02. Fair Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensuring Fairness in Regression Tasks\n",
    "\n",
    "This tutorial will focus on a real-world learning-with-constraint application.\n",
    "\n",
    "We will use the Moving Targets' APIs to train a `LinearRegression` learner while imposing a constraint on the whole sampled population leveraging the `RegressionMaster` template class. Our aim is indeed to satisfy ethical requirements via a metric for statistical fairness, namely the [**Disparate Impact Discrimination Index** ($\\mbox{DIDI}$)](https://arxiv.org/abs/1903.10598), so to reduce the level of both explicit and implicit discrimination learned by the model.\n",
    "\n",
    "> NOTE: since we will leverage on the [Gurobi](https://www.gurobi.com/) solver to deal with the master problem, you will need to install it separately. A free academic license for the solver can be obtained [here](https://www.gurobi.com/academia/academic-program-and-licenses/). Otherwise, you may change the line of code involving the backend object and use one among the other supported backends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "np.random.seed(0)\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_context('notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Communities & Crime Dataset\n",
    "\n",
    "Our regression task will be based upon the **Communities & Crime** dataset, in which the aim is to estimate the risk of violent crimes within a certain neighbourhood. The original version of the dataset can be found on the [UCI Repository](https://archive.ics.uci.edu/ml/datasets/communities+and+crime), but we will use a preprocessed version that can be found in the official Moving Targets' [github repository](https://github.com/moving-targets/moving-targets).\n",
    "\n",
    "While trying to maximize the predictive capabilities of our model, we aim as well at reducing the discrimination based on a protected categorical feature, i.e., **race** (*0* = primarily white neighbourhood, *1* = primarily black neighbourhood)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop</th>\n",
       "      <th>race</th>\n",
       "      <th>perHoush</th>\n",
       "      <th>pctBlack</th>\n",
       "      <th>pctWhite</th>\n",
       "      <th>pctAsian</th>\n",
       "      <th>pctHisp</th>\n",
       "      <th>pct12-21</th>\n",
       "      <th>pct12-29</th>\n",
       "      <th>pct16-24</th>\n",
       "      <th>...</th>\n",
       "      <th>pctForeignBorn</th>\n",
       "      <th>pctBornStateResid</th>\n",
       "      <th>pctSameHouse-5</th>\n",
       "      <th>pctSameCounty-5</th>\n",
       "      <th>pctSameState-5</th>\n",
       "      <th>landArea</th>\n",
       "      <th>popDensity</th>\n",
       "      <th>pctUsePubTrans</th>\n",
       "      <th>pctOfficDrugUnit</th>\n",
       "      <th>violentPerPop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.993000e+03</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.226789e+04</td>\n",
       "      <td>0.031109</td>\n",
       "      <td>2.706894</td>\n",
       "      <td>9.489207</td>\n",
       "      <td>83.506367</td>\n",
       "      <td>2.752052</td>\n",
       "      <td>8.485615</td>\n",
       "      <td>14.431179</td>\n",
       "      <td>27.619182</td>\n",
       "      <td>13.986508</td>\n",
       "      <td>...</td>\n",
       "      <td>7.609433</td>\n",
       "      <td>60.490316</td>\n",
       "      <td>51.318881</td>\n",
       "      <td>77.108851</td>\n",
       "      <td>87.730532</td>\n",
       "      <td>27.970045</td>\n",
       "      <td>2790.548771</td>\n",
       "      <td>3.064054</td>\n",
       "      <td>1.010472</td>\n",
       "      <td>589.098921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.021969e+05</td>\n",
       "      <td>0.173656</td>\n",
       "      <td>0.343221</td>\n",
       "      <td>14.075806</td>\n",
       "      <td>16.379820</td>\n",
       "      <td>4.649023</td>\n",
       "      <td>15.211553</td>\n",
       "      <td>4.479694</td>\n",
       "      <td>6.149047</td>\n",
       "      <td>5.898160</td>\n",
       "      <td>...</td>\n",
       "      <td>8.699132</td>\n",
       "      <td>17.058427</td>\n",
       "      <td>10.660005</td>\n",
       "      <td>10.791621</td>\n",
       "      <td>7.449883</td>\n",
       "      <td>114.856126</td>\n",
       "      <td>2920.757824</td>\n",
       "      <td>4.964081</td>\n",
       "      <td>2.913870</td>\n",
       "      <td>614.938163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000500e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.680000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>4.580000</td>\n",
       "      <td>9.380000</td>\n",
       "      <td>4.640000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>11.830000</td>\n",
       "      <td>27.950000</td>\n",
       "      <td>32.830000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.435800e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.490000</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>75.890000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>12.230000</td>\n",
       "      <td>24.380000</td>\n",
       "      <td>11.340000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>48.860000</td>\n",
       "      <td>44.670000</td>\n",
       "      <td>71.920000</td>\n",
       "      <td>84.730000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>1170.800000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>161.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.268600e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>3.140000</td>\n",
       "      <td>89.610000</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>13.620000</td>\n",
       "      <td>26.770000</td>\n",
       "      <td>12.540000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>62.520000</td>\n",
       "      <td>51.870000</td>\n",
       "      <td>79.310000</td>\n",
       "      <td>89.630000</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>1996.500000</td>\n",
       "      <td>1.220000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>374.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.318400e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>95.990000</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>8.610000</td>\n",
       "      <td>15.390000</td>\n",
       "      <td>29.180000</td>\n",
       "      <td>14.360000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.590000</td>\n",
       "      <td>74.360000</td>\n",
       "      <td>58.490000</td>\n",
       "      <td>84.700000</td>\n",
       "      <td>92.730000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>3272.200000</td>\n",
       "      <td>3.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>794.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.322564e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>96.670000</td>\n",
       "      <td>99.630000</td>\n",
       "      <td>57.460000</td>\n",
       "      <td>95.290000</td>\n",
       "      <td>54.400000</td>\n",
       "      <td>70.510000</td>\n",
       "      <td>63.620000</td>\n",
       "      <td>...</td>\n",
       "      <td>60.400000</td>\n",
       "      <td>93.140000</td>\n",
       "      <td>78.560000</td>\n",
       "      <td>96.590000</td>\n",
       "      <td>99.900000</td>\n",
       "      <td>3569.800000</td>\n",
       "      <td>44229.900000</td>\n",
       "      <td>54.330000</td>\n",
       "      <td>48.440000</td>\n",
       "      <td>4877.060000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                pop         race     perHoush     pctBlack     pctWhite  \\\n",
       "count  1.993000e+03  1993.000000  1993.000000  1993.000000  1993.000000   \n",
       "mean   5.226789e+04     0.031109     2.706894     9.489207    83.506367   \n",
       "std    2.021969e+05     0.173656     0.343221    14.075806    16.379820   \n",
       "min    1.000500e+04     0.000000     1.600000     0.000000     2.680000   \n",
       "25%    1.435800e+04     0.000000     2.490000     0.940000    75.890000   \n",
       "50%    2.268600e+04     0.000000     2.650000     3.140000    89.610000   \n",
       "75%    4.318400e+04     0.000000     2.850000    11.950000    95.990000   \n",
       "max    7.322564e+06     1.000000     5.280000    96.670000    99.630000   \n",
       "\n",
       "          pctAsian      pctHisp     pct12-21     pct12-29     pct16-24  ...  \\\n",
       "count  1993.000000  1993.000000  1993.000000  1993.000000  1993.000000  ...   \n",
       "mean      2.752052     8.485615    14.431179    27.619182    13.986508  ...   \n",
       "std       4.649023    15.211553     4.479694     6.149047     5.898160  ...   \n",
       "min       0.030000     0.120000     4.580000     9.380000     4.640000  ...   \n",
       "25%       0.620000     0.920000    12.230000    24.380000    11.340000  ...   \n",
       "50%       1.240000     2.350000    13.620000    26.770000    12.540000  ...   \n",
       "75%       2.740000     8.610000    15.390000    29.180000    14.360000  ...   \n",
       "max      57.460000    95.290000    54.400000    70.510000    63.620000  ...   \n",
       "\n",
       "       pctForeignBorn  pctBornStateResid  pctSameHouse-5  pctSameCounty-5  \\\n",
       "count     1993.000000        1993.000000     1993.000000      1993.000000   \n",
       "mean         7.609433          60.490316       51.318881        77.108851   \n",
       "std          8.699132          17.058427       10.660005        10.791621   \n",
       "min          0.180000           6.750000       11.830000        27.950000   \n",
       "25%          2.080000          48.860000       44.670000        71.920000   \n",
       "50%          4.490000          62.520000       51.870000        79.310000   \n",
       "75%          9.590000          74.360000       58.490000        84.700000   \n",
       "max         60.400000          93.140000       78.560000        96.590000   \n",
       "\n",
       "       pctSameState-5     landArea    popDensity  pctUsePubTrans  \\\n",
       "count     1993.000000  1993.000000   1993.000000     1993.000000   \n",
       "mean        87.730532    27.970045   2790.548771        3.064054   \n",
       "std          7.449883   114.856126   2920.757824        4.964081   \n",
       "min         32.830000     0.900000     10.000000        0.000000   \n",
       "25%         84.730000     7.400000   1170.800000        0.350000   \n",
       "50%         89.630000    13.700000   1996.500000        1.220000   \n",
       "75%         92.730000    25.800000   3272.200000        3.380000   \n",
       "max         99.900000  3569.800000  44229.900000       54.330000   \n",
       "\n",
       "       pctOfficDrugUnit  violentPerPop  \n",
       "count       1993.000000    1993.000000  \n",
       "mean           1.010472     589.098921  \n",
       "std            2.913870     614.938163  \n",
       "min            0.000000       0.000000  \n",
       "25%            0.000000     161.690000  \n",
       "50%            0.000000     374.050000  \n",
       "75%            0.000000     794.410000  \n",
       "max           48.440000    4877.060000  \n",
       "\n",
       "[8 rows x 104 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/moving-targets/moving-targets/main/res/communities.csv')\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "x, y = df.drop(columns='violentPerPop'), df['violentPerPop']\n",
    "fig = sns.boxplot(x=x['race'].map({0: 'white', 1: 'black'}), y=y)\n",
    "fig.set(xlabel='', ylabel='', title='Violent Per Population');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This boxplot shows how there exists a discrimination between mainly white vs. mainly black neighbourhoods.\n",
    "\n",
    "A simple way to understand whether this discrimination is direct (*disparate treatment*) or indirect (*disparate impact*) is to train a linear regression model and inspect the weight of the protected feature. As in the previous tutorial, we will do that leveraging the Moving Targets' APIs instead of the traditional `scikit-learn` models in order to exploit the possibility to scale the input data in a faster way via the `x_scaler` and `y_scaler` parameters.\n",
    "\n",
    "Recall that each Moving Targets' `Learner` object is essentially a wrapper of a machine learning model, which can be accessed via its `model` field. In this case, since we are wrapping a `scikit-learn` object, we can access its fields. In particular, we are interested to understand the weight (`coef_`) of each input feature (`feature_names_in_`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 features:\n",
      "\n",
      "rentUpperQ           1.981925e+11\n",
      "ownHousUperQ         1.829163e+11\n",
      "rentLowQ            -1.426295e+11\n",
      "ownHousLowQ         -1.240474e+11\n",
      "rentQrange          -8.455192e+10\n",
      "ownHousQrange       -7.150474e+10\n",
      "pctAllDivorc        -1.393157e-01\n",
      "pctPersOwnOccup     -1.370262e-01\n",
      "pctHousOwnerOccup    1.221527e-01\n",
      "pctMaleDivorc        8.685146e-02\n",
      "\n",
      "> race is at position 53/103 with weight -7.6444e-03\n"
     ]
    }
   ],
   "source": [
    "from moving_targets.learners import LinearRegression\n",
    "\n",
    "learner = LinearRegression(x_scaler='std', y_scaler='norm').fit(x, y)\n",
    "weights = pd.Series(learner.model.coef_, index=learner.model.feature_names_in_)\n",
    "weights = weights.sort_values(key=abs, ascending=False)\n",
    "\n",
    "print('Top 10 features:')\n",
    "print()\n",
    "print(weights.head(10).to_string())\n",
    "print()\n",
    "print(f'> race is at position {weights.index.get_loc(\"race\")}/{len(weights)} with weight {weights[\"race\"]:.4e}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the model is focusing its predictions mainly on $6$ features, while the remaining ones (**race** included) are practically negligible for the purpose of the predictive task. Still, the discrimination is evident from the previous plots, and it is likely to be carried out by one (or more) of the other features which act as *proxies* to identify the protected group, similarly to what happens in the [redlining](https://en.wikipedia.org/wiki/Redlining) discriminatory practice.\n",
    "\n",
    "However, in order to pose some constraint, we will need to somehow measure this level of discrimination. Due to the sensitivity and subjectivity of the task, this is still an open research area, but to the extent of our work we will rely on the **Disparate Impact Discrimination Index** for Regression Tasks ($\\mbox{DIDI}_r$), which has been introduced [here](https://arxiv.org/abs/1903.10598) and is defined as follows:\n",
    "\n",
    "$$\n",
    "\\mbox{DIDI}_r = \\sum_{p \\in P} \\mid \\bar{y} - \\bar{y_p} \\mid\n",
    "$$\n",
    "\n",
    "where $D$ is the dataset of the entire population and:\n",
    "* $P$ is the domain of the protected feature and $p$ represents each protected group (e.g., $P = \\{p_0=\\mbox{black}, p_1=\\mbox{hispanic}, p_2=\\mbox{asian}, ...\\}$)\n",
    "* $\\bar{y}$ is the average prediction on the whole population, i.e., $\\bar{y} = \\frac{1}{\\mid D \\mid} \\sum_{i \\in D} y_i$\n",
    "* $\\bar{y_p}$ is the average prediction on the protected population, i.e., $\\bar{y_p} = \\frac{1}{\\mid D_p \\mid} \\sum_{i \\in D_p} y_i$\n",
    "\n",
    "Intuitively, the *DIDI* measures the sum of the deviations from the overall mean respectively to each protected group. Thus, as it approaches a zero value, it means that we are reducing the level of discrimination. Let us see which is the violation level on our dataset using the `DIDI` class which can be found inside the Moving Targets' `metric` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DIDI: 1293.2071114749167\n"
     ]
    }
   ],
   "source": [
    "from moving_targets.metrics import DIDI\n",
    "\n",
    "didi = DIDI(protected='race', classification=False, percentage=False)\n",
    "print(f'DIDI: {didi(x=x, y=None, p=y)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A pitfall of the *DIDI* is that it is an absolute metric, therefore we have no ways to know whether this is a large amount of discrimination or not. Nonetheless, we can compare different *DIDI* values to understand which of the possible target vectors is less discriminative, or even the level of discrimination of a target vector $y'$ with respect to a target vector $y$ by computing their ratio.\n",
    "\n",
    "> This is exactly what the `percentage` parameter does: it compares the *DIDI* computed on the predictions $p$ with the *DIDI* computed on the original targets $y$ and yields their ratio. Indeed, here we passed no original targets `y` because we wanted the absolute *DIDI* value, to get the relative value we should have done:\n",
    "> ```python\n",
    "> didi = DIDI(protected='race', classification=False, percentage=True)\n",
    "> didi(x=x, y=y, p=y)\n",
    "> ```\n",
    "> which in this case would yield $1.0$ because the two target vectors are the same.\n",
    "\n",
    "Now, say that we measured a certain $\\mbox{DIDI}(y)$ on our original targets. Forcing this value to be exactly zero may lead to excessively poor performances, thus we can formulate our constraint as *20% of the initial discrimination*, or rather:\n",
    "$$\n",
    "\\mbox{DIDI}(p) \\leq 0.2 * \\mbox{DIDI}(y)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving Targets\n",
    "\n",
    "Similarly to the class balancing problem, here we have a non-differentiable constraint which is defined over the whole population. It follows that Moving Targets might turn out to be useful when solving this task.\n",
    "\n",
    "But first, split the data points into a train and test set and compute our reference *DIDI*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1300.5910106903016"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, shuffle=True)\n",
    "didi_train = didi(x=x_train, y=None, p=y_train)\n",
    "didi_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall the three main building blocks of the Moving Targets' library: `Learner`, `Master`, and `MACS`.\n",
    "\n",
    "This time, we will need to subclass the `RegressionMaster` class because we are dealing with a regression task, but the interface is the same: we need to formulate the constraints within the `build` method, and optionally handle some user-specified parameters within the `__init__` one.\n",
    "\n",
    "In particular, in this case the `__init__` method signature contains the usual `backend`, `loss`, `alpha`, and `stats` parameters, plus a `lb` an `ub` which defines the model variables lower and upper bounds, respectively. Differently from the `ClassificationMaster`, no `types` and `labelling` parameters are present, because a regression task always uses continuous targets. Finally, we have two task-specific parameters, namely `protected` and `didi`, which indicate the name of the protected feature and the maximal accepted level of (absolute) *DIDI*, respectively.\n",
    "\n",
    "In the `build` method, instead, we proceed by computing the *DIDI* as of the original formulation. Some important things to notice are:\n",
    "* the **indicator matrix**, which represents the one-hot encoded version of the protected column, is computed via the static `get_indicator_matrix` method of the `DIDI` class, and it is used to obtain the (binary) masking vectors identifying each protected group;\n",
    "* the *DIDI* is the sum of the **absolute value** of the deviations from the overall mean. Computing the absolute value is costly in Mixed-Integer Programming frameworks, but in this case the absolute value can be easily linearized with two greater-than-equal constraints since we aim at reducing their sum. Whenever this is not possible, the `Backend` object provides an `abs` method for all the backends supporting this operation;\n",
    "* since we cannot compute deviations in an explicit way due to the linearization of the absolute value, we need to rely on a vector of support variables, which can be initialized using the `Backend.add_<vtype>_variables` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from moving_targets.masters import RegressionMaster\n",
    "\n",
    "class FairRegression(RegressionMaster):\n",
    "    def __init__(self, protected, didi, backend='gurobi', loss='mse', alpha='harmonic', lb=0, ub=float('inf'), stats=False):\n",
    "        super().__init__(backend=backend, loss=loss, alpha=alpha, lb=lb, ub=ub, stats=stats)\n",
    "        self.protected = protected\n",
    "        self.didi = didi\n",
    "\n",
    "    def build(self, x, y, p):\n",
    "        # 1. RETRIEVE THE VARIABLES\n",
    "        z = super(FairRegression, self).build(x, y, p)\n",
    "\n",
    "        # 2. ADD THE CONSTRAINTS\n",
    "        #   > compute the indicator matrix\n",
    "        indicator_matrix = DIDI.get_indicator_matrix(x=x, protected=self.protected)\n",
    "        #   > add a continuous variable for each protected group\n",
    "        deviations = self.backend.add_continuous_variables(len(indicator_matrix), lb=0.0, name='deviations')\n",
    "        #   > compute the average output target for the whole dataset\n",
    "        total_avg = self.backend.mean(z)\n",
    "        for g, protected_group in enumerate(indicator_matrix):\n",
    "            #   > retrieve the subset of the variables having <label> as protected feature (i.e., the protected group)\n",
    "            z_protected = z[protected_group]\n",
    "            if len(z_protected) == 0:\n",
    "                continue\n",
    "            #   > compute the average output target for the protected group\n",
    "            protected_avg = self.backend.mean(z_protected)\n",
    "            #   > compute the partial deviation as the absolute value (which is linearized) of the difference between\n",
    "            #   > the total average samples and the average samples within the protected group\n",
    "            self.backend.add_constraint(deviations[g] >= total_avg - protected_avg)\n",
    "            self.backend.add_constraint(deviations[g] >= protected_avg - total_avg)\n",
    "        # > eventually compute the DIDI as the sum of this deviations and constrain it\n",
    "        didi = self.backend.sum(deviations)\n",
    "        self.backend.add_constraint(didi <= self.didi)\n",
    "\n",
    "        # 3. RETURN THE VARIABLES\n",
    "        return z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is now time to instantiate and fit the `MACS` object.\n",
    "\n",
    "As well as in the previous tutorial, we pass a custom callback to the `fit` method to visually address the level of discrimination during the iterations. Also, remember that even though the input data may be in each acceptable format (e.g., lists, numpy, pandas), the Moving Targets' library always expects and returns a numpy array for the output data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x1152 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from moving_targets import MACS\n",
    "from moving_targets.metrics import MSE, R2, DIDI\n",
    "from util.fairness import RegressionCallback\n",
    "\n",
    "master=FairRegression(protected='race', didi=0.2 * didi_train)\n",
    "learner=LinearRegression(x_scaler='std', y_scaler='norm')\n",
    "metrics=[MSE(), R2(), DIDI(protected='race', classification=False, percentage=True)]\n",
    "model = MACS(init_step='pretraining', master=master, learner=learner, metrics=metrics)\n",
    "\n",
    "\n",
    "callbacks = [RegressionCallback(protected='race', figsize=(16, 16), tight_layout=True)]\n",
    "val_data = dict(test=(x_test, y_test.values))\n",
    "history = model.fit(x=x_train, y=y_train.values, iterations=10, val_data=val_data, callbacks=callbacks, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And eventually plot the training metrics and statistics via the `History` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "history.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "We succeeded again at satisfying our constraint, not only on the training set but on the test set as well, meaning that Moving Targets owns generalization capabilities.\n",
    "\n",
    "Obviously, this has come at the expenses of predictive accuracy, but do not overestimate the importance of accuracy metrics in a fairness-related task. Indeed, we should prefer a less powerful non-discriminative model with respect to a more accurate but more discriminative one. Additionally, whenever the training data is biased due to historically discriminative practices, to seek for a high accuracy means to replicate the same discriminative practices, which were not accurate in first place. Thanks to Moving Targets, we might be able to find the correct trade-off!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}