tesis_mnav_heterogeneous_gnn_pyg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vp0qGc5z4AOk",
    "tags": []
   },
   "source": [
    "# MNAV - Heterogeneous GNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oDifkXbM4UUX"
   },
   "source": [
    "Dataset: MNAV\n",
    "\n",
    "Modelo: GNN con grafo heterogéneo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n4qV8TIQYl62",
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Importar Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 27989,
     "status": "ok",
     "timestamp": 1635510361213,
     "user": {
      "displayName": "Facundo Lezama",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjvX7z6H8HKQ9Q4HSRF20NaPjvwtJ3pQh0TGed2XQ=s64",
      "userId": "08104556030349101598"
     },
     "user_tz": 180
    },
    "id": "OjHf3j75Agti"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fede/.local/lib/python3.8/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 scipy\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from itertools import combinations\n",
    "from copy import deepcopy\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "import torch\n",
    "import networkx as nx\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "from torch_geometric.data import Data, HeteroData\n",
    "from torch_geometric.nn.conv.dna_conv import Linear\n",
    "from torch_geometric.utils import to_networkx, is_undirected, to_undirected\n",
    "\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GCNConv, ChebConv, SAGEConv, TAGConv, GraphConv, to_hetero, GATConv, Linear, BatchNorm, HeteroConv, HANConv\n",
    "from torch_geometric.loader import DataLoader\n",
    "import torch_geometric.transforms as T\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cMFznhF9wDxB"
   },
   "source": [
    "El dataset se encuentra disponible en https://github.com/ffedee7/posifi_mnav/tree/master/data_analysis. El dataset disponible está anonimizado pero se puede deshacer con el código de ese repositorio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 1270,
     "status": "ok",
     "timestamp": 1635454213295,
     "user": {
      "displayName": "Facundo Lezama",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjvX7z6H8HKQ9Q4HSRF20NaPjvwtJ3pQh0TGed2XQ=s64",
      "userId": "08104556030349101598"
     },
     "user_tz": 180
    },
    "id": "jXw5-YfeAgtp"
   },
   "outputs": [],
   "source": [
    "dataset = 'datos2.csv'\n",
    "df = pd.read_csv(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "height": 383
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    "id": "OIhn7QSoAgtq",
    "outputId": "37bf3f47-b29c-4af5-aec4-a23a675b0eef"
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   "outputs": [
    {
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     "text": [
      "(10469, 189)\n"
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       "      <th>count</th>\n",
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       "      <td>7.0</td>\n",
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       "      <th>mean</th>\n",
       "      <td>-83.642426</td>\n",
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       "      <td>-81.040950</td>\n",
       "      <td>-77.477950</td>\n",
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       "      <td>-82.051896</td>\n",
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       "      <td>-74.916214</td>\n",
       "      <td>-77.211208</td>\n",
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       "      <td>-95.0</td>\n",
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       "      <td>-84.0</td>\n",
       "      <td>-89.0</td>\n",
       "      <td>-88.0</td>\n",
       "      <td>-93.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-89.0</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.342463</td>\n",
       "      <td>11.268483</td>\n",
       "      <td>11.139033</td>\n",
       "      <td>9.777461</td>\n",
       "      <td>8.545359</td>\n",
       "      <td>8.589948</td>\n",
       "      <td>10.273665</td>\n",
       "      <td>9.073052</td>\n",
       "      <td>12.254007</td>\n",
       "      <td>8.259183</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>min</th>\n",
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       "      <td>-96.000000</td>\n",
       "      <td>-96.000000</td>\n",
       "      <td>-98.000000</td>\n",
       "      <td>-95.000000</td>\n",
       "      <td>-96.000000</td>\n",
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       "      <td>-96.000000</td>\n",
       "      <td>-95.000000</td>\n",
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       "      <td>-89.0</td>\n",
       "      <td>-88.0</td>\n",
       "      <td>-93.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-89.0</td>\n",
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       "      <td>-89.0</td>\n",
       "      <td>-88.0</td>\n",
       "      <td>-93.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-89.0</td>\n",
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       "      <td>-76.000000</td>\n",
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       "      <td>-89.0</td>\n",
       "      <td>-88.0</td>\n",
       "      <td>-93.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-89.0</td>\n",
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       "      <td>-79.000000</td>\n",
       "      <td>-66.000000</td>\n",
       "      <td>-72.000000</td>\n",
       "      <td>-71.000000</td>\n",
       "      <td>-69.000000</td>\n",
       "      <td>-77.000000</td>\n",
       "      <td>-79.000000</td>\n",
       "      <td>-70.000000</td>\n",
       "      <td>-67.000000</td>\n",
       "      <td>-73.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-97.0</td>\n",
       "      <td>-95.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-84.0</td>\n",
       "      <td>-89.0</td>\n",
       "      <td>-88.0</td>\n",
       "      <td>-93.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-89.0</td>\n",
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       "      <td>-89.0</td>\n",
       "      <td>-88.0</td>\n",
       "      <td>-93.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-89.0</td>\n",
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       "       wifi-c0:7b:bc:36:af:80  wifi-1c:1d:86:b8:ac:80  wifi-f8:4f:57:ab:ce:20  \\\n",
       "count             8278.000000             8313.000000             4127.000000   \n",
       "mean               -83.642426              -74.133406              -81.040950   \n",
       "std                  6.342463               11.268483               11.139033   \n",
       "min                -96.000000              -96.000000              -96.000000   \n",
       "25%                -89.000000              -83.000000              -91.000000   \n",
       "50%                -84.000000              -75.000000              -84.000000   \n",
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       "\n",
       "       wifi-dc:a5:f4:43:72:e0  wifi-c0:7b:bc:36:af:40  wifi-c0:7b:bc:36:9e:10  \\\n",
       "count             9365.000000            10289.000000             9230.000000   \n",
       "mean               -77.477950              -75.078725              -82.051896   \n",
       "std                  9.777461                8.545359                8.589948   \n",
       "min                -98.000000              -95.000000              -96.000000   \n",
       "25%                -85.000000              -81.000000              -89.000000   \n",
       "50%                -78.000000              -75.000000              -83.000000   \n",
       "75%                -71.000000              -69.000000              -77.000000   \n",
       "max                -42.000000              -44.000000              -41.000000   \n",
       "\n",
       "       wifi-1c:1d:86:d0:ef:00  wifi-dc:a5:f4:43:79:20  wifi-ec:08:6b:ec:36:12  \\\n",
       "count             2001.000000             9381.000000             4711.000000   \n",
       "mean               -84.896052              -74.916214              -77.211208   \n",
       "std                 10.273665                9.073052               12.254007   \n",
       "min                -99.000000              -94.000000              -96.000000   \n",
       "25%                -94.000000              -81.000000              -88.000000   \n",
       "50%                -88.000000              -76.000000              -81.000000   \n",
       "75%                -79.000000              -70.000000              -67.000000   \n",
       "max                -50.000000              -38.000000              -45.000000   \n",
       "\n",
       "       wifi-1c:1d:86:9f:99:20  ...  wifi-84:74:2a:01:41:27  \\\n",
       "count            10024.000000  ...                     7.0   \n",
       "mean               -78.225359  ...                   -97.0   \n",
       "std                  8.259183  ...                     0.0   \n",
       "min                -95.000000  ...                   -97.0   \n",
       "25%                -85.000000  ...                   -97.0   \n",
       "50%                -78.000000  ...                   -97.0   \n",
       "75%                -73.000000  ...                   -97.0   \n",
       "max                -42.000000  ...                   -97.0   \n",
       "\n",
       "       wifi-90:f6:52:f8:d1:dc  wifi-6a:fb:7e:26:34:bc  wifi-34:fa:40:08:f1:c8  \\\n",
       "count                     7.0                     7.0                     7.0   \n",
       "mean                    -95.0                   -90.0                   -84.0   \n",
       "std                       0.0                     0.0                     0.0   \n",
       "min                     -95.0                   -90.0                   -84.0   \n",
       "25%                     -95.0                   -90.0                   -84.0   \n",
       "50%                     -95.0                   -90.0                   -84.0   \n",
       "75%                     -95.0                   -90.0                   -84.0   \n",
       "max                     -95.0                   -90.0                   -84.0   \n",
       "\n",
       "       wifi-54:22:f8:90:0b:b6  wifi-b0:4e:26:78:8d:e2  wifi-d4:6e:0e:3a:9f:fa  \\\n",
       "count                     7.0                     7.0                     7.0   \n",
       "mean                    -89.0                   -88.0                   -93.0   \n",
       "std                       0.0                     0.0                     0.0   \n",
       "min                     -89.0                   -88.0                   -93.0   \n",
       "25%                     -89.0                   -88.0                   -93.0   \n",
       "50%                     -89.0                   -88.0                   -93.0   \n",
       "75%                     -89.0                   -88.0                   -93.0   \n",
       "max                     -89.0                   -88.0                   -93.0   \n",
       "\n",
       "       wifi-58:6d:8f:d7:68:d6  wifi-ac:84:c6:56:17:0e  wifi-00:87:01:6f:b6:ae  \n",
       "count                     7.0                     7.0                     7.0  \n",
       "mean                    -90.0                   -90.0                   -89.0  \n",
       "std                       0.0                     0.0                     0.0  \n",
       "min                     -90.0                   -90.0                   -89.0  \n",
       "25%                     -90.0                   -90.0                   -89.0  \n",
       "50%                     -90.0                   -90.0                   -89.0  \n",
       "75%                     -90.0                   -90.0                   -89.0  \n",
       "max                     -90.0                   -90.0                   -89.0  \n",
       "\n",
       "[8 rows x 188 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.describe()"
   ]
  },
  {
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      "displayName": "Facundo Lezama",
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   "outputs": [
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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>location</th>\n",
       "      <th>wifi-c0:7b:bc:36:af:80</th>\n",
       "      <th>wifi-1c:1d:86:b8:ac:80</th>\n",
       "      <th>wifi-f8:4f:57:ab:ce:20</th>\n",
       "      <th>wifi-dc:a5:f4:43:72:e0</th>\n",
       "      <th>wifi-c0:7b:bc:36:af:40</th>\n",
       "      <th>wifi-c0:7b:bc:36:9e:10</th>\n",
       "      <th>wifi-1c:1d:86:d0:ef:00</th>\n",
       "      <th>wifi-dc:a5:f4:43:79:20</th>\n",
       "      <th>wifi-ec:08:6b:ec:36:12</th>\n",
       "      <th>...</th>\n",
       "      <th>wifi-84:74:2a:01:41:27</th>\n",
       "      <th>wifi-90:f6:52:f8:d1:dc</th>\n",
       "      <th>wifi-6a:fb:7e:26:34:bc</th>\n",
       "      <th>wifi-34:fa:40:08:f1:c8</th>\n",
       "      <th>wifi-54:22:f8:90:0b:b6</th>\n",
       "      <th>wifi-b0:4e:26:78:8d:e2</th>\n",
       "      <th>wifi-d4:6e:0e:3a:9f:fa</th>\n",
       "      <th>wifi-58:6d:8f:d7:68:d6</th>\n",
       "      <th>wifi-ac:84:c6:56:17:0e</th>\n",
       "      <th>wifi-00:87:01:6f:b6:ae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>location_7</td>\n",
       "      <td>-84.0</td>\n",
       "      <td>-71.0</td>\n",
       "      <td>-92.0</td>\n",
       "      <td>-68.0</td>\n",
       "      <td>-77.0</td>\n",
       "      <td>-79.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>-92.0</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>location_3</td>\n",
       "      <td>-84.0</td>\n",
       "      <td>-72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-75.0</td>\n",
       "      <td>-79.0</td>\n",
       "      <td>-85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-87.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>location_8</td>\n",
       "      <td>-91.0</td>\n",
       "      <td>-82.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-78.0</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>-93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-79.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>location_10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-91.0</td>\n",
       "      <td>-85.0</td>\n",
       "      <td>-93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>location_3</td>\n",
       "      <td>-85.0</td>\n",
       "      <td>-66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>-79.0</td>\n",
       "      <td>-86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 189 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      location  wifi-c0:7b:bc:36:af:80  wifi-1c:1d:86:b8:ac:80  \\\n",
       "0   location_7                   -84.0                   -71.0   \n",
       "1   location_3                   -84.0                   -72.0   \n",
       "2   location_8                   -91.0                   -82.0   \n",
       "3  location_10                     NaN                   -95.0   \n",
       "4   location_3                   -85.0                   -66.0   \n",
       "\n",
       "   wifi-f8:4f:57:ab:ce:20  wifi-dc:a5:f4:43:72:e0  wifi-c0:7b:bc:36:af:40  \\\n",
       "0                   -92.0                   -68.0                   -77.0   \n",
       "1                     NaN                   -75.0                   -79.0   \n",
       "2                     NaN                   -78.0                   -80.0   \n",
       "3                     NaN                   -91.0                   -85.0   \n",
       "4                     NaN                   -80.0                   -79.0   \n",
       "\n",
       "   wifi-c0:7b:bc:36:9e:10  wifi-1c:1d:86:d0:ef:00  wifi-dc:a5:f4:43:79:20  \\\n",
       "0                   -79.0                   -90.0                   -74.0   \n",
       "1                   -85.0                     NaN                   -87.0   \n",
       "2                   -93.0                     NaN                   -79.0   \n",
       "3                   -93.0                     NaN                   -89.0   \n",
       "4                   -86.0                     NaN                   -88.0   \n",
       "\n",
       "   wifi-ec:08:6b:ec:36:12  ...  wifi-84:74:2a:01:41:27  \\\n",
       "0                   -92.0  ...                     NaN   \n",
       "1                     NaN  ...                     NaN   \n",
       "2                     NaN  ...                     NaN   \n",
       "3                     NaN  ...                     NaN   \n",
       "4                     NaN  ...                     NaN   \n",
       "\n",
       "   wifi-90:f6:52:f8:d1:dc  wifi-6a:fb:7e:26:34:bc  wifi-34:fa:40:08:f1:c8  \\\n",
       "0                     NaN                     NaN                     NaN   \n",
       "1                     NaN                     NaN                     NaN   \n",
       "2                     NaN                     NaN                     NaN   \n",
       "3                     NaN                     NaN                     NaN   \n",
       "4                     NaN                     NaN                     NaN   \n",
       "\n",
       "   wifi-54:22:f8:90:0b:b6  wifi-b0:4e:26:78:8d:e2  wifi-d4:6e:0e:3a:9f:fa  \\\n",
       "0                     NaN                     NaN                     NaN   \n",
       "1                     NaN                     NaN                     NaN   \n",
       "2                     NaN                     NaN                     NaN   \n",
       "3                     NaN                     NaN                     NaN   \n",
       "4                     NaN                     NaN                     NaN   \n",
       "\n",
       "   wifi-58:6d:8f:d7:68:d6  wifi-ac:84:c6:56:17:0e  wifi-00:87:01:6f:b6:ae  \n",
       "0                     NaN                     NaN                     NaN  \n",
       "1                     NaN                     NaN                     NaN  \n",
       "2                     NaN                     NaN                     NaN  \n",
       "3                     NaN                     NaN                     NaN  \n",
       "4                     NaN                     NaN                     NaN  \n",
       "\n",
       "[5 rows x 189 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gfqVFAK32Th4",
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Preprocesamiento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CL6O8p3ZERrS"
   },
   "source": [
    "Se definen los APs que se quieren usar tanto para la construcción del grafo como para el modelo.\n",
    "\n",
    "En este caso usamos solamente los APs que se encuentran dentro del MNAV, es decir que descartamos los APs que aparecen en las medidas pero que no son los que se instalaron."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1635454214396,
     "user": {
      "displayName": "Facundo Lezama",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjvX7z6H8HKQ9Q4HSRF20NaPjvwtJ3pQh0TGed2XQ=s64",
      "userId": "08104556030349101598"
     },
     "user_tz": 180
    },
    "id": "ynWQ8U8z50to"
   },
   "outputs": [],
   "source": [
    "APs_MAC_2_4 = ['wifi-dc:a5:f4:43:85:c0',\n",
    "'wifi-dc:a5:f4:43:27:e0',\n",
    "'wifi-f8:4f:57:ab:da:00',\n",
    "'wifi-5c:a4:8a:4c:05:c0',\n",
    "'wifi-1c:1d:86:ce:ef:b0',\n",
    "'wifi-dc:a5:f4:43:79:20',\n",
    "'wifi-c0:7b:bc:36:9e:10',\n",
    "'wifi-1c:1d:86:9f:99:20',\n",
    "'wifi-c0:7b:bc:36:af:40',\n",
    "'wifi-c0:7b:bc:36:af:80',\n",
    "'wifi-1c:1d:86:b6:ac:80',\n",
    "'wifi-dc:a5:f4:43:72:e0',\n",
    "'wifi-f8:4f:57:ab:d8:60',\n",
    "'wifi-dc:a5:f4:43:72:90',\n",
    "'wifi-f8:4f:57:ab:ce:20']\n",
    "\n",
    "APs_MAC_5 = ['wifi-dc:a5:f4:45:85:b0',\n",
    "'wifi-dc:a5:f4:45:27:e0',\n",
    "'wifi-f8:4f:57:ad:d9:60',\n",
    "'wifi-5c:a4:8a:4e:05:30',\n",
    "'wifi-1c:1d:86:d0:ef:00',\n",
    "'wifi-dc:a5:f4:45:79:10',\n",
    "'wifi-c0:7b:bc:38:9e:00',\n",
    "'wifi-1c:1d:86:a1:99:00',\n",
    "'wifi-c0:7b:bc:38:af:30',\n",
    "'wifi-c0:7b:bc:38:af:70',\n",
    "'wifi-1c:1d:86:b8:ac:80',\n",
    "'wifi-dc:a5:f4:45:72:d0',\n",
    "'wifi-f8:4f:57:ad:d7:c0',\n",
    "'wifi-dc:a5:f4:45:72:80',\n",
    "'wifi-f8:4f:57:ad:cd:80']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1635454214397,
     "user": {
      "displayName": "Facundo Lezama",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjvX7z6H8HKQ9Q4HSRF20NaPjvwtJ3pQh0TGed2XQ=s64",
      "userId": "08104556030349101598"
     },
     "user_tz": 180
    },
    "id": "q0BCpSLFAgts"
   },
   "outputs": [],
   "source": [
    "def preprocess_dataset(dataset_path, zone_to_remove=None, dataset_percentage=None):\n",
    "    df = pd.read_csv(dataset_path)\n",
    "    # paso los NaN a 0\n",
    "    df = df.fillna(0) \n",
    "\n",
    "    # sumo 100 a los valores de RSSI y ahora 0 es el minimo\n",
    "    df.iloc[:,1:] = 100 + df.iloc[:,1:] \n",
    "    values = df.iloc[:,1:]\n",
    "\n",
    "    # las medidas originales en 0 las asumo como que estaban muy lejos\n",
    "    # entonces las dejo en 0 que es el nuevo valor minimo\n",
    "    values[values==100] = 0 \n",
    "    df.iloc[:,1:] = values    \n",
    "    \n",
    "    # armo dos datsets: uno con las medidas solamente de la frecuencia\n",
    "    # 2.4GHz y otro con las frecuencias 2.4GHz y 5GHz\n",
    "    data_2_4 = df[['location'] + APs_MAC_2_4] # REVISAR PORQUE CREO QUE NO LO VUELVO A USAR\n",
    "    data_2_4_5 = df[['location'] + APs_MAC_2_4 + APs_MAC_5]\n",
    "    \n",
    "    if dataset_percentage:\n",
    "        data_2_4_5 = data_2_4_5.sample(frac=dataset_percentage)\n",
    "    \n",
    "    # paso las zonas por un ordinal encoder\n",
    "    enc = OrdinalEncoder(dtype=int)\n",
    "    y = enc.fit_transform(data_2_4_5['location'].values.reshape(-1,1))\n",
    "    X = data_2_4_5.iloc[:,1:].values\n",
    "\n",
    "    print(enc.categories_)\n",
    "\n",
    "    # separo el dataset en train y test 80-20\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)    \n",
    "    \n",
    "#     if zone_to_remove:\n",
    "#         X_train_modif = X_train[(y_train != zone_to_remove).reshape(-1)]\n",
    "#         y_train_modif = y_train[(y_train != zone_to_remove).reshape(-1)]\n",
    "#         y_train_modif[y_train_modif>zone_to_remove] = y_train_modif[y_train_modif>zone_to_remove] - 1\n",
    "        \n",
    "#         X_test_modif = X_test[(y_test != zone_to_remove).reshape(-1)]\n",
    "#         y_test_modif = y_test[(y_test != zone_to_remove).reshape(-1)]\n",
    "#         y_test_modif[y_test_modif>zone_to_remove] = y_test_modif[y_test_modif>zone_to_remove] - 1\n",
    "        \n",
    "#         print(\"X_train shape: \", X_train_modif.shape)\n",
    "#         return X_train_modif, X_test_modif, y_train_modif, y_test_modif, len(APs_MAC_2_4), len(np.unique(y_train_modif)), enc\n",
    "        \n",
    "    print(\"X_train shape: \", X_train.shape)\n",
    "    return X_train, X_test, y_train, y_test, len(APs_MAC_2_4), len(np.unique(y_train)), enc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fmC2Dsazdx7i",
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Grafo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cF2ZViCq3a_Q"
   },
   "source": [
    "En las siguientes celdas se describe un poco el dataset y se muestran las distribuciones de potencia por AP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1635454218455,
     "user": {
      "displayName": "Facundo Lezama",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjvX7z6H8HKQ9Q4HSRF20NaPjvwtJ3pQh0TGed2XQ=s64",
      "userId": "08104556030349101598"
     },
     "user_tz": 180
    },
    "id": "xHfjTzU5E2eq"
   },
   "outputs": [],
   "source": [
    "def ap_graph_creator(X_G, th=10, cols=None, prune_th=0):\n",
    "    \"\"\"\n",
    "    Dado un dataset y un threshold se arma un grafo basado en las medidas de RRSI\n",
    "    \"\"\"\n",
    "    columns = cols if cols else ['AP1', 'AP2', 'AP3', 'AP4', 'AP5', 'AP6', 'AP7', 'AP8', 'AP9', 'AP10', 'AP11', 'AP12', 'AP13', 'AP14', 'AP15']\n",
    "    df_data_train = pd.DataFrame(X_G, columns=columns)\n",
    "    df_G = pd.DataFrame(columns = ['from', 'to', 'weight'])\n",
    "\n",
    "    for ap in columns:\n",
    "        # para cada AP me quedo con las instancias donde el RSSI esta en el rango\n",
    "        # (max-th) intentando estimar las instancias mas cercanas al AP\n",
    "        max_val = df_data_train[ap].max()\n",
    "        # print(f\"Max: {max_val}\")\n",
    "        df_aux_i = df_data_train[df_data_train[ap]  > (max_val - th)]\n",
    "        df_aux_i = df_aux_i.drop(ap, axis=1)\n",
    "        # print(f\"Counts: {df_aux_i.shape[0]}\")\n",
    "        # import ipdb; ipdb.set_trace()\n",
    "        for k, v in df_aux_i.mean().items():\n",
    "            # print(v)\n",
    "            # armo las aristas con el promedio de RSSI que ven las instancias\n",
    "            # filtradas al resto de los APs\n",
    "            # weight = v\n",
    "            # if df_G.loc[(df_G['from'] == k) & (df_G['to'] == ap)].weight.any():\n",
    "            #     weight = np.mean([float(df_G.loc[(df_G['from'] == k) & (df_G['to'] == ap)].weight), weight])\n",
    "            #     df_G.loc[(df_G['from'] == k) & (df_G['to'] == ap)] = k, ap, weight\n",
    "            if v > prune_th:\n",
    "                df_G = df_G.append({'from':ap, 'to': k, 'weight': v}, ignore_index=True)\n",
    "        \n",
    "\n",
    "    edge_index_first_row = []\n",
    "    edge_index_second_row = []\n",
    "    edge_attr = []\n",
    "    for index, row in df_G.iterrows():\n",
    "        edge_index_first_row.append(columns.index(row['from']))\n",
    "        edge_index_second_row.append(columns.index(row['to']))\n",
    "        edge_attr.append([float(row.weight)])\n",
    "\n",
    "    edge_index = torch.tensor([edge_index_first_row, edge_index_second_row], dtype=torch.long)\n",
    "    edge_attr = torch.tensor(edge_attr, dtype=torch.float)                           \n",
    "    edge_index, edge_attr = to_undirected(edge_index, edge_attr, reduce=\"mean\")\n",
    "\n",
    "    return edge_index, edge_attr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1635454218455,
     "user": {
      "displayName": "Facundo Lezama",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjvX7z6H8HKQ9Q4HSRF20NaPjvwtJ3pQh0TGed2XQ=s64",
      "userId": "08104556030349101598"
     },
     "user_tz": 180
    },
    "id": "xHfjTzU5E2eq"
   },
   "outputs": [],
   "source": [
    "def zone_ap_graph_creator(X_G, y, cols=None, prune_th=0, zone_to_remove=None):\n",
    "\n",
    "    columns = cols if cols else ['AP1', 'AP2', 'AP3', 'AP4', 'AP5', 'AP6', 'AP7', 'AP8', 'AP9', 'AP10', 'AP11', 'AP12', 'AP13', 'AP14', 'AP15']\n",
    "    df_data_train = pd.DataFrame(X_G, columns=columns)\n",
    "    df_data_train['cls'] = y\n",
    "    df_G = pd.DataFrame(columns = ['from', 'to', 'weight'])\n",
    "\n",
    "    for zone in range(len(df_data_train['cls'].unique())): # np.sort(df_data_train['cls'].unique()):\n",
    "\n",
    "        filtered_instances = df_data_train.loc[df_data_train['cls'] == zone]\n",
    "        means = np.array(filtered_instances.mean())[:-1]\n",
    "\n",
    "        for ap, mean in enumerate(means):\n",
    "            if mean > prune_th:\n",
    "                # if zone_to_remove != zone:\n",
    "                if zone_to_remove == zone:\n",
    "                    mean = 0\n",
    "                df_G = df_G.append({'from':zone, 'to': ap, 'weight': mean}, ignore_index=True)\n",
    "\n",
    "    edge_index_first_row = []\n",
    "    edge_index_second_row = []\n",
    "    edge_attr = []\n",
    "    for index, row in df_G.iterrows():\n",
    "        edge_index_first_row.append(row['from'])\n",
    "        edge_index_second_row.append(row['to'])\n",
    "        edge_attr.append([float(row.weight)])\n",
    "    \n",
    "    edge_index = torch.tensor([[int(i) for i in edge_index_first_row], [int(i) for i in edge_index_second_row]], dtype=torch.long)\n",
    "    edge_attr = torch.tensor(edge_attr, dtype=torch.float)                           \n",
    "\n",
    "    return edge_index, edge_attr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dataset(X, y, graph, zone_to_remove=None):\n",
    "    dataset = []\n",
    "    # random = torch.randn(16,5)\n",
    "    one_hot = torch.nn.functional.one_hot(torch.arange(0,16)).float()\n",
    "    \n",
    "    for i in range(len(y)):\n",
    "        if y[i] != zone_to_remove:\n",
    "            data = deepcopy(graph)\n",
    "            data['aps'].x = torch.Tensor(X[i])\n",
    "\n",
    "            # data['zones'].x = torch.ones(data['zones'].num_nodes,1)\n",
    "            data['zones'].x = torch.zeros(data['zones'].num_nodes,1)\n",
    "            # data['zones'].x = torch.randn(data['zones'].num_nodes,5)\n",
    "            # data['zones'].x = one_hot\n",
    "            # data['zones'].x -= data['zones'].x.mean()\n",
    "            # data['zones'].x /= data['zones'].x.std()\n",
    "            data['zones'].y = torch.Tensor(y[i])\n",
    "            data['zones'].train_mask = torch.Tensor([True]*len(y))\n",
    "            data['zones'].val_mask = torch.Tensor([True]*len(y))\n",
    "            data['zones'].test_mask = torch.Tensor([True]*len(y))\n",
    "\n",
    "            dataset.append(data)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr, classes=16):\n",
    "    data = HeteroData()\n",
    "    data['aps', 'ap_ap', 'aps'].edge_index = ap_edge_index\n",
    "    ap_edge_attr = (ap_edge_attr - ap_edge_attr.mean())/ap_edge_attr.std()\n",
    "    data['aps', 'ap_ap', 'aps'].edge_attr = ap_edge_attr\n",
    "    data['aps'].num_nodes = 15\n",
    "\n",
    "    data['zones', 'zone_ap', 'aps'].edge_index = zone_ap_edge_index\n",
    "    zone_ap_edge_attr = (zone_ap_edge_attr - zone_ap_edge_attr.mean())/zone_ap_edge_attr.std()    \n",
    "    data['zones', 'zone_ap', 'aps'].edge_attr = torch.nn.functional.normalize(zone_ap_edge_attr, dim=0)\n",
    "    data['zones'].num_nodes = classes\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class HeteroGNN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels, out_channels, hidden_layers):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.hidden_convs = torch.nn.ModuleList()\n",
    "        for _ in range(hidden_layers):\n",
    "            conv = HeteroConv({\n",
    "                ('aps', 'ap_ap', 'aps'): GraphConv((-1, -1), hidden_channels),\n",
    "                ('zones', 'zone_ap', 'aps'): GraphConv((-1, -1), hidden_channels),\n",
    "                ('aps', 'rev_zone_ap', 'zones'): GraphConv((-1, -1), hidden_channels),\n",
    "            }, aggr='mean')\n",
    "            self.hidden_convs.append(conv)\n",
    "        \n",
    "        self.out_convs = torch.nn.ModuleList()\n",
    "        conv = HeteroConv({\n",
    "            ('aps', 'ap_ap', 'aps'): GraphConv((-1, -1), out_channels),\n",
    "            ('zones', 'zone_ap', 'aps'): GraphConv((-1, -1), out_channels),\n",
    "            ('aps', 'rev_zone_ap', 'zones'): GraphConv((-1, -1), out_channels),\n",
    "        }, aggr='mean')\n",
    "        self.out_convs.append(conv)        \n",
    "\n",
    "        # self.lin = Linear(hidden_channels, out_channels)\n",
    "\n",
    "    def forward(self, x_dict, edge_index_dict, edge_attr_dict):\n",
    "        for conv in self.hidden_convs:\n",
    "            x_dict = conv(x_dict, edge_index_dict, edge_attr_dict)\n",
    "            x_dict = {key: x.relu() for key, x in x_dict.items()}\n",
    "        for conv in self.out_convs:\n",
    "            x_dict = conv(x_dict, edge_index_dict, edge_attr_dict)\n",
    "           # x_dict = {key: x.relu() for key, x in x_dict.items()}\n",
    "\n",
    "        # out = self.lin(x_dict['zones'])\n",
    "        \n",
    "        return x_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class HeteroGNN_simplified(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels, out_channels, hidden_layers):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.hidden_convs = torch.nn.ModuleList()\n",
    "        for _ in range(hidden_layers):\n",
    "            conv = HeteroConv({\n",
    "                ('aps', 'ap_ap', 'aps'): GraphConv((-1, -1), hidden_channels),\n",
    "                # ('zones', 'zone_ap', 'aps'): GraphConv((-1, -1), hidden_channels),\n",
    "                # ('aps', 'rev_zone_ap', 'zones'): GraphConv((-1, -1), hidden_channels),\n",
    "            }, aggr='sum')\n",
    "            self.hidden_convs.append(conv)\n",
    "        \n",
    "        self.out_convs = torch.nn.ModuleList()\n",
    "        conv = HeteroConv({\n",
    "            ('aps', 'ap_ap', 'aps'): GraphConv((-1, -1), out_channels),\n",
    "            ('zones', 'zone_ap', 'aps'): GraphConv((-1, -1), out_channels),\n",
    "            ('aps', 'rev_zone_ap', 'zones'): GraphConv((-1, -1), out_channels),\n",
    "        }, aggr='sum')\n",
    "        self.out_convs.append(conv)        \n",
    "\n",
    "        # self.lin = Linear(hidden_channels, out_channels)\n",
    "\n",
    "    def forward(self, x_dict, edge_index_dict, edge_attr_dict):\n",
    "        initial_zones_signal = x_dict[\"zones\"]\n",
    "        for conv in self.hidden_convs:\n",
    "            x_dict = conv(x_dict, edge_index_dict, edge_attr_dict)\n",
    "            x_dict = {key: x.relu() for key, x in x_dict.items()}\n",
    "        x_dict[\"zones\"] = initial_zones_signal\n",
    "        for conv in self.out_convs:\n",
    "            x_dict = conv(x_dict, edge_index_dict, edge_attr_dict)\n",
    "           # x_dict = {key: x.relu() for key, x in x_dict.items()}\n",
    "\n",
    "        # out = self.lin(x_dict['zones'])\n",
    "        \n",
    "        return x_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YHsZyzkAZKlK",
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Entrenamiento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Armado dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 356,
     "status": "ok",
     "timestamp": 1635462144424,
     "user": {
      "displayName": "Facundo Lezama",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjvX7z6H8HKQ9Q4HSRF20NaPjvwtJ3pQh0TGed2XQ=s64",
      "userId": "08104556030349101598"
     },
     "user_tz": 180
    },
    "id": "0MsSCtZkUDbj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (2512, 30)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HeteroData(\n",
       "  \u001b[1maps\u001b[0m={ num_nodes=15 },\n",
       "  \u001b[1mzones\u001b[0m={ num_nodes=16 },\n",
       "  \u001b[1m(aps, ap_ap, aps)\u001b[0m={\n",
       "    edge_index=[2, 124],\n",
       "    edge_attr=[124, 1]\n",
       "  },\n",
       "  \u001b[1m(zones, zone_ap, aps)\u001b[0m={\n",
       "    edge_index=[2, 107],\n",
       "    edge_attr=[107, 1]\n",
       "  },\n",
       "  \u001b[1m(aps, rev_zone_ap, zones)\u001b[0m={\n",
       "    edge_index=[2, 107],\n",
       "    edge_attr=[107, 1]\n",
       "  }\n",
       ")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split de los datos y armado del objeto Data con el grafo\n",
    "\n",
    "X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset, dataset_percentage=0.3)\n",
    "ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=20, prune_th=20) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train, prune_th=20) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr)\n",
    "T.ToUndirected()(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  1,  1,  1,  1,  1,\n",
      "          1,  2,  2,  2,  2,  2,  2,  3,  3,  3,  4,  4,  4,  5,  5,  5,  5,  5,\n",
      "          5,  5,  5,  5,  5,  5,  6,  6,  6,  6,  6,  6,  6,  6,  7,  7,  7,  7,\n",
      "          7,  7,  7,  7,  7,  7,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  9,\n",
      "          9,  9,  9,  9,  9,  9,  9,  9, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11,\n",
      "         11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
      "         13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14],\n",
      "        [ 1,  2,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14,  0,  2,  5,  6, 12, 13,\n",
      "         14,  0,  1,  5, 12, 13, 14,  4,  7,  8,  3,  7,  8,  0,  1,  2,  6,  7,\n",
      "          8,  9, 11, 12, 13, 14,  0,  1,  5,  7,  8,  9, 11, 12,  0,  3,  4,  5,\n",
      "          6,  8,  9, 10, 11, 12,  0,  3,  4,  5,  6,  7,  9, 10, 11, 12, 13,  0,\n",
      "          5,  6,  7,  8, 10, 11, 12, 13,  0,  7,  8,  9, 11, 12, 13,  0,  5,  6,\n",
      "          7,  8,  9, 10, 12, 13,  0,  1,  2,  5,  6,  7,  8,  9, 10, 11, 13, 14,\n",
      "          0,  1,  2,  5,  8,  9, 10, 11, 12, 14,  0,  1,  2,  5, 12, 13]])\n"
     ]
    }
   ],
   "source": [
    "print(ap_edge_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Armado del dataset\n",
    "\n",
    "x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "y_training_data = y_train\n",
    "y_test_data = y_test\n",
    "\n",
    "#normalize (x-mean)/std\n",
    "mean = x_training_data.mean(axis=0)\n",
    "std = x_training_data.std(axis=0)\n",
    "\n",
    "x_training_data = x_training_data - mean\n",
    "x_training_data /= std\n",
    "x_test_data = x_test_data - mean\n",
    "x_test_data /= std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = build_dataset(x_training_data, y_training_data, data)\n",
    "test_dataset = build_dataset(x_test_data, y_test_data, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HeteroData(\n",
       "  \u001b[1maps\u001b[0m={\n",
       "    num_nodes=15,\n",
       "    x=[15, 2]\n",
       "  },\n",
       "  \u001b[1mzones\u001b[0m={\n",
       "    num_nodes=16,\n",
       "    x=[16, 1],\n",
       "    y=[1],\n",
       "    train_mask=[2512],\n",
       "    val_mask=[2512],\n",
       "    test_mask=[2512]\n",
       "  },\n",
       "  \u001b[1m(aps, ap_ap, aps)\u001b[0m={\n",
       "    edge_index=[2, 124],\n",
       "    edge_attr=[124, 1]\n",
       "  },\n",
       "  \u001b[1m(zones, zone_ap, aps)\u001b[0m={\n",
       "    edge_index=[2, 107],\n",
       "    edge_attr=[107, 1]\n",
       "  },\n",
       "  \u001b[1m(aps, rev_zone_ap, zones)\u001b[0m={\n",
       "    edge_index=[2, 107],\n",
       "    edge_attr=[107, 1]\n",
       "  }\n",
       ")"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# device = torch.device('cuda:1')\n",
    "# device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### HeteroGNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = HeteroGNN(20, 1, 4).to(device)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)\n",
    "loss = torch.nn.CrossEntropyLoss()\n",
    "scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.8)\n",
    "print_every = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with torch.no_grad():  # Initialize lazy modules.\n",
    "#     batch = train_dataset[0].to(device)\n",
    "#     out = model(batch.x_dict, batch.edge_index_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loss = []\n",
    "train_accuracy = []\n",
    "test_loss = []\n",
    "test_accuracy = []\n",
    "best_test_accuracy = 0\n",
    "\n",
    "\n",
    "m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "for epoch in range(250):\n",
    "    # print(f\"Epoch: {epoch+1}\")\n",
    "    \n",
    "    # TRAIN\n",
    "    model.train()\n",
    "    train_accuracy_epoch = []\n",
    "    train_loss_epoch = []\n",
    "    for data in train_loader:\n",
    "        \n",
    "        data = data.to(device)\n",
    "        out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "        out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "        loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))\n",
    "        loss_result.backward()\n",
    "        train_loss_epoch.append(loss_result.detach().cpu())\n",
    "        \n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        output = m(out_zones)\n",
    "        train_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "    # if scheduler.get_last_lr()[0] > 0.0005:\n",
    "    if (epoch+1)%20 == 0:\n",
    "        scheduler.step()\n",
    "\n",
    "    train_accuracy.append(np.mean(train_accuracy_epoch))\n",
    "    train_loss.append(np.mean(train_loss_epoch))\n",
    "    \n",
    "    \n",
    "    \n",
    "    # VALIDATION\n",
    "    model.eval()\n",
    "    test_accuracy_epoch = []\n",
    "    test_loss_epoch = []\n",
    "    for data in test_loader:\n",
    "        \n",
    "        data = data.to(device)\n",
    "        out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "        # out_zones = torch.mean(out[\"zones\"].cpu(),1)\n",
    "        # out_zones = out_zones.reshape(out_zones.shape[0]//num_classes,num_classes)\n",
    "        out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "        # out_zones = out.cpu().reshape(out.cpu().shape[0]//num_classes,num_classes)\n",
    "        \n",
    "        loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))        \n",
    "        test_loss_epoch.append(loss_result.detach().cpu())\n",
    "        \n",
    "        output = m(out_zones)\n",
    "        test_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "    test_accuracy.append(np.mean(test_accuracy_epoch))\n",
    "    if test_accuracy[-1] > best_test_accuracy:\n",
    "        best_test_accuracy = test_accuracy[-1]        \n",
    "        torch.save(model.state_dict(), \"MNAV_HeteroGNN_best_model.pth\")\n",
    "        \n",
    "    test_loss.append(np.mean(test_loss_epoch))\n",
    "\n",
    "    if (epoch+1)%print_every == 0:\n",
    "        print(f\"Epoch {epoch+1}, Train Loss {np.mean(train_loss_epoch)}, Val Loss {np.mean(test_loss_epoch)}\")\n",
    "    \n",
    "print(f\"Last LR: {scheduler.get_last_lr()}\")\n",
    "print(f\"Best Accuracy: Train {np.max(train_accuracy)}, Val {np.max(test_accuracy)}\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_loss, label=\"Train loss\")\n",
    "plt.plot(test_loss, label=\"Validation loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_accuracy, label=\"Train accuracy\")\n",
    "plt.plot(test_accuracy, label=\"Validation accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'out_zones' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m m \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mnn\u001b[39m.\u001b[39mSoftmax(dim\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m output \u001b[39m=\u001b[39m m(out_zones)\n\u001b[1;32m      3\u001b[0m accuracy \u001b[39m=\u001b[39m accuracy_score(data[\u001b[39m\"\u001b[39m\u001b[39mzones\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39my\u001b[39m.\u001b[39mcpu()\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m)\u001b[39m.\u001b[39mtype(torch\u001b[39m.\u001b[39mlong), np\u001b[39m.\u001b[39marray(torch\u001b[39m.\u001b[39margmax(output\u001b[39m.\u001b[39mcpu(), axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)))\n\u001b[1;32m      5\u001b[0m \u001b[39mprint\u001b[39m(accuracy)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'out_zones' is not defined"
     ]
    }
   ],
   "source": [
    "m = torch.nn.Softmax(dim=1)\n",
    "output = m(out_zones)\n",
    "accuracy = accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1)))\n",
    "\n",
    "print(accuracy)\n",
    "print(classification_report(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### HeteroGNNSimplified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = HeteroGNN_simplified(20, 1, 4).to(device)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)\n",
    "loss = torch.nn.CrossEntropyLoss()\n",
    "scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.8)\n",
    "print_every = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loss = []\n",
    "train_accuracy = []\n",
    "test_loss = []\n",
    "test_accuracy = []\n",
    "best_test_accuracy = 0\n",
    "\n",
    "\n",
    "m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "for epoch in range(250):\n",
    "    # print(f\"Epoch: {epoch+1}\")\n",
    "    \n",
    "    # TRAIN\n",
    "    model.train()\n",
    "    train_accuracy_epoch = []\n",
    "    train_loss_epoch = []\n",
    "    for data in train_loader:\n",
    "        \n",
    "        data = data.to(device)\n",
    "        out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "        out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "        loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))\n",
    "        loss_result.backward()\n",
    "        train_loss_epoch.append(loss_result.detach().cpu())\n",
    "        \n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        output = m(out_zones)\n",
    "        train_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "    # if scheduler.get_last_lr()[0] > 0.0005:\n",
    "    if (epoch+1)%40 == 0:\n",
    "        scheduler.step()\n",
    "\n",
    "    train_accuracy.append(np.mean(train_accuracy_epoch))\n",
    "    train_loss.append(np.mean(train_loss_epoch))\n",
    "    \n",
    "    \n",
    "    \n",
    "    # VALIDATION\n",
    "    model.eval()\n",
    "    test_accuracy_epoch = []\n",
    "    test_loss_epoch = []\n",
    "    for data in test_loader:\n",
    "        \n",
    "        data = data.to(device)\n",
    "        out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "        # out_zones = torch.mean(out[\"zones\"].cpu(),1)\n",
    "        # out_zones = out_zones.reshape(out_zones.shape[0]//num_classes,num_classes)\n",
    "        out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "        # out_zones = out.cpu().reshape(out.cpu().shape[0]//num_classes,num_classes)\n",
    "        \n",
    "        loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))        \n",
    "        test_loss_epoch.append(loss_result.detach().cpu())\n",
    "        \n",
    "        output = m(out_zones)\n",
    "        test_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "    test_accuracy.append(np.mean(test_accuracy_epoch))\n",
    "    if test_accuracy[-1] > best_test_accuracy:\n",
    "        best_test_accuracy = test_accuracy[-1]        \n",
    "        torch.save(model.state_dict(), \"MNAV_HeteroGNN_best_model.pth\")\n",
    "        \n",
    "    test_loss.append(np.mean(test_loss_epoch))\n",
    "\n",
    "    if (epoch+1)%print_every == 0:\n",
    "        print(f\"Epoch {epoch+1}, Train Loss {np.mean(train_loss_epoch)}, Val Loss {np.mean(test_loss_epoch)}\")\n",
    "    \n",
    "print(f\"Last LR: {scheduler.get_last_lr()}\")\n",
    "print(f\"Best Accuracy: Train {np.max(train_accuracy)}, Val {np.max(test_accuracy)}\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_loss, label=\"Train loss\")\n",
    "plt.plot(test_loss, label=\"Validation loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_accuracy, label=\"Train accuracy\")\n",
    "plt.plot(test_accuracy, label=\"Validation accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = torch.nn.Softmax(dim=1)\n",
    "output = m(out_zones)\n",
    "accuracy = accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1)))\n",
    "\n",
    "print(accuracy)\n",
    "print(classification_report(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Análisis sacando una zona"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La idea de este análisis es ver qué tan robusto es el algoritmo cuando se agrega una nueva zona. Lo interesante es ver si al agregar la nueva zona, sin reentrenar la red se tiene un buen desempeño."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zone removed:  0\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9481154580152672, Val 0.940305635148042\n",
      "Zone removed:  1\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9429866412213741, Val 0.938872970391595\n",
      "Zone removed:  2\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9446564885496184, Val 0.941738299904489\n",
      "Zone removed:  3\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9450143129770993, Val 0.9379178605539638\n",
      "Zone removed:  4\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9445031352235551, Val 0.94603629417383\n",
      "Zone removed:  5\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9451335877862596, Val 0.9422158548233047\n",
      "Zone removed:  6\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9468034351145038, Val 0.9364851957975168\n",
      "Zone removed:  7\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9478428298800436, Val 0.940305635148042\n",
      "Zone removed:  8\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9473998091603053, Val 0.9364851957975168\n",
      "Zone removed:  9\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9439067611777536, Val 0.9269340974212035\n",
      "Zone removed:  10\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9357108778625954, Val 0.9264565425023877\n",
      "Zone removed:  11\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9306331788440567, Val 0.9283667621776505\n",
      "Zone removed:  12\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9458492366412213, Val 0.937440305635148\n",
      "Zone removed:  13\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9252146946564885, Val 0.9235912129894938\n",
      "Zone removed:  14\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.9506202290076335, Val 0.9379178605539638\n",
      "Zone removed:  15\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n",
      "Last LR: [0.002097152000000001]\n",
      "Best Accuracy: Train 0.8788167938931297, Val 0.8758357211079274\n",
      "{'0': [0.940305635148042], '1': [0.938872970391595], '2': [0.941738299904489], '3': [0.9379178605539638], '4': [0.94603629417383], '5': [0.9422158548233047], '6': [0.9364851957975168], '7': [0.940305635148042], '8': [0.9364851957975168], '9': [0.9269340974212035], '10': [0.9264565425023877], '11': [0.9283667621776505], '12': [0.937440305635148], '13': [0.9235912129894938], '14': [0.9379178605539638], '15': [0.8758357211079274]}\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "batch_size = 16\n",
    "learning_rate = 0.01 \n",
    "print_every = 50\n",
    "\n",
    "zones_to_remove = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
    "accuracy = {\"0\":[],\"1\":[],\"2\":[],\"3\":[],\"4\":[],\"5\":[],\"6\":[],\"7\":[],\"8\":[],\"9\":[],\"10\":[],\"11\":[],\"12\":[],\"13\":[],\"14\":[],\"15\":[],}\n",
    "\n",
    "for zone_to_remove in zones_to_remove:\n",
    "    print('Zone removed: ', zone_to_remove)\n",
    "    \n",
    "    X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset, zone_to_remove=zone_to_remove)\n",
    "    ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "    zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train, zone_to_remove=zone_to_remove) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "    data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr, num_classes)\n",
    "    T.ToUndirected()(data)\n",
    "\n",
    "    # Armado del dataset\n",
    "\n",
    "    x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "    x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "    y_training_data = y_train\n",
    "    y_test_data = y_test\n",
    "\n",
    "    #normalize (x-mean)/std\n",
    "    mean = x_training_data.mean(axis=0)\n",
    "    std = x_training_data.std(axis=0)\n",
    "\n",
    "    x_training_data = x_training_data - mean\n",
    "    x_training_data /= std\n",
    "    x_test_data = x_test_data - mean\n",
    "    x_test_data /= std \n",
    "\n",
    "    train_dataset = build_dataset(x_training_data, y_training_data, data)\n",
    "    test_dataset = build_dataset(x_test_data, y_test_data, data) \n",
    "        \n",
    "    for _ in range(1):\n",
    "\n",
    "        model = HeteroGNN_simplified(20, 1, 4).to(device)\n",
    "\n",
    "        train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "        test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)\n",
    "        loss = torch.nn.CrossEntropyLoss()\n",
    "        scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.8)\n",
    "\n",
    "        train_loss = []\n",
    "        train_accuracy = []\n",
    "        test_loss = []\n",
    "        test_accuracy = []\n",
    "        best_test_accuracy = 0\n",
    "\n",
    "\n",
    "        m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "        for epoch in range(150):\n",
    "            # print(f\"Epoch: {epoch+1}\")\n",
    "\n",
    "            # TRAIN\n",
    "            model.train()\n",
    "            train_accuracy_epoch = []\n",
    "            train_loss_epoch = []\n",
    "            for data in train_loader:\n",
    "\n",
    "                data = data.to(device)\n",
    "                out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "                out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "                loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))\n",
    "                loss_result.backward()\n",
    "                train_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "                optimizer.step()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                output = m(out_zones)\n",
    "                train_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "            # if scheduler.get_last_lr()[0] > 0.0005:\n",
    "            if (epoch+1)%20 == 0:\n",
    "                scheduler.step()\n",
    "\n",
    "            train_accuracy.append(np.mean(train_accuracy_epoch))\n",
    "            train_loss.append(np.mean(train_loss_epoch))\n",
    "\n",
    "\n",
    "\n",
    "            # VALIDATION\n",
    "            model.eval()\n",
    "            test_accuracy_epoch = []\n",
    "            test_loss_epoch = []\n",
    "            for data in test_loader:\n",
    "\n",
    "                data = data.to(device)\n",
    "                out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "                out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "                loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))        \n",
    "                test_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "                output = m(out_zones)\n",
    "                test_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "            test_accuracy.append(np.mean(test_accuracy_epoch))\n",
    "            if test_accuracy[-1] > best_test_accuracy:\n",
    "                best_test_accuracy = test_accuracy[-1]        \n",
    "                torch.save(model.state_dict(), f\"MNAV_HeteroGNN_simplified_without_{zone_to_remove}_best_model_2.pth\")\n",
    "\n",
    "            test_loss.append(np.mean(test_loss_epoch))\n",
    "\n",
    "            # if (epoch+1)%print_every == 0:\n",
    "            #     print(f\"Epoch {epoch+1}, Train Loss {np.mean(train_loss_epoch)}, Val Loss {np.mean(test_loss_epoch)}\")\n",
    "\n",
    "        print(f\"Last LR: {scheduler.get_last_lr()}\")\n",
    "        print(f\"Best Accuracy: Train {np.max(train_accuracy)}, Val {np.max(test_accuracy)}\") \n",
    "    \n",
    "        accuracy[str(zone_to_remove)].append(np.max(test_accuracy))\n",
    "\n",
    "print(accuracy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Segunda prueba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zone removed:  0\n",
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[26], line 39\u001b[0m\n\u001b[1;32m     35\u001b[0m test_dataset \u001b[39m=\u001b[39m build_dataset(x_test_data, y_test_data, data, zone_to_remove\u001b[39m=\u001b[39mzone_to_remove) \n\u001b[1;32m     37\u001b[0m \u001b[39mfor\u001b[39;00m _ \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m1\u001b[39m):\n\u001b[0;32m---> 39\u001b[0m     model \u001b[39m=\u001b[39m HeteroGNN_simplified(\u001b[39m20\u001b[39;49m, \u001b[39m1\u001b[39;49m, \u001b[39m4\u001b[39;49m)\u001b[39m.\u001b[39;49mto(device)\n\u001b[1;32m     41\u001b[0m     train_loader \u001b[39m=\u001b[39m DataLoader(train_dataset, batch_size\u001b[39m=\u001b[39mbatch_size, shuffle\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m     42\u001b[0m     test_loader \u001b[39m=\u001b[39m DataLoader(test_dataset, batch_size\u001b[39m=\u001b[39m\u001b[39mlen\u001b[39m(test_dataset), shuffle\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py:989\u001b[0m, in \u001b[0;36mModule.to\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    985\u001b[0m         \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mto(device, dtype \u001b[39mif\u001b[39;00m t\u001b[39m.\u001b[39mis_floating_point() \u001b[39mor\u001b[39;00m t\u001b[39m.\u001b[39mis_complex() \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m    986\u001b[0m                     non_blocking, memory_format\u001b[39m=\u001b[39mconvert_to_format)\n\u001b[1;32m    987\u001b[0m     \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mto(device, dtype \u001b[39mif\u001b[39;00m t\u001b[39m.\u001b[39mis_floating_point() \u001b[39mor\u001b[39;00m t\u001b[39m.\u001b[39mis_complex() \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m, non_blocking)\n\u001b[0;32m--> 989\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_apply(convert)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py:641\u001b[0m, in \u001b[0;36mModule._apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_apply\u001b[39m(\u001b[39mself\u001b[39m, fn):\n\u001b[1;32m    640\u001b[0m     \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mchildren():\n\u001b[0;32m--> 641\u001b[0m         module\u001b[39m.\u001b[39;49m_apply(fn)\n\u001b[1;32m    643\u001b[0m     \u001b[39mdef\u001b[39;00m \u001b[39mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[1;32m    644\u001b[0m         \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39m_has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[1;32m    645\u001b[0m             \u001b[39m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[1;32m    646\u001b[0m             \u001b[39m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    651\u001b[0m             \u001b[39m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[1;32m    652\u001b[0m             \u001b[39m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py:641\u001b[0m, in \u001b[0;36mModule._apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_apply\u001b[39m(\u001b[39mself\u001b[39m, fn):\n\u001b[1;32m    640\u001b[0m     \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mchildren():\n\u001b[0;32m--> 641\u001b[0m         module\u001b[39m.\u001b[39;49m_apply(fn)\n\u001b[1;32m    643\u001b[0m     \u001b[39mdef\u001b[39;00m \u001b[39mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[1;32m    644\u001b[0m         \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39m_has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[1;32m    645\u001b[0m             \u001b[39m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[1;32m    646\u001b[0m             \u001b[39m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    651\u001b[0m             \u001b[39m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[1;32m    652\u001b[0m             \u001b[39m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n",
      "    \u001b[0;31m[... skipping similar frames: Module._apply at line 641 (2 times)]\u001b[0m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py:641\u001b[0m, in \u001b[0;36mModule._apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_apply\u001b[39m(\u001b[39mself\u001b[39m, fn):\n\u001b[1;32m    640\u001b[0m     \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mchildren():\n\u001b[0;32m--> 641\u001b[0m         module\u001b[39m.\u001b[39;49m_apply(fn)\n\u001b[1;32m    643\u001b[0m     \u001b[39mdef\u001b[39;00m \u001b[39mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[1;32m    644\u001b[0m         \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39m_has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[1;32m    645\u001b[0m             \u001b[39m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[1;32m    646\u001b[0m             \u001b[39m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    651\u001b[0m             \u001b[39m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[1;32m    652\u001b[0m             \u001b[39m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py:664\u001b[0m, in \u001b[0;36mModule._apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    660\u001b[0m \u001b[39m# Tensors stored in modules are graph leaves, and we don't want to\u001b[39;00m\n\u001b[1;32m    661\u001b[0m \u001b[39m# track autograd history of `param_applied`, so we have to use\u001b[39;00m\n\u001b[1;32m    662\u001b[0m \u001b[39m# `with torch.no_grad():`\u001b[39;00m\n\u001b[1;32m    663\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m--> 664\u001b[0m     param_applied \u001b[39m=\u001b[39m fn(param)\n\u001b[1;32m    665\u001b[0m should_use_set_data \u001b[39m=\u001b[39m compute_should_use_set_data(param, param_applied)\n\u001b[1;32m    666\u001b[0m \u001b[39mif\u001b[39;00m should_use_set_data:\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py:987\u001b[0m, in \u001b[0;36mModule.to.<locals>.convert\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    984\u001b[0m \u001b[39mif\u001b[39;00m convert_to_format \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m t\u001b[39m.\u001b[39mdim() \u001b[39min\u001b[39;00m (\u001b[39m4\u001b[39m, \u001b[39m5\u001b[39m):\n\u001b[1;32m    985\u001b[0m     \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mto(device, dtype \u001b[39mif\u001b[39;00m t\u001b[39m.\u001b[39mis_floating_point() \u001b[39mor\u001b[39;00m t\u001b[39m.\u001b[39mis_complex() \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m    986\u001b[0m                 non_blocking, memory_format\u001b[39m=\u001b[39mconvert_to_format)\n\u001b[0;32m--> 987\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39;49mto(device, dtype \u001b[39mif\u001b[39;49;00m t\u001b[39m.\u001b[39;49mis_floating_point() \u001b[39mor\u001b[39;49;00m t\u001b[39m.\u001b[39;49mis_complex() \u001b[39melse\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m, non_blocking)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/parameter.py:143\u001b[0m, in \u001b[0;36mUninitializedTensorMixin.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[39mif\u001b[39;00m kwargs \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    142\u001b[0m         kwargs \u001b[39m=\u001b[39m {}\n\u001b[0;32m--> 143\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m__torch_function__(func, types, args, kwargs)\n\u001b[1;32m    144\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    145\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mAttempted to use an uninitialized parameter in \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m    146\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mThis error happens when you are using a `LazyModule` or \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m    147\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mexplicitly manipulating `torch.nn.parameter.\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m` \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m    148\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mobjects. When using LazyModules Call `forward` with a dummy batch \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m    149\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mto initialize the parameters before calling torch functions\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(func, \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m))\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/cuda/__init__.py:229\u001b[0m, in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mCUDA_MODULE_LOADING\u001b[39m\u001b[39m'\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m os\u001b[39m.\u001b[39menviron:\n\u001b[1;32m    228\u001b[0m     os\u001b[39m.\u001b[39menviron[\u001b[39m'\u001b[39m\u001b[39mCUDA_MODULE_LOADING\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mLAZY\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m--> 229\u001b[0m torch\u001b[39m.\u001b[39;49m_C\u001b[39m.\u001b[39;49m_cuda_init()\n\u001b[1;32m    230\u001b[0m \u001b[39m# Some of the queued calls may reentrantly call _lazy_init();\u001b[39;00m\n\u001b[1;32m    231\u001b[0m \u001b[39m# we need to just return without initializing in that case.\u001b[39;00m\n\u001b[1;32m    232\u001b[0m \u001b[39m# However, we must not let any *other* threads in!\u001b[39;00m\n\u001b[1;32m    233\u001b[0m _tls\u001b[39m.\u001b[39mis_initializing \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "batch_size = 16\n",
    "learning_rate = 0.01 \n",
    "print_every = 50\n",
    "\n",
    "zones_to_remove = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
    "accuracy = {\"0\":[],\"1\":[],\"2\":[],\"3\":[],\"4\":[],\"5\":[],\"6\":[],\"7\":[],\"8\":[],\"9\":[],\"10\":[],\"11\":[],\"12\":[],\"13\":[],\"14\":[],\"15\":[],}\n",
    "\n",
    "for zone_to_remove in zones_to_remove:\n",
    "    print('Zone removed: ', zone_to_remove)\n",
    "    \n",
    "    X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset)\n",
    "    ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "    zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train, zone_to_remove=zone_to_remove) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "    data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr, num_classes)\n",
    "    T.ToUndirected()(data)\n",
    "\n",
    "    # Armado del dataset\n",
    "\n",
    "    x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "    x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "    y_training_data = y_train\n",
    "    y_test_data = y_test\n",
    "\n",
    "    #normalize (x-mean)/std\n",
    "    mean = x_training_data.mean(axis=0)\n",
    "    std = x_training_data.std(axis=0)\n",
    "\n",
    "    x_training_data = x_training_data - mean\n",
    "    x_training_data /= std\n",
    "    x_test_data = x_test_data - mean\n",
    "    x_test_data /= std \n",
    "\n",
    "    train_dataset = build_dataset(x_training_data, y_training_data, data, zone_to_remove=zone_to_remove)\n",
    "    test_dataset = build_dataset(x_test_data, y_test_data, data, zone_to_remove=zone_to_remove) \n",
    "        \n",
    "    for _ in range(1):\n",
    "\n",
    "        model = HeteroGNN_simplified(20, 1, 4).to(device)\n",
    "\n",
    "        train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "        test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)\n",
    "        loss = torch.nn.CrossEntropyLoss()\n",
    "        scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.8)\n",
    "\n",
    "        train_loss = []\n",
    "        train_accuracy = []\n",
    "        test_loss = []\n",
    "        test_accuracy = []\n",
    "        best_test_accuracy = 0\n",
    "\n",
    "\n",
    "        m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "        for epoch in range(150):\n",
    "            # print(f\"Epoch: {epoch+1}\")\n",
    "\n",
    "            # TRAIN\n",
    "            model.train()\n",
    "            train_accuracy_epoch = []\n",
    "            train_loss_epoch = []\n",
    "            for data in train_loader:\n",
    "\n",
    "                data = data.to(device)\n",
    "                out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "                out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "                loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))\n",
    "                loss_result.backward()\n",
    "                train_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "                optimizer.step()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                output = m(out_zones)\n",
    "                train_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "            # if scheduler.get_last_lr()[0] > 0.0005:\n",
    "            if (epoch+1)%20 == 0:\n",
    "                scheduler.step()\n",
    "\n",
    "            train_accuracy.append(np.mean(train_accuracy_epoch))\n",
    "            train_loss.append(np.mean(train_loss_epoch))\n",
    "\n",
    "\n",
    "\n",
    "            # VALIDATION\n",
    "            model.eval()\n",
    "            test_accuracy_epoch = []\n",
    "            test_loss_epoch = []\n",
    "            for data in test_loader:\n",
    "\n",
    "                data = data.to(device)\n",
    "                out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "                out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "                loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))        \n",
    "                test_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "                output = m(out_zones)\n",
    "                test_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "            test_accuracy.append(np.mean(test_accuracy_epoch))\n",
    "            if test_accuracy[-1] > best_test_accuracy:\n",
    "                best_test_accuracy = test_accuracy[-1]        \n",
    "                torch.save(model.state_dict(), f\"MNAV_HeteroGNN_simplified_without_{zone_to_remove}_best_model_2.pth\")\n",
    "\n",
    "            test_loss.append(np.mean(test_loss_epoch))\n",
    "\n",
    "            # if (epoch+1)%print_every == 0:\n",
    "            #     print(f\"Epoch {epoch+1}, Train Loss {np.mean(train_loss_epoch)}, Val Loss {np.mean(test_loss_epoch)}\")\n",
    "\n",
    "        print(f\"Last LR: {scheduler.get_last_lr()}\")\n",
    "        print(f\"Best Accuracy: Train {np.max(train_accuracy)}, Val {np.max(test_accuracy)}\") \n",
    "    \n",
    "        accuracy[str(zone_to_remove)].append(np.max(test_accuracy))\n",
    "\n",
    "print(accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n"
     ]
    }
   ],
   "source": [
    "# Split de los datos y armado del objeto Data con el grafo\n",
    "\n",
    "X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset)\n",
    "ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr)\n",
    "T.ToUndirected()(data)\n",
    "\n",
    "# Armado del dataset\n",
    "\n",
    "x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "y_training_data = y_train\n",
    "y_test_data = y_test\n",
    "\n",
    "#normalize (x-mean)/std\n",
    "mean = x_training_data.mean(axis=0)\n",
    "std = x_training_data.std(axis=0)\n",
    "\n",
    "x_training_data = x_training_data - mean\n",
    "x_training_data /= std\n",
    "x_test_data = x_test_data - mean\n",
    "x_test_data /= std\n",
    "\n",
    "train_dataset = build_dataset(x_training_data, y_training_data, data)\n",
    "test_dataset = build_dataset(x_test_data, y_test_data, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zone removed:  6\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.95      0.96       112\n",
      "           1       0.98      0.94      0.96       127\n",
      "           2       1.00      0.95      0.97        81\n",
      "           3       0.99      0.96      0.98        82\n",
      "           4       0.94      1.00      0.97        85\n",
      "           5       0.65      1.00      0.79        98\n",
      "           6       1.00      0.23      0.38        95\n",
      "           7       0.97      0.99      0.98        87\n",
      "           8       0.95      0.97      0.96        73\n",
      "           9       0.94      0.95      0.94       219\n",
      "          10       0.93      0.97      0.95       163\n",
      "          11       0.95      0.93      0.94       194\n",
      "          12       0.81      0.89      0.85       114\n",
      "          13       0.88      0.90      0.89       227\n",
      "          14       0.85      0.87      0.86       219\n",
      "          15       0.98      1.00      0.99       118\n",
      "\n",
      "    accuracy                           0.91      2094\n",
      "   macro avg       0.92      0.91      0.90      2094\n",
      "weighted avg       0.92      0.91      0.90      2094\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zones_to_remove = [6] #, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
    "for zone_to_remove in zones_to_remove:\n",
    "    print('Zone removed: ', zone_to_remove)\n",
    "    # model = HeteroGNN(20, 1, 4)\n",
    "    model = HeteroGNN_simplified(20, 1, 4)\n",
    "    model.load_state_dict(torch.load(f\"MNAV_HeteroGNN_simplified_without_{zone_to_remove}_best_model_2.pth\"))\n",
    "    model.to(device)\n",
    "\n",
    "    test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "    \n",
    "    # VALIDATION\n",
    "    model.eval()\n",
    "\n",
    "    for data in test_loader:\n",
    "        data = data.to(device)\n",
    "        data.x_dict[\"aps\"].requires_grad_()\n",
    "        data.x_dict[\"zones\"].requires_grad_()        \n",
    "        out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "        out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "        scores = (out_zones.gather(1, data[\"zones\"].y.cpu().view(-1, 1).type(torch.long)).squeeze())\n",
    "        scores.backward(torch.FloatTensor([1.0]*scores.shape[0]))\n",
    "        saliency, _ = torch.max(data.x_dict[\"aps\"].grad.data.abs(), dim=1)\n",
    "\n",
    "        \n",
    "        output = m(out_zones)\n",
    "        print(classification_report(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "        \n",
    "        for zone in range(num_classes):\n",
    "            # mask_gt = data[\"zones\"].y.cpu() == zone\n",
    "            # saliency_removed_zone = saliency.reshape((len(test_dataset),num_aps))[mask_gt]\n",
    "            # sal = np.mean(saliency_removed_zone.cpu().numpy(), axis=0).reshape(num_aps,1)\n",
    "\n",
    "            mask_pred = np.array(torch.argmax(output.cpu(), axis=1)) == zone\n",
    "            saliency_removed_zone_pred = saliency.reshape((len(test_dataset),num_aps))[mask_pred]\n",
    "            sal = np.mean(saliency_removed_zone_pred.cpu().numpy(), axis=0).reshape(num_aps,1)\n",
    "            if zone == 0:\n",
    "                sal_stack = sal\n",
    "            else:\n",
    "                sal_stack = np.concatenate((sal_stack, sal))\n",
    "\n",
    "        \n",
    "        # import ipdb; ipdb.set_trace()\n",
    "        \n",
    "        # plt.figure(figsize=[9,7])\n",
    "        # cf_matrix = confusion_matrix(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1)), normalize=\"true\")\n",
    "        # sns.heatmap(cf_matrix, annot=True, fmt=\".0%\", cmap=\"YlGnBu\", vmin=0, vmax=0.2, cbar=False)\n",
    "        # plt.figure(figsize=[9,7])\n",
    "        # sns.heatmap(saliency_removed_zone.cpu().numpy(), annot=False, fmt=\".0%\", cmap=\"YlGnBu\", cbar=True)\n",
    "        plt.figure(figsize=[9,7])\n",
    "        sns.heatmap(sal_stack.reshape((15,num_classes)), annot=False, fmt=\".0%\", cmap=\"YlGnBu\", cbar=True)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Grafo con una zona menos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "zone_to_remove = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HeteroData(\n",
       "  \u001b[1maps\u001b[0m={ num_nodes=15 },\n",
       "  \u001b[1mzones\u001b[0m={ num_nodes=16 },\n",
       "  \u001b[1m(aps, ap_ap, aps)\u001b[0m={\n",
       "    edge_index=[2, 210],\n",
       "    edge_attr=[210, 1]\n",
       "  },\n",
       "  \u001b[1m(zones, zone_ap, aps)\u001b[0m={\n",
       "    edge_index=[2, 233],\n",
       "    edge_attr=[233, 1]\n",
       "  },\n",
       "  \u001b[1m(aps, rev_zone_ap, zones)\u001b[0m={\n",
       "    edge_index=[2, 233],\n",
       "    edge_attr=[233, 1]\n",
       "  }\n",
       ")"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split de los datos y armado del objeto Data con el grafo\n",
    "\n",
    "X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset, zone_to_remove=zone_to_remove)\n",
    "ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train, zone_to_remove=zone_to_remove) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr, num_classes)\n",
    "T.ToUndirected()(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Armado del dataset\n",
    "\n",
    "x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "y_training_data = y_train\n",
    "y_test_data = y_test\n",
    "\n",
    "#normalize (x-mean)/std\n",
    "mean = x_training_data.mean(axis=0)\n",
    "std = x_training_data.std(axis=0)\n",
    "\n",
    "x_training_data = x_training_data - mean\n",
    "x_training_data /= std\n",
    "x_test_data = x_test_data - mean\n",
    "x_test_data /= std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = build_dataset(x_training_data, y_training_data, data, zone_to_remove)\n",
    "test_dataset = build_dataset(x_test_data, y_test_data, data, zone_to_remove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = HeteroGNN(20, 1, 4).to(device)\n",
    "model = HeteroGNN_simplified(20, 1, 4).to(device)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)\n",
    "loss = torch.nn.CrossEntropyLoss()\n",
    "scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.8)\n",
    "print_every = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5, Train Loss 0.913398802280426, Val Loss 0.7834773659706116\n",
      "Epoch 10, Train Loss 0.5863134264945984, Val Loss 1.216668725013733\n",
      "Epoch 15, Train Loss 0.45776939392089844, Val Loss 1.1149235963821411\n",
      "Epoch 20, Train Loss 0.668139636516571, Val Loss 0.7822443246841431\n",
      "Epoch 25, Train Loss 0.26792004704475403, Val Loss 0.5652636289596558\n",
      "Epoch 30, Train Loss 0.5831670165061951, Val Loss 0.42127475142478943\n",
      "Epoch 35, Train Loss 0.21870824694633484, Val Loss 0.21652105450630188\n",
      "Epoch 40, Train Loss 0.2791878283023834, Val Loss 0.2818174660205841\n",
      "Epoch 45, Train Loss 0.17977496981620789, Val Loss 0.3070118725299835\n",
      "Epoch 50, Train Loss 0.16598506271839142, Val Loss 0.28634798526763916\n",
      "Epoch 55, Train Loss 0.15063267946243286, Val Loss 0.2043921947479248\n",
      "Epoch 60, Train Loss 0.1883213222026825, Val Loss 0.1703384965658188\n",
      "Epoch 65, Train Loss 0.10930239409208298, Val Loss 0.23293007910251617\n",
      "Epoch 70, Train Loss 0.1569935828447342, Val Loss 0.17476709187030792\n",
      "Epoch 75, Train Loss 0.16707780957221985, Val Loss 0.16373233497142792\n",
      "Epoch 80, Train Loss 0.10026009380817413, Val Loss 0.18294358253479004\n",
      "Epoch 85, Train Loss 0.07412415742874146, Val Loss 0.15312491357326508\n",
      "Epoch 90, Train Loss 0.1042153537273407, Val Loss 0.14685870707035065\n",
      "Epoch 95, Train Loss 0.09242747724056244, Val Loss 0.18027351796627045\n",
      "Epoch 100, Train Loss 0.07050158083438873, Val Loss 0.16303251683712006\n",
      "Epoch 105, Train Loss 0.05333784967660904, Val Loss 0.1712801605463028\n",
      "Epoch 110, Train Loss 0.06232626363635063, Val Loss 0.20285195112228394\n",
      "Epoch 115, Train Loss 0.0541076622903347, Val Loss 0.17710553109645844\n",
      "Epoch 120, Train Loss 0.03726668283343315, Val Loss 0.1456785649061203\n",
      "Epoch 125, Train Loss 0.03664463013410568, Val Loss 0.16024301946163177\n",
      "Epoch 130, Train Loss 0.03013121336698532, Val Loss 0.1457684189081192\n",
      "Epoch 135, Train Loss 0.02754511870443821, Val Loss 0.16379953920841217\n",
      "Epoch 140, Train Loss 0.031130371615290642, Val Loss 0.16979801654815674\n",
      "Epoch 145, Train Loss 0.033238671720027924, Val Loss 0.1698557436466217\n",
      "Epoch 150, Train Loss 0.022703200578689575, Val Loss 0.15753775835037231\n",
      "Last LR: [0.00035184372088832035]\n",
      "Best Accuracy: Train 0.9948248407643312, Val 0.9728\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.98      0.98       112\n",
      "           1       0.98      0.97      0.98       127\n",
      "           2       1.00      0.96      0.98        81\n",
      "           3       0.96      0.96      0.96        82\n",
      "           4       0.95      0.98      0.97        85\n",
      "           5       0.95      0.96      0.95        98\n",
      "           6       0.96      0.95      0.95        95\n",
      "           7       1.00      0.99      0.99        87\n",
      "           8       0.96      0.96      0.96        73\n",
      "           9       0.95      0.96      0.96       219\n",
      "          10       0.98      0.95      0.97       163\n",
      "          11       0.95      0.96      0.95       194\n",
      "          12       0.98      1.00      0.99       114\n",
      "          13       0.98      0.98      0.98       227\n",
      "          14       0.00      0.00      0.00         0\n",
      "          15       1.00      1.00      1.00       118\n",
      "\n",
      "    accuracy                           0.97      1875\n",
      "   macro avg       0.91      0.91      0.91      1875\n",
      "weighted avg       0.97      0.97      0.97      1875\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fede/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/fede/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/fede/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "train_loss = []\n",
    "train_accuracy = []\n",
    "test_loss = []\n",
    "test_accuracy = []\n",
    "best_test_accuracy = 0\n",
    "\n",
    "\n",
    "m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "for epoch in range(150):\n",
    "    # print(f\"Epoch: {epoch+1}\")\n",
    "    \n",
    "    # TRAIN\n",
    "    model.train()\n",
    "    train_accuracy_epoch = []\n",
    "    train_loss_epoch = []\n",
    "    for data in train_loader:\n",
    "        # import ipdb; ipdb.set_trace()\n",
    "        \n",
    "        data = data.to(device)\n",
    "        \n",
    "        out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "        \n",
    "        out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "        loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))\n",
    "        loss_result.backward()\n",
    "        train_loss_epoch.append(loss_result.detach().cpu())\n",
    "        \n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        output = m(out_zones)\n",
    "        train_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "    # if scheduler.get_last_lr()[0] > 0.0005:\n",
    "    if (epoch+1)%10 == 0:\n",
    "        scheduler.step()\n",
    "\n",
    "    train_accuracy.append(np.mean(train_accuracy_epoch))\n",
    "    train_loss.append(np.mean(train_loss_epoch))\n",
    "    \n",
    "    \n",
    "    \n",
    "    # VALIDATION\n",
    "    model.eval()\n",
    "    test_accuracy_epoch = []\n",
    "    test_loss_epoch = []\n",
    "    for data in test_loader:\n",
    "        \n",
    "        data = data.to(device)\n",
    "        out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "        # out_zones = torch.mean(out[\"zones\"].cpu(),1)\n",
    "        # out_zones = out_zones.reshape(out_zones.shape[0]//num_classes,num_classes)\n",
    "        out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "        # out_zones = out.cpu().reshape(out.cpu().shape[0]//num_classes,num_classes)\n",
    "        \n",
    "        loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))        \n",
    "        test_loss_epoch.append(loss_result.detach().cpu())\n",
    "        \n",
    "        output = m(out_zones)\n",
    "        test_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "    test_accuracy.append(np.mean(test_accuracy_epoch))\n",
    "    if test_accuracy[-1] > best_test_accuracy:\n",
    "        best_test_accuracy = test_accuracy[-1]        \n",
    "        torch.save(model.state_dict(), \"MNAV_HeteroGNN_removed_zone_best_model_2.pth\")\n",
    "        \n",
    "    test_loss.append(np.mean(test_loss_epoch))\n",
    "\n",
    "    if (epoch+1)%print_every == 0:\n",
    "        print(f\"Epoch {epoch+1}, Train Loss {np.mean(train_loss_epoch)}, Val Loss {np.mean(test_loss_epoch)}\")\n",
    "    \n",
    "print(f\"Last LR: {scheduler.get_last_lr()}\")\n",
    "print(f\"Best Accuracy: Train {np.max(train_accuracy)}, Val {np.max(test_accuracy)}\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_loss, label=\"Train loss\")\n",
    "plt.plot(test_loss, label=\"Validation loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_accuracy, label=\"Train accuracy\")\n",
    "plt.plot(test_accuracy, label=\"Validation accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Test con todas las zonas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array(['location_1', 'location_10', 'location_11', 'location_12',\n",
      "       'location_13', 'location_14', 'location_15', 'location_16',\n",
      "       'location_2', 'location_3', 'location_4', 'location_5',\n",
      "       'location_6', 'location_7', 'location_8', 'location_9'],\n",
      "      dtype=object)]\n",
      "X_train shape:  (8375, 30)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HeteroData(\n",
       "  \u001b[1maps\u001b[0m={ num_nodes=15 },\n",
       "  \u001b[1mzones\u001b[0m={ num_nodes=16 },\n",
       "  \u001b[1m(aps, ap_ap, aps)\u001b[0m={\n",
       "    edge_index=[2, 210],\n",
       "    edge_attr=[210, 1]\n",
       "  },\n",
       "  \u001b[1m(zones, zone_ap, aps)\u001b[0m={\n",
       "    edge_index=[2, 233],\n",
       "    edge_attr=[233, 1]\n",
       "  },\n",
       "  \u001b[1m(aps, rev_zone_ap, zones)\u001b[0m={\n",
       "    edge_index=[2, 233],\n",
       "    edge_attr=[233, 1]\n",
       "  }\n",
       ")"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split de los datos y armado del objeto Data con el grafo\n",
    "\n",
    "X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset)\n",
    "ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr)\n",
    "T.ToUndirected()(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Armado del dataset\n",
    "\n",
    "x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "y_training_data = y_train\n",
    "y_test_data = y_test\n",
    "\n",
    "#normalize (x-mean)/std\n",
    "mean = x_training_data.mean(axis=0)\n",
    "std = x_training_data.std(axis=0)\n",
    "\n",
    "x_training_data = x_training_data - mean\n",
    "x_training_data /= std\n",
    "x_test_data = x_test_data - mean\n",
    "x_test_data /= std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = build_dataset(x_training_data, y_training_data, data)\n",
    "test_dataset = build_dataset(x_test_data, y_test_data, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zone removed:  6\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.95      0.96       112\n",
      "           1       0.98      0.94      0.96       127\n",
      "           2       1.00      0.95      0.97        81\n",
      "           3       0.99      0.96      0.98        82\n",
      "           4       0.94      1.00      0.97        85\n",
      "           5       0.65      1.00      0.79        98\n",
      "           6       1.00      0.23      0.38        95\n",
      "           7       0.97      0.99      0.98        87\n",
      "           8       0.95      0.97      0.96        73\n",
      "           9       0.94      0.95      0.94       219\n",
      "          10       0.93      0.97      0.95       163\n",
      "          11       0.95      0.93      0.94       194\n",
      "          12       0.81      0.89      0.85       114\n",
      "          13       0.88      0.90      0.89       227\n",
      "          14       0.85      0.87      0.86       219\n",
      "          15       0.98      1.00      0.99       118\n",
      "\n",
      "    accuracy                           0.91      2094\n",
      "   macro avg       0.92      0.91      0.90      2094\n",
      "weighted avg       0.92      0.91      0.90      2094\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for zone_to_remove in zones_to_remove:\n",
    "    print('Zone removed: ', zone_to_remove)\n",
    "    # model = HeteroGNN(20, 1, 4)\n",
    "    model = HeteroGNN_simplified(20, 1, 4)\n",
    "    model.load_state_dict(torch.load(f\"MNAV_HeteroGNN_simplified_without_{zone_to_remove}_best_model_2.pth\"))\n",
    "    model.to(device)\n",
    "\n",
    "    test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "    \n",
    "    # VALIDATION\n",
    "    model.eval()\n",
    "\n",
    "    for data in test_loader:\n",
    "        data = data.to(device)\n",
    "        out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "        out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "        output = m(out_zones)\n",
    "        print(classification_report(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "zona 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.95      0.96       112\n",
      "           1       0.98      0.94      0.96       127\n",
      "           2       1.00      0.95      0.97        81\n",
      "           3       0.99      0.96      0.98        82\n",
      "           4       0.94      1.00      0.97        85\n",
      "           5       0.65      1.00      0.79        98\n",
      "           6       1.00      0.23      0.38        95\n",
      "           7       0.97      0.99      0.98        87\n",
      "           8       0.95      0.97      0.96        73\n",
      "           9       0.94      0.95      0.94       219\n",
      "          10       0.93      0.97      0.95       163\n",
      "          11       0.95      0.93      0.94       194\n",
      "          12       0.81      0.89      0.85       114\n",
      "          13       0.88      0.90      0.89       227\n",
      "          14       0.85      0.87      0.86       219\n",
      "          15       0.98      1.00      0.99       118\n",
      "\n",
      "    accuracy                           0.91      2094\n",
      "   macro avg       0.92      0.91      0.90      2094\n",
      "weighted avg       0.92      0.91      0.90      2094\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# VALIDATION\n",
    "model.eval()\n",
    "\n",
    "for data in test_loader:\n",
    "    data = data.to(device)\n",
    "    out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "    out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "    output = m(out_zones)\n",
    "    print(classification_report(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d70bde7c0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[9,7])\n",
    "cf_matrix = confusion_matrix(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1)), normalize=\"true\")\n",
    "sns.heatmap(cf_matrix, annot=True, fmt=\".0%\", cmap=\"YlGnBu\", vmin=0, vmax=0.2, cbar=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "zona 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.95      0.96       112\n",
      "           1       0.98      0.94      0.96       127\n",
      "           2       1.00      0.95      0.97        81\n",
      "           3       0.99      0.96      0.98        82\n",
      "           4       0.94      1.00      0.97        85\n",
      "           5       0.65      1.00      0.79        98\n",
      "           6       1.00      0.23      0.38        95\n",
      "           7       0.97      0.99      0.98        87\n",
      "           8       0.95      0.97      0.96        73\n",
      "           9       0.94      0.95      0.94       219\n",
      "          10       0.93      0.97      0.95       163\n",
      "          11       0.95      0.93      0.94       194\n",
      "          12       0.81      0.89      0.85       114\n",
      "          13       0.88      0.90      0.89       227\n",
      "          14       0.85      0.87      0.86       219\n",
      "          15       0.98      1.00      0.99       118\n",
      "\n",
      "    accuracy                           0.91      2094\n",
      "   macro avg       0.92      0.91      0.90      2094\n",
      "weighted avg       0.92      0.91      0.90      2094\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# VALIDATION\n",
    "model.eval()\n",
    "\n",
    "for data in test_loader:\n",
    "    data = data.to(device)\n",
    "    out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "    out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "    output = m(out_zones)\n",
    "    print(classification_report(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d78ee4ee0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[9,7])\n",
    "cf_matrix = confusion_matrix(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1)), normalize=\"true\")\n",
    "sns.heatmap(cf_matrix, annot=True, fmt=\".0%\", cmap=\"YlGnBu\", vmin=0, vmax=0.2, cbar=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zone removed:  6\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'MNAV_HeteroGNN_simplified_without_6_best_model.pth'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[49], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[39m# model = HeteroGNN(20, 1, 4)\u001b[39;00m\n\u001b[1;32m      4\u001b[0m model \u001b[39m=\u001b[39m HeteroGNN_simplified(\u001b[39m20\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m4\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m model\u001b[39m.\u001b[39mload_state_dict(torch\u001b[39m.\u001b[39;49mload(\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mMNAV_HeteroGNN_simplified_without_\u001b[39;49m\u001b[39m{\u001b[39;49;00mzone_to_remove\u001b[39m}\u001b[39;49;00m\u001b[39m_best_model.pth\u001b[39;49m\u001b[39m\"\u001b[39;49m))\n\u001b[1;32m      6\u001b[0m model\u001b[39m.\u001b[39mto(device)\n\u001b[1;32m      8\u001b[0m test_loader \u001b[39m=\u001b[39m DataLoader(test_dataset, batch_size\u001b[39m=\u001b[39m\u001b[39mlen\u001b[39m(test_dataset), shuffle\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/serialization.py:771\u001b[0m, in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, **pickle_load_args)\u001b[0m\n\u001b[1;32m    768\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mencoding\u001b[39m\u001b[39m'\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m pickle_load_args\u001b[39m.\u001b[39mkeys():\n\u001b[1;32m    769\u001b[0m     pickle_load_args[\u001b[39m'\u001b[39m\u001b[39mencoding\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m--> 771\u001b[0m \u001b[39mwith\u001b[39;00m _open_file_like(f, \u001b[39m'\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m opened_file:\n\u001b[1;32m    772\u001b[0m     \u001b[39mif\u001b[39;00m _is_zipfile(opened_file):\n\u001b[1;32m    773\u001b[0m         \u001b[39m# The zipfile reader is going to advance the current file position.\u001b[39;00m\n\u001b[1;32m    774\u001b[0m         \u001b[39m# If we want to actually tail call to torch.jit.load, we need to\u001b[39;00m\n\u001b[1;32m    775\u001b[0m         \u001b[39m# reset back to the original position.\u001b[39;00m\n\u001b[1;32m    776\u001b[0m         orig_position \u001b[39m=\u001b[39m opened_file\u001b[39m.\u001b[39mtell()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/serialization.py:270\u001b[0m, in \u001b[0;36m_open_file_like\u001b[0;34m(name_or_buffer, mode)\u001b[0m\n\u001b[1;32m    268\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_open_file_like\u001b[39m(name_or_buffer, mode):\n\u001b[1;32m    269\u001b[0m     \u001b[39mif\u001b[39;00m _is_path(name_or_buffer):\n\u001b[0;32m--> 270\u001b[0m         \u001b[39mreturn\u001b[39;00m _open_file(name_or_buffer, mode)\n\u001b[1;32m    271\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    272\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mw\u001b[39m\u001b[39m'\u001b[39m \u001b[39min\u001b[39;00m mode:\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/torch/serialization.py:251\u001b[0m, in \u001b[0;36m_open_file.__init__\u001b[0;34m(self, name, mode)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, name, mode):\n\u001b[0;32m--> 251\u001b[0m     \u001b[39msuper\u001b[39m(_open_file, \u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(\u001b[39mopen\u001b[39;49m(name, mode))\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'MNAV_HeteroGNN_simplified_without_6_best_model.pth'"
     ]
    }
   ],
   "source": [
    "for zone_to_remove in zones_to_remove:\n",
    "    print('Zone removed: ', zone_to_remove)\n",
    "    # model = HeteroGNN(20, 1, 4)\n",
    "    model = HeteroGNN_simplified(20, 1, 4)\n",
    "    model.load_state_dict(torch.load(f\"MNAV_HeteroGNN_simplified_without_{zone_to_remove}_best_model_2.pth\"))\n",
    "    model.to(device)\n",
    "\n",
    "    test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "    \n",
    "    # VALIDATION\n",
    "    model.eval()\n",
    "\n",
    "    for data in test_loader:\n",
    "        data = data.to(device)\n",
    "        out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "        out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "        output = m(out_zones)\n",
    "        print(classification_report(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "        plt.figure(figsize=[9,7])\n",
    "        cf_matrix = confusion_matrix(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1)), normalize=\"true\")\n",
    "        sns.heatmap(cf_matrix, annot=True, fmt=\".0%\", cmap=\"YlGnBu\", vmin=0, vmax=0.2, cbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Análisis variando cantidad de muestras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### HeteroGNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "batch_size = 16\n",
    "learning_rate = 0.01  \n",
    "print_every = 5\n",
    "\n",
    "porcentajes = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]\n",
    "accuracy = {\"0.1\":[], \"0.2\":[], \"0.3\":[], \"0.4\":[], \"0.5\":[], \"0.6\":[], \"0.7\":[], \"0.8\":[], \"0.9\":[], \"1\":[]}\n",
    "\n",
    "for porc in porcentajes:\n",
    "    print('Porcentaje de datos: ', porc)\n",
    "    \n",
    "    for i in range(10):    \n",
    "\n",
    "        # Split de los datos y armado del objeto Data con el grafo\n",
    "        X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset, dataset_percentage=porc)\n",
    "        ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=10, prune_th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train, prune_th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr, num_classes)\n",
    "        T.ToUndirected()(data)\n",
    "\n",
    "        # Armado del dataset\n",
    "\n",
    "        x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "        x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "        y_training_data = y_train\n",
    "        y_test_data = y_test\n",
    "\n",
    "        #normalize (x-mean)/std\n",
    "        mean = x_training_data.mean(axis=0)\n",
    "        std = x_training_data.std(axis=0)\n",
    "\n",
    "        x_training_data = x_training_data - mean\n",
    "        x_training_data /= std\n",
    "        x_test_data = x_test_data - mean\n",
    "        x_test_data /= std   \n",
    "\n",
    "        train_dataset = build_dataset(x_training_data, y_training_data, data)\n",
    "        test_dataset = build_dataset(x_test_data, y_test_data, data) \n",
    "\n",
    "        model = HeteroGNN(20, 1, 4).to(device)\n",
    "\n",
    "        train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "        test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)\n",
    "        loss = torch.nn.CrossEntropyLoss()\n",
    "        scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.8)\n",
    "\n",
    "        train_loss = []\n",
    "        train_accuracy = []\n",
    "        test_loss = []\n",
    "        test_accuracy = []\n",
    "        best_test_accuracy = 0\n",
    "\n",
    "        m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "        for epoch in range(150):\n",
    "            # print(f\"Epoch: {epoch+1}\")\n",
    "\n",
    "            # TRAIN\n",
    "            model.train()\n",
    "            train_accuracy_epoch = []\n",
    "            train_loss_epoch = []\n",
    "            for data in train_loader:\n",
    "                # import ipdb; ipdb.set_trace()\n",
    "\n",
    "                data = data.to(device)\n",
    "\n",
    "                out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "\n",
    "                out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "                loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))\n",
    "                loss_result.backward()\n",
    "                train_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "                optimizer.step()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                output = m(out_zones)\n",
    "                train_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "            # if scheduler.get_last_lr()[0] > 0.0005:\n",
    "            if (epoch+1)%10 == 0:\n",
    "                scheduler.step()\n",
    "\n",
    "            train_accuracy.append(np.mean(train_accuracy_epoch))\n",
    "            train_loss.append(np.mean(train_loss_epoch))\n",
    "\n",
    "            # VALIDATION\n",
    "            model.eval()\n",
    "            test_accuracy_epoch = []\n",
    "            test_loss_epoch = []\n",
    "            for data in test_loader:\n",
    "\n",
    "                data = data.to(device)\n",
    "                out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "                # out_zones = torch.mean(out[\"zones\"].cpu(),1)\n",
    "                # out_zones = out_zones.reshape(out_zones.shape[0]//num_classes,num_classes)\n",
    "                out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "                # out_zones = out.cpu().reshape(out.cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "                loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))        \n",
    "                test_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "                output = m(out_zones)\n",
    "                test_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "            test_accuracy.append(np.mean(test_accuracy_epoch))\n",
    "            test_loss.append(np.mean(test_loss_epoch))\n",
    "\n",
    "        print(f\"Best Accuracy: Train {np.max(train_accuracy)}, Val {np.max(test_accuracy)}\")\n",
    "        accuracy[str(porc)].append(np.max(test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = {'0.3': [0.9364069952305246, 0.9268680445151033, 0.918918918918919, 0.9364069952305246, 0.9364069952305246], '0.4': [0.9534606205250596, 0.9486873508353222, 0.9486873508353222, 0.951073985680191, 0.9427207637231504], '0.5': [0.9474689589302769, 0.9398280802292264, 0.944603629417383, 0.944603629417383, 0.9474689589302769], '0.6': [0.954653937947494, 0.964200477326969, 0.964200477326969, 0.9562450278440732, 0.9578361177406524], '0.7': [0.9577080491132333, 0.9604365620736699, 0.9624829467939973, 0.961118690313779, 0.9556616643929059], '0.8': [0.9498507462686567, 0.9552238805970149, 0.9582089552238806, 0.955820895522388, 0.955820895522388], '0.9': [0.9639257294429708, 0.9612732095490716, 0.9671087533156498, 0.96657824933687, 0.96657824933687], '1': [0.9737344794651385, 0.9713467048710601, 0.9737344794651385, 0.9746895893027698, 0.9680038204393505]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aux = []\n",
    "aux.append(accuracy['0.3'])\n",
    "aux.append(accuracy['0.4'])\n",
    "aux.append(accuracy['0.5'])\n",
    "aux.append(accuracy['0.6'])\n",
    "aux.append(accuracy['0.7'])\n",
    "aux.append(accuracy['0.8'])\n",
    "aux.append(accuracy['0.9'])\n",
    "aux.append(accuracy['1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=[9,7])\n",
    "plt.boxplot(aux, showfliers=False) #, meanline=True, showmeans=True)\n",
    "plt.xticks([1, 2, 3, 4, 5, 6, 7, 8], [30, 40, 50, 60, 70, 80, 90, 100])\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Fingerprints sample size (%)')\n",
    "plt.grid()\n",
    "plt.savefig('MNAV_hetero_cant_muestras.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### HeteroGNNSimplified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "batch_size = 16\n",
    "learning_rate = 0.01  \n",
    "print_every = 5\n",
    "\n",
    "porcentajes = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]\n",
    "accuracy = {\"0.1\":[], \"0.2\":[], \"0.3\":[], \"0.4\":[], \"0.5\":[], \"0.6\":[], \"0.7\":[], \"0.8\":[], \"0.9\":[], \"1\":[]}\n",
    "\n",
    "for porc in porcentajes:\n",
    "    print('Porcentaje de datos: ', porc)   \n",
    "    \n",
    "    for i in range(10):    \n",
    "        \n",
    "        # Split de los datos y armado del objeto Data con el grafo\n",
    "        X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset, dataset_percentage=porc)\n",
    "        ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr, num_classes)\n",
    "        T.ToUndirected()(data)\n",
    "\n",
    "        # Armado del dataset\n",
    "\n",
    "        x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "        x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "        y_training_data = y_train\n",
    "        y_test_data = y_test\n",
    "\n",
    "        #normalize (x-mean)/std\n",
    "        mean = x_training_data.mean(axis=0)\n",
    "        std = x_training_data.std(axis=0)\n",
    "\n",
    "        x_training_data = x_training_data - mean\n",
    "        x_training_data /= std\n",
    "        x_test_data = x_test_data - mean\n",
    "        x_test_data /= std   \n",
    "\n",
    "        train_dataset = build_dataset(x_training_data, y_training_data, data)\n",
    "        test_dataset = build_dataset(x_test_data, y_test_data, data)  \n",
    "\n",
    "        model = HeteroGNN_simplified(20, 1, 4).to(device)\n",
    "\n",
    "        train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "        test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)\n",
    "        loss = torch.nn.CrossEntropyLoss()\n",
    "        scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.8)\n",
    "\n",
    "        train_loss = []\n",
    "        train_accuracy = []\n",
    "        test_loss = []\n",
    "        test_accuracy = []\n",
    "        best_test_accuracy = 0\n",
    "\n",
    "        m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "        for epoch in range(150):\n",
    "            # print(f\"Epoch: {epoch+1}\")\n",
    "\n",
    "            # TRAIN\n",
    "            model.train()\n",
    "            train_accuracy_epoch = []\n",
    "            train_loss_epoch = []\n",
    "            for data in train_loader:\n",
    "                # import ipdb; ipdb.set_trace()\n",
    "\n",
    "                data = data.to(device)\n",
    "\n",
    "                out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "\n",
    "                out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "                loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))\n",
    "                loss_result.backward()\n",
    "                train_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "                optimizer.step()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                output = m(out_zones)\n",
    "                train_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "            # if scheduler.get_last_lr()[0] > 0.0005:\n",
    "            if (epoch+1)%10 == 0:\n",
    "                scheduler.step()\n",
    "\n",
    "            train_accuracy.append(np.mean(train_accuracy_epoch))\n",
    "            train_loss.append(np.mean(train_loss_epoch))\n",
    "\n",
    "            # VALIDATION\n",
    "            model.eval()\n",
    "            test_accuracy_epoch = []\n",
    "            test_loss_epoch = []\n",
    "            for data in test_loader:\n",
    "\n",
    "                data = data.to(device)\n",
    "                out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "                # out_zones = torch.mean(out[\"zones\"].cpu(),1)\n",
    "                # out_zones = out_zones.reshape(out_zones.shape[0]//num_classes,num_classes)\n",
    "                out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "                # out_zones = out.cpu().reshape(out.cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "                loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))        \n",
    "                test_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "                output = m(out_zones)\n",
    "                test_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "            test_accuracy.append(np.mean(test_accuracy_epoch))\n",
    "            test_loss.append(np.mean(test_loss_epoch))\n",
    "\n",
    "        print(f\"Best Accuracy: Train {np.max(train_accuracy)}, Val {np.max(test_accuracy)}\")\n",
    "        accuracy[str(porc)].append(np.max(test_accuracy))\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Análisis variando cantidad de APs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "APs_MAC_2_4 = ['wifi-dc:a5:f4:43:85:c0',\n",
    "'wifi-dc:a5:f4:43:27:e0',\n",
    "'wifi-f8:4f:57:ab:da:00',\n",
    "'wifi-5c:a4:8a:4c:05:c0',\n",
    "'wifi-1c:1d:86:ce:ef:b0',\n",
    "'wifi-dc:a5:f4:43:79:20',\n",
    "'wifi-c0:7b:bc:36:9e:10',\n",
    "'wifi-1c:1d:86:9f:99:20',\n",
    "'wifi-c0:7b:bc:36:af:40',\n",
    "'wifi-c0:7b:bc:36:af:80',\n",
    "'wifi-1c:1d:86:b6:ac:80',\n",
    "'wifi-dc:a5:f4:43:72:e0',\n",
    "'wifi-f8:4f:57:ab:d8:60',\n",
    "'wifi-dc:a5:f4:43:72:90',\n",
    "'wifi-f8:4f:57:ab:ce:20']\n",
    "\n",
    "APs_MAC_5 = ['wifi-dc:a5:f4:45:85:b0',\n",
    "'wifi-dc:a5:f4:45:27:e0',\n",
    "'wifi-f8:4f:57:ad:d9:60',\n",
    "'wifi-5c:a4:8a:4e:05:30',\n",
    "'wifi-1c:1d:86:d0:ef:00',\n",
    "'wifi-dc:a5:f4:45:79:10',\n",
    "'wifi-c0:7b:bc:38:9e:00',\n",
    "'wifi-1c:1d:86:a1:99:00',\n",
    "'wifi-c0:7b:bc:38:af:30',\n",
    "'wifi-c0:7b:bc:38:af:70',\n",
    "'wifi-1c:1d:86:b8:ac:80',\n",
    "'wifi-dc:a5:f4:45:72:d0',\n",
    "'wifi-f8:4f:57:ad:d7:c0',\n",
    "'wifi-dc:a5:f4:45:72:80',\n",
    "'wifi-f8:4f:57:ad:cd:80']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "batch_size = 8\n",
    "learning_rate = 0.001  \n",
    "print_every = 5\n",
    "\n",
    "aps = ['AP1', 'AP2', 'AP3', 'AP4', 'AP5', 'AP6', 'AP7', 'AP8', 'AP9', 'AP10', 'AP11', 'AP12', 'AP13', 'AP14', 'AP15']\n",
    "APs_to_remove = [12] #, 9, 13, 2, 7] \n",
    "accuracy = {}\n",
    "\n",
    "for ap in APs_to_remove:\n",
    "    print('Nuevo AP removido: ', ap)\n",
    "    APs_MAC_2_4.pop(ap)\n",
    "    APs_MAC_5.pop(ap)\n",
    "    aps.pop(ap)\n",
    "    \n",
    "    # Split de los datos y armado del objeto Data con el grafo\n",
    "    X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset)\n",
    "    ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:len(aps)], th=10, cols=aps) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "    zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:len(aps)], y_train, cols=aps) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "    data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr, num_classes)\n",
    "    T.ToUndirected()(data)\n",
    "    \n",
    "    # Armado del dataset\n",
    "\n",
    "    x_training_data = np.reshape(X_train,(X_train.shape[0],len(aps),2))\n",
    "    x_test_data = np.reshape(X_test,(X_test.shape[0],len(aps),2))\n",
    "    y_training_data = y_train\n",
    "    y_test_data = y_test\n",
    "\n",
    "    #normalize (x-mean)/std\n",
    "    mean = x_training_data.mean(axis=0)\n",
    "    std = x_training_data.std(axis=0)\n",
    "\n",
    "    x_training_data = x_training_data - mean\n",
    "    x_training_data /= std\n",
    "    x_test_data = x_test_data - mean\n",
    "    x_test_data /= std   \n",
    "    \n",
    "    train_dataset = build_dataset(x_training_data, y_training_data, data)\n",
    "    test_dataset = build_dataset(x_test_data, y_test_data, data) \n",
    "  \n",
    "    model = HeteroGNN(20, 1, 4) #.to(device)\n",
    "\n",
    "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "    test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)\n",
    "    loss = torch.nn.CrossEntropyLoss()\n",
    "    scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.8)\n",
    "    \n",
    "    train_loss = []\n",
    "    train_accuracy = []\n",
    "    test_loss = []\n",
    "    test_accuracy = []\n",
    "    best_test_accuracy = 0\n",
    "\n",
    "    m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "    for epoch in range(100):\n",
    "        # print(f\"Epoch: {epoch+1}\")\n",
    "\n",
    "        # TRAIN\n",
    "        model.train()\n",
    "        train_accuracy_epoch = []\n",
    "        train_loss_epoch = []\n",
    "        for data in train_loader:\n",
    "            import ipdb; ipdb.set_trace()\n",
    "\n",
    "            # data = data.to(device)\n",
    "\n",
    "            out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "\n",
    "            out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "            loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))\n",
    "            loss_result.backward()\n",
    "            train_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            output = m(out_zones)\n",
    "            train_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "        # if scheduler.get_last_lr()[0] > 0.0005:\n",
    "        if (epoch+1)%20 == 0:\n",
    "            scheduler.step()\n",
    "\n",
    "        train_accuracy.append(np.mean(train_accuracy_epoch))\n",
    "        train_loss.append(np.mean(train_loss_epoch))\n",
    "\n",
    "        # VALIDATION\n",
    "        model.eval()\n",
    "        test_accuracy_epoch = []\n",
    "        test_loss_epoch = []\n",
    "        for data in test_loader:\n",
    "\n",
    "            data = data.to(device)\n",
    "            out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "            # out_zones = torch.mean(out[\"zones\"].cpu(),1)\n",
    "            # out_zones = out_zones.reshape(out_zones.shape[0]//num_classes,num_classes)\n",
    "            out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "            # out_zones = out.cpu().reshape(out.cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "            loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))        \n",
    "            test_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "            output = m(out_zones)\n",
    "            test_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "        test_accuracy.append(np.mean(test_accuracy_epoch))\n",
    "        test_loss.append(np.mean(test_loss_epoch))\n",
    "\n",
    "    print(f\"Best Accuracy: Train {np.max(train_accuracy)}, Val {np.max(test_accuracy)}\")\n",
    "    accuracy[str(porc)] = np.max(test_accuracy)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(train_loss, label=\"Train loss\")\n",
    "    plt.plot(test_loss, label=\"Validation loss\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(train_accuracy, label=\"Train accuracy\")\n",
    "    plt.plot(test_accuracy, label=\"Validation accuracy\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()    \n",
    "\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Análisis de APs caídos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entreno normalmente y después testeo con APs caídos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda:1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "lst = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14]\n",
    "accuracy_ap_caido_gnn = {\"1\":[], \"2\":[], \"3\":[]}\n",
    "\n",
    "m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "for ap in [1,2,3]:\n",
    "    print(\"Cantidad de APs caídos: \", ap)\n",
    "\n",
    "    for combo in combinations(lst, ap):  # 2 for pairs, 3 for triplets, etc\n",
    "        # Split de los datos y armado del objeto Data con el grafo\n",
    "\n",
    "        X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset)\n",
    "        ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=10, prune_th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train, prune_th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr)\n",
    "        T.ToUndirected()(data)\n",
    "\n",
    "        # Armado del dataset\n",
    "\n",
    "        x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "        x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "        y_training_data = y_train\n",
    "        y_test_data = y_test\n",
    "\n",
    "        #normalize (x-mean)/std\n",
    "        mean = x_training_data.mean(axis=0)\n",
    "        std = x_training_data.std(axis=0)\n",
    "\n",
    "        x_training_data = x_training_data - mean\n",
    "        x_training_data /= std\n",
    "        x_test_data = x_test_data - mean\n",
    "        x_test_data /= std     \n",
    "\n",
    "        for i in combo:        \n",
    "            x_test_data[:,i] = 0\n",
    "\n",
    "        test_dataset = build_dataset(x_test_data, y_test_data, data)     \n",
    "\n",
    "        model = HeteroGNN(20, 1, 4)\n",
    "        model.load_state_dict(torch.load(\"MNAV_HeteroGNN_best_model_968.pth\"))\n",
    "        model.to(device)\n",
    "        test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)  \n",
    "\n",
    "        # TEST\n",
    "        model.eval()\n",
    "        for data in test_loader:\n",
    "            data = data.to(device)\n",
    "            out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "            out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "            output = m(out_zones)\n",
    "        accuracy_ap_caido_gnn[str(ap)].append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "        print(accuracy_ap_caido_gnn[str(ap)][-1])\n",
    "    print(f\"    Accuracy promedio: {np.mean(accuracy_ap_caido_gnn[str(ap)])}\")\n",
    "\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_ap_caido_gnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ap in [1,2,3]:\n",
    "    print(f\"{ap}: {np.mean(accuracy_ap_caido_gnn[str(ap)])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14]\n",
    "X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset)\n",
    "\n",
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "neigh.fit(X_train, y_train)\n",
    "\n",
    "accuracy_ap_caido_knn = {\"1\":[], \"2\":[], \"3\":[]}\n",
    "\n",
    "for ap in [1,2,3]:\n",
    "    print(\"Cantidad de APs caídos: \", ap)\n",
    "    for combo in combinations(lst, ap):  # 2 for pairs, 3 for triplets, etc\n",
    "        X_aux_test = deepcopy(X_test)\n",
    "        for i in combo:\n",
    "            X_aux_test[:,i] = 0\n",
    "            X_aux_test[:,i+15] = 0\n",
    "\n",
    "        y_pred_knn = neigh.predict(X_aux_test)\n",
    "\n",
    "        ACC_test = accuracy_score(y_test, y_pred_knn)\n",
    "        accuracy_ap_caido_knn[str(ap)].append(ACC_test)\n",
    "    print(\"    Accuracy: \", np.mean(accuracy_ap_caido_knn[str(ap)]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_ap_caido_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Análisis de obstáculos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda:1')\n",
    "\n",
    "lst = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14]\n",
    "obstaculo_gnn_hetero = {\"5\":[], \"10\":[], \"15\":[]}\n",
    "obstaculos = [-5] #[-10, -15] # dB\n",
    "\n",
    "m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "for obstaculo in obstaculos:\n",
    "    print(f\"Obstáculo: {obstaculo}\")\n",
    "\n",
    "    for ap in lst:\n",
    "        \n",
    "        # Split de los datos y armado del objeto Data con el grafo        \n",
    "        X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset)\n",
    "        ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=10, prune_th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train, prune_th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr)\n",
    "        T.ToUndirected()(data)\n",
    "        \n",
    "        # Armado del dataset\n",
    "\n",
    "        x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "        x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "        y_training_data = y_train\n",
    "        y_test_data = y_test\n",
    "\n",
    "        x_test_data[:,ap] += obstaculo\n",
    "        \n",
    "        #normalize (x-mean)/std\n",
    "        mean = x_training_data.mean(axis=0)\n",
    "        std = x_training_data.std(axis=0)\n",
    "\n",
    "        x_training_data = x_training_data - mean\n",
    "        x_training_data /= std\n",
    "        x_test_data = x_test_data - mean\n",
    "        x_test_data /= std     \n",
    "\n",
    "        test_dataset = build_dataset(x_test_data, y_test_data, data)     \n",
    "\n",
    "        model = HeteroGNN(20, 1, 4)\n",
    "        model.load_state_dict(torch.load(\"MNAV_HeteroGNN_best_model_968.pth\"))\n",
    "        model.to(device)\n",
    "        test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)  \n",
    "\n",
    "        # TEST\n",
    "        model.eval()\n",
    "        for data in test_loader:\n",
    "            data = data.to(device)\n",
    "            out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "            out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "            output = m(out_zones)\n",
    "        obstaculo_gnn_hetero[str(abs(obstaculo))].append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obstaculo_gnn_hetero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cambio en GSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda:1')\n",
    "\n",
    "lst = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14]\n",
    "obstaculo_gnn_hetero_gso = {\"5\":[], \"10\":[], \"15\":[]}\n",
    "obstaculos = [-5] # [-10, -15] # dB\n",
    "\n",
    "m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "for obstaculo in obstaculos:\n",
    "    print(f\"Obstáculo: {obstaculo}\")\n",
    "\n",
    "    for ap in lst:\n",
    "        \n",
    "        # Split de los datos y armado del objeto Data con el grafo        \n",
    "        X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset)\n",
    "\n",
    "        # Build graph with modified fingerprints\n",
    "        X_train[:,ap] += obstaculo\n",
    "        X_train[:,ap+15] += obstaculo\n",
    "        \n",
    "        ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=10, prune_th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train, prune_th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr)\n",
    "        T.ToUndirected()(data)\n",
    "        \n",
    "        # Armado del dataset\n",
    "\n",
    "        x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "        x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "        y_training_data = y_train\n",
    "        y_test_data = y_test\n",
    "\n",
    "        x_test_data[:,ap] += obstaculo\n",
    "        \n",
    "        #normalize (x-mean)/std\n",
    "        mean = x_training_data.mean(axis=0)\n",
    "        std = x_training_data.std(axis=0)\n",
    "\n",
    "        x_training_data = x_training_data - mean\n",
    "        x_training_data /= std\n",
    "        x_test_data = x_test_data - mean\n",
    "        x_test_data /= std     \n",
    "\n",
    "        test_dataset = build_dataset(x_test_data, y_test_data, data)     \n",
    "\n",
    "        model = HeteroGNN(20, 1, 4)\n",
    "        model.load_state_dict(torch.load(\"MNAV_HeteroGNN_best_model_968.pth\"))\n",
    "        model.to(device)\n",
    "        test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)  \n",
    "\n",
    "        # TEST\n",
    "        model.eval()\n",
    "        for data in test_loader:\n",
    "            data = data.to(device)\n",
    "            out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "            out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "            output = m(out_zones)\n",
    "        obstaculo_gnn_hetero_gso[str(abs(obstaculo))].append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obstaculo_gnn_hetero_gso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obstaculo_gnn = {\"10\":[], \"15\":[]}\n",
    "obstaculo_gnn[\"10\"] = [0.9302769818529131, 0.9441260744985673, 0.9498567335243553, 0.938872970391595, 0.9379178605539638, 0.9336198662846227, 0.8720152817574021, 0.9326647564469914, 0.8667621776504298, 0.9274116523400191, 0.9274116523400191, 0.9383954154727794, 0.8046800382043935, 0.9450811843361987, 0.9097421203438395]\n",
    "obstaculo_gnn[\"15\"] = [0.887774594078319, 0.9240687679083095, 0.9422158548233047, 0.9149952244508118, 0.8982808022922636, 0.9097421203438395, 0.839541547277937, 0.899235912129895, 0.8166189111747851, 0.9063992359121299, 0.8863419293218721, 0.9250238777459407, 0.670487106017192, 0.9259789875835721, 0.8443170964660937]\n",
    "\n",
    "obstaculo_knn = {\"10\":[], \"15\":[]}\n",
    "obstaculo_knn[\"10\"].append(0.9493791786055397)\n",
    "obstaculo_knn[\"15\"].append(0.9350525310410697)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.944603629417383)\n",
    "obstaculo_knn[\"15\"].append(0.9369627507163324)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9479465138490927)\n",
    "obstaculo_knn[\"15\"].append(0.9288443170964661)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9412607449856734)\n",
    "obstaculo_knn[\"15\"].append(0.9202483285577842)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.941738299904489)\n",
    "obstaculo_knn[\"15\"].append(0.9216809933142311)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9364851957975168)\n",
    "obstaculo_knn[\"15\"].append(0.9106972301814709)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9044890162368673)\n",
    "obstaculo_knn[\"15\"].append(0.8419293218720153)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9293218720152817)\n",
    "obstaculo_knn[\"15\"].append(0.89207258834766)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9063992359121299)\n",
    "obstaculo_knn[\"15\"].append(0.8462273161413563)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9278892072588347)\n",
    "obstaculo_knn[\"15\"].append(0.8954154727793696)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9293218720152817)\n",
    "obstaculo_knn[\"15\"].append(0.8810888252148997)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9240687679083095)\n",
    "obstaculo_knn[\"15\"].append(0.8720152817574021)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9493791786055397)\n",
    "obstaculo_knn[\"15\"].append(0.9383954154727794)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9498567335243553)\n",
    "obstaculo_knn[\"15\"].append(0.9336198662846227)\n",
    "\n",
    "obstaculo_knn[\"10\"].append(0.9551098376313276)\n",
    "obstaculo_knn[\"15\"].append(0.9484240687679083)\n",
    "\n",
    "obstaculo_fcnn = {\"10\":[], \"15\":[]}\n",
    "\n",
    "obstaculo_fcnn[\"10\"].append(0.9188156638013372)\n",
    "obstaculo_fcnn[\"15\"].append(0.8533906399235912)\n",
    "obstaculo_fcnn[\"10\"].append(0.8767908309455588)\n",
    "obstaculo_fcnn[\"15\"].append(0.791308500477555)\n",
    "obstaculo_fcnn[\"10\"].append(0.8524355300859598)\n",
    "obstaculo_fcnn[\"15\"].append(0.7717287488061128)\n",
    "obstaculo_fcnn[\"10\"].append(0.8901623686723973)\n",
    "obstaculo_fcnn[\"15\"].append(0.8223495702005731)\n",
    "obstaculo_fcnn[\"10\"].append(0.8299904489016237)\n",
    "obstaculo_fcnn[\"15\"].append(0.7311365807067812)\n",
    "obstaculo_fcnn[\"10\"].append(0.8237822349570201)\n",
    "obstaculo_fcnn[\"15\"].append(0.7120343839541547)\n",
    "obstaculo_fcnn[\"10\"].append(0.7917860553963706)\n",
    "obstaculo_fcnn[\"15\"].append(0.6614135625596944)\n",
    "obstaculo_fcnn[\"10\"].append(0.9288443170964661)\n",
    "obstaculo_fcnn[\"15\"].append(0.8810888252148997)\n",
    "obstaculo_fcnn[\"10\"].append(0.8022922636103151)\n",
    "obstaculo_fcnn[\"15\"].append(0.6466093600764088)\n",
    "obstaculo_fcnn[\"10\"].append(0.8915950334288443)\n",
    "obstaculo_fcnn[\"15\"].append(0.7893982808022922)\n",
    "obstaculo_fcnn[\"10\"].append(0.893505253104107)\n",
    "obstaculo_fcnn[\"15\"].append(0.8166189111747851)\n",
    "obstaculo_fcnn[\"10\"].append(0.9001910219675263)\n",
    "obstaculo_fcnn[\"15\"].append(0.8314231136580706)\n",
    "obstaculo_fcnn[\"10\"].append(0.7717287488061128)\n",
    "obstaculo_fcnn[\"15\"].append(0.663323782234957)\n",
    "obstaculo_fcnn[\"10\"].append(0.779847182425979)\n",
    "obstaculo_fcnn[\"15\"].append(0.667621776504298)\n",
    "obstaculo_fcnn[\"10\"].append(0.7516714422158548)\n",
    "obstaculo_fcnn[\"15\"].append(0.6365807067812799)\n",
    "\n",
    "obstaculo_bracco = {\"10\":[], \"15\":[]}\n",
    "\n",
    "obstaculo_bracco[\"10\"].append(0.9584527220630372)\n",
    "obstaculo_bracco[\"15\"].append(0.9489016236867239)\n",
    "obstaculo_bracco[\"10\"].append(0.9613180515759312)\n",
    "obstaculo_bracco[\"15\"].append(0.9527220630372493)\n",
    "obstaculo_bracco[\"10\"].append(0.9489016236867239)\n",
    "obstaculo_bracco[\"15\"].append(0.9321872015281757)\n",
    "obstaculo_bracco[\"10\"].append(0.9474689589302769)\n",
    "obstaculo_bracco[\"15\"].append(0.9245463228271251)\n",
    "obstaculo_bracco[\"10\"].append(0.9355300859598854)\n",
    "obstaculo_bracco[\"15\"].append(0.9044890162368673)\n",
    "obstaculo_bracco[\"10\"].append(0.9221585482330468)\n",
    "obstaculo_bracco[\"15\"].append(0.8787010506208214)\n",
    "obstaculo_bracco[\"10\"].append(0.8724928366762178)\n",
    "obstaculo_bracco[\"15\"].append(0.779847182425979)\n",
    "obstaculo_bracco[\"10\"].append(0.9469914040114613)\n",
    "obstaculo_bracco[\"15\"].append(0.9102196752626552)\n",
    "obstaculo_bracco[\"10\"].append(0.8801337153772684)\n",
    "obstaculo_bracco[\"15\"].append(0.7774594078319007)\n",
    "obstaculo_bracco[\"10\"].append(0.9522445081184336)\n",
    "obstaculo_bracco[\"15\"].append(0.9283667621776505)\n",
    "obstaculo_bracco[\"10\"].append(0.9383954154727794)\n",
    "obstaculo_bracco[\"15\"].append(0.8925501432664756)\n",
    "obstaculo_bracco[\"10\"].append(0.9216809933142311)\n",
    "obstaculo_bracco[\"15\"].append(0.8619866284622731)\n",
    "obstaculo_bracco[\"10\"].append(0.9527220630372493)\n",
    "obstaculo_bracco[\"15\"].append(0.9340974212034384)\n",
    "obstaculo_bracco[\"10\"].append(0.9398280802292264)\n",
    "obstaculo_bracco[\"15\"].append(0.9087870105062082)\n",
    "obstaculo_bracco[\"10\"].append(0.9613180515759312)\n",
    "obstaculo_bracco[\"15\"].append(0.9522445081184336)\n",
    "\n",
    "obstaculo_gnn_gso = {\"10\":[], \"15\":[]}\n",
    "\n",
    "obstaculo_gnn_gso[\"10\"].append(0.937440305635148)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.9154727793696275)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.944603629417383)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.9336198662846227)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.9536771728748806)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.9512893982808023)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.933142311365807)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.9049665711556829)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.9441260744985673)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.8987583572110793)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.9412607449856734)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.9321872015281757)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.8820439350525311)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.8510028653295129)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.9336198662846227)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.8958930276981852)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.8629417382999045)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.8218720152817574)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.9274116523400191)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.9016236867239733)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.9269340974212035)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.890639923591213)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.937440305635148)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.9226361031518625)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.826647564469914)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.7048710601719198)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.9508118433619867)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.9307545367717287)\n",
    "obstaculo_gnn_gso[\"10\"].append(0.9097421203438395)\n",
    "obstaculo_gnn_gso[\"15\"].append(0.8443170964660937)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"GNN 10: {np.mean(obstaculo_gnn['10'])}\")\n",
    "print(f\"GNN 15: {np.mean(obstaculo_gnn['15'])}\")\n",
    "print(f\"KNN 10: {np.mean(obstaculo_knn['10'])}\")\n",
    "print(f\"KNN 15: {np.mean(obstaculo_knn['15'])}\")\n",
    "print(f\"FCNN 10: {np.mean(obstaculo_fcnn['10'])}\")\n",
    "print(f\"FCNN 15: {np.mean(obstaculo_fcnn['15'])}\")\n",
    "print(f\"BRACCO 10: {np.mean(obstaculo_bracco['10'])}\")\n",
    "print(f\"BRACCO 15: {np.mean(obstaculo_bracco['15'])}\")\n",
    "print(f\"GNN GSO 10: {np.mean(obstaculo_gnn_gso['10'])}\")\n",
    "print(f\"GNN GSO 15: {np.mean(obstaculo_gnn_gso['15'])}\")\n",
    "print(f\"GNN Hetero 10: {np.mean(obstaculo_gnn_hetero['10'])}\")\n",
    "print(f\"GNN Hetero 15: {np.mean(obstaculo_gnn_hetero['15'])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "neigh.fit(X_train, y_train)\n",
    "y_pred_knn = neigh.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred_knn))\n",
    "print(classification_report(y_test, y_pred_knn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## FCNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "clf = MLPClassifier().fit(X_train, y_train)\n",
    "y_pred_fcnn = clf.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred_fcnn))\n",
    "print(classification_report(y_test, y_pred_fcnn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Pruning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "batch_size = 16\n",
    "learning_rate = 0.01 \n",
    "print_every = 50\n",
    "\n",
    "prune_thresholds = [20]\n",
    "accuracy = {\"20\":[]}\n",
    "\n",
    "for prune_th in prune_thresholds:\n",
    "    print('Threshold: ', prune_th)\n",
    "        \n",
    "    for _ in range(10):\n",
    "        X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset, dataset_percentage=0.3)\n",
    "        ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=20, prune_th=prune_th) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train, prune_th=prune_th) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "        data = build_heterodata(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr)\n",
    "        T.ToUndirected()(data)\n",
    "\n",
    "        # Armado del dataset\n",
    "\n",
    "        x_training_data = np.reshape(X_train,(X_train.shape[0],15,2))\n",
    "        x_test_data = np.reshape(X_test,(X_test.shape[0],15,2))\n",
    "        y_training_data = y_train\n",
    "        y_test_data = y_test\n",
    "\n",
    "        #normalize (x-mean)/std\n",
    "        mean = x_training_data.mean(axis=0)\n",
    "        std = x_training_data.std(axis=0)\n",
    "\n",
    "        x_training_data = x_training_data - mean\n",
    "        x_training_data /= std\n",
    "        x_test_data = x_test_data - mean\n",
    "        x_test_data /= std \n",
    "\n",
    "        train_dataset = build_dataset(x_training_data, y_training_data, data)\n",
    "        test_dataset = build_dataset(x_test_data, y_test_data, data) \n",
    "    \n",
    "        model = HeteroGNN(20, 1, 4).to(device)\n",
    "\n",
    "        train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "        test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)\n",
    "        loss = torch.nn.CrossEntropyLoss()\n",
    "        scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.8)\n",
    "\n",
    "        train_loss = []\n",
    "        train_accuracy = []\n",
    "        test_loss = []\n",
    "        test_accuracy = []\n",
    "        best_test_accuracy = 0\n",
    "\n",
    "\n",
    "        m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "        for epoch in range(150):\n",
    "            # print(f\"Epoch: {epoch+1}\")\n",
    "\n",
    "            # TRAIN\n",
    "            model.train()\n",
    "            train_accuracy_epoch = []\n",
    "            train_loss_epoch = []\n",
    "            for data in train_loader:\n",
    "\n",
    "                data = data.to(device)\n",
    "                out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "                out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "\n",
    "                loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))\n",
    "                loss_result.backward()\n",
    "                train_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "                optimizer.step()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                output = m(out_zones)\n",
    "                train_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "            # if scheduler.get_last_lr()[0] > 0.0005:\n",
    "            if (epoch+1)%20 == 0:\n",
    "                scheduler.step()\n",
    "\n",
    "            train_accuracy.append(np.mean(train_accuracy_epoch))\n",
    "            train_loss.append(np.mean(train_loss_epoch))\n",
    "\n",
    "\n",
    "\n",
    "            # VALIDATION\n",
    "            model.eval()\n",
    "            test_accuracy_epoch = []\n",
    "            test_loss_epoch = []\n",
    "            for data in test_loader:\n",
    "\n",
    "                data = data.to(device)\n",
    "                out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "                out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//num_classes,num_classes)\n",
    "                loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))        \n",
    "                test_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "                output = m(out_zones)\n",
    "                test_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "            test_accuracy.append(np.mean(test_accuracy_epoch))\n",
    "            if test_accuracy[-1] > best_test_accuracy:\n",
    "                best_test_accuracy = test_accuracy[-1]        \n",
    "                torch.save(model.state_dict(), \"MNAV_HeteroGNN_best_model.pth\")\n",
    "\n",
    "            test_loss.append(np.mean(test_loss_epoch))\n",
    "\n",
    "            # if (epoch+1)%print_every == 0:\n",
    "            #     print(f\"Epoch {epoch+1}, Train Loss {np.mean(train_loss_epoch)}, Val Loss {np.mean(test_loss_epoch)}\")\n",
    "\n",
    "        print(f\"Last LR: {scheduler.get_last_lr()}\")\n",
    "        print(f\"Best Accuracy: Train {np.max(train_accuracy)}, Val {np.max(test_accuracy)}\") \n",
    "    \n",
    "        accuracy[str(prune_th)].append(np.max(test_accuracy))\n",
    "\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_prunning = accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_prunning = {\"20\": [0.9268680445151033, 0.9252782193958664, 0.9252782193958664, 0.931637519872814, 0.9332273449920508, 0.9220985691573926, 0.9268680445151033, 0.9475357710651828, 0.9427662957074722, 0.9125596184419714]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(accuracy_prunning[\"20\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aux = []\n",
    "aux.append(accuracy_prunning['20'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=[9,7])\n",
    "plt.boxplot(aux, showfliers=False) #, meanline=True, showmeans=True)\n",
    "plt.xticks([1], [30])\n",
    "plt.yticks([0.98, 0.96, 0.94, 0.92, 0.90])\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Fingerprints sample size (%)')\n",
    "plt.title('HeteroGNN prune_th=20')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Pruebas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Explainability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_heterodata_unnormalized(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr, classes=16):\n",
    "    data = HeteroData()\n",
    "    data['aps', 'ap_ap', 'aps'].edge_index = ap_edge_index\n",
    "    # ap_edge_attr = (ap_edge_attr - ap_edge_attr.mean())/ap_edge_attr.std()\n",
    "    data['aps', 'ap_ap', 'aps'].edge_attr = ap_edge_attr\n",
    "    data['aps'].num_nodes = 15\n",
    "\n",
    "    data['zones', 'zone_ap', 'aps'].edge_index = zone_ap_edge_index\n",
    "    # zone_ap_edge_attr = (zone_ap_edge_attr - zone_ap_edge_attr.mean())/zone_ap_edge_attr.std()    \n",
    "    # data['zones', 'zone_ap', 'aps'].edge_attr = torch.nn.functional.normalize(zone_ap_edge_attr, dim=0)\n",
    "    data['zones', 'zone_ap', 'aps'].edge_attr = zone_ap_edge_attr\n",
    "    data['zones'].num_nodes = classes\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split de los datos y armado del objeto Data con el grafo\n",
    "\n",
    "X_train, X_test, y_train, y_test, num_aps, num_classes, enc_train = preprocess_dataset(dataset)\n",
    "ap_edge_index, ap_edge_attr = ap_graph_creator(X_train[:,:15], th=10) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "zone_ap_edge_index, zone_ap_edge_attr = zone_ap_graph_creator(X_train[:,:15], y_train) #el grafo lo armo solo con los datos de 2.4Ghz\n",
    "data = build_heterodata_unnormalized(ap_edge_index, ap_edge_attr, zone_ap_edge_index, zone_ap_edge_attr)\n",
    "T.ToUndirected()(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data\n",
    "# data._edge_store_dict[('zones','zone_ap','aps')]\n",
    "edge_index = data._edge_store_dict[('zones','zone_ap','aps')][\"edge_index\"]\n",
    "edge_attr = data._edge_store_dict[('zones','zone_ap','aps')][\"edge_attr\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(edge_attr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = np.zeros((16, 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = 0\n",
    "# for zone in range(16):\n",
    "    # for ap in range(15):\n",
    "for i in range(len(edge_attr)):\n",
    "    zone = edge_index[0][i]\n",
    "    ap = edge_index[1][i]\n",
    "    matrix[zone,ap] = edge_attr[i]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=[12,12])\n",
    "ax = sns.heatmap(matrix, linewidth=0.5, cmap=\"YlGnBu\", vmin=10, vmax=35, annot=True)\n",
    "# plt.show()\n",
    "plt.savefig(\"analisis_sacando_zona.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0: 'location_1', \n",
    "# 1: 'location_10', \n",
    "# 2: 'location_11', \n",
    "# 3: 'location_12',\n",
    "# 4: 'location_13', \n",
    "# 5: 'location_14', \n",
    "# 6: 'location_15', \n",
    "# 7: 'location_16',\n",
    "# 8: 'location_2', \n",
    "# 9: 'location_3', \n",
    "# 10: 'location_4', \n",
    "# 11: 'location_5',\n",
    "# 12: 'location_6', \n",
    "# 13: 'location_7', \n",
    "# 14: 'location_8', \n",
    "# 15: 'location_9'],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_index_aps = data._edge_store_dict[('aps','ap_ap','aps')][\"edge_index\"]\n",
    "edge_attr_aps = data._edge_store_dict[('aps','ap_ap','aps')][\"edge_attr\"]\n",
    "matrix_aps = np.zeros((15, 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = 0\n",
    "for i in range(len(edge_attr_aps)):\n",
    "    ap1 = edge_index_aps[0][i]\n",
    "    ap2 = edge_index_aps[1][i]\n",
    "    matrix_aps[ap1,ap2] = edge_attr_aps[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=[12,11])\n",
    "ax_aps = sns.heatmap(matrix_aps, linewidth=0.5, cmap=\"YlGnBu\", vmin=20, vmax=80, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Simulacion agregando zona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_graph = HeteroData()\n",
    "train_graph['aps', 'ap_ap', 'aps'].edge_index = torch.tensor([[0, 0, 0, 1, 1, 2], [1, 2, 3, 2, 3, 3]])\n",
    "train_graph['aps', 'ap_ap', 'aps'].edge_attr = torch.tensor([[1], [1], [1], [1], [1], [1]])\n",
    "train_graph['aps'].num_nodes = 4\n",
    "\n",
    "train_graph['zones', 'zone_ap', 'aps'].edge_index = torch.tensor([[0, 0, 0, 0, 1, 1], [0, 1, 2, 3, 1, 2]])\n",
    "train_graph['zones', 'zone_ap', 'aps'].edge_attr = torch.tensor([[2], [5], [5], [1], [1], [5]])\n",
    "# train_graph['zones', 'zone_ap', 'aps'].edge_index = torch.tensor([[0, 0, 0, 0, 1, 1, 2, 2, 2], [0, 1, 2, 3, 1, 2, 0, 2, 3]])\n",
    "# train_graph['zones', 'zone_ap', 'aps'].edge_attr = torch.tensor([[2], [5], [5], [1], [1], [5], [0], [0], [0]])\n",
    "train_graph['zones'].num_nodes = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = HeteroData()\n",
    "graph['aps', 'ap_ap', 'aps'].edge_index = torch.tensor([[0, 0, 0, 1, 1, 2], [1, 2, 3, 2, 3, 3]])\n",
    "graph['aps', 'ap_ap', 'aps'].edge_attr = torch.tensor([[1], [1], [1], [1], [1], [1]])\n",
    "graph['aps'].num_nodes = 4\n",
    "\n",
    "graph['zones', 'zone_ap', 'aps'].edge_index = torch.tensor([[0, 0, 0, 0, 1, 1, 2, 2, 2], [0, 1, 2, 3, 1, 2, 0, 2, 3]])\n",
    "graph['zones', 'zone_ap', 'aps'].edge_attr = torch.tensor([[2], [5], [5], [1], [1], [5], [2], [5], [5]])\n",
    "graph['zones'].num_nodes = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "T.ToUndirected()(train_graph)\n",
    "T.ToUndirected()(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = []\n",
    "X_test = []\n",
    "y_train = []\n",
    "y_test = []\n",
    "x_0_train_sample = [1, 5, 5, 2]\n",
    "x_1_train_sample = [1, 3, 4, 1]\n",
    "x_test_sample = [1, 2, 5, 5]\n",
    "x_none_test_sample = [4, 2, 2, 1]\n",
    "for _ in range(20):\n",
    "    low_noise = np.random.normal(0,1,4)\n",
    "    X_train.append(x_0_train_sample + low_noise)\n",
    "    y_train.append([0])\n",
    "    low_noise = np.random.normal(0,1,4)\n",
    "    X_train.append(x_1_train_sample + low_noise)\n",
    "    y_train.append([1])    \n",
    "    \n",
    "    low_noise = np.random.normal(0,1,4)    \n",
    "    X_test.append(x_0_train_sample + low_noise)\n",
    "    y_test.append([0])\n",
    "    low_noise = np.random.normal(0,1,4)    \n",
    "    X_test.append(x_1_train_sample + low_noise)\n",
    "    y_test.append([1])    \n",
    "    low_noise = np.random.normal(0,1,4)\n",
    "    X_test.append(x_test_sample + low_noise)\n",
    "    y_test.append([2])\n",
    "    \n",
    "#     low_noise = np.random.normal(0,1,4)\n",
    "#     X_test.append(x_none_test_sample + low_noise)\n",
    "#     y_test.append([0])    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Armado del dataset\n",
    "\n",
    "x_training_data = np.reshape(X_train,(len(X_train),4,1))\n",
    "x_test_data = np.reshape(X_test,(len(X_test),4,1))\n",
    "y_training_data = y_train\n",
    "y_test_data = y_test\n",
    "\n",
    "# #normalize (x-mean)/std\n",
    "# mean = x_training_data.mean(axis=0)\n",
    "# std = x_training_data.std(axis=0)\n",
    "\n",
    "# x_training_data = x_training_data - mean\n",
    "# x_training_data /= std\n",
    "# x_test_data = x_test_data - mean\n",
    "# x_test_data /= std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_data(X, y, graph, zone_to_remove=None):\n",
    "    dataset = []\n",
    "\n",
    "    for i in range(len(y)):\n",
    "        if y[i] != zone_to_remove:\n",
    "            data = deepcopy(graph)\n",
    "            data['aps'].x = torch.Tensor(X[i])\n",
    "            data['zones'].x = torch.zeros(data['zones'].num_nodes,1)\n",
    "            data['zones'].y = torch.Tensor(y[i])\n",
    "        dataset.append(data)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = build_data(x_training_data, y_training_data, train_graph)\n",
    "test_dataset = build_data(x_test_data, y_test_data, graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 4\n",
    "learning_rate = 0.0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = HeteroGNN_simplified(20, 1, 4).to(device)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(train_dataset, batch_size=len(train_dataset), shuffle=False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)\n",
    "loss = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "train_loss = []\n",
    "train_accuracy = []\n",
    "\n",
    "m = torch.nn.Softmax(dim=1)\n",
    "\n",
    "for epoch in range(150):\n",
    "\n",
    "    # TRAIN\n",
    "    model.train()\n",
    "    train_accuracy_epoch = []\n",
    "    train_loss_epoch = []\n",
    "    optimizer.zero_grad()\n",
    "    for data in train_loader:\n",
    "\n",
    "        data = data.to(device)\n",
    "        out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "        out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//2,2)\n",
    "\n",
    "        loss_result = loss(out_zones.cpu(), data[\"zones\"].y.cpu().type(torch.long))\n",
    "        loss_result.backward()\n",
    "        train_loss_epoch.append(loss_result.detach().cpu())\n",
    "\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        output = m(out_zones)\n",
    "        train_accuracy_epoch.append(accuracy_score(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "\n",
    "\n",
    "    train_accuracy.append(np.mean(train_accuracy_epoch))\n",
    "    train_loss.append(np.mean(train_loss_epoch))\n",
    "\n",
    "    print(f\"Epoch {epoch+1}, Train Loss {np.mean(train_loss_epoch)}, Train Accuracy {np.mean(train_accuracy_epoch)}\")\n",
    "\n",
    "model.eval()\n",
    "\n",
    "for data in val_loader:\n",
    "    data = data.to(device)\n",
    "    out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "    out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//2,2)\n",
    "\n",
    "    output = m(out_zones)\n",
    "    print(classification_report(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "    plt.figure()\n",
    "    cf_matrix = confusion_matrix(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1)), normalize=\"true\")\n",
    "    sns.heatmap(cf_matrix, annot=True, fmt=\".0%\", cmap=\"YlGnBu\", cbar=False)    \n",
    "print(f\"Best Accuracy: Train {np.max(train_accuracy)}\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = {'size'   : 12}\n",
    "\n",
    "plt.rc('font', **font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "\n",
    "# VALIDATION\n",
    "model.eval()\n",
    "\n",
    "for data in test_loader:\n",
    "    data = data.to(device)\n",
    "    data.x_dict[\"aps\"].requires_grad_()\n",
    "    data.x_dict[\"zones\"].requires_grad_()\n",
    "    out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "    out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//3,3)\n",
    "\n",
    "    scores = (out_zones.gather(1, data[\"zones\"].y.cpu().view(-1, 1).type(torch.long)).squeeze())\n",
    "    scores.backward(torch.FloatTensor([1.0]*scores.shape[0]))\n",
    "    saliency, _ = torch.max(data.x_dict[\"aps\"].grad.data.abs(), dim=1)\n",
    "    saliency = saliency.reshape((len(test_dataset),4))\n",
    "\n",
    "    output = m(out_zones)\n",
    "    print(classification_report(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "    plt.figure()\n",
    "    cf_matrix = confusion_matrix(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1)), normalize=\"true\")\n",
    "    sns.heatmap(cf_matrix, annot=True, fmt=\".0%\", cmap=\"YlGnBu\", cbar=False)   \n",
    "\n",
    "plt.savefig(\"Agregado_zona_simple_test.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), shuffle=False)\n",
    "\n",
    "# VALIDATION\n",
    "model.eval()\n",
    "\n",
    "for data in val_loader:\n",
    "    data = data.to(device)\n",
    "    out = model(data.x_dict, data.edge_index_dict, data.edge_attr_dict) \n",
    "    out_zones = out[\"zones\"].cpu().reshape(out[\"zones\"].cpu().shape[0]//2,2)\n",
    "\n",
    "    output = m(out_zones)\n",
    "    print(classification_report(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1))))\n",
    "    plt.figure()\n",
    "    cf_matrix = confusion_matrix(data[\"zones\"].y.cpu().reshape(-1).type(torch.long), np.array(torch.argmax(output.cpu(), axis=1)), normalize=\"true\")\n",
    "    sns.heatmap(cf_matrix, annot=True, fmt=\".0%\", cmap=\"YlGnBu\", cbar=False) \n",
    "plt.savefig(\"Agregado_zona_simple_train.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### FCNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "clf = MLPClassifier(hidden_layer_sizes=(100,100,100,))\n",
    "clf.classes_ = np.array([0, 1, 2])\n",
    "clf.fit(X_train, y_train)\n",
    "print(clf.classes_)\n",
    "# y_pred_fcnn = clf.predict(X_test)\n",
    "# print(accuracy_score(y_test, y_pred_fcnn))\n",
    "# print(classification_report(y_test, y_pred_fcnn))\n",
    "\n",
    "y_pred_fcnn = clf.predict(X_train)\n",
    "print(accuracy_score(y_train, y_pred_fcnn))\n",
    "print(classification_report(y_train, y_pred_fcnn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Feedforward(torch.nn.Module):\n",
    "        def __init__(self, input_size, hidden_size):\n",
    "            super(Feedforward, self).__init__()\n",
    "            self.input_size = input_size\n",
    "            self.hidden_size  = hidden_size\n",
    "            self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)\n",
    "            self.relu = torch.nn.ReLU()\n",
    "            self.fc2 = torch.nn.Linear(self.hidden_size, 2)\n",
    "            # self.sigmoid = torch.nn.Sigmoid()\n",
    "        def forward(self, x):\n",
    "            hidden = self.fc1(x)\n",
    "            relu = self.relu(hidden)\n",
    "            output = self.fc2(relu)\n",
    "            # output = self.sigmoid(output)\n",
    "            return output\n",
    "\n",
    "        \n",
    "hidden_size = 100\n",
    "model = Feedforward(4, hidden_size)\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr = 0.001)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.eval()\n",
    "# y_pred = model(torch.tensor(X_test).type(torch.float))\n",
    "# before_train = criterion(y_pred.squeeze(), torch.tensor(y_test).reshape(-1))\n",
    "# print('Test loss before training' , before_train.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.train()\n",
    "epoch = 300\n",
    "for epoch in range(epoch):\n",
    "    optimizer.zero_grad()\n",
    "    # Forward pass\n",
    "    y_pred = model(torch.tensor(X_train).type(torch.float))\n",
    "    # Compute Loss\n",
    "    loss = criterion(y_pred.squeeze(), torch.tensor(y_train).reshape(-1))\n",
    "   \n",
    "\n",
    "    # Backward pass\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "print('Epoch {}: train loss: {}'.format(epoch, loss.item()))\n",
    "print(accuracy_score(y_train, np.array(torch.argmax(y_pred.cpu(), axis=1))))\n",
    "print(classification_report(y_train, np.array(torch.argmax(y_pred.cpu(), axis=1))))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fc2 = torch.nn.Linear(hidden_size, 3)\n",
    "model.eval()\n",
    "y_pred = model(torch.tensor(X_test).type(torch.float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.array(torch.argmax(y_pred.cpu(), axis=1))\n",
    "print(accuracy_score(y_test, np.array(torch.argmax(y_pred.cpu(), axis=1))))\n",
    "print(classification_report(y_test, np.array(torch.argmax(y_pred.cpu(), axis=1))))    "
   ]
  }
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