hw1.ipynb

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    "\"\"\"\n",
    "CS 281. Homework 1 2024. Machine Bias\n",
    "\n",
    "Your code and short answers will go into this file. \n",
    "\"\"\"\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "463929a9",
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   "source": [
    "## A. Machine Bias (6 points)\n",
    "\n",
    "\n",
    "### A1. \n",
    "\n",
    "### A2. \n",
    "\n",
    "### A3."
   ]
  },
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   "source": [
    "## B. Broward County dataset (17 points)"
   ]
  },
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     "text": [
      "Num rows: 7214\n",
      "Index(['id', 'name', 'first', 'last', 'compas_screening_date', 'sex', 'dob',\n",
      "       'age', 'age_cat', 'race', 'juv_fel_count', 'decile_score',\n",
      "       'juv_misd_count', 'juv_other_count', 'priors_count',\n",
      "       'days_b_screening_arrest', 'c_jail_in', 'c_jail_out', 'c_case_number',\n",
      "       'c_offense_date', 'c_arrest_date', 'c_days_from_compas',\n",
      "       'c_charge_degree', 'c_charge_desc', 'is_recid', 'r_case_number',\n",
      "       'r_charge_degree', 'r_days_from_arrest', 'r_offense_date',\n",
      "       'r_charge_desc', 'r_jail_in', 'r_jail_out', 'violent_recid',\n",
      "       'is_violent_recid', 'vr_case_number', 'vr_charge_degree',\n",
      "       'vr_offense_date', 'vr_charge_desc', 'type_of_assessment',\n",
      "       'decile_score.1', 'score_text', 'screening_date',\n",
      "       'v_type_of_assessment', 'v_decile_score', 'v_score_text',\n",
      "       'v_screening_date', 'in_custody', 'out_custody', 'priors_count.1',\n",
      "       'start', 'end', 'event', 'two_year_recid'],\n",
      "      dtype='object')\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>compas_screening_date</th>\n",
       "      <th>sex</th>\n",
       "      <th>dob</th>\n",
       "      <th>age</th>\n",
       "      <th>age_cat</th>\n",
       "      <th>race</th>\n",
       "      <th>...</th>\n",
       "      <th>v_decile_score</th>\n",
       "      <th>v_score_text</th>\n",
       "      <th>v_screening_date</th>\n",
       "      <th>in_custody</th>\n",
       "      <th>out_custody</th>\n",
       "      <th>priors_count.1</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>event</th>\n",
       "      <th>two_year_recid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>miguel hernandez</td>\n",
       "      <td>miguel</td>\n",
       "      <td>hernandez</td>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>Male</td>\n",
       "      <td>1947-04-18</td>\n",
       "      <td>69</td>\n",
       "      <td>Greater than 45</td>\n",
       "      <td>Other</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-08-14</td>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>2014-07-14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>327</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>kevon dixon</td>\n",
       "      <td>kevon</td>\n",
       "      <td>dixon</td>\n",
       "      <td>2013-01-27</td>\n",
       "      <td>Male</td>\n",
       "      <td>1982-01-22</td>\n",
       "      <td>34</td>\n",
       "      <td>25 - 45</td>\n",
       "      <td>African-American</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-01-27</td>\n",
       "      <td>2013-01-26</td>\n",
       "      <td>2013-02-05</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>ed philo</td>\n",
       "      <td>ed</td>\n",
       "      <td>philo</td>\n",
       "      <td>2013-04-14</td>\n",
       "      <td>Male</td>\n",
       "      <td>1991-05-14</td>\n",
       "      <td>24</td>\n",
       "      <td>Less than 25</td>\n",
       "      <td>African-American</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-04-14</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>marcu brown</td>\n",
       "      <td>marcu</td>\n",
       "      <td>brown</td>\n",
       "      <td>2013-01-13</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993-01-21</td>\n",
       "      <td>23</td>\n",
       "      <td>Less than 25</td>\n",
       "      <td>African-American</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2013-01-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1174</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>bouthy pierrelouis</td>\n",
       "      <td>bouthy</td>\n",
       "      <td>pierrelouis</td>\n",
       "      <td>2013-03-26</td>\n",
       "      <td>Male</td>\n",
       "      <td>1973-01-22</td>\n",
       "      <td>43</td>\n",
       "      <td>25 - 45</td>\n",
       "      <td>Other</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2013-03-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
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      "text/plain": [
       "   id                name   first         last compas_screening_date   sex  \\\n",
       "0   1    miguel hernandez  miguel    hernandez            2013-08-14  Male   \n",
       "1   3         kevon dixon   kevon        dixon            2013-01-27  Male   \n",
       "2   4            ed philo      ed        philo            2013-04-14  Male   \n",
       "3   5         marcu brown   marcu        brown            2013-01-13  Male   \n",
       "4   6  bouthy pierrelouis  bouthy  pierrelouis            2013-03-26  Male   \n",
       "\n",
       "          dob  age          age_cat              race  ...  v_decile_score  \\\n",
       "0  1947-04-18   69  Greater than 45             Other  ...               1   \n",
       "1  1982-01-22   34          25 - 45  African-American  ...               1   \n",
       "2  1991-05-14   24     Less than 25  African-American  ...               3   \n",
       "3  1993-01-21   23     Less than 25  African-American  ...               6   \n",
       "4  1973-01-22   43          25 - 45             Other  ...               1   \n",
       "\n",
       "   v_score_text  v_screening_date  in_custody  out_custody  priors_count.1  \\\n",
       "0           Low        2013-08-14  2014-07-07   2014-07-14               0   \n",
       "1           Low        2013-01-27  2013-01-26   2013-02-05               0   \n",
       "2           Low        2013-04-14  2013-06-16   2013-06-16               4   \n",
       "3        Medium        2013-01-13         NaN          NaN               1   \n",
       "4           Low        2013-03-26         NaN          NaN               2   \n",
       "\n",
       "  start   end event two_year_recid  \n",
       "0     0   327     0              0  \n",
       "1     9   159     1              1  \n",
       "2     0    63     0              1  \n",
       "3     0  1174     0              0  \n",
       "4     0  1102     0              0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
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   "source": [
    "## Load the COMPAS Dataset\n",
    "raw_data = pd.read_csv('compas-scores-two-years.csv')\n",
    "print('Num rows: %d' % len(raw_data))\n",
    "\n",
    "# what are the column names? What do they mean?\n",
    "print(raw_data.columns)\n",
    "\n",
    "## browse it\n",
    "raw_data.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa6c70e",
   "metadata": {},
   "source": [
    "### B1. Pre-processing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1de628d",
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   "source": [
    "# ## Apply the filtering described in question B.1 of hw1.pdf.\n",
    "# ## df should be a pandas.DataFrame object\n",
    "\n",
    "df =\n",
    "print('Q1: Num data points after filtering: %d' % len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf2f51a",
   "metadata": {},
   "source": [
    "### B2. Demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d286836",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Count the number of individuals across racial identities, age categories and\n",
    "# binary sex categories. Report your numbers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed86c51e",
   "metadata": {},
   "source": [
    "### B3. Distribution of COMPAS scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4838b676",
   "metadata": {},
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   "source": [
    "# a. Plot the distribution of scores for all individuals (a single histogram)\n",
    "\n",
    "# b. Plot the distribution of scores for (four separate histograms): \n",
    "# Black women, white women, Black men, white men\n",
    "\n",
    "# c. Repeat the plots, this time using stacked bars: color-coding the proportion\n",
    "# of each bar corresponding to individuals who did vs did not recidivate \n",
    "# (two_year_recid == 0 in one color, two_year_recid == 1 in another).\n",
    "# Hint: find pointers for creating stacked bars here https://www.pythoncharts.com/python/stacked-bar-charts/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9449045f",
   "metadata": {},
   "source": [
    "### d. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "110315df",
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   "source": [
    "### B4. Reflection point. \n",
    "#### a. \n",
    "\n",
    "#### b. \n",
    "\n",
    "#### c. \n",
    "\n",
    "#### d. "
   ]
  },
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   "cell_type": "markdown",
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    "## C. Fairness metrics of COMPAS scores\n",
    "\n",
    "For consistency with ProPublica’s analysis, we will assume that the decision threshold is at COMPAS risk 5 - in other words, that all individuals in the low risk category are recommended for release (d=0), but all individuals in the medium and high risk categories are not (d=1). \n",
    "\n",
    "Hint: add a binary column to your data frame that corresponds to this binary decision.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49cd98e",
   "metadata": {},
   "source": [
    "### C1. Intuition behind fairness metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f46c5137",
   "metadata": {},
   "source": [
    "#### a. Add your latex expression to the second column, Statistical Property, and a brief sentence in the third column.\n",
    "\n",
    "| Metric Name      | Statistical property | If this metric is satisfied, we can be sure that |\n",
    "| ----------- | ----------- | ----------- |\n",
    "| Independence        | $$ $$ |   |\n",
    "| Separation - FNR    | $$ $$ |   |\n",
    "| Separation - FPR    | $$ $$ |   |\n",
    "| Sufficiency - PPR   | $$ $$ |   |"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### b. "
   ]
  },
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    "### C2. Calculations\n",
    "For each of the following fairness metrics, calculate the appropriate statistic for 5 variants of the data: (1) all individuals, (2) Black women, (3) white women, (4) Black men, (5) white men. Assume the decision threshold of 5 in all cases except for sufficiency (using decile scores).\n",
    "\n",
    "* Independence.\n",
    "* Separation - false negative rate\n",
    "* Separation - false positive rate\n",
    "* Sufficiency - using decile scores\n",
    "* Sufficiency - using binary decisions\n"
   ]
  },
  {
   "cell_type": "code",
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   "id": "9c3e840c",
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   "source": [
    "# Before continuing, filter the data to include only\n",
    "# African-American and Cacuasian individuals\n",
    "\n",
    "df_bw = "
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### Independence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b2b818b",
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "338961f5",
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   "source": [
    "#### Separation - false negative rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d02f21f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5dab65e8",
   "metadata": {},
   "source": [
    "#### Separation - false positive rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57f6bb09",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "id": "87e6406d",
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   "source": [
    "#### Sufficiency - using decile scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04a7646c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "markdown",
   "id": "faeb4129",
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   "source": [
    "#### Sufficiency - using binary decisions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49e254c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9eb55406",
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   "source": [
    "### C3. Interpretation. \n",
    "a. Independence.\n",
    "\n",
    "\n",
    "\n",
    "b.  Separation\n",
    "\n",
    "    i. false negative rate\n",
    "\n",
    "    \n",
    "    ii. false positive rate\n",
    "\n",
    "\n",
    "\n",
    "c. Sufficiency (positive predictive value)\n",
    "\n",
    "    i. using decile scores \n",
    "    \n",
    "    ii. using binary decisions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d60401a",
   "metadata": {},
   "source": [
    "## D. Final reflection point (9 points)\n",
    "Please respond to the following questions (2-3 sentences or bullet points each).\n",
    "\n",
    "### D1. \n",
    "\n",
    "### D2.\n",
    "\n",
    "\n",
    "### D3. "
   ]
  }
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