example.ipynb

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   "source": [
    "import numpy as np\n",
    "import sklearn, sklearn.linear_model\n",
    "\n",
    "import loader\n",
    "import postprocess\n",
    "import utils\n",
    "\n",
    "data_dir = \"data/adult\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb9bdc2",
   "metadata": {},
   "source": [
    "## Attribute aware"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8105c1c6",
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    "execution": {
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>14423</td>\n",
       "      <td>22732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>1769</td>\n",
       "      <td>9918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Group   Female   Male\n",
       "Target               \n",
       "<=50K    14423  22732\n",
       ">50K      1769   9918"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Load the UCI Adult dataset\n",
    "\n",
    "(inputs, labels, label_names, groups,\n",
    " group_names) = loader.load_adult(data_dir, remove_sensitive_attr=False)\n",
    "\n",
    "n_classes = len(label_names)\n",
    "n_groups = len(group_names)\n",
    "\n",
    "# Normalize data\n",
    "scaler = sklearn.preprocessing.StandardScaler()\n",
    "inputs[:] = scaler.fit_transform(inputs)\n",
    "\n",
    "display(loader.dataset_stats(labels, label_names, groups, group_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3aed904d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Split data by 0.35/0.35/0.3 for pre-training, post-training, and testing\n",
    "\n",
    "(inputs_train, inputs_test, labels_train, labels_test, groups_train,\n",
    " groups_test) = sklearn.model_selection.train_test_split(\n",
    "     inputs,\n",
    "     labels,\n",
    "     groups,\n",
    "     test_size=0.3,\n",
    " )\n",
    "\n",
    "(inputs_pretrain, inputs_postproc, labels_pretrain, labels_postproc,\n",
    " groups_pretrain, groups_postproc) = sklearn.model_selection.train_test_split(\n",
    "     inputs_train,\n",
    "     labels_train,\n",
    "     groups_train,\n",
    "     test_size=0.5,\n",
    " )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0eded8b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Train a predictor for Y given X\n",
    "\n",
    "predictor_y = sklearn.linear_model.LogisticRegression()\n",
    "predictor_y.fit(inputs_pretrain, labels_pretrain)\n",
    "\n",
    "# Define predict functions\n",
    "predict_y = lambda x: predictor_y.predict_proba(x)\n",
    "predict_a = lambda x: np.where(x[['Sex_Female', 'Sex_Male']].values > 0, 1, 0)  # attribute aware\n",
    "predict_ay = lambda x: np.einsum('ij,ik->ijk', predict_a(x), predict_y(x)\n",
    "                                ).reshape(-1, n_groups, n_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3ff837fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attribute aware post-processing result for statistical parity on UCI Adult:\n",
      "  Original: accuracy=0.8490, delta_sp=0.1761\n",
      "  Post-processed: accuracy=0.8311, delta_sp=0.0043\n"
     ]
    }
   ],
   "source": [
    "## Post-process for statistical parity\n",
    "\n",
    "postprocessor = postprocess.PostProcessor(\n",
    "    n_classes,\n",
    "    n_groups,\n",
    "    pred_y_fn=predict_y,\n",
    "    pred_a_fn=predict_a,\n",
    "    criterion='sp',\n",
    "    alpha=0.001,\n",
    ")\n",
    "\n",
    "# Using cvxpy's default solver rather than Gurobi.\n",
    "# Okay for small scale datasets/problems, but can be very slow for larger ones.\n",
    "postprocessor.fit(inputs_postproc, solver=None)\n",
    "\n",
    "# Evaluate\n",
    "preds = np.argmax(predict_y(inputs_test), axis=1)\n",
    "preds_fair = postprocessor.predict(inputs_test)\n",
    "\n",
    "print(\n",
    "    f\"Attribute aware post-processing result for statistical parity on UCI Adult:\\n\"\n",
    "    f\"  Original: accuracy={1-utils.error_rate(labels_test, preds):.4f}, delta_sp={utils.delta_sp(preds, groups_test, n_classes, n_groups):.4f}\\n\"\n",
    "    f\"  Post-processed: accuracy={1-utils.error_rate(labels_test, preds_fair):.4f}, delta_sp={utils.delta_sp(preds_fair, groups_test, n_classes, n_groups):.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a87ea1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attribute aware post-processing result for binary equal opportunity on UCI Adult:\n",
      "  Original: accuracy=0.8490, delta_eopp=0.0805\n",
      "  Post-processed: accuracy=0.8486, delta_eopp=0.0212\n"
     ]
    }
   ],
   "source": [
    "## Post-process for binary equal opportunity\n",
    "\n",
    "postprocessor = postprocess.PostProcessor(\n",
    "    n_classes,\n",
    "    n_groups,\n",
    "    pred_ay_fn=predict_ay,\n",
    "    criterion='eopp',\n",
    "    alpha=0.001,\n",
    ")\n",
    "postprocessor.fit(inputs_postproc, solver=None)\n",
    "\n",
    "# Evaluate\n",
    "preds = np.argmax(predict_y(inputs_test), axis=1)\n",
    "preds_fair = postprocessor.predict(inputs_test)\n",
    "\n",
    "print(\n",
    "    f\"Attribute aware post-processing result for binary equal opportunity on UCI Adult:\\n\"\n",
    "    f\"  Original: accuracy={1-utils.error_rate(labels_test, preds):.4f}, delta_eopp={utils.delta_eopp(labels_test, preds, groups_test, n_classes, n_groups):.4f}\\n\"\n",
    "    f\"  Post-processed: accuracy={1-utils.error_rate(labels_test, preds_fair):.4f}, delta_eopp={utils.delta_eopp(labels_test, preds_fair, groups_test, n_classes, n_groups):.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c42a9065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attribute aware post-processing result for equalized odds on UCI Adult:\n",
      "  Original: accuracy=0.8490, delta_eo=0.0805\n",
      "  Post-processed: accuracy=0.8401, delta_eo=0.0510\n"
     ]
    }
   ],
   "source": [
    "## Post-process for equalized odds\n",
    "\n",
    "postprocessor = postprocess.PostProcessor(\n",
    "    n_classes,\n",
    "    n_groups,\n",
    "    pred_ay_fn=predict_ay,\n",
    "    criterion='eo',\n",
    "    alpha=0.001,\n",
    ")\n",
    "postprocessor.fit(inputs_postproc, solver=None)\n",
    "\n",
    "# Evaluate\n",
    "preds = np.argmax(predict_y(inputs_test), axis=1)\n",
    "preds_fair = postprocessor.predict(inputs_test)\n",
    "\n",
    "print(\n",
    "    f\"Attribute aware post-processing result for equalized odds on UCI Adult:\\n\"\n",
    "    f\"  Original: accuracy={1-utils.error_rate(labels_test, preds):.4f}, delta_eo={utils.delta_eo(labels_test, preds, groups_test, n_classes, n_groups):.4f}\\n\"\n",
    "    f\"  Post-processed: accuracy={1-utils.error_rate(labels_test, preds_fair):.4f}, delta_eo={utils.delta_eo(labels_test, preds_fair, groups_test, n_classes, n_groups):.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82857f02",
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     "start_time": "2024-03-02T22:54:16.081475",
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    },
    "tags": []
   },
   "source": [
    "## Attribute blind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80069fe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>14423</td>\n",
       "      <td>22732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>1769</td>\n",
       "      <td>9918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Group   Female   Male\n",
       "Target               \n",
       "<=50K    14423  22732\n",
       ">50K      1769   9918"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Load the UCI Adult dataset\n",
    "\n",
    "(inputs, labels, label_names, groups,\n",
    " group_names) = loader.load_adult(data_dir, remove_sensitive_attr=True)\n",
    "\n",
    "n_classes = len(label_names)\n",
    "n_groups = len(group_names)\n",
    "\n",
    "\n",
    "# Normalize data\n",
    "scaler = sklearn.preprocessing.StandardScaler()\n",
    "inputs[:] = scaler.fit_transform(inputs)\n",
    "\n",
    "display(loader.dataset_stats(labels, label_names, groups, group_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4aadf9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Split data by 0.35/0.35/0.3 for pre-training, post-training, and testing\n",
    "\n",
    "(inputs_train, inputs_test, labels_train, labels_test, groups_train,\n",
    " groups_test) = sklearn.model_selection.train_test_split(\n",
    "     inputs,\n",
    "     labels,\n",
    "     groups,\n",
    "     test_size=0.3,\n",
    " )\n",
    "\n",
    "(inputs_pretrain, inputs_postproc, labels_pretrain, labels_postproc,\n",
    " groups_pretrain, groups_postproc) = sklearn.model_selection.train_test_split(\n",
    "     inputs_train,\n",
    "     labels_train,\n",
    "     groups_train,\n",
    "     test_size=0.5,\n",
    " )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a46f9dcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rxian/anaconda3/envs/fair4/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "## Train predictors for Y given X, A given X, and (A, Y) given X\n",
    "\n",
    "predictor_y = sklearn.linear_model.LogisticRegression()\n",
    "predictor_y.fit(inputs_pretrain, labels_pretrain)\n",
    "\n",
    "predictor_a = sklearn.linear_model.LogisticRegression()\n",
    "predictor_a.fit(inputs_pretrain, groups_pretrain)\n",
    "\n",
    "predictor_ay = sklearn.linear_model.LogisticRegression()\n",
    "predictor_ay.fit(inputs_pretrain, groups_pretrain * n_classes + labels_pretrain)\n",
    "\n",
    "# Define predict functions\n",
    "predict_y = lambda x: predictor_y.predict_proba(x)\n",
    "predict_a = lambda x: predictor_a.predict_proba(x)\n",
    "predict_ay = lambda x: predictor_ay.predict_proba(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "033dc88e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attribute blind post-processing result for statistical parity on UCI Adult:\n",
      "  Original: accuracy=0.8482, delta_sp=0.1781\n",
      "  Post-processed: accuracy=0.8269, delta_sp=0.0046\n"
     ]
    }
   ],
   "source": [
    "## Post-process for statistical parity\n",
    "\n",
    "postprocessor = postprocess.PostProcessor(\n",
    "    n_classes,\n",
    "    n_groups,\n",
    "    pred_y_fn=predict_y,\n",
    "    pred_a_fn=predict_a,\n",
    "    criterion='sp',\n",
    "    alpha=0.001,\n",
    ")\n",
    "postprocessor.fit(inputs_postproc, solver=None)\n",
    "\n",
    "# Evaluate\n",
    "preds = np.argmax(predict_y(inputs_test), axis=1)\n",
    "preds_fair = postprocessor.predict(inputs_test)\n",
    "\n",
    "print(\n",
    "    f\"Attribute blind post-processing result for statistical parity on UCI Adult:\\n\"\n",
    "    f\"  Original: accuracy={1-utils.error_rate(labels_test, preds):.4f}, delta_sp={utils.delta_sp(preds, groups_test, n_classes, n_groups):.4f}\\n\"\n",
    "    f\"  Post-processed: accuracy={1-utils.error_rate(labels_test, preds_fair):.4f}, delta_sp={utils.delta_sp(preds_fair, groups_test, n_classes, n_groups):.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e4fcc6c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attribute blind post-processing result for binary equal opportunity on UCI Adult:\n",
      "  Original: accuracy=0.8482, delta_eopp=0.1077\n",
      "  Post-processed: accuracy=0.8465, delta_eopp=0.0523\n"
     ]
    }
   ],
   "source": [
    "## Post-process for binary equal opportunity\n",
    "\n",
    "postprocessor = postprocess.PostProcessor(\n",
    "    n_classes,\n",
    "    n_groups,\n",
    "    pred_ay_fn=predict_ay,\n",
    "    criterion='eopp',\n",
    "    alpha=0.001,\n",
    ")\n",
    "postprocessor.fit(inputs_postproc, solver=None)\n",
    "\n",
    "# Evaluate\n",
    "preds = np.argmax(predict_y(inputs_test), axis=1)\n",
    "preds_fair = postprocessor.predict(inputs_test)\n",
    "\n",
    "print(\n",
    "    f\"Attribute blind post-processing result for binary equal opportunity on UCI Adult:\\n\"\n",
    "    f\"  Original: accuracy={1-utils.error_rate(labels_test, preds):.4f}, delta_eopp={utils.delta_eopp(labels_test, preds, groups_test, n_classes, n_groups):.4f}\\n\"\n",
    "    f\"  Post-processed: accuracy={1-utils.error_rate(labels_test, preds_fair):.4f}, delta_eopp={utils.delta_eopp(labels_test, preds_fair, groups_test, n_classes, n_groups):.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21e589bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attribute blind post-processing result for equalized odds on UCI Adult:\n",
      "  Original: accuracy=0.8482, delta_eo=0.1077\n",
      "  Post-processed: accuracy=0.8374, delta_eo=0.0167\n"
     ]
    }
   ],
   "source": [
    "## Post-process for equalized odds\n",
    "\n",
    "postprocessor = postprocess.PostProcessor(\n",
    "    n_classes,\n",
    "    n_groups,\n",
    "    pred_ay_fn=predict_ay,\n",
    "    criterion='eo',\n",
    "    alpha=0.001,\n",
    ")\n",
    "postprocessor.fit(inputs_postproc, solver=None)\n",
    "\n",
    "# Evaluate\n",
    "preds = np.argmax(predict_y(inputs_test), axis=1)\n",
    "preds_fair = postprocessor.predict(inputs_test)\n",
    "\n",
    "print(\n",
    "    f\"Attribute blind post-processing result for equalized odds on UCI Adult:\\n\"\n",
    "    f\"  Original: accuracy={1-utils.error_rate(labels_test, preds):.4f}, delta_eo={utils.delta_eo(labels_test, preds, groups_test, n_classes, n_groups):.4f}\\n\"\n",
    "    f\"  Post-processed: accuracy={1-utils.error_rate(labels_test, preds_fair):.4f}, delta_eo={utils.delta_eo(labels_test, preds_fair, groups_test, n_classes, n_groups):.4f}\"\n",
    ")"
   ]
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