Calculate L1 Sensitivity For Threshold.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Calculate L1 Sensitivity For Threshold.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/resilientmax/60DaysOfUdacity/blob/master/Calculate%20L1%20Sensitivity%20For%20Threshold.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GP-lP-2i2lnl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RA4lcPxN2foQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_parallel_db(db, remove_index):\n",
        "\n",
        "    return torch.cat((db[0:remove_index], \n",
        "                      db[remove_index+1:]))\n",
        "\n",
        "\n",
        "def get_parallel_dbs(db):\n",
        "\n",
        "    parallel_dbs = list()\n",
        "\n",
        "    for i in range(len(db)):\n",
        "        pdb = get_parallel_db(db, i)\n",
        "        parallel_dbs.append(pdb)\n",
        "    \n",
        "    return parallel_dbs\n",
        "\n",
        "\n",
        "def create_db_and_parallels(num_entries):\n",
        "    \n",
        "    db = torch.rand(num_entries) > 0.5\n",
        "    pdbs = get_parallel_dbs(db)\n",
        "    \n",
        "    return db, pdbs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hyHLGV692U-u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sensitivity(query, n_entries=1000):\n",
        "\n",
        "    db, pdbs = create_db_and_parallels(n_entries)\n",
        "    \n",
        "    full_db_result = query(db)\n",
        "    \n",
        "    max_distance = 0\n",
        "    for pdb in pdbs:\n",
        "        pdb_result = query(pdb)\n",
        "\n",
        "        db_distance = torch.abs(pdb_result - full_db_result)\n",
        "\n",
        "        if(db_distance > max_distance):\n",
        "            max_distance = db_distance\n",
        "            \n",
        "    return max_distance"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxSogKXA2FBK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def query(db, threshold=5):\n",
        "    return (db.sum() > threshold).float()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMGfhO6z2P13",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "920cb636-3189-4d71-f0fd-72b643cfb4cc"
      },
      "source": [
        "for i in range(10):\n",
        "    sens_f = sensitivity(query, n_entries=10)\n",
        "    print(sens_f)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "0\n",
            "0\n",
            "0\n",
            "tensor(1.)\n",
            "0\n",
            "0\n",
            "tensor(1.)\n",
            "0\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EGF6m6tY3Q2-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}