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        "plt.plot(np.arange(.1, .9, .1),\n",
    
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        "         failure_rates[:, 0],\n",
        "         label='True Evaluation',\n",
        "         c='green')\n",
        "plt.plot(np.arange(0.1, 0.9, .1),\n",
        "         failure_rates[:, 1],\n",
        "         label='Labeled outcomes',\n",
        "         c='black')\n",
        "plt.plot(np.arange(0.1, 0.9, .1),\n",
        "         failure_rates[:, 2],\n",
        "         label='Human evaluation',\n",
        "         c='red')\n",
        "plt.plot(np.arange(0.1, 0.9, .1),\n",
        "         failure_rates[:, 3],\n",
        "         label='Contraction, log.',\n",
        "         c='blue')\n",
        "plt.plot(np.arange(0.1, 0.9, .1),\n",
        "         failure_rates[:, 4],\n",
        "         label='Causal effect',\n",
        "         c='magenta')\n",
    
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        "\n",
        "plt.title('Failure rate vs. Acceptance rate')\n",
        "plt.xlabel('Acceptance rate')\n",
        "plt.ylabel('Failure rate')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
       ]
      },
      {
    
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       "cell_type": "markdown",
    
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       "metadata": {},
       "source": [
    
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        "### Without unobservables\n",
        "\n",
        "\n",
        "#### Predictive model\n",
        "\n",
        "First build predictive models to give to cdf function."
    
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       ]
      },
      {
    
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       "cell_type": "code",
    
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       "execution_count": 119,
    
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       "metadata": {
        "scrolled": false
       },
       "outputs": [],
    
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       "source": [
    
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        "s_logreg = LogisticRegression(solver=\"lbfgs\")\n",
        "\n",
        "s_logreg = s_logreg.fit(s_train_labeled.dropna().X.values.reshape(-1, 1),\n",
        "                        s_train_labeled.result_Y.dropna())\n",
    
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        "\n",
    
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        "s_test = s_test.assign(\n",
        "    pred_Y=s_logreg.predict_proba(s_test.X.values.reshape(-1, 1))[:, 0])\n",
        "s_test_labeled = s_test_labeled.assign(\n",
        "    pred_Y=s_logreg.predict_proba(s_test_labeled.X.values.reshape(-1, 1))[:, 0])"
    
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       ]
    
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      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
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        "#### Visual comparison"
    
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       ]
      },
      {
       "cell_type": "code",
    
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       "execution_count": 120,
    
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       "metadata": {
        "scrolled": false
       },
    
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       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
    
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          "1 2 3 4 5 6 7 8 "
    
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         ]
        },
        {
         "data": {
    
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          "image/png": 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          "text/plain": [
           "<Figure size 1008x504 with 1 Axes>"
          ]
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ],
       "source": [
    
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        "f_rates_true = np.zeros(0)\n",
        "f_rates_human = np.zeros(0)\n",
    
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        "f_rates_cont = np.zeros(0)\n",
    
    Riku-Laine's avatar
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        "f_rates_caus = np.zeros(0)\n",
    
    Riku-Laine's avatar
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        "x_vals = np.arange(1, 9) / 10\n",
    
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        "\n",
        "for r in range(1, 9):\n",
    
    Riku-Laine's avatar
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        "    print(r, end=\" \")\n",
    
    Riku-Laine's avatar
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        "    \n",
    
    Riku-Laine's avatar
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        "    f_rates_cont = np.append(\n",
        "        f_rates_cont,\n",
        "        contraction(s_test_labeled, 'judgeID_J', 'decision_T', 'result_Y',\n",
        "                    'pred_Y', 'acceptanceRate_R', r / 10))\n",
    
    Riku-Laine's avatar
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        "    \n",
    
    Riku-Laine's avatar
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        "    f_rates_caus = np.append(\n",
        "        f_rates_caus,\n",
        "        np.sum((s_test_labeled.dropna().result_Y== 0) &\n",
        "               (cdf(s_test_labeled.dropna().X, s_logreg, 0) < r / 10)) /\n",
        "        s_test_labeled.dropna().result_Y.shape[0])\n",
        "    \n",
    
    Riku-Laine's avatar
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        "    #### True evaluation\n",
    
    Riku-Laine's avatar
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        "    # Sort by failure probabilities, subjects with the smallest risk are first.\n",
        "    s_sorted = s_test.sort_values(by='probabilities_Y',\n",
        "                                  inplace=False,\n",
        "                                  ascending=True)\n",
    
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        "\n",
        "    to_release = int(round(s_sorted.shape[0] * r / 10))\n",
        "\n",
    
    Riku-Laine's avatar
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        "    # Calculate failure rate as the ratio of failures to successes among those\n",
    
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        "    # who were given a positive decision, i.e. those whose probability of negative\n",
        "    # outcome was low enough.\n",
    
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        "    f_rates_true = np.append(f_rates_true,\n",
        "                             np.sum(s_sorted.result_Y[0:to_release] == 0)/s_sorted.shape[0])\n",
    
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        "\n",
        "    #### Human error rate\n",
        "    # Get judges with correct leniency as list\n",
        "    correct_leniency_list = s_test_labeled.judgeID_J[\n",
        "        s_test_labeled['acceptanceRate_R'].round(1) == r / 10].values\n",
        "\n",
        "    # Released are the people they judged and released, T = 1\n",
    
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        "    released = s_test_labeled[\n",
        "        s_test_labeled.judgeID_J.isin(correct_leniency_list)\n",
        "        & (s_test_labeled.decision_T == 1)]\n",
    
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        "    \n",
    
    Riku-Laine's avatar
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        "    # Get their failure rate, aka ratio of reoffenders to number of people judged in total\n",
        "    f_rates_human = np.append(\n",
        "        f_rates_human,\n",
        "        np.sum(released.result_Y == 0) / correct_leniency_list.shape[0])\n",
        "\n",
    
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        "plt.plot(x_vals, f_rates_cont, label=\"Contraction\")\n",
        "plt.plot(x_vals, f_rates_caus, label=\"Causal\")\n",
        "plt.plot(x_vals, f_rates_true, label=\"True evaluation\")\n",
        "plt.plot(x_vals, f_rates_human, label=\"Human evaluation\")\n",
    
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        "plt.title('Failure rate vs. Acceptance rate, simple data')\n",
        "plt.xlabel('Acceptance rate')\n",
        "plt.ylabel('Failure rate')\n",
        "plt.legend()\n",
        "plt.grid()\n",
    
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        "#plt.yscale(value=\"log\")\n",
    
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        "plt.show()"
       ]
    
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      }
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       "version": "3.7.3"
    
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      },
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       "toc_position": {
        "height": "calc(100% - 180px)",
        "left": "10px",
        "top": "150px",
        "width": "300.7px"
       },
    
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       "toc_section_display": true,
    
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       "toc_window_display": true
    
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      },
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