Mnist_SNA.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GuidoGiacomoMussini/SocialNetworkAnalysis--Algorithm/blob/main/Mnist_SNA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **0 - Initialization**"
      ],
      "metadata": {
        "id": "cltHRsg03gRx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 0.1 - Libraries"
      ],
      "metadata": {
        "id": "DDYYLpIa3qkl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "SbzzAp0UfFmd"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import shutil\n",
        "from glob import glob\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import random\n",
        "from sklearn.datasets import fetch_openml\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "\n",
        "import random as rnd\n",
        "from IPython.display import clear_output \n",
        "from tqdm.notebook import tqdm_notebook\n",
        "from collections import defaultdict, Counter, OrderedDict\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as T\n",
        "import torchvision.transforms.functional as Tf\n",
        "from keras.datasets import mnist\n",
        "\n",
        "\n",
        "import networkx as nx\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 0.2 - Functions"
      ],
      "metadata": {
        "id": "p_0S2gvX3vNN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#https://discuss.pytorch.org/t/how-to-show-a-image-in-jupyter-notebook-with-pytorch-easily/1229/4?u=ataraxy\n",
        "def show(img):\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)), interpolation='nearest')"
      ],
      "metadata": {
        "id": "5EiEE77AnxkS"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tensor_mean(tens):\n",
        "  ex = tens.numpy()\n",
        "  colmean = ex.mean(axis=1)\n",
        "  totmean = colmean.mean()\n",
        "  return totmean"
      ],
      "metadata": {
        "id": "H4834TouuonN"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def metrics_definition(net_train, y_train):\n",
        "  links_list, nodes_list, density_list, centrality_list, clust_list, transitivity_list, label = [], [], [], [], [], [], []\n",
        "\n",
        "  for i in tqdm_notebook(range(len(net_train))):\n",
        "    dic =nx.degree_centrality(net_train[i]) \n",
        "    dic = list(dic.values())\n",
        "    centrality_list.append(np.mean(dic))\n",
        "    links_list.append(net_train[i].number_of_edges())\n",
        "    nodes_list.append(net_train[i].number_of_nodes())\n",
        "    density_list.append(nx.density(net_train[i]))\n",
        "    clust_list.append(nx.average_clustering(net_train[i]))\n",
        "    transitivity_list.append(nx.transitivity(net_train[i]))\n",
        "\n",
        "  train_df = pd.DataFrame( columns = ['links', 'nodes', 'density', 'centrality', 'clustering', 'transitivity'])\n",
        "  train_df['links'] = links_list\n",
        "  train_df['nodes'] = nodes_list\n",
        "  train_df['density'] = density_list\n",
        "  train_df['centrality'] = centrality_list\n",
        "  train_df['clustering'] = clust_list\n",
        "  train_df['transitivity'] = transitivity_list\n",
        "  train_df['label'] = y_train\n",
        "  return train_df"
      ],
      "metadata": {
        "id": "GbnzdTfRhX9V"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def alg_mean(train_df, label1, label2):\n",
        "\n",
        "  avg_links_0, avg_nodes_0, avg_density_0, avg_centrality_0, avg_clust_0, avg_transitivity_0 = 0,0,0,0,0,0\n",
        "  avg_links_1, avg_nodes_1, avg_density_1, avg_centrality_1, avg_clust_1, avg_transitivity_1 = 0,0,0,0,0,0\n",
        "\n",
        "  avg_links_0 = train_df.loc[train_df['label'] == label1, 'links'].mean()\n",
        "  avg_nodes_0 = train_df.loc[train_df['label'] == label1, 'nodes'].mean()\n",
        "  avg_density_0 = train_df.loc[train_df['label'] == label1, 'density'].mean()\n",
        "  avg_centrality_0 = train_df.loc[train_df['label'] == label1, 'centrality'].mean()\n",
        "  avg_clust_0 = train_df.loc[train_df['label'] == label1, 'clustering'].mean()\n",
        "  avg_transitivity_0 = train_df.loc[train_df['label'] == label1, 'transitivity'].mean()\n",
        "\n",
        "  avg_links_1 = train_df.loc[train_df['label'] == label2, 'links'].mean()\n",
        "  avg_nodes_1 = train_df.loc[train_df['label'] == label2, 'nodes'].mean()\n",
        "  avg_density_1 = train_df.loc[train_df['label'] == label2, 'density'].mean()\n",
        "  avg_centrality_1 = train_df.loc[train_df['label'] == label2, 'centrality'].mean()\n",
        "  avg_clust_1 = train_df.loc[train_df['label'] == label2, 'clustering'].mean()\n",
        "  avg_transitivity_1 = train_df.loc[train_df['label'] == label2, 'transitivity'].mean()\n",
        "\n",
        "  tr_dist_0 = (avg_links_0, avg_nodes_0, avg_density_0, avg_centrality_0, avg_clust_0, avg_transitivity_0)\n",
        "  tr_dist_1 = (avg_links_1, avg_nodes_1, avg_density_1, avg_centrality_1, avg_clust_1, avg_transitivity_1)\n",
        "\n",
        "  return tr_dist_0, tr_dist_1"
      ],
      "metadata": {
        "id": "Bvv6V4n3i5hT"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def array_to_tuple(test_df):\n",
        "  tpl_list = []\n",
        "\n",
        "  for i in range(len(test_df)):\n",
        "    tpl = test_df.loc[i]\n",
        "    tpl_list.append((tpl[0], tpl[1], tpl[2], tpl[3], tpl[4], tpl[5]))\n",
        "  return tpl_list"
      ],
      "metadata": {
        "id": "2h9CILC7kDcP"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prediction(tpl_list, label1, label2):\n",
        "  predicted = []\n",
        "  for i in range(len(tpl_list)): \n",
        "    one = math.dist(tr_dist_1, tpl_list[i])\n",
        "    zero = math.dist(tr_dist_0, tpl_list[i])\n",
        "    if one > zero: \n",
        "      predicted.append(label1)\n",
        "    else:\n",
        "      predicted.append(label2)\n",
        "  return predicted"
      ],
      "metadata": {
        "id": "wEXPABY-kkvw"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1 -Dataset transformation**"
      ],
      "metadata": {
        "id": "5xDvVOGM32nr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1 - prepare the data"
      ],
      "metadata": {
        "id": "3DLeUzjD4-tR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "import the dataset"
      ],
      "metadata": {
        "id": "__-5luUr3_pi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, Y_train), (X_test, Y_test) = mnist.load_data()"
      ],
      "metadata": {
        "id": "hV8uZA4Nip_u"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "retrieve only the 3 and 5 images since they are the most mispredicted "
      ],
      "metadata": {
        "id": "Y9Q9xbjk4EIg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, y_train, x_test, y_test = [], [], [], []\n",
        "\n",
        "for i in range(len(X_train)): \n",
        "  if Y_train[i] == 5 or  Y_train[i] == 3:\n",
        "    x_train.append(X_train[i])\n",
        "    y_train.append(Y_train[i])\n",
        "\n",
        "for i in range(len(X_test)): \n",
        "  if Y_test[i] == 5 or  Y_test[i] == 3:\n",
        "    x_test.append(X_test[i])\n",
        "    y_test.append(Y_test[i])"
      ],
      "metadata": {
        "id": "Dax3VlPM3oRz"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Transform the images into tensor\n",
        "\n",
        "\n",
        "*   greyscale\n",
        "*   horizontal flip in te_flp\n",
        "\n"
      ],
      "metadata": {
        "id": "EHctOmGS4PWv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "transform2 = T.Grayscale()\n",
        "x_tr, x_te, x_te_flp = [], [], []\n",
        "\n",
        "for i in range(len(x_train)): \n",
        "  x_tr.append(transform2(torch.tensor(x_train[i]).unsqueeze_(-1).permute(2,0,1)))\n",
        "\n",
        "for i in range(len(x_test)): \n",
        "  x_te_flp.append(Tf.hflip(torch.tensor(x_test[i]).unsqueeze_(-1).permute(2,0,1)))\n",
        "\n",
        "for i in range(len(x_test)): \n",
        "  x_te.append(torch.tensor(x_test[i]).unsqueeze_(-1).permute(2,0,1))"
      ],
      "metadata": {
        "id": "6XZwLHQTmM79"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 - define the Adj. Matrix"
      ],
      "metadata": {
        "id": "zBwP1JBW5Hso"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "set to 'black' all the values smaller than the treshold (set to mean(matrix)\\10 in this case)"
      ],
      "metadata": {
        "id": "LY5hS6f94lvx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#trasform x_ train in 255-0 form\n",
        "for tens in tqdm_notebook(range(len(x_tr))): \n",
        "  ex = x_tr[tens]\n",
        "  mean = tensor_mean(ex)\n",
        "  for h in range(ex.shape[1]): \n",
        "    for l in range(ex.shape[2]):\n",
        "\n",
        "      if h == l: \n",
        "        ex[0][h][l] = 0\n",
        "      elif ex[0][h][l] > mean*10: \n",
        "        ex[0][h][l] = 0 \n",
        "\n",
        "  x_tr[tens] = ex"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "fdb5f5e46e224eebb3c6b95bb3966290",
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            "82e9188565db43c089e9e820f9c428ad",
            "f5eeeda2272546198910f6bceeea66e3",
            "feba04855b4c48208077b40f58bdf450"
          ]
        },
        "id": "Y5Y-fwXguczg",
        "outputId": "8bb74818-b993-4228-ff72-dfd16e694552"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/11552 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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    },
    {
      "cell_type": "code",
      "source": [
        "for tens in tqdm_notebook(range(len(x_te))): \n",
        "  ex = x_te[tens]\n",
        "  mean = tensor_mean(ex)\n",
        "  for h in range(ex.shape[1]): \n",
        "    for l in range(ex.shape[2]):\n",
        "\n",
        "      if h == l: \n",
        "        ex[0][h][l] = 0\n",
        "      elif ex[0][h][l] > mean*10: \n",
        "        ex[0][h][l] = 0 \n",
        "\n",
        "  x_te[tens] = ex"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        "id": "_FYVTEfextPN",
        "outputId": "f250080b-0235-4550-b93b-4d3755523b18"
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      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1902 [00:00<?, ?it/s]"
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    {
      "cell_type": "code",
      "source": [
        "for tens in tqdm_notebook(range(len(x_te_flp))): \n",
        "  ex = x_te_flp[tens]\n",
        "  mean = tensor_mean(ex)\n",
        "  for h in range(ex.shape[1]): \n",
        "    for l in range(ex.shape[2]):\n",
        "\n",
        "      if h == l: \n",
        "        ex[0][h][l] = 0\n",
        "      elif ex[0][h][l] > mean*10: \n",
        "        ex[0][h][l] = 0 \n",
        "\n",
        "  x_te_flp[tens] = ex"
      ],
      "metadata": {
        "id": "mfI5X_cgx1gV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        "outputId": "eb725b48-2ba3-41c5-cfe6-3c9fd0d01fd4"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1902 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e78cffc1f337468780d707d8006fc36a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show(torch.tensor(x_tr[1]))"
      ],
      "metadata": {
        "id": "w1l6FEd8ySdv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "cdd64be7-339a-4902-ebd5-17a1101db8c3"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-114-0595a3ce1c85>:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  show(torch.tensor(x_tr[1]))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAcSklEQVR4nO3df3DU9b3v8dfyIytoshhCfknAAApWIF4pxIgiSoaQzuHw63TAH3PB4+ARgy2i1ZuOgrSdSYtnrKND8cy9FeocwR8zQirH4tFgwqgJlgiXQ1tTwk1LkCQod7IbggmBfO4fXFdXEvS77OadhOdj5jtDdr/v7Mevq0++2d1vfM45JwAAetgA6wUAAC5NBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJgYZL2Ab+rs7NSxY8eUmJgon89nvRwAgEfOObW0tCgzM1MDBnR/ntPrAnTs2DFlZWVZLwMAcJHq6+s1cuTIbu/vdQFKTEyUJN2iH2iQBhuvBgDg1Rl16H29Ff7/eXfiFqANGzbo6aefVmNjo3JycvT8889r2rRp3zr35Y/dBmmwBvkIEAD0Of//CqPf9jJKXN6E8Oqrr2r16tVau3atPv74Y+Xk5KigoEDHjx+Px8MBAPqguATomWee0fLly3Xvvffqe9/7nl544QUNHTpUL774YjweDgDQB8U8QKdPn1Z1dbXy8/O/epABA5Sfn6/Kysrz9m9vb1coFIrYAAD9X8wD9Pnnn+vs2bNKS0uLuD0tLU2NjY3n7V9SUqJAIBDeeAccAFwazD+IWlxcrGAwGN7q6+utlwQA6AExfxdcSkqKBg4cqKampojbm5qalJ6eft7+fr9ffr8/1ssAAPRyMT8DSkhI0JQpU1RWVha+rbOzU2VlZcrLy4v1wwEA+qi4fA5o9erVWrp0qb7//e9r2rRpevbZZ9Xa2qp77703Hg8HAOiD4hKgxYsX67PPPtOaNWvU2NioG264QTt37jzvjQkAgEuXzznnrBfxdaFQSIFAQDM1jyshAEAfdMZ1qFylCgaDSkpK6nY/83fBAQAuTQQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAICJQdYLAOLBN+X6qOY6E7z/J/HpzMs9z/zpod94nulwZz3P9EezDv6T55nL5zVE9VidbW1RzeG74QwIAGCCAAEATMQ8QE899ZR8Pl/ENmHChFg/DACgj4vLa0DXX3+93n333a8eZBAvNQEAIsWlDIMGDVJ6eno8vjUAoJ+Iy2tAhw4dUmZmpsaMGaO7775bR44c6Xbf9vZ2hUKhiA0A0P/FPEC5ubnavHmzdu7cqY0bN6qurk633nqrWlpauty/pKREgUAgvGVlZcV6SQCAXijmASosLNQPf/hDTZ48WQUFBXrrrbfU3Nys1157rcv9i4uLFQwGw1t9fX2slwQA6IXi/u6AYcOG6dprr1VtbW2X9/v9fvn9/ngvAwDQy8T9c0AnT57U4cOHlZGREe+HAgD0ITEP0KOPPqqKigr97W9/04cffqgFCxZo4MCBuvPOO2P9UACAPizmP4I7evSo7rzzTp04cUIjRozQLbfcoqqqKo0YMSLWDwUA6MN8zjlnvYivC4VCCgQCmql5GuQbbL0cxJjLy/E8c2hZgueZX9+x1fOMJA32nfE8kz+k63d4XsiAKH740KlOzzM454YP/zmquewVxzzPnP38RFSP1Z+ccR0qV6mCwaCSkpK63Y9rwQEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJuL+C+mAr3O/+L+eZz6Z8EYcVoJLyf6bX4xqriD3Qc8z/v/gYqTfFWdAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMMHVsNGjPi3P8j40Ifbr6E5lm9/zzD+/tdz7A/m8j8hFMROlm278q+eZTVf/ZxxWgv6MMyAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQXI0WPyvr5h55nFrx2ZxxW0jXf6Q7PM9fU7YnDSmw1pwz3PPNuVaLnmfwhLZ5nonHHfy2Oai7pvT95numM6pEuTZwBAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmuBgper2zNbXWS7jkNC281vPMpITSKB7JH8WMd8eOJUc1d8Wp/xPjleDrOAMCAJggQAAAE54DtHv3bs2dO1eZmZny+Xzavn17xP3OOa1Zs0YZGRkaMmSI8vPzdejQoVitFwDQT3gOUGtrq3JycrRhw4Yu71+/fr2ee+45vfDCC9qzZ48uv/xyFRQUqK2t7aIXCwDoPzy/CaGwsFCFhYVd3uec07PPPqsnnnhC8+bNkyS99NJLSktL0/bt27VkyZKLWy0AoN+I6WtAdXV1amxsVH5+fvi2QCCg3NxcVVZWdjnT3t6uUCgUsQEA+r+YBqixsVGSlJaWFnF7Wlpa+L5vKikpUSAQCG9ZWVmxXBIAoJcyfxdccXGxgsFgeKuvr7deEgCgB8Q0QOnp6ZKkpqamiNubmprC932T3+9XUlJSxAYA6P9iGqDs7Gylp6errKwsfFsoFNKePXuUl5cXy4cCAPRxnt8Fd/LkSdXWfnVplLq6Ou3fv1/JyckaNWqUVq1apV/84he65pprlJ2drSeffFKZmZmaP39+LNcNAOjjPAdo7969uv3228Nfr169WpK0dOlSbd68WY899phaW1t1//33q7m5Wbfccot27typyy67LHarBgD0eT7nnLNexNeFQiEFAgHN1DwN8g22Xg7Qp322IroffU+45xPPM5uu/s+oHqsnLMiZE9Xc2c9PxHgll4YzrkPlKlUwGLzg6/rm74IDAFyaCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYMLzr2MAcPGOr7zZ88zSFW95nrkn6V89z0hS4oCEqOZ6ws8/u9HzjGs/HYeV4GJxBgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmOBipOhRA68f73nmr/de6XnmtlsOep6J1tGbTnqe+aj4ec8zner0PCP13EVFazvOeJ5ZvPERzzOjtjV5nulsOex5BvHHGRAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIKLkSJqbvoNnmeWbdrmeWbe5Z97nulRn3of+Yerpnqe+f2nf/T+QD3oR7WLPc9c9asPPc+c9TyB3oozIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABBcjRY8aKOd5ZkA//HvS28f2e54pyPR+AdMdn1Z7nonWzuu8X2j21ruLPM8EXq7yPIPeqf/9lw0A6BMIEADAhOcA7d69W3PnzlVmZqZ8Pp+2b98ecf+yZcvk8/kitjlz5sRqvQCAfsJzgFpbW5WTk6MNGzZ0u8+cOXPU0NAQ3rZu3XpRiwQA9D+e34RQWFiowsLCC+7j9/uVnp4e9aIAAP1fXF4DKi8vV2pqqsaPH68VK1boxIkT3e7b3t6uUCgUsQEA+r+YB2jOnDl66aWXVFZWpl/96leqqKhQYWGhzp7t+je5l5SUKBAIhLesrKxYLwkA0AvF/HNAS5YsCf950qRJmjx5ssaOHavy8nLNmjXrvP2Li4u1evXq8NehUIgIAcAlIO5vwx4zZoxSUlJUW1vb5f1+v19JSUkRGwCg/4t7gI4ePaoTJ04oIyMj3g8FAOhDPP8I7uTJkxFnM3V1ddq/f7+Sk5OVnJysdevWadGiRUpPT9fhw4f12GOPady4cSooKIjpwgEAfZvnAO3du1e33357+OsvX79ZunSpNm7cqAMHDuh3v/udmpublZmZqdmzZ+vnP/+5/H5/7FYNAOjzPAdo5syZcq77C0q+/fbbF7Ug9B2+D/Z7nvntfO9Xxfgfy4Z7nhn19mnPM5I08IszUc31iJu9j/zDVdE91O8//WN0g4AHXAsOAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJmL+K7mBCzn75796nhnzWBwWcokYOGJEVHP/eNVUzzNcQRtecQYEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJjgYqRAP9a0cFxUcx+uecvzTDQXMNXd3kfQf3AGBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCY4GKk/YzP7/c80/zD/xbVY11Z+ifPM50tLVE9FqSGR272PFP6o/VRPpr359HvP/2j55l/vMrzCPoRzoAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABNcjLQXa5s7zfNM4NEjnmcqxj3veUaSFvzxTu9DNf3vYqSDMtI9z3z6T2M8z7z60L96nskc5P2iotFqOtvueebUwlzPM0Pf2ON5Br0TZ0AAABMECABgwlOASkpKNHXqVCUmJio1NVXz589XTU1NxD5tbW0qKirS8OHDdcUVV2jRokVqamqK6aIBAH2fpwBVVFSoqKhIVVVVeuedd9TR0aHZs2ertbU1vM/DDz+sN998U6+//roqKip07NgxLVy4MOYLBwD0bZ7ehLBz586Irzdv3qzU1FRVV1drxowZCgaD+u1vf6stW7bojjvukCRt2rRJ1113naqqqnTTTTfFbuUAgD7tol4DCgaDkqTk5GRJUnV1tTo6OpSfnx/eZ8KECRo1apQqKyu7/B7t7e0KhUIRGwCg/4s6QJ2dnVq1apWmT5+uiRMnSpIaGxuVkJCgYcOGReyblpamxsbGLr9PSUmJAoFAeMvKyop2SQCAPiTqABUVFengwYN65ZVXLmoBxcXFCgaD4a2+vv6ivh8AoG+I6oOoK1eu1I4dO7R7926NHDkyfHt6erpOnz6t5ubmiLOgpqYmpad3/WE9v98vv7/nPiwHAOgdPJ0BOee0cuVKbdu2Tbt27VJ2dnbE/VOmTNHgwYNVVlYWvq2mpkZHjhxRXl5ebFYMAOgXPJ0BFRUVacuWLSotLVViYmL4dZ1AIKAhQ4YoEAjovvvu0+rVq5WcnKykpCQ99NBDysvL4x1wAIAIngK0ceNGSdLMmTMjbt+0aZOWLVsmSfr1r3+tAQMGaNGiRWpvb1dBQYF+85vfxGSxAID+w+ecc9aL+LpQKKRAIKCZmqdBvsHWyzF124EvPM88MvxgHFbSteve/RfvQyf737/TJTd3/RGDC1mXus/zTKc6Pc9Ea+nfCjzP1G4a73lm+P/yfuzQ+51xHSpXqYLBoJKSkrrdj2vBAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwERUvxEVkKS/5P+b9RL6MO9/96ts8/6bg5fv+e+eZyRp3PJDnmeGt3Jla3jDGRAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIKLkfZiu3403fPMSw9O8zzzv6e/6Hmmv/r3UJbnmYaOYZ5nXvzY+7/bcf/zrOeZMR/s9zwjSZ1RTQHecAYEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJjgYqS92MDyjz3PZH801PPMlB/92POMJP3uX571PDMxwed55o7/Wux5Jlie7nlGkka/+qnnmTN1f/c8c42qPc8A/Q1nQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACZ9zzlkv4utCoZACgYBmap4G+QZbLwcA4NEZ16FylSoYDCopKanb/TgDAgCYIEAAABOeAlRSUqKpU6cqMTFRqampmj9/vmpqaiL2mTlzpnw+X8T2wAMPxHTRAIC+z1OAKioqVFRUpKqqKr3zzjvq6OjQ7Nmz1draGrHf8uXL1dDQEN7Wr18f00UDAPo+T78RdefOnRFfb968WampqaqurtaMGTPCtw8dOlTp6dH9RkoAwKXhol4DCgaDkqTk5OSI219++WWlpKRo4sSJKi4u1qlTp7r9Hu3t7QqFQhEbAKD/83QG9HWdnZ1atWqVpk+frokTJ4Zvv+uuuzR69GhlZmbqwIEDevzxx1VTU6M33nijy+9TUlKidevWRbsMAEAfFfXngFasWKE//OEPev/99zVy5Mhu99u1a5dmzZql2tpajR079rz729vb1d7eHv46FAopKyuLzwEBQB/1XT8HFNUZ0MqVK7Vjxw7t3r37gvGRpNzcXEnqNkB+v19+vz+aZQAA+jBPAXLO6aGHHtK2bdtUXl6u7Ozsb53Zv3+/JCkjIyOqBQIA+idPASoqKtKWLVtUWlqqxMRENTY2SpICgYCGDBmiw4cPa8uWLfrBD36g4cOH68CBA3r44Yc1Y8YMTZ48OS7/AACAvsnTa0A+n6/L2zdt2qRly5apvr5e99xzjw4ePKjW1lZlZWVpwYIFeuKJJy74c8Cv41pwANC3xeU1oG9rVVZWlioqKrx8SwDAJYprwQEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATAyyXsA3OeckSWfUITnjxQAAPDujDklf/f+8O70uQC0tLZKk9/WW8UoAABejpaVFgUCg2/t97tsS1cM6Ozt17NgxJSYmyufzRdwXCoWUlZWl+vp6JSUlGa3QHsfhHI7DORyHczgO5/SG4+CcU0tLizIzMzVgQPev9PS6M6ABAwZo5MiRF9wnKSnpkn6CfYnjcA7H4RyOwzkch3Osj8OFzny+xJsQAAAmCBAAwESfCpDf79fatWvl9/utl2KK43AOx+EcjsM5HIdz+tJx6HVvQgAAXBr61BkQAKD/IEAAABMECABgggABAEz0mQBt2LBBV199tS677DLl5ubqo48+sl5Sj3vqqafk8/kitgkTJlgvK+52796tuXPnKjMzUz6fT9u3b4+43zmnNWvWKCMjQ0OGDFF+fr4OHTpks9g4+rbjsGzZsvOeH3PmzLFZbJyUlJRo6tSpSkxMVGpqqubPn6+ampqIfdra2lRUVKThw4friiuu0KJFi9TU1GS04vj4Lsdh5syZ5z0fHnjgAaMVd61PBOjVV1/V6tWrtXbtWn388cfKyclRQUGBjh8/br20Hnf99deroaEhvL3//vvWS4q71tZW5eTkaMOGDV3ev379ej333HN64YUXtGfPHl1++eUqKChQW1tbD680vr7tOEjSnDlzIp4fW7du7cEVxl9FRYWKiopUVVWld955Rx0dHZo9e7ZaW1vD+zz88MN688039frrr6uiokLHjh3TwoULDVcde9/lOEjS8uXLI54P69evN1pxN1wfMG3aNFdUVBT++uzZsy4zM9OVlJQYrqrnrV271uXk5Fgvw5Qkt23btvDXnZ2dLj093T399NPh25qbm53f73dbt241WGHP+OZxcM65pUuXunnz5pmsx8rx48edJFdRUeGcO/fvfvDgwe71118P7/OXv/zFSXKVlZVWy4y7bx4H55y77bbb3I9//GO7RX0Hvf4M6PTp06qurlZ+fn74tgEDBig/P1+VlZWGK7Nx6NAhZWZmasyYMbr77rt15MgR6yWZqqurU2NjY8TzIxAIKDc395J8fpSXlys1NVXjx4/XihUrdOLECeslxVUwGJQkJScnS5Kqq6vV0dER8XyYMGGCRo0a1a+fD988Dl96+eWXlZKSookTJ6q4uFinTp2yWF63et3FSL/p888/19mzZ5WWlhZxe1pamj755BOjVdnIzc3V5s2bNX78eDU0NGjdunW69dZbdfDgQSUmJlovz0RjY6Mkdfn8+PK+S8WcOXO0cOFCZWdn6/Dhw/rpT3+qwsJCVVZWauDAgdbLi7nOzk6tWrVK06dP18SJEyWdez4kJCRo2LBhEfv25+dDV8dBku666y6NHj1amZmZOnDggB5//HHV1NTojTfeMFxtpF4fIHylsLAw/OfJkycrNzdXo0eP1muvvab77rvPcGXoDZYsWRL+86RJkzR58mSNHTtW5eXlmjVrluHK4qOoqEgHDx68JF4HvZDujsP9998f/vOkSZOUkZGhWbNm6fDhwxo7dmxPL7NLvf5HcCkpKRo4cOB572JpampSenq60ap6h2HDhunaa69VbW2t9VLMfPkc4PlxvjFjxiglJaVfPj9WrlypHTt26L333ov49S3p6ek6ffq0mpubI/bvr8+H7o5DV3JzcyWpVz0fen2AEhISNGXKFJWVlYVv6+zsVFlZmfLy8gxXZu/kyZM6fPiwMjIyrJdiJjs7W+np6RHPj1AopD179lzyz4+jR4/qxIkT/er54ZzTypUrtW3bNu3atUvZ2dkR90+ZMkWDBw+OeD7U1NToyJEj/er58G3HoSv79++XpN71fLB+F8R38corrzi/3+82b97s/vznP7v777/fDRs2zDU2NlovrUc98sgjrry83NXV1bkPPvjA5efnu5SUFHf8+HHrpcVVS0uL27dvn9u3b5+T5J555hm3b98+9/e//90559wvf/lLN2zYMFdaWuoOHDjg5s2b57Kzs90XX3xhvPLYutBxaGlpcY8++qirrKx0dXV17t1333U33niju+aaa1xbW5v10mNmxYoVLhAIuPLyctfQ0BDeTp06Fd7ngQcecKNGjXK7du1ye/fudXl5eS4vL89w1bH3bcehtrbW/exnP3N79+51dXV1rrS01I0ZM8bNmDHDeOWR+kSAnHPu+eefd6NGjXIJCQlu2rRprqqqynpJPW7x4sUuIyPDJSQkuKuuusotXrzY1dbWWi8r7t577z0n6bxt6dKlzrlzb8V+8sknXVpamvP7/W7WrFmupqbGdtFxcKHjcOrUKTd79mw3YsQIN3jwYDd69Gi3fPnyfveXtK7++SW5TZs2hff54osv3IMPPuiuvPJKN3ToULdgwQLX0NBgt+g4+LbjcOTIETdjxgyXnJzs/H6/GzdunPvJT37igsGg7cK/gV/HAAAw0etfAwIA9E8ECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgIn/B0ts4a0az1hAAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "reshape to obtain a Matrix"
      ],
      "metadata": {
        "id": "3H5a1WxN5aP8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dim1, dim2 = 28, 28\n",
        "train, test, test_flp = [], [], []\n",
        "\n",
        "for i in range(len(x_tr)):\n",
        "  train.append(x_tr[i].reshape(dim1, dim2).numpy())\n",
        "\n",
        "for i in range(len(x_te)):\n",
        "  test.append(x_te[i].reshape(dim1, dim2).numpy())\n",
        "\n",
        "for i in range(len(x_te_flp)):\n",
        "  test_flp.append(x_te_flp[i].reshape(dim1, dim2).numpy())"
      ],
      "metadata": {
        "id": "MWZBHAbV6ss9"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(train[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "izVv7fNNgdn8",
        "outputId": "d7cc2a3b-b401-4fdc-dc69-39ae2924650f"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3 - Trasform the Adj. Matrices in to a network object"
      ],
      "metadata": {
        "id": "sCHLR15r5gn-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "net_train, net_test, net_test_flp = [], [], []\n",
        "\n",
        "for i in range(len(train)):\n",
        "  provnet = nx.from_numpy_array(train[i], parallel_edges=False, create_using=None)\n",
        "  provnet.remove_nodes_from(list(nx.isolates(provnet)))\n",
        "  net_train.append(provnet)\n",
        "\n",
        "for i in range(len(test)):\n",
        "  provnet = nx.from_numpy_array(test[i], parallel_edges=False, create_using=None)\n",
        "  provnet.remove_nodes_from(list(nx.isolates(provnet)))\n",
        "  net_test.append(provnet)\n",
        "\n",
        "for i in range(len(test_flp)):\n",
        "  provnet = nx.from_numpy_array(test_flp[i], parallel_edges=False, create_using=None)\n",
        "  provnet.remove_nodes_from(list(nx.isolates(provnet)))\n",
        "  net_test_flp.append(provnet)"
      ],
      "metadata": {
        "id": "G1Po5mmCg1zs"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2 - Algorithm**"
      ],
      "metadata": {
        "id": "KDseHQ0q5r7k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 - Training "
      ],
      "metadata": {
        "id": "FV681OZJ52am"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "derive the metrics"
      ],
      "metadata": {
        "id": "SmPJQ9Az5zaO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = metrics_definition(net_train, y_train)\n",
        "test_df = metrics_definition(net_test, y_test)\n",
        "test_flp_df = metrics_definition(net_test_flp, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "5f5619871246443ebc6ef78e4231c836",
            "c87bf51d385c446e86c1e02a3728391e",
            "4c85facae76c4757afcf53a053761780",
            "ad411d4c1e9e4bf0804ac65cab31b2bf",
            "4b9baab9a7f64088a543de4194c8843e",
            "54c7b0face9b4e4ca8df2bcdd0139edb",
            "869488516a3b4609b8a1ba1ac6d321a2",
            "ecc5088d80e5425c91c1dfc272875516",
            "780fc85098f0420a81c9b670afab8b7a",
            "4ea8d934283943e295450461b2a7814f",
            "784ff703fb4f4da19a7ef730f74069e1",
            "1680efa7c744414394e88732c2b1b9ef",
            "ce0a7774973246f59955bb07626013fa",
            "80365b6b46064e60b6a27bc5973ccc5c",
            "ea77cb20e3184d6abe3af58dad246a24",
            "24b4c271d779451db10c56c3e63ac799",
            "c3bc39f4ec7c4ef393eaf06f3f0cea8c",
            "fef3dc9477804d75a5a0c71334a4b855",
            "ee8002ba648f4e2d97f726e8d4205a95",
            "6e8c41fad9174b55a5308405c13a02b2",
            "b1ca371d2f544616b63ac7eff6fa300b",
            "6cd4383e863d44b19e5c77a0690da694",
            "531d8dad504249189477bc9bdf20529b",
            "a9e966c072dd4c13bd52dfd5ec5a9be3",
            "20b4dfc6bfd24e2680478fe8a8fe3021",
            "525618c804d04aaa8b18f588864e248b",
            "37d5d47da70948aeb2bcbbca9ed7ed9a",
            "52e29a406f6147abbe9d3062256ff209",
            "1421fcef8a4f4cf4b47af8701eca4192",
            "9179ae23fe984e01ab6b0513d598902e",
            "1f20971790cf4b26aa07af56e7982617",
            "96097549f2df408c9973f12a387f801d",
            "bdc7221f414148b2a3269b2df2815767"
          ]
        },
        "id": "ZJwrHuRvhWTX",
        "outputId": "1f752df4-7c50-4ce7-8a5f-35c3ef8c2acc"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/11552 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f5619871246443ebc6ef78e4231c836"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1902 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1680efa7c744414394e88732c2b1b9ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1902 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "531d8dad504249189477bc9bdf20529b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "train the algorithm"
      ],
      "metadata": {
        "id": "FEnt2EHkPBHM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tr_dist_0, tr_dist_1 = alg_mean(train_df, 3, 5)\n",
        "te_dist_0, te_dist_1 = alg_mean(test_df, 3, 5)\n",
        "te_flp_dist_0, te_flp_dist_1 = alg_mean(test_flp_df, 3, 5)"
      ],
      "metadata": {
        "id": "g_eUiP7Ph1Yq"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "transform the results in tuples"
      ],
      "metadata": {
        "id": "wa4TOo1NPFZa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tpl_te_list = array_to_tuple(test_df)\n",
        "tpl_te_flp_list = array_to_tuple(test_flp_df)"
      ],
      "metadata": {
        "id": "T8Ztwf_3kQSX"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 - Prediction"
      ],
      "metadata": {
        "id": "yunbkPQV6BdC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predict_te, predict_te_flp = prediction(tpl_te_list, 3, 5), prediction(tpl_te_flp_list, 3, 5)"
      ],
      "metadata": {
        "id": "j7MqiPhNk0Gl"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Results"
      ],
      "metadata": {
        "id": "ImYD19g76GTB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ytest = test_df['label']\n",
        "ytest_flp = test_flp_df['label']\n",
        "\n",
        "cnf_test = metrics.confusion_matrix(ytest, predict_te)\n",
        "report_te = metrics.classification_report(ytest, predict_te)\n",
        "\n",
        "\n",
        "cnf_test_flp = metrics.confusion_matrix(ytest, predict_te_flp)\n",
        "report_te_flp = metrics.classification_report(ytest, predict_te_flp)\n"
      ],
      "metadata": {
        "id": "lCXk5tkYlBK-"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"----------------------------TEST--------------------------------\\n\")\n",
        "print(report_te, \"\\n Confusion matrix \\n\", cnf_test)\n",
        "print(\"\\n---------------------------------------------------------------- \")\n",
        "print(\"---------------------------------------------------------------- \\n\")\n",
        "print(\"----------------------------FLIP TEST---------------------------\\n\")\n",
        "print(report_te_flp, \"\\n Confusion matrix \\n\", cnf_test_flp)"
      ],
      "metadata": {
        "id": "LSYYtcr2lR63"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnf_test_flp, print(report_te_flp)"
      ],
      "metadata": {
        "id": "UywaphwAlTpV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}