GA_Student_Mentor.ipynb

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      "name": "Public FINAL VERSION",
      "version": "0.3.2",
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      "collapsed_sections": []
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      "name": "python3",
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  "cells": [
    {
      "metadata": {
        "id": "C4xBAeOQdbT7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Author**: Dee\n",
        "\n",
        "**Email**: deetungsten@gmail.com\n",
        "\n",
        "**Date**: December 12th, 2018\n",
        "\n",
        "**Version**: 0.9\n",
        "\n",
        "**Purpose**: For Pytorch Facebook Challenge Student-Mentor Program. Optimizes and pairs students and mentors automatically according to their experience and language. \n"
      ]
    },
    {
      "metadata": {
        "id": "S5TsOfPEd5qP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "1.   Importing the necessary files from Google Drive\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "cs5RfqwwdiK7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install deap\n",
        "!pip install pydrive\n",
        "import os\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# 1. Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "# choose a local (colab) directory to store the data.\n",
        "local_download_path = os.path.expanduser('~/data')\n",
        "try:\n",
        "  os.makedirs(local_download_path)\n",
        "except: pass\n",
        "\n",
        "# 2. Auto-iterate using the query syntax\n",
        "#    https://developers.google.com/drive/v2/web/search-parameters\n",
        "file_list = drive.ListFile(\n",
        "    {'q': \"'REPLACE FOLDER ID HERE (KEEP PARENTHESIS)' in parents\"}).GetList()\n",
        "\n",
        "for f in file_list:\n",
        "  # 3. Create & download by id.\n",
        "  print('title: %s, id: %s' % (f['title'], f['id']))\n",
        "  fname = os.path.join(local_download_path, f['title'])\n",
        "  print('downloading to {}'.format(fname))\n",
        "  f_ = drive.CreateFile({'id': f['id']})\n",
        "  f_.GetContentFile(fname)\n",
        "  \n",
        "print(\"Files successfully imported from Google Drive\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gIoyawbgeDIl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "1.   Imports the CSV files and load them to a pandas dataframe\n",
        "2.   Sets the weights of importance of the traits (500 for language, 200 for skill difference)\n",
        "\n",
        "1.   Goes through all of the students and mentors and assign the relationship weights\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "9Q1pfTUkfaU1",
        "colab_type": "code",
        "outputId": "250d3e00-8f75-4e9d-e971-f2a46f914940",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        }
      },
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "\n",
        "from deap import base, creator, tools\n",
        "import pandas as pd\n",
        "language_weight = 500\n",
        "skill_weights = 200\n",
        "\n",
        "skill = {'Beginner':0, 'Intermediate':1, 'Advanced':2}\n",
        "\n",
        "students = pd.read_csv(\"/root/data/students.csv\")\n",
        "mentors = pd.read_csv(\"/root/data/mentors.csv\")\n",
        "\n",
        "print(\"List of Students\")\n",
        "display(students)\n",
        "\n",
        "print(\"List of Mentors\")\n",
        "display(mentors)\n",
        "\n",
        "number_of_students = len(students)\n",
        "number_of_mentors = len(mentors)\n",
        "\n",
        "relationship_m = np.zeros((number_of_students, number_of_mentors))\n",
        "\n",
        "for index_s, student in students.iterrows():\n",
        "    student_level = skill[student['Skill']]\n",
        "    for index_m, mentor in mentors.iterrows():\n",
        "        score = 0\n",
        "        if student['Language'] == mentor['Language']:\n",
        "            score += language_weight\n",
        "        mentor_level = skill[mentor['Skill']]\n",
        "        skill_difference = mentor_level - student_level\n",
        "        if skill_difference > 0:\n",
        "            score += skill_weights\n",
        "        relationship_m[index_s,index_m] = score\n",
        "\n",
        "print(\"Relationship Matrix\")\n",
        "print(relationship_m)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "List of Students\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Skill</th>\n",
              "      <th>Language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Test1</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>Chinese</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Test2</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Italian</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Test3</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Test4</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>Vietnamese</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Test5</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name         Skill    Language\n",
              "0  Test1      Beginner     Chinese\n",
              "1  Test2  Intermediate     Italian\n",
              "2  Test3      Beginner     English\n",
              "3  Test4      Beginner  Vietnamese\n",
              "4  Test5  Intermediate     English"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "List of Mentors\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Skill</th>\n",
              "      <th>Language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Test6</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>Chinese</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Test7</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>Italian</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Test8</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Test9</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Vietnamese</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Test10</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "     Name         Skill    Language\n",
              "0   Test6      Advanced     Chinese\n",
              "1   Test7      Advanced     Italian\n",
              "2   Test8  Intermediate     English\n",
              "3   Test9  Intermediate  Vietnamese\n",
              "4  Test10      Advanced     English"
            ]
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          "text": [
            "[[700. 200. 200. 200. 200.]\n",
            " [200. 700.   0.   0. 200.]\n",
            " [200. 200. 700. 200. 700.]\n",
            " [200. 200. 200. 700. 200.]\n",
            " [200. 200. 500.   0. 700.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aGXCE7szei-7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   Creates two lists to match the students and mentors. Each list contains the possible choice index. For the current version, the number of students and mentors needs to be balanced (equal) but this be changed in future versions\n",
        "2.   Defines a mututation function which just shuffles the order randomly\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "0pTuhlgXb28K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import array\n",
        "import random\n",
        "import json\n",
        "\n",
        "import numpy\n",
        "\n",
        "from deap import algorithms\n",
        "from deap import base\n",
        "from deap import creator\n",
        "from deap import tools\n",
        "from random import shuffle\n",
        "\n",
        "distance_map = relationship_m\n",
        "IND_SIZE = students.shape[0]\n",
        "\n",
        "toolbox = base.Toolbox()\n",
        "\n",
        "creator.create(\"FitnessMin\", base.Fitness, weights=(1.0,))\n",
        "creator.create(\"Individual\", list, typecode='i', fitness=creator.FitnessMin)\n",
        "\n",
        "toolbox.register(\"indices1\", random.sample, range(IND_SIZE), IND_SIZE)\n",
        "toolbox.register(\"indices2\", random.sample, range(IND_SIZE), IND_SIZE)\n",
        "\n",
        "toolbox.register(\"individual\", tools.initCycle, creator.Individual,\n",
        "             (toolbox.indices1, toolbox.indices2))\n",
        "\n",
        "def myMutation(individual):\n",
        "    \n",
        "    shuffle(individual[0])\n",
        "    shuffle(individual[1])\n",
        "    return (individual,)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dM-JCDXUfDVu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   Defines a cost function. In this case it is just the sum of the relationship cost.\n",
        "2.   Runs the genetic algorithm to maximize the cost function (which maximizes the relationship)\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "Q3S63XjUfKap",
        "colab_type": "code",
        "outputId": "932a237d-1698-4084-8d4e-d002ab7e7b93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 7511
        }
      },
      "cell_type": "code",
      "source": [
        "toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n",
        "                            \n",
        "\n",
        "                 \n",
        "def evalrel(individual):\n",
        "    cost = 0\n",
        "    for gene1, gene2 in zip(individual[0], individual[1]):\n",
        "        cost += relationship_m[gene1][gene2]\n",
        "    return cost,\n",
        "                 \n",
        "\n",
        "\n",
        "toolbox.register(\"mate\", tools.cxTwoPoint)\n",
        "toolbox.register(\"select\", tools.selBest)\n",
        "toolbox.register(\"evaluate\", evalrel)\n",
        "toolbox.register(\"mutate\", myMutation)\n",
        "\n",
        "\n",
        "random.seed(169)\n",
        "\n",
        "\n",
        "NGEN = 50\n",
        "MU = 50\n",
        "LAMBDA = 100\n",
        "CXPB = 0.7\n",
        "MUTPB = 0.2\n",
        "\n",
        "pop = toolbox.population(n=MU)\n",
        "hof = tools.ParetoFront()\n",
        "stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
        "stats.register(\"avg\", numpy.mean, axis=0)\n",
        "stats.register(\"std\", numpy.std, axis=0)\n",
        "stats.register(\"min\", numpy.min, axis=0)\n",
        "stats.register(\"max\", numpy.max, axis=0)\n",
        "\n",
        "algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,\n",
        "                          halloffame=hof)\n",
        "\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gen\tnevals\tavg    \tstd           \tmin   \tmax    \n",
            "0  \t50    \t[1498.]\t[564.08864552]\t[600.]\t[2500.]\n",
            "1  \t89    \t[2160.]\t[328.6335345] \t[1800.]\t[3500.]\n",
            "2  \t87    \t[2428.]\t[249.03011866]\t[2100.]\t[3500.]\n",
            "3  \t95    \t[2604.]\t[369.3020444] \t[2300.]\t[3500.]\n",
            "4  \t90    \t[2732.]\t[394.68468427]\t[2500.]\t[3500.]\n",
            "5  \t92    \t[2852.]\t[435.54104284]\t[2500.]\t[3500.]\n",
            "6  \t89    \t[3024.]\t[434.30864601]\t[2500.]\t[3500.]\n",
            "7  \t88    \t[3372.]\t[96.]         \t[3300.]\t[3500.]\n",
            "8  \t95    \t[3396.]\t[99.91996797] \t[3300.]\t[3500.]\n",
            "9  \t91    \t[3456.]\t[82.84926071] \t[3300.]\t[3500.]\n",
            "10 \t86    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "11 \t90    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "12 \t88    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "13 \t94    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "14 \t85    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "15 \t96    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "16 \t94    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "17 \t89    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "18 \t87    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "19 \t89    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "20 \t90    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "21 \t91    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "22 \t93    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "23 \t90    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "24 \t93    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "25 \t91    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "26 \t95    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "27 \t94    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "28 \t90    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "29 \t89    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "30 \t89    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "31 \t90    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "32 \t93    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "33 \t91    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "34 \t94    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "35 \t91    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "36 \t94    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "37 \t93    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "38 \t90    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "39 \t93    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "40 \t89    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "41 \t90    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "42 \t91    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "43 \t85    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "44 \t87    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "45 \t88    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "46 \t91    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "47 \t90    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "48 \t92    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "49 \t90    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n",
            "50 \t86    \t[3500.]\t[0.]          \t[3500.]\t[3500.]\n"
          ],
          "name": "stdout"
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          "output_type": "execute_result",
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              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[1, 0, 4, 2, 3], [1, 0, 4, 2, 3]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[0, 2, 3, 4, 1], [0, 2, 3, 4, 1]],\n",
              "  [[0, 2, 3, 4, 1], [0, 2, 3, 4, 1]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 0, 2], [4, 3, 1, 0, 2]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]],\n",
              "  [[4, 3, 1, 2, 0], [4, 3, 1, 2, 0]]],\n",
              " [{'avg': array([1498.]),\n",
              "   'gen': 0,\n",
              "   'max': array([2500.]),\n",
              "   'min': array([600.]),\n",
              "   'nevals': 50,\n",
              "   'std': array([564.08864552])},\n",
              "  {'avg': array([2160.]),\n",
              "   'gen': 1,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([1800.]),\n",
              "   'nevals': 89,\n",
              "   'std': array([328.6335345])},\n",
              "  {'avg': array([2428.]),\n",
              "   'gen': 2,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([2100.]),\n",
              "   'nevals': 87,\n",
              "   'std': array([249.03011866])},\n",
              "  {'avg': array([2604.]),\n",
              "   'gen': 3,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([2300.]),\n",
              "   'nevals': 95,\n",
              "   'std': array([369.3020444])},\n",
              "  {'avg': array([2732.]),\n",
              "   'gen': 4,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([2500.]),\n",
              "   'nevals': 90,\n",
              "   'std': array([394.68468427])},\n",
              "  {'avg': array([2852.]),\n",
              "   'gen': 5,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([2500.]),\n",
              "   'nevals': 92,\n",
              "   'std': array([435.54104284])},\n",
              "  {'avg': array([3024.]),\n",
              "   'gen': 6,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([2500.]),\n",
              "   'nevals': 89,\n",
              "   'std': array([434.30864601])},\n",
              "  {'avg': array([3372.]),\n",
              "   'gen': 7,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3300.]),\n",
              "   'nevals': 88,\n",
              "   'std': array([96.])},\n",
              "  {'avg': array([3396.]),\n",
              "   'gen': 8,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3300.]),\n",
              "   'nevals': 95,\n",
              "   'std': array([99.91996797])},\n",
              "  {'avg': array([3456.]),\n",
              "   'gen': 9,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3300.]),\n",
              "   'nevals': 91,\n",
              "   'std': array([82.84926071])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 10,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 86,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 11,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 90,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 12,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 88,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
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              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 94,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 14,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 85,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 15,\n",
              "   'max': array([3500.]),\n",
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              "   'nevals': 96,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 16,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 94,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 17,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 89,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 18,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 87,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 19,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 89,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 20,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 90,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 21,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 91,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 22,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 93,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 23,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 90,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 24,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 93,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 25,\n",
              "   'max': array([3500.]),\n",
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              "   'nevals': 91,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 26,\n",
              "   'max': array([3500.]),\n",
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              "   'nevals': 95,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
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              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
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              "   'nevals': 90,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
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              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 89,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 30,\n",
              "   'max': array([3500.]),\n",
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              "   'nevals': 89,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 31,\n",
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              "   'nevals': 90,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 32,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 93,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 33,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 91,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 34,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 94,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 35,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 91,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 36,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 94,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 37,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 93,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 38,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 90,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 39,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 93,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 40,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 89,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 41,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 90,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 42,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 91,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 43,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 85,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 44,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 87,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 45,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 88,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 46,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 91,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 47,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 90,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 48,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 92,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 49,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 90,\n",
              "   'std': array([0.])},\n",
              "  {'avg': array([3500.]),\n",
              "   'gen': 50,\n",
              "   'max': array([3500.]),\n",
              "   'min': array([3500.]),\n",
              "   'nevals': 86,\n",
              "   'std': array([0.])}])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "3Oe-DLXFfPdm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   Double checks that the student languages are the same and the skill level difference is appropriate\n",
        "2.   Output the name and language\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "IBoassOjYcEu",
        "colab_type": "code",
        "outputId": "a6f0ec16-fa01-472a-c3b8-580a9c99d9e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"MATCHED RESULTS\")\n",
        "student_list = []\n",
        "mentors_list = []\n",
        "language_list = []\n",
        "for student_idx,mentor_idx in zip(hof.items[-1][0],hof.items[-1][1]):\n",
        "      flag_language = True if (students['Language'][student_idx] == mentors['Language'][mentor_idx]) else print(\"Language Error\") \n",
        "      \n",
        "      student_skill = skill[students['Skill'][student_idx]]\n",
        "      mentor_skill = skill[mentors['Skill'][student_idx]]\n",
        "      flag_skill = True if (mentor_skill >=student_skill) else print(\"Skill Error\") \n",
        "      if flag_language and flag_skill:\n",
        "        student_list.append(students['Name'][student_idx])\n",
        "        mentors_list.append(mentors['Name'][mentor_idx])\n",
        "        language_list.append(mentors['Language'][mentor_idx])\n",
        "    \n",
        "result = pd.DataFrame({'Students':student_list, \"Mentors\":mentors_list, \"Langugage\":language_list})\n",
        "\n",
        "display(result)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MATCHED RESULTS\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Langugage</th>\n",
              "      <th>Mentors</th>\n",
              "      <th>Students</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Italian</td>\n",
              "      <td>Test7</td>\n",
              "      <td>Test2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chinese</td>\n",
              "      <td>Test6</td>\n",
              "      <td>Test1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>English</td>\n",
              "      <td>Test10</td>\n",
              "      <td>Test5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>English</td>\n",
              "      <td>Test8</td>\n",
              "      <td>Test3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Vietnamese</td>\n",
              "      <td>Test9</td>\n",
              "      <td>Test4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Langugage Mentors Students\n",
              "0     Italian   Test7    Test2\n",
              "1     Chinese   Test6    Test1\n",
              "2     English  Test10    Test5\n",
              "3     English   Test8    Test3\n",
              "4  Vietnamese   Test9    Test4"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}