bias_scenarios.ipynb

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      "metadata": {
        "id": "YE8TUKl8P4Ea"
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      "source": [
        "from utils import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2I3bM005IpiS"
      },
      "source": [
        "# Scenarios"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1re0ZRgUQ1Cu"
      },
      "source": [
        "## S1 - no historical bias"
      ]
    },
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          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.002790 -0.001676  0.635310\n",
            "Q -0.002790  1.000000  0.001225 -0.298573\n",
            "A -0.001676  0.001225  1.000000  0.002482\n",
            "Y  0.635310 -0.298573  0.002482  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
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              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>0.210188</td>\n",
              "      <td>0.210188</td>\n",
              "      <td>0.094284</td>\n",
              "      <td>0.075239</td>\n",
              "      <td>0.038963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.372727</td>\n",
              "      <td>86.421212</td>\n",
              "      <td>86.384848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.134439</td>\n",
              "      <td>0.146577</td>\n",
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              "                                      Y    Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  0.210188  0.210188    0.094284   0.075239   \n",
              "ACC score                         100.0     100.0   86.372727  86.421212   \n",
              "ACC score difference                0.0       0.0    0.134439   0.146577   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.038963  \n",
              "ACC score                      86.384848  \n",
              "ACC score difference            0.110201  "
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          },
          "execution_count": 9,
          "metadata": {},
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      ],
      "source": [
        "# no additional bias\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
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          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 1, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "Correlation between R and P:  [[1.         0.89928525]\n",
            " [0.89928525 1.        ]]\n",
            "The correlation matrix is:  \n",
            "           P         Q         A         Y\n",
            "P  1.000000  0.000310 -0.108182  0.570987\n",
            "Q  0.000310  1.000000  0.001225 -0.298573\n",
            "A -0.108182  0.001225  1.000000  0.002482\n",
            "Y  0.570987 -0.298573  0.002482  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
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              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>0.210188</td>\n",
              "      <td>0.210188</td>\n",
              "      <td>0.77221</td>\n",
              "      <td>7.414714</td>\n",
              "      <td>0.542702</td>\n",
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              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>81.160606</td>\n",
              "      <td>81.057576</td>\n",
              "      <td>81.151515</td>\n",
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              "      <th>ACC score difference</th>\n",
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              "                                      Y    Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  0.210188  0.210188     0.77221   7.414714   \n",
              "ACC score                         100.0     100.0   81.160606  81.057576   \n",
              "ACC score difference                0.0       0.0    0.484058   0.714324   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.542702  \n",
              "ACC score                      81.151515  \n",
              "ACC score difference            0.502237  "
            ]
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          "execution_count": 10,
          "metadata": {},
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      ],
      "source": [
        "# measurment on R\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":1, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
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          "height": 525
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        "id": "hEkwfT_cSLVh",
        "outputId": "97c44671-a391-4f01-d5ba-b03d67e4304a"
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          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': True, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           Q         A         Y\n",
            "Q  1.000000  0.001225 -0.298573\n",
            "A  0.001225  1.000000  0.002482\n",
            "Y -0.298573  0.002482  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
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              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
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              "      <th>DP</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>0.210188</td>\n",
              "      <td>0.210188</td>\n",
              "      <td>1.163635</td>\n",
              "      <td>1.163635</td>\n",
              "      <td>0.951568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>62.766667</td>\n",
              "      <td>62.766667</td>\n",
              "      <td>62.751515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.368279</td>\n",
              "      <td>0.368279</td>\n",
              "      <td>0.410698</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d014f325-feab-4435-8c7a-4c937201a50d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d014f325-feab-4435-8c7a-4c937201a50d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d014f325-feab-4435-8c7a-4c937201a50d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                      Y    Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  0.210188  0.210188    1.163635   1.163635   \n",
              "ACC score                         100.0     100.0   62.766667  62.766667   \n",
              "ACC score difference                0.0       0.0    0.368279   0.368279   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.951568  \n",
              "ACC score                      62.751515  \n",
              "ACC score difference            0.410698  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# omission\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":True, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "9etdc05uSr5h",
        "outputId": "46a8e26f-4f8d-4dc8-e74d-8343ca8c7215"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 0, 'p_u': 0.1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.007685 -0.001163  0.635270\n",
            "Q -0.007685  1.000000 -0.002525 -0.306182\n",
            "A -0.001163 -0.002525  1.000000 -0.000779\n",
            "Y  0.635270 -0.306182 -0.000779  1.000000 \n",
            " The value counts is:  \n",
            " 0    33493\n",
            "1     3350\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>1.940033</td>\n",
              "      <td>1.940033</td>\n",
              "      <td>0.703916</td>\n",
              "      <td>0.57056</td>\n",
              "      <td>0.35842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.268459</td>\n",
              "      <td>86.296011</td>\n",
              "      <td>86.307031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.961994</td>\n",
              "      <td>1.792298</td>\n",
              "      <td>2.004423</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "          const element = document.querySelector('#df-6e57f139-5130-46a4-bb1c-64d0be6a85bc');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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            "text/plain": [
              "                                      Y    Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  1.940033  1.940033    0.703916    0.57056   \n",
              "ACC score                         100.0     100.0   86.268459  86.296011   \n",
              "ACC score difference                0.0       0.0    1.961994   1.792298   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference    0.35842  \n",
              "ACC score                      86.307031  \n",
              "ACC score difference            2.004423  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# sample\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":0.1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "Rn3cJtC4n9qT",
        "outputId": "ed865533-f4a9-4b2e-e607-babbb5bd7f10"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 1.5, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "                R         Q         A         Y    Y_real\n",
            "R       1.000000 -0.002790 -0.001676  0.628497  0.603973\n",
            "Q      -0.002790  1.000000  0.001225 -0.291867 -0.313287\n",
            "A      -0.001676  0.001225  1.000000 -0.122700  0.002057\n",
            "Y       0.628497 -0.291867 -0.122700  1.000000  0.882952\n",
            "Y_real  0.603973 -0.313287  0.002057  0.882952  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
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              "      <th>DP</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>12.943638</td>\n",
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              "      <td>0.105574</td>\n",
              "      <td>0.027654</td>\n",
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              "      <th>ACC score</th>\n",
              "      <td>93.624242</td>\n",
              "      <td>100.0</td>\n",
              "      <td>83.342424</td>\n",
              "      <td>84.457576</td>\n",
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              "      <td>12.749197</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a4c6dece-531e-4c54-bed3-61b7efa791be button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a4c6dece-531e-4c54-bed3-61b7efa791be');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                       Y    Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  12.943638  0.194441    11.22728   0.105574   \n",
              "ACC score                      93.624242     100.0   83.342424  84.457576   \n",
              "ACC score difference           12.749197       0.0    4.012125   0.048894   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.027654  \n",
              "ACC score                      84.324242  \n",
              "ACC score difference            0.012482  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# measurement bias on Y (P_Y as target). Performance are calculated on Y\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":1.5, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict, y_bias_meas=True)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "id": "B8MufxWnSyxX",
        "outputId": "42811774-ab70-4e1c-b68f-8faca772d58a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 0, 'p_u': 0.1, 'l_r': True, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.005347 -0.332019  0.639318\n",
            "Q -0.005347  1.000000  0.001442 -0.299489\n",
            "A -0.332019  0.001442  1.000000 -0.250970\n",
            "Y  0.639318 -0.299489 -0.250970  1.000000 \n",
            " The value counts is:  \n",
            " 0    33493\n",
            "1     3350\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Can t mitigate with CDP.\n",
            "Report output. \n",
            "\n",
            "problem in:  Demographic parity difference\n",
            "problem in:  Demographic parity ratio\n",
            "problem in:  Equalized odds ratio\n",
            "problem in:  Equalized odds difference\n",
            "problem in:  ACC score\n",
            "problem in:  F1 score\n",
            "problem in:  Precision\n",
            "problem in:  Recall\n",
            "problem in:  ACC score difference\n",
            "problem in:  F1 score difference\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n",
            "problem in:  Mutual information A, y_pred\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-bbf385fd-dd83-4387-8f1d-e164553c0c0f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>44.187733</td>\n",
              "      <td>44.187733</td>\n",
              "      <td>45.205494</td>\n",
              "      <td>45.429749</td>\n",
              "      <td>0.587281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.7203</td>\n",
              "      <td>86.747851</td>\n",
              "      <td>83.41415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.407774</td>\n",
              "      <td>9.510802</td>\n",
              "      <td>26.022519</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bbf385fd-dd83-4387-8f1d-e164553c0c0f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bbf385fd-dd83-4387-8f1d-e164553c0c0f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  44.187733  44.187733   45.205494  45.429749   \n",
              "ACC score                          100.0      100.0     86.7203  86.747851   \n",
              "ACC score difference                 0.0        0.0    9.407774   9.510802   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.587281  \n",
              "ACC score                       83.41415  \n",
              "ACC score difference           26.022519  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# representation bias\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":0.1, \"l_r\":True, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aiWqzxlWTHfS"
      },
      "source": [
        "## S2 - historical bias compensation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "JiMgxJpMS_eP",
        "outputId": "5b83cf06-f0d0-4505-f056-7d4228695d63"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': -1, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000  0.065214 -0.174944  0.633145\n",
            "Q  0.065214  1.000000 -0.391901 -0.252705\n",
            "A -0.174944 -0.391901  1.000000 -0.008692\n",
            "Y  0.633145 -0.252705 -0.008692  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d741028a-60d2-4a16-8a5c-bfc81f1245b5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>1.38592</td>\n",
              "      <td>1.38592</td>\n",
              "      <td>0.123829</td>\n",
              "      <td>0.833859</td>\n",
              "      <td>0.343013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.342424</td>\n",
              "      <td>86.451515</td>\n",
              "      <td>86.387879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.049579</td>\n",
              "      <td>0.073861</td>\n",
              "      <td>0.017071</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d741028a-60d2-4a16-8a5c-bfc81f1245b5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d741028a-60d2-4a16-8a5c-bfc81f1245b5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                     Y   Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  1.38592  1.38592    0.123829   0.833859   \n",
              "ACC score                        100.0    100.0   86.342424  86.451515   \n",
              "ACC score difference               0.0      0.0    0.049579   0.073861   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.343013  \n",
              "ACC score                      86.387879  \n",
              "ACC score difference            0.017071  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# no additional bias\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":-1,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "c2-Q95haVCUe",
        "outputId": "f8f4a92d-2126-4c36-ea4d-7ccce016dc29"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': -1, 'l_m': 1, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "Correlation between R and P:  [[1.         0.90538424]\n",
            " [0.90538424 1.        ]]\n",
            "The correlation matrix is:  \n",
            "           P         Q         A         Y\n",
            "P  1.000000  0.100867 -0.259059  0.562825\n",
            "Q  0.100867  1.000000 -0.391901 -0.252705\n",
            "A -0.259059 -0.391901  1.000000 -0.008692\n",
            "Y  0.562825 -0.252705 -0.008692  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>1.38592</td>\n",
              "      <td>1.38592</td>\n",
              "      <td>2.166053</td>\n",
              "      <td>5.590341</td>\n",
              "      <td>0.942543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>81.118182</td>\n",
              "      <td>81.090909</td>\n",
              "      <td>81.066667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.399195</td>\n",
              "      <td>0.708276</td>\n",
              "      <td>0.308267</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
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            "text/plain": [
              "                                     Y   Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  1.38592  1.38592    2.166053   5.590341   \n",
              "ACC score                        100.0    100.0   81.118182  81.090909   \n",
              "ACC score difference               0.0      0.0    0.399195   0.708276   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.942543  \n",
              "ACC score                      81.066667  \n",
              "ACC score difference            0.308267  "
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# measurment on R\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":-1,\n",
        "              \"l_m\":1, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "1muewBavWoBM",
        "outputId": "d7ec2c45-6b29-45fc-aa86-8d20f54947a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': -1, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': True, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           Q         A         Y\n",
            "Q  1.000000 -0.391901 -0.252705\n",
            "A -0.391901  1.000000 -0.008692\n",
            "Y -0.252705 -0.008692  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "  <div id=\"df-94fb8eeb-1308-4af0-8318-a82c25300993\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>1.38592</td>\n",
              "      <td>1.38592</td>\n",
              "      <td>9.791949</td>\n",
              "      <td>27.093546</td>\n",
              "      <td>0.937277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>62.7</td>\n",
              "      <td>61.587879</td>\n",
              "      <td>62.354545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.659164</td>\n",
              "      <td>1.565483</td>\n",
              "      <td>0.01975</td>\n",
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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            "text/plain": [
              "                                     Y   Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  1.38592  1.38592    9.791949  27.093546   \n",
              "ACC score                        100.0    100.0        62.7  61.587879   \n",
              "ACC score difference               0.0      0.0    0.659164   1.565483   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.937277  \n",
              "ACC score                      62.354545  \n",
              "ACC score difference             0.01975  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# omission\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":-1,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":True, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "F32GTesTWvEB",
        "outputId": "2f9f69d6-a416-402b-ed2b-20f736ef1056"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': -1, 'l_m': 0, 'p_u': 0.1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000  0.016263 -0.101782  0.632553\n",
            "Q  0.016263  1.000000 -0.227652 -0.294001\n",
            "A -0.101782 -0.227652  1.000000 -0.003284\n",
            "Y  0.632553 -0.294001 -0.003284  1.000000 \n",
            " The value counts is:  \n",
            " 0    33493\n",
            "1     3350\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>0.952145</td>\n",
              "      <td>0.952145</td>\n",
              "      <td>1.611251</td>\n",
              "      <td>0.726413</td>\n",
              "      <td>0.588721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.27948</td>\n",
              "      <td>86.2905</td>\n",
              "      <td>86.362134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.37411</td>\n",
              "      <td>1.519567</td>\n",
              "      <td>1.19836</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dd32804a-0ea6-436d-bb19-9c5cc98f6ab3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                      Y    Y True Unmitigated       FTU  \\\n",
              "Demographic parity difference  0.952145  0.952145    1.611251  0.726413   \n",
              "ACC score                         100.0     100.0    86.27948   86.2905   \n",
              "ACC score difference                0.0       0.0     1.37411  1.519567   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.588721  \n",
              "ACC score                      86.362134  \n",
              "ACC score difference             1.19836  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# sample\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":-1,\n",
        "              \"l_m\":0, \"p_u\":0.1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "BGgebOdspCj6",
        "outputId": "c34e61ba-a405-49aa-c9aa-39f6e78b3d57"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 1.5, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': -1, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "                R         Q         A         Y    Y_real\n",
            "R       1.000000  0.065214 -0.174944  0.645688  0.602124\n",
            "Q       0.065214  1.000000 -0.391901 -0.197834 -0.266431\n",
            "A      -0.174944 -0.391901  1.000000 -0.132825 -0.005467\n",
            "Y       0.645688 -0.197834 -0.132825  1.000000  0.880709\n",
            "Y_real  0.602124 -0.266431 -0.005467  0.880709  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "\n",
              "  <div id=\"df-7d645dcf-cc3a-4496-b720-1d147db383f5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>14.307192</td>\n",
              "      <td>1.291376</td>\n",
              "      <td>11.512405</td>\n",
              "      <td>3.8701</td>\n",
              "      <td>0.439987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>93.490909</td>\n",
              "      <td>100.0</td>\n",
              "      <td>83.693939</td>\n",
              "      <td>84.2</td>\n",
              "      <td>84.593939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>13.015815</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.381949</td>\n",
              "      <td>0.721527</td>\n",
              "      <td>0.466208</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "                                       Y    Y True Unmitigated       FTU  \\\n",
              "Demographic parity difference  14.307192  1.291376   11.512405    3.8701   \n",
              "ACC score                      93.490909     100.0   83.693939      84.2   \n",
              "ACC score difference           13.015815       0.0    3.381949  0.721527   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.439987  \n",
              "ACC score                      84.593939  \n",
              "ACC score difference            0.466208  "
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# measurement bias on Y (P_Y as target). Performance are calculated on Y\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":1.5, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":-1,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict, y_bias_meas=True)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "id": "Za0sCcszWzuu",
        "outputId": "07d4d683-8665-4ab5-ade5-87202d8d8905"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': -1, 'l_m': 0, 'p_u': 0.1, 'l_r': True, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000  0.088878 -0.416227  0.645537\n",
            "Q  0.088878  1.000000 -0.224779 -0.227457\n",
            "A -0.416227 -0.224779  1.000000 -0.263621\n",
            "Y  0.645537 -0.227457 -0.263621  1.000000 \n",
            " The value counts is:  \n",
            " 0    33493\n",
            "1     3350\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Can t mitigate with CDP.\n",
            "Report output. \n",
            "\n",
            "problem in:  Demographic parity difference\n",
            "problem in:  Demographic parity ratio\n",
            "problem in:  Equalized odds ratio\n",
            "problem in:  Equalized odds difference\n",
            "problem in:  ACC score\n",
            "problem in:  F1 score\n",
            "problem in:  Precision\n",
            "problem in:  Recall\n",
            "problem in:  ACC score difference\n",
            "problem in:  F1 score difference\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n",
            "problem in:  Mutual information A, y_pred\n"
          ]
        },
        {
          "data": {
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              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>45.721075</td>\n",
              "      <td>45.721075</td>\n",
              "      <td>45.272154</td>\n",
              "      <td>45.43581</td>\n",
              "      <td>1.843118</td>\n",
              "    </tr>\n",
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              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9bb3c535-e316-4cb4-90f9-88828f6df775 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9bb3c535-e316-4cb4-90f9-88828f6df775');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  45.721075  45.721075   45.272154   45.43581   \n",
              "ACC score                          100.0      100.0   86.819484  86.847035   \n",
              "ACC score difference                 0.0        0.0   11.032022  11.001716   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   1.843118  \n",
              "ACC score                      83.144148  \n",
              "ACC score difference            29.39222  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# representation bias\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":-1,\n",
        "              \"l_m\":0, \"p_u\":0.1, \"l_r\":True, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oO8mW4guW-Nd"
      },
      "source": [
        "## S3 - Historical bias on R"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "vK7e3JGFW7sW",
        "outputId": "993da7a0-4c54-4261-b489-19cd980d8ba3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.002959 -0.174944  0.640068\n",
            "Q -0.002959  1.000000  0.001225 -0.291867\n",
            "A -0.174944  0.001225  1.000000 -0.122700\n",
            "Y  0.640068 -0.291867 -0.122700  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "\n",
              "  <div id=\"df-4613dd25-6510-4030-a579-1d9f512dcdad\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>12.943638</td>\n",
              "      <td>12.943638</td>\n",
              "      <td>12.560752</td>\n",
              "      <td>12.948759</td>\n",
              "      <td>0.015566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.657576</td>\n",
              "      <td>86.621212</td>\n",
              "      <td>85.542424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.168488</td>\n",
              "      <td>2.095774</td>\n",
              "      <td>2.253742</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4613dd25-6510-4030-a579-1d9f512dcdad')\"\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-4613dd25-6510-4030-a579-1d9f512dcdad button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4613dd25-6510-4030-a579-1d9f512dcdad');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
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            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  12.943638  12.943638   12.560752  12.948759   \n",
              "ACC score                          100.0      100.0   86.657576  86.621212   \n",
              "ACC score difference                 0.0        0.0    2.168488   2.095774   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.015566  \n",
              "ACC score                      85.542424  \n",
              "ACC score difference            2.253742  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# no additional bias\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "crFBfxhaYOEb",
        "outputId": "3d5073c5-7df5-4f76-f596-f38aaab540e6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 1, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "Correlation between R and P:  [[1.         0.90538424]\n",
            " [0.90538424 1.        ]]\n",
            "The correlation matrix is:  \n",
            "           P         Q         A         Y\n",
            "P  1.000000  0.000113 -0.259059  0.580852\n",
            "Q  0.000113  1.000000  0.001225 -0.291867\n",
            "A -0.259059  0.001225  1.000000 -0.122700\n",
            "Y  0.580852 -0.291867 -0.122700  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>12.943638</td>\n",
              "      <td>12.943638</td>\n",
              "      <td>11.814556</td>\n",
              "      <td>19.069283</td>\n",
              "      <td>0.687892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>81.484848</td>\n",
              "      <td>81.360606</td>\n",
              "      <td>80.627273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.491581</td>\n",
              "      <td>2.182536</td>\n",
              "      <td>2.340378</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ec7414e-72ff-4947-8b59-6c38097c9b3e')\"\n",
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              "      </button>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-5ec7414e-72ff-4947-8b59-6c38097c9b3e button.colab-df-convert');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  12.943638  12.943638   11.814556  19.069283   \n",
              "ACC score                          100.0      100.0   81.484848  81.360606   \n",
              "ACC score difference                 0.0        0.0    2.491581   2.182536   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.687892  \n",
              "ACC score                      80.627273  \n",
              "ACC score difference            2.340378  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# measurment on R\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":1, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "WLKEq3zvYPKR",
        "outputId": "62b8bc9f-0a2e-4a81-b6ca-44338a4f4e90"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': True, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           Q         A         Y\n",
            "Q  1.000000  0.001225 -0.291867\n",
            "A  0.001225  1.000000 -0.122700\n",
            "Y -0.291867 -0.122700  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>12.943638</td>\n",
              "      <td>12.943638</td>\n",
              "      <td>36.690088</td>\n",
              "      <td>1.163635</td>\n",
              "      <td>0.951568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>64.548485</td>\n",
              "      <td>62.624242</td>\n",
              "      <td>62.615152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.291679</td>\n",
              "      <td>2.556106</td>\n",
              "      <td>2.562163</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  12.943638  12.943638   36.690088   1.163635   \n",
              "ACC score                          100.0      100.0   64.548485  62.624242   \n",
              "ACC score difference                 0.0        0.0    1.291679   2.556106   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.951568  \n",
              "ACC score                      62.615152  \n",
              "ACC score difference            2.562163  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# omission\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":True, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "y7rtMTktYPtk",
        "outputId": "537cd234-b7bd-422b-a0bc-e19ea43bd741"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 0, 'p_u': 0.1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.007392 -0.101782  0.635198\n",
            "Q -0.007392  1.000000 -0.002525 -0.305691\n",
            "A -0.101782 -0.002525  1.000000 -0.066942\n",
            "Y  0.635198 -0.305691 -0.066942  1.000000 \n",
            " The value counts is:  \n",
            " 0    33493\n",
            "1     3350\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>9.908602</td>\n",
              "      <td>9.908602</td>\n",
              "      <td>11.199183</td>\n",
              "      <td>10.253739</td>\n",
              "      <td>0.047646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.362134</td>\n",
              "      <td>86.345603</td>\n",
              "      <td>86.180295</td>\n",
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              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.868329</td>\n",
              "      <td>0.953181</td>\n",
              "      <td>1.265005</td>\n",
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              "</table>\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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            "text/plain": [
              "                                      Y    Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  9.908602  9.908602   11.199183  10.253739   \n",
              "ACC score                         100.0     100.0   86.362134  86.345603   \n",
              "ACC score difference                0.0       0.0    0.868329   0.953181   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.047646  \n",
              "ACC score                      86.180295  \n",
              "ACC score difference            1.265005  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# sample\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":0.1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "65MQr93Cqs4I",
        "outputId": "300c28fb-0d7c-48f1-fd0d-f8ad3ef66dd8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 1.5, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "                R         Q         A         Y    Y_real\n",
            "R       1.000000 -0.002959 -0.174944  0.637131  0.610068\n",
            "Q      -0.002959  1.000000  0.001225 -0.283430 -0.307326\n",
            "A      -0.174944  0.001225  1.000000 -0.244939 -0.128151\n",
            "Y       0.637131 -0.283430 -0.244939  1.000000  0.890457\n",
            "Y_real  0.610068 -0.307326 -0.128151  0.890457  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-07b24e5a-728e-49c1-aa52-5c586ce2cb0f\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>24.798318</td>\n",
              "      <td>13.188308</td>\n",
              "      <td>25.064487</td>\n",
              "      <td>13.131121</td>\n",
              "      <td>0.129787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>94.193939</td>\n",
              "      <td>100.0</td>\n",
              "      <td>84.036364</td>\n",
              "      <td>84.863636</td>\n",
              "      <td>83.681818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>11.61001</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.691165</td>\n",
              "      <td>1.502474</td>\n",
              "      <td>4.896843</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07b24e5a-728e-49c1-aa52-5c586ce2cb0f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-07b24e5a-728e-49c1-aa52-5c586ce2cb0f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-07b24e5a-728e-49c1-aa52-5c586ce2cb0f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  24.798318  13.188308   25.064487  13.131121   \n",
              "ACC score                      94.193939      100.0   84.036364  84.863636   \n",
              "ACC score difference            11.61001        0.0    1.691165   1.502474   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.129787  \n",
              "ACC score                      83.681818  \n",
              "ACC score difference            4.896843  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# measurement bias on Y (P_Y as target). Performance are calculated on Y\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":1.5, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict, y_bias_meas=True)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "id": "6jGgteDwYVgb",
        "outputId": "02ec4161-ad45-4bb5-8cf4-c231feda4af0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 0, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 0, 'p_u': 0.1, 'l_r': True, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.005293 -0.416227  0.645785\n",
            "Q -0.005293  1.000000  0.001442 -0.291786\n",
            "A -0.416227  0.001442  1.000000 -0.277194\n",
            "Y  0.645785 -0.291786 -0.277194  1.000000 \n",
            " The value counts is:  \n",
            " 0    33493\n",
            "1     3350\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Can t mitigate with CDP.\n",
            "Report output. \n",
            "\n",
            "problem in:  Demographic parity difference\n",
            "problem in:  Demographic parity ratio\n",
            "problem in:  Equalized odds ratio\n",
            "problem in:  Equalized odds difference\n",
            "problem in:  ACC score\n",
            "problem in:  F1 score\n",
            "problem in:  Precision\n",
            "problem in:  Recall\n",
            "problem in:  ACC score difference\n",
            "problem in:  F1 score difference\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n",
            "problem in:  Mutual information A, y_pred\n"
          ]
        },
        {
          "data": {
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              "\n",
              "  <div id=\"df-9f959c68-b981-45fb-9ca0-85c540a924e7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>48.612122</td>\n",
              "      <td>48.612122</td>\n",
              "      <td>45.575221</td>\n",
              "      <td>46.690508</td>\n",
              "      <td>1.538714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.676218</td>\n",
              "      <td>86.841525</td>\n",
              "      <td>82.868636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.122972</td>\n",
              "      <td>12.941132</td>\n",
              "      <td>27.422468</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-9f959c68-b981-45fb-9ca0-85c540a924e7 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9f959c68-b981-45fb-9ca0-85c540a924e7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  48.612122  48.612122   45.575221  46.690508   \n",
              "ACC score                          100.0      100.0   86.676218  86.841525   \n",
              "ACC score difference                 0.0        0.0   13.122972  12.941132   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   1.538714  \n",
              "ACC score                      82.868636  \n",
              "ACC score difference           27.422468  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# representation bias\n",
        "param_dict = {\"dim\":100000, \"l_y\":0, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":0.1, \"l_r\":True, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aGubXoHHYkPX"
      },
      "source": [
        "## S4 - Historical bias on Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "YCmNDWYoYlG_",
        "outputId": "f696a066-9fbe-4442-d559-8c03d48921ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.002790 -0.001676  0.628497\n",
            "Q -0.002790  1.000000  0.001225 -0.291867\n",
            "A -0.001676  0.001225  1.000000 -0.122700\n",
            "Y  0.628497 -0.291867 -0.122700  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>12.943638</td>\n",
              "      <td>12.943638</td>\n",
              "      <td>11.22728</td>\n",
              "      <td>0.105574</td>\n",
              "      <td>0.027654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.5</td>\n",
              "      <td>85.548485</td>\n",
              "      <td>85.518182</td>\n",
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              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.301879</td>\n",
              "      <td>2.132528</td>\n",
              "      <td>2.374963</td>\n",
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              "</table>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ef12140-0641-4b1d-a98d-ea1131c995c5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1ef12140-0641-4b1d-a98d-ea1131c995c5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  12.943638  12.943638    11.22728   0.105574   \n",
              "ACC score                          100.0      100.0        86.5  85.548485   \n",
              "ACC score difference                 0.0        0.0    2.301879   2.132528   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.027654  \n",
              "ACC score                      85.518182  \n",
              "ACC score difference            2.374963  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# no additional bias\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "jIFHvr_FYsdQ",
        "outputId": "e87d3b1f-fc23-43ed-df35-9f2c173dedb7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 1, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "Correlation between R and P:  [[1.         0.89928525]\n",
            " [0.89928525 1.        ]]\n",
            "The correlation matrix is:  \n",
            "           P         Q         A         Y\n",
            "P  1.000000  0.000310 -0.108182  0.578410\n",
            "Q  0.000310  1.000000  0.001225 -0.291867\n",
            "A -0.108182  0.001225  1.000000 -0.122700\n",
            "Y  0.578410 -0.291867 -0.122700  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d4b8806a-4c05-4f0a-af3e-ba5e82599a3a\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>12.943638</td>\n",
              "      <td>12.943638</td>\n",
              "      <td>10.911588</td>\n",
              "      <td>7.353967</td>\n",
              "      <td>0.590781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>81.5</td>\n",
              "      <td>81.339394</td>\n",
              "      <td>80.639394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.691576</td>\n",
              "      <td>2.855271</td>\n",
              "      <td>2.219162</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d4b8806a-4c05-4f0a-af3e-ba5e82599a3a')\"\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-d4b8806a-4c05-4f0a-af3e-ba5e82599a3a button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-d4b8806a-4c05-4f0a-af3e-ba5e82599a3a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  12.943638  12.943638   10.911588   7.353967   \n",
              "ACC score                          100.0      100.0        81.5  81.339394   \n",
              "ACC score difference                 0.0        0.0    2.691576   2.855271   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.590781  \n",
              "ACC score                      80.639394  \n",
              "ACC score difference            2.219162  "
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# measurment on R\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":1, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "NQzwmKh1Z4RP",
        "outputId": "df558319-8600-4157-95b9-98cf946de0de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': True, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           Q         A         Y\n",
            "Q  1.000000  0.001225 -0.291867\n",
            "A  0.001225  1.000000 -0.122700\n",
            "Y -0.291867 -0.122700  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "\n",
              "  <div id=\"df-5f2b8bbc-be26-4ad4-a63a-84f7322782b3\">\n",
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              "      <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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>12.943638</td>\n",
              "      <td>12.943638</td>\n",
              "      <td>36.690088</td>\n",
              "      <td>1.163635</td>\n",
              "      <td>0.951568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>64.548485</td>\n",
              "      <td>62.624242</td>\n",
              "      <td>62.615152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.291679</td>\n",
              "      <td>2.556106</td>\n",
              "      <td>2.562163</td>\n",
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              "</table>\n",
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              "\n",
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              "      border: none;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  12.943638  12.943638   36.690088   1.163635   \n",
              "ACC score                          100.0      100.0   64.548485  62.624242   \n",
              "ACC score difference                 0.0        0.0    1.291679   2.556106   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.951568  \n",
              "ACC score                      62.615152  \n",
              "ACC score difference            2.562163  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# omission\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":True, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "6FXNAUQgZ9mh",
        "outputId": "b3d1856c-28db-423b-b582-870d6454c5f1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 0, 'p_u': 0.1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.007685 -0.001163  0.631742\n",
            "Q -0.007685  1.000000 -0.002525 -0.305691\n",
            "A -0.001163 -0.002525  1.000000 -0.066942\n",
            "Y  0.631742 -0.305691 -0.066942  1.000000 \n",
            " The value counts is:  \n",
            " 0    33493\n",
            "1     3350\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>9.908602</td>\n",
              "      <td>9.908602</td>\n",
              "      <td>10.665829</td>\n",
              "      <td>0.509931</td>\n",
              "      <td>0.067498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.384175</td>\n",
              "      <td>86.141724</td>\n",
              "      <td>86.158254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.977419</td>\n",
              "      <td>1.555913</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ],
            "text/plain": [
              "                                      Y    Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  9.908602  9.908602   10.665829   0.509931   \n",
              "ACC score                         100.0     100.0   86.384175  86.141724   \n",
              "ACC score difference                0.0       0.0    0.977419   1.555913   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.067498  \n",
              "ACC score                      86.158254  \n",
              "ACC score difference            1.307428  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# sample\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":0.1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "jWv0BygrrGAb",
        "outputId": "975e4c9f-56e7-4683-efcd-358876e7605c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 1.5, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "                R         Q         A         Y    Y_real\n",
            "R       1.000000 -0.002790 -0.001676  0.603998  0.597067\n",
            "Q      -0.002790  1.000000  0.001225 -0.283430 -0.307326\n",
            "A      -0.001676  0.001225  1.000000 -0.244939 -0.128151\n",
            "Y       0.603998 -0.283430 -0.244939  1.000000  0.890457\n",
            "Y_real  0.597067 -0.307326 -0.128151  0.890457  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>24.798318</td>\n",
              "      <td>13.188308</td>\n",
              "      <td>23.100961</td>\n",
              "      <td>0.19083</td>\n",
              "      <td>0.064428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>94.193939</td>\n",
              "      <td>100.0</td>\n",
              "      <td>84.345455</td>\n",
              "      <td>83.493939</td>\n",
              "      <td>83.906061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>11.61001</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.073095</td>\n",
              "      <td>5.721154</td>\n",
              "      <td>4.654337</td>\n",
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              "</table>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  24.798318  13.188308   23.100961    0.19083   \n",
              "ACC score                      94.193939      100.0   84.345455  83.493939   \n",
              "ACC score difference            11.61001        0.0    1.073095   5.721154   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.064428  \n",
              "ACC score                      83.906061  \n",
              "ACC score difference            4.654337  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# measurement bias on Y (P_Y as target). Performance are calculated on Y\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":1.5, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict, y_bias_meas=True)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "id": "foUQO3lMaB2v",
        "outputId": "2046c71b-55ca-49c5-8164-fdb1652b8d94"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 0, 'l_h_q': 0, 'l_m': 0, 'p_u': 0.1, 'l_r': True, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.005347 -0.332019  0.642284\n",
            "Q -0.005347  1.000000  0.001442 -0.291786\n",
            "A -0.332019  0.001442  1.000000 -0.277194\n",
            "Y  0.642284 -0.291786 -0.277194  1.000000 \n",
            " The value counts is:  \n",
            " 0    33493\n",
            "1     3350\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Can t mitigate with CDP.\n",
            "Report output. \n",
            "\n",
            "problem in:  Demographic parity difference\n",
            "problem in:  Demographic parity ratio\n",
            "problem in:  Equalized odds ratio\n",
            "problem in:  Equalized odds difference\n",
            "problem in:  ACC score\n",
            "problem in:  F1 score\n",
            "problem in:  Precision\n",
            "problem in:  Recall\n",
            "problem in:  ACC score difference\n",
            "problem in:  F1 score difference\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n",
            "problem in:  Mutual information A, y_pred\n"
          ]
        },
        {
          "data": {
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              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
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              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>48.612122</td>\n",
              "      <td>48.612122</td>\n",
              "      <td>45.575221</td>\n",
              "      <td>46.720815</td>\n",
              "      <td>1.544775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.676218</td>\n",
              "      <td>86.836015</td>\n",
              "      <td>82.808023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.122972</td>\n",
              "      <td>12.947193</td>\n",
              "      <td>27.355793</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9bc3f5ce-5d2c-4121-b10e-6af08fae118e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-9bc3f5ce-5d2c-4121-b10e-6af08fae118e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9bc3f5ce-5d2c-4121-b10e-6af08fae118e');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  48.612122  48.612122   45.575221  46.720815   \n",
              "ACC score                          100.0      100.0   86.676218  86.836015   \n",
              "ACC score difference                 0.0        0.0   13.122972  12.947193   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   1.544775  \n",
              "ACC score                      82.808023  \n",
              "ACC score difference           27.355793  "
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# representation bias\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":0,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":0.1, \"l_r\":True, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85su9gMNadJe"
      },
      "source": [
        "## S5 - historical bias on R and Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "QUPJQnL2aop4",
        "outputId": "155dc794-b0d2-4025-81f2-76b6708f61b7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.002959 -0.174944  0.637131\n",
            "Q -0.002959  1.000000  0.001225 -0.283430\n",
            "A -0.174944  0.001225  1.000000 -0.244939\n",
            "Y  0.637131 -0.283430 -0.244939  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
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              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>24.798318</td>\n",
              "      <td>24.798318</td>\n",
              "      <td>25.064487</td>\n",
              "      <td>13.131121</td>\n",
              "      <td>0.129787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.860606</td>\n",
              "      <td>86.00303</td>\n",
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              "      <th>ACC score difference</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  24.798318  24.798318   25.064487  13.131121   \n",
              "ACC score                          100.0      100.0   86.860606   86.00303   \n",
              "ACC score difference                 0.0        0.0    3.956293   3.780848   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.129787  \n",
              "ACC score                      83.063636  \n",
              "ACC score difference            3.660704  "
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# no additional bias\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "swinOZUSauIh",
        "outputId": "6d57abcd-650f-4839-8b00-174bf0abbfc9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 1, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "Correlation between R and P:  [[1.         0.90538424]\n",
            " [0.90538424 1.        ]]\n",
            "The correlation matrix is:  \n",
            "           P         Q         A         Y\n",
            "P  1.000000  0.000113 -0.259059  0.590810\n",
            "Q  0.000113  1.000000  0.001225 -0.283430\n",
            "A -0.259059  0.001225  1.000000 -0.244939\n",
            "Y  0.590810 -0.283430 -0.244939  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2ff30843-104d-4e58-9048-adb55e7d538c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2ff30843-104d-4e58-9048-adb55e7d538c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  24.798318  24.798318   27.027517  18.772584   \n",
              "ACC score                          100.0      100.0   82.030303  81.642424   \n",
              "ACC score difference                 0.0        0.0    5.073201   5.036979   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference    0.63316  \n",
              "ACC score                      78.660606  \n",
              "ACC score difference            4.589578  "
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# measurment on R\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":1, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "BGeKcMGiavKZ",
        "outputId": "8f8a42b4-f3bc-438b-9097-59175a033aa7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': True, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           Q         A         Y\n",
            "Q  1.000000  0.001225 -0.283430\n",
            "A  0.001225  1.000000 -0.244939\n",
            "Y -0.283430 -0.244939  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>24.798318</td>\n",
              "      <td>24.798318</td>\n",
              "      <td>36.690088</td>\n",
              "      <td>1.163635</td>\n",
              "      <td>0.951568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.421212</td>\n",
              "      <td>62.054545</td>\n",
              "      <td>62.045455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.660695</td>\n",
              "      <td>4.07105</td>\n",
              "      <td>4.077108</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3fa1c3dd-2a61-4804-86d0-a98e6f9cf407')\"\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3fa1c3dd-2a61-4804-86d0-a98e6f9cf407 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3fa1c3dd-2a61-4804-86d0-a98e6f9cf407');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  24.798318  24.798318   36.690088   1.163635   \n",
              "ACC score                          100.0      100.0   66.421212  62.054545   \n",
              "ACC score difference                 0.0        0.0    4.660695    4.07105   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.951568  \n",
              "ACC score                      62.045455  \n",
              "ACC score difference            4.077108  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# omission\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":True, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "CEQ9QTKwavx-",
        "outputId": "a4b40249-34ee-4f6c-c4e3-07c2a3a36741"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 0, 'p_u': 0.1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.007392 -0.101782  0.633864\n",
            "Q -0.007392  1.000000 -0.002525 -0.301842\n",
            "A -0.101782 -0.002525  1.000000 -0.124015\n",
            "Y  0.633864 -0.301842 -0.124015  1.000000 \n",
            " The value counts is:  \n",
            " 0    33493\n",
            "1     3350\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7927472c-c5e3-456f-b4db-74192c6b6c5d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>20.642083</td>\n",
              "      <td>20.642083</td>\n",
              "      <td>19.156915</td>\n",
              "      <td>10.290136</td>\n",
              "      <td>1.328073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.554992</td>\n",
              "      <td>86.334582</td>\n",
              "      <td>85.557637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.189555</td>\n",
              "      <td>1.431975</td>\n",
              "      <td>6.513473</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7927472c-c5e3-456f-b4db-74192c6b6c5d')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7927472c-c5e3-456f-b4db-74192c6b6c5d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7927472c-c5e3-456f-b4db-74192c6b6c5d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  20.642083  20.642083   19.156915  10.290136   \n",
              "ACC score                          100.0      100.0   86.554992  86.334582   \n",
              "ACC score difference                 0.0        0.0    4.189555   1.431975   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   1.328073  \n",
              "ACC score                      85.557637  \n",
              "ACC score difference            6.513473  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# sample\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":0.1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "ezOcaXh_vhiP",
        "outputId": "0c317c1e-c2c8-430e-8b31-0c470c8dbbed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 1.5, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 0, 'p_u': 1, 'l_r': False, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "                R         Q         A         Y    Y_real\n",
            "R       1.000000 -0.002959 -0.174944  0.619543  0.611526\n",
            "Q      -0.002959  1.000000  0.001225 -0.270694 -0.293878\n",
            "A      -0.174944  0.001225  1.000000 -0.359151 -0.255519\n",
            "Y       0.619543 -0.270694 -0.359151  1.000000  0.902292\n",
            "Y_real  0.611526 -0.293878 -0.255519  0.902292  1.000000 \n",
            " The value counts is:  \n",
            " 1    33506\n",
            "0    33494\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Report output. \n",
            "\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n"
          ]
        },
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>36.822925</td>\n",
              "      <td>26.24909</td>\n",
              "      <td>37.392013</td>\n",
              "      <td>13.258432</td>\n",
              "      <td>0.105889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>94.712121</td>\n",
              "      <td>100.0</td>\n",
              "      <td>84.609091</td>\n",
              "      <td>84.257576</td>\n",
              "      <td>81.181818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>10.573835</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.097434</td>\n",
              "      <td>6.605725</td>\n",
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            "text/plain": [
              "                                       Y    Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  36.822925  26.24909   37.392013  13.258432   \n",
              "ACC score                      94.712121     100.0   84.609091  84.257576   \n",
              "ACC score difference           10.573835       0.0    0.097434   6.605725   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   0.105889  \n",
              "ACC score                      81.181818  \n",
              "ACC score difference           11.255328  "
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# measurement bias on Y (P_Y as target). Performance are calculated on Y\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":1.5, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":1, \"l_r\":False, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict, y_bias_meas=True)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "id": "-KSsofcVawf6",
        "outputId": "9203e5b4-4da0-4f76-e547-93c885f481e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The parameters for data generetion are:  {'dim': 100000, 'l_y': 1.5, 'l_m_y': 0, 'thr_supp': 1, 'l_h_r': 1.5, 'l_h_q': 0, 'l_m': 0, 'p_u': 0.1, 'l_r': True, 'l_o': False, 'l_y_b': 0, 'l_q': 2, 'sy': 2, 'l_r_q': 0} \n",
            "\n",
            "Start creation dataset. \n",
            "\n",
            "The correlation matrix is:  \n",
            "           R         Q         A         Y\n",
            "R  1.000000 -0.005293 -0.416227  0.644387\n",
            "Q -0.005293  1.000000  0.001442 -0.290510\n",
            "A -0.416227  0.001442  1.000000 -0.289102\n",
            "Y  0.644387 -0.290510 -0.289102  1.000000 \n",
            " The value counts is:  \n",
            " 0    33493\n",
            "1     3350\n",
            "Name: A, dtype: int64 \n",
            "\n",
            "Fitting models. \n",
            "\n",
            "Mitigate models. \n",
            "\n",
            "Can t mitigate with CDP.\n",
            "Report output. \n",
            "\n",
            "problem in:  Demographic parity difference\n",
            "problem in:  Demographic parity ratio\n",
            "problem in:  Equalized odds ratio\n",
            "problem in:  Equalized odds difference\n",
            "problem in:  ACC score\n",
            "problem in:  F1 score\n",
            "problem in:  Precision\n",
            "problem in:  Recall\n",
            "problem in:  ACC score difference\n",
            "problem in:  F1 score difference\n",
            "problem in:  Flip\n",
            "problem in:  Flip\n",
            "problem in:  Mutual information A, y_pred\n"
          ]
        },
        {
          "data": {
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              "\n",
              "  <div id=\"df-69d5f2f7-b117-423c-9d08-f60383ea8537\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y</th>\n",
              "      <th>Y True</th>\n",
              "      <th>Unmitigated</th>\n",
              "      <th>FTU</th>\n",
              "      <th>DP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Demographic parity difference</th>\n",
              "      <td>50.721366</td>\n",
              "      <td>50.721366</td>\n",
              "      <td>46.993575</td>\n",
              "      <td>48.430113</td>\n",
              "      <td>1.054034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>86.621115</td>\n",
              "      <td>86.791933</td>\n",
              "      <td>82.510469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ACC score difference</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>14.116929</td>\n",
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              "  </style>\n",
              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "                                       Y     Y True Unmitigated        FTU  \\\n",
              "Demographic parity difference  50.721366  50.721366   46.993575  48.430113   \n",
              "ACC score                          100.0      100.0   86.621115  86.791933   \n",
              "ACC score difference                 0.0        0.0   14.116929  13.929027   \n",
              "\n",
              "                                      DP  \n",
              "Demographic parity difference   1.054034  \n",
              "ACC score                      82.510469  \n",
              "ACC score difference           29.761844  "
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# representation bias\n",
        "param_dict = {\"dim\":100000, \"l_y\":1.5, \"l_m_y\":0, \"thr_supp\":1, \"l_h_r\":1.5,  \"l_h_q\":0,\n",
        "              \"l_m\":0, \"p_u\":0.1, \"l_r\":True, \"l_o\":False, \"l_y_b\":0,\n",
        "              \"l_q\":2, \"sy\":2, \"l_r_q\":0}\n",
        "dtf_res = pipeline(param_dict)\n",
        "# Select only DP diff, Accuracy, Accuracy difference for the models Unmitigated, FTU and DP postprocessed\n",
        "dtf_res.loc[['Demographic parity difference', 'ACC score', 'ACC score difference'], ['Y', 'Y True', 'Unmitigated', 'FTU', 'DP']]*100"
      ]
    }
  ],
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      "name": "Bias Dataset Generation.ipynb",
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