances['routing_demand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "511859d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grc_index\n",
       "(1, 0)       7.0\n",
       "(1, 7)      14.0\n",
       "(1, 12)     16.0\n",
       "(1, 17)     19.0\n",
       "(1, 22)     19.0\n",
       "            ... \n",
       "(57, 38)    14.0\n",
       "(57, 43)    14.0\n",
       "(57, 48)    13.0\n",
       "(57, 49)    12.0\n",
       "(57, 54)     1.0\n",
       "Name: routing_demand, Length: 1300, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances['grc_index'].value_counts()\n",
    "instances.groupby('grc_index')['routing_demand'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "95c6908e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0, 0): 0,\n",
       " (0, 1): 0,\n",
       " (0, 2): 0,\n",
       " (0, 3): 0,\n",
       " (0, 4): 0,\n",
       " (0, 5): 0,\n",
       " (0, 6): 0,\n",
       " (0, 7): 0,\n",
       " (0, 8): 0,\n",
       " (0, 9): 0,\n",
       " (0, 10): 0,\n",
       " (0, 11): 0,\n",
       " (0, 12): 0,\n",
       " (0, 13): 0,\n",
       " (0, 14): 0,\n",
       " (0, 15): 0,\n",
       " (0, 16): 0,\n",
       " (0, 17): 0,\n",
       " (0, 18): 0,\n",
       " (0, 19): 0,\n",
       " (0, 20): 0,\n",
       " (0, 21): 0,\n",
       " (0, 22): 0,\n",
       " (0, 23): 0,\n",
       " (0, 24): 0,\n",
       " (0, 25): 0,\n",
       " (0, 26): 0,\n",
       " (0, 27): 0,\n",
       " (0, 28): 0,\n",
       " (0, 29): 0,\n",
       " (0, 30): 0,\n",
       " (0, 31): 0,\n",
       " (0, 32): 0,\n",
       " (0, 33): 0,\n",
       " (0, 34): 0,\n",
       " (0, 35): 0,\n",
       " (0, 36): 0,\n",
       " (0, 37): 0,\n",
       " (0, 38): 0,\n",
       " (0, 39): 0,\n",
       " (0, 40): 0,\n",
       " (0, 41): 0,\n",
       " (0, 42): 0,\n",
       " (0, 43): 0,\n",
       " (0, 44): 0,\n",
       " (0, 45): 0,\n",
       " (0, 46): 0,\n",
       " (0, 47): 0,\n",
       " (0, 48): 0,\n",
       " (0, 49): 0,\n",
       " (0, 50): 0,\n",
       " (0, 51): 0,\n",
       " (0, 52): 0,\n",
       " (0, 53): 0,\n",
       " (0, 54): 0,\n",
       " (0, 55): 0,\n",
       " (0, 56): 0,\n",
       " (1, 0): 0,\n",
       " (1, 1): 0,\n",
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       " ...}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extract congestion data from GRCs and apply values to individual cell instances\n",
    "congestion_dict = {}\n",
    "for l in list(congestion_data['layerList']): \n",
    "    d = 0 \n",
    "    c = 0\n",
    "    lyr=list(congestion_data['layerList']).index(l)\n",
    "    for i in range(len(congestion_data['demand'][lyr])):\n",
    "        for j in range(len(congestion_data['demand'][lyr][i])):\n",
    "            d += congestion_data['demand'][lyr][i][j]\n",
    "            #c += congestion_data['capacity'][lyr][i][j]\n",
    "            congestion_dict[(i, j)] = d\n",
    "        \n",
    "congestion_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c3bdf271",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "total = 0\n",
    "for key, val in congestion_dict.items():\n",
    "    print(val)\n",
    "    total += val\n",
    "#total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a9d4f5b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0, 40, 40)\n",
    "\n",
    "pyplot.hist(list(congestion_dict.values()), bins, alpha = 0.3, label = 'grc-based demand')\n",
    "pyplot.hist(instances['routing_demand'], bins, alpha = 0.3, label = 'cell-based demand')\n",
    "\n",
    "pyplot.legend(loc='upper right')\n",
    "pyplot.title('Routing Demand')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "144fba99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract congestion data from GRCs and apply values to individual cell instances\n",
    "for k in range(instances.shape[0]):\n",
    "    #print(k)\n",
    "    xloc = instances.iloc[k]['xloc']; yloc = instances.iloc[k]['yloc']\n",
    "    i,j=getGRCIndex(xloc,yloc,xbst,ybst)\n",
    "    d = 0 \n",
    "    c = 0\n",
    "    for l in list(congestion_data['layerList']): \n",
    "        lyr=list(congestion_data['layerList']).index(l)\n",
    "        d += congestion_data['demand'][lyr][i][j]\n",
    "        c += congestion_data['capacity'][lyr][i][j]\n",
    "    demand[k] = d\n",
    "    capacity[k] = c\n",
    "    indices.append((i, j))\n",
    "        \n",
    "instances['routing_demand'] = demand\n",
    "instances['routing_capacity'] = capacity\n",
    "instances['grc_index'] = indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "35f7a4e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(congestion_data['demand'][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1aae84d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#including column for x and y coord of grc index\n",
    "instances['grc_xcoord'] = instances['grc_index'].str[0]\n",
    "instances['grc_ycoord'] = instances['grc_index'].str[1]\n",
    "\n",
    "#middle of x and y coords\n",
    "middle_x = (max(instances['grc_xcoord'].array) + min(instances['grc_xcoord'].array)) / 2\n",
    "middle_y = (max(instances['grc_ycoord'].array) + min(instances['grc_ycoord'].array)) / 2\n",
    "\n",
    "#divide instances by quadrant\n",
    "quad1 = instances[(instances['grc_xcoord'] < middle_x) & (instances['grc_ycoord'] < middle_y)]\n",
    "quad2 = instances[(instances['grc_xcoord'] > middle_x) & (instances['grc_ycoord'] < middle_y)]\n",
    "quad3 = instances[(instances['grc_xcoord'] < middle_x) & (instances['grc_ycoord'] > middle_y)]\n",
    "quad4 = instances[(instances['grc_xcoord'] > middle_x) & (instances['grc_ycoord'] > middle_y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f46a54e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>xloc</th>\n",
       "      <th>yloc</th>\n",
       "      <th>cell</th>\n",
       "      <th>orient</th>\n",
       "      <th>routing_demand</th>\n",
       "      <th>routing_capacity</th>\n",
       "      <th>grc_index</th>\n",
       "      <th>individual_pins</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>grc_pin_count</th>\n",
       "      <th>grc_cell_count</th>\n",
       "      <th>grc_xcoord</th>\n",
       "      <th>grc_ycoord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>out_reg_31_</td>\n",
       "      <td>13</td>\n",
       "      <td>512</td>\n",
       "      <td>7680</td>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(5, 0)</td>\n",
       "      <td>6</td>\n",
       "      <td>3840</td>\n",
       "      <td>1536</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>out_reg_30_</td>\n",
       "      <td>14</td>\n",
       "      <td>35456</td>\n",
       "      <td>41472</td>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>(27, 22)</td>\n",
       "      <td>6</td>\n",
       "      <td>3840</td>\n",
       "      <td>1536</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>out_reg_29_</td>\n",
       "      <td>15</td>\n",
       "      <td>512</td>\n",
       "      <td>26112</td>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>(17, 0)</td>\n",
       "      <td>6</td>\n",
       "      <td>3840</td>\n",
       "      <td>1536</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>out_reg_28_</td>\n",
       "      <td>16</td>\n",
       "      <td>27776</td>\n",
       "      <td>1536</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>(1, 17)</td>\n",
       "      <td>6</td>\n",
       "      <td>3840</td>\n",
       "      <td>1536</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>out_reg_24_</td>\n",
       "      <td>20</td>\n",
       "      <td>640</td>\n",
       "      <td>1536</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>(1, 0)</td>\n",
       "      <td>6</td>\n",
       "      <td>3840</td>\n",
       "      <td>1536</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3828</th>\n",
       "      <td>U7600</td>\n",
       "      <td>3828</td>\n",
       "      <td>28800</td>\n",
       "      <td>19968</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(13, 18)</td>\n",
       "      <td>5</td>\n",
       "      <td>896</td>\n",
       "      <td>1536</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3829</th>\n",
       "      <td>U7601</td>\n",
       "      <td>3829</td>\n",
       "      <td>32128</td>\n",
       "      <td>19968</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(13, 20)</td>\n",
       "      <td>5</td>\n",
       "      <td>896</td>\n",
       "      <td>1536</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3830</th>\n",
       "      <td>U7602</td>\n",
       "      <td>3830</td>\n",
       "      <td>28032</td>\n",
       "      <td>23040</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(15, 17)</td>\n",
       "      <td>5</td>\n",
       "      <td>896</td>\n",
       "      <td>1536</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3831</th>\n",
       "      <td>U7603</td>\n",
       "      <td>3831</td>\n",
       "      <td>29696</td>\n",
       "      <td>19968</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>(13, 19)</td>\n",
       "      <td>4</td>\n",
       "      <td>768</td>\n",
       "      <td>1536</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3832</th>\n",
       "      <td>U7604</td>\n",
       "      <td>3832</td>\n",
       "      <td>18432</td>\n",
       "      <td>10752</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>(7, 11)</td>\n",
       "      <td>5</td>\n",
       "      <td>896</td>\n",
       "      <td>1536</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>908 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name    id   xloc   yloc  cell  orient  routing_demand  \\\n",
       "13    out_reg_31_    13    512   7680    65       6            10.0   \n",
       "14    out_reg_30_    14  35456  41472    65       6            23.0   \n",
       "15    out_reg_29_    15    512  26112    65       6            14.0   \n",
       "16    out_reg_28_    16  27776   1536    65       0            19.0   \n",
       "20    out_reg_24_    20    640   1536    65       0             7.0   \n",
       "...           ...   ...    ...    ...   ...     ...             ...   \n",
       "3828        U7600  3828  28800  19968    56       6            27.0   \n",
       "3829        U7601  3829  32128  19968    56       0            23.0   \n",
       "3830        U7602  3830  28032  23040    56       6            27.0   \n",
       "3831        U7603  3831  29696  19968    50       6            27.0   \n",
       "3832        U7604  3832  18432  10752    46       0            25.0   \n",
       "\n",
       "      routing_capacity grc_index  individual_pins  width  height  \\\n",
       "13                36.0    (5, 0)                6   3840    1536   \n",
       "14                30.0  (27, 22)                6   3840    1536   \n",
       "15                30.0   (17, 0)                6   3840    1536   \n",
       "16                32.0   (1, 17)                6   3840    1536   \n",
       "20                34.0    (1, 0)                6   3840    1536   \n",
       "...                ...       ...              ...    ...     ...   \n",
       "3828              36.0  (13, 18)                5    896    1536   \n",
       "3829              36.0  (13, 20)                5    896    1536   \n",
       "3830              36.0  (15, 17)                5    896    1536   \n",
       "3831              36.0  (13, 19)                4    768    1536   \n",
       "3832              28.0   (7, 11)                5    896    1536   \n",
       "\n",
       "      grc_pin_count  grc_cell_count  grc_xcoord  grc_ycoord  \n",
       "13                6               1           5           0  \n",
       "14               17               3          27          22  \n",
       "15               12               2          17           0  \n",
       "16               12               2           1          17  \n",
       "20                6               1           1           0  \n",
       "...             ...             ...         ...         ...  \n",
       "3828             14               3          13          18  \n",
       "3829             15               3          13          20  \n",
       "3830             20               4          15          17  \n",
       "3831             14               3          13          19  \n",
       "3832             20               4           7          11  \n",
       "\n",
       "[908 rows x 16 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quad1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b0b0fb41",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_matrix_quad1 = quad1.drop(columns=['name', 'id', 'cell', 'orient', 'routing_demand', 'routing_capacity', 'grc_index'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1605a74d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.15384615, 1.        , 0.        ],\n",
       "       [0.84520124, 1.        , 1.        , 0.        ],\n",
       "       [0.        , 0.61538462, 1.        , 0.        ],\n",
       "       ...,\n",
       "       [0.66563467, 0.53846154, 0.14814815, 0.        ],\n",
       "       [0.70588235, 0.46153846, 0.11111111, 0.        ],\n",
       "       [0.43343653, 0.23076923, 0.14814815, 0.        ]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#normalize huge features\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "feature_matrix_quad1 = scaler.fit_transform(feature_matrix_quad1[['xloc', 'yloc', 'width', 'height']])\n",
    "feature_matrix_quad1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "02374064",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAT(\n",
      "  (gat1): GATConv(4, 8, heads=8)\n",
      "  (gat2): GATConv(64, 1, heads=1)\n",
      ")\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "Encountered an index error. Please ensure that all indices in 'edge_index' point to valid indices in the interval [0, 907] (got interval [0, 3951])",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torch_geometric/nn/conv/message_passing.py:266\u001b[0m, in \u001b[0;36mMessagePassing._lift\u001b[0;34m(self, src, edge_index, dim)\u001b[0m\n\u001b[1;32m    265\u001b[0m     index \u001b[38;5;241m=\u001b[39m edge_index[dim]\n\u001b[0;32m--> 266\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msrc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex_select\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode_dim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    267\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mIndexError\u001b[39;00m, \u001b[38;5;167;01mRuntimeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "\u001b[0;31mIndexError\u001b[0m: index out of range in self",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[66], line 16\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28mprint\u001b[39m(gat)\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# Train\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m model, out \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;66;03m#, train_mask, val_mask)\u001b[39;00m\n\u001b[1;32m     17\u001b[0m out\n",
      "Cell \u001b[0;32mIn[48], line 36\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(model, x, y, edge_index)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epochs\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m     34\u001b[0m     \u001b[38;5;66;03m# Training\u001b[39;00m\n\u001b[1;32m     35\u001b[0m     optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 36\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     37\u001b[0m     loss \u001b[38;5;241m=\u001b[39m criterion(out, y)\n\u001b[1;32m     38\u001b[0m     \u001b[38;5;66;03m#acc = accuracy(out[train_mask].argmax(dim=1), y[train_mask])\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Cell \u001b[0;32mIn[48], line 16\u001b[0m, in \u001b[0;36mGAT.forward\u001b[0;34m(self, x, edge_index)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, edge_index):\n\u001b[1;32m     15\u001b[0m     h \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mdropout(x, p\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.6\u001b[39m, training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining)\n\u001b[0;32m---> 16\u001b[0m     h \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgat1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     17\u001b[0m     h \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39melu(h)\n\u001b[1;32m     18\u001b[0m     h \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mdropout(h, p\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.6\u001b[39m, training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torch_geometric/nn/conv/gat_conv.py:252\u001b[0m, in \u001b[0;36mGATConv.forward\u001b[0;34m(self, x, edge_index, edge_attr, size, return_attention_weights)\u001b[0m\n\u001b[1;32m    246\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m    247\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe usage of \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124medge_attr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madd_self_loops\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    248\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msimultaneously is currently not yet supported for \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    249\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124medge_index\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m in a \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSparseTensor\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m form\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    251\u001b[0m \u001b[38;5;66;03m# edge_updater_type: (alpha: OptPairTensor, edge_attr: OptTensor)\u001b[39;00m\n\u001b[0;32m--> 252\u001b[0m alpha \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge_updater\u001b[49m\u001b[43m(\u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_attr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medge_attr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[38;5;66;03m# propagate_type: (x: OptPairTensor, alpha: Tensor)\u001b[39;00m\n\u001b[1;32m    255\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpropagate(edge_index, x\u001b[38;5;241m=\u001b[39mx, alpha\u001b[38;5;241m=\u001b[39malpha, size\u001b[38;5;241m=\u001b[39msize)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torch_geometric/nn/conv/message_passing.py:523\u001b[0m, in \u001b[0;36mMessagePassing.edge_updater\u001b[0;34m(self, edge_index, **kwargs)\u001b[0m\n\u001b[1;32m    519\u001b[0m         edge_index, kwargs \u001b[38;5;241m=\u001b[39m res\n\u001b[1;32m    521\u001b[0m size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_input(edge_index, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 523\u001b[0m coll_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_collect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_edge_user_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    524\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    526\u001b[0m edge_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minspector\u001b[38;5;241m.\u001b[39mdistribute(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124medge_update\u001b[39m\u001b[38;5;124m'\u001b[39m, coll_dict)\n\u001b[1;32m    527\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39medge_update(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39medge_kwargs)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torch_geometric/nn/conv/message_passing.py:329\u001b[0m, in \u001b[0;36mMessagePassing._collect\u001b[0;34m(self, args, edge_index, size, kwargs)\u001b[0m\n\u001b[1;32m    327\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, Tensor):\n\u001b[1;32m    328\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_size(size, dim, data)\n\u001b[0;32m--> 329\u001b[0m             data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_lift\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    331\u001b[0m         out[arg] \u001b[38;5;241m=\u001b[39m data\n\u001b[1;32m    333\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_torch_sparse_tensor(edge_index):\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/torch_geometric/nn/conv/message_passing.py:269\u001b[0m, in \u001b[0;36mMessagePassing._lift\u001b[0;34m(self, src, edge_index, dim)\u001b[0m\n\u001b[1;32m    267\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mIndexError\u001b[39;00m, \u001b[38;5;167;01mRuntimeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    268\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m index\u001b[38;5;241m.\u001b[39mmin() \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m index\u001b[38;5;241m.\u001b[39mmax() \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m src\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnode_dim):\n\u001b[0;32m--> 269\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\n\u001b[1;32m    270\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEncountered an index error. Please ensure that all \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    271\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindices in \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124medge_index\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m point to valid indices in \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    272\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe interval [0, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msrc\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnode_dim)\u001b[38;5;250m \u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    273\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(got interval \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    274\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mint\u001b[39m(index\u001b[38;5;241m.\u001b[39mmin())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mint\u001b[39m(index\u001b[38;5;241m.\u001b[39mmax())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m])\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    275\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    276\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n",
      "\u001b[0;31mIndexError\u001b[0m: Encountered an index error. Please ensure that all indices in 'edge_index' point to valid indices in the interval [0, 907] (got interval [0, 3951])"
     ]
    }
   ],
   "source": [
    "#move features into tensor\n",
    "#x = instances.drop(columns=['name', 'id', 'routing_demand'])\n",
    "x = torch.tensor(feature_matrix_quad1, dtype=torch.float)\n",
    "\n",
    "#move target into tensor\n",
    "y = torch.tensor(quad1['routing_demand'].values, dtype=torch.float)\n",
    "\n",
    "#%%time\n",
    "\n",
    "\n",
    "# Create GAT\n",
    "gat = GAT(feature_matrix_quad1.shape[1], 8, 1)\n",
    "print(gat)\n",
    "\n",
    "# Train\n",
    "model, out = train(gat, x, y, edge_index)#, train_mask, val_mask)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f24d8949",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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