Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FairSourcing demo \n",
    "## This notebook shows all the different functionalities of the FairSourcing project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the modules\n",
    "from metrics.diversity import *\n",
    "from metrics.fairness import *\n",
    "from decision_maker import *\n",
    "from team import *\n",
    "from candidate import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate the data, after editing your desired parameters in the params.yaml file\n",
    "--> run the following in a terminal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cd data\\ generation/\n",
    "# python generate_fake_team.py\n",
    "# python generate_fake_team_objectives.py\n",
    "# python generate_fake_candidates.py\n",
    "# python generate_fake_recruiters.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data\n",
    "The necessary data contains: the list of employees, either one population, or segmented into teams. Each team should have at leat one manager and one recruiter. The team should have objectives and importance scores present for each social metric of interest. The teams also have a list of past candidates along with the decisions they received both at the recruiter and manager steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the data files into dataframes\n",
    "employees_df = pd.read_csv('data/generated_team.csv', sep=',', encoding='utf-8', index_col=0)\n",
    "objectives_df = pd.read_csv('data/generated_objectives.csv', sep=',', encoding='utf-8', index_col=0)\n",
    "decision_maker_df = pd.read_csv('data/generated_recruiters.csv', sep=',', encoding='utf-8', index_col=0)\n",
    "candidates_df = pd.read_csv('data/generated_candidates.csv', sep=',', encoding='utf-8', index_col=0)\n",
    "#has link to a job that is linked to a recruiter that has their history of decision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the environment\n",
    "Specify which are the considered criteria, which are the teams present in the former files and in the richness, the theoretical space for each criteria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define important values\n",
    "criteria = ['age', 'gender', 'language']\n",
    "teams = ['sales', 'product', 'communication', 'finance']\n",
    "richness = {'age': ['20s', '30s', '40s', '50s'], 'gender': ['M', 'F', 'O'], 'language': ['fr', 'de', 'en', 'it']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper functions to read the objectives dataframe, given a team, metric type and criteria, return for the first the objective and the second the importance\n",
    "get_objective = lambda t, m, c: objectives_df[(objectives_df['team']== t) & (objectives_df['metric'] == m) & \n",
    "                             (objectives_df['criteria'] == c)]['target'].values[0]\n",
    "\n",
    "get_importance = lambda t, m, c: objectives_df[(objectives_df['team']== t) & (objectives_df['metric'] == m) & \n",
    "                             (objectives_df['criteria'] == c)]['importance'].values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create our instance\n",
    "We are now ready to start experimenting. We will select one of the team, create a DecisionMaker object for the manager with the data of the corresponding employee (flagged as manager of this team). Next, we retreive the list of vacancies that this team had, and which recruiters were responsible for them. With the recruiter ids, we create a list of DecisionMaker objects (one for each recruiter). Then we create our list of Diversity metrics objects and the list of Fairness metrics object, one of each for each of the listed criterias. We can then finalize our Team object with the manager, recruiter, employees, diversity metrics, candidates and fairness metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select a team and create a Team object\n",
    "t = teams[0]\n",
    "# Get the DecisionMakers\n",
    "manager = DecisionMaker(\n",
    "            str(employees_df[(employees_df['team'] == 'sales') & (employees_df['manager'] ==1)]['emp_id'].values[0]),\n",
    "            t,\n",
    "            'hired',\n",
    "            dict(zip(criteria, employees_df[(employees_df['team'] == 'sales') & (employees_df['manager'] ==1)][criteria].values[0]))\n",
    "            )\n",
    "vacancies = candidates_df[candidates_df['team'] == t]['job_id'].unique()\n",
    "recruiter_ids = decision_maker_df[decision_maker_df['job_id'].isin(vacancies)]['recr_id'].unique()\n",
    "\n",
    "recruiters = [DecisionMaker(str(recr_id), t, 'transfer',\n",
    "                dict(zip(criteria,decision_maker_df[decision_maker_df['recr_id'] == recr_id][criteria].values[0] ))\n",
    "                ) for recr_id in recruiter_ids]\n",
    "\n",
    "assert len(recruiter_ids) == len(recruiters)\n",
    "\n",
    "#Create the metrics of interest\n",
    "diversity_metrics = [Diversity(c, get_objective(t, 'diversity', c), get_importance(t, 'diversity', c), richness[c]) for c in criteria]\n",
    "\n",
    "fairness_metrics= [Fairness(c, get_objective(t, 'fairness', c), get_importance(t, 'fairness', c)) for c in criteria]\n",
    "\n",
    "\n",
    "team = Team(t, manager, recruiters, employees_df[employees_df['team'] == t], diversity_metrics, candidates_df[candidates_df['team'] == t], fairness_metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First step - Evaluate\n",
    "We will start by inspecting the metrics for diversity and fairness criteria of the team, one by one. <br>\n",
    "We also show the visualisation of the overall diversity and fairness levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inspect_diversity(team: Team, criteria_id: int):\n",
    "    \"\"\"\n",
    "    Helper function to compute the value of a diversity metric, visualise it, and if the objective is not met, compute the distance and difficulty.\n",
    "    \"\"\"\n",
    "    metric = team._diversity_metrics[criteria_id]\n",
    "    criteria_name = metric._criteria\n",
    "    \n",
    "    div, obj_reached = metric.compute(team._diversity_state)\n",
    "    print('For the team ', team._team_id, ' the level of diversity in the dimension '+criteria_name+' is:', div, '. Is their goal met? ', obj_reached)\n",
    "    metric.visualise(team._diversity_state)\n",
    "    if not obj_reached:\n",
    "        increments, reached_diversity = metric.distance(team._diversity_state)\n",
    "        difficulty = metric.difficulty(div, increments, reached_diversity)\n",
    "        print('In order to fill the gap, the team need to add the following profiles ', str(increments), '. This represents a difficulty of ', difficulty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the team  sales  the level of diversity in the dimension age is: 0.9211 . Is their goal met?  True\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inspect_diversity(team, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the team  sales  the level of diversity in the dimension gender is: 0.6518 . Is their goal met?  False\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In order to fill the gap, the team need to add the following profiles  {'M': 0, 'F': 0, 'O': 213} . This represents a difficulty of  2169.0428\n"
     ]
    }
   ],
   "source": [
    "inspect_diversity(team, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the team  sales  the level of diversity in the dimension language is: 0.6114 . Is their goal met?  False\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In order to fill the gap, the team need to add the following profiles  {'de': 0, 'fr': 0, 'en': 108, 'it': 289} . This represents a difficulty of  2862.2927\n"
     ]
    }
   ],
   "source": [
    "inspect_diversity(team, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Overview of Diversity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7763500000000001"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Measure Overall diversity of this team\n",
    "team.compute_social_metric('Diversity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualise the team diversity metrics\n",
    "team.visualise_social_metric('Diversity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inspect_fairness(team, criteria_id, decision, richness):\n",
    "    \"\"\"\n",
    "    Helper function to compute the value of a diversity metric, visualise it, and if the objective is not met, compute the distance and difficulty.\n",
    "    \"\"\"\n",
    "    metric = team._fairness_metrics[criteria_id]\n",
    "    criteria_name = metric._criteria\n",
    "    \n",
    "    fair, obj_reached = metric.compute(team._fairness_state, decision)\n",
    "    print('For the team ', team._team_id, ' the level of fairness in the dimension '+criteria_name+' is:', fair, '. Is their goal met? ', obj_reached)\n",
    "    metric.visualise(team._fairness_state, decision, len(richness[criteria_name]))\n",
    "    if not obj_reached:\n",
    "        swaps, _, reached_fairness = metric.distance(team._fairness_state, decision)\n",
    "        difficulty = metric.difficulty(fair, swaps, reached_fairness)\n",
    "        print('In order to fill the gap, the team need to swap the following decisions ', str(swaps), '. This represents a difficulty of ', difficulty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the team  sales  the level of fairness in the dimension age is: 0.9672 . Is their goal met?  True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inspect_fairness(team, 0, 'transfer', richness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: you can select which decision you would like to see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the team  sales  the level of fairness in the dimension age is: 0.3701 . Is their goal met?  False\n"
     ]
    },
    {
     "data": {
      "image/png": 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2ZtGiRadjFAAAgFGnsm3btoG/Ua3GWlpa8q0nN9Z6jCQe9eS1Jk9uysGDnbUeA17D3mQksz8ZqexNRqqJE8dk9+7dgz7Pl+kBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUTvgBAAAUbkytBxiMarWaay59V63HSJIcPXas1iMAAAAMyBkWfsnLL3fUegwAAIAzikc9AQAACif8AAAACif8AAAACif8AAAACif8AAAACif8AAAACif8AAAACif8AAAACif8AAAACif8AAAACif8AAAACif8AAAACif8AAAACif8AAAACjem1gMMTjVTp04a1BlHurvTcahnmOYBAAAY+c6o8Kurq8s1ty0Z1Dnfuv1L6YjwAwAARi+PegIAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRO+AEAABRuzHAtfPPNN6epqSlJct555+Xaa6/N/fffn7q6urzjHe/I4sWLh+vSAAAA/JRhCb+enp4kSVtb2/HX/uiP/ii33HJLzj///KxcuTLPPvtsZs+ePRyXBwAA4KcMS/g999xz6e7uTltbW3p7e7N48eIcPXo006ZNS5K84x3vyJNPPpnx48dn9erVqa+vT7VazR/8wR/k3HPP/bnrPv/886lufnlQs9x0003p7a2e0t/T2Dg+ixYtybx5809pHQAAgFoYlvAbN25cfuM3fiPXXHNN9u7dmzvuuCMTJkw4fryxsTE/+tGPsnXr1lx88cX5rd/6rezcuTOdnZ39rnvkyJHkSO+gZnnxxRdP6m/4WQ8/vF74AQAAZ6RhCb/p06dn2rRpqVQqmT59epqamvLqq68eP97V1ZUJEybkPe95T9avX5877rgjTU1NWbJkSb/rjh8/PhlfP6hZLjxn2pDc8bvuuvef0hoAAAC1Mizh96//+q954YUX8ru/+7s5cOBAuru7M27cuLz00ks5//zz88QTT2Tx4sXZtGlTLrnkkixevDgbNmzI+vXr88lPfvLnrnvRRRelMu/nPwr6eh68/cHs399xqn8SAADAGWtYwu8973lPVq9enc9+9rOpVCr5xCc+kbq6uvzFX/xF+vr68va3vz1z5szJWWedlXvuuScPPfRQ+vr68tGPfnQ4xgEAABjVhiX8Ghoa8od/+IeveX3VqlUn/Dxt2rSsXLlyOEYAAADg//MF7gAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUTfgAAAIUbU+sBBqOvry/fuv1LgzrnSHf3ME0DAABwZjijwi+pZP/+jloPAQAAcEbxqCcAAEDhhB8AAEDhhB8AAEDhhB8AAEDhhB8AAEDhhB8AAEDhzqivc+jr683EiWfUyIwix4712J+MSPYmI5n9yUhlbzJSNTQ0nNR5Z9Rufuqpp2o9AgAAwBnHo54AAACFE34AAACFE34AAACFE34AAACFE34AAACFG5Gf6tnX15cvfvGLee6559LQ0JCPf/zjueCCC44ff/TRR/Poo4+mrq4uCxcuzPz582s4LaPJG+3Nf/7nf87GjRuTJPPmzcvixYtrNSqj0Bvtz5/8zp133pnLLrss733ve2s0KaPNG+3NzZs35+///u+TJLNnz87SpUtTqVRqNS6jyBvtza997WvZsGFDKpVKPvShD2XBggU1nJbR6vvf/37+5m/+Jm1tbSe8vmnTpnzlK19JfX19rr766vzqr/5qv+uMyDt+3/3ud9PT05PPfe5zufHGG7N27drjx1555ZV84xvfyMqVK3Pbbbdl3bp1OXr0aA2nZTTpb2++9NJL2bBhQ1auXJnPfe5zeeKJJ/Lcc8/VblhGnf7250/87d/+bV599dUaTMdo1t/e7OrqyoMPPpjPfOYzWbVqVc4777wcOnSohtMymvS3Nw8fPpyvf/3rufPOO7NixYrcf//9NZyU0Wr9+vW577770tPTc8Lrx44dywMPPJAVK1akra0tjz76aA4ePNjvWiMy/Hbu3JnW1tYkyZw5c7J79+7jx3bt2pXm5uY0NDRkwoQJmTZtWp5//vlajcoo09/ePPfcc3Prrbemvr4+lUolvb29GTt2bK1GZRTqb38myX/8x3+kUqkc/x04Xfrbm0899VTe9KY3Ze3atbn11ltz9tln5+yzz67VqIwy/e3NcePGZerUqenu7k53d3fq6kbk/22mcOeff35uueWW17y+Z8+eTJs2LRMnTkxDQ0MuueSSfO973+t3rRG5g7u6utLU1HT857q6uvT29iZJOjs7TzjW2NiYw4cPn/YZGZ3625tjxozJWWedlWq1mrVr12bWrFmZPn16rUZlFOpvf77wwgvZsGFDfvM3f7NW4zGK9bc3Ozo6smPHjtx444357Gc/m69//ev54Q9/WKtRGWX625tJcs455+RTn/pUbr755rzvfe+rxYiMcldccUXGjHntu/N+du+OHz8+nZ2d/a41It/j19jYmK6uruM/9/X1pb6+PknS1NR0wrGurq5MmDDhtM/I6NTf3kySnp6e3HvvvWlsbMzSpUtrMSKjWH/789vf/nYOHDiQP/3TP82+ffsyZsyYnHfeee7+cVr0tzcnTZqUt7zlLZkyZUqS5JJLLskPfvAD/+GM06K/vblly5a88sorue+++5Ikt99+e5qbm3PxxRfXZFb4aT+7d48cOfKGTTQi7/g1Nzdn8+bNSf7fmxkvuuii48fe+ta3ZufOnenp6cnhw4ezZ8+evOlNb6rVqIwy/e3NarWaVatW5c1vfnN+7/d+74QghNOhv/150003ZdWqVWlra8u73/3uXH/99aKP06a/vTl79uz893//dw4dOpTe3t4888wzmTlzZq1GZZTpb29OmDAhY8eOTUNDQ8aOHZsJEyZ4yowRY8aMGdm7d286Ojpy9OjRfO9738ucOXP6Paeybdu26mmab8B+8glLzz//fKrVaj7xiU9k8+bNueCCC3LZZZcd/1TParWaD37wg7niiitqPTKjRH97s6+vL5///OdP+Jfuwx/+cN72trfVcGJGkzf6386f+PKXv5zJkyf7VE9Omzfam//+7/+ef/qnf0qSXHnllfnABz5Q44kZLd5ob/7d3/1dnnjiiVQqlTQ3N+emm27yibOcdvv27Ut7e3tWrVqVDRs2pKurK9dee+3xT/WsVqu5+uqr8+u//uv9rjMiww8AAIChMyIf9QQAAGDoCD8AAIDCCT8AAIDCCT8AAIDCCT8AAIDCCT8ARr3HHnssf/3Xf33Ca+3t7Tl69Oig13rkkUfy5S9/eahGA4AhMabWAwDASPTpT3+61iMAwJARfgCQ5JlnnklbW1sOHTqUa6+9Nv/4j/+Yu+++O2vWrElHR0deffXVfOYzn8n69euzc+fO9PX15frrr8+VV16ZnTt35v7778+ECRNSX1+fOXPm1PrPAYATCD8ASFJfX5/bbrst+/fvz8qVK0849ku/9Eu5/vrrs3nz5uzbty8rV65MT09P/uRP/iRvf/vb85d/+Ze55ZZbMn369KxZs6ZGfwEA/HzCDwCSzJ49O5VKJZMnT053d/cJx6ZPn54keeGFF7J79+6sWLEiSXLs2LHs27cvBw8ePP47zc3Neemll07v8ADwBoQfALyBurr/91loF154YVpaWvLxj388fX19eeihhzJt2rScc8452bNnT2bMmJFdu3Zl4sSJNZ4YAE4k/ABggObPn5/t27fn1ltvzZEjR/LOd74zjY2NWbZsWe6+++40NTVl/Pjxwg+AEaeybdu2aq2HAAAAYPj4Hj8AAIDCCT8AAIDCCT8AAIDCCT8AAIDCCT8AAIDCCT8AAIDCCT8AAIDCCT8AAIDC/V/FyOTxIszV6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In order to fill the gap, the team need to swap the following decisions  {'30s': 2, '40s': -7, '20s': 2, '50s': 3} . This represents a difficulty of  11.6647\n"
     ]
    }
   ],
   "source": [
    "inspect_fairness(team, 0, 'hired', richness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the team  sales  the level of fairness in the dimension gender is: 0.7087 . Is their goal met?  False\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In order to fill the gap, the team need to swap the following decisions  {'M': 3, 'F': 0, 'O': -3} . This represents a difficulty of  13.6924\n"
     ]
    }
   ],
   "source": [
    "inspect_fairness(team, 1, 'transfer', richness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the team  sales  the level of fairness in the dimension language is: 0.7769 . Is their goal met?  False\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAFxCAYAAADOPGGRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAalUlEQVR4nO3dfZCddWH28evsG9m8Lg2bF2JSERJD6aBJQaToPEOK0VY6ahFphTJafCUqltqRQqA2TDAzjhHtMBQZWqFRVAaLiJ22MEGN1McyQ1AaiQFbAoT4JMgu2SSb3ezZ8/zRmhGBZRNy9t789vP5a7Ob3PdF5ifD1/vs2dpDDz3UCAAAAEVoqXoAAAAAh4/IAwAAKIjIAwAAKIjIAwAAKIjIAwAAKIjIAwAAKEhb1QNezEknnZShoeGqZ8ALam2tpV7300cYf5xNxjPnk/HK2WS8qtUa2bx580H/uXEbeY1G0tu7t+oZ8IK6uiY7n4xLzibjmfPJeOVsMl5NnXpoueblmgAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUZtz9CoZZGurunVT0DXpTzyXgxtG8wPX0DVc8AAMaJ8Rt5LS3Z8I5Lq54BMO698Z/WJiIPAPhfXq4JAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQEJEHAABQkKZH3uDgYD70oQ81+zYAAADEkzwAAICitDXjov39/fn85z+f3bt3Z+7cuUmSrVu35qabbkqSTJ06NStWrMiUKVOacXuAcWXL/l25d+DnGUi9Kde/8cILU683kiStrbUDHx8unZ2Tcu65787Spacc1usCAM3RlMj7t3/7t8yfPz/nn39+tmzZkoceeijXX399VqxYkfnz5+eee+7JHXfckfPPP78ZtwcYV+4b3JHtw/3Nu8G2bc279v+66647RB4AHCGaEnlPPfVUfud3fidJsmjRorS2tmbbtm258cYbkyRDQ0MHnvABlO6MjlkZbOKTvM653U1/knf22W8/rNcEAJqnKZE3f/78/PSnP83rXve6/Nd//Vfq9XqOPfbYfPSjH013d3c2b96cnp6eZtwaYNxZ1D49i9qnN+36b7xlbXbu7EuSdHVNTm/v3qbdCwAY/5oSecuXL88XvvCFXHHFFZk3b17a29vzgQ98IH/7t3+ber2eWq2Wiy++uBm3BgAAmNCaEnkdHR35xCc+8bzPr1q1qhm3AwAA4H/5EQoAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFEXkAAAAFaat6wItpDA/njf+0tuoZAOPe0L7BqicAAOPI+I281PL0zr6qZ8AL6uqanN7evVXPAACA5/FyTQAAgIKIPAAAgIKIPAAAgIKIPAAAgIKIPAAAgIKIPAAAgIKIPAAAgIKIPAAAgIKIPAAAgIKIPAAAgIKIPAAAgIKIPAAAgIKIPAAAgIKIPAAAgIK0VT3gxdRqSXf3tKpnwItyPjlc6vvreaZ3b9UzAIBCjOPIq+X/rb+/6hkATTd72alVTwAACuLlmgAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAUReQAAAAVpa8ZFh4aGcsMNN2T79u1pNBr5kz/5k9x000056aSTsnXr1tRqtXzyk5/MlClTmnF7YIxtfGRzbvve3dk3OFD1lCNS65eOSr3eODzXaq0dtmsdCTo7J+Xcc9+dpUtPqXoKAIwbTYm8e+65J9OnT8+KFSvS19eXlStXZmBgIG94wxvyvve9L9dee202btyYN7zhDc24PTDG7vq/381jP99W9QwmqLvuukPkAcCvaErkPf7443n44YfzyCOPJEmGh4fT19eX4447Lkkyc+bMDA4ONuPWQAXOfv3/Sf/goCd5h6i105O8Q9XZOSlnn/32qmcAwLjSlMibN29eZs6cmXPOOScDAwO5/fbb893vfje1Wq0ZtwMqtmTh4ixZuLjqGUes2ctOzc6dfYflWl1dk9Pbu/ewXAsAODI15Y1Xli9fnm3btuXKK6/MFVdcke7uboEHAAAwBpryJK+9vT0f+9jHnvO5N73pTQc+/tM//dNm3BYAAGDC8yMUAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACiLyAAAACnJQkdfX19esHQAAABwGbaP5TZs2bcqNN96Y4eHhnH766enu7s5ZZ53V7G0AAAAcpFE9ybv11ltz9dVXp6urK+ecc07+9V//tdm7AAAAOASjiryWlpZMmzYttVotHR0d6ezsbPYuAAAADsGoIm/OnDlZt25d+vr68o1vfCPd3d3N3gUAAMAhGFXkffCDH0x3d3dOPPHETJo0KR/+8IebvQsAAIBDMKrI27BhQ4466qgsXLgwkydPzg9+8IM8/PDDzd4GAADAQRrVu2t+//vfz+DgYBYtWpRHH300g4ODaWlpyfHHH5/3vve9zd4IAADAKI0q8ur1ej71qU+lpaUlw8PDWb16da688spcfvnlzd4HAADAQRjVyzX7+vpSr9eT/E/w7d69O0myf//+5i0DAADgoI3qSd5b3vKWXHrppZk/f362bduWt73tbbn99tvz2te+ttn7AAAAOAijiryzzjorp512WrZv3565c+dm2rRpqdfraW1tbdqwRqOR2ctObdr1AcaL+v561RMAgIKMKvK2bNmS9evXp16vp9Fo5JlnnslVV13V1GGNRvL0031NvQccqq6uyent3Vv1DAAAeJ5RfU/eF7/4xZx00knZs2dPuru7M3369GbvAgAA4BCMKvKmTZuWN77xjZk8eXLOO++8/OIXv2j2LgAAAA7BqCKvVqvl8ccfz8DAQLZt23bg3TUBAAAYX0YVee95z3vyxBNP5K1vfWuuvfbaLFu2rNm7AAAAOASjeuOVBQsWZMGCBUmSz3zmM00dBAAAwKEbVeRddNFFqdVqaTQa2b17d2bPnp0vfOELzd4GAADAQRpV5N10000HPt6xY0e+/vWvN20QAAAAh25U35P3q2bNmpVt27Y1YwsAAAAv06ie5K1duza1Wi1J0tPTkxkzZjR1FAAAAIdmVJH35je/+cDH7e3tOf7445s2CAAAgEM3qsjbtGnTc379n//5n5k5c2bOOOOMtLWN6hIAAACMgVF9T95jjz2W7du3p6urKzt27MiPf/zjPPjgg7nuuuuavQ8AAICDMKrI27t3by655JIsX748H/nIR9LS0pJLLrkkO3bsaPY+AAAADsKoIm/Pnj3ZtWtXkqSvry979+7N0NBQBgYGmjoOAACAgzOqb6g777zzctlll2Xy5Mnp7+/P+973vtx55535vd/7vaYNq9WS7u5pTbs+vFzOJ+PVWJ7N/UND6e3pH7P7AQAvbVSRd8opp2Tp0qXZtWtXZsyYkVqtliVLljR1WK1Wyz0/vq+p9wDg5Tnr5DOqngAA/JpRRd6PfvSjfOtb38r+/fsPfO5v/uZvmjYKAACAQzOqyPuHf/iHvPe9780xxxzT7D0AAAC8DKOKvGOOOSavec1rmr0FAACAl2lUkTdjxozccMMNOe644w58bvny5U0bBQAAwKEZVeTNnj07SdLb29vUMQAAALw8o4q8d73rXenp6cnQ0FAajUZ6enqavQsAAIBDMKrIu+6667Jly5bs27cvg4ODmT17dtasWdPsbQAAABykltH8pq1bt+baa6/Na1/72nz+859PR0dHs3cBAABwCEYVeVOnTk2tVsvAwECmT5/e7E0AAAAcolFF3vHHH59vfvObOfroo7N27doMDg42excAAACHYMTvyVu3bl1qtdqBN1up1WrZvn17Fi5cOFb7AAAAOAgjRt68efOe97kFCxY0bQwAAAAvz4iRd+aZZ47VDgAAAA6DUX1PHgAAAEcGkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFCQMYm8er2eq666Kpdffnl27949FrcEAACYkNrG4ibPPPNM+vv785nPfGYsbgdwRHh000+z4V/WZ2BgsOoph+yWjhtSrzeqnnFQOjsn5dxz352lS0+pegoANMWYRN4NN9yQ7du35+/+7u+yY8eO7Nu3LxdffHFe8YpXjMXtAcalH957X37+5PaqZ7wsPVUPOER33XWHyAOgWGMSeR/4wAeydu3aHH300Wlvb89FF100FrcFGNdOO/OMDA4MHNFP8iZ3TDoin+Sdffbbq54BAE0zJpH3q+bNmzfWtwQYl0446dU54aRXVz3jZTnr5DOyc2df1TMAgF8x5u+uWavVxvqWAAAAE4YfoQAAAFCQMXm55qxZs7JmzZqxuBUAAMCE5kkeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQUQeAABAQdqqHvBiGo1Gzjr5jKpnADCC/UNDVU8AAH7NOI685Omn+6qeAS+oq2tyenv3Vj0DnsfZBAC8XBMAAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgbVUPeHGNdHdPq3oEvCjnk/HK2Xxx+wYG0rdrsOoZANBU4zbyWlpactaV7656BgAFuefqr6QvIg+Asnm5JgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEFEHgAAQEHaxvJmGzduzM6dO5Mky5YtS1vbmN4eAACgeGP6JG/JkiVZvnx5vvGNb2R4eHgsbw0AADAhjOmjtPXr1+cf//Ef09/fn7Vr1+ayyy4by9sDR7jGMwPJE7uTeqPqKRyhLrzwwtSdn8q0ttaO2L//zs5JOffcd2fp0lOqngLwksb89ZLLli3Lfffdl0svvXSsbw0c6Z7ak+wZqnoFR7Bt27ZVPYEj2F133SHygCOCb4oDjhzHTknqnuRx6ObNnHPEPkkqwZH+JO/ss99e9QyAUakk8mq1WhqNI/Nf8kB1ar9xVPIbR1U9gyPYLVffkp07+6qeMWF1dU1Ob+/eqmcAFK+SH6HwW7/1W1m9erXQAwAAOMzG9EnesmXLxvJ2AAAAE44fhg4AAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFCQtqoHvJjh4eHcc/VXqp4BQEH2DQxUPQEAmm7cRl5Sy86dfVWPgBfU1TU5vb17q54Bz+NsAgBergkAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFAQkQcAAFCQcfsjFIaH65k6ddzOY4IbGhp0PhmXnE3GM+eT8crZZLxqb28/pD83bk/z5s2bq54AAABwxPFyTQAAgIKIPAAAgIKIPAAAgIKIPAAAgIKIPAAAgIJU/u6aw8PDufHGG/PYY4+lvb09H/7whzN37twDX7/77rtz9913p6WlJe985ztzyimnVLiWieSlzua3vvWt3HfffUmSpUuX5l3veldVU5mAXup8/vL3XHPNNTn11FPz5je/uaKlTDQvdTYfeOCBfP3rX0+SvOpVr8r73//+1Gq1quYygbzU2bzzzjuzYcOG1Gq1nHPOOTnttNMqXMtEtWXLlqxbty6rVq16zufvv//+3HbbbWltbc2yZcvypje9acTrVP4k7z/+4z8yODiYT3/607ngggty8803H/haT09P/vmf/zmrV6/OlVdemS9/+cvZv39/hWuZSEY6mz//+c+zYcOGrF69Op/+9Kfz4IMP5rHHHqtuLBPOSOfzl2699dbs3r27gnVMZCOdzf7+/txyyy25/PLLs2bNmsyaNSu7du2qcC0TyUhnc8+ePfn2t7+da665JldddVX+/u//vsKlTFR33HFHrr/++gwODj7n80NDQ/nSl76Uq666KqtWrcrdd9+d3t7eEa9VeeQ9/PDDWbJkSZJk0aJF+dnPfnbga48++mgWL16c9vb2TJkyJXPmzMnWrVurmsoEM9LZPOaYY7Jy5cq0tramVqulXq+no6OjqqlMQCOdzyT5wQ9+kFqtduD3wFgZ6Wxu3rw5CxYsyM0335yVK1dmxowZmTFjRlVTmWBGOptHHXVUuru7MzAwkIGBgbS0VP6fyExAs2fPzl/+5V8+7/NPPvlk5syZk6lTp6a9vT0nnnhifvKTn4x4rcpPcH9/fyZPnnzg1y0tLanX60mSvXv3PudrnZ2d2bNnz5hvZGIa6Wy2tbVl+vTpaTQaufnmm3Pcccfl2GOPrWoqE9BI5/Pxxx/Phg0b8sd//MdVzWMCG+ls9vX1ZdOmTbngggtyxRVX5Nvf/naeeuqpqqYywYx0NpNk5syZueSSS/KJT3wif/AHf1DFRCa4008/PW1tz/9uul8/u5MmTcrevXtHvFbl35PX2dmZ/v7+A78eHh5Oa2trkmTy5MnP+Vp/f3+mTJky5huZmEY6m0kyODiY6667Lp2dnXn/+99fxUQmsJHO53e+850888wz+dSnPpUdO3akra0ts2bN8lSPMTHS2Zw2bVqOP/74HH300UmSE088Mf/93//t/yRjTIx0Njdu3Jienp5cf/31SZKrr746ixcvzsKFCyvZCr/q18/uvn37XrKJKn+St3jx4jzwwANJ/ucbDX/zN3/zwNdOOOGEPPzwwxkcHMyePXvy5JNPZsGCBVVNZYIZ6Ww2Go2sWbMmr3zlK/OhD33oOfEHY2Gk83nhhRdmzZo1WbVqVc4888z84R/+ocBjzIx0Nl/1qlfliSeeyK5du1Kv1/PII49k/vz5VU1lghnpbE6ZMiUdHR1pb29PR0dHpkyZ4tVjjBuveMUrsn379vT19WX//v35yU9+kkWLFo34Z2oPPfRQY4z2vaBfvtPR1q1b02g0smLFijzwwAOZO3duTj311APvrtloNPJHf/RHOf3006ucywQy0tkcHh7O5z73uef8D+z888/Pq1/96goXM5G81L87f+lrX/taurq6vLsmY+alzub3v//9fPOb30yS/O7v/m7e8Y53VLyYieKlzuZXv/rVPPjgg6nValm8eHEuvPBC7/zKmNuxY0fWrl2bNWvWZMOGDenv78/y5csPvLtmo9HIsmXL8vu///sjXqfyyAMAAODwqfzlmgAAABw+Ig8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg+AIg0ODuaee+457NfdsmVLVqxYkXXr1h32awPA4SDyAChSb29vUyLvwQcfzFvf+tZccMEFh/3aAHA4tFU9AACa4fbbb8+TTz6Zd77znTn55JOzb9++XHzxxfnOd76Tn/3sZ+nr68srX/nKfOQjH8nXvva17NixI88++2x27tyZ97znPVmyZEm+/OUvZ9OmTanX63n961+f3/7t38769evT1taWmTNnZurUqfnKV76SlpaWzJkzJx/84Afzve99L+vXr0+j0ch5552Xk08+ueq/CgAmGJEHQJHOOeecbN26NUuWLMnu3btz0UUXZe/evZk6dWr++q//OsPDw/n4xz+eX/ziF0mStra2rFy5Mj/60Y9y5513ZsmSJdmwYUNWrVqVo48+Ovfee28WLlyYM888M11dXXnd616Xj370o1m9enVmzJiRW2+9Nffee29aW1szderUXHbZZRX/DQAwUYk8AIo3b968JElHR0eeffbZrF27Np2dndm3b1/q9XqS5LjjjkuSzJw5M/v370+SfPzjH8+6devS29ubJUuWPOeau3btSk9PTz772c8m+Z/vATz55JMzZ86cHHvssWP1jwYAzyPyAChSrVZLo9E48HGSbNy4MU8//XT+4i/+Is8++2x++MMfPu/3/NL+/fvz7//+7/nzP//zJMkll1ySM84448DXp02blpkzZ+aTn/xkpkyZkvvvvz+TJk3Kzp07n3ctABhLIg+AIs2YMSNDQ0MZHBw88LkTTjght912W1auXJlarZbZs2enp6fnBf98e3t7pk6dmr/6q79KR0dHXvOa16S7u/vA11taWvJnf/Znueaaa9JoNNLZ2ZmPfexj2blzZ9P/2QBgJLWHHnqoUfUIAAAADg8/QgEAAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAgIg8AAKAg/x+aNKSxVMe5sQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In order to fill the gap, the team need to swap the following decisions  {'de': 4, 'fr': -4, 'en': 3, 'it': -3} . This represents a difficulty of  39.5704\n"
     ]
    }
   ],
   "source": [
    "inspect_fairness(team, 2, 'transfer', richness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Overview of Fairness metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7903749999999999"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Measure Overall fairness of this team \n",
    "team.compute_social_metric('Fairness', 'transfer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.480675"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Measure Overall fairness of this team \n",
    "team.compute_social_metric('Fairness', 'hired')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualise the team Fairness metrics\n",
    "team.visualise_social_metric('Fairness', 'transfer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualise the team Fairness metrics\n",
    "team.visualise_social_metric('Fairness', 'hired')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second Step - Decision Makers\n",
    "In this part we quantify the bias and visualise the pattern of decisions of a decision maker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compute the decision landscape of the recruiters for this team\n",
    "recruiter = team._recruiters[0]\n",
    "# Extract the job ids of this recruiter and then the candidates to those vacancies\n",
    "recr_jobs = decision_maker_df[decision_maker_df['recr_id'].astype(str) == recruiter._pers_id]['job_id'].values\n",
    "recr_data = candidates_df[candidates_df.job_id.isin(recr_jobs)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the data for the manager case\n",
    "manager = team._manager\n",
    "mng_data = candidates_df[candidates_df['team'] == manager._team_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "mmg_fairness, mng_distr = manager.get_fairness(mng_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the fairness of the decision maker and the probabilities of positive decisions per distance\n",
    "recr_fairness, recr_distr = recruiter.get_fairness(recr_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise the fairness\n",
    "recruiter.visualise_fairness(recr_data , recr_distr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise the landscape of decisions\n",
    "# You have one dimension across the plot, the second for the lines, the third with the columns.\n",
    "# The size of the bubble shows how many candidates were in that group and the color is the probability of positive decision\n",
    "# The green star locates the attributes of the recriuter.\n",
    "recruiter.visualise_landscape(recr_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Third Step - Candidate\n",
    "In this part we first instantiate a Candidate object with a team and some set of attributes. <br>\n",
    "Then we compute the impact both on the diversity and fairness metrics for this team, with the objectives and importances taken into consideration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This candidate has a diversity impact =  0.00029999999999996696  and a fairness impact =  -0.018274999999999986\n"
     ]
    }
   ],
   "source": [
    "#Compute the impact of a new candidate to this team & visualize\n",
    "candidate = Candidate('0', 'sales', {'age': '50s', 'gender': 'O', 'language': 'it'}, 'transfer')\n",
    "\n",
    "div_impact, fair_impact = team.compute_cand_agg_impacts(candidate)\n",
    "print('This candidate has a diversity impact = ', div_impact, ' and a fairness impact = ', fair_impact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "team.visualise_cand_impacts(candidate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fourth step - Active Sourcing\n",
    "The final step is an assistance to the recruiters, given a team, we compute the optimal profiles in terms of demographics to support the active sourcing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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>attr</th>\n",
       "      <th>div_impact</th>\n",
       "      <th>fair_impact</th>\n",
       "      <th>tot_impact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>{'age': '50s', 'gender': 'M', 'language': 'de'}</td>\n",
       "      <td>-0.000025</td>\n",
       "      <td>0.031775</td>\n",
       "      <td>0.0159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>{'age': '50s', 'gender': 'F', 'language': 'de'}</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.030225</td>\n",
       "      <td>0.0151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>{'age': '50s', 'gender': 'M', 'language': 'fr'}</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.010550</td>\n",
       "      <td>0.0053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>{'age': '50s', 'gender': 'F', 'language': 'fr'}</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>0.0046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>{'age': '50s', 'gender': 'M', 'language': 'en'}</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.0018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               attr  div_impact  fair_impact  \\\n",
       "37  {'age': '50s', 'gender': 'M', 'language': 'de'}   -0.000025     0.031775   \n",
       "41  {'age': '50s', 'gender': 'F', 'language': 'de'}    0.000075     0.030225   \n",
       "36  {'age': '50s', 'gender': 'M', 'language': 'fr'}    0.000075     0.010550   \n",
       "40  {'age': '50s', 'gender': 'F', 'language': 'fr'}    0.000175     0.009000   \n",
       "38  {'age': '50s', 'gender': 'M', 'language': 'en'}    0.000125     0.003500   \n",
       "\n",
       "    tot_impact  \n",
       "37      0.0159  \n",
       "41      0.0151  \n",
       "36      0.0053  \n",
       "40      0.0046  \n",
       "38      0.0018  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compute the optimal next profile\n",
    "team.compute_optimal_profile(richness, 'hired', n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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