diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index 20b7a23..30dd65f 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -18,7 +18,7 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
       - name: Unit Tests
diff --git a/README.md b/README.md
index 316e846..724742e 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
 OxonFair is an expressive toolkit designed to enforce a wide-range of fairness definitions and to customize binary classifier behavior.
 The toolkit is designed to overcome a range of shortcomings in existing fairness toolkits for high-capacity models that overfit to the training data.
-It is designed and works for computer vision and NLP problems alongside tabular data.
+Unlike other toolkits it is designed and works for computer vision and NLP problems alongside tabular data.
 
 Check out the colab demo [here](https://colab.research.google.com/drive/1CfcS3AX7M2MO1wW33wU1LDiY5DwtyyxH?usp=sharing) or read the [preprint](https://arxiv.org/abs/2407.13710).
 
@@ -115,6 +115,16 @@ OxonFair is build from the ground up to avoid these issues. It is a postprocessi
 
 That said, we make several additional design decisions which we believe make for a better experience for data scientists:
 
+#### Direct support for pytorch including NLP and Computer Vision
+
+See [here](./examples/pytorch_minimal_demo.ipynb) for an example.
+In brief, the steps are:
+
+1. Train your network.
+2. Call `DeepFairPredictor` with the network output on validation data.
+3. Call `fit` to enforce fairness. Use `evaluate_groups` and `plot_frontier` to explore trade-offs.
+4. Use `merge_heads_pytorch` to generate a fair network.  
+
 #### Fine-grained control of behavior
 
 ##### Wide Choice of performance measure
diff --git a/examples/fairret.ipynb b/examples/fairret.ipynb
index a8e628c..d1cf478 100644
--- a/examples/fairret.ipynb
+++ b/examples/fairret.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "However, performance and fairness remains noticeably worse than OxonFair on a network of the same architecture.\n",
     "\n",
-    "Increasing the regularization weight to 5 or 10 increases fairness, but the classifier is strongly degraded.\n",
+    "Increasing the regularization weight to 5 or 10 increases fairness, but the classifier is strongly degraded. Their simple pipeline demo uses 1, but this doesn't seem to be helping with fairness.\n",
     "\n",
     "If you know how to make it work better, please submit a patch."
    ]
@@ -251,10 +251,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Set up a fairpredictor using the neural network over the validation data\n",
     "fpred = oxonfair.DeepFairPredictor(val['target'],\n",
     "                                   val_output,\n",
     "                                   groups=val['groups'])\n",
     "\n",
+    "# Enforce EO to within 1%\n",
     "fpred.fit(gm.accuracy, gm.equal_opportunity, 0.01)\n",
     "# Varying the grid with can improve accuracy at the cost of a longer search time."
    ]
@@ -276,9 +278,7 @@
     {
      "data": {
       "text/plain": [
-       "0    0.838531\n",
-       "0    0.054564\n",
-       "dtype: float64"
+       "(0.8439227461953184, 0.04137239076421528)"
       ]
      },
      "execution_count": 13,
@@ -289,8 +289,8 @@
    "source": [
     "# Compute\n",
     "import pandas as pd\n",
-    "fairret_score = pd.concat((oxonfair.performance.evaluate(test['target'],test_output_1head.reshape(-1)).loc['Accuracy'],\n",
-    "                           oxonfair.performance.evaluate_fairness(test['target'],test_output_1head.reshape(-1),test['groups']).loc['Equal Opportunity']))\n",
+    "fairret_score = ((oxonfair.performance.evaluate(test['target'],test_output_1head.reshape(-1))['Accuracy'],\n",
+    "                  oxonfair.performance.evaluate_fairness(test['target'],test_output_1head.reshape(-1),test['groups'])['Equal Opportunity']))\n",
     "fairret_score"
    ]
   },
@@ -318,7 +318,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x3180a4fd0>"
+       "<matplotlib.legend.Legend at 0x1683c2cd0>"
       ]
      },
      "execution_count": 15,
@@ -327,7 +327,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -340,7 +340,7 @@
     "from matplotlib import pyplot as plt\n",
     "fpred2.plot_frontier(data=test, name_frontier='XGBoost Front')\n",
     "fpred.plot_frontier(data=test_network, new_plot=False,name_frontier='Neural Net Front')\n",
-    "plt.scatter(y=fairret_score.iloc[0],x=fairret_score.iloc[1],label='Fairrets',marker='X')\n",
+    "plt.scatter(y=fairret_score[0],x=fairret_score[1],label='Fairrets',marker='X')\n",
     "plt.legend()\n"
    ]
   },
@@ -362,7 +362,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -375,6 +375,72 @@
     "fpred2.plot_frontier(name_frontier='XGBoost Front')\n",
     "fpred.plot_frontier(new_plot=False,name_frontier='Neural Net Front')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Finally let's show how the 2-headed network can be merged into a single-headed network that makes fair predictions.\n",
+    "# We copy the network and create a fair version\n",
+    "import copy\n",
+    "fair_network= copy.deepcopy(network)\n",
+    "# We replace the final linear layer with a 1 dimensional head.\n",
+    "fair_network[-1] = fpred.merge_heads_pytorch(network[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Evaluate the new fair network on test\n",
+    "test_output_fair=np.asarray(fair_network(torch.tensor(np.asarray(test['data'])).float()).detach())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8499283423189791, 0.03749450062510884)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((oxonfair.performance.evaluate(test['target'],test_output_fair.reshape(-1))['Accuracy'],\n",
+    "  oxonfair.performance.evaluate_fairness(test['target'],test_output_fair.reshape(-1),test['groups'])['Equal Opportunity']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8439227461953184, 0.04137239076421528)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The same score for fairrets was\n",
+    "fairret_score"
+   ]
   }
  ],
  "metadata": {
diff --git a/examples/high-dim_fairlearn_comparision.ipynb b/examples/high-dim_fairlearn_comparision.ipynb
index 95ca804..33a889c 100644
--- a/examples/high-dim_fairlearn_comparision.ipynb
+++ b/examples/high-dim_fairlearn_comparision.ipynb
@@ -1451,7 +1451,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/examples/multi_group_fairlearn_comparision.ipynb b/examples/multi_group_fairlearn_comparision.ipynb
index fb99307..e3a6484 100644
--- a/examples/multi_group_fairlearn_comparision.ipynb
+++ b/examples/multi_group_fairlearn_comparision.ipynb
@@ -28,7 +28,7 @@
      "output_type": "stream",
      "text": [
       "Training time of xgboost without fairness\n",
-      "0.8215227920000006\n"
+      "0.6205382919999991\n"
      ]
     }
    ],
@@ -142,8 +142,8 @@
     "         sensitive_features=train_data['groups'])\n",
     "    stop=time.perf_counter()\n",
     "    score=evaluate_fairness(test_data['target'], fair.predict(test_data['data']), test_data['groups'], metrics={'a':gm.accuracy,'b':gm.demographic_parity},verbose=False)\n",
-    "    fairl[0,i]=score[0]['a']#['updated']\n",
-    "    fairl[1,i]=score[0]['b']#['updated']\n",
+    "    fairl[0,i]=score['a']#['updated']\n",
+    "    fairl[1,i]=score['b']#['updated']\n",
     "    fairl[2,i]=stop-start\n",
     "    train_data['groups'][train_data['groups']==name]=' Other'\n",
     "    test_data['groups'][test_data['groups']==name]=' Other'"
@@ -224,56 +224,56 @@
        "      <td>OxonFair</td>\n",
        "      <td>0.868619</td>\n",
        "      <td>0.019085</td>\n",
-       "      <td>44.246316</td>\n",
+       "      <td>41.136526</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>FairLearn</td>\n",
        "      <td>0.865001</td>\n",
        "      <td>0.037732</td>\n",
-       "      <td>46.683562</td>\n",
+       "      <td>46.391113</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>OxonFair</td>\n",
        "      <td>0.867663</td>\n",
        "      <td>0.012305</td>\n",
-       "      <td>0.816369</td>\n",
+       "      <td>0.804408</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>FairLearn</td>\n",
        "      <td>0.866639</td>\n",
        "      <td>0.016227</td>\n",
-       "      <td>28.097114</td>\n",
+       "      <td>27.712763</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>OxonFair</td>\n",
        "      <td>0.867936</td>\n",
        "      <td>0.021317</td>\n",
-       "      <td>0.067256</td>\n",
+       "      <td>0.071277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>FairLearn</td>\n",
        "      <td>0.865070</td>\n",
        "      <td>0.007104</td>\n",
-       "      <td>24.851400</td>\n",
+       "      <td>25.013719</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>OxonFair</td>\n",
        "      <td>0.868823</td>\n",
        "      <td>0.003093</td>\n",
-       "      <td>0.053665</td>\n",
+       "      <td>0.049922</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>FairLearn</td>\n",
        "      <td>0.869165</td>\n",
        "      <td>0.002346</td>\n",
-       "      <td>20.321553</td>\n",
+       "      <td>20.080540</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -281,14 +281,14 @@
       ],
       "text/plain": [
        "        Name  Accuracy  Demographic Parity       Time\n",
-       "0   OxonFair  0.868619            0.019085  44.246316\n",
-       "0  FairLearn  0.865001            0.037732  46.683562\n",
-       "1   OxonFair  0.867663            0.012305   0.816369\n",
-       "1  FairLearn  0.866639            0.016227  28.097114\n",
-       "2   OxonFair  0.867936            0.021317   0.067256\n",
-       "2  FairLearn  0.865070            0.007104  24.851400\n",
-       "3   OxonFair  0.868823            0.003093   0.053665\n",
-       "3  FairLearn  0.869165            0.002346  20.321553"
+       "0   OxonFair  0.868619            0.019085  41.136526\n",
+       "0  FairLearn  0.865001            0.037732  46.391113\n",
+       "1   OxonFair  0.867663            0.012305   0.804408\n",
+       "1  FairLearn  0.866639            0.016227  27.712763\n",
+       "2   OxonFair  0.867936            0.021317   0.071277\n",
+       "2  FairLearn  0.865070            0.007104  25.013719\n",
+       "3   OxonFair  0.868823            0.003093   0.049922\n",
+       "3  FairLearn  0.869165            0.002346  20.080540"
       ]
      },
      "execution_count": 7,
@@ -299,44 +299,6 @@
    "source": [
     "results"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{tabular}{llrrr}\n",
-      "\\toprule\n",
-      " & Name & Accuracy & Demographic Parity & Time \\\\\n",
-      "\\midrule\n",
-      "0 & OxonFair & 0.868619 & 0.019085 & 44.246316 \\\\\n",
-      "0 & FairLearn & 0.865001 & 0.037732 & 46.683562 \\\\\n",
-      "1 & OxonFair & 0.867663 & 0.012305 & 0.816369 \\\\\n",
-      "1 & FairLearn & 0.866639 & 0.016227 & 28.097114 \\\\\n",
-      "2 & OxonFair & 0.867936 & 0.021317 & 0.067256 \\\\\n",
-      "2 & FairLearn & 0.865070 & 0.007104 & 24.851400 \\\\\n",
-      "3 & OxonFair & 0.868823 & 0.003093 & 0.053665 \\\\\n",
-      "3 & FairLearn & 0.869165 & 0.002346 & 20.321553 \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(results.to_latex())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/pytorch_minimal_demo.ipynb b/examples/pytorch_minimal_demo.ipynb
index 9825f91..2938f21 100644
--- a/examples/pytorch_minimal_demo.ipynb
+++ b/examples/pytorch_minimal_demo.ipynb
@@ -10,7 +10,17 @@
     "\n",
     "In general, we strongly recommend that you *do not* use neural networks on tabular data. Boosting is typically higher performing and much faster to run.\n",
     "\n",
-    "However, this is a toy example, and unlike computer vision examples, it should train and run in a matter of minutes. The techniques shown will directly apply to computer vision and NLP without modification."
+    "However, this is a toy example, and unlike computer vision examples, it will train and run in a matter of minutes. The techniques shown directly apply to computer vision and NLP without modification.\n",
+    "\n",
+    "In brief, the steps are:\n",
+    "1. Train a two-headed network.   \n",
+    "    The first head should predict the target attribute using binary cross entropy.  \n",
+    "    The second head should predict binarized protected attributes (use a 1-hot or 0,1 encoding) using mean squared error.\n",
+    "2. Create a FairPredictor using `DeepFairPredictor` with the output of the network on validation data.\n",
+    "3. Call fit to enforce fairness. Use `evaluate_groups` and `plot_frontier` to explore trade-offs. \n",
+    "4. Finalize the network. Use `merge_heads_pytorch` to generate a fair network with a single head.  \n",
+    "    If you are not using pytorch you can still do this by hand.  \n",
+    "    Call the methods `extract_coefficients` or `extract_coefficients_1_hot` and look at the code in `merge_heads_pytorch` or read the paper for instructions."
    ]
   },
   {
@@ -232,7 +242,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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nCQsL00rO8vLylBvCw8N73SQAwIu4e96dTqdzLKuq6rzqzLnUbUyPj8rJybHdUFdX52aTAACv0PtMMhqN4sZsSdPU1KRNm4xGY3t7u9Vq7bF0+fJl5+e5cuWK82RL4+/vH+Sk100CALxI7zNp1KhRRqOxtLRUW21vby8rK4uLixNCREdH+/r6OkqNjY1nzpzRSrGxsTab7fjx41rp2LFjNptNKwEA7nMunePQ2tr66aefasu1tbU1NTXBwcHDhw/PzMzMzc2NiIiIiIjIzc0dNGhQamqqEEJRlLS0tOXLl4eEhAQHB2dlZUVFRcXHxwshxo0bN2vWrEWLFm3ZskUI8cwzzyQlJd3mpDsAwP3DpUw6efLk9OnTteVly5YJIRYuXFhYWLhy5cq2trbFixdbrdaYmJhDhw4FBgZqwzZs2KDX61NSUtra2mbMmFFYWOjj46OV9uzZs2TJEu2svOTk5JsvewIA3J++3/VJ/YLrkwDIqc/PBef6JL7vDgAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAsyCQAgCzIJACALMgkAIAu3Munbb7/95S9/OWrUqIEDB44ePfrll1/u6urSSqqqrlmzxmQyDRw4cNq0aWfPnnU8ym63Z2RkhIaGBgQEJCcn19fXu7UHAIB7hVuZtG7dujfffLOgoOD8+fP5+fm//e1vX3/9da2Un5+/fv36goKCEydOGI3GmTNntrS0aKXMzMzi4uKioqLy8vLW1takpKTOzk539wMA4P307jy4srLyxz/+8ezZs4UQI0eO3Ldv38mTJ4UQqqpu3Lhx9erVTz/9tBBi586dBoNh79696enpNptt+/btu3btio+PF0Ls3r07PDz88OHDiYmJfbE7AAAv5tY86cknn/yf//mfixcvCiH+8pe/lJeX/+hHPxJC1NbWWiyWhIQEbZi/v//UqVMrKiqEENXV1R0dHY6SyWSKjIzUSgCA+5xb86RVq1bZbLaxY8f6+Ph0dnb+5je/+ad/+ichhMViEUIYDAbHSIPBcOnSJa3k5+c3ePBg55I23pndbrfb7dpyc3OzO00CALyFW/Ok/fv37969e+/evadOndq5c+e///u/79y501HV6XSOZVVVnVed9VjKy8tTbggPD3enSQCAt3Ark1asWJGdnT1//vyoqCiz2fzCCy/k5eUJIYxGo7gxW9I0NTVp0yaj0dje3m61Wm8uOcvJybHdUFdX506TAABv4VYmXb9+/YEH/u8ZfHx8tHPBR40aZTQaS0tLte3t7e1lZWVxcXFCiOjoaF9fX0epsbHxzJkzWsmZv79/kBN3mgQAeAu3Pk+aM2fOb37zm+HDhz/88MOnT59ev379v/zLvwghdDpdZmZmbm5uREREREREbm7uoEGDUlNThRCKoqSlpS1fvjwkJCQ4ODgrKysqKko7Bw8AcJ9zK5Nef/31F198cfHixU1NTSaTKT09/Ve/+pVWWrlyZVtb2+LFi61Wa0xMzKFDhwIDA7XShg0b9Hp9SkpKW1vbjBkzCgsLfXx83N0PAID306mq2t893EFzc7OiKDabjYN4AKQyMvudvn3CL16Z3bdPKJs7vp/zfXcAAFmQSQAAWZBJAABZkEkAAFmQSQAAWZBJAABZkEkAAFmQSQAAWZBJAABZkEkAAFmQSQAAWZBJAABZkEkAAFmQSQAAWZBJAABZkEkAAFmQSQAAWZBJAABZkEkAAFmQSQAAWZBJAABZkEkAAFmQSQAAWZBJAABZkEkAAFmQSQAAWZBJAABZkEkAAFmQSQAAWZBJAABZkEkAAFm4m0l//etf//mf/zkkJGTQoEGPPvpodXW1tl1V1TVr1phMpoEDB06bNu3s2bOOh9jt9oyMjNDQ0ICAgOTk5Pr6ejd7AADcG9zKJKvVOmXKFF9f3z//+c/nzp179dVXH3zwQa2Un5+/fv36goKCEydOGI3GmTNntrS0aKXMzMzi4uKioqLy8vLW1takpKTOzk43dwMAcA/Qqara6wdnZ2d/8MEHR48e7bZdVVWTyZSZmblq1SohhN1uNxgM69atS09Pt9lsQ4YM2bVr17x584QQDQ0N4eHhBw8eTExMvNVPaW5uVhTFZrMFBQX1ulUA6HMjs9/p2yf84pXZffuEsrnj+7lb86S333570qRJ//iP/xgWFjZx4sRt27Zp22tray0WS0JCgrbq7+8/derUiooKIUR1dXVHR4ejZDKZIiMjtZIzu93e7MSdJgEA3sKtTPr88883b94cERHx7rvvPvvss0uWLPn9738vhLBYLEIIg8HgGGkwGLSNFovFz89v8ODBN5ec5eXlKTeEh4e70yQAwFu4lUldXV2PPfZYbm7uxIkT09PTFy1atHnzZkdVp9M5llVVdV511mMpJyfHdkNdXZ07TQIAvIVbmTR06NDx48c7VseNG/fll18KIYxGo7gxW9I0NTVp0yaj0dje3m61Wm8uOfP39w9y4k6TAABv4VYmTZky5eOPP3asXrx4ccSIEUKIUaNGGY3G0tJSbXt7e3tZWVlcXJwQIjo62tfX11FqbGw8c+aMVgIA3Of07jz4hRdeiIuLy83NTUlJOX78+NatW7du3SqE0Ol0mZmZubm5ERERERERubm5gwYNSk1NFUIoipKWlrZ8+fKQkJDg4OCsrKyoqKj4+Pi+2RsAgDdzK5Mef/zx4uLinJycl19+edSoURs3blywYIFWWrlyZVtb2+LFi61Wa0xMzKFDhwIDA7XShg0b9Hp9SkpKW1vbjBkzCgsLfXx83N0PAID3c+v6JM/g+iQAcuL6pO/r7l6fBABAHyKTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALIgkwAAsiCTAACyIJMAALJw655+APpd727hc8/fpwdeinkSAEAWZBIAQBZkEgBAFmQSAEAWZBIAQBZkEgBAFmQSAEAWZBIAQBZkEgBAFmQSAEAWZBIAQBZkEgBAFmQSAEAWZBIAQBZ9dq+KvLy8f/u3f1u6dOnGjRuFEKqqrl27duvWrVarNSYm5o033nj44Ye1kXa7PSsra9++fW1tbTNmzNi0adOwYcP6qg3gntS7G1IAXqdv5kknTpzYunXrI4884tiSn5+/fv36goKCEydOGI3GmTNntrS0aKXMzMzi4uKioqLy8vLW1takpKTOzs4+aQMA4NX6IJNaW1sXLFiwbdu2wYMHa1tUVd24cePq1auffvrpyMjInTt3Xr9+fe/evUIIm822ffv2V199NT4+fuLEibt37/7oo48OHz7sfhsAAG/XB8funnvuudmzZ8fHx//617/WttTW1losloSEBG3V399/6tSpFRUV6enp1dXVHR0djpLJZIqMjKyoqEhMTHR+TrvdbrfbteXm5mb3mwTgotscJ+TutLjb3M2koqKiU6dOnThxwnmjxWIRQhgMBscWg8Fw6dIlreTn5+eYUWklbbyzvLy8tWvXutkb4F08+aERH1BBTm4du6urq1u6dOnu3bsHDBhwc1Wn0zmWVVV1XnXWYyknJ8d2Q11dnTtNAgC8hVuZVF1d3dTUFB0drdfr9Xp9WVnZa6+9ptfrtRmS8+ynqalJ22g0Gtvb261W680lZ/7+/kFO3GkSAOAt3Dp2N2PGjI8++six+otf/GLs2LGrVq0aPXq00WgsLS2dOHGiEKK9vb2srGzdunVCiOjoaF9f39LS0pSUFCFEY2PjmTNn8vPz3dsLALgX8GGeW5kUGBgYGRnpWA0ICAgJCdG2ZGZm5ubmRkRERERE5ObmDho0KDU1VQihKEpaWtry5ctDQkKCg4OzsrKioqLi4+Pd3A0AwD2gz66Z7WblypVtbW2LFy/Wrpk9dOhQYGCgVtqwYYNer09JSdGumS0sLPTx8blLbQAAvIhOVdX+7uEOmpubFUWx2Wx8sIR7m/znwt0nh49c58l/snvjl3/H9/O7NU8C0CP5gwfoR3wHKwBAFmQSAEAWHLsD4CrOVMbdxjwJACALMgkAIAsyCQAgCz5PAnqJD1eAPsc8CQAgC+ZJQN/jwligd5gnAQBkwTwJAG6JKa+HMU8CAMiCTAIAyIJMAgDIgkwCAMiCTAIAyIJMAgDIgkwCAMiCTAIAyIJMAgDIgkwCAMiC7xYC0Ae4cwf6BJkE8H4KyIJMAm6Hr+AEPInPkwAAsiCTAACyIJMAALIgkwAAsuAcB3gfTpMD7lVuzZPy8vIef/zxwMDAsLCwuXPnfvzxx46Sqqpr1qwxmUwDBw6cNm3a2bNnHSW73Z6RkREaGhoQEJCcnFxfX+9ODwCAe4ZbmVRWVvbcc89VVVWVlpZ+++23CQkJ165d00r5+fnr168vKCg4ceKE0WicOXNmS0uLVsrMzCwuLi4qKiovL29tbU1KSurs7HR3PwAA3s+tY3clJSWO5R07doSFhVVXV//whz9UVXXjxo2rV69++umnhRA7d+40GAx79+5NT0+32Wzbt2/ftWtXfHy8EGL37t3h4eGHDx9OTEx0c08AyIljrXBdn53jYLPZhBDBwcFCiNraWovFkpCQoJX8/f2nTp1aUVEhhKiuru7o6HCUTCZTZGSkVnJmt9ubnfRVkwAAmfXNOQ6qqi5btuzJJ5+MjIwUQlgsFiGEwWBwDDAYDJcuXdJKfn5+gwcPdi5p453l5eWtXbu2T3oDNHwjAyC/vpknPf/88x9++OG+ffucN+p0OseyqqrOq856LOXk5NhuqKur65MmAQCS64NMysjIePvtt997771hw4ZpW4xGo7gxW9I0NTVp0yaj0dje3m61Wm8uOfP39w9y4n6TAAD5uXXsTlXVjIyM4uLi999/f9SoUY7to0aNMhqNpaWlEydOFEK0t7eXlZWtW7dOCBEdHe3r61taWpqSkiKEaGxsPHPmTH5+vnt7gXtQ7w61cYAO8GpuZdJzzz23d+/e//zP/wwMDNRmRYqiDBw4UKfTZWZm5ubmRkRERERE5ObmDho0KDU1VRuQlpa2fPnykJCQ4ODgrKysqKgo7Rw83IeIEADO3MqkzZs3CyGmTZvm2LJjx46f//znQoiVK1e2tbUtXrzYarXGxMQcOnQoMDBQG7Nhwwa9Xp+SktLW1jZjxozCwkIfHx932gAA3Bt0qqr2dw930NzcrCiKzWbjg6V7D/Ok+5z81yfJ8ycq/+/KFXd8P+c7WAEAsiCTAACyIJMAALIgkwAAsuD+SQD6DV/Pim7IJNx18py5BEByZBIAGTGFuj/xeRIAQBZkEgBAFmQSAEAWZBIAQBac44C+wcl1ANxHJt3LOHMJcAX/o5IHx+4AALIgkwAAsiCTAACy4PMkr9e7Q+F81ARAQmQSuiOu4L3u4b/ee3jXnJFJ+B44PQnAXUUmAfAy/N/oHkYmAbgvkGRegUzyDrycANwPOBccACALMgkAIAsyCQAgCzIJACALznGQCCcyALjPMU8CAMiCTAIAyIJjd57GAToAfete+iq8fpsnbdq0adSoUQMGDIiOjj569Gh/tQEAkEf/zJP279+fmZm5adOmKVOmbNmy5amnnjp37tzw4cP7pZm7gckQAPSCTlVVz//UmJiYxx57bPPmzdrquHHj5s6dm5eX1+Pg5uZmRVFsNltQUJAHe3QLmQRAcv1yWO+O7+f9ME9qb2+vrq7Ozs52bElISKioqHAeY7fb7Xa7tmyz2YQQzc3NnmzSFZEvvdvfLQBAL/XLm6r2Q28zF+qHTPrqq686OzsNBoNji8FgsFgszmPy8vLWrl3rvCU8PNxD/QHAfUDZ2G8/uqWlRVGUHkv9dt6dTqdzLKuq6rwqhMjJyVm2bJm23NXV9be//S0kJKTbmLutubk5PDy8rq7Oi44ZCu9sm549xhvbpmeP8UDbqqq2tLSYTKZbDeiHTAoNDfXx8XGeGDU1NTlPm4QQ/v7+/v7+jtUHH3zQY+11ExQU5F1/VRpvbJuePcYb26Znj7nbbd9qhqTph3PB/fz8oqOjS0tLHVtKS0vj4uI83wkAQCr9c+xu2bJlZrN50qRJsbGxW7du/fLLL5999tl+6QQAII/+yaR58+Z9/fXXL7/8cmNjY2Rk5MGDB0eMGNEvndyGv7//Sy+95HwI0St4Y9v07DHe2DY9e4wMbffP9UkAANyM72AFAMiCTAIAyIJMAgDIgkwCAMiCTOrOarWazWZFURRFMZvNV69evXlMR0fHqlWroqKiAgICTCbTz372s4aGBo936tL9PsrKyqKjowcMGDB69Og333zTwx3e7I49v/XWWzNnzhwyZEhQUFBsbOy77/b/lwq6fl+VDz74QK/XP/roo55q7XZcadtut69evXrEiBH+/v5/93d/9x//8R8ebrIbV3res2fPhAkTBg0aNHTo0F/84hdff/21h5t0duTIkTlz5phMJp1Od+DAgVsNk+pl6ErP/fkyVPFds2bNioyMrKioqKioiIyMTEpKunnM1atX4+Pj9+/ff+HChcrKypiYmOjoaA/3WVRU5Ovru23btnPnzi1dujQgIODSpUvdxnz++eeDBg1aunTpuXPntm3b5uvr+8c//tHDfTpzpeelS5euW7fu+PHjFy9ezMnJ8fX1PXXqVL90q3GlZ83Vq1dHjx6dkJAwYcIEz/bYAxfbTk5OjomJKS0tra2tPXbs2AcffOD5Vh1c6fno0aMPPPDA7373u88///zo0aMPP/zw3Llz+6VbzcGDB1evXv2nP/1JCFFcXNzjGNlehq703I8vQzLpO86dOyeEqKqq0lYrKyuFEBcuXLj9o44fPy6EuNVb1V3yxBNPPPvss47VsWPHZmdndxuzcuXKsWPHOlbT09MnT57sof564krP3YwfP37t2rV3ua/bcb3nefPm/fKXv3zppZdkyCRX2v7zn/+sKMrXX3/t2dZuyZWef/vb344ePdqx+tprrw0bNsxD/d3Wbd7fZXsZOtym5248+TLk2N13VFZWKooSExOjrU6ePFlRlG730biZzWbT6XSe/FI+7X4fCQkJji033+9DCFFZWek8JjEx8eTJkx0dHR7q8rtc7NlZV1dXS0tLcHDw3e+uZ673vGPHjs8+++yll17yYHe35GLbb7/99qRJk/Lz83/wgx889NBDWVlZbW1tnu30/7jYc1xcXH19/cGDB1VVvXz58h//+MfZs2W/t7dUL8Ne8PDLsN++F1xOFoslLCzMeUtYWFi3+2h0880332RnZ6empnryyxZdud+HEMJisXQb8+2333711VdDhw71UKNOXOzZ2auvvnrt2rWUlJS7313PXOz5k08+yc7OPnr0qF4vxQvKxbY///zz8vLyAQMGFBcXf/XVV4sXL/7b3/7WXx8pudhzXFzcnj175s2b980333z77bfJycmvv/66Zzv93qR6GfaCh1+G9908ac2aNbpbOHnypPjuTTRET/fRcNbR0TF//vyurq5Nmzbd9dZvcvv7ffQ4Rty0gx7mSs+affv2rVmzZv/+/d3+l+B5t++5s7MzNTV17dq1Dz30kMdbu507/qq7urp0Ot2ePXueeOKJH/3oR+vXry8sLOzHqZJwoedz584tWbLkV7/6VXV1dUlJSW1trVd8VaZsL0PXef5lKMV/6zzp+eefnz9/fo+lkSNHfvjhh5cvX3beeOXKlW730XDo6OhISUmpra393//9Xw9/I70r9/sQQhiNxm5j9Hp9SEiIh7r8Lhd71uzfvz8tLe0Pf/hDfHy8pxrsgSs9t7S0nDx58vTp088//7wQoqurS1VVvV5/6NChf/iHf/B0x0IIl3/VQ4cO/cEPfuC4ccC4ceNUVa2vr4+IiPBcrze42HNeXt6UKVNWrFghhHjkkUcCAgL+/u///te//rXMcw6pXobfS7+8DO+7eVJoaOjYWxgwYEBsbKzNZtPOWRBCHDt2zGaz9XgfDS2QPvnkk8OHD3v+z8vF+33ExsY6jzl06NCkSZN8fX091OV3uX6Pkn379v385z/fu3dvv39U4ErPQUFBH330Uc0Nzz777JgxY2pqahyfSnqei7/qKVOmNDQ0tLa2aqsXL1584IEHhg0b5rlGnbjY8/Xr1x944P/etXx8fMRtb6QtA6lehq7rt5ehZ06l8CKzZs165JFHKisrKysro6KinM8FHzNmzFtvvaWqakdHR3Jy8rBhw2pqahpvsNvtnuxTO3F2+/bt586dy8zMDAgI+OKLL1RVzc7ONpvN2hjtJNQXXnjh3Llz27dv7/eTUF3pee/evXq9/o033nD8Yq9evSp5z84kOe/OlbZbWlqGDRv205/+9OzZs2VlZREREf/6r/8qec87duzQ6/WbNm367LPPysvLJ02a9MQTT/Rjzy0tLadPnz59+rQQYv369adPn9bOv5X5ZehKz/34MiSTuvv6668XLFgQGBgYGBi4YMECq9XqKAkhduzYoapqbW3tzen+3nvvebjVN954Y8SIEX5+fo899lhZWZm2ceHChVOnTnWMef/99ydOnOjn5zdy5MjNmzd7uMOb3bHnqVOndvvFLly4sL+61bjye3aQJJNU19o+f/58fHz8wIEDhw0btmzZsuvXr/dPrze40vNrr702fvz4gQMHDh06dMGCBfX19f3Tq6qqqvree+/1+Ocq88vQlZ778WXIvSoAALK47z5PAgBIi0wCAMiCTAIAyIJMAgDIgkwCAMiCTAIAyIJMAgDIgkwCAMiCTAIAyIJMAgDIgkwCAMiCTAIAyOL/AbsTNaTnFIQdAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -326,38 +336,38 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
-       "      <td>0.852170</td>\n",
-       "      <td>0.837183</td>\n",
+       "      <td>0.847011</td>\n",
+       "      <td>0.831859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Balanced Accuracy</th>\n",
-       "      <td>0.733593</td>\n",
-       "      <td>0.704507</td>\n",
+       "      <td>0.737473</td>\n",
+       "      <td>0.704057</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F1 score</th>\n",
-       "      <td>0.621037</td>\n",
-       "      <td>0.569511</td>\n",
+       "      <td>0.622626</td>\n",
+       "      <td>0.566420</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MCC</th>\n",
-       "      <td>0.557051</td>\n",
-       "      <td>0.504594</td>\n",
+       "      <td>0.544495</td>\n",
+       "      <td>0.489700</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
-       "      <td>0.803368</td>\n",
-       "      <td>0.775354</td>\n",
+       "      <td>0.759862</td>\n",
+       "      <td>0.739658</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
-       "      <td>0.506160</td>\n",
-       "      <td>0.450034</td>\n",
+       "      <td>0.527379</td>\n",
+       "      <td>0.458932</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ROC AUC</th>\n",
-       "      <td>0.907850</td>\n",
-       "      <td>0.894249</td>\n",
+       "      <td>0.902655</td>\n",
+       "      <td>0.888533</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -365,13 +375,13 @@
       ],
       "text/plain": [
        "                   original   updated\n",
-       "Accuracy           0.852170  0.837183\n",
-       "Balanced Accuracy  0.733593  0.704507\n",
-       "F1 score           0.621037  0.569511\n",
-       "MCC                0.557051  0.504594\n",
-       "Precision          0.803368  0.775354\n",
-       "Recall             0.506160  0.450034\n",
-       "ROC AUC            0.907850  0.894249"
+       "Accuracy           0.847011  0.831859\n",
+       "Balanced Accuracy  0.737473  0.704057\n",
+       "F1 score           0.622626  0.566420\n",
+       "MCC                0.544495  0.489700\n",
+       "Precision          0.759862  0.739658\n",
+       "Recall             0.527379  0.458932\n",
+       "ROC AUC            0.902655  0.888533"
       ]
      },
      "execution_count": 12,
@@ -424,43 +434,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Statistical Parity</th>\n",
-       "      <td>0.147951</td>\n",
-       "      <td>0.015993</td>\n",
+       "      <td>0.153123</td>\n",
+       "      <td>0.019611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Predictive Parity</th>\n",
-       "      <td>0.055324</td>\n",
-       "      <td>0.253787</td>\n",
+       "      <td>0.000197</td>\n",
+       "      <td>0.270249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equal Opportunity</th>\n",
-       "      <td>0.120342</td>\n",
-       "      <td>0.297030</td>\n",
+       "      <td>0.100120</td>\n",
+       "      <td>0.262595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in False Negative Rate</th>\n",
-       "      <td>0.120342</td>\n",
-       "      <td>0.297030</td>\n",
+       "      <td>0.100120</td>\n",
+       "      <td>0.262595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equalized Odds</th>\n",
-       "      <td>0.084993</td>\n",
-       "      <td>0.162143</td>\n",
+       "      <td>0.078853</td>\n",
+       "      <td>0.145533</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Conditional Use Accuracy</th>\n",
-       "      <td>0.083531</td>\n",
-       "      <td>0.213775</td>\n",
+       "      <td>0.056217</td>\n",
+       "      <td>0.219375</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Accuracy</th>\n",
-       "      <td>0.112128</td>\n",
-       "      <td>0.117919</td>\n",
+       "      <td>0.114673</td>\n",
+       "      <td>0.108514</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment Equality</th>\n",
-       "      <td>0.163322</td>\n",
-       "      <td>1.458469</td>\n",
+       "      <td>0.123767</td>\n",
+       "      <td>1.587736</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -468,14 +478,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.147951  0.015993\n",
-       "Predictive Parity                                0.055324  0.253787\n",
-       "Equal Opportunity                                0.120342  0.297030\n",
-       "Average Group Difference in False Negative Rate  0.120342  0.297030\n",
-       "Equalized Odds                                   0.084993  0.162143\n",
-       "Conditional Use Accuracy                         0.083531  0.213775\n",
-       "Average Group Difference in Accuracy             0.112128  0.117919\n",
-       "Treatment Equality                               0.163322  1.458469"
+       "Statistical Parity                               0.153123  0.019611\n",
+       "Predictive Parity                                0.000197  0.270249\n",
+       "Equal Opportunity                                0.100120  0.262595\n",
+       "Average Group Difference in False Negative Rate  0.100120  0.262595\n",
+       "Equalized Odds                                   0.078853  0.145533\n",
+       "Conditional Use Accuracy                         0.056217  0.219375\n",
+       "Average Group Difference in Accuracy             0.114673  0.108514\n",
+       "Treatment Equality                               0.123767  1.587736"
       ]
      },
      "execution_count": 13,
@@ -502,7 +512,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -530,7 +540,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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etWrVs2fPHj58GBgYSFcTi8WEEA0Njezs7Hbt2kmeITw8fN68efTr0tJSW1tb1b0paC5qOMeEIiMs6A5Ayed29ba3AEBBKkxgWlpabm5uycnJo0aNovckJyePGDFCqlpFRYWGxj9h8Hg8QghFUY6Ojrdu3WL2L1mypKysbOPGjbXzk7a2tra2tkreA6gNNZxjQsERFugABFAR1XYhzps3b9KkSe7u7p6entu2bcvLy5s5cyYhJDw8/MmTJz/++CMhJDAwcPr06TExMXQXYlhYWM+ePW1sbAghzs7OzKmMjY2l9sB7RT3nmFCwgYUOQABVUG0CCwoKKioqWrFihVAodHZ2PnHihJ2dHSFEKBTm5eXRdYKDg8vKyjZv3jx//nxjY+OBAweuXbtWpVEBGzV6QLmqoYEF0Fw4FNWilggqLS01MjIqKSkxNDRs7lhA+dTqd2AAoFL1fp9jMl9gEzR3AICBBAYsg+dJAEBDAgMle1T6qLy6XCymbj8tfVleaaKn7WxjyOVy9DT17Aztmjs6AGg5kMBAmR6VPhp2aJj03ut//f/xUceRwwBAWZDAoJFkzgpfXl0u5xD5pQAADYIEBtIUWa+krtGAYpnLNf5NfikAQIMggcG/KDJOXc4iWK9E8iZTvv201NlC+TEDwPtJ5SsyA4sosl5JXbPCE0KWH8ssel0p5/wvy+WVAgA0CBIY/EV+ZhL93fsnf1b40jfVci5hoocpKwFAadCF2MLV9UCr9n4F1yuRP++7oY6mnFJnG0yPAgBKgwTWktX1QEvm/soasZxTMXlL/rzvZvry2lhczJoBAMqDLsQWq64HWlEnMmXuf/hC3hh3Jm/Rs8LXTkQcQgRG/J528qYl1NPUa+CbAACoEybzbZlEYqrP2hSZXYKSi9kzOIRYGWoTwnlWKnu9kt8XDmS6H+nUSGrNCh8z0TXAWYCZOABAKTCZ73uqrgdahMjIXoQQipCC0sq5gzp+d/puveuVyF8Ei8lSGDEPACqFBMZudY3RkD/Uoi725rqKLM9IMCs8AKgBJDAWk/OjY/lDLepiacD3bGemYGbCrPAA0LyQwNhKznQYAc4CeqhFQYn0Ay1CCJdDKIrIfNDV08GUIDMBAEtgFCIr1fujYx6XExHoRP5+iEXjEMIhZLq3Q+39pNaDLgAANYcExkr1/uiY/D3Uwtron75EayN+zETX8CFOMvfXftAFAKDO0IXISvLHaDCldQ21wBAMAGgBkMDUUb0LmsgfoyFZWtcDLTzoAgC2QwJTO4osaFLXGA3JsRgAAC0bnoGpF0UWNCGE1DVGg2AsBgC8N5DA1IiCC5rQ6hqjgbEYAPCeQBeiGlFwQRMGxmIAwPsMCUyNKDi2UBLGYgDAewtdiGpE8bGFAACABKZG6llqC2MLAQAkIIGpEYwtBABQHBKYesHYQgAABWEQhwqJxFRqTtHlBy8I4Xi2M+vV1kyRVhTGFgIAKAIJTFWSbgsXHbz1qqKa3tx89r6xruaa0V0UaUthbCEAQL3QhagSSbeFM3ddY7IX7VVF9cxac2oAAEDjIIEpn0hMRR69U1dp7Tk1AACgEZDAlC8tt7igtLKuUma9LgAAeBdIYMp3OrNAfgX5M24AAIAikMCUTCSmDl1/Ir8O5tQAAHh3GIWoBJLrT4opqri8Wk5lzKkBAKAUSGDvSmr9SWMdTfn1MacGAIBSIIG9E3r9Sckxha/eyGt+zR3UEXNqAAAoBZ6BNZ7M9SflsDbUnj2wvQoDAgB4n6AF1nh1rT9ZG91jGDn8A3QeAgAoC1pgjSd/uLzkwzBMyAsAoHRogTVS0m1h3MWHcir8MN6Vy+VgQl4AABVBAmsM+ulXXaUcQqyN+L3aKTT3PAAANA66EBtD/tMvCmPlAQBUDwmsMeQ//frIyx6PuwAAVA0JrMHqffrl62TdVLEAALy/8AysYRR5+oWZogAAmgBaYA2Dp18AAGoCCaxh5K+EgqdfAABNBgmsYeSvhIKnXwAATQYJrGF6OpgKjPi1uwg5WCcFAKBpIYHJIxJTl3OKjlx/cjmnSCSmCCE8Lici0In8Pb0hjX6Np18AAE0JoxDrJLXQl8CIHxHoFOAsCHAWxEx0lSyy/ruo+YIFAHjvcChK8fVAWKC0tNTIyKikpMTQ0PBdzlN7oS+6bcXMySu5CjOmOgQAULp6v8/RApNB5kJfFCEcQpYfy/R1suZxOTwux7OdWfPEBwAAeAYmU10/9qIIEZa8TcstbvqQAABAChKYDPJ/7CW/FAAAmgYSmAzyf+wlvxQAAJoGEpgM9I+9ZBbhx14AAGoCCUwGHpfj3Er2oJfhXQUYcAgAoA6QwGQ4cfNpcmahzKKjN4T0L5oBAKB5IYFJE4mpJUdu11WKUYgAAGoCCUxaWm5xcXm1nAoYhQgAoA6QwKTVm58wChEAQB0ggUmTn5/M9LQwChEAQB2oPIFFR0c7ODjw+Xw3N7cLFy7IrLN79+6uXbvq6uoKBIKpU6cWFRXR+2NjY729vU1MTExMTAYNGpSWlqbqaAkhPR1MjXU16ypdOcIZoxABANSBahNYYmJiWFjY4sWLMzIyvL29Bw8enJeXJ1Xn999/nzx5ckhIyJ07d/bv33/lypVp06bRRefOnfvwww/Pnj17+fLlNm3a+Pn5PXnyRKUBy6erxfN3xpKVAABqQbWz0Xt4eLi6usbExNCbnTt3HjlyZFRUlGSdr7/+OiYmJicnh97ctGnTunXr8vPzpU4lEolMTEw2b948efJkOVd899noL+cUfRibWlfp3um9MIcvAEATqPf7XIUtsKqqqvT0dD8/P2aPn5/fpUuXpKr17t378ePHJ06coCjq2bNnBw4cGDp0aO2zVVRUVFdXm5rKeP5UWVlZKuEdw8ZEiAAArKDCBPbixQuRSGRlZcXssbKyKigokKrWu3fv3bt3BwUFaWlpWVtbGxsbb9q0qfbZFi1a1KpVq0GDBtUuioqKMvqbra3tO4aNiRABAFhB5YM4OJx/hjxQFCW5ScvMzJwzZ86yZcvS09OTkpJyc3NnzpwpVWfdunV79+49ePAgny8jf4SHh5f8rXbfY0PREyHWHqfBwUSIAADqRIULWpqbm/N4PMkmV2FhoWSDjBYVFeXl5fX5558TQlxcXPT09Ly9vVetWiUQCOgKX3/99erVq0+fPu3i4iLzQtra2tra2soKm8flRAQ6fbLrGocQ5vEgnc8iAp0wBBEAQE2osAWmpaXl5uaWnJzM7ElOTu7du7dUtYqKCi73nzB4PB4hhBlasn79+pUrVyYlJbm7u6suVEkiMWWkozXVy95ET4vZaW3Ej5noGuAsaJoYAACgXipsgRFC5s2bN2nSJHd3d09Pz23btuXl5dHdg+Hh4U+ePPnxxx8JIYGBgdOnT4+JifH39xcKhWFhYT179rSxsSGErFu3bunSpXv27LG3t6dbcvr6+vr6+qoLOOm2cPmxTGY5ZlM9zVHdWg1ysu7pYIq2FwCAWlFtAgsKCioqKlqxYoVQKHR2dj5x4oSdnR0hRCgUMj8ICw4OLisr27x58/z5842NjQcOHLh27Vq6KDo6uqqqasyYMcwJIyIiIiMjVRRt0m3hJ7uuSf6q4GV59faLD3sgewEAqB/V/g6s6TX6d2AiMdVnbQrT9mJwCLE24v++cCByGABAU2rO34GxS1puce3sRQihsIQKAIBaQgL7C36/DADALkhgf8HvlwEA2AUJ7C/4/TIAALsggf2F/v0y+fs3yzT8fhkAQG0hgf0jwFkQM9HV2uif3kL8fhkAQG2p9ndgrBPgLPB1sk7LLS4se2tpwMfvlwEA1BYSmDQel4MVvwAA1B+6EAEAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJWQwAAAgJUUSmD29vYrVqzIy8tTdTQAAAAKUiiBzZ8//8iRI23btvX19d23b19lZaWqwwIAAJBPoQT22Wefpaenp6enOzk5zZkzRyAQzJ49+9q1a6oODgAAoC4ciqIadEB1dXV0dPTChQurq6udnZ1DQ0OnTp3K4XBUFF9DlZaWGhkZlZSUGBoaNncsAADQePV+n2sofq7q6upDhw7Fx8cnJyf36tUrJCTk6dOnixcvPn369J49e5QUMAAAgEIUSmDXrl2Lj4/fu3cvj8ebNGnShg0bHB0d6SI/P7++ffuqMkIAAAAZFEpgPXr08PX1jYmJGTlypKampmSRk5PTuHHjVBMbAABAnRRKYA8ePLCzs5NZpKenFx8fr9SQAAAA6qfQKMTCwsI//vhDcs8ff/xx9epV1YQEAABQP4US2KxZs/Lz8yX3PHnyZNasWaoJCQAAoH4KJbDMzExXV1fJPd27d8/MzFRNSAAAAPVTKIFpa2s/e/ZMco9QKNTQaMAQfAAAAOVSKIH5+vqGh4eXlJTQm69evfryyy99fX1VGRgAAIA8CrWivvnmm759+9rZ2XXv3p0Qcv36dSsrq507d6o4NgAAgDoplMBatWp18+bN3bt337hxQ0dHZ+rUqR9++KHUD8IAAACakqLPsfT09D7++GOVhgIAAKC4BgzEyMzMzMvLq6qqYvYMHz5cBSEBAADUT9GZOEaNGnXr1i0O56/Z6+np50UikWqjAwAAqINCoxBDQ0MdHByePXumq6t7586d3377zd3d/dy5cyqODQAAoE4KtcAuX76ckpJiYWHB5XK5XG6fPn2ioqLmzJmTkZGh6vgAAABkUqgFJhKJ9PX1CSHm5uZPnz4lhNjZ2WVnZ6s2NAAAgLop1AJzdna+efNm27ZtPTw81q1bp6WltW3btrZt26o6OAAAgLoolMCWLFlSXl5OCFm1atWwYcO8vb3NzMwSExNVHBsAAECd/hpV2CDFxcUmJib0QER1U1paamRkVFJSYmho2NyxAABA49X7fV7/M7CamhoNDY3bt28ze0xNTdUzewEAwPuj/gSmoaFhZ2eHn3wBAIBaUWgU4pIlS8LDw4uLi1UdDQAAgIIUGsTx/fff379/38bGxs7OTk9Pj9l/7do1lQUGAAAgj0IJbOTIkSoOAwAAoGEaMwpRnWEUIgBAy6CEUYgAAABqSKEuRC6XK3PcPIYmAgBAc1EogR06dIh5XV1dnZGRsWPHjuXLl6ssKgAAgHo08hnYnj17EhMTjxw5ovSA3hGegQEAtAyqegbm4eFx+vTpdwgMAADgnTQmgb1582bTpk2tW7dWejQAAAAKUugZmOTUvRRFlZWV6erq7tq1S5WBAQAAyKNQAtuwYQOTwLhcroWFhYeHh4mJiSoDAwAAkEehBBYcHKziMAAAABpGoWdg8fHx+/fvl9yzf//+HTt2qCYkAACA+imUwNasWWNubi65x9LScvXq1YocGx0d7eDgwOfz3dzcLly4ILPO7t27u3btqqurKxAIpk6dWlRUxBT9/PPPTk5O2traTk5Okj9HAwCA95xCCezRo0cODg6Se+zs7PLy8uo9MDExMSwsbPHixRkZGd7e3oMHD6591O+//z558uSQkJA7d+7s37//ypUr06ZNo4suX74cFBQ0adKkGzduTJo0aezYsX/88Ydi7wsAAFo6SgG2trZHjhyR3HP48OFWrVrVe2DPnj1nzpzJbDo6Oi5atEiqzvr169u2bctsfv/9961bt6Zfjx07NiAggCny9/cfN26c/CuWlJQQQkpKSuqNDQAA1Fm93+cKtcDGjRs3Z86cs2fPikQikUiUkpISGho6btw4+UdVVVWlp6f7+fkxe/z8/C5duiRVrXfv3o8fPz5x4gRFUc+ePTtw4MDQoUPposuXL0se7u/vX/twAAB4Pyk0CnHVqlWPHj3y8fHR0NAghIjF4smTJ9f7DOzFixcikcjKyorZY2VlVVBQIFWtd+/eu3fvDgoKevv2bU1NzfDhwzdt2kQXFRQU1Hs4IaSysrKyspJ+XVpaqsg7AgAAtlOoBaalpZWYmJidnb179+6DBw/m5ORs375dS0tLkWMlp7GnKKr2rPaZmZlz5sxZtmxZenp6UlJSbm7uzJkzFT+cEBIVFWX0N1tbW0WiAgAAtlOoBUbr0KFDhw4dFK9vbm7O4/Ek20yFhYWSLSpaVFSUl5fX559/TghxcXHR09Pz9vZetWqVQCCwtrau93BCSHh4+Lx58+jXpaWlyGEAAO8DhVpgY8aMWbNmjeSe9evX//e//5V/lJaWlpubW3JyMrMnOTm5d+/eUtUqKiq43H/C4PF4hBCKogghnp6ekoefOnWq9uGEEG1tbUMJirwjAABgPUWGgpibm9+8eVNyz82bNy0tLes9cN++fZqamnFxcZmZmWFhYXp6eg8fPqQoatGiRZMmTaLrxMfHa2hoREdH5+Tk/P777+7u7j179qSLLl68yOPx1qxZk5WVtWbNGg0NjdTUVPlXxChEAICWod7vc4W6EF+/fi31xEtTU1OR4RJBQUFFRUUrVqwQCoXOzs4nTpyws7MjhAiFQuYHYcHBwWVlZZs3b54/f76xsfHAgQPXrl1LF/Xu3Xvfvn1LlixZunRpu3btEhMTPTw8GpKdAQCgxVJoQcsePXoEBgYuW7aM2RMZGXns2LH09HRVxtYYWNASAKBlqPf7XKEW2NKlS//zn//k5OQMHDiQEHLmzJk9e/YcOHBAmZECAAA0hEIJbPjw4YcPH169evWBAwd0dHS6du2akpKCJg4AADQjhboQJb169Wr37t1xcXE3btwQiUQqCqvR0IUIANAy1Pt9rtAwelpKSsrEiRNtbGw2b948ZMiQq1evKilIAACABqu/C/Hx48cJCQnbt28vLy8fO3ZsdXU1vcRJEwQHAABQl3paYEOGDHFycsrMzNy0adPTp0+ZWQoBAACaVz0tsFOnTs2ZM+eTTz5p0CRSAAAAqlZPC+zChQtlZWXu7u4eHh6bN29+/vx504QFAAAgXz0JzNPTMzY2VigUzpgxY9++fa1atRKLxcnJyWVlZU0THwAAgEwNG0afnZ0dFxe3c+fOV69e+fr6Hj16VHWRNQ6G0QMAtAzKHEZPCOnUqdO6deseP368d+9eZYQHAADQSA3+IbOaQwsMAKBlUHILDAAAQE0ggQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACupPIFFR0c7ODjw+Xw3N7cLFy7UrhAcHMz5tw8++IAp/e677zp16qSjo2Nrazt37ty3b9+qOmAAAGAF1SawxMTEsLCwxYsXZ2RkeHt7Dx48OC8vT6rOxo0bhX/Lz883NTX973//Sxft3r170aJFERERWVlZcXFxiYmJ4eHhKg0YAADYgkNRlOrO7uHh4erqGhMTQ2927tx55MiRUVFRddU/fPjw6NGjc3Nz7ezsCCGzZ8/Oyso6c+YMXTp//vy0tDSZzThGaWmpkZFRSUmJoaGh8t4HAAA0tXq/z1XYAquqqkpPT/fz82P2+Pn5Xbp0Sc4hcXFxgwYNorMXIaRPnz7p6elpaWmEkAcPHpw4cWLo0KG1j6qsrCyVoNQ3AQAAakpDdad+8eKFSCSysrJi9lhZWRUUFNRVXygU/vrrr3v27GH2jBs37vnz53369KEoqqam5pNPPlm0aFHtA6OiopYvX67c4AEAQM2pfBAHh8NhXlMUJbkpJSEhwdjYeOTIkcyec+fOffXVV9HR0deuXTt48ODx48dXrlxZ+8Dw8PCSv+Xn5ys1fAAAUFMqbIGZm5vzeDzJJldhYaFkg0wSRVHbt2+fNGmSlpYWs3Pp0qWTJk2aNm0aIaRLly7l5eUff/zx4sWLudx/5V1tbW1tbW3VvAkAAFBTKmyBaWlpubm5JScnM3uSk5N79+4ts/L58+fv378fEhIiubOiokIyV/F4PIqiVDrqBAAA2EKFLTBCyLx58yZNmuTu7u7p6blt27a8vLyZM2cSQsLDw588efLjjz8yNePi4jw8PJydnSUPDwwM/Pbbb7t37+7h4XH//v2lS5cOHz6cx+OpNGYAAGAF1SawoKCgoqKiFStWCIVCZ2fnEydO0CMMhUKh5A/CSkpKfv75540bN0odvmTJEg6Hs2TJkidPnlhYWAQGBn711VcqDRgAANhCtb8Da3r4HRgAQMvQnL8DAwAAUB0kMAAAYCUkMAAAYCUkMAAAYCUkMAAAYCUkMAAAYCUkMAAAYCUkMAAAYCUkMAAAYCUkMAAAYCXVzoUIACCHSCSqrq5u7iig+WlpaUmtk6UIJDAAaAYURRUUFLx69aq5AwG1wOVyHRwcJNeDVAQSGAA0Azp7WVpa6urqylmoHd4HYrH46dOnQqGwTZs2DboZkMAAoKmJRCI6e5mZmTV3LKAWLCwsnj59WlNTo6mpqfhRGMQBAE2Nfu6lq6vb3IGAuqA7D0UiUYOOQgIDgOaBnkNgNO5mQAIDAABWQgIDAFC5yMjIbt26NXcULQ0SGAC814KDgzn/dv/+/Xc/LYfDOXz4MLO5YMGCM2fOvPtpQRJGIQIAa4jEVFpucWHZW0sDfk8HUx5XOU/RAgIC4uPjmU0LCwvmdVVVVUN/nCSTvr6+vr5+gw6prq5u0JC89xBaYADADkm3hX3WpnwYmxq67/qHsal91qYk3RYq5cza2trWEnx8fGbPnj1v3jxzc3NfX19CyPnz53v27KmtrS0QCBYtWlRTU0Mf2L9//zlz5nzxxRempqbW1taRkZH0fnt7e0LIqFGjOBwO/VqqCzE+Pr5z5858Pt/R0TE6Opre+fDhQw6H89NPP/Xv35/P5+/atUsp764FQwIDABZIui38ZNc1YclbZk9BydtPdl1TVg6TsmPHDg0NjYsXL27duvXJkydDhgzp0aPHjRs3YmJi4uLiVq1aJVlTT0/vjz/+WLdu3YoVK5KTkwkhV65cIYTEx8cLhUL6taTY2NjFixd/9dVXWVlZq1evXrp06Y4dO5jShQsXzpkzJysry9/fXxVvrSVBFyIAqDuRmFp+LJP6906KEA4hy49l+jpZv2Nf4vHjx5n+vcGDBxNC2rdvv27dOnrP4sWLbW1tN2/ezOFwHB0dnz59unDhwmXLltFz97m4uERERBBCOnTosHnz5jNnzvj6+tKdkMbGxtbW1rUvt3Llym+++Wb06NGEEAcHh8zMzK1bt06ZMoUuDQsLo4ugXkhgAKDu0nKLJdteDIoQYcnbtNxiz3bvNKPHgAEDYmJi6Nd6enoffvihu7s7U5qVleXp6cn8UMnLy+v169ePHz9u06YNIcTFxYWpKRAICgsL5V/r+fPn+fn5ISEh06dPp/fU1NQYGRkxFSQvDfIhgQGAuissk5G9FCxVhJ6eXvv27aX2MK8pipL8mS1FUUTih7eS4yw4HI5YLJZ/LbpCbGysh4cHs5PH48m8NMiHBAYA6s7SgN/o0nfn5OT0888/M2ns0qVLBgYGrVq1kn+UpqamzImRrKysWrVq9eDBgwkTJqgk3PcJBnEAgLrr6WAqMOLXfszFIURgxO/pYKrSq3/66af5+fmfffbZn3/+eeTIkYiIiHnz5tW7eJW9vf2ZM2cKCgpevnwpVRQZGRkVFbVx48a7d+/eunUrPj7+22+/VVn4LRkSGACoOx6XExHoRAiRzGH064hAJ2X9GqwurVq1OnHiRFpaWteuXWfOnBkSErJkyZJ6j/rmm2+Sk5NtbW27d+8uVTRt2rT//e9/CQkJXbp06devX0JCgoODg2pib+E4dH9ui1FaWmpkZFRSUmJoaNjcsQCAbG/fvs3NzXVwcODzG9D7l3RbuPxYJjOaQ2DEjwh0CnAWqCZGaFIyb4l6v8/xDAwA2CHAWeDrZK2KmTiApZDAAIA1eFzOO46Yh5YEz8AAAICVkMAAAICVkMAAAICVkMAAAICVkMAAAICVkMAAAICVkMAAABqAXnby+vXr8qslJCQYGxur+irKwuFwDh8+3PTXfUdIYADwvqPXN7GxsdHS0rKzswsNDS0qKqqrsq2trVAodHZ2ln/OoKCgu3fvKjtSlVPk3QUHB48cObKpIpIHCQwA3msPHjxwd3e/e/fu3r1779+/v2XLljNnznh6ehYXF9euXFVVxePxrK2tNTTqmQVCR0fH0tJSNSErpLq6uhFHKfjuGqGqqkrp50QCAwBWqX5DfvuaVL9R1vlmzZqlpaV16tSpfv36tWnTZvDgwadPn37y5MnixYvpCvb29qtWrQoODjYyMpo+fbpUJ9vRo0c7dOigo6MzYMCAHTt2cDicV69ekX93IUZGRnbr1m3nzp329vZGRkbjxo0rKyuji5KSkvr06WNsbGxmZjZs2LCcnJx6A7a3t1+5cuX48eP19fVtbGw2bdrEFHE4nC1btowYMUJPT2/VqlWEkGPHjrm5ufH5/LZt2y5fvrympoauee/evb59+/L5fCcnp+TkZOYMUu/uzp07Q4cONTQ0NDAw8Pb2zsnJiYyM3LFjx5EjRzgcDofDOXfuHCHk1q1bAwcO1NHRMTMz+/jjj1+/fk0fTrfVoqKibGxsOnbs2OA/m/oggQEAq2QeJSkrSdYxpZysuLj45MmTn376qY6ODrPT2tp6woQJiYmJzFzn69evd3Z2Tk9PX7p0qeThDx8+HDNmzMiRI69fvz5jxgwm59WWk5Nz+PDh48ePHz9+/Pz582vWrKH3l5eXz5s378qVK2fOnOFyuaNGjap3SUw6HhcXl2vXroWHh8+dO1cyA0VERIwYMeLWrVsfffTRyZMnJ06cOGfOnMzMzK1btyYkJHz11VeEELFYPHr0aB6Pl5qaumXLloULF8q8ypMnT+gkl5KSkp6e/tFHH9XU1CxYsGDs2LEBAQFCoVAoFPbu3buioiIgIMDExOTKlSv79+8/ffr07NmzmZOcOXMmKysrOTn5+PHj9b6vBqNalpKSEkJISUlJcwcCAHV68+ZNZmbmmzdvGnPw7iAqwpDaM04pkaSmphJCDh06JLWfXqDr2bNnFEXZ2dmNHDmSKcrNzSWEZGRkUBS1cOFCZ2dnpohOYC9fvqQoKj4+3sjIiN4fERGhq6tbWlpKb37++eceHh61gyksLCSE3Lp1S+oqUuzs7AICApjNoKCgwYMH068JIWFhYUyRt7f36tWrmc2dO3cKBAKKok6ePMnj8fLz8+n9v/76K/MhSF43PDzcwcGhqqpKKoApU6aMGDGC2dy2bZuJicnr16/pzV9++YXL5RYUFNA1raysKisra78LKTJviXq/zzGZLwCovdeF5PIPRFxDCCE5Zwgh5P5pcnIxIYRwNYjnbKJvodwL0smAXoKZEOLu7i6zWnZ2do8ePZjNnj171nVCe3t7AwMD+rVAIKBzFSEkJydn6dKlqampL168oNteeXl59Y4Q8fT0lHz93XffMZuSoaanp1+5coVudRFCRCLR27dvKyoqsrKy2rRp07p169pnk3T9+nVvb29NTU35wWRlZXXt2lVPT4/e9PLyEovF2dnZVlZWhJAuXbpoaWnJP0OjIYEBgNorKyCpMURUSTjcv1ayFItIajShxISnTZz/0+gE1r59ew6Hk5mZKTWs7s8//zQxMTE3N6c3mW9nKRRFMUmO/J32ZJJMAxwOh+knDAwMtLW1jY2NtbGxEYvFzs7OjRjsIBmDZKhisXj58uWjR4+WrMzn86XilDxckmS3qhxSH4LUOev66JQCz8AAQO0JXMiM88S8IyGEUKJ//te8I5lxnghcGn1iMzMzX1/f6OjoN2/+GRVSUFCwe/fuoKCgur7ZGY6OjleuXGE2r1692qCrFxUVZWVlLVmyxMfHp3Pnzi9fvlTwQLrnk3nt6Ogos5qrq2t2dnb7f+NyuU5OTnl5eU+fPqWrXb58WebhLi4uFy5cqD2aUUtLSyQSMZtOTk7Xr18vLy+nNy9evMjlclUxZKM2JDAAYAPLziTklPTOkFPEsvM7nnjz5s2VlZX+/v6//fZbfn5+UlKSr69vq1atmJ43OWbMmPHnn38uXLjw7t27P/30U0JCAqm7QVObiYmJmZnZtm3b7t+/n5KSMm/ePAUPvHjx4rp16+7evfvDDz/s378/NDRUZrVly5b9+OOPkZGRd+7cycrKSkxMXLJkCSFk0KBBnTp1mjx58o0bNy5cuFDX2JPZs2eXlpaOGzfu6tWr9+7d27lzZ3Z2NiHE3t7+5s2b2dnZL168qK6unjBhAp/PnzJlyu3bt8+ePfvZZ59NmjSJ7j9UNSQwAGCJR5cIJTFCjxKTR7KbDg3SoUOHq1evtmvXLigoqF27dh9//PGAAQMuX75sampa77EODg4HDhw4ePCgi4tLTEwMnQm0tbUVvDSXy923b196erqzs/PcuXPXr1+v4IHz589PT0/v3r37ypUrv/nmG39/f5nV/P39jx8/npyc3KNHj169en377bd2dnb0dQ8dOlRZWdmzZ89p06bVlarNzMxSUlJev37dr18/Nze32NhYuiN0+vTpnTp1cnd3t7CwuHjxoq6u7smTJ4uLi3v06DFmzBgfH5/Nmzcr+EbeEUdOpy0blZaWGhkZlZSUGBoaNncsACDb27dvc3NzHRwc+Hx+Aw47NIPc2EfaeBLfleTUEpKfSrp+SEZtUVmYDfbVV19t2bIlPz9fpVext7cPCwsLCwtT6VWamMxbot7vcwziAACWEHQnFp1J788Il0emniCXNhGNhuQ/1YiOju7Ro4eZmdnFixfXr18v+RMoUDUkMABgiV4z/3nN5ZE+Yc0WiYR79+6tWrWquLi4TZs28+fPDw8Pb+6I3iNIYAAAjbdhw4YNGzY05RUfPnzYlJdTZxjEAQAArIQEBgAArIQEBgAArIQEBgAArIQEBgAArIQEBgAArIQEBgCgBPTqwyo6udRCyarG4XAOHz7c9NdtKCQwAHiv9e/fX2papsOHDys+IW+DqHk+qM3W1lYoFMpfn0ylmVs+JDAAgJav9qooiuDxeNbW1hoayp/yohHLntWGBCZNJKYu5xQduf7kck6RSNyiZjoGYK9HpY8yizJr//eo9JFKrxsZGdmtW7etW7fa2trq6ur+97//ffXqFV0kEonmzZtnbGxsZmb2xRdfSE6MnpSU1KdPH7po2LBhOTk59H4HBwdCSPfu3TkcTv/+/emd8fHxnTt35vP5jo6O0dHRzEnS0tK6d+/O5/Pd3d0zMjLqitDe3n7lypXjx4/X19e3sbHZtGkTU8ThcLZs2TJixAg9Pb1Vq1YRQo4dO+bm5sbn89u2bbt8+fKamhq65r179/r27cvn852cnJKTk5kzSDUZ79y5M3ToUENDQwMDA29v75ycnMjIyB07dhw5coTD4XA4nHPnzhFCbt26NXDgQB0dHTMzs48//vj169f04XRbLSoqysbGRikLhmEqqX9Jui2MPJpZUPqW3rQ25EcOdwpwFjRvVADvuUelj4YdGlZX6fFRx+0M7VR39fv37//000/Hjh0rLS0NCQmZNWvW7t27CSHffPPN9u3b4+LinJycvvnmm0OHDg0cOJA+pLy8fN68eV26dCkvL1+2bNmoUaOuX7/O5XLT0tJ69ux5+vTpDz74QEtLixASGxsbERGxefPm7t27Z2RkTJ8+XU9Pb8qUKeXl5cOGDRs4cOCuXbtyc3PrWvGLtn79+i+//DIyMvLkyZNz5851dHT09fWliyIiIqKiojZs2MDj8U6ePDlx4sTvv/+ezj0ff/wxXUEsFo8ePdrc3Dw1NbW0tLSuee6fPHnSt2/f/v37p6SkGBoaXrx4saamZsGCBVlZWaWlpfHx8YQQU1PTioqKgICAXr16XblypbCwcNq0abNnz6ZXSiOEnDlzxtDQMDk5WTkLoVAtS0lJCSGkpKSkEcf+euup3cLjtf/79dZTpccJ8D578+ZNZmbmmzdvFKx/58Ud5wTnuv678+LOuwTTr1+/0NBQyT2HDh1ivhsjIiJ4PF5+fj69+euvv3K5XKFQSFGUQCBYs2YNvb+6urp169YjRoyoff7CwkJCyK1btyiKys3NJYRkZGQwpba2tnv27GE2V65c6enpSVHU1q1bTU1Ny8vL6f0xMTFSBzLs7OwCAgKYzaCgoMGDB9OvCSFhYWFMkbe39+rVq5nNnTt3CgQCiqJOnjwp9R4JIYcOHZIKODw83MHBoaqqSiqAKVOmSL7xbdu2mZiYvH79mt785ZdfuFxuQUEBXdPKyqqysrL2u5B5S9T7fY4uxL+IxNSig7dkFoUfvIW+RID3Vps2bVq3bk2/9vT0FIvF2dnZJSUlQqHQ09OT3q+hoeHu7s4ckpOTM378+LZt2xoaGtLdhnl5ebXP/Pz58/z8/JCQEP2/rVq1iu5vzMrK6tq1q66uLnNdORFKlnp6emZlZTGbklGlp6evWLGCudb06dOFQmFFRUVWVpbUe5R5levXr3t7e9NrWspBR66np0dvenl50Z8YvdmlSxe66akU6EL8S+qDolcVsh9yvqyoTn1Q5NXevIlDAoAmYGhoSP9Ln/Hq1au6VlCkRyfWO0YxMDDQ1tY2NjbWxsZGLBY7OzvLHLMgFosJIbGxsR4eHsxOHo9HCKHeoYdNMjwmkdCXW758+ejRoyUr8/l8qWvV9e50dHQUuTpFUbXPwOyRjOfdqbwFFh0dTS+y6ebmduHChdoVgoODOf/2wQcfMKWvXr2aNWuWQCDg8/mdO3c+ceKEiuK8lPOi0aUAwF6Ojo5Xr16V3HPlypVOnToxm3l5eU+fPqVfX758mcvlduzY0cjISCAQpKam0vtramrS09Pp10VFRVlZWUuWLPHx8encufPLly+ZU9GND5FIRG9aWVm1atXqwYMH7SXQLTYnJ6cbN268efOGrslcSCbJ0tTUVEdHR5nVXF1ds7Oz2/8bl8t1cnKSeo8yD3dxcblw4ULt0YxaWlrMO6Ijv379enl5Ob158eJF+hOTE3+jqTaBJSYmhoWFLV68OCMjw9vbe/DgwbXb0Rs3bhT+LT8/39TU9L///S9dVFVV5evr+/DhwwMHDmRnZ8fGxrZq1UpFoT55+abRpQDAXp9++mlOTs6sWbNu3Lhx9+7dH374IS4u7vPPP2cq8Pn8KVOm3Lhx48KFC3PmzBk7dqy1tTUhJDQ0dM2aNYcOHfrzzz8//fRTZnSiiYmJmZnZtm3b7t+/n5KSMm/ePOZUlpaWOjo6SUlJz549o5t9kZGRUVFRGzduvHv37q1bt+Lj47/99ltCyPjx47lcbkhISGZm5okTJ77++ms5b+HixYvr1q2jg9+/f39dIz6WLVv2448/RkZG3rlzJysrKzExccmSJYSQQYMGderUafLkyfR7XLx4sczDZ8+eXVpaOm7cuKtXr967d2/nzp10x6C9vf3Nmzezs7NfvHhRXV09YcIE+hO7ffv22bNnP/vss0mTJllZWTXgj0Rhqk1g3377bUhIyLRp0zp37vzdd9/Z2trSjyIlGRkZWf/t6tWrL1++nDp1Kl20ffv24uLiw4cPe3l52dnZ9enTp2vXrioK1cZY3trk8ksBgL3s7e0vXLiQk5Pj5+fXo0ePhISEhIQE5p/RhJD27duPHj16yJAhfn5+zs7OzEj3+fPnT548OTg42NPT08DAYNSoUfR+Lpe7b9++9PR0Z2fnuXPnrl+/njmVhobG999/v3XrVhsbmxEjRhBCpk2b9r///S8hIaFLly79+vVLSEigW2D6+vrHjh3LzMzs3r374sWL165dK+ctzJ8/Pz09vXv37itXrvzmm2/8/f1lVvP39z9+/HhycnKPHj169er17bff2tnZ0QEfOnSosrKyZ8+e06ZN++qrr2QebmZmlpKS8vr16379+rm5ucXGxtLPw6ZPn96pUyd3d3cLC4uLFy/q6uqePHmyuLi4R48eY8aM8fHx2bx5s8J/Gg3DeZeeVvmqqqp0dXX379/P/LmGhoZev379/PnzdR0SGBhYWVl56tQpenPIkCGmpqa6urpHjhyxsLAYP378woUL6Q7iupSWlhoZGZWUlNTVhV2Xi/deTIj7o67S3SEeXh3wDAxAOd6+fZubm0s/XFCkfmZRZtDxoLpKE4clOpk5KS+6f4mMjDx8+LA6z51hb28fFhZW19h3tpB5S9T7fa7CQRwvXrwQiUSSLUcrK6uCgoK66guFwl9//XXPnj3MngcPHqSkpEyYMOHEiRP37t2bNWtWTU3NsmXLpA6srKysrKykX5eWljYu2l7tzIx1NWWO4zDW1ezVzqxxpwWAd6enKe/Jv/xSaMFUPgpRcjiKzNEpjISEBGNjY8k5tcRisaWl5bZt23g8npub29OnT9evX187gUVFRS1fvvwd4+RxOWtGd5m561rtojWju/C4KpkYDQAUYWdod3zU8fLq8tpFepp6Kv0VM6gzFSYwc3NzHo8n2eQqLCys61EeRVHbt2+fNGmS5E8EBAKBpqYm02fYuXPngoKCqqoqqZ8RhIeHM49JS0tLbW1tGxdwgLNgy0TXyKN3Ckr/as9ZG2pHDv8AM3EANLvmylKRkZGRkZHNcmkFPXz4sLlDaDYqTGBaWlpubm7JycnMM7Dk5GT6uWVt58+fv3//fkhIiOROLy+vPXv2iMViLpdLCLl7965AIKj9IzhtbW1tbW2lxBzgLPB1sk7LLS4se2tpwO/pYIq2FwCAelLtKMR58+b973//2759e1ZW1ty5c/Py8mbOnEkICQ8Pnzx5smTNuLg4Dw8PqUn7P/nkk6KiotDQ0Lt37/7yyy+rV6+eNWuWSgMmhPC4HM92ZiO6tfJsZ4bsBQCgtlT7DCwoKKioqGjFihX0ijInTpygR20KhULJH4SVlJT8/PPPGzdulDrc1tb21KlTc+fOdXFxadWqVWho6MKFC1UaMAA0GdUNgQbWadzNoMJh9M2i0cPoAaDJiESiu3fvWlpamplhfC8QQkhJScnTp0/bt28vOddicw6jBwCQicfjGRsb09O06+rqqmj5Y2ALsVj8/PlzXV3dhq6ciQQGAM2Ano2JzmEAXC63TZs2Df2nDBIYADQDDocjEAgsLS0bt9Q9tDBaWlr0aPMGQQIDgGbD4/HkTw4HIAcWtAQAAFZCAgMAAFZCAgMAAFZqac/A6J+1NXpOegAAUBP0N7mcHyu3tARWVlZGCGn0fL4AAKBWysrKjIyMZBa1tJk4xGLx06dPDQwM3uWnkfSU9vn5+Wo+nQfiVC62xEnYEyriVC62xEmUFCpFUWVlZTY2NnWNsG9pLTAul9u6dWulnMrQ0FD97xKCOJWNLXES9oSKOJWLLXESZYRaV9uLhkEcAADASkhgAADASkhgMmhra0dERChrkUzVQZzKxZY4CXtCRZzKxZY4SVOF2tIGcQAAwHsCLTAAAGAlJDAAAGAlJDAAAGAlJDAAAGCl9yKBRUdHOzg48Pl8Nze3CxcuyKxz/vx5Nzc3Pp/ftm3bLVu2SBb9/PPPTk5O2traTk5Ohw4dauiZmybO2NhYb29vExMTExOTQYMGpaWlMUWRkZEcCfRKuM0VZ0JCAuff3r5926AzN02c/fv3l4pz6NChdJHSP09FQhUKhePHj+/UqROXyw0LC5MqVZ9bVE6canWLyolTrW5ROXGq2y168OBBX19fCwsLQ0NDT0/PkydPSpaq6halWrp9+/ZpamrGxsZmZmaGhobq6ek9evRIqs6DBw90dXVDQ0MzMzNjY2M1NTUPHDhAF126dInH461evTorK2v16tUaGhqpqamKn7nJ4hw/fvwPP/yQkZGRlZU1depUIyOjx48f00UREREffPCB8G+FhYWNDvLd44yPjzc0NBRKaNCZmyzOoqIiJsLbt2/zeLz4+Hi6SLmfp4Kh5ubmzpkzZ8eOHd26dQsNDZUsUqtbVE6canWLyolTrW5ROXGq2y0aGhq6du3atLS0u3fvhoeHa2pqXrt2jS5S3S3a8hNYz549Z86cyWw6OjouWrRIqs4XX3zh6OjIbM6YMaNXr17067FjxwYEBDBF/v7+48aNU/zMTRanpJqaGgMDgx07dtCbERERXbt2bXRgyo0zPj7eyMio0WdusjglbdiwwcDA4PXr1/Smcj9PBUNl9OvXT+qLTK1uUTlxSmr2W1ROnGp1i8qJU5Ja3aI0Jyen5cuX069Vd4u28C7Eqqqq9PR0Pz8/Zo+fn9+lS5ekql2+fFmyjr+//9WrV6urq2UW0YcreOYmi1NSRUVFdXW1qakps+fevXs2NjYODg7jxo178OBB44JUVpyvX7+2s7Nr3br1sGHDMjIyGnTmpoyTERcXN27cOD09PWaPsj5PxUOVQ61uUQU1+y0qn/rcogpSt1tULBaXlZUxf76qu0VbeAJ78eKFSCSysrJi9lhZWRUUFEhVKygokKpTU1Pz4sULmUX04QqeucnilLRo0aJWrVoNGjSI3vTw8Pjxxx9PnjwZGxtbUFDQu3fvoqKi5orT0dExISHh6NGje/fu5fP5Xl5e9+7dU/zMTRYnIy0t7fbt29OmTWP2KPHzVDxUOdTqFlVQs9+icqjVLaoINbxFv/nmm/Ly8rFjx9KbqrtFW9ps9DJJLq1CUZTMlVak6kjukXO4Imdusjhp69at27t377lz5/h8Pr1n8ODB9IsuXbp4enq2a9dux44d8+bNa5Y4e/Xq1atXL3q/l5eXq6vrpk2bvv/+e8XP3DRxMuLi4pydnXv27MnsUfrnqWCojTu86T/SeqnJLVoXdbtF66Vut+jevXsjIyOPHDliaWmpyOHv8iG08BaYubk5j8eTTOmFhYWSCZ9mbW0tVUdDQ8PMzExmEX24gmdusjhpX3/99erVq0+dOuXi4iLzQnp6el26dKH/RdmMcdK4XG6PHj3oYNTz86yoqNi3b5/kv22lvOPnqXiocqjVLVovNblFFdTst2i91O0WTUxMDAkJ+emnn5jmNVHlLdrCE5iWlpabm1tycjKzJzk5uXfv3lLVPD09JeucOnXK3d1dU1NTZhF9uIJnbrI4CSHr169fuXJlUlKSu7t7XReqrKzMysoSCATNGCeDoqjr16/Twajh50kI+emnnyorKydOnFjXhd7x81Q8VDnU6haVT31uUQU1+y1aL7W6Rffu3RscHLxnzx5mQD9Nhbeo4uM9WIoephkXF5eZmRkWFqanp/fw4UOKohYtWjRp0iS6Dj2ceu7cuZmZmXFxcZLDqS9evMjj8dasWZOVlbVmzZraA0Brn7lZ4ly7dq2WltaBAweYsbNlZWV00fz588+dO/fgwYPU1NRhw4YZGBg0Y5yRkZFJSUk5OTkZGRlTp07V0ND4448/5J+5WeKk9enTJygoSOrMyv08FQyVoqiMjIyMjAw3N7fx48dnZGTcuXOH3q9Wt6icONXqFpUTp1rdonLipKnPLbpnzx4NDY0ffviB+fN99eoVXaS6W7TlJzCKon744Qc7OzstLS1XV9fz58/TO6dMmdKvXz+mzrlz57p3766lpWVvbx8TEyN5+P79+zt16qSpqeno6Pjzzz/Xe+ZmidPOzk7qnyYRERF0UVBQkEAg0NTUtLGxGT16tNRfgCaOMywsrE2bNlpaWhYWFvSIo3rP3CxxUhSVnZ1N/2tR6rRK/zwVDFXqz9fOzo4pUqtbtK441e0WrStOdbtF5fy5q9Ut2q9fP6lQp0yZwhyuolsUy6kAAAArtfBnYAAA0FIhgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQG0BAkJCcbGxjKLgoODR44c2ZTBREZGduvWrSmvCO8nJDB4jwQHB9OLrGtqalpZWfn6+m7fvl0sFjd3XKq1cePGhIQExetLfkpt27ZdsGBBeXl5g664YMGCM2fOMGdr4vQJ7w8kMHi/BAQECIXChw8f/vrrrwMGDAgNDR02bFhNTU1zx/WXqqoqpZ/TyMiorsZZXehP6cGDB6tWrYqOjl6wYIGCB1IUVVNTo6+vX3vxAQClQwKD94u2tra1tXWrVq1cXV2//PLLI0eO/Prrr0wDpaSk5OOPP7a0tDQ0NBw4cOCNGzfo/XSf2Pbt29u0aaOvr//JJ5+IRKJ169ZZW1tbWlp+9dVXzPnz8vJGjBihr69vaGg4duzYZ8+eMUWrVq2ytLQ0MDCYNm3aokWLmE42uo0SFRVlY2PTsWNHQsiuXbvc3d0NDAysra3Hjx9fWFhI1zx37hyHw/nll1+6du3K5/M9PDxu3bol+e5OnjzZuXNnfX19OgNJnp9+LRaL165d2759e21t7TZt2khGXvtTsrW1HT9+/IQJEw4fPlxvVCdPnnR3d9fW1r5w4QLThRgZGbljx44jR47QTbpz584NHDhw9uzZzIWKioq0tbVTUlIa8mcI8BckMHivDRw4sGvXrgcPHiSEUBQ1dOjQgoKCEydOpKenu7q6+vj4FBcX0zVzcnJ+/fXXpKSkvXv3bt++fejQoY8fPz5//vzatWuXLFmSmppKn2HkyJHFxcXnz59PTk7OyckJCgqiD9+9e/dXX321du3a9PT0Nm3axMTESIZx5syZrKys5OTk48ePE0KqqqpWrlx548aNw4cP5+bmBgcHS1b+/PPPv/766ytXrlhaWg4fPry6upreX1FR8fXXX+/cufO3337Ly8uT2WwKDw9fu3bt0qVLMzMz9+zZo8jaSzo6OvQl5Ef1xRdfREVFZWVlSS70tWDBgrFjx9LZVCgU9u7de9q0aXv27KmsrGQ+FhsbmwEDBtQbBoAMjZ2bGIB9pkyZMmLECKmdQUFBnTt3pijqzJkzhoaGb9++ZYratWu3detWiqIiIiJ0dXVLS0vp/f7+/vb29iKRiN7s1KlTVFQURVGnTp3i8Xh5eXn0/jt37hBC0tLSKIry8PCYNWsWc2YvL6+uXbsyUVlZWVVWVsqMOS0tjRBCrzxy9uxZQsi+ffvooqKiIh0dncTERIqi4uPjCSH379+ni3744QcrKyupd11aWqqtrR0bG6v4p/THH3+YmZmNHTu23qgOHz7MlEZEREi+O8nP/O3bt6ampnTMFEV169YtMjJSfjwAdUELDN531N+rmKenp79+/drMzEz/b7m5uTk5OXQ1e3t7AwMD+rWVlZWTkxOXy2U26f60rKwsW1tbW1tber+Tk5OxsXFWVhYhJDs7W3LRd8nXhJAuXbpoaWkxmxkZGSNGjLCzszMwMOjfvz8hJC8vjyn19PSkX5iamnbq1Ik+PyFEV1e3Xbt29GuBQMB08TGysrIqKyt9fHzq/UyOHz+ur6/P5/M9PT379u27adOmeqOSs0alJG1t7YkTJ27fvp0Qcv369Rs3bki15AAUp9HcAQA0s6ysLAcHB0KIWCwWCATnzp2TLGWGP0iu1EyP0JPcpIcyMrmQIblHsoj69zJGenp6zOvy8nI/Pz8/P79du3ZZWFjk5eX5+/vLGdzBnFYqJKrWSkk6Ojp1nUTKgAEDYmJi6AWl6NPWG5XkW5Bv2rRp3bp1e/z48fbt2318fGqvEwagILTA4L2WkpJy69at//znP4QQV1fXgoICDQ2N9hLMzc0VP5uTk1NeXl5+fj69mZmZWVJS0rlzZ0JIp06d6G432tWrV+s6yZ9//vnixYs1a9Z4e3s7OjrWbkjRz9sIIS9fvrx7966jo6OC4XXo0EFHR4cZ4C6Hnp5e+/bt7ezsmKRYb1R10dLSEolEknu6dOni7u4eGxu7Z8+ejz76SMHzANSGBAbvl8rKyoKCgidPnly7dm316tUjRowYNmzY5MmTCSGDBg3y9PQcOXLkyZMnHz58eOnSpSVLlsjJNLUNGjTIxcVlwoQJ165dS0tLmzx5cr9+/ei+tc8++ywuLm7Hjh337t1btWrVzZs3pdpqDHo54E2bNj148ODo0aMrV66UqrBixYozZ87cvn07ODjY3Nxc8V9Z8fn8hQsXfvHFFz/++GNOTk5qampcXJyCx9YbVV3s7e1v3ryZnZ394sULZrzJtGnT1qxZIxKJRo0apeB5AGpDAoP3S1JSkkAgsLe3DwgIOHv27Pfff3/kyBEej0cI4XA4J06c6Nu370cffdSxY8dx48Y9fPhQkXF6DA6Hc/jwYRMTk759+w4aNKht27aJiYl00YQJE8LDwxcsWODq6koP4ePz+TJPYmFhkZCQsH//ficnpzVr1nz99ddSFdasWRMaGurm5iYUCo8ePSr58KxeS5cunT9//rJlyzp37hwUFKR4Q6reqOoyffr0Tp06ubu7W1hYXLx4kd754YcfamhojB8/vq4PAUARMjrKAUDVfH19ra2td+7c2aCjzp07N2DAgJcvXzb0h8nqJj8/397e/sqVK66urs0dC7AYBnEANIWKiootW7b4+/vzeLy9e/eePn06OTm5uYNqBtXV1UKhcNGiRb169UL2gneEBAbQFOj+yVWrVlVWVnbq1Onnn38eNGhQcwfVDC5evDhgwICOHTseOHCguWMB1kMXIgAAsBIGcQAAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACshgQEAACv9H+jz4j2PACTSAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -602,38 +612,38 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
-       "      <td>0.851937</td>\n",
-       "      <td>0.837769</td>\n",
+       "      <td>0.850545</td>\n",
+       "      <td>0.840799</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Balanced Accuracy</th>\n",
-       "      <td>0.730736</td>\n",
-       "      <td>0.717201</td>\n",
+       "      <td>0.742136</td>\n",
+       "      <td>0.737372</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F1 score</th>\n",
-       "      <td>0.616949</td>\n",
-       "      <td>0.589089</td>\n",
+       "      <td>0.631090</td>\n",
+       "      <td>0.618375</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MCC</th>\n",
-       "      <td>0.555631</td>\n",
-       "      <td>0.511426</td>\n",
+       "      <td>0.555519</td>\n",
+       "      <td>0.529675</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
-       "      <td>0.809789</td>\n",
-       "      <td>0.747762</td>\n",
+       "      <td>0.770864</td>\n",
+       "      <td>0.725138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
-       "      <td>0.498289</td>\n",
-       "      <td>0.485969</td>\n",
+       "      <td>0.534223</td>\n",
+       "      <td>0.539014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ROC AUC</th>\n",
-       "      <td>0.905296</td>\n",
-       "      <td>0.888789</td>\n",
+       "      <td>0.902339</td>\n",
+       "      <td>0.888278</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -641,13 +651,13 @@
       ],
       "text/plain": [
        "                   original   updated\n",
-       "Accuracy           0.851937  0.837769\n",
-       "Balanced Accuracy  0.730736  0.717201\n",
-       "F1 score           0.616949  0.589089\n",
-       "MCC                0.555631  0.511426\n",
-       "Precision          0.809789  0.747762\n",
-       "Recall             0.498289  0.485969\n",
-       "ROC AUC            0.905296  0.888789"
+       "Accuracy           0.850545  0.840799\n",
+       "Balanced Accuracy  0.742136  0.737372\n",
+       "F1 score           0.631090  0.618375\n",
+       "MCC                0.555519  0.529675\n",
+       "Precision          0.770864  0.725138\n",
+       "Recall             0.534223  0.539014\n",
+       "ROC AUC            0.902339  0.888278"
       ]
      },
      "execution_count": 17,
@@ -700,43 +710,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Statistical Parity</th>\n",
-       "      <td>0.147951</td>\n",
-       "      <td>0.027185</td>\n",
+       "      <td>0.154577</td>\n",
+       "      <td>0.046572</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Predictive Parity</th>\n",
-       "      <td>0.055324</td>\n",
-       "      <td>0.257181</td>\n",
+       "      <td>0.008904</td>\n",
+       "      <td>0.245004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equal Opportunity</th>\n",
-       "      <td>0.120342</td>\n",
-       "      <td>0.273454</td>\n",
+       "      <td>0.104299</td>\n",
+       "      <td>0.231262</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in False Negative Rate</th>\n",
-       "      <td>0.120342</td>\n",
-       "      <td>0.273454</td>\n",
+       "      <td>0.104299</td>\n",
+       "      <td>0.231262</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equalized Odds</th>\n",
-       "      <td>0.084993</td>\n",
-       "      <td>0.149802</td>\n",
+       "      <td>0.080281</td>\n",
+       "      <td>0.124072</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Conditional Use Accuracy</th>\n",
-       "      <td>0.083531</td>\n",
-       "      <td>0.210693</td>\n",
+       "      <td>0.059483</td>\n",
+       "      <td>0.198959</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Accuracy</th>\n",
-       "      <td>0.112128</td>\n",
-       "      <td>0.105141</td>\n",
+       "      <td>0.112338</td>\n",
+       "      <td>0.095505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment Equality</th>\n",
-       "      <td>0.163322</td>\n",
-       "      <td>1.792626</td>\n",
+       "      <td>0.137020</td>\n",
+       "      <td>2.032479</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -744,14 +754,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.147951  0.027185\n",
-       "Predictive Parity                                0.055324  0.257181\n",
-       "Equal Opportunity                                0.120342  0.273454\n",
-       "Average Group Difference in False Negative Rate  0.120342  0.273454\n",
-       "Equalized Odds                                   0.084993  0.149802\n",
-       "Conditional Use Accuracy                         0.083531  0.210693\n",
-       "Average Group Difference in Accuracy             0.112128  0.105141\n",
-       "Treatment Equality                               0.163322  1.792626"
+       "Statistical Parity                               0.154577  0.046572\n",
+       "Predictive Parity                                0.008904  0.245004\n",
+       "Equal Opportunity                                0.104299  0.231262\n",
+       "Average Group Difference in False Negative Rate  0.104299  0.231262\n",
+       "Equalized Odds                                   0.080281  0.124072\n",
+       "Conditional Use Accuracy                         0.059483  0.198959\n",
+       "Average Group Difference in Accuracy             0.112338  0.095505\n",
+       "Treatment Equality                               0.137020  2.032479"
       ]
      },
      "execution_count": 18,
@@ -760,7 +770,7 @@
     }
    ],
    "source": [
-    "fpred.evaluate_fairness()"
+    "fpred.evaluate_fairness(test_network)"
    ]
   },
   {
@@ -778,7 +788,7 @@
     {
      "data": {
       "text/plain": [
-       "(-2.084, 1.782)"
+       "(-2.107, 1.824)"
       ]
      },
      "execution_count": 19,
@@ -827,15 +837,9 @@
    "source": [
     "# We copy the network and create a fair version\n",
     "import copy\n",
-    "fair_network= copy.deepcopy(network)\n",
+    "fair_network = copy.deepcopy(network)\n",
     "# We replace the final linear layer with a 1 dimensional head.\n",
-    "fair_network[4]=nn.Linear(50,1)\n",
-    "#Now we merge the weights \n",
-    "fair_network[4].weight.data[:] = (network[4].weight[0] + fpred.extract_coefficients()[0]*network[4].weight[1]).data\n",
-    "# and the biases\n",
-    "fair_network[4].bias.data[:] = (network[4].bias[0] + fpred.extract_coefficients()[0]*network[4].bias[1]).data\n",
-    "# and add the extra bias term\n",
-    "fair_network[4].bias.data += fpred.extract_coefficients()[1]\n"
+    "fair_network[-1] = fpred.merge_heads_pytorch(network[-1])"
    ]
   },
   {
@@ -869,70 +873,15 @@
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Accuracy</th>\n",
-       "      <td>0.837851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Balanced Accuracy</th>\n",
-       "      <td>0.717372</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>F1 score</th>\n",
-       "      <td>0.589382</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MCC</th>\n",
-       "      <td>0.511719</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Precision</th>\n",
-       "      <td>0.747895</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Recall</th>\n",
-       "      <td>0.486311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ROC AUC</th>\n",
-       "      <td>0.888800</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                          0\n",
-       "Accuracy           0.837851\n",
-       "Balanced Accuracy  0.717372\n",
-       "F1 score           0.589382\n",
-       "MCC                0.511719\n",
-       "Precision          0.747895\n",
-       "Recall             0.486311\n",
-       "ROC AUC            0.888800"
+       "Accuracy             0.840799\n",
+       "Balanced Accuracy    0.737372\n",
+       "F1 score             0.618375\n",
+       "MCC                  0.529675\n",
+       "Precision            0.725138\n",
+       "Recall               0.539014\n",
+       "ROC AUC              0.888278\n",
+       "Name: 0, dtype: float64"
       ]
      },
      "execution_count": 22,
@@ -942,7 +891,6 @@
    ],
    "source": [
     "# As expected the accuracy is identical to what was reported before.\n",
-    "\n",
     "oxonfair.performance.evaluate(test['target'],test_fair_output.reshape(-1))"
    ]
   },
@@ -960,75 +908,16 @@
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Statistical Parity</th>\n",
-       "      <td>0.016946</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Predictive Parity</th>\n",
-       "      <td>0.268903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Equal Opportunity</th>\n",
-       "      <td>0.301356</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Average Group Difference in False Negative Rate</th>\n",
-       "      <td>0.301356</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Equalized Odds</th>\n",
-       "      <td>0.166554</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Conditional Use Accuracy</th>\n",
-       "      <td>0.219282</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Average Group Difference in Accuracy</th>\n",
-       "      <td>0.105828</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Treatment Equality</th>\n",
-       "      <td>2.084835</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                                                        0\n",
-       "Statistical Parity                               0.016946\n",
-       "Predictive Parity                                0.268903\n",
-       "Equal Opportunity                                0.301356\n",
-       "Average Group Difference in False Negative Rate  0.301356\n",
-       "Equalized Odds                                   0.166554\n",
-       "Conditional Use Accuracy                         0.219282\n",
-       "Average Group Difference in Accuracy             0.105828\n",
-       "Treatment Equality                               2.084835"
+       "Statistical Parity                                 0.046572\n",
+       "Predictive Parity                                  0.245004\n",
+       "Equal Opportunity                                  0.231262\n",
+       "Average Group Difference in False Negative Rate    0.231262\n",
+       "Equalized Odds                                     0.124072\n",
+       "Conditional Use Accuracy                           0.198959\n",
+       "Average Group Difference in Accuracy               0.095505\n",
+       "Treatment Equality                                 2.032479\n",
+       "Name: 0, dtype: float64"
       ]
      },
      "execution_count": 23,
@@ -1114,7 +1003,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.75, 0.8835872235872236)"
+       "(0.75, 0.8792014742014742)"
       ]
      },
      "execution_count": 26,
@@ -1123,7 +1012,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/setup.py b/setup.py
index 89290cc..1683e59 100644
--- a/setup.py
+++ b/setup.py
@@ -4,7 +4,7 @@
 
 FAIR = "oxonfair"
 
-version = "0.2.1.5"
+version = "0.2.1.6"
 
 PYTHON_REQUIRES = ">=3.8"
 
diff --git a/src/oxonfair/learners/fair.py b/src/oxonfair/learners/fair.py
index 073de39..4824e0b 100644
--- a/src/oxonfair/learners/fair.py
+++ b/src/oxonfair/learners/fair.py
@@ -398,14 +398,14 @@ def frontier_thresholds(self):
         "Returns the thresholds corresponding to the found frontier"
         assert self.frontier, "Call fit before frontier_thresholds"
         return self.frontier[1]
-    
+
     def frontier_scores(self):
         "Returns the scores (i.e. objective and constraint) corresponding to the found frontier"
         assert self.frontier, "Call fit before frontier_scores"
         return self.frontier[0]
-    
+
     def set_threshold(self, threshold):
-        """Set the thresholds. 
+        """Set the thresholds.
            This code allows the manual overriding of the thresholds found by fit to enforce different trade-offs.
            """
         self.offset = threshold
@@ -600,17 +600,17 @@ def evaluate_fairness(self, data=None, groups=None, factor=None, *,
         score = y_pred_proba[:, 1] - y_pred_proba[:, 0]
         collect = perf.evaluate_fairness(labels, score, groups, factor,
                                          metrics=metrics, verbose=verbose, threshold=0)
-        collect.columns = ['original']
 
         if self.frontier is not None:
             y_pred_proba = np.asarray(self.predict_proba(data))
-            score = y_pred_proba[:, 1]-y_pred_proba[:, 0]
+            score = (y_pred_proba[:, 1]-y_pred_proba[:, 0])/2
             new_pd = perf.evaluate_fairness(labels, score, groups, factor,
                                             metrics=metrics, verbose=verbose,
                                             threshold=0)
 
-            new_pd.columns = ['updated']
             collect = pd.concat([collect, new_pd], axis='columns')
+            collect.columns = ['original', 'updated']
+
         return collect
 
     def fairness_metrics(self, y_true: np.ndarray, proba, groups: np.ndarray,
@@ -812,7 +812,34 @@ def extract_coefficients_1_hot(self):
         Such that head_1 + a.dot(head_2) has the same outputs as our fair classifier.
         This can be used to merge the coefficients of the two heads, creating a single-headed fair classifier.
         """
-        return self.offset[:, 0]
+        return -self.offset[:, 0]
+
+    def merge_heads_pytorch(self, heads):
+        """Merges multiple heads into a single head of the same form, that enforces fairness.
+        heads is assumed to be a 2-d torch linear layer of dimension: backbone width by number of heads.
+        The first head is assumed to be the classifier response, and the remainder of heads encode the attributes.
+        If the number of heads is two we asumme the second-head was trained to enocde a binary attributes with labels roughly 0 and 1.
+        If the number of heads is more than two we assume all heads except the first encode an approximate 1-hot embedding of the attributes"""
+        from torch.nn import Linear
+        from torch import Tensor
+        assert isinstance(heads, Linear)
+        assert heads.out_features > 1
+        out = Linear(heads.in_features, 1, dtype=heads.weight.dtype)
+        if heads.out_features == 2:
+            assert self.offset.shape[0] == 2, 'Dimension mismatch between training data and heads'
+            coeff = self.extract_coefficients()
+            # Now we merge the weights
+            out.weight.data[:] = (heads.weight[0] + coeff[0]*heads.weight[1]).data
+            # and the biases
+            out.bias.data[:] = (heads.bias[0] + coeff[0]*heads.bias[1]).data
+            # and add the extra bias term
+            out.bias.data += coeff[1]
+        else:
+            assert self.offset.shape[0] == heads.out_features-1, 'Dimension mismatch between training data and heads'
+            coeff = Tensor(self.extract_coefficients_1_hot())
+            out.weight.data[:] = (heads.weight[0] + coeff.inner(heads.weight[1:].T)).data
+            out.bias.data[:] = (heads.bias[0] + coeff.dot(heads.bias[1:])).data
+        return out
 
 
 def _needs_groups(func) -> bool:
diff --git a/src/oxonfair/learners/fair_frontier.py b/src/oxonfair/learners/fair_frontier.py
index 798854e..44545e2 100644
--- a/src/oxonfair/learners/fair_frontier.py
+++ b/src/oxonfair/learners/fair_frontier.py
@@ -45,7 +45,7 @@ def compute_metrics(metrics: Sequence[Callable], y_true: np.ndarray, proba: np.n
         then select the max and compute a fairness metric """
     scores = np.zeros((len(metrics), weights.shape[-1]))
     y_true = np.asarray(y_true)
-    assert weights[:, 1, :].sum() == 0
+    # assert weights[:, 1, :].sum() == 0
     weights = weights[:, 0, :]
     proba = proba[:, 0] - proba[:, 1]
 
@@ -61,7 +61,7 @@ def compute_metrics(metrics: Sequence[Callable], y_true: np.ndarray, proba: np.n
     for i in range(weights.shape[-1]):
         threshold_assignment.dot(weights[:, i], out=tmp)
         tmp += proba  # [:, np.newaxis]
-        pred = (tmp <= 0)
+        pred = (tmp < 0)  # <= may be causing a mismatch
 
         # np.dot(threshold_assignment, weights[:, i], tmp)
         # tmp += proba
diff --git a/src/oxonfair/utils/performance.py b/src/oxonfair/utils/performance.py
index fc3bb01..0153d6a 100644
--- a/src/oxonfair/utils/performance.py
+++ b/src/oxonfair/utils/performance.py
@@ -90,7 +90,7 @@ def evaluate_fairness(target, prediction, groups, factor=None, *,
         values[i] = dispatch_metric(
             metrics[k], target, prediction, groups, factor, threshold=threshold)
 
-    return pd.DataFrame(values, index=names)
+    return pd.DataFrame(values, index=names)[0]
 
 
 def evaluate_per_group(target, prediction, groups, factor=None, *,
diff --git a/tests/test_check_style.py b/tests/test_check_style.py
index 34f4e52..7a9118d 100755
--- a/tests/test_check_style.py
+++ b/tests/test_check_style.py
@@ -54,7 +54,7 @@ def test_check_style_examples():
 
 
 def test_md_links():
-    missing_links = lc.check_links('./', ext='.md')
+    missing_links = lc.check_links('./', ext='.md', recurse=True,)
     for link in missing_links:
         warnings.warn(link)
     assert missing_links == []
diff --git a/tests/unittests/test_additional_constraints.py b/tests/unittests/test_additional_constraints.py
index 6021e74..8e5383b 100644
--- a/tests/unittests/test_additional_constraints.py
+++ b/tests/unittests/test_additional_constraints.py
@@ -71,7 +71,7 @@ def test_active_constraints(use_fast=True):
     cpredictor = fair.FairPredictor(predictor, test_dict, "sex_ Female", use_fast=use_fast)
 
     cpredictor.fit(gm.accuracy, gm.recall.diff, 0.005, additional_constraints=((gm.pos_pred_rate, .7),))
-    assert cpredictor.evaluate(metrics={'m': gm.pos_pred_rate})['updated'][0] > .7
+    assert cpredictor.evaluate(metrics={'m': gm.pos_pred_rate})['updated'][0] >= .7
 
 
 def test_active_constraints_slow():
diff --git a/tests/unittests/test_scipy.py b/tests/unittests/test_scipy.py
index fc663d1..36e5541 100644
--- a/tests/unittests/test_scipy.py
+++ b/tests/unittests/test_scipy.py
@@ -26,23 +26,32 @@
 total_data = total_data.drop(columns="class")
 total_data = pd.get_dummies(total_data)
 
-train = total_data.sample(frac=0.5)
-val_test = total_data.drop(train.index)
-train_y = y.iloc[train.index]
-val_test_y = y.drop(train_y.index)
-val = val_test.sample(frac=0.4)
-test = val_test.drop(val.index)
-val_y = y.iloc[val.index]
-test_y = val_test_y.drop(val.index)
-predictor = classifier_type()
-predictor.fit(train, train_y)
 
-train_dict = {"data": train, "target": train_y}
-val_dict = {"data": val, "target": val_y}
-test_dict = {"data": test, "target": test_y}
+def resample():
+    global train, train_y, val, val_y, test, test_y, predictor
+    global train_dict, val_dict, test_dict
+    global train_dict_g, val_dict_g, test_dict_g
+    train = total_data.sample(frac=0.5)
+    val_test = total_data.drop(train.index)
+    train_y = y.iloc[train.index]
+    val_test_y = y.drop(train_y.index)
+    val = val_test.sample(frac=0.4)
+    test = val_test.drop(val.index)
+    val_y = y.iloc[val.index]
+    test_y = val_test_y.drop(val.index)
+
+    predictor = classifier_type()
+    predictor.fit(train, train_y)
+
+    train_dict = {"data": train, "target": train_y}
+    val_dict = {"data": val, "target": val_y}
+    test_dict = {"data": test, "target": test_y}
+
+    val_dict_g = fair.DataDict(val_y, val, val['sex_ Female'])
+    test_dict_g = fair.DataDict(test_y, test, test['sex_ Female'])
 
-val_dict_g = fair.DataDict(val_y, val, val['sex_ Female'])
-test_dict_g = fair.DataDict(test_y, test, test['sex_ Female'])
+
+resample()
 
 
 def test_base_functionality(val_dict=val_dict, test_dict=test_dict):
@@ -93,8 +102,9 @@ def test_conflict_groups():
 def test_fit_creates_updated(use_fast=True):
     """eval should return 'updated' iff fit has been called"""
     fpredictor = FairPredictor(predictor, val_dict, use_fast=use_fast)
-    assert 'updated' not in fpredictor.evaluate().columns
+    assert isinstance(fpredictor.evaluate(), pd.Series)
     fpredictor.fit(gm.accuracy, gm.recall, 0)  # constraint is intentionally slack
+    assert not isinstance(fpredictor.evaluate(), pd.Series)
     assert 'updated' in fpredictor.evaluate().columns
 
 
@@ -196,9 +206,9 @@ def test_recall_diff(use_fast=True):
     # Evaluate the change in fairness (recall difference corresponds to EO)
     measures = fpredictor.evaluate_fairness(verbose=False)
 
-    assert measures["original"]["recall.diff"] > 0.025 
+    assert measures["original"]["recall.diff"] > 0.025
 
-    assert measures["updated"]["recall.diff"] < 0.025 + 1e-4
+    assert measures["updated"]["recall.diff"] <= 0.025
     measures = fpredictor.evaluate(verbose=False)
     acc = measures["updated"]["accuracy"]
     fpredictor.fit(gm.accuracy, gm.recall.diff, 0.025, greater_is_better_const=True)
@@ -270,11 +280,33 @@ def test_recall_diff_slow():
 def test_recall_diff_hybrid():
     test_recall_diff('hybrid')
 
+""" too slow and disabled
+def test_many_recall_diff_hybrid(many=200):
+    count = 0
+    for i in range(many):
+        resample()
+        try:
+            test_recall_diff_hybrid()
+        except:
+            count += 1
+    assert count == 0
+
+
+def test_many_recall_diff_slow(many=200):
+    count = 0
+    for i in range(many):
+        resample()
+        try:
+            test_recall_diff_slow()
+        except:
+            count += 1
+    assert count == 0
+
 
 def test_min_recall_slow():
     "test slow pathway"
     test_min_recall(False)
-
+"""
 
 def test_min_recall_hybrid():
     test_min_recall('hybrid')
diff --git a/tests/unittests/test_torch.py b/tests/unittests/test_torch.py
new file mode 100644
index 0000000..748716f
--- /dev/null
+++ b/tests/unittests/test_torch.py
@@ -0,0 +1,82 @@
+from oxonfair import dataset_loader, DeepFairPredictor, performance
+from oxonfair import group_metrics as gm
+import numpy as np
+from torch import nn, optim, tensor
+import torch.nn.functional as F
+from numpy import random
+from sklearn.preprocessing import OneHotEncoder
+import copy
+train, _, _ = dataset_loader.compas(train_proportion=1.0, test_proportion=0, groups='race',
+                                    replace_groups={'Asian': 'Other', 'Hispanic': 'Other', 'Native American': 'Other'}, seperate_groups=True)
+
+
+# Define a custom loss that trains the two-heads as required.
+def loss(network, x, y, g):
+    output = network(x)
+    loss0 = F.binary_cross_entropy_with_logits(output[:, 0], y)
+    loss1 = F.mse_loss(output[:, 1:], g)
+    return loss0 + loss1
+
+
+def test_1_hot(head_width=4):
+    target = tensor(train['target']).float()
+    data = tensor(np.asarray(train['data'])).float()
+
+    groups = tensor(OneHotEncoder(sparse_output=False).fit_transform(train['groups'].reshape(-1, 1))).float()
+    groups_bin = groups[:, 0]
+
+    std = train['data'].std()
+    train['data'] = train['data'] / std
+
+    # define a basic nn with 2 hidden-layers. 1 of width 100, and the second width 50.
+    network = nn.Sequential(nn.Linear(train['data'].shape[1], 100),
+                            nn.SELU(),
+                            nn.Linear(100, 50),
+                            nn.SELU(),
+                            nn.Linear(50, head_width))
+    optimizer = optim.Adam(network.parameters(), lr=1e-4)
+    # Train the network
+    batch_size = 50
+
+    for epoch in range(100):
+        # shuffle data
+        perm = random.permutation(target.shape[0])
+        target = target[perm]
+        data = data[perm]
+        groups = groups[perm]
+        for step in range(target.shape[0]//batch_size):  # This discards the final incomplete batch
+            optimizer.zero_grad()
+            el = loss(network,
+                      data[step*batch_size:(1+step)*batch_size],
+                      target[step*batch_size:(1+step)*batch_size],
+                      groups[step*batch_size:(1+step)*batch_size, :head_width-1])
+            el.backward()
+            optimizer.step()
+
+    train_output = np.asarray(network(tensor(np.asarray(train['data'])).float()).detach())
+    if head_width > 2:
+        fpred = DeepFairPredictor(train['target'],
+                                  train_output,
+                                  groups=train['groups'])
+    else:
+        fpred = DeepFairPredictor(train['target'],
+                                  train_output,
+                                  groups=groups_bin)
+
+    fpred.fit(gm.accuracy, gm.demographic_parity, 0.01)
+    fair_network = copy.deepcopy(network)
+    fair_network[-1] = fpred.merge_heads_pytorch(network[-1])
+    output_fair = np.asarray(fair_network(tensor(np.asarray(train['data'])).float()).detach())
+    if head_width > 2:
+        assert performance.evaluate_fairness(train['target'], output_fair.reshape(-1), train['groups']).loc['Statistical Parity'] < 0.01
+        assert (performance.evaluate_fairness(train['target'], output_fair.reshape(-1), train['groups'])
+                == fpred.evaluate_fairness()['updated']).all()
+        assert np.isclose(performance.evaluate(train['target'], output_fair.reshape(-1)), fpred.evaluate()['updated'], atol=0.001).all()
+    else:
+        assert performance.evaluate_fairness(train['target'], output_fair.reshape(-1), groups_bin).loc['Statistical Parity'] < 0.01
+        assert (performance.evaluate_fairness(train['target'], output_fair.reshape(-1), groups_bin) == fpred.evaluate_fairness()['updated']).all()
+        assert np.isclose(performance.evaluate(train['target'], output_fair.reshape(-1)), fpred.evaluate()['updated'], atol=0.001).all()
+
+
+def test_bin():
+    test_1_hot(2)