data_eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Exploration\n",
    "\n",
    "`loader = METRLADatasetLoader()`\n",
    "`dataset = loader.get_dataset()`\n",
    "- `num_timesteps_in=12`, `num_timesteps_out=12` are defaults. Each timestep is 5 minutes, so 12 timesteps will equate to 1 hour. (12*5 mins = 60 mins).\n",
    "\n",
    "`next(iter(dataset))` - shows the first sample.\n",
    "- Output will be Data(x=[207, 2, 12], edge_index=[2, 1722], edge_attr=[1722], y=[207, 12])\n",
    "- Here, each x is the dimensions of [num_sensors, num_features (speed, time), num_timesteps]. We will have 34249 such x's in the entire dataset since the data is taken over 4 months and there are approx 34249 5-minute intervals in 4 months.\n",
    "- edge_index has dimension [2, num_edges] where one row is source_node and the second row is destination_node.\n",
    "- edge_attr has shape [num_edges] and has weights for the edge at ith index of edge_index.\n",
    "- y is the output which has shape [201, 12]. We will have 34249 such y's.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kedar\\anaconda3\\envs\\torch\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import typing\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from torch_geometric_temporal.dataset import METRLADatasetLoader\n",
    "from torch_geometric_temporal.signal import temporal_signal_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.7.1-cp38-cp38-win_amd64.whl (7.6 MB)\n",
      "     ---------------------------------------- 7.6/7.6 MB 18.1 MB/s eta 0:00:00\n",
      "Collecting contourpy>=1.0.1\n",
      "  Downloading contourpy-1.0.7-cp38-cp38-win_amd64.whl (162 kB)\n",
      "     -------------------------------------- 163.0/163.0 kB 9.5 MB/s eta 0:00:00\n",
      "Collecting cycler>=0.10\n",
      "  Downloading cycler-0.11.0-py3-none-any.whl (6.4 kB)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in c:\\users\\kedar\\anaconda3\\envs\\torch\\lib\\site-packages (from matplotlib) (5.2.0)\n",
      "Collecting fonttools>=4.22.0\n",
      "  Downloading fonttools-4.39.0-py3-none-any.whl (1.0 MB)\n",
      "     ---------------------------------------- 1.0/1.0 MB 21.6 MB/s eta 0:00:00\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\kedar\\anaconda3\\envs\\torch\\lib\\site-packages (from matplotlib) (9.4.0)\n",
      "Requirement already satisfied: numpy>=1.20 in c:\\users\\kedar\\anaconda3\\envs\\torch\\lib\\site-packages (from matplotlib) (1.23.5)\n",
      "Collecting kiwisolver>=1.0.1\n",
      "  Downloading kiwisolver-1.4.4-cp38-cp38-win_amd64.whl (55 kB)\n",
      "     ---------------------------------------- 55.4/55.4 kB ? eta 0:00:00\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\kedar\\anaconda3\\envs\\torch\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\kedar\\anaconda3\\envs\\torch\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\kedar\\anaconda3\\envs\\torch\\lib\\site-packages (from matplotlib) (22.0)\n",
      "Requirement already satisfied: zipp>=3.1.0 in c:\\users\\kedar\\anaconda3\\envs\\torch\\lib\\site-packages (from importlib-resources>=3.2.0->matplotlib) (3.11.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\kedar\\anaconda3\\envs\\torch\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Installing collected packages: kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
      "Successfully installed contourpy-1.0.7 cycler-0.11.0 fonttools-4.39.0 kiwisolver-1.4.4 matplotlib-3.7.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# %pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34249"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# len(dataset.features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = METRLADatasetLoader()\n",
    "\n",
    "dataset = loader.get_dataset()\n",
    "\n",
    "train_dataset, test_dataset = temporal_signal_split(dataset, train_ratio = 0.8)\n",
    "\n",
    "# DATASET INFO\n",
    "# num_timesteps_in=12, num_timesteps_out=12 are defaults - equates to 1 hour (5 min intervals)\n",
    "\n",
    "\n",
    "# next(iter(dataset)) # Showing first sample\n",
    "# x = [num_sensors, num_features(speed, time), num_timesteps]. Each timestep is 5 minutes. \n",
    "# Each data point has a sliding window of 5 minutes.\n",
    "# edge_index is ------ of shape [2, 1722] where 1722 is number of edges\n",
    "# edge_attr "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(x=[207, 2, 12], edge_index=[2, 1722], edge_attr=[1722], y=[207, 12])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(iter(dataset)) # Showing first sample\n",
    "# x = [num_sensors, num_features(speed, time), num_timesteps]. Each timestep is 5 minutes. \n",
    "# Each data point has a sliding window of 5 minutes.\n",
    "# edge_index is ------ of shape [2, 1722]\n",
    "# edge_attr "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.0000, 0.2223, 0.5088,  ..., 0.2779, 0.3204, 1.0000])\n",
      "tensor([[  0,   0,   0,  ..., 206, 206, 206],\n",
      "        [  0,  13,  37,  ..., 187, 198, 206]])\n",
      "[[  0   0   0 ... 206 206 206]\n",
      " [  0  13  37 ... 187 198 206]]\n"
     ]
    }
   ],
   "source": [
    "temp = next(iter(dataset))\n",
    "print(temp.edge_attr)\n",
    "print(temp.edge_index)\n",
    "\n",
    "print(np.array(temp.edge_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.53317183,  0.44864222,  0.51461655, -2.6521542 , -2.6521542 ,\n",
       "         0.18474454,  0.63831854,  0.49606127,  0.74965036,  0.48987615,\n",
       "         0.57509303,  0.4280252 ],\n",
       "       [-1.7291821 , -1.7171322 , -1.7050823 , -1.6930324 , -1.6809825 ,\n",
       "        -1.6689326 , -1.6568825 , -1.6448327 , -1.6327827 , -1.6207328 ,\n",
       "        -1.6086829 , -1.596633  ]], dtype=float32)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# len(dataset.edge_index[0])\n",
    "# len(dataset.features) # 34249\n",
    "# dataset.features[0].shape\n",
    "dataset.features[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.45276558, 0.49606127, 0.5201146 , 0.58883774, 0.5764676 ,\n",
       "        0.56409734, 0.5764676 , 0.66305894, 0.61357814, 0.47750598,\n",
       "        0.52561224, 0.49606127],\n",
       "       [0.34340465, 0.35545453, 0.36750463, 0.37955448, 0.39160436,\n",
       "        0.40365425, 0.41570434, 0.4277542 , 0.43980408, 0.45185396,\n",
       "        0.46390384, 0.47595394]], dtype=float32)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.features[32428][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.44864222,  0.51461655, -2.6521542 , -2.6521542 ,  0.18474454,\n",
       "         0.63831854,  0.49606127,  0.74965036,  0.48987615,  0.57509303,\n",
       "         0.4280252 ,  0.37235928],\n",
       "       [-1.7171322 , -1.7050823 , -1.6930324 , -1.6809825 , -1.6689326 ,\n",
       "        -1.6568825 , -1.6448327 , -1.6327827 , -1.6207328 , -1.6086829 ,\n",
       "        -1.596633  , -1.584583  ]], dtype=float32)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.features[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.37235928, 0.24522121, 0.49606127, 0.65206325, 0.11258511,\n",
       "       0.5311103 , 0.50911874, 0.47132087, 0.42184007, 0.3909146 ,\n",
       "       0.47613147, 0.56409734], dtype=float32)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dataset.targets[0][0]\n",
    "#len(dataset.targets) # 34249\n",
    "dataset.targets[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(207, 12)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.targets[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.edge_index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([207, 2, 12])\n",
      "torch.Size([207, 12])\n"
     ]
    }
   ],
   "source": [
    "print(temp.x.shape)\n",
    "print(temp.y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1722])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.edge_index.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.edge_attr[-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0   0   0 ... 206 206 206]\n",
      "[  0  13  37 ... 187 198 206]\n",
      "[1.         0.22234692 0.5088465  ... 0.27790868 0.32041067 1.        ]\n",
      "[('0', '13', 0.22234692), ('0', '37', 0.5088465), ('0', '42', 0.13740046), ('0', '54', 0.4409315), ('0', '111', 0.21944809), ('0', '116', 0.41578582), ('0', '125', 0.10258746), ('0', '140', 0.11251228), ('0', '142', 0.7216228), ('0', '145', 0.87776124), ('0', '199', 0.11980409), ('1', '2', 0.3909554), ('1', '7', 0.39045706), ('1', '28', 0.573732), ('1', '78', 0.20909733), ('1', '107', 0.16216348), ('1', '177', 0.92621875), ('1', '183', 0.15487239), ('2', '1', 0.7174379), ('2', '28', 0.17432716), ('2', '49', 0.441055), ('2', '78', 0.92367977), ('2', '123', 0.29931417), ('2', '129', 0.16393073), ('2', '177', 0.4829466), ('2', '183', 0.5701921), ('3', '4', 0.63372165), ('3', '12', 0.11933213), ('3', '15', 0.52255076), ('3', '17', 0.7409004), ('3', '33', 0.27014008), ('3', '80', 0.5133506), ('3', '144', 0.19402061), ('3', '161', 0.16145374), ('3', '188', 0.26349494), ('3', '191', 0.4547534), ('3', '192', 0.9461079), ('3', '193', 0.15311207), ('3', '195', 0.37622455), ('4', '3', 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      "1515\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = nx.DiGraph()\n",
    "\n",
    "# print(temp.edge_attr)\n",
    "# print(temp.edge_index)\n",
    "\n",
    "nodes = np.array(temp.edge_index[0])\n",
    "#print(nodes)\n",
    "\n",
    "targets = np.array(temp.edge_index[1])\n",
    "#print(targets)\n",
    "\n",
    "weight_range = len(temp.edge_attr)\n",
    "weights = np.array(temp.edge_attr)\n",
    "#weights = weight_range * weights\n",
    "\n",
    "#print(weights)\n",
    "\n",
    "pairs = []\n",
    "\n",
    "for i in range(len(nodes)):\n",
    "    if (nodes[i] != targets[i]):\n",
    "        pair = (str(nodes[i]), str(targets[i]), weights[i])\n",
    "        pairs.append(pair)\n",
    "\n",
    "#print(pairs)\n",
    "#print(len(pairs))\n",
    "\n",
    "for p in pairs:\n",
    "    G.add_edge(p[0], p[1], weight = p[2])\n",
    "\n",
    "weights = nx.get_edge_attributes(G,'weight').values()\n",
    "\n",
    "pos = nx.fruchterman_reingold_layout(G)\n",
    "\n",
    "nx.draw(G, pos, width=list(weights))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 92,
     "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": [
    "# Visualize traffic over time\n",
    "sensor_number = 1\n",
    "hours = 24\n",
    "sensor_labels = [bucket.y[sensor_number][0].item() for bucket in list(dataset)[:hours]]\n",
    "plt.plot(data=sensor_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch_geometric_temporal.signal.static_graph_temporal_signal.StaticGraphTemporalSignal at 0x21801805a60>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[:hours]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dataset' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m temp1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(\u001b[43mdataset\u001b[49m))\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1000\u001b[39m):\n\u001b[0;32m      3\u001b[0m     temp1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(dataset))\n",
      "\u001b[1;31mNameError\u001b[0m: name 'dataset' is not defined"
     ]
    }
   ],
   "source": [
    "temp1 = next(iter(dataset))\n",
    "for i in range(1000):\n",
    "    temp1 = next(iter(dataset))\n",
    "print(temp1.edge_attr)\n",
    "print(temp1.edge_index)\n",
    "\n",
    "print(np.array(temp1.edge_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'temp1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mtemp1\u001b[49m\u001b[38;5;241m.\u001b[39medge_index\n",
      "\u001b[1;31mNameError\u001b[0m: name 'temp1' is not defined"
     ]
    }
   ],
   "source": [
    "temp1.edge_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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