ADULT_ML (1).ipynb

{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HsuHcu1LurU1",
        "outputId": "90d51485-0319-4275-fcfb-b32246a5a24f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting aif360[LFR]\n",
            "  Downloading aif360-0.4.0-py3-none-any.whl (175 kB)\n",
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            "\u001b[33mWARNING: aif360 0.4.0 does not provide the extra 'lfr'\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.22.1 in /usr/local/lib/python3.7/dist-packages (from aif360[LFR]) (1.0.2)\n",
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            "  Downloading memory_profiler-0.60.0.tar.gz (38 kB)\n",
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            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
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            "Building wheels for collected packages: memory-profiler\n",
            "  Building wheel for memory-profiler (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for memory-profiler: filename=memory_profiler-0.60.0-py3-none-any.whl size=31284 sha256=043e232ded89d050c4356844741fc515a15593d12f893f5d4247edd60ad76a20\n",
            "  Stored in directory: /root/.cache/pip/wheels/67/2b/fb/326e30d638c538e69a5eb0aa47f4223d979f502bbdb403950f\n",
            "Successfully built memory-profiler\n",
            "Installing collected packages: slicer, shap, memory-profiler, tempeh, aif360\n",
            "Successfully installed aif360-0.4.0 memory-profiler-0.60.0 shap-0.40.0 slicer-0.0.7 tempeh-0.1.12\n"
          ]
        }
      ],
      "source": [
        "!pip install 'aif360[LFR]'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1TS-esZyvTy9",
        "outputId": "2bb8ee8c-d98d-4a43-abcf-31e4526395c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/aif360/data/raw/adult\n"
          ]
        }
      ],
      "source": [
        "cd /usr/local/lib/python3.7/dist-packages/aif360/data/raw/adult"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading adult dataset"
      ],
      "metadata": {
        "id": "QjLW1N3X9Wze"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mytBLHzvaq5",
        "outputId": "a86c9895-70d5-4973-84f2-64398ec3699d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-05-11 02:57:17--  https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3974305 (3.8M) [application/x-httpd-php]\n",
            "Saving to: ‘adult.data’\n",
            "\n",
            "adult.data          100%[===================>]   3.79M  6.92MB/s    in 0.5s    \n",
            "\n",
            "2022-05-11 02:57:18 (6.92 MB/s) - ‘adult.data’ saved [3974305/3974305]\n",
            "\n",
            "--2022-05-11 02:57:18--  https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5229 (5.1K) [application/x-httpd-php]\n",
            "Saving to: ‘adult.names’\n",
            "\n",
            "adult.names         100%[===================>]   5.11K  --.-KB/s    in 0s      \n",
            "\n",
            "2022-05-11 02:57:19 (113 MB/s) - ‘adult.names’ saved [5229/5229]\n",
            "\n",
            "--2022-05-11 02:57:19--  https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2003153 (1.9M) [application/x-httpd-php]\n",
            "Saving to: ‘adult.test’\n",
            "\n",
            "adult.test          100%[===================>]   1.91M  3.92MB/s    in 0.5s    \n",
            "\n",
            "2022-05-11 02:57:20 (3.92 MB/s) - ‘adult.test’ saved [2003153/2003153]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\n",
        "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names\n",
        "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install pytorch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aH0fpzJ3DSkB",
        "outputId": "44a42cea-e061-49c5-8fdb-30a756230fcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pytorch\n",
            "  Downloading pytorch-1.0.2.tar.gz (689 bytes)\n",
            "Building wheels for collected packages: pytorch\n",
            "  Building wheel for pytorch (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for pytorch\u001b[0m\n",
            "\u001b[?25h  Running setup.py clean for pytorch\n",
            "Failed to build pytorch\n",
            "Installing collected packages: pytorch\n",
            "    Running setup.py install for pytorch ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[31mERROR: Command errored out with exit status 1: /usr/bin/python3 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-gjd13w20/pytorch_962e70a32ee64b1dbe112b56133d9193/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-gjd13w20/pytorch_962e70a32ee64b1dbe112b56133d9193/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-3j148gja/install-record.txt --single-version-externally-managed --compile --install-headers /usr/local/include/python3.7/pytorch Check the logs for full command output.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install fairlearn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lUNbxeFDfEw",
        "outputId": "d0064bf3-ca38-4fb9-9572-0bc987dc8500"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting fairlearn\n",
            "  Downloading fairlearn-0.7.0-py3-none-any.whl (177 kB)\n",
            "\u001b[?25l\r\u001b[K     |█▉                              | 10 kB 21.1 MB/s eta 0:00:01\r\u001b[K     |███▊                            | 20 kB 12.9 MB/s eta 0:00:01\r\u001b[K     |█████▌                          | 30 kB 9.1 MB/s eta 0:00:01\r\u001b[K     |███████▍                        | 40 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |█████████▎                      | 51 kB 8.5 MB/s eta 0:00:01\r\u001b[K     |███████████                     | 61 kB 9.9 MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 71 kB 9.5 MB/s eta 0:00:01\r\u001b[K     |██████████████▊                 | 81 kB 9.3 MB/s eta 0:00:01\r\u001b[K     |████████████████▋               | 92 kB 10.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████▌             | 102 kB 10.5 MB/s eta 0:00:01\r\u001b[K     |████████████████████▎           | 112 kB 10.5 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▏         | 122 kB 10.5 MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 133 kB 10.5 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▉      | 143 kB 10.5 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▊    | 153 kB 10.5 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▌  | 163 kB 10.5 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▍| 174 kB 10.5 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 177 kB 10.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.7/dist-packages (from fairlearn) (1.21.6)\n",
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            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.1->fairlearn) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.25.1->fairlearn) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.1->fairlearn) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.1->fairlearn) (3.1.0)\n",
            "Installing collected packages: fairlearn\n",
            "Successfully installed fairlearn-0.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xUVCpjjjvfJu"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "import numpy as np\n",
        "from aif360.datasets import AdultDataset\n",
        "from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions import load_preproc_data_adult\n",
        "from aif360.metrics import ClassificationMetric\n",
        "from aif360.algorithms.preprocessing.reweighing import Reweighing\n",
        "import pdb\n",
        "from sklearn.preprocessing import StandardScaler  #MinMaxScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import *\n",
        "import matplotlib.pyplot as plt\n",
        "np.random.seed(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBU__PuOvmx5",
        "outputId": "66d65e5c-119b-41f2-b5f6-8d1d2fc33034"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training data size (34189, 18)\n",
            "dataset feature names ['race', 'sex', 'Age (decade)=10', 'Age (decade)=20', 'Age (decade)=30', 'Age (decade)=40', 'Age (decade)=50', 'Age (decade)=60', 'Age (decade)=>=70', 'Education Years=6', 'Education Years=7', 'Education Years=8', 'Education Years=9', 'Education Years=10', 'Education Years=11', 'Education Years=12', 'Education Years=<6', 'Education Years=>12']\n"
          ]
        }
      ],
      "source": [
        "\n",
        "privileged_groups = [{'sex': 1.0}]\n",
        "unprivileged_groups = [{'sex': 0.0}]\n",
        "dataset_orig = load_preproc_data_adult(['sex'])\n",
        "\n",
        "\n",
        "\n",
        "train, test = dataset_orig.split([0.7], shuffle=True)\n",
        "print(\"training data size\", train.features.shape)\n",
        "print(\"dataset feature names\", train.feature_names)\n",
        "\n",
        "#Normalize the dataset, both train and test. This should always be done in any machine learning pipeline!\n",
        "scale_orig = StandardScaler()\n",
        "X_train = scale_orig.fit_transform(train.features)\n",
        "y_train = train.labels.ravel()\n",
        "\n",
        "X_test = scale_orig.transform(test.features) \n",
        "y_test = test.labels.ravel()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Code adopted from https://machinelearningmastery.com/hyperparameter-optimization-with-random-search-and-grid-search/\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import  accuracy_score \n",
        "\n",
        "# define dataset\n",
        "\n",
        "# define models and parameters\n",
        "model = LogisticRegression()\n",
        "solvers = [ 'liblinear']\n",
        "penalty = ['l2']\n",
        "c_values = np.logspace(np.log10(100),np.log10(0.00001),num=20)\n",
        "# define grid search\n",
        "grid = dict(solver=solvers,penalty=penalty,C=c_values)\n",
        "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=1, random_state=1)\n",
        "grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy',error_score=0)\n",
        "grid_result = grid_search.fit(X_train, y_train)\n",
        "best_model = grid_result.best_estimator_\n",
        "yhat = best_model.predict(X_test)\n",
        "\t# evaluate the model\n",
        "acc = accuracy_score(y_test, yhat)\n",
        "\t# store the result\n",
        "# summarize results\n",
        "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
        "means = grid_result.cv_results_['mean_test_score']\n",
        "stds = grid_result.cv_results_['std_test_score']\n",
        "params = grid_result.cv_results_['params']\n",
        "for mean, stdev, param in zip(means, stds, params):\n",
        "    print('accuracy',\"%f (%f) with: %r\" % (mean, stdev, param))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJ3BfEiu0Rua",
        "outputId": "e4d3f5aa-825a-44d4-90c8-f6aa92320e13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best: 0.804089 using {'C': 100.0, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 100.0, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 42.81332398719393, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 18.329807108324356, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 7.847599703514615, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 3.3598182862837827, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 1.438449888287663, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 0.6158482110660267, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 0.26366508987303583, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 0.11288378916846895, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 0.048329302385717574, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 0.0206913808111479, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.803884 (0.004325) with: {'C': 0.008858667904100831, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.803943 (0.004333) with: {'C': 0.003792690190732254, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.803855 (0.004323) with: {'C': 0.0016237767391887243, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.802919 (0.004244) with: {'C': 0.0006951927961775605, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.800579 (0.004101) with: {'C': 0.00029763514416313193, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.797215 (0.002723) with: {'C': 0.00012742749857031348, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.797186 (0.002743) with: {'C': 5.455594781168526e-05, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.796455 (0.002362) with: {'C': 2.335721469090126e-05, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.795168 (0.002705) with: {'C': 1e-05, 'penalty': 'l2', 'solver': 'liblinear'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learner = LogisticRegression(solver='liblinear', random_state=1, C = 0.01, penalty = \"l2\") \n",
        "learner.fit(X_train,y_train)\n",
        "predictions = learner.predict(X_test)\n",
        "\n",
        "test_pred = test.copy()\n",
        "test_pred.labels = predictions\n",
        "print(\"Accuracy\", sum(predictions==y_test)/len(y_test))\n",
        "metric = ClassificationMetric(test, test_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)\n",
        "metric_arrs = {}\n",
        "metric_arrs['eq_opp_diff']=(metric.equal_opportunity_difference())\n",
        "print(metric_arrs)"
      ],
      "metadata": {
        "id": "0Cfk4rFBfSmj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "714eb5a1-6d1e-45e6-c47f-4faa07ceb79f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.8042039172865625\n",
            "{'eq_opp_diff': -0.4414141414141414}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_validate\n",
        "learner = LogisticRegression(solver='liblinear', random_state=1, C = 0.01, penalty = \"l2\") \n",
        "learner.fit(X_train,y_train)\n",
        "predictions = learner.predict(X_test)\n",
        "cv_results = cross_validate(learner,X_train,y_train,cv = 5,return_estimator = True)\n",
        "\n",
        "cv_results['test_score']\n",
        "cv_model = cv_results['estimator'][list(cv_results['test_score']).index(max(cv_results['test_score']))]\n",
        "\n",
        "cv_predictions = test.copy()\n",
        "cv_predictions.labels = cv_model.predict(test.features).reshape(-1,1)\n",
        "\n",
        "# Measure Fairness metrics\n",
        "metric = ClassificationMetric(test, cv_predictions, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)\n",
        "metric_arrs = {}\n",
        "metric_arrs['eq_opp_diff']=(metric.equal_opportunity_difference())\n",
        "metric_arrs['avg_odds_diff']=(metric.average_odds_difference())\n",
        "metric_arrs['bal_acc']=((metric.true_positive_rate() + metric.true_negative_rate()) / 2)\n",
        "metric_arrs['disp_imp']=(metric.disparate_impact())\n",
        "print(metric_arrs)"
      ],
      "metadata": {
        "id": "E-dFRcHgq8BF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f176f20f-d1c0-427b-931d-49286178c9fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eq_opp_diff': 0.0, 'avg_odds_diff': 0.0, 'bal_acc': 0.5, 'disp_imp': nan}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "invalid value encountered in double_scalars\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistic Regression With PyTorch"
      ],
      "metadata": {
        "id": "B0SpsAbm-jQO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.autograd import Variable\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.datasets as dsets\n",
        "import torch.utils.data as Data"
      ],
      "metadata": {
        "id": "gtYg4r8tfuE8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class LogisticRegression_torch(torch.nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(LogisticRegression_torch, self).__init__()\n",
        "        self.linear = torch.nn.Linear(input_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        outputs = torch.sigmoid(self.linear(x))\n",
        "        return outputs"
      ],
      "metadata": {
        "id": "iYQd5jK9sooS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "GPU = True\n",
        "device_idx = 0\n",
        "if GPU:\n",
        "    device = torch.device(\"cuda:\" + str(device_idx) if torch.cuda.is_available() else \"cpu\")\n",
        "else:\n",
        "    device = torch.device(\"cpu\")\n",
        "BATCH_SIZE = 64\n",
        "learning_rate = 0.0001\n",
        "\n",
        "# building a DataTensor\n",
        "train_dataset = Data.TensorDataset(torch.tensor(X_train).float(), torch.Tensor(y_train).float())\n",
        "\n",
        "if device == 0:\n",
        "    num_workers = 2\n",
        "else:\n",
        "    num_workers = 0\n",
        "\n",
        "# Data Loader for training the model\n",
        "loader_train = Data.DataLoader(\n",
        "    dataset=train_dataset, \n",
        "    batch_size=BATCH_SIZE, \n",
        "    shuffle=True, num_workers=num_workers)"
      ],
      "metadata": {
        "id": "3H7uMgdKrjdg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "criterion = torch.nn.BCELoss(reduction='sum')\n",
        "epochs = 3\n",
        "accuracies = []\n",
        "metrix = {}\n",
        "lambdas = [0.0, 0.00001, 0.0001, 0.001, 0.01, 0.1, 0.2, 0.3, 0.5, 0.7, 0.9]\n",
        "lambdas = np.concatenate((np.array(lambdas), np.linspace(1, 100, num=100)))\n",
        "for r_lambda in lambdas:\n",
        "    print('Lambda:', r_lambda,'\\n')\n",
        "    model = LogisticRegression_torch(X_train.shape[1], 1)\n",
        "    optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)  \n",
        "    for epoch in range(epochs):\n",
        "        train_loss = 0.0\n",
        "        for i, (x, y) in enumerate(loader_train):\n",
        "        #using input and labels for training \n",
        "\n",
        "            inputs = Variable(x.to(device))\n",
        "            labels = Variable(y.to(device))\n",
        "\n",
        "          \n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # using input getting the output\n",
        "            outputs = model(inputs)\n",
        "\n",
        "            # Regularization factor\n",
        "            reg = 0\n",
        "            for param in model.parameters():\n",
        "                reg += 0.5 * (param ** 2).sum()\n",
        "\n",
        "            # find loss for output\n",
        "            loss = criterion(outputs.reshape(outputs.shape[0]), labels) + \\\n",
        "                r_lambda * reg\n",
        "                \n",
        "            train_loss += loss.item()\n",
        "            \n",
        "            # get gradients w.r.t to parameters\n",
        "            loss.backward()\n",
        "            \n",
        "            # update parameters\n",
        "            optimizer.step()\n",
        "        \n",
        "\n",
        "        print('epoch [{}/{}], Training loss:{:.6f}'.format(\n",
        "            epoch + 1, \n",
        "            epochs, \n",
        "            train_loss / len(loader_train.dataset))) #training has finished \n",
        "    with torch.no_grad():\n",
        "        model.eval()\n",
        "        out = model(Variable(torch.Tensor(X_test).to(device))).detach().cpu()\n",
        "        pred = (out >= 0.5).int().numpy().squeeze()\n",
        "        accuracy = sum((y_test == pred))/len(y_test)\n",
        "    #accuracy from validation\n",
        "\n",
        "    print('Accuracy: ', accuracy,'\\n')\n",
        "    accuracies.append(accuracy)\n",
        "\n",
        "    test_pred = test.copy()\n",
        "    test_pred.labels = pred.reshape(-1,1)\n",
        "    metric = ClassificationMetric(test, test_pred,unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)\n",
        "    metric_arrs = {}\n",
        "    metric_arrs['eq_opp_diff']=(metric.equal_opportunity_difference())\n",
        "    print(metric_arrs)\n",
        "\n",
        "    metrix[r_lambda] = {}\n",
        "    metrix[r_lambda]['accuracy'] = accuracy\n",
        "    metrix[r_lambda]['privilaged'] = metric.performance_measures(privileged=True)\n",
        "    metrix[r_lambda]['unprivilaged'] = metric.performance_measures(privileged=False)\n",
        "\n",
        "    met = metric.binary_confusion_matrix(privileged=True)\n",
        "    PR_priv = (met['TP'] + met['FP']) / (met['TP'] + met['FP'] + met['TN'] + met['FN'])\n",
        "    metrix[r_lambda]['privilaged']['PR'] = PR_priv\n",
        "\n",
        "    met = metric.binary_confusion_matrix(privileged=False)\n",
        "    PR_unpriv = (met['TP'] + met['FP']) / (met['TP'] + met['FP'] + met['TN'] + met['FN'])\n",
        "    metrix[r_lambda]['unprivilaged']['PR'] = PR_unpriv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "751hJwFlsdzN",
        "outputId": "373170ba-520c-4aad-b6d7-1db97e7ee037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lambda: 0.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.586180\n",
            "epoch [2/3], Training loss:0.473647\n",
            "epoch [3/3], Training loss:0.445917\n",
            "Accuracy:  0.8038626902340817 \n",
            "\n",
            "{'eq_opp_diff': -0.435016835016835}\n",
            "Lambda: 1e-05 \n",
            "\n",
            "epoch [1/3], Training loss:0.597402\n",
            "epoch [2/3], Training loss:0.475149\n",
            "epoch [3/3], Training loss:0.446723\n",
            "Accuracy:  0.8050228622125162 \n",
            "\n",
            "{'eq_opp_diff': -0.4377104377104377}\n",
            "Lambda: 0.0001 \n",
            "\n",
            "epoch [1/3], Training loss:0.576622\n",
            "epoch [2/3], Training loss:0.473604\n",
            "epoch [3/3], Training loss:0.446437\n",
            "Accuracy:  0.801610591687709 \n",
            "\n",
            "{'eq_opp_diff': -0.3692411924119241}\n",
            "Lambda: 0.001 \n",
            "\n",
            "epoch [1/3], Training loss:0.593786\n",
            "epoch [2/3], Training loss:0.480416\n",
            "epoch [3/3], Training loss:0.449420\n",
            "Accuracy:  0.8023612912031666 \n",
            "\n",
            "{'eq_opp_diff': -0.35307957625030795}\n",
            "Lambda: 0.01 \n",
            "\n",
            "epoch [1/3], Training loss:0.614691\n",
            "epoch [2/3], Training loss:0.481845\n",
            "epoch [3/3], Training loss:0.449285\n",
            "Accuracy:  0.8027025182556473 \n",
            "\n",
            "{'eq_opp_diff': -0.35947688264761435}\n",
            "Lambda: 0.1 \n",
            "\n",
            "epoch [1/3], Training loss:0.567074\n",
            "epoch [2/3], Training loss:0.472963\n",
            "epoch [3/3], Training loss:0.447685\n",
            "Accuracy:  0.8046133897495393 \n",
            "\n",
            "{'eq_opp_diff': -0.45555555555555555}\n",
            "Lambda: 0.2 \n",
            "\n",
            "epoch [1/3], Training loss:0.611305\n",
            "epoch [2/3], Training loss:0.483840\n",
            "epoch [3/3], Training loss:0.452523\n",
            "Accuracy:  0.7983348119838941 \n",
            "\n",
            "{'eq_opp_diff': -0.24149215734581586}\n",
            "Lambda: 0.3 \n",
            "\n",
            "epoch [1/3], Training loss:0.628789\n",
            "epoch [2/3], Training loss:0.488558\n",
            "epoch [3/3], Training loss:0.455645\n",
            "Accuracy:  0.801610591687709 \n",
            "\n",
            "{'eq_opp_diff': -0.3692411924119241}\n",
            "Lambda: 0.5 \n",
            "\n",
            "epoch [1/3], Training loss:0.592277\n",
            "epoch [2/3], Training loss:0.481239\n",
            "epoch [3/3], Training loss:0.455579\n",
            "Accuracy:  0.8027025182556473 \n",
            "\n",
            "{'eq_opp_diff': -0.35947688264761435}\n",
            "Lambda: 0.7 \n",
            "\n",
            "epoch [1/3], Training loss:0.577784\n",
            "epoch [2/3], Training loss:0.479987\n",
            "epoch [3/3], Training loss:0.457901\n",
            "Accuracy:  0.8009963829932437 \n",
            "\n",
            "{'eq_opp_diff': -0.37765870082943254}\n",
            "Lambda: 0.9 \n",
            "\n",
            "epoch [1/3], Training loss:0.559973\n",
            "epoch [2/3], Training loss:0.480481\n",
            "epoch [3/3], Training loss:0.460599\n",
            "Accuracy:  0.8024295366136628 \n",
            "\n",
            "{'eq_opp_diff': -0.3635172866880184}\n",
            "Lambda: 1.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.591135\n",
            "epoch [2/3], Training loss:0.485918\n",
            "epoch [3/3], Training loss:0.463623\n",
            "Accuracy:  0.8022248003821743 \n",
            "\n",
            "{'eq_opp_diff': -0.35408967726040896}\n",
            "Lambda: 2.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.570245\n",
            "epoch [2/3], Training loss:0.493752\n",
            "epoch [3/3], Training loss:0.477869\n",
            "Accuracy:  0.804476898928547 \n",
            "\n",
            "{'eq_opp_diff': -0.45656565656565656}\n",
            "Lambda: 3.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.644271\n",
            "epoch [2/3], Training loss:0.512052\n",
            "epoch [3/3], Training loss:0.492047\n",
            "Accuracy:  0.8020200641506858 \n",
            "\n",
            "{'eq_opp_diff': -0.38136240453313625}\n",
            "Lambda: 4.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.609973\n",
            "epoch [2/3], Training loss:0.513566\n",
            "epoch [3/3], Training loss:0.501328\n",
            "Accuracy:  0.802566027434655 \n",
            "\n",
            "{'eq_opp_diff': -0.3625071856779174}\n",
            "Lambda: 5.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.607741\n",
            "epoch [2/3], Training loss:0.522103\n",
            "epoch [3/3], Training loss:0.511241\n",
            "Accuracy:  0.8027707636661434 \n",
            "\n",
            "{'eq_opp_diff': -0.3699145930853248}\n",
            "Lambda: 6.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.592596\n",
            "epoch [2/3], Training loss:0.527502\n",
            "epoch [3/3], Training loss:0.519796\n",
            "Accuracy:  0.7988125298573671 \n",
            "\n",
            "{'eq_opp_diff': -0.22937094522460377}\n",
            "Lambda: 7.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.606145\n",
            "epoch [2/3], Training loss:0.533890\n",
            "epoch [3/3], Training loss:0.527542\n",
            "Accuracy:  0.8023612912031666 \n",
            "\n",
            "{'eq_opp_diff': -0.38775971093044265}\n",
            "Lambda: 8.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.633001\n",
            "epoch [2/3], Training loss:0.543073\n",
            "epoch [3/3], Training loss:0.535206\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 9.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.608867\n",
            "epoch [2/3], Training loss:0.545341\n",
            "epoch [3/3], Training loss:0.541201\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 10.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.621701\n",
            "epoch [2/3], Training loss:0.552768\n",
            "epoch [3/3], Training loss:0.547615\n",
            "Accuracy:  0.7981983211629018 \n",
            "\n",
            "{'eq_opp_diff': -0.23778845364211215}\n",
            "Lambda: 11.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.633408\n",
            "epoch [2/3], Training loss:0.557077\n",
            "epoch [3/3], Training loss:0.552815\n",
            "Accuracy:  0.7981983211629018 \n",
            "\n",
            "{'eq_opp_diff': -0.23778845364211215}\n",
            "Lambda: 12.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.638629\n",
            "epoch [2/3], Training loss:0.562632\n",
            "epoch [3/3], Training loss:0.558022\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 13.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.642087\n",
            "epoch [2/3], Training loss:0.566440\n",
            "epoch [3/3], Training loss:0.562527\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 14.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.627736\n",
            "epoch [2/3], Training loss:0.569724\n",
            "epoch [3/3], Training loss:0.566929\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 15.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.616701\n",
            "epoch [2/3], Training loss:0.572964\n",
            "epoch [3/3], Training loss:0.570930\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 16.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.622329\n",
            "epoch [2/3], Training loss:0.576485\n",
            "epoch [3/3], Training loss:0.574647\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 17.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.654940\n",
            "epoch [2/3], Training loss:0.580057\n",
            "epoch [3/3], Training loss:0.578146\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 18.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.609957\n",
            "epoch [2/3], Training loss:0.582173\n",
            "epoch [3/3], Training loss:0.581441\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 19.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.668399\n",
            "epoch [2/3], Training loss:0.586724\n",
            "epoch [3/3], Training loss:0.584641\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 20.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.648031\n",
            "epoch [2/3], Training loss:0.588695\n",
            "epoch [3/3], Training loss:0.587534\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 21.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.634619\n",
            "epoch [2/3], Training loss:0.591588\n",
            "epoch [3/3], Training loss:0.590369\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 22.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.638880\n",
            "epoch [2/3], Training loss:0.594107\n",
            "epoch [3/3], Training loss:0.593001\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 23.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.656115\n",
            "epoch [2/3], Training loss:0.596654\n",
            "epoch [3/3], Training loss:0.595486\n",
            "Accuracy:  0.798744284446871 \n",
            "\n",
            "{'eq_opp_diff': -0.253613369467028}\n",
            "Lambda: 24.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.647783\n",
            "epoch [2/3], Training loss:0.598598\n",
            "epoch [3/3], Training loss:0.597835\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 25.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.632924\n",
            "epoch [2/3], Training loss:0.600500\n",
            "epoch [3/3], Training loss:0.600075\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 26.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.645818\n",
            "epoch [2/3], Training loss:0.602923\n",
            "epoch [3/3], Training loss:0.602239\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 27.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.630360\n",
            "epoch [2/3], Training loss:0.604688\n",
            "epoch [3/3], Training loss:0.604281\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 28.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.658090\n",
            "epoch [2/3], Training loss:0.606757\n",
            "epoch [3/3], Training loss:0.606222\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 29.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.629035\n",
            "epoch [2/3], Training loss:0.608291\n",
            "epoch [3/3], Training loss:0.608088\n",
            "Accuracy:  0.798744284446871 \n",
            "\n",
            "{'eq_opp_diff': -0.253613369467028}\n",
            "Lambda: 30.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.659010\n",
            "epoch [2/3], Training loss:0.610330\n",
            "epoch [3/3], Training loss:0.609890\n",
            "Accuracy:  0.798744284446871 \n",
            "\n",
            "{'eq_opp_diff': -0.253613369467028}\n",
            "Lambda: 31.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.650569\n",
            "epoch [2/3], Training loss:0.611896\n",
            "epoch [3/3], Training loss:0.611588\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 32.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.676524\n",
            "epoch [2/3], Training loss:0.613622\n",
            "epoch [3/3], Training loss:0.613227\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 33.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.657117\n",
            "epoch [2/3], Training loss:0.615036\n",
            "epoch [3/3], Training loss:0.614812\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 34.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.669046\n",
            "epoch [2/3], Training loss:0.616547\n",
            "epoch [3/3], Training loss:0.616308\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 35.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.661387\n",
            "epoch [2/3], Training loss:0.618086\n",
            "epoch [3/3], Training loss:0.617790\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 36.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.656141\n",
            "epoch [2/3], Training loss:0.619378\n",
            "epoch [3/3], Training loss:0.619190\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 37.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.654880\n",
            "epoch [2/3], Training loss:0.620725\n",
            "epoch [3/3], Training loss:0.620506\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 38.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.657541\n",
            "epoch [2/3], Training loss:0.621937\n",
            "epoch [3/3], Training loss:0.621816\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 39.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.645859\n",
            "epoch [2/3], Training loss:0.623155\n",
            "epoch [3/3], Training loss:0.623073\n",
            "Accuracy:  0.7979253395209173 \n",
            "\n",
            "{'eq_opp_diff': -0.25933727519093375}\n",
            "Lambda: 40.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.658132\n",
            "epoch [2/3], Training loss:0.624424\n",
            "epoch [3/3], Training loss:0.624273\n",
            "Accuracy:  0.7975841124684365 \n",
            "\n",
            "{'eq_opp_diff': -0.25293996879362735}\n",
            "Lambda: 41.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.677028\n",
            "epoch [2/3], Training loss:0.625582\n",
            "epoch [3/3], Training loss:0.625435\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 42.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.657079\n",
            "epoch [2/3], Training loss:0.626643\n",
            "epoch [3/3], Training loss:0.626557\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 43.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.667723\n",
            "epoch [2/3], Training loss:0.627774\n",
            "epoch [3/3], Training loss:0.627676\n",
            "Accuracy:  0.7975841124684365 \n",
            "\n",
            "{'eq_opp_diff': -0.25293996879362735}\n",
            "Lambda: 44.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.665608\n",
            "epoch [2/3], Training loss:0.628744\n",
            "epoch [3/3], Training loss:0.628694\n",
            "Accuracy:  0.798744284446871 \n",
            "\n",
            "{'eq_opp_diff': -0.253613369467028}\n",
            "Lambda: 45.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.651821\n",
            "epoch [2/3], Training loss:0.629756\n",
            "epoch [3/3], Training loss:0.629740\n",
            "Accuracy:  0.7972428854159558 \n",
            "\n",
            "{'eq_opp_diff': -0.24277736716761109}\n",
            "Lambda: 46.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.663631\n",
            "epoch [2/3], Training loss:0.630786\n",
            "epoch [3/3], Training loss:0.630738\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 47.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.661268\n",
            "epoch [2/3], Training loss:0.631720\n",
            "epoch [3/3], Training loss:0.631657\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 48.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.673924\n",
            "epoch [2/3], Training loss:0.632658\n",
            "epoch [3/3], Training loss:0.632608\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 49.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.660118\n",
            "epoch [2/3], Training loss:0.633533\n",
            "epoch [3/3], Training loss:0.633513\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 50.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.683772\n",
            "epoch [2/3], Training loss:0.634419\n",
            "epoch [3/3], Training loss:0.634388\n",
            "Accuracy:  0.7980618303419095 \n",
            "\n",
            "{'eq_opp_diff': -0.23705346144370534}\n",
            "Lambda: 51.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.666821\n",
            "epoch [2/3], Training loss:0.635259\n",
            "epoch [3/3], Training loss:0.635233\n",
            "Accuracy:  0.7974476216474442 \n",
            "\n",
            "{'eq_opp_diff': -0.26977498562864416}\n",
            "Lambda: 52.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.677224\n",
            "epoch [2/3], Training loss:0.636069\n",
            "epoch [3/3], Training loss:0.636051\n",
            "Accuracy:  0.7974476216474442 \n",
            "\n",
            "{'eq_opp_diff': -0.23957460786729082}\n",
            "Lambda: 53.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.672385\n",
            "epoch [2/3], Training loss:0.636889\n",
            "epoch [3/3], Training loss:0.636826\n",
            "Accuracy:  0.7975841124684365 \n",
            "\n",
            "{'eq_opp_diff': -0.2491746735649175}\n",
            "Lambda: 54.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.665390\n",
            "epoch [2/3], Training loss:0.637670\n",
            "epoch [3/3], Training loss:0.637616\n",
            "Accuracy:  0.7984713028048863 \n",
            "\n",
            "{'eq_opp_diff': -0.2677547836084421}\n",
            "Lambda: 55.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.673584\n",
            "epoch [2/3], Training loss:0.638400\n",
            "epoch [3/3], Training loss:0.638391\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 56.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.667791\n",
            "epoch [2/3], Training loss:0.639140\n",
            "epoch [3/3], Training loss:0.639123\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 57.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.680659\n",
            "epoch [2/3], Training loss:0.639865\n",
            "epoch [3/3], Training loss:0.639854\n",
            "Accuracy:  0.7979253395209173 \n",
            "\n",
            "{'eq_opp_diff': -0.25933727519093375}\n",
            "Lambda: 58.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.666111\n",
            "epoch [2/3], Training loss:0.640532\n",
            "epoch [3/3], Training loss:0.640536\n",
            "Accuracy:  0.7979253395209173 \n",
            "\n",
            "{'eq_opp_diff': -0.25933727519093375}\n",
            "Lambda: 59.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.669106\n",
            "epoch [2/3], Training loss:0.641223\n",
            "epoch [3/3], Training loss:0.641218\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 60.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.676885\n",
            "epoch [2/3], Training loss:0.641893\n",
            "epoch [3/3], Training loss:0.641874\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 61.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.672018\n",
            "epoch [2/3], Training loss:0.642531\n",
            "epoch [3/3], Training loss:0.642525\n",
            "Accuracy:  0.798744284446871 \n",
            "\n",
            "{'eq_opp_diff': -0.253613369467028}\n",
            "Lambda: 62.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.671208\n",
            "epoch [2/3], Training loss:0.643160\n",
            "epoch [3/3], Training loss:0.643148\n",
            "Accuracy:  0.797788848699925 \n",
            "\n",
            "{'eq_opp_diff': -0.27617229202595056}\n",
            "Lambda: 63.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.674842\n",
            "epoch [2/3], Training loss:0.643777\n",
            "epoch [3/3], Training loss:0.643758\n",
            "Accuracy:  0.7974476216474442 \n",
            "\n",
            "{'eq_opp_diff': -0.26977498562864416}\n",
            "Lambda: 64.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.679633\n",
            "epoch [2/3], Training loss:0.644368\n",
            "epoch [3/3], Training loss:0.644368\n",
            "Accuracy:  0.7974476216474442 \n",
            "\n",
            "{'eq_opp_diff': -0.26977498562864416}\n",
            "Lambda: 65.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.680100\n",
            "epoch [2/3], Training loss:0.644939\n",
            "epoch [3/3], Training loss:0.644947\n",
            "Accuracy:  0.7984713028048863 \n",
            "\n",
            "{'eq_opp_diff': -0.2677547836084421}\n",
            "Lambda: 66.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.679668\n",
            "epoch [2/3], Training loss:0.645536\n",
            "epoch [3/3], Training loss:0.645525\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 67.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.671794\n",
            "epoch [2/3], Training loss:0.646087\n",
            "epoch [3/3], Training loss:0.646081\n",
            "Accuracy:  0.7974476216474442 \n",
            "\n",
            "{'eq_opp_diff': -0.26977498562864416}\n",
            "Lambda: 68.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.674383\n",
            "epoch [2/3], Training loss:0.646634\n",
            "epoch [3/3], Training loss:0.646624\n",
            "Accuracy:  0.7972428854159558 \n",
            "\n",
            "{'eq_opp_diff': -0.24277736716761109}\n",
            "Lambda: 69.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.691731\n",
            "epoch [2/3], Training loss:0.647135\n",
            "epoch [3/3], Training loss:0.647159\n",
            "Accuracy:  0.7969699037739713 \n",
            "\n",
            "{'eq_opp_diff': -0.2516958199885029}\n",
            "Lambda: 70.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.678759\n",
            "epoch [2/3], Training loss:0.647673\n",
            "epoch [3/3], Training loss:0.647669\n",
            "Accuracy:  0.7975841124684365 \n",
            "\n",
            "{'eq_opp_diff': -0.2491746735649175}\n",
            "Lambda: 71.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.681999\n",
            "epoch [2/3], Training loss:0.648185\n",
            "epoch [3/3], Training loss:0.648168\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 72.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.686399\n",
            "epoch [2/3], Training loss:0.648675\n",
            "epoch [3/3], Training loss:0.648672\n",
            "Accuracy:  0.7970381491844674 \n",
            "\n",
            "{'eq_opp_diff': -0.2105567873860557}\n",
            "Lambda: 73.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.683025\n",
            "epoch [2/3], Training loss:0.649171\n",
            "epoch [3/3], Training loss:0.649136\n",
            "Accuracy:  0.7976523578789326 \n",
            "\n",
            "{'eq_opp_diff': -0.27347868933234787}\n",
            "Lambda: 74.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.678047\n",
            "epoch [2/3], Training loss:0.649623\n",
            "epoch [3/3], Training loss:0.649633\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 75.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.680070\n",
            "epoch [2/3], Training loss:0.650106\n",
            "epoch [3/3], Training loss:0.650092\n",
            "Accuracy:  0.7975158670579403 \n",
            "\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "Lambda: 76.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.686537\n",
            "epoch [2/3], Training loss:0.650582\n",
            "epoch [3/3], Training loss:0.650548\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.24345076784101174}\n",
            "Lambda: 77.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.679530\n",
            "epoch [2/3], Training loss:0.651005\n",
            "epoch [3/3], Training loss:0.650993\n",
            "Accuracy:  0.7971063945949635 \n",
            "\n",
            "{'eq_opp_diff': -0.25961238400262787}\n",
            "Lambda: 78.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.677454\n",
            "epoch [2/3], Training loss:0.651436\n",
            "epoch [3/3], Training loss:0.651429\n",
            "Accuracy:  0.798744284446871 \n",
            "\n",
            "{'eq_opp_diff': -0.253613369467028}\n",
            "Lambda: 79.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.675573\n",
            "epoch [2/3], Training loss:0.651856\n",
            "epoch [3/3], Training loss:0.651872\n",
            "Accuracy:  0.7981300757524057 \n",
            "\n",
            "{'eq_opp_diff': -0.25759218198242584}\n",
            "Lambda: 80.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.692768\n",
            "epoch [2/3], Training loss:0.652295\n",
            "epoch [3/3], Training loss:0.652284\n",
            "Accuracy:  0.7974476216474442 \n",
            "\n",
            "{'eq_opp_diff': -0.26977498562864416}\n",
            "Lambda: 81.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.685649\n",
            "epoch [2/3], Training loss:0.652686\n",
            "epoch [3/3], Training loss:0.652679\n",
            "Accuracy:  0.7977206032894287 \n",
            "\n",
            "{'eq_opp_diff': -0.22543319372587664}\n",
            "Lambda: 82.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.690631\n",
            "epoch [2/3], Training loss:0.653093\n",
            "epoch [3/3], Training loss:0.653094\n",
            "Accuracy:  0.7984713028048863 \n",
            "\n",
            "{'eq_opp_diff': -0.2677547836084421}\n",
            "Lambda: 83.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.675919\n",
            "epoch [2/3], Training loss:0.653491\n",
            "epoch [3/3], Training loss:0.653493\n",
            "Accuracy:  0.7975841124684365 \n",
            "\n",
            "{'eq_opp_diff': -0.2189742958035641}\n",
            "Lambda: 84.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.685964\n",
            "epoch [2/3], Training loss:0.653862\n",
            "epoch [3/3], Training loss:0.653890\n",
            "Accuracy:  0.7974476216474442 \n",
            "\n",
            "{'eq_opp_diff': -0.2660096903999343}\n",
            "Lambda: 85.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.679926\n",
            "epoch [2/3], Training loss:0.654263\n",
            "epoch [3/3], Training loss:0.654235\n",
            "Accuracy:  0.7976523578789326 \n",
            "\n",
            "{'eq_opp_diff': -0.19917056746325038}\n",
            "Lambda: 86.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.682032\n",
            "epoch [2/3], Training loss:0.654633\n",
            "epoch [3/3], Training loss:0.654631\n",
            "Accuracy:  0.7981300757524057 \n",
            "\n",
            "{'eq_opp_diff': -0.25759218198242584}\n",
            "Lambda: 87.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.676662\n",
            "epoch [2/3], Training loss:0.654953\n",
            "epoch [3/3], Training loss:0.654985\n",
            "Accuracy:  0.7975841124684365 \n",
            "\n",
            "{'eq_opp_diff': -0.22273959103227395}\n",
            "Lambda: 88.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.683796\n",
            "epoch [2/3], Training loss:0.655333\n",
            "epoch [3/3], Training loss:0.655343\n",
            "Accuracy:  0.796901658363475 \n",
            "\n",
            "{'eq_opp_diff': -0.22739180422107252}\n",
            "Lambda: 89.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.691852\n",
            "epoch [2/3], Training loss:0.655701\n",
            "epoch [3/3], Training loss:0.655694\n",
            "Accuracy:  0.7984713028048863 \n",
            "\n",
            "{'eq_opp_diff': -0.2677547836084421}\n",
            "Lambda: 90.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.683344\n",
            "epoch [2/3], Training loss:0.656035\n",
            "epoch [3/3], Training loss:0.656049\n",
            "Accuracy:  0.797857094110421 \n",
            "\n",
            "{'eq_opp_diff': -0.20483288166214997}\n",
            "Lambda: 91.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.682765\n",
            "epoch [2/3], Training loss:0.656365\n",
            "epoch [3/3], Training loss:0.656390\n",
            "Accuracy:  0.7971746400054597 \n",
            "\n",
            "{'eq_opp_diff': -0.2839163997700583}\n",
            "Lambda: 92.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.691098\n",
            "epoch [2/3], Training loss:0.656705\n",
            "epoch [3/3], Training loss:0.656701\n",
            "Accuracy:  0.7979253395209173 \n",
            "\n",
            "{'eq_opp_diff': -0.22913689742958035}\n",
            "Lambda: 93.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.677393\n",
            "epoch [2/3], Training loss:0.657025\n",
            "epoch [3/3], Training loss:0.657027\n",
            "Accuracy:  0.7971063945949635 \n",
            "\n",
            "{'eq_opp_diff': -0.22941200624127456}\n",
            "Lambda: 94.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.682264\n",
            "epoch [2/3], Training loss:0.657365\n",
            "epoch [3/3], Training loss:0.657377\n",
            "Accuracy:  0.7984713028048863 \n",
            "\n",
            "{'eq_opp_diff': -0.2677547836084421}\n",
            "Lambda: 95.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.692879\n",
            "epoch [2/3], Training loss:0.657669\n",
            "epoch [3/3], Training loss:0.657670\n",
            "Accuracy:  0.7984713028048863 \n",
            "\n",
            "{'eq_opp_diff': -0.2677547836084421}\n",
            "Lambda: 96.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.684021\n",
            "epoch [2/3], Training loss:0.657974\n",
            "epoch [3/3], Training loss:0.657985\n",
            "Accuracy:  0.7975158670579403 \n",
            "\n",
            "{'eq_opp_diff': -0.2903137061673647}\n",
            "Lambda: 97.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.681123\n",
            "epoch [2/3], Training loss:0.658288\n",
            "epoch [3/3], Training loss:0.658313\n",
            "Accuracy:  0.7975158670579403 \n",
            "\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "Lambda: 98.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.682623\n",
            "epoch [2/3], Training loss:0.658584\n",
            "epoch [3/3], Training loss:0.658566\n",
            "Accuracy:  0.7984030573943902 \n",
            "\n",
            "{'eq_opp_diff': -0.2472160630697216}\n",
            "Lambda: 99.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.688385\n",
            "epoch [2/3], Training loss:0.658884\n",
            "epoch [3/3], Training loss:0.658868\n",
            "Accuracy:  0.7975841124684365 \n",
            "\n",
            "{'eq_opp_diff': -0.22273959103227395}\n",
            "Lambda: 100.0 \n",
            "\n",
            "epoch [1/3], Training loss:0.678828\n",
            "epoch [2/3], Training loss:0.659151\n",
            "epoch [3/3], Training loss:0.659163\n",
            "Accuracy:  0.7975158670579403 \n",
            "\n",
            "{'eq_opp_diff': -0.19843557526484357}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(lambdas, accuracies)\n",
        "plt.title('Accuracy for Logistic Regression on Adult dataset')\n",
        "plt.xlabel('r_lambda')\n",
        "plt.ylabel('Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "1J0L4_wwsezG",
        "outputId": "46424038-9e58-4d3a-c64a-c6c1aa5aba9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TPR_priv = []\n",
        "TPR_non_priv = []\n",
        "TNR_priv = []\n",
        "TNR_non_priv = []\n",
        "for l in metrix:\n",
        "    TPR_priv.append(metrix[l]['privilaged']['TPR'])\n",
        "    TPR_non_priv.append(metrix[l]['unprivilaged']['TPR'])\n",
        "    TNR_priv.append(metrix[l]['privilaged']['TNR'])\n",
        "    TNR_non_priv.append(metrix[l]['unprivilaged']['TNR'])\n",
        "\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10,5))\n",
        "fig.suptitle('Finding the Equal Odds')\n",
        "axs[0].plot(lambdas, TPR_non_priv)\n",
        "axs[0].plot(lambdas, TPR_priv)\n",
        "axs[0].set_title('TPR')\n",
        "axs[0].set(xlabel='r_lambda', ylabel='TPR')\n",
        "axs[0].legend(['Female', 'Male'])\n",
        "\n",
        "axs[1].plot(lambdas, TNR_non_priv)\n",
        "axs[1].plot(lambdas, TNR_priv)\n",
        "axs[1].set_title('TNR')\n",
        "axs[1].set(xlabel='r_lambda', ylabel='TNR')\n",
        "axs[1].legend(['Female', 'Male'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "y73gBhiTvXjb",
        "outputId": "4b248d59-6876-4ff8-eaac-e811af55ff3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3ee44eaa90>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PPP_priv= []\n",
        "PPP_non_priv= []\n",
        "NPP_priv= []\n",
        "NPP_non_priv = []\n",
        "for l in metrix:\n",
        "    PPP_priv.append(metrix[l]['privilaged']['PPV'])\n",
        "    PPP_non_priv.append(metrix[l]['unprivilaged']['PPV'])\n",
        "    NPP_priv.append(metrix[l]['privilaged']['NPV'])\n",
        "    NPP_non_priv.append(metrix[l]['unprivilaged']['NPV'])\n",
        "\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10,5))\n",
        "fig.suptitle('Finding the Predictive Parity')\n",
        "axs[0].plot(lambdas, PPP_non_priv)\n",
        "axs[0].plot(lambdas, PPP_priv)\n",
        "axs[0].set_title('PPP')\n",
        "axs[0].set(xlabel='r-lambda', ylabel='PPP')\n",
        "axs[0].legend(['Female', 'Male'])\n",
        "\n",
        "axs[1].plot(lambdas, NPP_non_priv)\n",
        "axs[1].plot(lambdas, NPP_priv)\n",
        "axs[1].set_title('NPP')\n",
        "axs[1].set(xlabel='r-lambda', ylabel='NPP')\n",
        "axs[1].legend(['Female', 'Male'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "2ksdVJsivik2",
        "outputId": "e4570060-3e85-48d4-c1ed-3cb47213abc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3ee43de390>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RW = Reweighing(unprivileged_groups=unprivileged_groups, \n",
        "                privileged_groups=privileged_groups)\n",
        "\n",
        "train_rw = RW.fit_transform(train)\n",
        "\n",
        "train_weights = train_rw.instance_weights"
      ],
      "metadata": {
        "id": "PmHALReR0khM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import  accuracy_score \n",
        "\n",
        "# define dataset\n",
        "\n",
        "\n",
        "# define models and parameters\n",
        "model = LogisticRegression()\n",
        "solvers = [ 'liblinear']\n",
        "penalty = ['l2']\n",
        "c_values = np.logspace(np.log10(100),np.log10(0.00001),num=20)\n",
        "# define grid search\n",
        "grid = dict(solver=solvers,penalty=penalty,C=c_values)\n",
        "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=1, random_state=1)\n",
        "grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy',error_score=0)\n",
        "grid_result = grid_search.fit(X_train, y_train)\n",
        "best_model = grid_result.best_estimator_\n",
        "yhat = best_model.predict(X_test)\n",
        "\t# evaluate the model\n",
        "acc = accuracy_score(y_test, yhat)\n",
        "\t# store the result\n",
        "# summarize results\n",
        "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
        "means = grid_result.cv_results_['mean_test_score']\n",
        "stds = grid_result.cv_results_['std_test_score']\n",
        "params = grid_result.cv_results_['params']\n",
        "for mean, stdev, param in zip(means, stds, params):\n",
        "    print('accuracy',\"%f (%f) with: %r\" % (mean, stdev, param))"
      ],
      "metadata": {
        "id": "Ff-ol7oN0647",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9334b83c-40d1-4038-c5b9-7536c0436af5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best: 0.804089 using {'C': 100.0, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 100.0, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 42.81332398719393, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 18.329807108324356, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 7.847599703514615, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 3.3598182862837827, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 1.438449888287663, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 0.6158482110660267, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 0.26366508987303583, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 0.11288378916846895, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 0.048329302385717574, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.804089 (0.004089) with: {'C': 0.0206913808111479, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.803884 (0.004325) with: {'C': 0.008858667904100831, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.803943 (0.004333) with: {'C': 0.003792690190732254, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.803855 (0.004323) with: {'C': 0.0016237767391887243, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.802919 (0.004244) with: {'C': 0.0006951927961775605, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.800579 (0.004101) with: {'C': 0.00029763514416313193, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.797215 (0.002723) with: {'C': 0.00012742749857031348, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.797186 (0.002743) with: {'C': 5.455594781168526e-05, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.796455 (0.002362) with: {'C': 2.335721469090126e-05, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.795168 (0.002705) with: {'C': 1e-05, 'penalty': 'l2', 'solver': 'liblinear'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_validate\n",
        "learner = LogisticRegression(solver='liblinear', random_state=1, C = 0.01, penalty = \"l2\") \n",
        "learner.fit(X_train,y_train)\n",
        "predictions = learner.predict(X_test)\n",
        "cv_results = cross_validate(learner,X_train,y_train,cv =5,return_estimator = True)\n",
        "\n",
        "cv_results['test_score']\n",
        "cv_model = cv_results['estimator'][list(cv_results['test_score']).index(max(cv_results['test_score']))]\n",
        "\n",
        "cv_predictions = test.copy()\n",
        "cv_predictions.labels = cv_model.predict(test.features).reshape(-1,1)\n",
        "\n",
        "# Measure Fairness metrics\n",
        "metric = ClassificationMetric(test, cv_predictions, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)\n",
        "metric_arrs = {}\n",
        "metric_arrs['eq_opp_diff']=(metric.equal_opportunity_difference())\n",
        "metric_arrs['avg_odds_diff']=(metric.average_odds_difference())\n",
        "metric_arrs['bal_acc']=((metric.true_positive_rate() + metric.true_negative_rate()) / 2)\n",
        "metric_arrs['disp_imp']=(metric.disparate_impact())\n",
        "print(metric_arrs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6nkLB3WTXtdz",
        "outputId": "642cc441-6815-42f0-8c19-5604303ef249"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eq_opp_diff': 0.0, 'avg_odds_diff': 0.0, 'bal_acc': 0.5, 'disp_imp': nan}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "invalid value encountered in double_scalars\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# weight Tensor\n",
        "weights = torch.FloatTensor(train_rw.instance_weights)\n",
        "\n",
        "BATCH_SIZE = 64\n",
        "learning_rate = 0.0001\n",
        "\n",
        "\n",
        "# weight and reweight with tensordataset\n",
        "rw_train_dataset = Data.TensorDataset(torch.tensor(X_train).float(), \n",
        "                                      torch.Tensor(y_train).float(), \n",
        "                                      weights)\n",
        "# Loading data \n",
        "loader_train = Data.DataLoader(\n",
        "    dataset=rw_train_dataset, \n",
        "    batch_size=BATCH_SIZE, \n",
        "    shuffle=False, num_workers=num_workers)"
      ],
      "metadata": {
        "id": "lrp6foWSwJvq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "epochs = 5\n",
        "accuracies = []\n",
        "metrix_rw = {}\n",
        "lambdas = [0.0, 0.00001, 0.0001, 0.001, 0.01, 0.1]\n",
        "lambdas = np.concatenate((np.array(lambdas), np.linspace(1, 10, num=10)))\n",
        "for r_lambda in lambdas:\n",
        "    model = LogisticRegression_torch(X_train.shape[1], 1)\n",
        "    optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)\n",
        "    for epoch in range(epochs):\n",
        "        train_loss = 0.0\n",
        "        for i, (x, y, w) in enumerate(loader_train):\n",
        "        \n",
        "\n",
        "            inputs = Variable(x.to(device))\n",
        "            labels = Variable(y.to(device))\n",
        "\n",
        "             \n",
        "           \n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            \n",
        "            outputs = model(inputs)\n",
        "\n",
        "            # Regularization\n",
        "            reg = 0\n",
        "            for param in model.parameters():\n",
        "                reg += 0.5 * (param ** 2).mean()\n",
        "                #reg += param.abs().sum()\n",
        "            # r_lambda = 0\n",
        "            \n",
        "            # criterion\n",
        "            criterion = torch.nn.BCELoss(weight=w, reduction='sum')\n",
        "            # get loss for the predicted output\n",
        "            loss = criterion(outputs.reshape(outputs.shape[0]), labels) + \\\n",
        "                r_lambda * reg\n",
        "            \n",
        "                \n",
        "            train_loss += loss.item()\n",
        "            \n",
        "            # get gradients w.r.t to parameters\n",
        "            loss.backward()\n",
        "            \n",
        "            # update parameters\n",
        "            optimizer.step()\n",
        "        #training done\n",
        "\n",
        "        print('epoch [{}/{}], Training loss:{:.6f}'.format(\n",
        "            epoch + 1, \n",
        "            epochs, \n",
        "            train_loss / len(loader_train.dataset)))\n",
        "    with torch.no_grad():\n",
        "        model.eval()\n",
        "        out = model(Variable(torch.Tensor(X_test).to(device))).detach().cpu()\n",
        "        pred = (out >= 0.5).int().numpy().squeeze()\n",
        "        accuracy = sum((y_test == pred))/len(y_test)\n",
        "        print('Accuracy: ', accuracy)\n",
        "        accuracies.append(accuracy)\n",
        "#testing done\n",
        "    test_pred = test.copy()\n",
        "    test_pred.labels = pred.reshape(-1,1)\n",
        "    metric_rew = ClassificationMetric(test, test_pred,unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups)\n",
        "    metric_arrs = {}\n",
        "    metric_arrs['eq_opp_diff']=(metric.equal_opportunity_difference())\n",
        "    print(metric_arrs)\n",
        "    \n",
        "    metrix_rw[r_lambda] = {}\n",
        "    metrix_rw[r_lambda]['accuracy'] = accuracy\n",
        "    metrix_rw[r_lambda]['privilaged'] = metric_rew.performance_measures(privileged=True)\n",
        "    metrix_rw[r_lambda]['unprivilaged'] = metric_rew.performance_measures(privileged=False)\n",
        "\n",
        "    met = metric_rew.binary_confusion_matrix(privileged=True)\n",
        "    PR_priv = (met['TP'] + met['FP']) / (met['TP'] + met['FP'] + met['TN'] + met['FN'])\n",
        "    metrix_rw[r_lambda]['privilaged']['PR'] = PR_priv\n",
        "\n",
        "    met = metric_rew.binary_confusion_matrix(privileged=False)\n",
        "    PR_unpriv = (met['TP'] + met['FP']) / (met['TP'] + met['FP'] + met['TN'] + met['FN'])\n",
        "    metrix_rw[r_lambda]['unprivilaged']['PR'] = PR_unpriv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPo9P3q-wbIT",
        "outputId": "9da5e1c2-3188-48bc-cbfe-5fd7dc0f4136"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch [1/5], Training loss:0.562379\n",
            "epoch [2/5], Training loss:0.482955\n",
            "epoch [3/5], Training loss:0.461448\n",
            "epoch [4/5], Training loss:0.453151\n",
            "epoch [5/5], Training loss:0.449210\n",
            "Accuracy:  0.7905548351873336\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.584412\n",
            "epoch [2/5], Training loss:0.487166\n",
            "epoch [3/5], Training loss:0.463144\n",
            "epoch [4/5], Training loss:0.453966\n",
            "epoch [5/5], Training loss:0.449625\n",
            "Accuracy:  0.7905548351873336\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.603322\n",
            "epoch [2/5], Training loss:0.492960\n",
            "epoch [3/5], Training loss:0.465044\n",
            "epoch [4/5], Training loss:0.454690\n",
            "epoch [5/5], Training loss:0.449924\n",
            "Accuracy:  0.7905548351873336\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.621426\n",
            "epoch [2/5], Training loss:0.494599\n",
            "epoch [3/5], Training loss:0.465088\n",
            "epoch [4/5], Training loss:0.454613\n",
            "epoch [5/5], Training loss:0.449909\n",
            "Accuracy:  0.7905548351873336\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.582137\n",
            "epoch [2/5], Training loss:0.486498\n",
            "epoch [3/5], Training loss:0.463141\n",
            "epoch [4/5], Training loss:0.454153\n",
            "epoch [5/5], Training loss:0.449860\n",
            "Accuracy:  0.7905548351873336\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.595248\n",
            "epoch [2/5], Training loss:0.490854\n",
            "epoch [3/5], Training loss:0.464664\n",
            "epoch [4/5], Training loss:0.455195\n",
            "epoch [5/5], Training loss:0.450933\n",
            "Accuracy:  0.7905548351873336\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.598048\n",
            "epoch [2/5], Training loss:0.494373\n",
            "epoch [3/5], Training loss:0.472595\n",
            "epoch [4/5], Training loss:0.465832\n",
            "epoch [5/5], Training loss:0.463156\n",
            "Accuracy:  0.7905548351873336\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.558906\n",
            "epoch [2/5], Training loss:0.496453\n",
            "epoch [3/5], Training loss:0.481848\n",
            "epoch [4/5], Training loss:0.477099\n",
            "epoch [5/5], Training loss:0.475243\n",
            "Accuracy:  0.7905548351873336\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.567206\n",
            "epoch [2/5], Training loss:0.504038\n",
            "epoch [3/5], Training loss:0.490737\n",
            "epoch [4/5], Training loss:0.486866\n",
            "epoch [5/5], Training loss:0.485489\n",
            "Accuracy:  0.7905548351873336\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.567020\n",
            "epoch [2/5], Training loss:0.508611\n",
            "epoch [3/5], Training loss:0.497792\n",
            "epoch [4/5], Training loss:0.494995\n",
            "epoch [5/5], Training loss:0.494094\n",
            "Accuracy:  0.7904183443663414\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.590453\n",
            "epoch [2/5], Training loss:0.516928\n",
            "epoch [3/5], Training loss:0.504938\n",
            "epoch [4/5], Training loss:0.502312\n",
            "epoch [5/5], Training loss:0.501615\n",
            "Accuracy:  0.787893264177984\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.619561\n",
            "epoch [2/5], Training loss:0.527087\n",
            "epoch [3/5], Training loss:0.512835\n",
            "epoch [4/5], Training loss:0.509540\n",
            "epoch [5/5], Training loss:0.508591\n",
            "Accuracy:  0.7876885279464956\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.626158\n",
            "epoch [2/5], Training loss:0.530477\n",
            "epoch [3/5], Training loss:0.517663\n",
            "epoch [4/5], Training loss:0.515177\n",
            "epoch [5/5], Training loss:0.514544\n",
            "Accuracy:  0.7866648467890535\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.590359\n",
            "epoch [2/5], Training loss:0.529510\n",
            "epoch [3/5], Training loss:0.521539\n",
            "epoch [4/5], Training loss:0.520134\n",
            "epoch [5/5], Training loss:0.519825\n",
            "Accuracy:  0.7842080120111923\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.622158\n",
            "epoch [2/5], Training loss:0.537651\n",
            "epoch [3/5], Training loss:0.527128\n",
            "epoch [4/5], Training loss:0.525186\n",
            "epoch [5/5], Training loss:0.524721\n",
            "Accuracy:  0.7839350303692076\n",
            "{'eq_opp_diff': -0.19843557526484357}\n",
            "epoch [1/5], Training loss:0.605721\n",
            "epoch [2/5], Training loss:0.539034\n",
            "epoch [3/5], Training loss:0.530844\n",
            "epoch [4/5], Training loss:0.529412\n",
            "epoch [5/5], Training loss:0.529101\n",
            "Accuracy:  0.7839350303692076\n",
            "{'eq_opp_diff': -0.19843557526484357}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(lambdas, accuracies)\n",
        "plt.title('Accuracy for Logistic Regression on Adult dataset')\n",
        "plt.xlabel('r-lambda')\n",
        "plt.ylabel('Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "y_sVX__4y-bC",
        "outputId": "d833fb48-7ca2-4909-ae5d-4642f216e7ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TPR_priv_rw = []\n",
        "TPR_non_priv_rw = []\n",
        "TNR_priv_rw = []\n",
        "TNR_non_priv_rw = []\n",
        "for l in metrix_rw:\n",
        "    TPR_priv_rw.append(metrix_rw[l]['privilaged']['TPR'])\n",
        "    TPR_non_priv_rw.append(metrix_rw[l]['unprivilaged']['TPR'])\n",
        "    TNR_priv_rw.append(metrix_rw[l]['privilaged']['TNR'])\n",
        "    TNR_non_priv_rw.append(metrix_rw[l]['unprivilaged']['TNR'])\n",
        "\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10,5))\n",
        "fig.suptitle('Finding the Equal Odds')\n",
        "axs[0].plot(lambdas, TPR_non_priv_rw)\n",
        "axs[0].plot(lambdas, TPR_priv_rw)\n",
        "axs[0].set_title('TPR')\n",
        "axs[0].set(xlabel='r-lambda', ylabel='TPR')\n",
        "axs[0].legend(['Female', 'Male'])\n",
        "\n",
        "axs[1].plot(lambdas, TNR_non_priv_rw)\n",
        "axs[1].plot(lambdas, TNR_priv_rw)\n",
        "axs[1].set_title('TNR')\n",
        "axs[1].set(xlabel='r-lambda', ylabel='TNR')\n",
        "axs[1].legend(['Female', 'Male'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "VWTALo1tzY20",
        "outputId": "10c4b592-7d51-4bf9-ee37-15a7a9291281"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3ee4150610>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PPP_priv_rw = []\n",
        "PPP_non_priv_rw = []\n",
        "NPP_priv_rw = []\n",
        "NPP_non_priv_rw = []\n",
        "for l in metrix_rw:\n",
        "    PPP_priv_rw.append(metrix_rw[l]['privilaged']['PPV'])\n",
        "    PPP_non_priv_rw.append(metrix_rw[l]['unprivilaged']['PPV'])\n",
        "    NPP_priv_rw.append(metrix_rw[l]['privilaged']['NPV'])\n",
        "    NPP_non_priv_rw.append(metrix_rw[l]['unprivilaged']['NPV'])\n",
        "\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10,5))\n",
        "fig.suptitle('Investigating Predictive Parity')\n",
        "axs[0].plot(lambdas, PPP_non_priv_rw)\n",
        "axs[0].plot(lambdas, PPP_priv_rw)\n",
        "axs[0].set_title('PPP')\n",
        "axs[0].set(xlabel='r-lambda', ylabel='PPP')\n",
        "axs[0].legend(['Female', 'Male'])\n",
        "axs[0].set(ylim=(0,1))\n",
        "\n",
        "axs[1].plot(lambdas, NPP_non_priv_rw)\n",
        "axs[1].plot(lambdas, NPP_priv_rw)\n",
        "axs[1].set_title('NPP')\n",
        "axs[1].set(xlabel='r-lambda', ylabel='NPP')\n",
        "axs[1].legend(['Female', 'Male'])\n",
        "axs[1].set(ylim=(0.2,1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "SRSim6pqzpb-",
        "outputId": "90015db3-baba-4f79-82ef-49742e425323"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.2, 1.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model selection"
      ],
      "metadata": {
        "id": "iMwPyAfdZNXc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "learning_rate = 0.0001\n",
        "\n",
        "\n",
        "if device == 0:\n",
        "    num_workers = 2\n",
        "else:\n",
        "    num_workers = 0\n",
        "\n",
        "\n",
        "def get_loader(x_train, y_train, W):\n",
        "    train_dataset = Data.TensorDataset(torch.tensor(x_train).float(), \n",
        "                                      torch.Tensor(y_train).float(), \n",
        "                                      W.float())\n",
        "    \n",
        "    return Data.DataLoader(dataset=train_dataset,\n",
        "                           batch_size=BATCH_SIZE, \n",
        "                           shuffle=True, num_workers=num_workers)\n",
        "  "
      ],
      "metadata": {
        "id": "ou9Qrz0kZw7a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reweighihting\n",
        "W = torch.FloatTensor(train_rw.instance_weights)\n",
        "loader_train = get_loader(X_train, y_train, W)"
      ],
      "metadata": {
        "id": "AhHazRptZ1gA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class LogisticRegression(torch.nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(LogisticRegression, self).__init__()\n",
        "        self.linear = torch.nn.Linear(input_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        outputs = torch.sigmoid(self.linear(x))\n",
        "        return outputs"
      ],
      "metadata": {
        "id": "aOMMg44cZWDa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "r_lambda = 24\n",
        "num_splits = 5\n",
        "ACC=[]\n",
        "for split in range(num_splits):\n",
        "    \n",
        "    \n",
        "    loader_train = get_loader(X_train, y_train, W)\n",
        "    model = LogisticRegression_torch(X_train.shape[1], 1)\n",
        "    optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        train_loss = 0.0\n",
        "        for i, (x, y, w) in enumerate(loader_train):\n",
        "        \n",
        "\n",
        "            inputs = Variable(x.to(device))\n",
        "            labels = Variable(y.to(device))\n",
        "\n",
        "            \n",
        "            optimizer.zero_grad()\n",
        "\n",
        "           \n",
        "            outputs = model(inputs)\n",
        "\n",
        "            # Regularization\n",
        "            reg = 0\n",
        "            for param in model.parameters():\n",
        "                reg += 0.5 * (param ** 2).mean()\n",
        "            \n",
        "            \n",
        "            criterion = torch.nn.BCELoss(weight=w, reduction='sum')\n",
        "            # get loss for the predicted output\n",
        "            loss = criterion(outputs.reshape(outputs.shape[0]), labels) + \\\n",
        "                r_lambda * reg\n",
        "            \n",
        "                \n",
        "            train_loss += loss.item()\n",
        "            \n",
        "            \n",
        "            loss.backward()\n",
        "            \n",
        "            \n",
        "            optimizer.step()\n",
        "        \n",
        "\n",
        "        print('epoch [{}/{}], Training loss:{:.6f}'.format(\n",
        "            epoch + 1, \n",
        "            epochs, \n",
        "            train_loss / len(loader_train.dataset)))\n",
        "        #training finish\n",
        "    with torch.no_grad():\n",
        "        model.eval()\n",
        "        out = model(Variable(torch.Tensor(X_test).to(device))).detach().cpu()\n",
        "        pred = (out >= 0.5).int().numpy().squeeze()\n",
        "        accuracy = sum((y_test == pred))/len(y_test)\n",
        "\n",
        "    ACC.append(accuracy)\n",
        "\n",
        "    \n",
        "print('\\nAccuracy:')\n",
        "print('Mean:', np.mean(ACC), 'S.D.:', np.std(ACC))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdMk91YsjRGf",
        "outputId": "2a2609e2-02a2-43f4-adf8-1e8b0b826fc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch [1/5], Training loss:0.662785\n",
            "epoch [2/5], Training loss:0.598567\n",
            "epoch [3/5], Training loss:0.593424\n",
            "epoch [4/5], Training loss:0.592666\n",
            "epoch [5/5], Training loss:0.592485\n",
            "epoch [1/5], Training loss:0.638230\n",
            "epoch [2/5], Training loss:0.598300\n",
            "epoch [3/5], Training loss:0.593614\n",
            "epoch [4/5], Training loss:0.592829\n",
            "epoch [5/5], Training loss:0.592609\n",
            "epoch [1/5], Training loss:0.670833\n",
            "epoch [2/5], Training loss:0.599540\n",
            "epoch [3/5], Training loss:0.593619\n",
            "epoch [4/5], Training loss:0.592780\n",
            "epoch [5/5], Training loss:0.592583\n",
            "epoch [1/5], Training loss:0.636478\n",
            "epoch [2/5], Training loss:0.596220\n",
            "epoch [3/5], Training loss:0.592948\n",
            "epoch [4/5], Training loss:0.592475\n",
            "epoch [5/5], Training loss:0.592398\n",
            "epoch [1/5], Training loss:0.635102\n",
            "epoch [2/5], Training loss:0.596632\n",
            "epoch [3/5], Training loss:0.593132\n",
            "epoch [4/5], Training loss:0.592579\n",
            "epoch [5/5], Training loss:0.592433\n",
            "\n",
            "Accuracy:\n",
            "Mean: 0.7615368866443731 S.D.: 0.0015286971951136152\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis of algorithmic fairness methods beyond binary sensitive features, for\n",
        "example, using Race. "
      ],
      "metadata": {
        "id": "Nm3uoI0YsX-d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Priviled are the white male\n",
        "privileged_groups = [{'sex': 1, 'race': 1}]\n",
        "unprivileged_groups = [{'sex': 1, 'race': 0}, {'sex': 0, 'race': 0}, {'sex': 0, 'race': 1}]"
      ],
      "metadata": {
        "id": "xAvCpk1rpXKM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class LogisticRegression_torch(torch.nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(LogisticRegression_torch, self).__init__()\n",
        "        self.linear = torch.nn.Linear(input_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        outputs = torch.sigmoid(self.linear(x))\n",
        "        return outputs"
      ],
      "metadata": {
        "id": "GSXotEt8sxuL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cpu\")\n",
        "BATCH_SIZE = 32\n",
        "learning_rate = 0.0001\n",
        "\n",
        "# Data Tensor\n",
        "train_dataset = Data.TensorDataset(torch.tensor(X_train).float(), torch.Tensor(y_train).float())\n",
        "\n",
        "if device == 0:\n",
        "    num_workers = 2\n",
        "else:\n",
        "    num_workers = 0\n",
        "\n",
        "# Data Loader\n",
        "loader_train = Data.DataLoader(\n",
        "    dataset=rw_train_dataset, \n",
        "    batch_size=BATCH_SIZE, \n",
        "    shuffle=False, num_workers=num_workers)\n"
      ],
      "metadata": {
        "id": "Nkp93BrdtEdF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.metrics import confusion_matrix as CM\n",
        "epochs = 4\n",
        "\n",
        "metrix_rw = {}\n",
        "\n",
        "lambdas = [0.0, 0.00001, 0.0001, 0.001, 0.01, 0.1]\n",
        "lambdas = np.concatenate((np.array(lambdas), np.linspace(1, 10, num=10)))\n",
        "for r_lambda in lambdas:\n",
        "    model = LogisticRegression_torch(X_train.shape[1], 1)\n",
        "    optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)\n",
        "    print('Lambda:', r_lambda,'\\n')\n",
        "    for epoch in range(epochs):\n",
        "        train_loss = 0.0\n",
        "        for i, (x, y, w) in enumerate(loader_train):\n",
        "       \n",
        "\n",
        "            inputs = Variable(x.to(device))\n",
        "            labels = Variable(y.to(device))\n",
        "\n",
        "           \n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            \n",
        "            outputs = model(inputs)\n",
        "\n",
        "            \n",
        "            reg = 0\n",
        "            for param in model.parameters():\n",
        "                reg += 0.5 * (param ** 2).mean()\n",
        "            \n",
        "           \n",
        "            criterion = torch.nn.BCELoss(weight=w, reduction='sum')\n",
        "           \n",
        "            loss = criterion(outputs.reshape(outputs.shape[0]), labels) + \\\n",
        "                r_lambda * reg\n",
        "            \n",
        "                \n",
        "            train_loss += loss.item()\n",
        "            \n",
        "            \n",
        "            loss.backward()\n",
        "            \n",
        "            \n",
        "            optimizer.step()\n",
        "        \n",
        "#training has finished\n",
        "        print('epoch [{}/{}], Training loss:{:.6f}'.format(\n",
        "            epoch + 1, \n",
        "            epochs, \n",
        "            train_loss / len(loader_train.dataset))) #testing started\n",
        "    with torch.no_grad():\n",
        "        model.eval()\n",
        "        out = model(Variable(torch.Tensor(X_test).to(device))).detach().cpu()\n",
        "        pred = (out >= 0.5).int().numpy().squeeze()\n",
        "    \n",
        "    accuracy = sum((y_test == pred))/len(y_test)\n",
        "    print('Accuracy: ', accuracy,'\\n')\n",
        "\n",
        "    test_pred = test.copy()\n",
        "    test_pred.labels = pred.reshape(-1,1)\n",
        "    #White Men \n",
        "    white_male = test.labels[(( test.features[:,1] == 1.0) & ( test.features[:,0] == 1.0))]\n",
        "    white_male_pred = pred[(( test.features[:,1] == 1.0) & ( test.features[:,0] == 1.0))]\n",
        "    #Nonwhite Men\n",
        "    nonwhite_male =  test.labels[(( test.features[:,1] == 1.0) & ( test.features[:,0] == 0.0))]\n",
        "    nonwhite_male_pred = pred[(( test.features[:,1] == 1.0) & ( test.features[:,0] == 0.0))]\n",
        "\n",
        "    #White Female\n",
        "    white_female =  test.labels[(( test.features[:,1] == 0.0) & ( test.features[:,0] == 1.0))]\n",
        "    white_female_pred = pred[(( test.features[:,1] == 0.0) & ( test.features[:,0] == 1.0))]\n",
        "    #Nonwhite Men\n",
        "    nonwhite_female =  test.labels[(( test.features[:,1] == 0.0) & ( test.features[:,0] == 0.0))]\n",
        "    nonwhite_female_pred = pred[(( test.features[:,1] == 0.0) & ( test.features[:,0] == 0.0))]\n",
        "        \n",
        "    metrix_rw[r_lambda] = {}\n",
        "    metrix_rw[r_lambda]['accuracy'] = accuracy\n",
        "    metrix_rw[r_lambda]['privilaged'] = {}\n",
        "    metrix_rw[r_lambda]['unprivilaged'] = {}\n",
        "\n",
        "    # GET TPR and TNR for each unique group\n",
        "    tn, fp, fn, tp = CM(white_male, white_male_pred).ravel()\n",
        "    metrix_rw[r_lambda]['privilaged']['White Male'] =  (tp/(tp+fn), tn/(tn+fp))\n",
        "\n",
        "\n",
        "    tn, fp, fn, tp = CM(nonwhite_male, nonwhite_male_pred).ravel()\n",
        "    metrix_rw[r_lambda]['unprivilaged']['Nonwhite Male'] = (tp/(tp+fn), tn/(tn+fp))\n",
        "\n",
        "\n",
        "    tn, fp, fn, tp = CM(nonwhite_female, nonwhite_female_pred).ravel()\n",
        "    metrix_rw[r_lambda]['unprivilaged']['Nonwhite Female'] = (tp/(tp+fn), tn/(tn+fp))\n",
        "\n",
        "\n",
        "    tn, fp, fn, tp = CM(white_female, white_female_pred).ravel()\n",
        "    metrix_rw[r_lambda]['unprivilaged']['White Female'] = (tp/(tp+fn), tn/(tn+fp))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgNgdgmJylgi",
        "outputId": "90f7464f-7dc8-4d52-9f76-034797a6c2b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lambda: 0.0 \n",
            "\n",
            "epoch [1/4], Training loss:0.611689\n",
            "epoch [2/4], Training loss:0.494943\n",
            "epoch [3/4], Training loss:0.465829\n",
            "epoch [4/4], Training loss:0.455133\n",
            "Accuracy:  0.7905548351873336 \n",
            "\n",
            "Lambda: 1e-05 \n",
            "\n",
            "epoch [1/4], Training loss:0.589176\n",
            "epoch [2/4], Training loss:0.489619\n",
            "epoch [3/4], Training loss:0.463895\n",
            "epoch [4/4], Training loss:0.454255\n",
            "Accuracy:  0.7905548351873336 \n",
            "\n",
            "Lambda: 0.0001 \n",
            "\n",
            "epoch [1/4], Training loss:0.587050\n",
            "epoch [2/4], Training loss:0.486700\n",
            "epoch [3/4], Training loss:0.462893\n",
            "epoch [4/4], Training loss:0.453916\n",
            "Accuracy:  0.7905548351873336 \n",
            "\n",
            "Lambda: 0.001 \n",
            "\n",
            "epoch [1/4], Training loss:0.587995\n",
            "epoch [2/4], Training loss:0.487787\n",
            "epoch [3/4], Training loss:0.462594\n",
            "epoch [4/4], Training loss:0.453444\n",
            "Accuracy:  0.7905548351873336 \n",
            "\n",
            "Lambda: 0.01 \n",
            "\n",
            "epoch [1/4], Training loss:0.609936\n",
            "epoch [2/4], Training loss:0.493280\n",
            "epoch [3/4], Training loss:0.465136\n",
            "epoch [4/4], Training loss:0.454959\n",
            "Accuracy:  0.7905548351873336 \n",
            "\n",
            "Lambda: 0.1 \n",
            "\n",
            "epoch [1/4], Training loss:0.555071\n",
            "epoch [2/4], Training loss:0.482992\n",
            "epoch [3/4], Training loss:0.463033\n",
            "epoch [4/4], Training loss:0.455467\n",
            "Accuracy:  0.7905548351873336 \n",
            "\n",
            "Lambda: 1.0 \n",
            "\n",
            "epoch [1/4], Training loss:0.605010\n",
            "epoch [2/4], Training loss:0.504508\n",
            "epoch [3/4], Training loss:0.483811\n",
            "epoch [4/4], Training loss:0.477771\n",
            "Accuracy:  0.7905548351873336 \n",
            "\n",
            "Lambda: 2.0 \n",
            "\n",
            "epoch [1/4], Training loss:0.608051\n",
            "epoch [2/4], Training loss:0.516133\n",
            "epoch [3/4], Training loss:0.499761\n",
            "epoch [4/4], Training loss:0.495550\n",
            "Accuracy:  0.7900771173138607 \n",
            "\n",
            "Lambda: 3.0 \n",
            "\n",
            "epoch [1/4], Training loss:0.599544\n",
            "epoch [2/4], Training loss:0.523144\n",
            "epoch [3/4], Training loss:0.511610\n",
            "epoch [4/4], Training loss:0.509116\n",
            "Accuracy:  0.7871425646625264 \n",
            "\n",
            "Lambda: 4.0 \n",
            "\n",
            "epoch [1/4], Training loss:0.621979\n",
            "epoch [2/4], Training loss:0.534115\n",
            "epoch [3/4], Training loss:0.522588\n",
            "epoch [4/4], Training loss:0.520394\n",
            "Accuracy:  0.7861188835050843 \n",
            "\n",
            "Lambda: 5.0 \n",
            "\n",
            "epoch [1/4], Training loss:0.659838\n",
            "epoch [2/4], Training loss:0.544985\n",
            "epoch [3/4], Training loss:0.531953\n",
            "epoch [4/4], Training loss:0.529684\n",
            "Accuracy:  0.7839350303692076 \n",
            "\n",
            "Lambda: 6.0 \n",
            "\n",
            "epoch [1/4], Training loss:0.577578\n",
            "epoch [2/4], Training loss:0.542269\n",
            "epoch [3/4], Training loss:0.538010\n",
            "epoch [4/4], Training loss:0.537238\n",
            "Accuracy:  0.7839350303692076 \n",
            "\n",
            "Lambda: 7.0 \n",
            "\n",
            "epoch [1/4], Training loss:0.629683\n",
            "epoch [2/4], Training loss:0.553295\n",
            "epoch [3/4], Training loss:0.545160\n",
            "epoch [4/4], Training loss:0.543850\n",
            "Accuracy:  0.7823653859277964 \n",
            "\n",
            "Lambda: 8.0 \n",
            "\n",
            "epoch [1/4], Training loss:0.583683\n",
            "epoch [2/4], Training loss:0.554516\n",
            "epoch [3/4], Training loss:0.550349\n",
            "epoch [4/4], Training loss:0.549475\n",
            "Accuracy:  0.782228895106804 \n",
            "\n",
            "Lambda: 9.0 \n",
            "\n",
            "epoch [1/4], Training loss:0.617936\n",
            "epoch [2/4], Training loss:0.562809\n",
            "epoch [3/4], Training loss:0.556008\n",
            "epoch [4/4], Training loss:0.554690\n",
            "Accuracy:  0.7821606496963079 \n",
            "\n",
            "Lambda: 10.0 \n",
            "\n",
            "epoch [1/4], Training loss:0.647140\n",
            "epoch [2/4], Training loss:0.568433\n",
            "epoch [3/4], Training loss:0.560629\n",
            "epoch [4/4], Training loss:0.559268\n",
            "Accuracy:  0.7816829318228349 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ACC  = []\n",
        "for l in metrix_rw:\n",
        "    ACC.append(metrix_rw[l]['accuracy'])\n",
        "\n",
        "\n",
        "fig, axs = plt.subplots(1, 1, figsize=(6,3))\n",
        "axs.plot(lambdas, ACC)\n",
        "axs.set_title('Accuracy with race and sex feature')\n",
        "axs.set(xlabel='r-lambda', ylabel = 'Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "EcBrLNro0Nzy",
        "outputId": "a6f0a1f0-07c2-4b9f-d89f-b8595a4b9a3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Accuracy'), Text(0.5, 0, 'r-lambda')]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Excluding the sensetive attribute \"Gende or Sex\""
      ],
      "metadata": {
        "id": "zv5_d33iaSqV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "GhrNappb0ZCD",
        "outputId": "af601782-effb-4041-fe55-8717e3a92349"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               race           sex  Income Binary  Age (decade)=10  \\\n",
              "count  48842.000000  48842.000000   48842.000000     48842.000000   \n",
              "mean       0.855043      0.668482       0.239282         0.051390   \n",
              "std        0.352061      0.470764       0.426649         0.220795   \n",
              "min        0.000000      0.000000       0.000000         0.000000   \n",
              "25%        1.000000      0.000000       0.000000         0.000000   \n",
              "50%        1.000000      1.000000       0.000000         0.000000   \n",
              "75%        1.000000      1.000000       0.000000         0.000000   \n",
              "max        1.000000      1.000000       1.000000         1.000000   \n",
              "\n",
              "       Age (decade)=20  Age (decade)=30  Age (decade)=40  Age (decade)=50  \\\n",
              "count     48842.000000     48842.000000     48842.000000     48842.000000   \n",
              "mean          0.245793         0.264711         0.219565         0.135519   \n",
              "std           0.430561         0.441184         0.413956         0.342280   \n",
              "min           0.000000         0.000000         0.000000         0.000000   \n",
              "25%           0.000000         0.000000         0.000000         0.000000   \n",
              "50%           0.000000         0.000000         0.000000         0.000000   \n",
              "75%           0.000000         1.000000         0.000000         0.000000   \n",
              "max           1.000000         1.000000         1.000000         1.000000   \n",
              "\n",
              "       Age (decade)=60  Age (decade)=>=70  Education Years=6  \\\n",
              "count     48842.000000       48842.000000       48842.000000   \n",
              "mean          0.062528           0.020495           0.028439   \n",
              "std           0.242115           0.141686           0.166224   \n",
              "min           0.000000           0.000000           0.000000   \n",
              "25%           0.000000           0.000000           0.000000   \n",
              "50%           0.000000           0.000000           0.000000   \n",
              "75%           0.000000           0.000000           0.000000   \n",
              "max           1.000000           1.000000           1.000000   \n",
              "\n",
              "       Education Years=7  Education Years=8  Education Years=9  \\\n",
              "count       48842.000000       48842.000000       48842.000000   \n",
              "mean            0.037099           0.013452           0.323164   \n",
              "std             0.189007           0.115199           0.467690   \n",
              "min             0.000000           0.000000           0.000000   \n",
              "25%             0.000000           0.000000           0.000000   \n",
              "50%             0.000000           0.000000           0.000000   \n",
              "75%             0.000000           0.000000           1.000000   \n",
              "max             1.000000           1.000000           1.000000   \n",
              "\n",
              "       Education Years=10  Education Years=11  Education Years=12  \\\n",
              "count        48842.000000        48842.000000        48842.000000   \n",
              "mean             0.222718            0.042197            0.032779   \n",
              "std              0.416075            0.201041            0.178060   \n",
              "min              0.000000            0.000000            0.000000   \n",
              "25%              0.000000            0.000000            0.000000   \n",
              "50%              0.000000            0.000000            0.000000   \n",
              "75%              0.000000            0.000000            0.000000   \n",
              "max              1.000000            1.000000            1.000000   \n",
              "\n",
              "       Education Years=<6  Education Years=>12  \n",
              "count        48842.000000         48842.000000  \n",
              "mean             0.052209             0.247942  \n",
              "std              0.222451             0.431823  \n",
              "min              0.000000             0.000000  \n",
              "25%              0.000000             0.000000  \n",
              "50%              0.000000             0.000000  \n",
              "75%              0.000000             0.000000  \n",
              "max              1.000000             1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-26e487ee-dd6e-428c-bf94-dc664d37e43a\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>Income Binary</th>\n",
              "      <th>Age (decade)=10</th>\n",
              "      <th>Age (decade)=20</th>\n",
              "      <th>Age (decade)=30</th>\n",
              "      <th>Age (decade)=40</th>\n",
              "      <th>Age (decade)=50</th>\n",
              "      <th>Age (decade)=60</th>\n",
              "      <th>Age (decade)=&gt;=70</th>\n",
              "      <th>Education Years=6</th>\n",
              "      <th>Education Years=7</th>\n",
              "      <th>Education Years=8</th>\n",
              "      <th>Education Years=9</th>\n",
              "      <th>Education Years=10</th>\n",
              "      <th>Education Years=11</th>\n",
              "      <th>Education Years=12</th>\n",
              "      <th>Education Years=&lt;6</th>\n",
              "      <th>Education Years=&gt;12</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "      <td>48842.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.855043</td>\n",
              "      <td>0.668482</td>\n",
              "      <td>0.239282</td>\n",
              "      <td>0.051390</td>\n",
              "      <td>0.245793</td>\n",
              "      <td>0.264711</td>\n",
              "      <td>0.219565</td>\n",
              "      <td>0.135519</td>\n",
              "      <td>0.062528</td>\n",
              "      <td>0.020495</td>\n",
              "      <td>0.028439</td>\n",
              "      <td>0.037099</td>\n",
              "      <td>0.013452</td>\n",
              "      <td>0.323164</td>\n",
              "      <td>0.222718</td>\n",
              "      <td>0.042197</td>\n",
              "      <td>0.032779</td>\n",
              "      <td>0.052209</td>\n",
              "      <td>0.247942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.352061</td>\n",
              "      <td>0.470764</td>\n",
              "      <td>0.426649</td>\n",
              "      <td>0.220795</td>\n",
              "      <td>0.430561</td>\n",
              "      <td>0.441184</td>\n",
              "      <td>0.413956</td>\n",
              "      <td>0.342280</td>\n",
              "      <td>0.242115</td>\n",
              "      <td>0.141686</td>\n",
              "      <td>0.166224</td>\n",
              "      <td>0.189007</td>\n",
              "      <td>0.115199</td>\n",
              "      <td>0.467690</td>\n",
              "      <td>0.416075</td>\n",
              "      <td>0.201041</td>\n",
              "      <td>0.178060</td>\n",
              "      <td>0.222451</td>\n",
              "      <td>0.431823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26e487ee-dd6e-428c-bf94-dc664d37e43a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      <script>\n",
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              "          document.querySelector('#df-26e487ee-dd6e-428c-bf94-dc664d37e43a 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-26e487ee-dd6e-428c-bf94-dc664d37e43a');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "lets check the dataframe of adult data set"
      ],
      "metadata": {
        "id": "VZoRKP7naVro"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop down the sex column to check further analysis"
      ],
      "metadata": {
        "id": "8gi_OxkubNeN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(columns=['sex'])"
      ],
      "metadata": {
        "id": "yKUBsaDEW8ZY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "target_attribute = df['race']\n",
        "\n",
        "x_train, x_test, Y_train, Y_test = train_test_split(df, target_attribute, test_size=0.3)"
      ],
      "metadata": {
        "id": "WXYfF8OzoqXM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  logistic regression\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import  accuracy_score \n",
        "\n",
        "# define dataset\n",
        "\n",
        "\n",
        "# define models and parameters\n",
        "model = LogisticRegression()\n",
        "solvers = [ 'liblinear']\n",
        "penalty = ['l2']\n",
        "c_values = np.logspace(np.log10(100),np.log10(0.00001),num=20)\n",
        "# define grid search\n",
        "grid = dict(solver=solvers,penalty=penalty,C=c_values)\n",
        "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=1, random_state=1)\n",
        "grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy',error_score=0)\n",
        "grid_result = grid_search.fit(x_train, Y_train)\n",
        "best_model = grid_result.best_estimator_\n",
        "yhat = best_model.predict(x_test)\n",
        "\t# evaluate the model\n",
        "acc = accuracy_score(Y_test, yhat)\n",
        "\t# store the result\n",
        "# summarize results\n",
        "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
        "means = grid_result.cv_results_['mean_test_score']\n",
        "stds = grid_result.cv_results_['std_test_score']\n",
        "params = grid_result.cv_results_['params']\n",
        "for mean, stdev, param in zip(means, stds, params):\n",
        "    print('accuracy',\"%f (%f) with: %r\" % (mean, stdev, param))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "93NwmXbfp7cv",
        "outputId": "3a7dc72b-f5c8-4d5b-a5de-a3e3ae335e19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best: 1.000000 using {'C': 100.0, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 100.0, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 42.81332398719393, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 18.329807108324356, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 7.847599703514615, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 3.3598182862837827, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 1.438449888287663, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 0.6158482110660267, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 0.26366508987303583, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 0.11288378916846895, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 0.048329302385717574, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 0.0206913808111479, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 0.008858667904100831, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 1.000000 (0.000000) with: {'C': 0.003792690190732254, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.997485 (0.000466) with: {'C': 0.0016237767391887243, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.856445 (0.000067) with: {'C': 0.0006951927961775605, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.856445 (0.000067) with: {'C': 0.00029763514416313193, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.856445 (0.000067) with: {'C': 0.00012742749857031348, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.856445 (0.000067) with: {'C': 5.455594781168526e-05, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.856445 (0.000067) with: {'C': 2.335721469090126e-05, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "accuracy 0.856445 (0.000067) with: {'C': 1e-05, 'penalty': 'l2', 'solver': 'liblinear'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adult Data Analysis"
      ],
      "metadata": {
        "id": "r-hDPMusRpAx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "#creating model \n",
        "classifier = (LogisticRegression())\n",
        "\n",
        "#fitting  training data to model\n",
        "classifier.fit(X_train , y_train)\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "#print(list(zip(y_test , y_pred)))"
      ],
      "metadata": {
        "id": "GMo_tgfPoE1L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Precision, recall, F1 score \n",
        "from sklearn.metrics import confusion_matrix , accuracy_score , classification_report\n",
        "\n",
        "cfm = confusion_matrix(y_test , y_pred)\n",
        "\n",
        "print( cfm , \"\\n\")\n",
        "\n",
        "print(  classification_report(y_test , y_pred) , \"\\n\")\n",
        "\n",
        "acc = accuracy_score(y_test , y_pred)\n",
        "print(\"accuracy :\" , acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "serfUgz-nyDr",
        "outputId": "3b3e612d-f9c6-4c44-ab0b-0bfde3d1cb5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[10473   718]\n",
            " [ 2151  1311]] \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.83      0.94      0.88     11191\n",
            "         1.0       0.65      0.38      0.48      3462\n",
            "\n",
            "    accuracy                           0.80     14653\n",
            "   macro avg       0.74      0.66      0.68     14653\n",
            "weighted avg       0.79      0.80      0.78     14653\n",
            " \n",
            "\n",
            "accuracy : 0.8042039172865625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_roc_curve(fpr, tpr, label=None):\n",
        "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
        "    plt.plot([0, 1], [0, 1], 'k--')\n",
        "    plt.axis([0, 1, 0, 1])\n",
        "    plt.xlabel('False Positive Rate', fontsize=16)\n",
        "    plt.ylabel('True Positive Rate', fontsize=16)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plot_roc_curve(fpr, tpr)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "DCZfC9QAo6hp",
        "outputId": "4f028258-9029-4e9d-b3db-bb58ae17ac75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "precisions, recalls, thresholds = precision_recall_curve(y_test.tolist() , y_pred_class)\n",
        "def  plot_precision_vs_recall(precisions, recalls):\n",
        "    plt.plot(recalls, precisions, \"b-\", linewidth=2)\n",
        "    plt.xlabel(\"Recall\", fontsize=16)\n",
        "    plt.ylabel(\"Precision\", fontsize=16)\n",
        "    plt.axis([0, 1, 0, 1])\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plot_precision_vs_recall(precisions, recalls)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "l_9i0Gaso-kB",
        "outputId": "65c53e84-4f4a-4ff6-ab67-7188d1bbf4b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = dataset_orig.metadata['params']['df'].copy()\n",
        "#biasness with the help of income\n",
        "male_more_40k = sum(df[((df['sex'] == 1.0))]['Income Binary'] == 1.0)\n",
        "#check with the income bracket of 40K\n",
        "\n",
        "female_40k = sum(df[((df['sex'] == 0.0))]['Income Binary'] == 0.0)\n",
        "male_40k = sum(df[((df['sex'] == 1.0))]['Income Binary'] == 0.0)\n",
        "\n",
        "female_40k = sum(df[((df['sex'] == 0.0))]['Income Binary'] == 1.0)\n",
        "\n",
        "print('Male')\n",
        "print('<=40k:', male_40k,'\\t>40k:', male_40k, '\\tTotal:', male_40k + male_40k)\n",
        "print('Female')\n",
        "print('<=40k:', female_40k, '\\t>40k:', female_40k, '\\tTotal:', female_40k + female_40k)\n",
        "print('\\n\\t\\t\\t\\tTotal:', male_40k + male_40k + female_40k + female_40k)\n",
        "\n",
        "labels = ['Female', 'Male']\n",
        "less_40k = [female_40k, male_40k]\n",
        "more_40k = [female_40k, male_40k]\n",
        "\n",
        "x = np.arange(len(labels))  \n",
        "width = 0.4  \n",
        "\n",
        "fig, ax = plt.subplots(figsize=(7,5))\n",
        "rects1 = ax.bar(x - width/2, less_40k, width, label=' Income <= 40k')\n",
        "rects2 = ax.bar(x + width/2, more_40k, width, label='Income > 40k')\n",
        "\n",
        "ax.set_ylabel('Counts')\n",
        "ax.set_title('Income of male and female')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.legend()\n",
        "\n",
        "fig.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "r-0xbK3weUI1",
        "outputId": "feb81ec5-644a-4f00-98ed-3dbab2a47ed6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Male\n",
            "<=40k: 22732 \t>50k: 9918 \tTotal: 32650\n",
            "Female\n",
            "<=40k: 14423 \t>50k: 1769 \tTotal: 16192\n",
            "\n",
            "\t\t\t\tTotal: 48842\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "ADULT_ML.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}