diff --git a/examples/demo_gerryfair.ipynb b/examples/demo_gerryfair.ipynb
index efdb5983..daaabbe1 100644
--- a/examples/demo_gerryfair.ipynb
+++ b/examples/demo_gerryfair.ipynb
@@ -1,884 +1,998 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "import sys\n",
-    "sys.path.append(\"../\")\n",
-    "from aif360.algorithms.inprocessing import GerryFairClassifier\n",
-    "from aif360.algorithms.inprocessing.gerryfair.clean import array_to_tuple\n",
-    "from aif360.algorithms.inprocessing.gerryfair.auditor import Auditor\n",
-    "from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions import load_preproc_data_adult\n",
-    "from sklearn import svm\n",
-    "from sklearn import tree\n",
-    "from sklearn.kernel_ridge import KernelRidge\n",
-    "from sklearn import linear_model\n",
-    "from aif360.metrics import BinaryLabelDatasetMetric\n",
-    "from IPython.display import Image\n",
-    "import pickle\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# load data set\n",
-    "data_set = load_preproc_data_adult(sub_samp=1000, balance=True)\n",
-    "max_iterations = 500"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**instantiate, fit, and predict** \n",
-    "\n",
-    "\n",
-    "We first demonstrate how to instantiate a `GerryFairClassifier`, `train` it with respect to rich subgroup fairness, and `predict` the label of a new example. We remark that when we set the `print_flag = True` at each iteration of the algorithm we print the error, fairness violation, and violated group size of most recent model. The error is the classification error of the classifier. At each round the Learner tries to find a classifier that minimizes the classification error plus a weighted sum of the fairness disparities on all the groups that the Auditor has found up until that point. By contrast the Auditor tries to find the group at each round with the greatest rich subgroup disparity with respect to the Learner's model. We define `violated group size` as the size (as a fraction of the dataset size) of this group, and the `fairness violation` as the `violated group size` times the difference in the statistical rate (FP or FN rate) on the group vs. the whole population. \n",
-    "\n",
-    "In the example below we set `max_iterations=500` which is an order of magnitude less than the time to convergence observed in [the rich subgroup fairness empirical paper](https://arxiv.org/abs/1808.08166), but advise that this can be highly dataset dependent. Our target $\\gamma$-disparity is $\\gamma = .005$, our statistical rate is false positive rate or `FP`, and our cost-sensitive classification oracle is linear regression (more on that below). \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    }
-   },
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iteration: 1, error: 0.263, fairness violation: 0.028780000000000007, violated group size: 0.217\n",
-      "iteration: 2, error: 0.3815, fairness violation: 0.014390000000000003, violated group size: 0.217\n",
-      "iteration: 3, error: 0.42099999999999993, fairness violation: 0.009593333333333339, violated group size: 0.283\n",
-      "iteration: 4, error: 0.44075, fairness violation: 0.007195000000000002, violated group size: 0.217\n",
-      "iteration: 5, error: 0.45260000000000006, fairness violation: 0.005756000000000001, violated group size: 0.217\n",
-      "iteration: 6, error: 0.4605000000000001, fairness violation: 0.004796666666666668, violated group size: 0.283\n",
-      "iteration: 7, error: 0.4661428571428572, fairness violation: 0.004111428571428572, violated group size: 0.217\n",
-      "iteration: 8, error: 0.470375, fairness violation: 0.0035975000000000017, violated group size: 0.217\n",
-      "iteration: 9, error: 0.4691111111111112, fairness violation: 0.0033906666666666677, violated group size: 0.283\n",
-      "iteration: 10, error: 0.4681, fairness violation: 0.003225200000000001, violated group size: 0.283\n",
-      "iteration: 11, error: 0.4672727272727271, fairness violation: 0.0030898181818181836, violated group size: 0.283\n",
-      "iteration: 12, error: 0.4665833333333333, fairness violation: 0.0029769999999999996, violated group size: 0.217\n",
-      "iteration: 13, error: 0.466, fairness violation: 0.0028815384615384627, violated group size: 0.283\n",
-      "iteration: 14, error: 0.4655000000000001, fairness violation: 0.0027997142857142865, violated group size: 0.217\n",
-      "iteration: 15, error: 0.46506666666666674, fairness violation: 0.002728800000000001, violated group size: 0.217\n",
-      "iteration: 16, error: 0.4646875, fairness violation: 0.0026667500000000007, violated group size: 0.217\n",
-      "iteration: 17, error: 0.4643529411764707, fairness violation: 0.002612000000000001, violated group size: 0.283\n",
-      "iteration: 18, error: 0.46405555555555567, fairness violation: 0.002563333333333334, violated group size: 0.217\n",
-      "iteration: 19, error: 0.4637894736842106, fairness violation: 0.0025197894736842096, violated group size: 0.217\n",
-      "iteration: 20, error: 0.46354999999999996, fairness violation: 0.0024806000000000008, violated group size: 0.283\n",
-      "iteration: 21, error: 0.4633333333333334, fairness violation: 0.0024451428571428584, violated group size: 0.217\n",
-      "iteration: 22, error: 0.4631363636363638, fairness violation: 0.0024129090909090914, violated group size: 0.283\n",
-      "iteration: 23, error: 0.46295652173913054, fairness violation: 0.002383478260869566, violated group size: 0.217\n",
-      "iteration: 24, error: 0.4627916666666667, fairness violation: 0.002356500000000001, violated group size: 0.283\n",
-      "iteration: 25, error: 0.4626400000000001, fairness violation: 0.0023316800000000018, violated group size: 0.283\n",
-      "iteration: 26, error: 0.4625000000000001, fairness violation: 0.0023087692307692314, violated group size: 0.217\n",
-      "iteration: 27, error: 0.4623703703703705, fairness violation: 0.0022875555555555557, violated group size: 0.217\n",
-      "iteration: 28, error: 0.46224999999999994, fairness violation: 0.0022678571428571426, violated group size: 0.217\n",
-      "iteration: 29, error: 0.46213793103448264, fairness violation: 0.0022495172413793106, violated group size: 0.217\n",
-      "iteration: 30, error: 0.46203333333333335, fairness violation: 0.0022324000000000003, violated group size: 0.217\n",
-      "iteration: 31, error: 0.46193548387096783, fairness violation: 0.0022163870967741935, violated group size: 0.217\n",
-      "iteration: 32, error: 0.46184375, fairness violation: 0.0022013749999999993, violated group size: 0.217\n",
-      "iteration: 33, error: 0.459969696969697, fairness violation: 0.0023319393939393944, violated group size: 0.283\n",
-      "iteration: 34, error: 0.4582058823529412, fairness violation: 0.002454823529411765, violated group size: 0.217\n",
-      "iteration: 35, error: 0.45654285714285714, fairness violation: 0.0025706857142857144, violated group size: 0.217\n",
-      "iteration: 36, error: 0.4549722222222221, fairness violation: 0.0026801111111111114, violated group size: 0.283\n",
-      "iteration: 37, error: 0.4534864864864866, fairness violation: 0.0027836216216216214, violated group size: 0.283\n",
-      "iteration: 38, error: 0.45207894736842097, fairness violation: 0.0028816842105263162, violated group size: 0.283\n",
-      "iteration: 39, error: 0.4507435897435898, fairness violation: 0.0029747179487179492, violated group size: 0.217\n",
-      "iteration: 40, error: 0.44947499999999996, fairness violation: 0.0030631000000000005, violated group size: 0.217\n",
-      "iteration: 41, error: 0.44826829268292684, fairness violation: 0.0031471707317073175, violated group size: 0.283\n",
-      "iteration: 42, error: 0.4471190476190476, fairness violation: 0.0032272380952380955, violated group size: 0.217\n",
-      "iteration: 43, error: 0.44602325581395347, fairness violation: 0.0033035813953488386, violated group size: 0.283\n",
-      "iteration: 44, error: 0.44497727272727267, fairness violation: 0.0033764545454545453, violated group size: 0.283\n",
-      "iteration: 45, error: 0.4439777777777778, fairness violation: 0.003446088888888888, violated group size: 0.217\n",
-      "iteration: 46, error: 0.44302173913043474, fairness violation: 0.0035126956521739122, violated group size: 0.217\n",
-      "iteration: 47, error: 0.44210638297872346, fairness violation: 0.0035764680851063826, violated group size: 0.217\n",
-      "iteration: 48, error: 0.4412291666666666, fairness violation: 0.003637583333333332, violated group size: 0.217\n",
-      "iteration: 49, error: 0.4403877551020407, fairness violation: 0.0036962040816326523, violated group size: 0.217\n",
-      "iteration: 50, error: 0.4395600000000001, fairness violation: 0.0037524800000000003, violated group size: 0.217\n",
-      "iteration: 51, error: 0.43876470588235295, fairness violation: 0.0038065490196078425, violated group size: 0.217\n",
-      "iteration: 52, error: 0.438, fairness violation: 0.003858538461538461, violated group size: 0.283\n",
-      "iteration: 53, error: 0.4372641509433963, fairness violation: 0.003908566037735848, violated group size: 0.217\n",
-      "iteration: 54, error: 0.4365555555555556, fairness violation: 0.003956740740740741, violated group size: 0.283\n",
-      "iteration: 55, error: 0.4358181818181819, fairness violation: 0.004003163636363636, violated group size: 0.217\n",
-      "iteration: 56, error: 0.4351071428571429, fairness violation: 0.004047928571428571, violated group size: 0.217\n",
-      "iteration: 57, error: 0.4344736842105262, fairness violation: 0.004091122807017543, violated group size: 0.217\n",
-      "iteration: 58, error: 0.43381034482758624, fairness violation: 0.004132827586206895, violated group size: 0.217\n",
-      "iteration: 59, error: 0.4331694915254237, fairness violation: 0.0041731186440677965, violated group size: 0.283\n",
-      "iteration: 60, error: 0.43254999999999993, fairness violation: 0.004212066666666666, violated group size: 0.217\n",
-      "iteration: 61, error: 0.4319508196721312, fairness violation: 0.004249737704918031, violated group size: 0.217\n",
-      "iteration: 62, error: 0.4313709677419356, fairness violation: 0.004286193548387096, violated group size: 0.217\n",
-      "iteration: 63, error: 0.43080952380952386, fairness violation: 0.004321492063492062, violated group size: 0.283\n",
-      "iteration: 64, error: 0.430265625, fairness violation: 0.004355687499999999, violated group size: 0.283\n",
-      "iteration: 65, error: 0.4297384615384615, fairness violation: 0.004388830769230769, violated group size: 0.283\n",
-      "iteration: 66, error: 0.42922727272727274, fairness violation: 0.004420969696969697, violated group size: 0.217\n",
-      "iteration: 67, error: 0.42873134328358203, fairness violation: 0.004452149253731343, violated group size: 0.217\n",
-      "iteration: 68, error: 0.42824999999999996, fairness violation: 0.0044824117647058815, violated group size: 0.283\n",
-      "iteration: 69, error: 0.42778260869565227, fairness violation: 0.004511797101449274, violated group size: 0.217\n",
-      "iteration: 70, error: 0.42732857142857145, fairness violation: 0.004540342857142856, violated group size: 0.283\n",
-      "iteration: 71, error: 0.42688732394366197, fairness violation: 0.004568084507042252, violated group size: 0.217\n",
-      "iteration: 72, error: 0.4264583333333332, fairness violation: 0.004595055555555555, violated group size: 0.283\n",
-      "iteration: 73, error: 0.42604109589041106, fairness violation: 0.004621287671232876, violated group size: 0.217\n",
-      "iteration: 74, error: 0.4256351351351351, fairness violation: 0.0046468108108108095, violated group size: 0.283\n",
-      "iteration: 75, error: 0.42524, fairness violation: 0.004671653333333331, violated group size: 0.217\n",
-      "iteration: 76, error: 0.4248552631578947, fairness violation: 0.004695842105263155, violated group size: 0.217\n",
-      "iteration: 77, error: 0.42448051948051946, fairness violation: 0.004719402597402596, violated group size: 0.217\n",
-      "iteration: 78, error: 0.4239871794871795, fairness violation: 0.00475905128205128, violated group size: 0.217\n",
-      "iteration: 79, error: 0.42363291139240505, fairness violation: 0.004781215189873418, violated group size: 0.283\n",
-      "iteration: 80, error: 0.42328750000000015, fairness violation: 0.004802824999999999, violated group size: 0.283\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+      },
+      "source": [
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Trusted-AI/AIF360/blob/master/examples/demo_gerryfair.ipynb)\n"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iteration: 81, error: 0.4229506172839506, fairness violation: 0.004823901234567901, violated group size: 0.283\n",
-      "iteration: 82, error: 0.4226219512195123, fairness violation: 0.004844463414634145, violated group size: 0.217\n",
-      "iteration: 83, error: 0.4221807228915662, fairness violation: 0.004880216867469879, violated group size: 0.217\n",
-      "iteration: 84, error: 0.42175, fairness violation: 0.004915119047619047, violated group size: 0.217\n",
-      "iteration: 85, error: 0.4214470588235294, fairness violation: 0.004933882352941174, violated group size: 0.217\n",
-      "iteration: 86, error: 0.4210348837209302, fairness violation: 0.004967348837209301, violated group size: 0.217\n",
-      "iteration: 87, error: 0.420632183908046, fairness violation: 0.005000045977011494, violated group size: 0.283\n",
-      "iteration: 88, error: 0.42035227272727277, fairness violation: 0.0050172045454545434, violated group size: 0.217\n",
-      "iteration: 89, error: 0.4200786516853933, fairness violation: 0.005033977528089887, violated group size: 0.217\n",
-      "iteration: 90, error: 0.4198111111111112, fairness violation: 0.005050377777777776, violated group size: 0.283\n",
-      "iteration: 91, error: 0.4195824175824176, fairness violation: 0.0050664175824175805, violated group size: 0.217\n",
-      "iteration: 92, error: 0.4193695652173913, fairness violation: 0.005082108695652173, violated group size: 0.217\n",
-      "iteration: 93, error: 0.41916129032258065, fairness violation: 0.005097462365591397, violated group size: 0.217\n",
-      "iteration: 94, error: 0.41895744680851066, fairness violation: 0.005112489361702126, violated group size: 0.217\n",
-      "iteration: 95, error: 0.41875789473684216, fairness violation: 0.005127199999999998, violated group size: 0.217\n",
-      "iteration: 96, error: 0.41856250000000006, fairness violation: 0.005141604166666665, violated group size: 0.283\n",
-      "iteration: 97, error: 0.418979381443299, fairness violation: 0.005106494845360823, violated group size: 0.217\n",
-      "iteration: 98, error: 0.41938775510204085, fairness violation: 0.005072102040816325, violated group size: 0.217\n",
-      "iteration: 99, error: 0.4197878787878788, fairness violation: 0.0050384040404040376, violated group size: 0.217\n",
-      "iteration: 100, error: 0.42018000000000005, fairness violation: 0.0050053799999999985, violated group size: 0.217\n",
-      "iteration: 101, error: 0.42056435643564366, fairness violation: 0.004973009900990098, violated group size: 0.217\n",
-      "iteration: 102, error: 0.42094117647058826, fairness violation: 0.00494127450980392, violated group size: 0.217\n",
-      "iteration: 103, error: 0.4213106796116506, fairness violation: 0.004910155339805824, violated group size: 0.217\n",
-      "iteration: 104, error: 0.4216730769230769, fairness violation: 0.004879634615384614, violated group size: 0.217\n",
-      "iteration: 105, error: 0.4220285714285715, fairness violation: 0.004849695238095237, violated group size: 0.217\n",
-      "iteration: 106, error: 0.4223773584905662, fairness violation: 0.004820320754716981, violated group size: 0.283\n",
-      "iteration: 107, error: 0.42271962616822434, fairness violation: 0.004791495327102803, violated group size: 0.217\n",
-      "iteration: 108, error: 0.4230555555555556, fairness violation: 0.0047632037037037035, violated group size: 0.217\n",
-      "iteration: 109, error: 0.4233853211009175, fairness violation: 0.00473543119266055, violated group size: 0.217\n",
-      "iteration: 110, error: 0.4237090909090908, fairness violation: 0.004708163636363636, violated group size: 0.217\n",
-      "iteration: 111, error: 0.424027027027027, fairness violation: 0.004681387387387387, violated group size: 0.283\n",
-      "iteration: 112, error: 0.42433928571428586, fairness violation: 0.004655089285714286, violated group size: 0.283\n",
-      "iteration: 113, error: 0.4241238938053097, fairness violation: 0.004671504424778761, violated group size: 0.217\n",
-      "iteration: 114, error: 0.42442982456140343, fairness violation: 0.004645754385964912, violated group size: 0.283\n",
-      "iteration: 115, error: 0.42473043478260875, fairness violation: 0.0046204521739130425, violated group size: 0.283\n",
-      "iteration: 116, error: 0.42502586206896553, fairness violation: 0.0045955862068965524, violated group size: 0.283\n",
-      "iteration: 117, error: 0.42481196581196584, fairness violation: 0.004611948717948717, violated group size: 0.217\n",
-      "iteration: 118, error: 0.4251016949152542, fairness violation: 0.004587576271186439, violated group size: 0.217\n",
-      "iteration: 119, error: 0.42489075630252104, fairness violation: 0.004603731092436974, violated group size: 0.217\n",
-      "iteration: 120, error: 0.4251750000000001, fairness violation: 0.0045798333333333325, violated group size: 0.217\n",
-      "iteration: 121, error: 0.4249669421487604, fairness violation: 0.004595785123966942, violated group size: 0.283\n",
-      "iteration: 122, error: 0.4247622950819671, fairness violation: 0.0046114754098360656, violated group size: 0.217\n",
-      "iteration: 123, error: 0.42456097560975614, fairness violation: 0.00462691056910569, violated group size: 0.217\n",
-      "iteration: 124, error: 0.42436290322580644, fairness violation: 0.004642096774193548, violated group size: 0.217\n",
-      "iteration: 125, error: 0.4241680000000001, fairness violation: 0.00465704, violated group size: 0.217\n",
-      "iteration: 126, error: 0.4239761904761905, fairness violation: 0.004671746031746031, violated group size: 0.217\n",
-      "iteration: 127, error: 0.42425196850393704, fairness violation: 0.004648629921259842, violated group size: 0.217\n",
-      "iteration: 128, error: 0.4240625, fairness violation: 0.004663171874999999, violated group size: 0.217\n",
-      "iteration: 129, error: 0.4238759689922481, fairness violation: 0.004677488372093024, violated group size: 0.283\n",
-      "iteration: 130, error: 0.42369230769230776, fairness violation: 0.004691584615384614, violated group size: 0.217\n",
-      "iteration: 131, error: 0.42351145038167937, fairness violation: 0.004705465648854962, violated group size: 0.217\n",
-      "iteration: 132, error: 0.4233333333333333, fairness violation: 0.004719136363636364, violated group size: 0.283\n",
-      "iteration: 133, error: 0.423157894736842, fairness violation: 0.0047326015037594, violated group size: 0.217\n",
-      "iteration: 134, error: 0.4229850746268656, fairness violation: 0.004745865671641791, violated group size: 0.217\n",
-      "iteration: 135, error: 0.42281481481481475, fairness violation: 0.004758933333333335, violated group size: 0.283\n",
-      "iteration: 136, error: 0.4226470588235294, fairness violation: 0.004771808823529411, violated group size: 0.217\n",
-      "iteration: 137, error: 0.42248175182481745, fairness violation: 0.004784496350364964, violated group size: 0.283\n",
-      "iteration: 138, error: 0.42231884057971014, fairness violation: 0.004797000000000002, violated group size: 0.283\n",
-      "iteration: 139, error: 0.42215827338129497, fairness violation: 0.004809323741007196, violated group size: 0.283\n",
-      "iteration: 140, error: 0.42200000000000004, fairness violation: 0.004821471428571429, violated group size: 0.217\n",
-      "iteration: 141, error: 0.4218439716312057, fairness violation: 0.0048334468085106394, violated group size: 0.217\n",
-      "iteration: 142, error: 0.42169014084507045, fairness violation: 0.004845253521126761, violated group size: 0.283\n",
-      "iteration: 143, error: 0.4215384615384616, fairness violation: 0.004856895104895106, violated group size: 0.283\n",
-      "iteration: 144, error: 0.4213888888888888, fairness violation: 0.004868375, violated group size: 0.217\n",
-      "iteration: 145, error: 0.42124137931034483, fairness violation: 0.004879696551724138, violated group size: 0.217\n",
-      "iteration: 146, error: 0.4210958904109589, fairness violation: 0.00489086301369863, violated group size: 0.217\n",
-      "iteration: 147, error: 0.4209523809523809, fairness violation: 0.004901877551020409, violated group size: 0.217\n",
-      "iteration: 148, error: 0.42081081081081084, fairness violation: 0.004912743243243244, violated group size: 0.217\n",
-      "iteration: 149, error: 0.42067114093959734, fairness violation: 0.004923463087248323, violated group size: 0.283\n",
-      "iteration: 150, error: 0.4205333333333334, fairness violation: 0.004934040000000001, violated group size: 0.217\n",
-      "iteration: 151, error: 0.4203973509933776, fairness violation: 0.004944476821192053, violated group size: 0.217\n",
-      "iteration: 152, error: 0.4202631578947368, fairness violation: 0.0049547763157894754, violated group size: 0.283\n",
-      "iteration: 153, error: 0.4201307189542483, fairness violation: 0.00496494117647059, violated group size: 0.283\n",
-      "iteration: 154, error: 0.42, fairness violation: 0.004974974025974027, violated group size: 0.283\n",
-      "iteration: 155, error: 0.4198709677419355, fairness violation: 0.0049848774193548395, violated group size: 0.217\n",
-      "iteration: 156, error: 0.4197435897435898, fairness violation: 0.004994653846153847, violated group size: 0.217\n",
-      "iteration: 157, error: 0.4196178343949045, fairness violation: 0.0050043057324840766, violated group size: 0.217\n"
-     ]
+      "cell_type": "code",
+      "source": [
+        "!pip install aif360"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nTdJbsvOhAeU",
+        "outputId": "3da8ff56-8a28-47a3-c8e9-b751018039c6"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting aif360\n",
+            "  Downloading aif360-0.5.0-py3-none-any.whl (214 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m214.1/214.1 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.23.5)\n",
+            "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.11.2)\n",
+            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.5.3)\n",
+            "Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.2.2)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from aif360) (3.7.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->aif360) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->aif360) (2023.3.post1)\n",
+            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->aif360) (1.3.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->aif360) (3.2.0)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (1.1.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (0.11.0)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (4.42.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (23.1)\n",
+            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (3.1.1)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.24.0->aif360) (1.16.0)\n",
+            "Installing collected packages: aif360\n",
+            "Successfully installed aif360-0.5.0\n"
+          ]
+        }
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iteration: 158, error: 0.4194936708860761, fairness violation: 0.005013835443037975, violated group size: 0.217\n",
-      "iteration: 159, error: 0.41937106918239, fairness violation: 0.005023245283018869, violated group size: 0.283\n",
-      "iteration: 160, error: 0.41925000000000007, fairness violation: 0.0050325375, violated group size: 0.217\n",
-      "iteration: 161, error: 0.4191304347826087, fairness violation: 0.005041714285714285, violated group size: 0.217\n",
-      "iteration: 162, error: 0.41901234567901235, fairness violation: 0.005050777777777778, violated group size: 0.283\n",
-      "iteration: 163, error: 0.41889570552147243, fairness violation: 0.005059730061349694, violated group size: 0.283\n",
-      "iteration: 164, error: 0.4191402439024391, fairness violation: 0.005039463414634148, violated group size: 0.283\n",
-      "iteration: 165, error: 0.41938181818181824, fairness violation: 0.005019442424242424, violated group size: 0.217\n",
-      "iteration: 166, error: 0.4192650602409639, fairness violation: 0.005028421686746988, violated group size: 0.217\n",
-      "iteration: 167, error: 0.41950299401197605, fairness violation: 0.005008706586826348, violated group size: 0.217\n",
-      "iteration: 168, error: 0.41973809523809513, fairness violation: 0.004989226190476189, violated group size: 0.217\n",
-      "iteration: 169, error: 0.41997041420118336, fairness violation: 0.0049699763313609474, violated group size: 0.283\n",
-      "iteration: 170, error: 0.4202, fairness violation: 0.00495095294117647, violated group size: 0.283\n",
-      "iteration: 171, error: 0.4204269005847953, fairness violation: 0.004932152046783625, violated group size: 0.217\n",
-      "iteration: 172, error: 0.42065116279069764, fairness violation: 0.00491356976744186, violated group size: 0.217\n",
-      "iteration: 173, error: 0.4208728323699421, fairness violation: 0.004895202312138728, violated group size: 0.217\n",
-      "iteration: 174, error: 0.42109195402298855, fairness violation: 0.004877045977011494, violated group size: 0.217\n",
-      "iteration: 175, error: 0.4213085714285715, fairness violation: 0.004859097142857142, violated group size: 0.217\n",
-      "iteration: 176, error: 0.42152272727272727, fairness violation: 0.0048413522727272715, violated group size: 0.217\n",
-      "iteration: 177, error: 0.42173446327683617, fairness violation: 0.00482380790960452, violated group size: 0.217\n",
-      "iteration: 178, error: 0.42161235955056187, fairness violation: 0.004833280898876404, violated group size: 0.217\n",
-      "iteration: 179, error: 0.42182122905027924, fairness violation: 0.004815977653631285, violated group size: 0.217\n",
-      "iteration: 180, error: 0.4220277777777778, fairness violation: 0.004798866666666665, violated group size: 0.217\n",
-      "iteration: 181, error: 0.42223204419889504, fairness violation: 0.004781944751381214, violated group size: 0.283\n",
-      "iteration: 182, error: 0.4224340659340659, fairness violation: 0.004765208791208789, violated group size: 0.217\n",
-      "iteration: 183, error: 0.4226338797814208, fairness violation: 0.004748655737704917, violated group size: 0.217\n",
-      "iteration: 184, error: 0.4228315217391304, fairness violation: 0.004732282608695651, violated group size: 0.217\n",
-      "iteration: 185, error: 0.4230270270270271, fairness violation: 0.004716086486486487, violated group size: 0.217\n",
-      "iteration: 186, error: 0.4229032258064515, fairness violation: 0.0047257311827957, violated group size: 0.283\n",
-      "iteration: 187, error: 0.4230962566844919, fairness violation: 0.0047097433155080205, violated group size: 0.217\n",
-      "iteration: 188, error: 0.4229734042553191, fairness violation: 0.00471931914893617, violated group size: 0.283\n",
-      "iteration: 189, error: 0.4231640211640213, fairness violation: 0.004703534391534391, violated group size: 0.217\n",
-      "iteration: 190, error: 0.42304210526315794, fairness violation: 0.004713042105263158, violated group size: 0.217\n",
-      "iteration: 191, error: 0.4232303664921467, fairness violation: 0.004697455497382198, violated group size: 0.217\n",
-      "iteration: 192, error: 0.42310937499999995, fairness violation: 0.004706895833333333, violated group size: 0.217\n",
-      "iteration: 193, error: 0.42329533678756476, fairness violation: 0.004691502590673575, violated group size: 0.283\n",
-      "iteration: 194, error: 0.4231752577319588, fairness violation: 0.004700876288659792, violated group size: 0.217\n",
-      "iteration: 195, error: 0.4230564102564103, fairness violation: 0.004710153846153845, violated group size: 0.217\n",
-      "iteration: 196, error: 0.4229387755102041, fairness violation: 0.004719336734693878, violated group size: 0.283\n",
-      "iteration: 197, error: 0.4228223350253807, fairness violation: 0.004728426395939086, violated group size: 0.283\n",
-      "iteration: 198, error: 0.4227070707070707, fairness violation: 0.004737424242424242, violated group size: 0.217\n",
-      "iteration: 199, error: 0.4228894472361809, fairness violation: 0.004722341708542713, violated group size: 0.217\n",
-      "iteration: 200, error: 0.42277499999999996, fairness violation: 0.004731279999999999, violated group size: 0.217\n",
-      "iteration: 201, error: 0.4226616915422886, fairness violation: 0.004740129353233829, violated group size: 0.217\n",
-      "iteration: 202, error: 0.4225495049504951, fairness violation: 0.00474889108910891, violated group size: 0.217\n",
-      "iteration: 203, error: 0.42243842364532025, fairness violation: 0.004757566502463053, violated group size: 0.283\n",
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-      "iteration: 205, error: 0.422219512195122, fairness violation: 0.004774663414634145, violated group size: 0.217\n",
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-      "iteration: 207, error: 0.4220048309178744, fairness violation: 0.00479142995169082, violated group size: 0.217\n",
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-      "iteration: 214, error: 0.421285046728972, fairness violation: 0.0048476448598130835, violated group size: 0.217\n",
-      "iteration: 215, error: 0.42118604651162794, fairness violation: 0.004855376744186045, violated group size: 0.217\n",
-      "iteration: 216, error: 0.4210879629629629, fairness violation: 0.004863037037037037, violated group size: 0.283\n",
-      "iteration: 217, error: 0.42099078341013824, fairness violation: 0.004870626728110601, violated group size: 0.283\n",
-      "iteration: 218, error: 0.42089449541284396, fairness violation: 0.004878146788990825, violated group size: 0.217\n",
-      "iteration: 219, error: 0.42079908675799094, fairness violation: 0.004885598173515983, violated group size: 0.283\n",
-      "iteration: 220, error: 0.4207045454545455, fairness violation: 0.004892981818181818, violated group size: 0.283\n",
-      "iteration: 221, error: 0.4206108597285068, fairness violation: 0.004900298642533936, violated group size: 0.283\n",
-      "iteration: 222, error: 0.4205180180180179, fairness violation: 0.004907549549549549, violated group size: 0.217\n",
-      "iteration: 223, error: 0.4204260089686098, fairness violation: 0.004914735426008968, violated group size: 0.217\n",
-      "iteration: 224, error: 0.4203348214285714, fairness violation: 0.004921857142857143, violated group size: 0.283\n",
-      "iteration: 225, error: 0.4202444444444444, fairness violation: 0.004928915555555555, violated group size: 0.217\n",
-      "iteration: 226, error: 0.42015486725663725, fairness violation: 0.004935911504424777, violated group size: 0.217\n",
-      "iteration: 227, error: 0.4200660792951542, fairness violation: 0.004942845814977973, violated group size: 0.217\n",
-      "iteration: 228, error: 0.4199780701754386, fairness violation: 0.004949719298245614, violated group size: 0.217\n",
-      "iteration: 229, error: 0.4198908296943231, fairness violation: 0.004956532751091703, violated group size: 0.283\n",
-      "iteration: 230, error: 0.419804347826087, fairness violation: 0.004963286956521739, violated group size: 0.283\n",
-      "iteration: 231, error: 0.4197186147186147, fairness violation: 0.004969982683982686, violated group size: 0.283\n",
-      "iteration: 232, error: 0.4196336206896552, fairness violation: 0.004976620689655175, violated group size: 0.283\n",
-      "iteration: 233, error: 0.419549356223176, fairness violation: 0.004983201716738197, violated group size: 0.283\n",
-      "iteration: 234, error: 0.419465811965812, fairness violation: 0.0049897264957264945, violated group size: 0.217\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "pycharm": {
+          "is_executing": false
+        },
+        "id": "8MX9m3k_g9Ft"
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "import sys\n",
+        "sys.path.append(\"../\")\n",
+        "from aif360.algorithms.inprocessing import GerryFairClassifier\n",
+        "from aif360.algorithms.inprocessing.gerryfair.clean import array_to_tuple\n",
+        "from aif360.algorithms.inprocessing.gerryfair.auditor import Auditor\n",
+        "from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions import load_preproc_data_adult\n",
+        "from sklearn import svm\n",
+        "from sklearn import tree\n",
+        "from sklearn.kernel_ridge import KernelRidge\n",
+        "from sklearn import linear_model\n",
+        "from aif360.metrics import BinaryLabelDatasetMetric\n",
+        "from IPython.display import Image\n",
+        "import pickle\n",
+        "import matplotlib.pyplot as plt"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iteration: 235, error: 0.4193829787234044, fairness violation: 0.00499619574468085, violated group size: 0.217\n",
-      "iteration: 236, error: 0.41930084745762713, fairness violation: 0.005002610169491525, violated group size: 0.283\n",
-      "iteration: 237, error: 0.4192194092827004, fairness violation: 0.005008970464135021, violated group size: 0.283\n",
-      "iteration: 238, error: 0.41913865546218493, fairness violation: 0.00501527731092437, violated group size: 0.283\n",
-      "iteration: 239, error: 0.4190585774058578, fairness violation: 0.005021531380753138, violated group size: 0.283\n",
-      "iteration: 240, error: 0.4189791666666667, fairness violation: 0.005027733333333333, violated group size: 0.217\n",
-      "iteration: 241, error: 0.41890041493775937, fairness violation: 0.005033883817427385, violated group size: 0.217\n",
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-      "iteration: 243, error: 0.4187448559670781, fairness violation: 0.005046032921810699, violated group size: 0.217\n",
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-      "iteration: 248, error: 0.41908064516129034, fairness violation: 0.005017798387096774, violated group size: 0.283\n",
-      "iteration: 249, error: 0.4192409638554217, fairness violation: 0.005004618473895581, violated group size: 0.217\n",
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-      "iteration: 252, error: 0.4197142857142857, fairness violation: 0.004965706349206349, violated group size: 0.217\n",
-      "iteration: 253, error: 0.4198695652173912, fairness violation: 0.00495294071146245, violated group size: 0.217\n",
-      "iteration: 254, error: 0.42002362204724414, fairness violation: 0.00494027559055118, violated group size: 0.217\n",
-      "iteration: 255, error: 0.4201764705882353, fairness violation: 0.004927709803921568, violated group size: 0.217\n",
-      "iteration: 256, error: 0.420328125, fairness violation: 0.004915242187499999, violated group size: 0.217\n",
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-      "iteration: 258, error: 0.42062790697674424, fairness violation: 0.004890596899224807, violated group size: 0.217\n",
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-      "iteration: 260, error: 0.42069615384615383, fairness violation: 0.004884692307692307, violated group size: 0.217\n",
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-      "iteration: 264, error: 0.4208295454545454, fairness violation: 0.004873151515151516, violated group size: 0.283\n",
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-      "iteration: 288, error: 0.4219618055555556, fairness violation: 0.004777486111111113, violated group size: 0.283\n",
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-      "iteration: 291, error: 0.42193814432989696, fairness violation: 0.004778941580756014, violated group size: 0.283\n",
-      "iteration: 292, error: 0.42186301369863016, fairness violation: 0.0047848698630136985, violated group size: 0.217\n",
-      "iteration: 293, error: 0.4217883959044368, fairness violation: 0.004790757679180888, violated group size: 0.217\n",
-      "iteration: 294, error: 0.42171428571428576, fairness violation: 0.004796605442176871, violated group size: 0.217\n",
-      "iteration: 295, error: 0.4216406779661017, fairness violation: 0.004802413559322035, violated group size: 0.217\n",
-      "iteration: 296, error: 0.4215675675675675, fairness violation: 0.004808182432432432, violated group size: 0.217\n",
-      "iteration: 297, error: 0.4214949494949495, fairness violation: 0.00481391245791246, violated group size: 0.283\n",
-      "iteration: 298, error: 0.4214228187919464, fairness violation: 0.0048196040268456385, violated group size: 0.217\n",
-      "iteration: 299, error: 0.42135117056856186, fairness violation: 0.0048252575250836115, violated group size: 0.217\n",
-      "iteration: 300, error: 0.42128, fairness violation: 0.004830873333333335, violated group size: 0.283\n",
-      "iteration: 301, error: 0.42120930232558146, fairness violation: 0.004836451827242525, violated group size: 0.217\n",
-      "iteration: 302, error: 0.42113907284768215, fairness violation: 0.004841993377483444, violated group size: 0.217\n",
-      "iteration: 303, error: 0.42106930693069305, fairness violation: 0.004847498349834984, violated group size: 0.217\n",
-      "iteration: 304, error: 0.4211940789473684, fairness violation: 0.004837263157894738, violated group size: 0.283\n",
-      "iteration: 305, error: 0.4211245901639345, fairness violation: 0.004842747540983607, violated group size: 0.283\n",
-      "iteration: 306, error: 0.4210555555555555, fairness violation: 0.004848196078431373, violated group size: 0.217\n",
-      "iteration: 307, error: 0.42098697068403895, fairness violation: 0.004853609120521175, violated group size: 0.283\n",
-      "iteration: 308, error: 0.4209188311688312, fairness violation: 0.004858987012987015, violated group size: 0.283\n",
-      "iteration: 309, error: 0.42085113268608415, fairness violation: 0.00486433009708738, violated group size: 0.283\n"
-     ]
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import urllib\n",
+        "import aif360\n",
+        "\n",
+        "# Obtain the location where it is installed\n",
+        "LIB_PATH = aif360.__file__.rsplit(\"aif360\", 1)[0]\n",
+        "\n",
+        "# check if the data got download properly\n",
+        "def check_data_or_download(destn, files, data_source_directory):\n",
+        "    check = all(item in os.listdir(destn) for item in files)\n",
+        "    if check:\n",
+        "        print(\"Adult dataset is available for us\")\n",
+        "    else:\n",
+        "        print(\"Some files are missing. Downloading now.\")\n",
+        "        for data_file in files:\n",
+        "            _ = urllib.request.urlretrieve(data_source_directory + data_file,\n",
+        "                                           os.path.join(destn, data_file))\n",
+        "\n",
+        "# Download adult dataset\n",
+        "data_source_directory = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/\"\n",
+        "destn = os.path.join(LIB_PATH, \"aif360\", \"data\", \"raw\", \"adult\")\n",
+        "files = [\"adult.data\", \"adult.test\", \"adult.names\"]\n",
+        "\n",
+        "check_data_or_download(destn, files, data_source_directory)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R7RtdPi0hmfH",
+        "outputId": "190a4308-9e85-4da3-96cb-ae042eb09453"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Some files are missing. Downloading now.\n"
+          ]
+        }
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iteration: 310, error: 0.4207838709677419, fairness violation: 0.00486963870967742, violated group size: 0.283\n",
-      "iteration: 311, error: 0.420717041800643, fairness violation: 0.004874913183279744, violated group size: 0.217\n",
-      "iteration: 312, error: 0.42065064102564104, fairness violation: 0.0048801538461538466, violated group size: 0.217\n",
-      "iteration: 313, error: 0.42058466453674115, fairness violation: 0.0048853610223642185, violated group size: 0.217\n",
-      "iteration: 314, error: 0.42051910828025474, fairness violation: 0.004890535031847135, violated group size: 0.217\n",
-      "iteration: 315, error: 0.4204539682539683, fairness violation: 0.004895676190476191, violated group size: 0.217\n",
-      "iteration: 316, error: 0.4203892405063292, fairness violation: 0.004900784810126583, violated group size: 0.217\n",
-      "iteration: 317, error: 0.4203249211356468, fairness violation: 0.004905861198738172, violated group size: 0.283\n",
-      "iteration: 318, error: 0.4202610062893082, fairness violation: 0.00491090566037736, violated group size: 0.217\n",
-      "iteration: 319, error: 0.4201974921630094, fairness violation: 0.004915918495297806, violated group size: 0.217\n",
-      "iteration: 320, error: 0.4201343749999999, fairness violation: 0.004920900000000002, violated group size: 0.217\n",
-      "iteration: 321, error: 0.4200716510903427, fairness violation: 0.004925850467289721, violated group size: 0.217\n",
-      "iteration: 322, error: 0.4200093167701862, fairness violation: 0.0049307701863354056, violated group size: 0.283\n",
-      "iteration: 323, error: 0.4199473684210526, fairness violation: 0.00493565944272446, violated group size: 0.283\n",
-      "iteration: 324, error: 0.41988580246913576, fairness violation: 0.004940518518518519, violated group size: 0.217\n",
-      "iteration: 325, error: 0.41982461538461535, fairness violation: 0.004945347692307694, violated group size: 0.217\n",
-      "iteration: 326, error: 0.4197638036809816, fairness violation: 0.004950147239263805, violated group size: 0.283\n",
-      "iteration: 327, error: 0.4197033639143731, fairness violation: 0.004954917431192661, violated group size: 0.283\n",
-      "iteration: 328, error: 0.4196432926829268, fairness violation: 0.004959658536585366, violated group size: 0.217\n",
-      "iteration: 329, error: 0.41958358662613987, fairness violation: 0.004964370820668694, violated group size: 0.283\n",
-      "iteration: 330, error: 0.41952424242424236, fairness violation: 0.004969054545454545, violated group size: 0.217\n",
-      "iteration: 331, error: 0.41946525679758306, fairness violation: 0.00497370996978852, violated group size: 0.217\n",
-      "iteration: 332, error: 0.41940662650602417, fairness violation: 0.004978337349397591, violated group size: 0.217\n",
-      "iteration: 333, error: 0.4193483483483482, fairness violation: 0.004982936936936937, violated group size: 0.217\n",
-      "iteration: 334, error: 0.4192904191616766, fairness violation: 0.004987508982035928, violated group size: 0.217\n",
-      "iteration: 335, error: 0.4192328358208956, fairness violation: 0.004992053731343284, violated group size: 0.283\n",
-      "iteration: 336, error: 0.4191755952380953, fairness violation: 0.00499657142857143, violated group size: 0.283\n",
-      "iteration: 337, error: 0.4191186943620178, fairness violation: 0.0050010623145400595, violated group size: 0.217\n",
-      "iteration: 338, error: 0.41906213017751476, fairness violation: 0.005005526627218935, violated group size: 0.217\n",
-      "iteration: 339, error: 0.4190058997050148, fairness violation: 0.005009964601769911, violated group size: 0.217\n",
-      "iteration: 340, error: 0.41894999999999993, fairness violation: 0.005014376470588236, violated group size: 0.283\n",
-      "iteration: 341, error: 0.41889442815249267, fairness violation: 0.005018762463343108, violated group size: 0.217\n",
-      "iteration: 342, error: 0.41883918128654973, fairness violation: 0.005023122807017544, violated group size: 0.217\n",
-      "iteration: 343, error: 0.41878425655976675, fairness violation: 0.0050274577259475225, violated group size: 0.283\n",
-      "iteration: 344, error: 0.4187296511627907, fairness violation: 0.005031767441860465, violated group size: 0.217\n",
-      "iteration: 345, error: 0.4186753623188406, fairness violation: 0.005036052173913045, violated group size: 0.283\n",
-      "iteration: 346, error: 0.4186213872832369, fairness violation: 0.005040312138728323, violated group size: 0.217\n",
-      "iteration: 347, error: 0.41856772334293946, fairness violation: 0.005044547550432276, violated group size: 0.283\n",
-      "iteration: 348, error: 0.41851436781609197, fairness violation: 0.005048758620689655, violated group size: 0.217\n",
-      "iteration: 349, error: 0.418461318051576, fairness violation: 0.005052945558739255, violated group size: 0.283\n",
-      "iteration: 350, error: 0.4185771428571428, fairness violation: 0.005043468571428572, violated group size: 0.283\n",
-      "iteration: 351, error: 0.4186923076923077, fairness violation: 0.005034045584045584, violated group size: 0.217\n",
-      "iteration: 352, error: 0.4188068181818182, fairness violation: 0.005024676136363637, violated group size: 0.283\n",
-      "iteration: 353, error: 0.4189206798866855, fairness violation: 0.005015359773371105, violated group size: 0.217\n",
-      "iteration: 354, error: 0.41903389830508475, fairness violation: 0.005006096045197741, violated group size: 0.283\n",
-      "iteration: 355, error: 0.41914647887323936, fairness violation: 0.004996884507042254, violated group size: 0.283\n",
-      "iteration: 356, error: 0.4192584269662922, fairness violation: 0.004987724719101122, violated group size: 0.217\n",
-      "iteration: 357, error: 0.41936974789915965, fairness violation: 0.0049786162464986, violated group size: 0.217\n",
-      "iteration: 358, error: 0.41948044692737424, fairness violation: 0.004969558659217878, violated group size: 0.217\n",
-      "iteration: 359, error: 0.41959052924791085, fairness violation: 0.004960551532033426, violated group size: 0.283\n",
-      "iteration: 360, error: 0.4195361111111111, fairness violation: 0.004964855555555557, violated group size: 0.283\n",
-      "iteration: 361, error: 0.4196454293628808, fairness violation: 0.004955911357340723, violated group size: 0.283\n",
-      "iteration: 362, error: 0.4197541436464089, fairness violation: 0.004947016574585636, violated group size: 0.217\n",
-      "iteration: 363, error: 0.4198622589531681, fairness violation: 0.004938170798898072, violated group size: 0.283\n",
-      "iteration: 364, error: 0.41996978021978026, fairness violation: 0.004929373626373626, violated group size: 0.217\n",
-      "iteration: 365, error: 0.42007671232876714, fairness violation: 0.004920624657534246, violated group size: 0.217\n",
-      "iteration: 366, error: 0.42018306010928963, fairness violation: 0.004911923497267759, violated group size: 0.217\n",
-      "iteration: 367, error: 0.4202888283378746, fairness violation: 0.004903269754768393, violated group size: 0.217\n",
-      "iteration: 368, error: 0.42039402173913043, fairness violation: 0.00489466304347826, violated group size: 0.217\n",
-      "iteration: 369, error: 0.4204986449864499, fairness violation: 0.00488610298102981, violated group size: 0.283\n",
-      "iteration: 370, error: 0.4206027027027027, fairness violation: 0.0048775891891891885, violated group size: 0.217\n",
-      "iteration: 371, error: 0.4207061994609164, fairness violation: 0.004869121293800538, violated group size: 0.217\n",
-      "iteration: 372, error: 0.4208091397849463, fairness violation: 0.004860698924731182, violated group size: 0.217\n",
-      "iteration: 373, error: 0.420911528150134, fairness violation: 0.004852321715817694, violated group size: 0.217\n",
-      "iteration: 374, error: 0.420855614973262, fairness violation: 0.004856754010695187, violated group size: 0.217\n",
-      "iteration: 375, error: 0.4209573333333334, fairness violation: 0.004848432, violated group size: 0.217\n",
-      "iteration: 376, error: 0.42105851063829786, fairness violation: 0.004840154255319148, violated group size: 0.217\n",
-      "iteration: 377, error: 0.4211591511936339, fairness violation: 0.004831920424403182, violated group size: 0.217\n",
-      "iteration: 378, error: 0.4211031746031746, fairness violation: 0.004836359788359788, violated group size: 0.217\n",
-      "iteration: 379, error: 0.42120316622691284, fairness violation: 0.004828179419525066, violated group size: 0.217\n",
-      "iteration: 380, error: 0.42130263157894726, fairness violation: 0.004820042105263157, violated group size: 0.217\n",
-      "iteration: 381, error: 0.42124671916010503, fairness violation: 0.004824477690288715, violated group size: 0.283\n",
-      "iteration: 382, error: 0.42134554973821986, fairness violation: 0.004816392670157068, violated group size: 0.283\n",
-      "iteration: 383, error: 0.42144386422976504, fairness violation: 0.004808349869451696, violated group size: 0.217\n",
-      "iteration: 384, error: 0.42154166666666676, fairness violation: 0.004800348958333333, violated group size: 0.283\n"
-     ]
+      "cell_type": "code",
+      "source": [
+        "# load data set\n",
+        "data_set = load_preproc_data_adult(sub_samp=1000, balance=True)\n",
+        "max_iterations = 500"
+      ],
+      "metadata": {
+        "id": "KZhYY9zVhp1i"
+      },
+      "execution_count": 6,
+      "outputs": []
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iteration: 385, error: 0.4214857142857143, fairness violation: 0.00480478961038961, violated group size: 0.217\n",
-      "iteration: 386, error: 0.4215829015544041, fairness violation: 0.0047968393782383415, violated group size: 0.217\n",
-      "iteration: 387, error: 0.42152713178294576, fairness violation: 0.004801266149870801, violated group size: 0.283\n",
-      "iteration: 388, error: 0.4216237113402061, fairness violation: 0.004793365979381444, violated group size: 0.283\n",
-      "iteration: 389, error: 0.42171979434447304, fairness violation: 0.004785506426735219, violated group size: 0.217\n",
-      "iteration: 390, error: 0.4218153846153847, fairness violation: 0.00477768717948718, violated group size: 0.217\n",
-      "iteration: 391, error: 0.4219104859335038, fairness violation: 0.004769907928388747, violated group size: 0.217\n",
-      "iteration: 392, error: 0.4218545918367348, fairness violation: 0.00477434693877551, violated group size: 0.217\n",
-      "iteration: 393, error: 0.42194910941475816, fairness violation: 0.004766615776081424, violated group size: 0.217\n",
-      "iteration: 394, error: 0.42204314720812186, fairness violation: 0.004758923857868021, violated group size: 0.283\n",
-      "iteration: 395, error: 0.4221367088607595, fairness violation: 0.00475127088607595, violated group size: 0.217\n",
-      "iteration: 396, error: 0.42208080808080806, fairness violation: 0.004755712121212122, violated group size: 0.283\n",
-      "iteration: 397, error: 0.42202518891687657, fairness violation: 0.004760130982367758, violated group size: 0.217\n",
-      "iteration: 398, error: 0.42196984924623115, fairness violation: 0.004764527638190955, violated group size: 0.283\n",
-      "iteration: 399, error: 0.42191478696741846, fairness violation: 0.004768902255639098, violated group size: 0.217\n",
-      "iteration: 400, error: 0.42186, fairness violation: 0.004773255, violated group size: 0.217\n",
-      "iteration: 401, error: 0.4218054862842893, fairness violation: 0.004777586034912718, violated group size: 0.283\n",
-      "iteration: 402, error: 0.4217512437810945, fairness violation: 0.00478189552238806, violated group size: 0.217\n",
-      "iteration: 403, error: 0.4218436724565757, fairness violation: 0.0047743374689826305, violated group size: 0.283\n",
-      "iteration: 404, error: 0.42178960396039605, fairness violation: 0.004778633663366336, violated group size: 0.217\n",
-      "iteration: 405, error: 0.4217358024691357, fairness violation: 0.004782908641975308, violated group size: 0.217\n",
-      "iteration: 406, error: 0.4216822660098523, fairness violation: 0.004787162561576355, violated group size: 0.217\n",
-      "iteration: 407, error: 0.4216289926289926, fairness violation: 0.004791395577395577, violated group size: 0.283\n",
-      "iteration: 408, error: 0.421575980392157, fairness violation: 0.004795607843137254, violated group size: 0.217\n",
-      "iteration: 409, error: 0.4215232273838631, fairness violation: 0.004799799511002444, violated group size: 0.217\n",
-      "iteration: 410, error: 0.42147073170731714, fairness violation: 0.004803970731707317, violated group size: 0.283\n",
-      "iteration: 411, error: 0.4214184914841849, fairness violation: 0.0048081216545012165, violated group size: 0.217\n",
-      "iteration: 412, error: 0.4213665048543689, fairness violation: 0.004812252427184466, violated group size: 0.283\n",
-      "iteration: 413, error: 0.42131476997578693, fairness violation: 0.004816363196125908, violated group size: 0.217\n",
-      "iteration: 414, error: 0.42126328502415455, fairness violation: 0.004820454106280194, violated group size: 0.217\n",
-      "iteration: 415, error: 0.4212120481927711, fairness violation: 0.004824525301204821, violated group size: 0.283\n",
-      "iteration: 416, error: 0.42116105769230766, fairness violation: 0.004828576923076923, violated group size: 0.217\n",
-      "iteration: 417, error: 0.4211103117505996, fairness violation: 0.004832609112709832, violated group size: 0.283\n",
-      "iteration: 418, error: 0.42105980861244025, fairness violation: 0.004836622009569378, violated group size: 0.283\n",
-      "iteration: 419, error: 0.42100954653937933, fairness violation: 0.004840615751789977, violated group size: 0.217\n",
-      "iteration: 420, error: 0.42110000000000003, fairness violation: 0.0048332238095238084, violated group size: 0.217\n",
-      "iteration: 421, error: 0.42104988123515436, fairness violation: 0.004837206650831354, violated group size: 0.217\n",
-      "iteration: 422, error: 0.42100000000000004, fairness violation: 0.004841170616113744, violated group size: 0.217\n",
-      "iteration: 423, error: 0.420950354609929, fairness violation: 0.004845115839243499, violated group size: 0.217\n",
-      "iteration: 424, error: 0.42104009433962253, fairness violation: 0.004837783018867924, violated group size: 0.217\n",
-      "iteration: 425, error: 0.4209905882352941, fairness violation: 0.004841717647058822, violated group size: 0.217\n",
-      "iteration: 426, error: 0.42094131455399053, fairness violation: 0.004845633802816901, violated group size: 0.217\n",
-      "iteration: 427, error: 0.42089227166276344, fairness violation: 0.004849531615925057, violated group size: 0.217\n",
-      "iteration: 428, error: 0.4208434579439252, fairness violation: 0.004853411214953271, violated group size: 0.217\n",
-      "iteration: 429, error: 0.4207948717948717, fairness violation: 0.0048572727272727274, violated group size: 0.283\n",
-      "iteration: 430, error: 0.42074651162790694, fairness violation: 0.004861116279069767, violated group size: 0.217\n",
-      "iteration: 431, error: 0.4206983758700697, fairness violation: 0.004864941995359629, violated group size: 0.283\n",
-      "iteration: 432, error: 0.420650462962963, fairness violation: 0.00486875, violated group size: 0.217\n",
-      "iteration: 433, error: 0.42060277136258656, fairness violation: 0.0048725404157043874, violated group size: 0.217\n",
-      "iteration: 434, error: 0.42055529953917054, fairness violation: 0.0048763133640553, violated group size: 0.283\n",
-      "iteration: 435, error: 0.42050804597701147, fairness violation: 0.004880068965517241, violated group size: 0.217\n",
-      "iteration: 436, error: 0.4204610091743119, fairness violation: 0.004883807339449542, violated group size: 0.217\n",
-      "iteration: 437, error: 0.4204141876430207, fairness violation: 0.004887528604118992, violated group size: 0.217\n",
-      "iteration: 438, error: 0.42036757990867574, fairness violation: 0.004891232876712329, violated group size: 0.217\n",
-      "iteration: 439, error: 0.4203211845102506, fairness violation: 0.0048949202733485206, violated group size: 0.283\n",
-      "iteration: 440, error: 0.42027499999999995, fairness violation: 0.00489859090909091, violated group size: 0.283\n",
-      "iteration: 441, error: 0.42022902494331066, fairness violation: 0.004902244897959184, violated group size: 0.283\n",
-      "iteration: 442, error: 0.42018325791855204, fairness violation: 0.004905882352941177, violated group size: 0.217\n",
-      "iteration: 443, error: 0.42013769751693003, fairness violation: 0.004909503386004516, violated group size: 0.283\n",
-      "iteration: 444, error: 0.42009234234234244, fairness violation: 0.004913108108108108, violated group size: 0.217\n",
-      "iteration: 445, error: 0.420047191011236, fairness violation: 0.004916696629213483, violated group size: 0.217\n",
-      "iteration: 446, error: 0.42000224215246645, fairness violation: 0.004920269058295964, violated group size: 0.217\n",
-      "iteration: 447, error: 0.4199574944071588, fairness violation: 0.004923825503355704, violated group size: 0.217\n",
-      "iteration: 448, error: 0.41991294642857147, fairness violation: 0.004927366071428571, violated group size: 0.217\n",
-      "iteration: 449, error: 0.41986859688195993, fairness violation: 0.004930890868596881, violated group size: 0.217\n",
-      "iteration: 450, error: 0.41982444444444433, fairness violation: 0.004934399999999999, violated group size: 0.217\n",
-      "iteration: 451, error: 0.41978048780487814, fairness violation: 0.004937893569844789, violated group size: 0.217\n",
-      "iteration: 452, error: 0.41973672566371684, fairness violation: 0.004941371681415928, violated group size: 0.217\n",
-      "iteration: 453, error: 0.41969315673289187, fairness violation: 0.0049448344370860925, violated group size: 0.217\n",
-      "iteration: 454, error: 0.41964977973568285, fairness violation: 0.004948281938325991, violated group size: 0.283\n",
-      "iteration: 455, error: 0.41960659340659345, fairness violation: 0.004951714285714286, violated group size: 0.283\n",
-      "iteration: 456, error: 0.41956359649122804, fairness violation: 0.004955131578947367, violated group size: 0.217\n",
-      "iteration: 457, error: 0.41952078774617063, fairness violation: 0.0049585339168490145, violated group size: 0.217\n",
-      "iteration: 458, error: 0.41947816593886456, fairness violation: 0.004961921397379911, violated group size: 0.217\n",
-      "iteration: 459, error: 0.4194357298474945, fairness violation: 0.00496529411764706, violated group size: 0.283\n",
-      "iteration: 460, error: 0.4193934782608696, fairness violation: 0.004968652173913044, violated group size: 0.283\n",
-      "iteration: 461, error: 0.41935140997830805, fairness violation: 0.004971995661605205, violated group size: 0.283\n",
-      "iteration: 462, error: 0.41930952380952374, fairness violation: 0.0049753246753246735, violated group size: 0.217\n",
-      "iteration: 463, error: 0.41926781857451406, fairness violation: 0.004978639308855291, violated group size: 0.217\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MEge2s2Wg9Fu"
+      },
+      "source": [
+        "**instantiate, fit, and predict**\n",
+        "\n",
+        "\n",
+        "We first demonstrate how to instantiate a `GerryFairClassifier`, `train` it with respect to rich subgroup fairness, and `predict` the label of a new example. We remark that when we set the `print_flag = True` at each iteration of the algorithm we print the error, fairness violation, and violated group size of most recent model. The error is the classification error of the classifier. At each round the Learner tries to find a classifier that minimizes the classification error plus a weighted sum of the fairness disparities on all the groups that the Auditor has found up until that point. By contrast the Auditor tries to find the group at each round with the greatest rich subgroup disparity with respect to the Learner's model. We define `violated group size` as the size (as a fraction of the dataset size) of this group, and the `fairness violation` as the `violated group size` times the difference in the statistical rate (FP or FN rate) on the group vs. the whole population.\n",
+        "\n",
+        "In the example below we set `max_iterations=500` which is an order of magnitude less than the time to convergence observed in [the rich subgroup fairness empirical paper](https://arxiv.org/abs/1808.08166), but advise that this can be highly dataset dependent. Our target $\\gamma$-disparity is $\\gamma = .005$, our statistical rate is false positive rate or `FP`, and our cost-sensitive classification oracle is linear regression (more on that below).\n"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iteration: 464, error: 0.41922629310344817, fairness violation: 0.004981939655172413, violated group size: 0.217\n",
-      "iteration: 465, error: 0.41918494623655916, fairness violation: 0.004985225806451612, violated group size: 0.217\n",
-      "iteration: 466, error: 0.4191437768240344, fairness violation: 0.004988497854077254, violated group size: 0.283\n",
-      "iteration: 467, error: 0.41910278372591, fairness violation: 0.004991755888650964, violated group size: 0.283\n",
-      "iteration: 468, error: 0.4190619658119658, fairness violation: 0.004994999999999998, violated group size: 0.217\n",
-      "iteration: 469, error: 0.4190213219616205, fairness violation: 0.0049982302771855, violated group size: 0.217\n",
-      "iteration: 470, error: 0.41898085106382993, fairness violation: 0.005001446808510636, violated group size: 0.283\n",
-      "iteration: 471, error: 0.41894055201698516, fairness violation: 0.005004649681528661, violated group size: 0.217\n",
-      "iteration: 472, error: 0.41890042372881364, fairness violation: 0.005007838983050847, violated group size: 0.283\n",
-      "iteration: 473, error: 0.4188604651162791, fairness violation: 0.005011014799154333, violated group size: 0.217\n",
-      "iteration: 474, error: 0.41882067510548526, fairness violation: 0.005014177215189871, violated group size: 0.217\n",
-      "iteration: 475, error: 0.41878105263157905, fairness violation: 0.0050173263157894735, violated group size: 0.283\n",
-      "iteration: 476, error: 0.41874159663865557, fairness violation: 0.0050204621848739485, violated group size: 0.217\n",
-      "iteration: 477, error: 0.4188259958071279, fairness violation: 0.005013576519916141, violated group size: 0.217\n",
-      "iteration: 478, error: 0.41878661087866115, fairness violation: 0.005016707112970709, violated group size: 0.217\n",
-      "iteration: 479, error: 0.4188705636743216, fairness violation: 0.005009858037578285, violated group size: 0.217\n",
-      "iteration: 480, error: 0.41883125000000004, fairness violation: 0.005012983333333334, violated group size: 0.283\n",
-      "iteration: 481, error: 0.4187920997920998, fairness violation: 0.005016095634095634, violated group size: 0.283\n",
-      "iteration: 482, error: 0.4187531120331951, fairness violation: 0.0050191950207468874, violated group size: 0.283\n",
-      "iteration: 483, error: 0.4188364389233955, fairness violation: 0.00501239751552795, violated group size: 0.217\n",
-      "iteration: 484, error: 0.41891942148760325, fairness violation: 0.005005628099173555, violated group size: 0.283\n",
-      "iteration: 485, error: 0.4190020618556701, fairness violation: 0.004998886597938144, violated group size: 0.283\n",
-      "iteration: 486, error: 0.41896296296296304, fairness violation: 0.005001995884773661, violated group size: 0.217\n",
-      "iteration: 487, error: 0.4190451745379877, fairness violation: 0.004995289527720739, violated group size: 0.283\n",
-      "iteration: 488, error: 0.4191270491803279, fairness violation: 0.004988610655737704, violated group size: 0.283\n",
-      "iteration: 489, error: 0.4192085889570552, fairness violation: 0.004981959100204497, violated group size: 0.217\n",
-      "iteration: 490, error: 0.41916938775510204, fairness violation: 0.004985077551020407, violated group size: 0.283\n",
-      "iteration: 491, error: 0.4192505091649695, fairness violation: 0.004978460285132381, violated group size: 0.217\n",
-      "iteration: 492, error: 0.4192113821138212, fairness violation: 0.004981573170731706, violated group size: 0.217\n",
-      "iteration: 493, error: 0.41917241379310355, fairness violation: 0.004984673427991887, violated group size: 0.283\n",
-      "iteration: 494, error: 0.41913360323886634, fairness violation: 0.004987761133603237, violated group size: 0.217\n",
-      "iteration: 495, error: 0.4192141414141415, fairness violation: 0.004981191919191918, violated group size: 0.217\n",
-      "iteration: 496, error: 0.4192943548387097, fairness violation: 0.004974649193548386, violated group size: 0.217\n",
-      "iteration: 497, error: 0.419374245472837, fairness violation: 0.004968132796780683, violated group size: 0.217\n",
-      "iteration: 498, error: 0.4194538152610441, fairness violation: 0.004961642570281124, violated group size: 0.217\n",
-      "iteration: 499, error: 0.41953306613226454, fairness violation: 0.0049551783567134255, violated group size: 0.217\n"
-     ]
-    }
-   ],
-   "source": [
-    "C = 100\n",
-    "print_flag = True\n",
-    "gamma = .005\n",
-    "\n",
-    "\n",
-    "fair_model = GerryFairClassifier(C=C, printflag=print_flag, gamma=gamma, fairness_def='FP',\n",
-    "             max_iters=max_iterations, heatmapflag=False)\n",
-    "\n",
-    "# fit method\n",
-    "fair_model.fit(data_set, early_termination=True)\n",
-    "\n",
-    "# predict method. If threshold in (0, 1) produces binary predictions\n",
-    "\n",
-    "dataset_yhat = fair_model.predict(data_set, threshold=False)\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3-d heatmaps**\n",
-    "\n",
-    "We now show to generate a 3d-heatmap of unfairness using the `generate_heatmap` method. The $X-Y$ axes in the plot represent the coefficients of the linear threshold function that defines a protected subgroup with respect to the first two sensitive attributes. Which $2$ attributes are considered sensitive can be overwritten with the `col_index` argument. The $Z$-axes is the $\\gamma$-disparity (FP) of the corresponding subgroup defined by the linear threshold function. This is important because it allows us to (1) visualize convergence as the heatmap flattens and (2) brute force check the fairness in low-dimensions without relying on a heuristic auditor. See the [the rich subgroup fairness empirical paper](https://arxiv.org/abs/1808.08166) for a discussion of these plots. Note that in the below plot no group has a $\\gamma$-disparity of greater than $.005$, which we would expect since the set of linear threshold functions on two attributes is a subset of the set of linear threshold functions on all protected attributes, and the final model is $\\gamma$-fair. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "pycharm": {
+          "is_executing": true
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qEigaAI9g9Fv",
+        "outputId": "5a2852d9-132a-4ed0-a0c3-7eb96686c9e8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "iteration: 1, error: 0.257, fairness violation: 0.041308000000000004, violated group size: 0.263\n",
+            "iteration: 2, error: 0.3785, fairness violation: 0.020654000000000002, violated group size: 0.263\n",
+            "iteration: 3, error: 0.419, fairness violation: 0.013769333333333342, violated group size: 0.263\n",
+            "iteration: 4, error: 0.43925, fairness violation: 0.010327000000000001, violated group size: 0.263\n",
+            "iteration: 5, error: 0.4514, fairness violation: 0.008261600000000001, violated group size: 0.237\n",
+            "iteration: 6, error: 0.4595000000000001, fairness violation: 0.006884666666666671, violated group size: 0.263\n",
+            "iteration: 7, error: 0.4652857142857143, fairness violation: 0.005901142857142857, violated group size: 0.237\n",
+            "iteration: 8, error: 0.469625, fairness violation: 0.005163500000000002, violated group size: 0.237\n",
+            "iteration: 9, error: 0.47300000000000003, fairness violation: 0.00458977777777778, violated group size: 0.237\n",
+            "iteration: 10, error: 0.4757000000000001, fairness violation: 0.0041308000000000004, violated group size: 0.237\n",
+            "iteration: 11, error: 0.47790909090909084, fairness violation: 0.0037552727272727273, violated group size: 0.263\n",
+            "iteration: 12, error: 0.47974999999999995, fairness violation: 0.0034423333333333354, violated group size: 0.263\n",
+            "iteration: 13, error: 0.4813076923076924, fairness violation: 0.0031775384615384643, violated group size: 0.263\n",
+            "iteration: 14, error: 0.48264285714285726, fairness violation: 0.0029505714285714296, violated group size: 0.237\n",
+            "iteration: 15, error: 0.48380000000000006, fairness violation: 0.0027538666666666674, violated group size: 0.237\n",
+            "iteration: 16, error: 0.4848125, fairness violation: 0.002581750000000001, violated group size: 0.237\n",
+            "iteration: 17, error: 0.4857058823529412, fairness violation: 0.0024298823529411775, violated group size: 0.237\n",
+            "iteration: 18, error: 0.4865, fairness violation: 0.00229488888888889, violated group size: 0.237\n",
+            "iteration: 19, error: 0.48721052631578954, fairness violation: 0.002174105263157895, violated group size: 0.237\n",
+            "iteration: 20, error: 0.4878499999999999, fairness violation: 0.0020654000000000015, violated group size: 0.263\n",
+            "iteration: 21, error: 0.4884285714285715, fairness violation: 0.001967047619047619, violated group size: 0.237\n",
+            "iteration: 22, error: 0.48895454545454553, fairness violation: 0.0018776363636363643, violated group size: 0.237\n",
+            "iteration: 23, error: 0.4894347826086957, fairness violation: 0.0017960000000000012, violated group size: 0.263\n",
+            "iteration: 24, error: 0.48987500000000006, fairness violation: 0.0017211666666666686, violated group size: 0.263\n",
+            "iteration: 25, error: 0.49028000000000016, fairness violation: 0.0016523200000000003, violated group size: 0.237\n",
+            "iteration: 26, error: 0.49065384615384616, fairness violation: 0.0015887692307692321, violated group size: 0.263\n",
+            "iteration: 27, error: 0.4910000000000001, fairness violation: 0.0015299259259259272, violated group size: 0.263\n",
+            "iteration: 28, error: 0.49132142857142846, fairness violation: 0.0014752857142857144, violated group size: 0.237\n",
+            "iteration: 29, error: 0.4916206896551723, fairness violation: 0.0014244137931034486, violated group size: 0.237\n",
+            "iteration: 30, error: 0.49190000000000006, fairness violation: 0.0013769333333333335, violated group size: 0.237\n",
+            "iteration: 31, error: 0.49216129032258077, fairness violation: 0.0013325161290322588, violated group size: 0.263\n",
+            "iteration: 32, error: 0.49240625, fairness violation: 0.0012908750000000006, violated group size: 0.237\n",
+            "iteration: 33, error: 0.4926363636363636, fairness violation: 0.001251757575757576, violated group size: 0.237\n",
+            "iteration: 34, error: 0.49285294117647066, fairness violation: 0.0012149411764705886, violated group size: 0.237\n",
+            "iteration: 35, error: 0.49305714285714286, fairness violation: 0.0011802285714285717, violated group size: 0.237\n",
+            "iteration: 36, error: 0.4932499999999999, fairness violation: 0.001147444444444445, violated group size: 0.237\n",
+            "iteration: 37, error: 0.49343243243243257, fairness violation: 0.0011164324324324328, violated group size: 0.237\n",
+            "iteration: 38, error: 0.49360526315789466, fairness violation: 0.0010870526315789476, violated group size: 0.237\n",
+            "iteration: 39, error: 0.4937692307692308, fairness violation: 0.001059179487179488, violated group size: 0.263\n",
+            "iteration: 40, error: 0.4939249999999999, fairness violation: 0.0010327000000000008, violated group size: 0.263\n",
+            "iteration: 41, error: 0.4940731707317074, fairness violation: 0.0010075121951219515, violated group size: 0.237\n",
+            "iteration: 42, error: 0.4942142857142858, fairness violation: 0.0009835238095238096, violated group size: 0.237\n",
+            "iteration: 43, error: 0.4943488372093023, fairness violation: 0.0009606511627906982, violated group size: 0.263\n",
+            "iteration: 44, error: 0.49447727272727265, fairness violation: 0.0009388181818181818, violated group size: 0.263\n",
+            "iteration: 45, error: 0.49460000000000004, fairness violation: 0.000917955555555556, violated group size: 0.263\n",
+            "iteration: 46, error: 0.4947173913043478, fairness violation: 0.0008980000000000004, violated group size: 0.263\n",
+            "iteration: 47, error: 0.49482978723404264, fairness violation: 0.0008788936170212767, violated group size: 0.263\n",
+            "iteration: 48, error: 0.4949374999999999, fairness violation: 0.0008605833333333339, violated group size: 0.263\n",
+            "iteration: 49, error: 0.49504081632653046, fairness violation: 0.0008430204081632653, violated group size: 0.237\n",
+            "iteration: 50, error: 0.49514, fairness violation: 0.0008261600000000004, violated group size: 0.263\n",
+            "iteration: 51, error: 0.49523529411764705, fairness violation: 0.0008099607843137258, violated group size: 0.263\n",
+            "iteration: 52, error: 0.49532692307692294, fairness violation: 0.0007943846153846153, violated group size: 0.263\n",
+            "iteration: 53, error: 0.4954150943396228, fairness violation: 0.0007793962264150945, violated group size: 0.263\n",
+            "iteration: 54, error: 0.4936111111111111, fairness violation: 0.0009580740740740742, violated group size: 0.237\n",
+            "iteration: 55, error: 0.49372727272727274, fairness violation: 0.0009406545454545454, violated group size: 0.263\n",
+            "iteration: 56, error: 0.4938392857142857, fairness violation: 0.0009238571428571433, violated group size: 0.263\n",
+            "iteration: 57, error: 0.49394736842105263, fairness violation: 0.0009076491228070175, violated group size: 0.237\n",
+            "iteration: 58, error: 0.49405172413793097, fairness violation: 0.0008920000000000001, violated group size: 0.237\n",
+            "iteration: 59, error: 0.49415254237288136, fairness violation: 0.0008768813559322037, violated group size: 0.263\n",
+            "iteration: 60, error: 0.4942499999999999, fairness violation: 0.0008622666666666666, violated group size: 0.237\n",
+            "iteration: 61, error: 0.49434426229508205, fairness violation: 0.0008481311475409838, violated group size: 0.237\n",
+            "iteration: 62, error: 0.4942741935483872, fairness violation: 0.000834451612903226, violated group size: 0.237\n",
+            "iteration: 63, error: 0.4943650793650794, fairness violation: 0.0008212063492063492, violated group size: 0.263\n",
+            "iteration: 64, error: 0.492859375, fairness violation: 0.0009861250000000002, violated group size: 0.263\n",
+            "iteration: 65, error: 0.49250769230769237, fairness violation: 0.0010365846153846153, violated group size: 0.237\n",
+            "iteration: 66, error: 0.4923787878787878, fairness violation: 0.0010352424242424242, violated group size: 0.237\n",
+            "iteration: 67, error: 0.49249253731343284, fairness violation: 0.0010197910447761196, violated group size: 0.263\n",
+            "iteration: 68, error: 0.4926029411764706, fairness violation: 0.0010047941176470586, violated group size: 0.237\n",
+            "iteration: 69, error: 0.49271014492753634, fairness violation: 0.000990231884057971, violated group size: 0.237\n",
+            "iteration: 70, error: 0.4928142857142858, fairness violation: 0.0009760857142857143, violated group size: 0.237\n",
+            "iteration: 71, error: 0.491338028169014, fairness violation: 0.0011225633802816905, violated group size: 0.237\n",
+            "iteration: 72, error: 0.4901249999999999, fairness violation: 0.001251805555555556, violated group size: 0.237\n",
+            "iteration: 73, error: 0.49004109589041106, fairness violation: 0.0012476438356164388, violated group size: 0.263\n",
+            "iteration: 74, error: 0.48977027027027026, fairness violation: 0.0012884324324324327, violated group size: 0.237\n",
+            "iteration: 75, error: 0.4884266666666666, fairness violation: 0.0014166133333333335, violated group size: 0.237\n",
+            "iteration: 76, error: 0.4879736842105262, fairness violation: 0.0014665789473684216, violated group size: 0.263\n",
+            "iteration: 77, error: 0.4868831168831168, fairness violation: 0.001582961038961039, violated group size: 0.237\n",
+            "iteration: 78, error: 0.4856153846153846, fairness violation: 0.001696358974358975, violated group size: 0.263\n",
+            "iteration: 79, error: 0.484506329113924, fairness violation: 0.001806886075949368, violated group size: 0.263\n",
+            "iteration: 80, error: 0.48330000000000006, fairness violation: 0.0019265000000000005, violated group size: 0.237\n",
+            "iteration: 81, error: 0.4821234567901235, fairness violation: 0.002043160493827161, violated group size: 0.263\n",
+            "iteration: 82, error: 0.4811707317073171, fairness violation: 0.0021454146341463418, violated group size: 0.237\n",
+            "iteration: 83, error: 0.48024096385542164, fairness violation: 0.002245204819277109, violated group size: 0.237\n",
+            "iteration: 84, error: 0.4791428571428571, fairness violation: 0.002353904761904763, violated group size: 0.237\n",
+            "iteration: 85, error: 0.4782705882352941, fairness violation: 0.002454470588235295, violated group size: 0.237\n",
+            "iteration: 86, error: 0.477406976744186, fairness violation: 0.0025471860465116286, violated group size: 0.237\n",
+            "iteration: 87, error: 0.47637931034482767, fairness violation: 0.0026486666666666677, violated group size: 0.237\n",
+            "iteration: 88, error: 0.47537499999999994, fairness violation: 0.0027478409090909107, violated group size: 0.263\n",
+            "iteration: 89, error: 0.4743932584269664, fairness violation: 0.002844786516853933, violated group size: 0.263\n",
+            "iteration: 90, error: 0.4736222222222222, fairness violation: 0.002934311111111111, violated group size: 0.237\n",
+            "iteration: 91, error: 0.47268131868131874, fairness violation: 0.0030270769230769236, violated group size: 0.237\n",
+            "iteration: 92, error: 0.4717608695652174, fairness violation: 0.003117826086956522, violated group size: 0.237\n",
+            "iteration: 93, error: 0.4708602150537634, fairness violation: 0.0032066236559139794, violated group size: 0.263\n",
+            "iteration: 94, error: 0.46997872340425534, fairness violation: 0.003293531914893617, violated group size: 0.237\n",
+            "iteration: 95, error: 0.46911578947368426, fairness violation: 0.003378610526315791, violated group size: 0.263\n",
+            "iteration: 96, error: 0.46827083333333336, fairness violation: 0.0034619166666666674, violated group size: 0.263\n",
+            "iteration: 97, error: 0.46744329896907216, fairness violation: 0.0035435051546391766, violated group size: 0.237\n",
+            "iteration: 98, error: 0.46663265306122453, fairness violation: 0.003623428571428572, violated group size: 0.237\n",
+            "iteration: 99, error: 0.46583838383838383, fairness violation: 0.003701737373737375, violated group size: 0.237\n",
+            "iteration: 100, error: 0.4650599999999999, fairness violation: 0.0037784800000000016, violated group size: 0.237\n",
+            "iteration: 101, error: 0.4642970297029704, fairness violation: 0.003853702970297031, violated group size: 0.237\n",
+            "iteration: 102, error: 0.4635490196078431, fairness violation: 0.003927450980392159, violated group size: 0.237\n",
+            "iteration: 103, error: 0.46281553398058256, fairness violation: 0.003999766990291263, violated group size: 0.237\n",
+            "iteration: 104, error: 0.4620961538461538, fairness violation: 0.00407069230769231, violated group size: 0.237\n",
+            "iteration: 105, error: 0.46139047619047613, fairness violation: 0.004140266666666668, violated group size: 0.237\n",
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+            "iteration: 461, error: 0.4551149674620391, fairness violation: 0.004613683297180044, violated group size: 0.237\n",
+            "iteration: 462, error: 0.45519047619047615, fairness violation: 0.004603696969696971, violated group size: 0.237\n",
+            "iteration: 463, error: 0.45528725701943845, fairness violation: 0.004593753779697626, violated group size: 0.237\n",
+            "iteration: 464, error: 0.4553836206896551, fairness violation: 0.004583853448275864, violated group size: 0.263\n",
+            "iteration: 465, error: 0.4554795698924731, fairness violation: 0.004573995698924732, violated group size: 0.263\n",
+            "iteration: 466, error: 0.45553862660944205, fairness violation: 0.004565197424892706, violated group size: 0.263\n",
+            "iteration: 467, error: 0.4555674518201284, fairness violation: 0.004565571734475376, violated group size: 0.263\n",
+            "iteration: 468, error: 0.4556623931623932, fairness violation: 0.00455581623931624, violated group size: 0.237\n",
+            "iteration: 469, error: 0.4557569296375267, fairness violation: 0.004546102345415778, violated group size: 0.237\n",
+            "iteration: 470, error: 0.4557872340425532, fairness violation: 0.004545506382978723, violated group size: 0.237\n",
+            "iteration: 471, error: 0.45588110403397036, fairness violation: 0.004535855626326964, violated group size: 0.237\n",
+            "iteration: 472, error: 0.4559745762711865, fairness violation: 0.004526245762711867, violated group size: 0.263\n",
+            "iteration: 473, error: 0.45600422832980975, fairness violation: 0.0045256955602537, violated group size: 0.237\n",
+            "iteration: 474, error: 0.456097046413502, fairness violation: 0.004516147679324895, violated group size: 0.237\n",
+            "iteration: 475, error: 0.45618947368421053, fairness violation: 0.004506640000000003, violated group size: 0.263\n",
+            "iteration: 476, error: 0.4562478991596639, fairness violation: 0.0044991638655462185, violated group size: 0.237\n",
+            "iteration: 477, error: 0.45624109014675046, fairness violation: 0.004501656184486373, violated group size: 0.237\n",
+            "iteration: 478, error: 0.4563138075313808, fairness violation: 0.004493230125523012, violated group size: 0.263\n",
+            "iteration: 479, error: 0.45637160751565764, fairness violation: 0.004485828810020879, violated group size: 0.263\n",
+            "iteration: 480, error: 0.4564625000000001, fairness violation: 0.004476483333333333, violated group size: 0.237\n",
+            "iteration: 481, error: 0.45641787941787937, fairness violation: 0.00447900207900208, violated group size: 0.263\n",
+            "iteration: 482, error: 0.45650829875518667, fairness violation: 0.004469709543568465, violated group size: 0.263\n",
+            "iteration: 483, error: 0.4565983436853003, fairness violation: 0.004460455486542443, violated group size: 0.263\n",
+            "iteration: 484, error: 0.4566880165289256, fairness violation: 0.004451239669421488, violated group size: 0.263\n",
+            "iteration: 485, error: 0.45677731958762885, fairness violation: 0.004442061855670102, violated group size: 0.237\n",
+            "iteration: 486, error: 0.45680246913580247, fairness violation: 0.004442674897119343, violated group size: 0.263\n",
+            "iteration: 487, error: 0.45679671457905546, fairness violation: 0.004444258726899385, violated group size: 0.263\n",
+            "iteration: 488, error: 0.4568852459016393, fairness violation: 0.004435151639344263, violated group size: 0.263\n",
+            "iteration: 489, error: 0.4569734151329243, fairness violation: 0.004426081799591004, violated group size: 0.263\n",
+            "iteration: 490, error: 0.4570612244897959, fairness violation: 0.004417048979591837, violated group size: 0.237\n",
+            "iteration: 491, error: 0.45714867617107935, fairness violation: 0.004408052953156824, violated group size: 0.263\n",
+            "iteration: 492, error: 0.45723780487804877, fairness violation: 0.004400056910569106, violated group size: 0.237\n",
+            "iteration: 493, error: 0.45732454361054764, fairness violation: 0.004391131845841786, violated group size: 0.237\n",
+            "iteration: 494, error: 0.45741093117408893, fairness violation: 0.0043822429149797575, violated group size: 0.237\n",
+            "iteration: 495, error: 0.45749696969696974, fairness violation: 0.0043733898989899, violated group size: 0.263\n",
+            "iteration: 496, error: 0.4574536290322581, fairness violation: 0.004376995967741938, violated group size: 0.263\n",
+            "iteration: 497, error: 0.4575392354124748, fairness violation: 0.004368189134808854, violated group size: 0.263\n",
+            "iteration: 498, error: 0.45759036144578313, fairness violation: 0.004360369477911648, violated group size: 0.237\n",
+            "iteration: 499, error: 0.4575811623246493, fairness violation: 0.004363030060120242, violated group size: 0.263\n"
+          ]
+        }
+      ],
+      "source": [
+        "C = 100\n",
+        "print_flag = True\n",
+        "gamma = .005\n",
+        "\n",
+        "\n",
+        "fair_model = GerryFairClassifier(C=C, printflag=print_flag, gamma=gamma, fairness_def='FP',\n",
+        "             max_iters=max_iterations, heatmapflag=False)\n",
+        "\n",
+        "# fit method\n",
+        "fair_model.fit(data_set, early_termination=True)\n",
+        "\n",
+        "# predict method. If threshold in (0, 1) produces binary predictions\n",
+        "\n",
+        "dataset_yhat = fair_model.predict(data_set, threshold=False)\n",
+        "\n",
+        "\n"
+      ]
+    },
     {
-     "data": {
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jcODAAVRWVqYIm1arTUlFJr+WeDxe1Ini5yJUwM5hkpuPAfCOB1LgOA7hcBinTp2CwWBAd3d31jvEfAUsFovBbrfD6XSipaUlZWhlLhFeuu1jsRhGR0exsLDAX1iIZ1+ywJdLFeJS03/kD/B+8g4EAgHMzs5idHQU0WgUKpUqQdQMBkPKxbfQa2BLHYHJhWQ0amtrU4QtFArxTdputxuBQIAXNpfLhZMnT0KhUCAQCKR1itm7dy++8pWvIB6P46abbsJdd92V8PNwOIzrr78ehw4dQk1NDX7zm9+gvb0dALB79248+eSTUCqVePTRR3HZZZchFArhoosuQjgcRiwWwyc/+Uk8+ODyyhhQATsHSdd8rFKpUmYoiUFcLGw2G+LxOBobG9HV1SXp2LlYPQFANBqF3W7ny+/TTVvOZZxK8vZEJKemptDc3Iyuri5+rYOsc3Acx69xGI3GhNYC4frX+ZAiJK4jQqLRKG+nNT09jUAggFgsBrVazQtbMBgsaEqsGAImNZqUQjqjZ2HEVlNTk7B9OBzG6dOnMTs7C5fLhcsuuwyBQAA33XQTbr311oRz/dKXvoRXXnkFLS0t2LJlC3bt2oW+vj5+myeffBJVVVUYHh7Gc889hzvvvBO/+c1vcPz4cTz33HM4duwYJicn8ZGPfASnT59GRUUF/vd//xdGoxHRaBQXXnghduzYgcHBwZTXUOj3vlBQATtHyDb5GMgeHZFm4NHRUZjNZqxbtw4ul0v2AEk5RCIRhEIhHDx4ECtXrsT27duzrkvkaj0lFK7W1lZs374dwJmLscFgSLhrFvYS+Xw+uFwuzMzMYHx8HJ2tTdlf1y33Sz7HTJRDGlEMtVoNi8WSUnQQiUTg9/t5cRsZGeENlIXRmsFgkJ0OLHTEVIz9yXlNxCty/fr16OrqwhtvvIE33niDj9iEHDx4EN3d3ejs7AQAXHvttdizZ0+CgO3ZswcPPPAAAOCTn/wkbr31VnAchz179uDaa69FRUUFOjo60N3djYMHD2L79u18tBeNRhGNRkWbrhmGwVNPPYXa2loMDg6meFuWEipgyxypk48BpI3ASE/V2NgYqqureRcLAFhYWEgZEFgISN/Y3NwcFAoFBgcHJX355QokwzCIRqOwWq2Ymprim6vJsdKJs7CXqL6+HiqVCmq1GvX19fDMTUs6dqboLFuBh+lfrpV0jHJDo9Gguroa1dXVCAQCaGpqgslkShC2iYmJhBRatrUhQqGdMwq9ppZPD9jCwgIfrZKITcjExATvQAIALS0tOHDgQNptiF+ny+XCxMREQlTV0tKCiYkJAGfeg4GBAQwPD+NLX/oStm3blrBP8n2bmZnBCy+8gNdffx1XXXUVNm3aJHlkUjGhArZMIaXw8Xhc0uRj4MyHWhiBkdL08fFx1NfXp/gUkucsLi4W7LxDoRBsNhvcbjfa29vR09ODAwcOFKWQIhaLYX5+Hh6PB52dnZJFUgxyfkvhhl5u5FpOT9JODMOgoqICFRUVKSm0dNV8Yv1XxUghlosglqoCUalU4siRI1hYWMAnPvEJHD16FBdccEHKdnfffTfuvvtuvPjii7j99tthNBpxyy234FOf+lRJvxPll9SkZIRUFIZCIRw5ciRtA7IYJP0Wi8UwMjKC/fv3Ix6PY9u2bejp6RFdDyhUVWEoFMLx48fxzjvvwGKxYPv27WhqauLdugvllgGAf30HDhyAQqFAe3s72tra8vqilbKIQzF4SUmOmy/ZBIdEGrW1tWhra0NfXx82b96MLVu2oKurCyaTCcFgEHa7HYcOHYLX68WJEydgs9kwMzPDR3G5Uk4RmMfjyShgzc3NcDgc/P/Hx8fR3NycdptYLAaPx4OamhpJz7VYLPinf/on7N27V/T4kUgEp06dQm9vL77xjW9gfn4eu3fvxurVq/GHP/xB9ustFDQCWyaINR/7fD5ZkUs0GkUoFMKBAwcyFkoIyVfAFhcXYbPZ4PV6ExqehRA3jnybXmOxGMbGxvgKxsHBQUxNTcma4JwJjuMkNTBLWf86H4o/cp1ena7/6sCBA+js7BT1OMxlQGYxBCzX/S0sLGRsYt6yZQuGhoZgs9nQ3NyM5557Dv/1X/+VsM2uXbvwzDPPYPv27XjxxRfxoQ99CAzDYNeuXfjXf/1X3HHHHZicnMTQ0BC2bt2K2dlZfi0zGAzilVdewZ133plybJZl8eMf/xgnTpzA+++/jw996EP485//jLa2NtjtdlxyySXYtWtXTq87X6iAlTnZBkhKIRQKYXR0FC6XCwqFImuhhBClUimpclEIwzD82ItAIIDOzk709fWlvaDkWrlIEBMuoSN4uVlJLccG51zSiMWYoJzO41BsQCaxjhIKm1arLdqAzGK6cKhUKjz22GO47LLLEI/HceONN2Lt2rX4xje+gc2bN2PXrl34/Oc/j89+9rPo7u5GdXU1nnvuOQDA2rVrcc0116Cvrw8qlQqPP/44lEolnE4nPve5z/HLENdccw127tyZcmyv14vXX38d9913HzZt2sQ/znEcGhsb8R//8R85veZCQAWsTBETLrkXUWH0097ejt7eXuzfv1/WRUVuBOb3+xEMBnH06FF0dXWhtrY263nnKmBC4SLNzskXpHzFkXC2+rE0Tc/lWo2YiWIImBjp5ojF43Fe2DweDyYnJxEKhfgCncXFRXg8HphMJmg0mrxvUvJNIWazkbriiitwxRVXJDz2zW9+k/+3VqvFCy+8IPrce+65B/fcc0/CY+vXr8fhw4eznpter8e2bdsSxMvr9eKpp57C7bffjptuuinrPooFFbAyQ+rkY4JYmoaULy8uLqZEP+RCLNeJIxs+nw8jIyMIh8PQarXYuHFjwliTbMeQIzIcx2FkZCSjcBGK5YUIFC8NGLnlfmDuVFH2vZQU2olDLkqlEiaTCSaTKeHxeDzONxQvLCxgYmIC4XAYSqUyIVozGo2y+sTyjcCS16VKTSgUwvvvv4+//vWvePbZZ3H11VcjGAyiubkZ77//Pv7whz/g9ttvL+nvmApYmSB38jFwtiyeOCB4vV5YrVbEYjF0dnaiuro6ZR+kErFQAiY8ZldXF6qrq3H48OGCDKlMhkRcgUBAcul9oSIwADBozr8KRCFy04iFduIoFEqlEmazGWq1Gt3d3fzjsVgswbjXbrfzriNCURNzHSHPl3rTlkw5jlIJh8M4duwY/vKXv2Bubg73338/fD4fotEoFhYWcPHFFwMozmBQqVABKyGk+TgSicBqtaKzs1NWqpAIGIm4AKCzszPjSAaypiXVcy2dgHk8HlitVrAsm+KNKDftmG2kCvFFnJycRHNzM4xGI1auXCnp4ljYNbDs+yEFHOmiMykFHjVpoq/llkbMtYgj3b6KDemdSuc6QgpH/H4//x0SilokEslrlEq5GflWVlbihhtuwNq1azExMYGPf/zjmJqaQjgcRlVVFd8HVsoyeipgJUCs+XhmZibhblDKPmKxGI4cOQKdToeenh5JjYVyxSX5AuR2uzEyMgKGYdDV1SX6pct1SGUyQkNfYXHG9PS05DRoId3oKfIp1PtWSiujTK4jRNicTifm5uYwPz8PnU6X4hOZ7SJfjqNU/v73v4PjOKxZswbHjx/H3r17E5rUdTpdyc2HqYAtIbk0H4vtY2ZmBjabDdFoFF1dXWhqym5tRJDqh5jM/Pw8rFYrVCpVVrHMJQITikxyxJWPoW8m70SWZTE+Pg6n05ngeyiWImIYRkoAljflvv7FPXk/rBd/NsH4eCnuwAstYIWI6DQaDTQaDZ99OHr0KDo6OqBSqXgrLaHrSEVFRYKwCd87r9ebMXNSaCNfh8OB66+/HtPT02AYBl/4whfwla98RfR9stvt+NGPfgSGYeDxeBAOh+FwOHDPPffgW9/6VsGrOeVABWwJiUajiMfjOQkXy7K8T6HFYsGGDRvgcDhk3wHJEReO4zA/P49AIICxsTGsXr06ZUFcDLlTlkkRR7qIS2z/UgVMLIVIrLPsdjvq6+uxevVq3upIaEwrvOBomMI1WudT/FEOacSqqioEAgE4HI4U5wzyfiVbIeVLOblmZNqnWq2GRqNBRUUFqqur+Z8R415ip0VcR5xOJ376059iYWEBr7zyCjZt2oRVq1YlFI8Uw8hXpVLh+9//Pvr7++Hz+TAwMIBLL72U3yfHcfwa17Fjx7B///601xqaQjxPyNTDla46kGVZTExMYGxsDLW1tRgYGODHqidbQ0lBSgTGcRzm5uYwMjICnU7HVxVKJRdnjenpaVitVtGIKxm5ERjZluM4OJ1OjI6Oora2Flu2bIFarUYkEoFOp0tIhyZfcDTa7DcKhTLwLXdqampSLKGI8bHf7+cbjAOBAI4dO8aLmtFoREVFRU5pxXK3kQIyNzIT416tVptgGh2Px9Hd3Y2bbroJY2Nj2Lt3LxobG/HII4/w2xTLyLexsREAYDKZsGbNGkxMTPD7ZBgGkUgEGo0GDz/8MAYHB/GlL32poO9XIaACtoRkuvCq1WrEYjH+zisWi/E+hQ0NDdiyZYuoT6HcdGCmCIyMURkZGYHBYMAFF1wAg8GA/fv3y1qQlxrlxeNxjI2NwW63w2KxyDL0lROBsSwLp9MJm82G6urqBM/HdKmk5AuOFAeObJyrApfOOePgwYNoa2uD3+9P6MMSlqvzEW6WcvXlIGC5nKNSqcTq1auhUChw9913i37HimXkSxgdHcXhw4dTjHzJ+9PR0YF3330Xb7zxBlasWMGnP8uh6IQKWJmgUqn4cQbJzbnpekvIhGK5x0kWPbKuNjIyApPJhPXr10Ov1/M/l2v1lC1CIsI1OTmJpqYmdHV1geM4yRcUqTPBSAp0bm4OGo0G/f39OZc5J1OMHjCpAlfqNKLUcnqGYUQbjIXl6sIhmcJZYuQP+cwthxQikF/RSikKhfx+P66++mr84Ac/SFnXJmJcX1+PF154AceOHeOjx+npafzsZz/D+vXrC1ptKhcqYGUCwzAYGRmB1+vlZ1Vl+4Ll4hSvVCr58SjC+V+VlZXYuHGj6NoFiaikCli6CIwI18TEBJqbm7Ft2zaoVCo4nc6U+UeZyCaQJAVqtVr51OCaNWvSbk8rDJeWdOXqpKrP7/fD6XQiEAggHo9Dq9VCqVQiGo3C7/eLTsyWSykLD5IJhUL8soAYcox8W1paJBv5RqNRXH311bjuuuvwz//8zynHJd+La6+9FjfffDP/+1lcXMTCwgJfNU0bmc8TxH7RZLyIy+VCU1NT0X0KVSoV/H4/JicnMTo6iqqqqoT5X+mOI7coQxgZxuNxvjijqakpJaosVNk9x3FwuVywWq3Q6/VYv349GIbBiRMnJO9bDLkGvvlEZ+l6wJYbubQuJFf1AWdHrkxNTcHtdsNut/M3bcnl6lIMfAmFFrB8qhqz9YAVw8iX4zh8/vOfx5o1a3DHHXdkPL/a2lq8/vrrUKlU/Eicjo6OhCxNqaACViIWFxcxMjICn8+Hjo4OPn0i585S7hoYy7Jwu928kAgLQjKRa1k8ES5y10ciLrHt8ym7B86U+Q8PD0Or1fJrd8CZ8uJCjmrJlXNt/StbGrFQLhxk5Ar5fXZ0dABInZgtNPAVW19LFrZCC1g+a3SlMPL9xz/+gWeffRbr1q3jC7S+853vpHgtAsAPf/hD7Nu3Dy+88ALq6+sxMzMDAJibm0uotCwFVMCWEDICZWRkBKFQCB0dHVi7di2/7iV37IdUARNWMprNZtTU1GD16tWyjiO3+Xl+fh7T09OiEVcy+URgbrcbw8PDUKvV6OvrS1lvkeLEUcocPkGuwJV6HSwbhS66SLYrSp6YTSAGvn6/H/Pz8xgbG0MkEuHtoArhmiFGMWeBAYU38r3wwgslR40//vGPcfz4cTgcDuzbtw9vvPEGXnrpJVrEcb4RjUZx+vRptLe3p/gUqlQqfm1KKtkEjDTqOhwO1NfXY8uWLbxtlRzkVBU6HA7Y7XZUVFSkjbiSkStgDMPwvWkMw2TsTyukF2IhWG5jVHKl0D6IUgUxnYEvsYMivX7z8/NgWRbz8/MJEVuujdn5RHTZZoGVEuLvqFAoEAgEMDU1hf7+ftx8883YvXt3qU+PCthSotFoMExcnn0AACAASURBVDAwIPoztVqNQCAga3/pBEy45tTQ0ICtW7fyTYikYVgO2QQseY1r3bp1mJyclFX0IVVkSLpIqVRi7dq1We8CpXohpovCCjXA8lwkUxqx0E70+ab8ku2gxsbGoFarUV1dzff6ZWvMziSg+URgXq+3LKIZMaLRKK688koEg0FcddVVePDBB2E2m9HQ0FDqUwNABaxsIGX0cki+8AudLJqamkQjoFyan9MViwiFq7GxkT+e3+/Pe00rGb/fj+HhYUSjUdTU1MBisUj60mcTMOKIUkyz2GIJXDmnEYuRQpQz2iQbsVgMer0eFRUVfGECIV1jNnBmNpZYY3Y+AlbOEZhOp8O3v/1tAMDdd9+NJ554AiqVCnfffXeJz+wMVMCWmHQXy1w9CoGzY0aId2CmNadcKheTI7B0wkXIZU0rneAFAgEMDw8jHA7zC9R2uz0vKylK8SmGgC3V/tI1ZpPJz6QxWzhHTKlUgmEYuN1uSY3ZQrxerywj76XGbrfjb3/7G1iWxbp167Bhw4ayiRipgJUJarVadgQWjUYRDodx4MABtLS0SOodyyWtQ3pwsgmXcPt8I7DFxUVYrVYsLi7ywkXOXa4XYjpisRjsdjsmJyf5ogDSeJtpmGGh17GWcwoyXRqx0IUxcmbYSd2f3Igp3eRncgO5uLiYtjGbrK+JHbMcnegJo6OjuPfeezE+Po7e3l48++yz8Pv9eOKJJ9IuhywlVMCWmEJEYJFIBHa7HTMzM2AYRrIFU64wDMPb0mQSLkI+AhYMBvn2gq6uLtTW1qZcCPMdkSIU4tbWVmzZsoVPGwmr11Y21mffWRJL3QNWDmnE48ePJ1yoNRpN0asQ86WQgqhSqaDRaKDVahMmQwgbsycnJxMas0nhiNPphNvtzihghXaiB4Abb7wRL730Eurr63H06NGUY5Lf35tvvgmv14vXXnuN/9mzzz6Lb3/72/jd735X8oZwKmBlgpSLfiQSgc1mw9zcHNra2rB9+3a89dZbRUuRxeNxjI+PY3R0FDqdrmhVhSStefz4cXg8HnR2dqKvry/tHbxCoZAdrQKJ7QSNjY288EciEXAcl3J3XYgCjuUcXUmltbUVfr8fbrcbDocjoYl9cnKSF7Z8LnTl7oVI1tSEZGrMDgQC8Hq92L17N44ePYqDBw9i/fr1+MAHPoDbbrst4TwL7USvVCpxww034NZbb8X1118v+nrId89isaC+vh6Tk5PQaDSwWCwIhUJoa2tL2K5UUAFbYtL9wjN9EMLhMGw2G+bn59HW1oaenh7+y0wiN7npkEwpHiJc4+PjaGxsRF9fH+bm5mR5IUoV1UgkktDQvWbNmqxfCrkCyXEc7zxSV1eXUJVJyR+xsvXp6WnMzs4iHo+n2EKRGwViCivlIliMFGIp+sBIY7ZOp0NtbS2ef/55XH311fj5z3/Oj1cRUiwn+osuugijo6Npz5NcHxQKBd555x3ccsstuOSSS3D48GGMj4/j8ssvx/PPP4++vj5ccMEF8t+wAkEFrIwhNlNutxvt7e3o7e1N+bLnUvxBBCD5CywUroaGBj7i8ng8sisXsxGJRDA6Ooq5uTm0t7fDYDBILs2VKmDEpJjc7Qpd6EuBWHqxEBFaqdOIYutgSqUSer0+wUWdRB9+v5+v7ltcXIRCoeCr+9K5ZxQjhVgu+/N4PKirq4Ner0/x7Cy2E306yM1CS0sLbrnlFgSDQXi9XvT19aG5uRl2ux1vvvkmbrjhBipglLOQLzkx9u3o6MDq1avT3qXmMxOMfOHSCRdB7ppWJqLRKEZHRzEzM4O2tjYMDg5CoVBkvBtMRoqZL/FEJIvncpxHKPkjlvITRh/C6j6he4bL5eLdM9RqNZ9+DIfDBU2VFzolmU8ZfTgcLvgA0EJRVVWFz3zmM9DpdPzacVNTU9ZxREsFFbAlJlO6hGEYvP/++1hcXERHR0fGdSBCPjPBsglX8vb5QCr+pqameLf9XC8gmUrjibVURUUF1q1bB71ej3379sneH13/kkfIkWiYbAZglhiQpHPPEBZBhEIhHD9+PKXJ2Gg0yjLxFVLoKslcBCybABTLiT4bJKL83ve+h8HBQVxzzTW444478Mwzz2Dnzp145JFH0NDQQNfAKGd6nUjJeGtrK9atWyf5g5FrA7TD4cDc3FxG4RJun6uACUeotLa2FqRiUiwC83q9GBoagkKhyGgtda5SqjRi+KPXZvx5srARtK3px9sQhEUQ09PT6O/vB8MwKU3GJA0ptITK1AZRDDJNY5ZCuu97MZzo5XDkyBHcfPPNOHLkCAKBAObn53HdddfhyJEjuPzyy0vuI0oFbIkR/rL9fj+sVivC4TC/SGs2m2V9IOREYMQbcXZ2FitWrJBcVZhLmpLjOIyOjvJ3fVJ61KQiFLBAIIChoSHEYjH09PQUvcGykD1gyzlCyyZc2RATtkyiRi6U6ZqMk9OQdrtdtBfLYDAUpew71zUwkipNRzGc6AHg05/+NF577TXMzc2hpaUFDz74ID7/+c+nHL+pqQmvvvoqXnvtNVx11VVQKBRwuVwJziWlhApYCfD5fLBarYhGo+js7OSbdGdnZ3Oa75UtAhOa+jY0NKC5uRk1NTWSUx5y+q7IsRYXFxGLxbI60eeCQqFAJBLh0609PT1ZxzosxZ3i+TAHLF/hykS2aC3T709KGnJiYgKBQAAsy/IFUkTYck1DCsnl+aVwogeAX//61xmPSYTua1/7Gn70ox9BpVJh586dAM7YaZHJzDSFeJ4RDAZx6tQpdHV1JfSHALm5cahUqrTTjJOFi5SPW61W2eNRssGyLCYnJ2G327FixQqYzWasXLlSlnhJERnSUuB2u7F+/XrRRmex85cjYMt1/atYacRiipYUQo4TWNdgTBA4KSlIQLwXKxaL4Z133oHRaOTd6YWzxJKbsotJuRr5kqnpPT09ePzxx1FRUYFgMIiFhQU8/fTTKdeuUkEFbInR6/XYvHmz6M9yKcgQe0464crnOOlI7rHasmULNBoNvF6v7CnOmUqlo9Eo38Td3NyMWCyWkELKBEk5FrLqjFJa8llbI8bAdXV1KWnIQCCAQCAgmoYUTn8uVBpSSgRWCp5++mncc889ePDBB6FQKHj3EGKHddNNN5XF94kKWBmRawRGxEgoXCtWrEjbsFuIqkKO4zA1NQWbzYaamhpeuHI9BjH0Tb4wCKsXSdl9JBLB3Nyc5H1nM/T1er3gOA4GgwGeuWnJ+5VDcnpxOax/lTryygUpwpZuvUqpVMJsNsNsNic8HolE+JErwjSkTqfj2zQ4jsspTV2uTvQ7d+4EwzBYv349/9oDgQBmZ2cRCoXKQrwAKmBLTqYPuEql4sc2SIUY7Y6NjWUVLuFx5A7PBM6W/M7MzGBkZAQWiwUDAwOoqKhI2TafKctAol8hMSomX5pcBmCKCZjH4+ErF5vqquEJ+iXvs1wpRBpxOQpXNoTCxgDosUi/9Gk0GlRXVyesswq9M71eL8LhMN566y3ZaUiPx1M26TgCy7JgWRaHDh3CpZdeCrPZDI7joNFoSup7KAYVsBKQydBXTgTGsixmZmYwOzsLo9Eo2SIplwiMYRheuMxmMzZt2gStVluwYxBRSvYrFKuUzFcchZWLLStqJe+HsByip1w5F8UrHfmkIYXVkCaTCcFgEOvWrePTkOmqIckfMvk5WwqxGEa+2fbp9Xrx2GOPoaqqCl6vF06nEz09PWfes1AIq1atwle/+tWs79FSQAWsjFCr1ZLWpoQX+bq6OhiNRlnzhOSsgRFXCzJOfMOGDSmmpWLkImDT09NwOp1Z/QpzjcBCoRCsViv8fj/amlZIfr5clpvAnU+iJQW5wiZ04ciWhvT7/RgfH0cgEMBDDz0Ej8eDlStXorW1FevXr0dnZyefpSmGkS+ArPvUarW45ZZbYLfb8eSTT6KrqwsbN27E2NgYfv/73/OCW+oeMIAKWEnIdaSKULhIqlClUslaDwKki8v8/DyGh4eh1WphNpuxatUqyZY3cvwKZ2dn+deQLiUpJJchlVarFV6vF52tTagxG2Q9l1CoHrByEjgqXtJJ17smxUZKLA35wgsv4D/+4z9gsVjw7rvv4te//jV+/etf82m6Yhj5Asi6T61Wy48Y0mg0eOyxx/ifffzjH8f995/5/FIBoySQrohDKFz19fV5u6lnE0pix6RWq9HX1wej0Yh3331XdlVhtu1dLheGh4dhMBhQV1eH1tbWrOIFSO89icfjsNvtmJ+fR0dHBxzsRgDDkp4rl3LqAZOyDkaFqzCEHCcQ19XltDakUqnAsiyuuOIKXHTRRSk/L5aRb7Z9kopdEjW+/vrraG1thU6nw2uvvcZfe0rtgwhQASsrkoWl0MJFSCcupKhBqVSm2DHlWlUoxsLCAoaGhqDRaHDBBRfAYDDg5MmTeQ2pFCJ835qamhCtvxynA2d+dtidmmrdVCVd1MopesoFKlyFRdu6BgtTUzk365fjNGZSLNXf348rr7wS3/ve97Bx40acPHkSwWAQ9957b8J2pYQKWAnINhMsF+GSE84nC6XX68Xw8DA4jktrxyRXwEhflxCfz4ehoSEASBFIuetaYnAch+npaYyMjKCurg5e08Xw+rI/T0zUAHnCRihngaPiVTiklOVLIVMVYrGMfKUa/Or1etx+++3YsWMHDh8+jB07dmDbtm38ay11+hCgAlZWsCyLSCSC/fv3y4q40s33yra93+/H0NAQ4vE4uru7M94J5iJgpFSfVP1Fo1H09PSIHkdM8OQwNzeH4eFhmM1meIwXwSOvG0EUImxEyHIVJ2F6cakEjopV8Ugu5ojFYhkrcjORScCKYeTLcVzWfSbT29uL3t7enF5fsaECVgKS71yENkwsy2Lz5s2S1oIIyfO9srG4uIjFxUUcP36cNwDNRi4pxHA4jKNHjyIQCKC7uzujAWimlGMmPB4PTp8+jYqKCrj1F8JdGIMRALlFYOVA0CxtMChFHlKqEOUSDAbTVvUWy8hXbJ/p4DiO/14qFAreULlcoAJWQoTCVV9fjy1btuDw4cOy0xFEwLKJ3uLiIj+2RaPRYMuWLZI/jHIELBwOY3JyEh6PB2vXrkVdXV3W48hNIcbjcRw+fBgsy2KuYrvk50mBChdFSLa+sFxTiKQIItN3oxhGvmL7TAfDMAU34y4k5Xtm5zAcx2F8fDxBuEi3PhEjOR+abFWFwWCQ73/q6upCbW0t9u/fL+tOSoqACf0K6+rqoNVqUV9fL2n/UgUsFApheHj4zGj6yn8CCmwMkI94lWr9K1m43BUNqApPleRczjWkNDXnE4EB5bGWlI6XX34ZgUAAWq0WNTU1qKqqQkdHR9mIWnmcxXmG1+tFMBhM8Q8EpDczC0knYKFQCCMjI/B4POjq6sLatWsTvixyCj+Ea1rJiPkVer1evmxX6v4jkUjan0ciEYyMjMDtdmPB8EFgxUbJ+5bCUkRdhRa4TBGXuyL1Z43zRxEyyHceOR85Oh0Ax3HQeY4mOGhUVFSkfGdyjcDyHYJZLMh14ac//SneffddPPXUU1ixYgXcbjf8fj/eeustDAwM0D6w8xWLxQKDQbyZNpcJy8kDJ8nIkfn5eXR2dmLNmjUpHzQielLL8lUqFRYXFxMeY1mWn7ac7FeYq5VUMkJxbG9vhy1ygeR9yiG5YGOpyLUH7FT1hQCAlbHE8x1TdcMEcU9HZ7X4e0ejtbOQiGtz65nPN/E79Hg8mJiYQDgchkqlShC1aDSaU0Ti9XpTHDvKAaGAvfLKKxgdHcUf//hHzM3N4fvf/z7a2toAlEfkSAWsBGQz9M01AotEIrDZbHC5XGhvb0dvb2/aYxGBkSpgwiIL4dpdQ0ODqF9hvn1jQmf9lpYWeE0X4z2X5N3lTC59YoWc0iyXMZV0C7F0JEdrrfa/I2aqOe+iteR0ITHmNRgMWLHirPVYNBrl/Q4nJyfh9Xpx+PBh3p2e/NFqtRm/6+XYAwacvT5xHAedTodgMIh//OMfuPjii/GnP/0Jd9xxR4nP8CxUwMqMXEaqMAyDyclJ2Gw2tLW1oaenJ2uToVyhVCqViMVicDqdsNlsqK2tzVjmPzoXR0VNN46PJTZi9a00iW5PIjCO4+B0OjE6Oor6+np4TRfjuEfyaRYFKX1i5dL/ZVLm56jfav87AEDlc8HoS71j8DeUZzl1PkgdjklQq9WwWCy8+Ph8PmzevJmP1nw+H6amphAMBqFUKhOc6Y1GI3+zJ2eUyvz8PD71qU9hdHQU7e3teP7550XL75955hl8+9vfBgDce++9+NznPgcAOHToEG644QYEg0FcccUV+OEPfwiGYfDCCy/ggQcewIkTJ3Dw4EFs3ryZF7B/+Zd/QTgcxtVXX42nn34af/7zn1FbW1tW88sYrhz8QM5D0q0nORwOcByHlStXZt0HSa85HA6YzWZs3LhRcnf80aNH0draKunDyHEcxsbGMDw8jKamJnR2dqateEwWLCn0rTTxzt3hcBgWiwWj0XWy97PUSEk3kugsncDlkkIk6UMxchUwIly5sJxFTa54ifHWW29hy5Ytoj+LxWJ8tEb+xONxvPLKK7DZbAgGg3jkkUfQ1dWVcT3s61//Oqqrq3HXXXfh4Ycfhtvtxn/+538mbDM/P4/Nmzfj7bffBsMwGBgYwKFDh1BVVYWtW7fi0UcfxbZt23DFFVfgy1/+Mnbs2IETJ05AoVDgi1/8Ih555JG0w3b/9Kc/weFw4LOf/Wza5Y9SQCOwEpHOkFatViMQCGR8biwWw9jYGCYnJ9Ha2oq+vj4sLCzIsnaRGoERv8KKigpUVlZizRrxL3wuwpX4XA20tT3QAuAAtMEN+0J5zUkilLLMfi+zEx1YKNj+8hEugnFKXITLWdgKIVxSIP6EwhtFjuNQW1uLZ599Fu+88w7uvfdeDA8P48UXX+RNdpPZs2cPXnvtNQDA5z73OVxyySUpAvaXv/wFl156Kd/Xeemll2Lv3r245JJL4PV6eW/E66+/Hr///e+xY8eOtN9nAJiamsKrr74KhmHQ0dGBrq4uzMzMoKOjI5+3pKBQASszMgkLGfJI7F+2b98OpVKJhYUF2etm2daokv0KVSoVjh49mrJdPsKVjTaLO+WxUopaqfvD9jI7C7avQghXNsSErdSipm1dcyZias2+rRRySWAxDIPOzk50dHSgublZ0myt6elpNDY2AgAaGhowPZ06OVzM/HdiYoIvskp+PBMejwd33nknPB4P3G43XC4X5ufnUVNTg/fff78sKhABKmBlh1gZvbCgobGxEYODgwlFE/kUfiSTzq8wFoslbF9M4cpEKUSt1MIFLD/xSoeYqC1VwYi2dU3BHdTz6QFbWFhI8CH8yEc+gqmp1IrQhx56KOH/S+GGYbfb8eabb+LUKfHouhzEC6ACVjKkTGVOnv8lVu1HnpNvBBYIBDA8PIxIJCLqV0i8CkslXJkQEzVP2ICFYPpR7lIphHjlu/51rohXOopdMCJMFxY6csjHyNfr9SbYOL366qtpt12xYgWcTicaGxvhdDpFDQKam5v5NCNwxqj3kksuQXNzM8bHxxMeT2fgS2hoaMBNN92EAwcOoLq6GjqdDhqNhp8mXS5QASsziIBNTExgdHQ063Ri8pxcIrBwOMy7dGTzKzzh8KOyuTg9WMWgsiKAyorUtUS50ZqwArEUkVghxQsAHG0XpzxWjqIGANrAmSGnuUZnYutc+QiOGPlEYHLK6Ilp71133YVnnnkGV111Vco2l112Ge6++2643Wdu6F5++WXs3r0b1dXVMJvNePPNN7Ft2zb86le/wm233ZbxeAzDYGJiAl/4whfwsY99DBzHIRKJoL+/H9dddx1NIZ7viP3yOY7D3Nwc3G43zGazqFOHGKTEXQ4sy2JqagpTU1Po6upK61dYjhFXPuSTgizk2BUpyBWvXCsQy03UYqbEm6hchOw9pw9a93sJfVk6na7gAhaPx5dEwO666y5cc801ePLJJ9HW1obnn38eAPD222/jiSeewC9+8QtUV1fjvvvu4ysiv/GNb/AFHT/+8Y/5MvodO3Zgx44dAIDf/e53uO222zA7O4uPfexj2LBhA15++WX85S9/wZtvvolvfetb8Pl8CIfDcLvdkoy/lxJaRl8iYrEYn8Ijc6xsNhssFgtcLhcuvDB9qbQY+/btwwc+8IGs2xG/wqmpKej1egwMDJwXwiWXfNfViKhpfvJgzilEMQHrqEpfgZhvD1g2lkLUksVLjExCRqIujuPO+GUKyteDwTMzduLxOFauXMn3Z+Xj6+dyubCwsICuri7Zz7322mvx+OOPo729PefjFxoSWf3ud7+Dw+HAl7/85VKfUkZoBFZCOI7D7OwsrFYrKisrsWnTJmi1Wuzbt6/gxyKl906nEytXrsQFF1wAp9OZIl7nu3AR8llXK0SDc6FTh/lSbPGKbfl44gMn/2/abbWBuRQRS04XMgwDnU4HnU6Huro6/nG32833WjqdTgQCAcTjcdkuGvx555lCTDcLrFSQuYIzMzP4yU9+gsnJSfT398NisaCyshK9vb1l5R5CBaxEeL1evP/++zAajdi4cSN0Ol1RjsOyLBwOB2/JNDg4CKVSCb/fn5J2pOKVmWzraoVKJeYiXr/6HzW+VATNW3LhIqz+IP9PvlFYIGrCtKLcni6dTpdQVs5xXIKLhtPpRCgUSvE8NBgMKenHfFKSfr8fRqMxp+cWC9JL2tzcjHXr1uHEiRPYt28fgsEgTpw4gR/84Ae46aabCp6KzRUqYCVCrVZj/fr1ohU9DMOAZVlZjcnJz0n2K0wuvRdWIVLhyg8Src1zZ9Nf1Uxuxo3lEnktSbownXilQyBqBLlzkMUuvAzDQK/XQ6/XJ1T3RaNRPv04MTGBQOCMQ71er+dFLRQK5XTzSVZu5HzHlwKGYRCPx7Fz507s3Jn+s1gO4gVQASsZBoMhrech8UPMZSqzWq3G1NRUVr9ClUoFbW0PFa8i8ZarBwCwpWZI8nPOF/GSLVwFRE7koFarUVVVlZDmY1kWi4uL8Pv9cLvdmJmZAXDGtYKImslkgl6vlyRO5VDJJ4TjOCiVSrz77rvYs2cPAoEAdDod9Ho9otEorr/++oRm6VJDBawMkTphWYhSqcT09DTGx8dhsVgwMDBQUL9CijT+fIhc7Dj0djBphSy5gON8EK9SChch39SXQqHghQo4awtlMBj4aM1ut/Ojh4QmviaTib+ZlJNhKZaR79e+9jX88Y9/hEajQVdXF55++mmYTCYolUp85zvfgVarxQUXXIBYLAa/34+pqSnZ1c7FhgpYiSjkSBVSCQUg43oaFa7icVa4xMkUkUkVr3QViL/6H2kjcaTgaLu4KCI2230xjNGo5PE9xSKfsncxSBGHRqNBdXV1Qpk5y7K8kS8xq45Gozh27BgOHToEhmFw8uRJ9PT0ZBTVhx9+GB/+8Id5I9+HH35Y1Mj3wQcfTDDy3bVrF6qqqnDLLbfg5z//OW/ku3fvXuzYsQOXXnopdu/eDZVKhTvvvBO7d+/G7t27+f395je/Kbuy+WSogJUhUkeqeDwenD59Gmq1GrW1tWhpaREVLypcxSObcCVDhOxynInAChV5fWlnatWkkMdfqsq6DUGsLwzILTpzr/7wmchkbg42mw2xWAxarZaPSLJV/BW6yycej8vKbEjZXzrxUSgUMJlMvB0bcOb1tLS0IBaL4fDhw7j//vsxNDSEhx9+GB/96EdF91MsI1/h8QYHB/Hiiy/yUeFXv/pV3H///RgYGEBjYyMsFgtMJpPocNxSQgWsDMkWgRG/Qo7j0NvbC7PZjKGhIVpVuMRkE69TtjNpxHQsZdpQqnhlQk7D8+ma9ejs7IQJSLmAC/uzxCr+TCYTDAYDP+S00I3HpXTiYBgGTU1N2Lp1K44cOYJf/epXWZ+zFEa+Tz31FD71qU/x/z906BAOHjzIX2dCoRDcbjfefPNNaiVFyZxCTBeBCf0Ku7u7E/LgQtGjwlVc5EZdYpTLmle+iImaElEo/t8aUDLp+rOEFX8Oh4MfKaTVahEOhzE/P5+whpQrpRYwgsfjSRixUkoj34ceeggqlQrXXXcd/9jjjz+OkZGRBLGKRCKSnIGWEipgJSSToa9w4GUoFMLw8DD8fj/vV5j8IVapVFiImbBAxatoFEK4Ck0h17/ypbH+zPszNTUluzw8XcWf2+2G1WqFy+XC6OhoQgqSRGtSm46BwgsYx3E5lcIn20iVysj3l7/8JV566SX89a9/5d/DSCSCSy65BO+99x4/vFalUhV07bBQlN8ZUfihlpFIBFarFW63G11dXVi7dm0G26fy6Y4/18hHuLKlEZcCOetfuUCEiyC3hzEdCoUCWq0WBoMBPT1n1g45jkM4HIbP54Pf78f09DSCwSCUSiW/pkbK2MWEqlwacMvByHfv3r347ne/i7///e8JkVYoFEIgEMDXv/51XHjhhdDr9VCr1airq8ONN95YgFdfOKiAlZB0ERgAzM7O8tNPV69enfYOk6YLi0s5Rl3lRLJ4AYUdWZIshgzDQKvVQqvVpk1Bjo+PJzQdC4WtnARMLJISo1hGvrfeeivC4TAuvfRSAGcKOZ544gkolUp8+tOfhtFoxOzsLBYXF+FyuXhxLBcneoCa+ZaUaDQKlmX5/xO/wvHxcajVamzbtk3SnSwVscJTSOEqRASWrYQ+U4RVjAhMTLgIDocDKpWKLzzIB4/Hg6mpKfT2yp8NRpqOSbTm8/ng9XphsVhgNpsTXOpzuSBzHIe3336bFw057N69G/39/fjkJz8p+7nFRChODocDwJnlCZPJBI1GQ9fAKKkQv0KSnx4YGMDJkyclp2H6Vprg8/kwOjqKdevWAaCilivFiLiKlUaUIl6FJpNwEQqVQgTyS/klNx0DwMGDB9Hb24tAIACfzyeagkzne1jIstB9JwAAIABJREFUcytHI1/gTIQ7NTWFF198EW+88QZmZ2dhsVgwMTGBnTt34p577ino7zdfqICVEI7jMD4+zvsVkonLsVgspwnLwuf0rTxbuhyPx2G32xFS1Yk9lYJzN1X4+EtL/7qKmULMF2EVZG3tWUd74jbh8/lSfA+FwiaMQPIVsHJydQfOvtf/+Mc/cODAAfT19eHw4cO47bbb8MMf/rCsLKQIVMBKiN1uRzAYTBlcKTTalYpY7xjLspiYmMDY2BiamprQ25y4sG21WhFWS8vDn6ucq8IlpFAR2uMvVeErV85mbT4upOgs1d2+SqWCxWJJEBVhClLopFFRUQGj0ciX9Oci2OUagQHA2NgYNm3ahPXr12N8fBwXX3wxRkZGcPDgQVx//fUFby7PBypgJaSzs1M00srl7lUoYGRA5sjICOrq6tIa+iqVSlQqvQlltcD5k35cSvEqh2rEdEhdI/vKlbMJ40bUanWCowYxsC2XFGK+iKUgSRWk3+/H3NwcAoEA3nrrLSiVyoT3IlsKshwjMHLdqa2tBcdx0Gq1WFxcxG9/+1u8/vrrOQ3tLDZUwM4RFAoFOI7D3NwchoeHYTabMxr6AukdP4TpR8K5JGrnQtRViv6vurq6hMq/SCTCp91cLhcWFxf5cRzE1dxoNOYlQOW03gIkVkEqFAqoVCp0d3fzKUix0StiKUi/3w+z2Zz1eMUy8r3vvvuwZ88eKBQK1NfX45e//CWampoAnCnbj8fjqKmpgdVqxbe+9S1cccUV+Ld/+zcA5TNKBaBViCWFZdm0nof79u3D9u3bJUdjHo8HBw8eRH19PXp6eiTZvTidTgSDQXR2dmbdluM4OBwOnDp1Cl1dXWhra8OpCXG3hXKl1MKVawQmVoEoFLB00RNZ/ypUClFKAUc8HsexY8eg1+v5i7pYObtUR41CVjSyLIt33nkHmzdvzntfADAzM4PFxUW0t7enPR4ZvUIqIa1WK372s5/B4/HggQcewKZNm9DV1ZVWpL/+9a+jurqaN/J1u92iRr6bN29OMPI9dOgQqqqqsHXrVjz66KO8ke+Xv/xl7NixA16vlxfQRx99FMePH8cTTzwB4OzSBvFYraysBMdxOTdtFxMagZUpwvlemfD7/RgaGgLLstBqtdiwYYPsY2Rjbm4OQ0NDqKyshMFg4AVvuURqpRYuQjmmEaWmDx9/qQrfltDDqlQqoVarsWLFCt4DMXktSeioIRS1ioqKlBu2QprvxuPxgl6As6U3hSnIhoYGAMCGDRuwadMmfOYzn8GpU6fwwgsv4OKLL8att94quo9iGfkKo79AIJAwEPfll1/GM888g66uLlRXV2N6ehqtra1Yu3YtLr/8csn9a0sBFbAyhfghphMwYi8VCATQ09OD6upq7Nu3T9aCcrZiEeJ2r9FosGHDBuh0Ouzfvz/jPpOrH99++22YGvsknU8xKBfxOhcg2QLix8cwjKggJKf90q0lhUIh+Hw++Hw+TE5OIhwOQ61WpzQeF7IgpBijVOSgUCjQ0dEBlUqF++67L+v2xTTyveeee/CrX/0KlZWV+Nvf/sY/fuzYMXR3d+PCCy+E0WjE3r178e677+Lw4cN49dVX8d3vfpcX5FJDBayE5DITLBKJYGRkBPPz8+ju7kZdXR2/H6lRW7ZjLC4uYmhoCNFolHe7zwXiJi4UNY7jMDMzA1e4uI7W57JwyUkfFhK1Wg2WZfk/APgbIKGoSbmJEpazC+/oI5EIn26bm5uD2+3G3NwcXC5XglN9LuswsVisoBEYiSLl4vf7Exz6S2Xk+9BDD+Ghhx7C7t278dhjj+H+++8HAOzfvx+vvvoqf47XXnstPvrRj+K///u/8YlPfAJOp5MKGCUzKpUqYX0sFovBbrdjamoK7e3t6O3tFTX0lSNgyREY8V5cWFhAT09PQp9MLiSfn8fjwalTp6DX69Hd3Z3y5S9E+rHchWup04iFWP8iYqhUKhOEg2VZcByX8Dep0OM4DtFoFAqFgv8cSBEPjUaDmpoa1NTUAABOnTqFuro6KJXKjD1aUtbVWJYti9EsCwsLCU70pTLyJVx33XW44oor8MADDwAAuru78d3vfhdXXXUVjEYj/H4/ZmZm+BuXchpySQWshGQbqRKLxcCyLMbHx+FwONDc3Izt27envRDIneRMto/H4xgdHcXU1FRW78VcEEZ0a9asSbj7BM4OLWwyx3H06FGoVCqYzWaYzWZ42UqxXaZQ7sJVjsiJ0nbfnHqhJp9DpVLJu8k4nU6sWrUKOp2OX/gXRmukEIA8N5uosSzLpxWFF33hutrc3FzKuhoRNuG6WiwWK8tRKpkolpHv0NAQb5C8Z8+ehO/8Pffcg0ceeQTPPPMMKioqcOjQIdx8882oqKjA6tWr+ZuLcoAKWIlJZ+irVCr5kvgVK1bwLh2ZkCtgDMNgcXERb775ZlZxFCJ1nS0ajSIUCuHdd99NG9GRCxzLsjAajRgcHEQsFoPX64XX60XYd8ZMVKlUwmw2w2Qywced7Z+hwlV6FhYW+Ehpy5YtKSJBBEwYqZHIXywFKfwMpiujF66rkTUi4bqax+PBxMREwrqa8BwKcYOWawQmR8CKZeR711134dSpU1AoFGhra+MrEAGgr68PP/vZzzAyMoJgMIj77ruPP9/HH39c9ustJlTAygzSyzU2NgatVpvi0pEJqQLGcRxmZ2cxPDyMWCyG7du3S047SlnjYFkWY2NjmJiYgEKhwODgYMr2QuEi+yXbqNXqhDQScEYMFxYWMD4+joWFU1CpVHjb/RFJ51xuyEkjJpfQS+n/khNZ5ZNijEQiGBoaQiQSwbp169K2bqSLtsRSkEBipBaLxSSLTbZ1tampKfj9/oTG43zW1fKJwKQ2MdfU1OCvf/1ryuObN2/GL37xC/7/N954o+iok82bN+Po0aMpj//2t7/NeFyVSoVVq1ZJOsdSQgWsjHC73RgaGoJOp0NnZyfC4bAs92cpAkaOodfr0d/fj3feeUfWlFtyDLHzIg4gVqsVDQ0NGBwcxIEDBxIuQJmEKx0cx/Hl1w0NDVi/fj2USiUGo3489j/GjM+l5AcRw/379/NrTiaTCYFAAE6nE52dnaivr88pohGmIAlEzOLxOCYnJxEKhaBQKBLWg8m6mtSCDLKuFolEYDab0dramrbx2GAwJDhqZPpuLIWAUTJDBazEMAwDr9eLoaEhAODXiFwuF/x+v6x9ZRIw0i/GcRz6+voSSprlkK703u124/Tp0zCZTNi8eTPfu0P6S8jfZF1EakUVKeU3Go3o7+9PEM58x8ufz8itUhwcHEQwGMTMzAxOnDgBhmGgVCoxOTkJn88Hk8kEs9ksazqyGAqFAj6fDydPnoTFYsG2bdv4qF/4+RGmIYnrR7ZiEWHKL533YSAQgN/vx+zsLGw2G2KxGHQ6HS9qZKyIsG9KLlTACgcVsBIzNDQEl8uFnp6eBIsYUsQhB5VKhXA4nPBYKBSC1WqF3+/n+8XyIVnAAoEATp8+DY7jsHbt2hRhJKX0wtSjlC99MBjkU5xr1qzJWXDLlVyqEZPTh0s5RiUej8PhcMDn86G/vx9Go5H3BfR6vbxHYjAYhEaj4S/2ZrMZer1ekqjF43FYrVZ4PB6sXr06pdgnOVIjf2dKQQrTl/F4PONNj0Kh4M9buK4WDAbh9/tT1tXC4TBmZmZ4H0ipwu31etHR0SFpW0pmqICVmPb2dnR2dqYtiZeDSqVCIBAAcCa9YbPZMDs7i87OTvT19aX9guXS/ByJRDA8PAyv14tVq1aJCiPHcVCpVBgaGuKHCGbrmyHnTfrcslU8fXVXGP/nD4VxajgXEEZWhRS4t956C21tbVi1ahX/WRH6AoqtOXm9Xn6iL5m3JawQFN7IzM7Owmq1oqWlBT09PVk/j5nW1dIVi4RCIWi1Wv6GSsqNFMMw0Ov10Ov1Ca8xHA7jnXfeQTAYTHiNwvRj8msk0AiscFABKzEajUY0JUecOORAesfsdjvGx8fR2tqKwcHBjF9UIkhSc/kMw8DhcMDj8aCzsxNr1qzJWKCxZs0aeDwe3m0hFAqhoqKCL5M3m82oqKgAx3GYnJyEw+FAa2srtm7dWjZjy881pKYPyXaf/f9Ooadns+SUbXIvF3DmxoS4boyNjcHv9/NFF4FAAGq1GuvXr5fk4ZkJYdRFCIfDGB4eRigUQltbm2iklslZJN1rVKlUCT6IyTPFyBKAwWDgRY0sGUipQiyWkS/h+9//Pv793/8ds7Ozefd8lgoqYGWK3AiM4zgsLCzA6XSira1NUtm98DjZtiUCMz09jfr6etGSe7ECjYqKCtTX1/N3r8K0k9fr5b/osVgMRqMR7e3tZTsnqdCUozeiGH19+VuBqVQqVFVV8b9bjuMwNjaG8fFx1NTUgGVZHD16FCzLwmAw8C0Tcox/kyGuLyMjI2hvb0dDQwN/Ac9UAUnIVCwitv6VaV2NTH/+4he/iKmpKUxNTeGDH/wgLrroIlx00UWi5//www/jwx/+MG/k+/DDD4sa+T744IMJRr67du1CVVUVbrnlFvz85z/njXz37t3Ll9E7HA68/PLLWLlyZQ7vbPlABazEpIsy5EQfLpeLryy0WCzo7u6W/FwpwzNdLhdOnz4Ni8WClpYWmM3mhC9v8gJ7pgINYdpJr9fzd6MrV65EJBKB1+uF0+lEOBzmm1KFkZrYfs/VNKKwhL4Y41OWcg1NiLBIY3BwMGVti1zwSVoxFovxFZBE2LIZ/IZCIZw8eRIqlQoDAwMpVbOZKiClFItEo1FJZffCdbWmpib8/e9/x5VXXolvfvObGBsbw+TkZNrnFsvIFwC++tWv8m4byxkqYMsYn8+H06dPQ6lUYt26dVCr1Xjvvfdk7SNb5eKpU6egVCqxYcMG6PV6jI6OJggeKXmWU6BBLKuIEbEwnZIuUhsfH+dFjQiayWTKyYvuXKWQ/ofF8FKMxWIYGRmBx+NJW5gjvOATOI7jXTfcbjfsdjsikUjCDY7wszA+Po6JiQn09PTIco1IJ2rkb6HAzc/PQ6lUIhqNJnzupXz+fT4fNm3ahG3btmXcrlhGvnv27EFzc7OsyRXlChWwEiOlByp5m2AwiKGhIYTDYaxatYoXAJZlZRd+iEVg4XAYQ0NDCAQC6O3tTUiJkO1z6eeKx+MYGxvD9PR0VssqsQKBZKcFh8PBixowKOt1lwtLnUbMRZiOHz/O3zSkK0zIBmmcb21tlVSkIYRhGBgMBhgMBt5ENrkCcnJyEoFAAJFIBHq9Hq2trdBqtXm7biQLUzQaxenTpxGJRNDb2wulUsnfxAHZnUXINiQtutRGvouLi/jOd76Dl19+Oe99lQNUwMqY5AKLaDSa4ERfW1ub8KEm49zlIIzAYrEYRkdHMTMzg66uLqxduzblS6NQKBAIBPg7T6mNyNPT07DZbGhsbMTWrVtzugiKOS0QUWtqGsN/HVje+fxypampCT6fDw6Hgy9MMBqNCetU6dJpoVCItyzq7+8v2Gwv4Q1ObW0tbDYbIpEIVq9eDeBMqbrVak2pgDSbzTAYDDl9/mZmZmC1WtHR0YEVK1akfO6FEVqyY7+wWERoHbfURr5WqxU2m42Pvsb///bOO6zJs/vj3zBlI6iAICJ7yZDh6FLUUq2iVquob8VZ29pqrQuLWrRVrB12aJ21Wluk1dbxVsQ96mApbtlD9giQAIGQcf/+4Pc8bwIBAgQBvT/XxdUSniTPE5N87/uc7zknLw9DhgxBfHx8t+kw3xaogHUxyoxU4XA4bLzcxsZGzsrcUdTV1SEWi5Gbm4unT5/CyspKoXOR+WAaGBigsLAQ8fHxCt2Ejc+rsrISaWlpMDAwUJiL6ChMQS2XywXwfAqYovxX4/xVZ4T8GBobEyQSCeu2KygoQHV1NdvLkhEJfX19FBYWoqCgoM2hvLbA9GE0MzODr68v+75t3IasuroafD4fOTk5rANS1tbfkgjX19cjOTmZNUk09x5uLa8mkUiwd+9eFBcXK/X57YxGvoMHD0ZJSQl7fxsbGyQmJlIXIkX1aGhoID8/H0VFRejfv3+ThHdHYYo0c3NzYW5uDn9//yaOr8ZJbV1dXXh7ewMAG85j3IRMnQ1T78V8UDqrEJnpuch00Ue6yp/imdAZYcSOGDRkxVBRF3p1dXUYGRk16Q5fU1MDPp+P3NxclJWVQV1dHcbGxqiqahiTY2hoqLLuKWKxGGlpaaitrW2xDyPQUJIi64AE/ifCfD5foQgztnemhRkze6+tMKKWlZWFpUuXYsiQIcjMzFTqvp3VyPd5gkMUtUKnPDMIIaivr29yW1lZGR48eABTU1O4uLgovXO5efMmhg8f3uoKj8/nIyUlBRKJBKampuxohcbnIWvQUCZUWFNTg/T0dPB4PLa+S0dHR26n1tFdGNOMODMzE2ZmZrC2tmaFvae6ERsLWGsOxNZ2YIoETJljGh+nSMCaQywWIyMjA1VVVXB2doaenh7rKGRyVSKRCLq6umz4sT3vByaUN3DgQFhYWKgsGiHrgKyoqEBJSQk4HA6MjY1hZGTECltbwqASiQS7du1CVFQUfvzxR7z00ksqOVdKA3QH1sUoGvqYmpoKbW1tmJubo2/fvm36gLdWmNzYAMLsomRpj0FDKpUiPz8feXl5sLa2hqenJxvvr6urA5/Pl3OQMV9izI+yK/OqqiqkpaVBW1sb3t7eKsupvGio2kLPiIq1tbVciFvRyJPmHIWy7lJF4WihUIjk5GSoqal1SjiaGdHCFN57eHjAxMREaQdk4/NNTU3F0qVL4e/vjxs3bkBHR0el50uhAtYt4HA4bE9BiUTC9oHLyspqVzcORYXJjIW5rKyMnc3F1LPI1rm0VbgAoKysDBkZGTA1NYWfn5/cc8saL8zMzNjnEQgE4PP5KCsrQ2ZmJsRiMVvAyvzIPo6s9d7R0RGGhoZtel26O82FEVU9PkXVj9VavVVjmnMUMoscWXcpk2Nlut8XFRXB0dGx0/I1AoEAT548gb6+vtxcs+bOl+kswoTPtbS08PTpU5SUlKCgoAAXL17Ejh07MGLEiE45XwoVsG5BcnIyKioqmiS729sPUfY+zKRcZmfU2KAhO5W5rcJVXV2N1NRUaGlpwdPTU+maLNkvMdmVOZNDYVbzEokEenp6bL7C1ta21WnRz2tRc0dRtclDdgJzR00azS1yhEIhSktLkZqaytrRc3JyUF5ezgpbW5roNgfTFaSwsBDOzs6t9ilsae5YTk4O/vrrL5SXl0NLSwuhoaHYtWsX3NzcOnSOFMVQAesGWFpaKnQWampqss15lYURJKaNTkZGBvr27auwtRRj7a2oqEB+fj7rHmvtC0EoFLIW5caFyO2Fw+Gw4ab+/fuzea60tDTo6enBxMQEeXl5ePr0KWvhZr7EVGlsochTW1vbJDzG4/GQkpLC7rg74/VnWpeVlpbCw8ODfY8x3VqY1ky1tbVy07qZ7vfK2uSrq6vx5MkT9O7du0PXIhaL8dNPP+Hvv//Gzp072SJlJhdM6RyogHUDDA0NFdZvtXcHVllZidTUVHZopaKdEWPQ0NHRgbOzM/h8PrKysiAQCKChoSEXytPR0QGHw2lSiKyoka8qkN3Z+fj4yJ2/VCqVc48x+TtZUQOa1sv0BJR1Iz7LFlDJyclsA2bGlCESieDq6tpk3Imq4PF4SE5ORr9+/eDn5ycnRlpaWujTp49cGFEkErFGkaysLNTU1LRa+yWVSpGdnY2ysjI4Ozt3KCT95MkTfPTRR3jttddw/fp1uferKhZ3lOahAtaNaWtHeoFAgNLSUqipqWHw4MEKv2AU5bkaW4xFIhHbwqm4uBgCgQBAw86rb9++8PDwYEVNlYhEItbFJtthRBY1NTUZoWpA1hKdl5eHEWbJuFmsuEFqT6K9+a/2Clzjx2pwIHqzu6HMzEx2oOPDhw+hpaUltxPu6HtCLBYjPT0dNTU1cHd3h56enlL309TUhImJidxIH9nu90ztF2PS0NTURGlpaZPasfac7/fff49Tp07hp59+Yq3slGcHFbBuQHMfemV3YPX19cjMzERFRQVbwNhYvNpi0NDU1GTHYTCFyLq6uqwjKzk5Wa4voZGRUYfs8VKplO1fZ2NjAycnpzZ9ESqqS7p5ql2n0i2QtdCrgo7kv2pra5GSkgINDQ0MHTpU7t+4pWGWbc1RMa2mrK2t2/zvr4jG3e+Bhs9JSkoKysvLYWBggLKyMpSVlckVYCsbkn78+DGWLl2KgIAAXL9+nYYJuwgqYN2Y1gRMKpUiJyeH7dDh5OSEgoICuV1be52FjN1eIpHA1dW1yWq4JXs8I2jKjMJgHIx9+vSBv7+/yvIpPdXMkZJFMKiTp8kou0PLyclBYWFhswNLtbW10bdvX7kC38Y5KoFAAE1NzSY5KuY9KBQKkZKSAgAqbTXVmMrKSiQnJ8PCwgLu7u5yY1WY3XthYSFSU1NbHOkiEonw3Xff4fTp09i1axd8fHw65XwpykEFrBvQ0k5IUQiREIKioiJkZmbC3NxcrkOHhoYG6urq2jTiRBaRSITs7GxUVFTAzs6uWXdZa/Z4ZhSGRCJRaLpgygY0NDTa5GCkPDvEYnGbjQ2KclSyE5pLSkrYPCvTV3PQoEGwsrLqlHyqbFhS0cBMRSFp2YJmxgh1/Phx3L17FwUFBfD398fJkydZBy2l66AC1o1RU1OTa/wJABUVFUhNTYWBgQH8/PyahO2YqcxtHXHSuBDZ3t6+zV8oiuzxsi2GCgsL2fAjAFhYWMDc3FzlBakA/t+92fN2YAzdof7Lzs5OJY/deEKzQCDAo0ePoK6uDktLS9YF29hNqKen1yFRY+bkWVlZtSks2XiGl0gkwoULFyCVSvHOO++gsrISs2bNws6dO1Uy7JPSfqiAdQOU+WAxOxYAzSa4CSHQ0tJiQzdMXsjQ0LDZVTQhBFwulw3jNS5E7ijMl4Genh6kUikqKythZ2cHfX19tsN5VVUVexxzvu398hKLxcjKykJFRQXmveqIX66ZqexaeiKd2eS3rTAh75KSkiZjegB5N2FmZqZcJ3lG2JTpJC878sTLy6tDu/sHDx5g6dKlGDduHC5cuNApiy1K+6EC1s2RSqV4/Pgxqqqq4ODgoDAXIZvn6tWrF0aMGMGG8oqKipCWlgZCCPT19VmB0NfXR01NDdLS0tpciNxWuFwu0tPTm3TqkP0CY1xjja3QTHjHyMioRZcbIQSFhYXIycnBgAED2rWD7A6UFAta3H111RTljsLn85GcnMwukhSJUHNuQianlp2djZqaGnaxw7w3ZEWNMYPY2NjA3Ny83e+B+vp6fP3117hw4QL27NkDLy+v9l04pVOhAtZNkUgkyM7OhkAggK2trcKaq5YMGrJFwUCDEDICkZ2djfLyckilUvTp0wcmJiZyIUdVIRAIkJqaCjU1NdZ63xyKXGOydn4md8JYtxkh1tbWBp/PR2pqKgwNDeHr66uyjufPkpJiQZuOb25n1R6BU/RYs4c/wdOnHS8Wl0gkSE9PR1VVFdzc3JS2xjNoaGg0a5GXHY/C3K6hoQFHR0f07t273e/le/fuYdmyZZgwYQKuXbtGd13dGCpg3QDZDxpTc5OdnY3+/fujd+/eMDU1bXJMWw0aTA1MeXk5BAIBXF1d0bt3b/aLID09XU4gGJFQ1FS1NUQiEbKyslBZWQkHBwc5UWoLsnZ+Bsa6zYzt4PP54HA4MDMzg6mpaZOcYXd3I7ZVuNpCR8KHAwcObOLMa2sHlLKyMqSnp8PKykqlM+xkFzvMsNTMzExYWlpCXV0dhYWFSEtLY9/zsm7ClsKPQqEQX331FS5fvoz9+/fDw8NDJecbExODZcuWQSKRYOHChQgNDZX7+/Lly3H58mUADYu+kpISVFY2lFKoq6tj8ODBAABra2ucOtWD60M6ASpg3Qgul4vU1FS2rY2WlhZ4PB7EYjFrL2484kQZgwbjWmREUXYicuPVbX19PXg8Hjvji6n3YnY8RkZGze5wCCHIz89Hbm4urK2t2zw6Xhm0tbVhamoKgUAAkUgEFxcXGBoaNukWznS7b6gN655usc4Ur47CfOFbWloC+J8Zh8fjtSpq9fX1bGPqjuagWoJpJKypqQk/P78m70uJRMIWM+fl5bE7NdkOHbq6utDU1MTdu3exbNkyTJ48GdeuXVPZLl4ikWDJkiU4f/48rKys4Ofnh6CgIDnzx/bt29n///HHH5GUlMT+rqOjg7t376rkXJ5HqIB1AwghuH37NtTV1eHp6Sln9ZXtbdieeq62TkTW0tKSq+1h6r14PB7Ky8uRnZ0NkUgEPT09VtQMDQ3B4/GQnp7Oiq8qjSCyMM4yps0QswvQ1dVt1s7f3QSsK4WrvTk0WWceg6K2XvX19RCLxTAzM4OVlVWnhHOZKEVubm6LjYSZgZqKpkkzXVvWrVuH9PR0VFdXY+HChRg9erRKezvGx8fD3t4etra2AIDg4GCcPHmyWffikSNHsHHjRpU9//MOFbBugJqaGlxcXBROldXQ0IBQKGQLmpUVLoFAgPT0dEilUoWFyMoiW+8lO06CWY3n5eWhrKwMQMNuTkdHBwKBAPr6+u1u0dPc9TD5NE9PzxbzaY3t/E5O3TuM2Fa6cnyKLLI1VLW1tewoEgsLCwgEAlbUCCFypgt9ff12iwTzPLq6uvD19W3zQkm2a8udO3fA5XIxd+5cjB07Fvfu3cPvv//ONuJVBfn5+RgwYAD7u5WVFeLi4hQem5OTg6ysLAQEBLC31dXVsdcZGhqKyZMnq+zcngeogHUT9PT05PI3zI7LyMgIaWlprCOP+fApGqAHyOef7O3tFboWOwqHw0GvXr3Y9kGenp7s6Hg+n4+nT5+yvedkDRftGX0hkUiQlZUFLpfbrAuzJ3Hm9+vwGTOk05/nWdnnZceqODk5sflO2X8n2Z3owaRXAAAgAElEQVRafn6+wgbMrYkaIQS5ubkoKCiQe572UFdXh61bt+LmzZs4dOgQO+pk2LBh7X5MVRAVFYVp06bJvQ45OTmwtLREZmYmAgICMHjwYJXV5z0PUAHrZjDGDMak0bdvX/Tr14915PF4PBQVFbFjLhiBMDAwQElJCfLy8jBw4MBOyT8x51dQUICnT582sasz4sqsOBkLtCKTiKyLUNF5Msn5rKwsNnegyh1dV3Dm9+sAgNsX7jT527MQNVVTVVWFJ0+ewNTUVC6v2pjmul0wC57WRK2mpgaPHz+GsbFxh8e3JCYmYvny5ZgxYwauXLnSaaFuBktLS+Tm5rK/5+XlsXnFxkRFRWHnzp1N7g8Atra2GDlyJJKSkqiAycAhjW1blC6B6Z4ha9BoSYCYgX+VlZUoLi4Gl8uFuro6evfuzQqJqmdlVVRUIC0tDcbGxhg0aFC78huyLkIejwehUAgdHR05Uaurq0NKSgr09PRgZ2enMhtzV4URGeFqK41FjclfKWOh78gxDV3om0cikSAzMxOVlZVwcXGBvr5+i8cri2x+is/no7q6GkKhEFKpFJaWlujXr1+rTsLmqKurw5YtWxAfH489e/bAxcVFJefcGmKxGI6Ojrh48SIsLS3h5+eHyMjIJgMuk5OT8cYbbyArK4v93FdUVEBXVxfa2tooKyvD8OHDW8yfvYjQHVg3gBCCHTt2wMPDQ+lRJRwOByKRCAUFBdDS0sKIESOgra0t5xRjmqQyHS6MjIza1eGCaewrlUrbVcsjS+MGsLImkZKSEjx8+BASiQTGxsbQ1dVlC5p74tDK9goXg+xOzWfMkG7RVYMx0VhaWsLX11elu3zZ/BSzu+vfvz9MTU1RXV3dxEmorD0+Pj4eK1aswMyZM3Hp0iWV7bpas8cfPHgQq1atgoGBAZydnWFkZIRly5bBzc0NkyZNQkJCAvT09LBu3TpkZWUhODhY7vV88uQJFi9eDDU1NUilUoSGhlLxagQVsG6AWCyGrq4uIiMjsWbNGqipqcHLyws+Pj7w9fWFo6Oj3Bc4MxG5trYWDg4OcqEZpoCZCT0wVmLZDhfMwEpmx9NcPk0sFiM7OxtcLhf29vYdGhvfHBwOB9ra2hCJRKisrISTkxP69esHgUDAhkuZTiKNjQDdOaTYUfFiaE9osaNCd/v2bbmBpr169YJIJEJaWppK2jO1hFQqZUcDubq6srs72ZxXYydhY1HT0dFhc8qbN2/G7du38fvvv8PZ2Vll56mMPR4AZsyYgR07dsjdVl5ejgcPHuDhw4fgcDjw8fHB7du3m+T1RowYgQcPHqjsnJ9HqIB1AzQ1NbF48WIsXrwYhBBUV1fj9u3biIuLQ0REBFJTU9GnTx94eHiguLgYBgYGWLt2Lfr169fqCliRlZgZecHj8VBQUIC6ujq21osJPZaVlSEnJ6fT80/l5eVIS0tj8yiMUCsS4urqavB4PNYkwvTJU8YkUlNTg9es7uBq3vBOuQ4GVQkXoPq8mLIW+sGDB7NhvIKCAlRXV0MkEsHU1JTt7KLqri1AQ8lHSkoKzM3NW9zdKZr/JrtQu3nzJtatWwc+nw87OzuEhISofAffVnu8LGfPnsXYsWNZo8vYsWMRExODmTNnqvQcXwSogHUzOBwODAwMMHLkSIwcORJAw5fFvn37EBERAWdnZ5SUlCAoKAj29vbsLs3b2xv6+vpKfak0HnkhG8bLz89HWVkZ1NTUYGJiAkIIqqqqOmR9VoRsWHLw4MEKSwhkUfSlxZhEmPCjIpOIpqYmu6J3dHTE1TyVXYIc3Vm4FNHSLo15f+jp6YHH48HExAQDBw5EbW0tK2rMokd2p9beWV5Mu6nq6mql3guKYBZqWlpauHnzJiwtLXH06FEIBALcvn0bjx8/hoODQ7vOTxHK2uP/+usvXLt2DY6Ojti+fTsGDBig8L75+fkqO7cXCSpgPQAOhwMbGxvcvn2bXbVJJBKkpqYiNjYWJ0+exGeffQaRSAQPDw/4+PjAz88PLi4uShktGMNIaWkpJBIJhg4dCh0dHTafxrjEGHHtSMd4iUTCdiRvqQhVGRT1yWNMIjweD5mZmaipqWGLnCUSSbufqyW6MlzYGcha1mWHWerr68vlLmVf69zcXDlDDvPTmgGnvLwcqampKmk3devWLaxatQohISH49ttv2QXXiBEj2v2YHWHixImYOXMmtLW1sWfPHoSEhODSpUvtfryqqqomk9ZfdKiA9RBef/11ud/V1dXh4uICFxcXzJs3D0DDrubu3buIjY3FDz/8gMePH8PAwABDhgyBn58ffHx8MGDAALlwINM0uKysDHZ2dnKDCBt3XpBIJGxoiRl3wUzblc2nKYIQgtLSUmRmZsLCwqJF23VH0NbWhq6uLnJzc2FgYABvb29IpVLweDxwuVy80j8L/xa8pJLn6o67LlUYPRITE1u1rDO1gL169UK/fv0AtDylW1bUmEGtaWlpEAqFHc6p1dTUYNOmTXj48CH++OMPle60mkMZe7zs4mzhwoVYvXo1e98rV67I3ZeJtihCJBIhNjYWtbW1GDVqFG7dugUfH58OmameF6iN/jmGmfWVkJCA2NhYJCQksH0KfXx8UFNTg6KiIqxfvx5WVlbtEhTZ3omytnjZNlNCoRCpqanQ1taGg4NDp3X3lp0F5uTkJBdulKWjdnpVCpci2itmSyZUtChgyljo3/K6A2dnZ5Wt9GXbejE/dXV1EIvF6NOnD/r37w8jI6N2OQMJIbhx4wbWrFmD+fPn44MPPlBJmLs1d+G3336Lffv2ISMjA76+vjh06BCmTZuGyMhIeHh4sM13+/Xrh3PnzgEAjh8/ji+//BKxsbEoLy+Hj48P7txpcJkOGTJELrqiiPPnz+O7775DWloapkyZgoiIiG5tYnpWUAF7wZBKpTh16hRCQ0NhYGAAHR0d8Pl8uLq6sqHHwYMHtzufQQhBbW0teDweKisrUVpaCrFYDGNjY/Tt25e1Pqvywydb9DxgwABYWlq2Gopqr4h1tngpQhlBa61GTNljtixU67Q5avX19UhJSYFUKsWAAQPY3Rqfz4dUKoWenp7cTq0lMaqpqUF4eDiSk5Oxd+9elRX3SiQSODo6yrkLjxw5ImfOuHz5MoYOHYorV65g3rx5qKmpwdq1axEWFgYtLS0cO3YMQUFBWLt2LU6dOsWGunft2sU6IQ8cOIAtW7YAAMLCwtgoCgPztcz8W6SkpGDcuHEYMmQI9u3bh969e0Mqlb7wIkYF7AXk7NmzsLKyYosp6+vr8eDBA8TGxiI+Ph4PHjyAlpYWvL29WVGzs7NT+sMi25V+4MCBMDMzQ01NDbtLYyYwd7TNFABUV1cjJSUFOjo6sLe3V3p311YB6wrhaglFRc6thQ+VEbDWipjbg+wCw9bWlm26LAvT7Z4RtKqqKrbbvWx4WktLC9evX8eaNWuwaNEivP/++yr9Er916xbCw8Nx9uxZAEBERAQAYO3atQqPT0pKwocffogbN24AaMgTMrb+9iKRSFjxvn79OsRiMTw9PVFRUYEDBw7A3Nwcc+bMkSufeVGhObAXkMDAQLnftbS04OPjAx8fHyxZsgSEEPD5fCQmJiI2NhafffYZMjIyYG5uzroefX190bdv3yaiU1lZKTcShgkNMatqKysrAM23mZId29LSLlAsFiMzMxM8Hg+Ojo7Nhgs7SncTLoa2Fjl31SRnoVCI5ORkaGhotDhsVLbbvewIF9keikuXLkVhYSHq6+vxwQcfYNiwYSrfLbal+S4A/Pzzzxg3bhz7uyqa7zLi9csvv2Djxo14+eWXIRaLsW/fPgwbNgwxMTG4f/8+Xn75ZaSlpfXY6eOqgAoYpQkcDgdGRkYYPXo0Ro8eDeB/u6q4uDjExsZix44dKC8vh6OjI3x8fGBjY4Pjx4/j/fffh7u7e6sJ5tYchHl5eRAKhdDV1ZXLp6mrq7OzzQYMGNDuno/KDLrsruLF0BlFzo8ePZKb79Xe3Y1sz0wHBwc5c5CyMLt0AwMD1jT0ySefwM/PD3fu3MHhw4fh4+PTrvNTBb/99hsSExNx9epV9rb2Nt9ldl2MUWrixInw9fXFo0ePIJVKsXHjRmzYsAHbt2/Ho0ePsHfvXsyePRurVq2Cvb19Z15mt4YKGEUpOBwOrKysYGVlhalTpwJo2AXdu3cPW7Zswfbt2+Hi4oLVq1fD09OT3ak5OTkpnaBX1GaKMQCUlJQgJSUFtbW10NbWhqWlJQwNDTuloPZ5FC5lsbKykutwwZROMKKmp6fXqqjJjjzp6Gy4qqoqrF+/HtnZ2Thx4gRsbGwAdF7neGWb7164cAGbN2/G1atX5SIFbW2+y2RwmF2XmpoaevfuDR8fH1y/fp197EWLFmHFihU4ffo01qxZg3PnzuHtt9/GxIkTO37RPRgqYJR2w4x29/f3R2RkJLS0tNjC0fj4eGzbto0NJzIhSn9/f1hYWCglOsxcLy0tLfD5fGhoaGDIkCHgcDhyY1uYUTNM6FGZXpLN8SKLF4BmO1zweDxkZ2ezvSllzRZM/pIQgry8POTn53d45AkhBFevXsXatWuxZMkS7N69+5kYFvz8/JCWloasrCxYWloiKioKkZGRcsckJSVh8eLFiImJYUsIgKbNd2/cuMFa55uDeZ+eOXMG33zzDYYPHw43Nzd8//338Pb2xtGjRzFz5kzY2tpi5syZ+Oyzz/Dyyy83Kat5UaEmDkqnQghBSUkJ4uPjWSt/YWEhBg0axObShgwZAgMDgyaiQwhBYWEhcnJyYG1tjf79+zc7A435kuXz+RAIBNDW1pbLpzVn7mDCiM+7cKnSwMGM9mF+BAIB1NXVUVdXBwMDAzg4OLSryJ2Bz+dj3bp1yMvLw969e2Ftbd2ux2lMa/Z4oVCIOXPm4Nq1a6ioqICZmRneffddhIWFISAgAE+ePGEFu6CgABYWDZO+ra2tcerUKdy8eVOu+e7HH3+MBQsWNDmPS5cuQSqVYsyYMQCAf//9F+vXr8ePP/6I6OhoREZG4uzZs0hKSsKqVatw8eJFmJmZsYs2d3d3lbwezwNUwCjPHKlUirS0NMTFxSEuLg537tyBUCiEm5sbK2o1NTV48OABRo0aBTs7uzaPbmEs2oyo1dfXszZtRtjU1dVBCEFgcGInXalqUMWuSxmXYnsciFKpFE+fPkVhYSH69+/PmnPa02qKEILLly8jLCwMH330EebPn6+yXZcy9viffvoJ9+/fx+7duxEVFYXjx4/jjz/+wOPHjzFz5kzEx8ejoKAAY8aMQWpqartrzq5duwYXFxd2YXX48GEIhUIYGxvj888/x7p16/D2228DAObOnYuysjL8888/KnkdnjdoCJHyzFFTU4OTkxOcnJwwZ84cAA2r37t37+Ly5ctYsGAB+Hw+nJyckJWVxYqajY2N0l9oirpENO5wLxaLIRKJELFaH3Z2djA0NGQf//UZCZ1z8e2AcRx2l1ZTDLIDLYcOHSr3b8O0mmLqAZ8+fdpsVw4A4PF4WLduHQoLC/HPP//IOQFVgTLNd0+ePInw8HAAwLRp0/Dhhx+CEIKTJ08iODgY2traGDRoEOzt7REfH4/hw5VvDC2VStmWba+++iqio6Nx6tQp7NixA2ZmZnj77bfx+uuvIzY2Fjo6OsjNzUVVVRX279+P69e7d3SgK6EC1gUcPXoU4eHhePLkCeLj4+Hr66vwuOZCHszsIC6XCx8fHxw+fLjTuls8K7S1tTF06FCcOXMGn376Kf7zn/+gsrISCQkJiIuLw7Fjx1iHFyNoPj4+MDExaVM+TU9PD/369UNGRgZ4PB5sbGwgFotZ0wKT3/n1+wEwMjKSGzXT1aLW3knOqrbQS6VSZGVloby8XG7kiSyyraaYui/ZIveysjJkZmZi+/btKC8vR2ZmJt555x189913ndLvTxl7vOwxGhoaMDIyApfLRX5+vpxpRNnmu4zBiBDCivvNmzeho6MDHx8ffPXVV/jnn3/g4uKCkJAQmJiYQEdHBxcuXMDKlSuxcuVKuLq6tthm6kWHClgX4O7ujr///huLFy9u9piW5g2tWbMGy5cvR3BwMN577z38/PPPeP/995/hFXQezAoYAExMTBAYGMjWrTHhqtjYWFy7dg3ffPMN+Hw+nJ2dWVHz9PRssR8jU1BrbW2tsHksk99hdmq1tbVsKCxy56AmDWp7iqipahgmj8dDcnIyzMzM4OPj06YQH4fDga6uLnR1dWFhYYHKykoYGRlBLBZjxYoVSE9PR2BgIE6cOCFnjuipVFRUsAss5vN8584dbNu2Dd7e3li0aBEOHjyIiIgIvP/++5g/fz6Sk5ORnJyMTZs2ISgoqKsvodtDBawLUGaceXMhDxcXF1y6dIl1RoWEhCA8PPy5EbCWUFNTg42NDWxsbBAcHAygQXAePnyIuLg4/Prrr7h//z7U1dXZLiK+vr5wcHDA48ePweVy0a9fP/j4+DS7Y9XU1ISpqSnbiFU2FFZRUYHs7GyIxWI2n/bnHgcYGBiw+ZCuFjSgcyY5SyQSZGRkgM/nK1Xn1xKEEJw7dw6fffYZVqxYgXfeeafTHYbK2OOZY6ysrCAWi8Hj8WBqaqq0tZ5BKBQiMTER0dHR2Lx5MwDgxIkT4HK5iI+PZ4+bNWsWrly5gsOHD2Pz5s24fPkyKioqYGpq2uMjKs8KKmDdlOZCHlwuF8bGxmxtzYs+S0hTUxPe3t7w9vbGe++9xw4ETUxMRFxcHDZu3IjY2Fj06tULkyZNwssvvwwTExOYmZkpHXpUFApjRs0UFhYiJSUFAKCnp4cvVjSEjdzc3Ni5Vl0hau3Jl3G5XLm8FAMz8sTS0rLdheMMFRUVWLt2LSorKxETE8MOyOxslLHHBwUF4dChQxg+fDiOHTuGgIAAcDgcBAUFYdasWfjkk09QUFCAtLQ0+Pv7K3yeAwcO4Mcff8Tly5cxYsQInDt3Dq+//jqqqqrY+sb6+npwOBxoamoiPDwcb7zxBl566SW8+eabrLORohxUwDqJMWPGoKioqMntmzdvxqRJk1T6XGKxGGPHjkV2djZsbGzw559/NqnBuXz5MpYvX87+npycjKioKEyePBlz587F1atX2fqfgwcPwsvLS6Xn+KxgCm9HjRqFkSNH4o033kBoaCimTp3KTrnevXs3ysrKmgwEVdb6zeFw5CZGM/VPOTk56N27NyQSCe7du8eOmjn8gzXby+9Z5NPaI17LJpairKxhhppEIoGenh4MDAxQWVkJiUQCT09P6OjotPucCCGIiYnBxo0bsXr1asyaNeuZNqLV0NDAjh07EBgYCIlEgvnz58PNzQ0bNmyAr68vgoKCsGDBArzzzjuwt7eHiYkJoqKiAABubm6YPn06XF1doaGhgZ07dzZxIDK5vZs3b2Lz5s0ghCA9PR2zZ8/G9evXYW5uDrFYjNLSUlbIrl27hmHDhuHQoUPw8PB4Zq/F8wS10XchI0eOxNdff63QxNFcU9HQ0FD07dsXRUVF0NDQwK1btzBr1iwsXrwYoaGh2Lp1KyoqKvDll182+7zl5eWwt7dHXl4edHV1MXfuXEyYMAHTpk3rnAvtQsRiscJOEBKJBCkpKWwD46SkJIjF4iYDQVvrIlFTU4Pk5GTo6zc4GWWPr6+vl7PyM9Zypli48W6no6LWEZeirIVeKpUiPz8fWVlZ0NXVZQeBMh05jIyMlOrIwVBeXo7Q0FBUV1dj165dnbbLKC8vx4wZM1pcyN29exfvv/8++Hw+1NXVERYWhhkzZgBAuxdyv/zyC86fP4+wsDB89dVXKCoqglQqxfHjx/HDDz+wQ2fnzp0LHR0dBAQE4Nq1a0hJScHevXvZ7iKUtkN3YN2U5kIeHA4Ho0aNwrFjxxAcHIxDhw5BIBAgJCQEQENObOTIkS0K2LFjxzBu3Lh2jW7vaTQnQOrq6nB1dYWrqyvmz58PABAIBEhKSkJcXBy+++47PHnyBAYGBuwuzc/PD5aWllBTU0NNTQ3y8vLA4/GanT2mpaWFPn36sH0AmYGPzHBN2d2OkZERju51hL6+frvyaaqy2ItEIqSkpLCTuZnaLaYjB5/PR05OjlIdUAghiI6Oxueff47Q0FAEBwd36q5r69atGD16NLuQ27p1a5PPga6uLn799Vc4ODigoKAAPj4+CAwMhLGxMQDgq6++atNCrqKiArt378aPP/4IJycnqKmpIT09Hfv374eenh4++eQTBAUFYc+ePdi9ezf++OMPnD17FoaGhoiOju5Qmy0K3YF1CcePH8dHH32E0tJSGBsbw8vLC2fPnkVBQQEWLlyI6OhoAEB0dDQ+/vhjNuQRFhYGAMjMzERwcDDKy8vh7e2N8+fPo7KyEkDDl0bv3r3Z3xUREBCATz75BBMmTADQsPK8desWtLW1MXr0aGzdurXd88CeJwghKCsrQ3x8POLi4pCQkIC8vDzo6+ujqKgIy5Ytw9tvvw1jY+N254WYMSLMLq2qqooNgxoZGUEikbBjafr379+k6FpVwhWxSB3FxcXIzMxsduRJYxR15NDW1sbVq1dhaGiIixcvQl1dHTt37oS5ublKzrMlnJyccOXKFVhYWKCwsBAjR45k85PN4enpiWPHjsHBwaHNkQjGJr98+XJcvnwZ5ubm+Pnnn7F//34QQjBr1iw4OjoiISEBc+fOxW+//QZvb2/U1tZ2KBxL+R9UwHoILeXUQkJC5ASrd+/eqKhQXPtTWFgIDw8PFBQUsOGrwsJCmJubo76+HiEhIUhISGAdf4rCMEDDDoaZPMu00gGezxo1BkII5s+fj/z8fEyYMAHp6elITExEbW2t3EBQd3f3Di0AJBIJuFwuMjIyIBKJoKGhAW1tbbkuIrKlAmv3STp8bcH+D6Gurg5HR8cO/XvV1dVh+/btiImJgVAoBIfDga2tLQ4dOtTp86uMjY3btJCLj49HSEgIHj16BDU1NaUXcrLzuqqrq/H666+jqKgIW7ZsQXBwMNLT07Fx40aMHj0aU6dOhYGBAT777DOYmppi6dKlnXPxLyhUwJ4D2rLy/P7779lxDIoIDg7G3bt3kZyc3GI+rbnBfdOnT8dbb73F1qh5eno+Vxb/5ORkdqouQ319Pe7fv8/m0x4+fAhtbW25gaC2trZKhc9kx5DY29vLOdeYXRqPx4NQKISOjo7cqBnZfFpbRW3FlIp2jTyRpaysDKtWrYJUKmU7TBBCkJGRATs7O5VMDVDlQm7kyJE4dOgQW6Qsu5B79913YWdnhw0bNii8f3V1Ne7cuQMXFxfo6+vj4sWL2L9/P3744QdYW1sjKioKZ8+exaxZszB27NhOmZpAoQL2XLBq1SqYmpqysf/y8nJs27ZN4bHDhg1DREQERo0axd5WWFgICwsLEEJgYmKC2bNnY8eOHS2KoSIBI4Q0MZjIGlFeFAgh4PF47EDQhIQEZGZmwsLCQm4gaJ8+feS+1AQCAZ48eQI9PT3Y29u3mB+R7WrBhPAkEoncBGPZeV6tCVpHJjETQnDq1Cls2bIF69atw/Tp07vky1rZhRyfz8fIkSPx6aefNhsuvHLlCr7++muFPQhPnDiB8PBwTJo0CX/++Sd+/fVXODs7Y+PGjZBIJNi+fTsAYNmyZRg7diwbqqeoHipgzwFcLhfTp0/H06dPMXDgQPz5558wMTFBYmIidu/ejf379wMAsrOz8dJLLyE3N1duNxAQEIDS0lIQQpCamory8nLo6+u3GIbR0NCAl5eX3OTZsrIyDBs2DOnp6QCA3NxcjBs3Dg8fPnw2L0Q3hhCC3NxctoFxQkICKisr4ejoCC8vL2RkZKBv375YunRpu8eQyE4w5vF4qKqqYodCyhot8vPzkZ+fj5MP/AB0TLxKS0uxcuVKqKur44cffujSDhrKLOTq6+sxbtw4TJw4ER9//LHc32QXcsuXL0evXr2wZcsWuc+KWCzGkiVLsGLFCujq6sLLywvh4eH48MMPkZSUhE2bNmHy5MkICQlhXaeUzoMK2AuIKsIw+fn5cpNnL168CCMjIzkBe/DgAYYPHw4LC4tnamvuKYjFYhw9ehRhYWGwtLREXV0dAMDLy0tuIGh7u54zz8Hs0LhcLng8HjQ1NWFmZgZjY2MYGRm1K19HCMGJEyfw5ZdfYsOGDZg6dWqn7LqUscYDDTlZFxcX5OTkgBCCYcOG4c8//wSPx8OECRPYxdS4ceOwaNEiuLm5sfdl3leyCzkvLy/s3LmTfe/V19dDS0sLT58+RXh4OIyNjfHvv//i008/xZQpUyAUCqGtrc0aOBYtWqTy14LSFCpgFDna4+Ri3FtTp06VCyH+5z//QXx8PFJTU5vNp6WmpoLD4cjZmp88eQJjY+Pnuj6N4fDhw/Dz84OzszPb4YMpuE5ISGAHgjLNi/38/JQeCMpACEFOTg6Ki4vh7OyMXr16yeXTmC7xsvm0lsKXJSUlWLFiBbS1tfHDDz90OHfWEqtXr4aJiUmrNY6dlZPl8XhYvnw5CCFwdnbGmjVrMGHCBPB4PPz7778AGnJ/W7duxbp16+QmGlA6HypgFDmUCcM0njw7fPhwdjTF22+/jalTpyI4OBjGxsZYs2YN1q5d+0xszc8jTANi2YGgRUVFsLW1lRsIqq+vr1DUqqur8eTJE5iYmGDQoEEKv1yZUTOyRddSqVSucFlbWxtaWlr466+/8NVXX2Hjxo2YMmVKp+e6lF1QqSonK5VKoaamBkIIqqqq8NZbb2HYsGGYOHEiNm/ejIEDB2LlypV45ZVX8Pnnn0MoFGLnzp0ICgrC559/rvLrp7QMFTCKHMrk01qaPCtbo/b06VNUVVVBW1u7U23NLxpSqRSpqalsPi0pKQlCoRDu7u6sqNnZ2eG3336Dj48PXFxc2jyiRCqVsoXLhYWFWLRoEUQiEXr16oXly5dj7NixbKPpzkRZa3xHc6yzSHQAABVdSURBVLKyLkFGCMViMZYtW4aff/6Z3ZFaWlri7NmzKC0txZ07d3Dnzh289957eOWVVzrl+iktQwWM0iG6i635RUcoFLJdRM6cOYO4uDi4u7vLdeUfOHBgm8NbUqkUx44dw7fffovQ0FCYmZkhISGhRadrW3lWOVllTEW//vor9u7di7CwMPj7+8PPzw8nT55kax43bNiA/v3747333uvoZVNUAaFQOglHR0dSUFBACCGkoKCAODo6KjyOx+MRb29vcvToUYV/P3PmDBkwYADR1dUlERERTf5eV1dHpk+fTuzs7Ii/vz/Jyspi/7ZlyxZiZ2dHHB0dSUxMTMcvqpuTnp5ORo0aRZKTk0lZWRk5c+YMCQ8PJ+PHjydubm7kjTfeIOvXrycnTpwgubm5pLq6mtTU1Cj8ycjIIEFBQeSdd94hXC63S65H2feQLCEhIeTo0aNEKpUSU1NTIhKJCCGE3Lx5k7z++uvN3u/UqVNkxIgR5Pbt2+xtW7duJV5eXqSoqIjweDzyxhtvkOjo6A5eFUVVUAF7wZFIJEQqlXbKY69cuZIVnIiICLJq1aomxwiFQhIQEEC2b9/e5G8FBQVELBYTW1tbMnfuXLJy5Uri4eFBHj16JHfczp07yeLFiwkhhBw5coRMnz6dEELIo0ePiIeHB6mrqyOZmZnE1taWiMViVV9mt6O5f0+JREIyMjJIZGQk+fjjj8lLL71EPD09yYwZM8hXX31FLl++TLhcLqmqqiI///wzGTx4MDl16lSnvT+UQZn3UHl5OamrqyOEEFJaWkrs7e3Z98i0adPIkSNHCCGELF68mOzcubPZ5woLCyObNm0ihDS8LxkWLlxI5s2bR9zd3UloaKhqLoyiEqiAUTqNsrIyEhAQQOzt7cno0aPZVXxCQgJZsGABIYSQw4cPEw0NDeLp6cn+JCUlEUIIGTVqFLG1tSV6enpk9uzZpKqqimzZsoVs2bJF7nlef/11cvPmTUIIISKRiJiamhKpVNrkWNnjKA3U19eT27dvk59++omEhISQIUOGEDMzMxIcHEzKy8s75Tm5XC4ZM2YMsbe3J2PGjFH4PJcuXSKenp7Ezc2N6OvrEw6HQzw8PAiXyyUhISHEwsKCmJiYEE9PT/LLL78Qd3d34uHhQdzd3cn+/fvZx8nIyCB+fn7Ezs6OTJs2jQgEgmbP67fffiNLly6VO5+amhpCCCHV1dUkNTVVha8CRRVQAXuBOX/+PDlw4AC5c+dOs8d05eqbEEKOHj3Kih0hhPz6669kyZIlcse4ubmR3Nxc9ndbW1tSWlpKlixZQg4fPszePn/+/GbDlJQGpFIpyc3NJRKJpNOeY9WqVXK7qtWrV7d4PJfLJb1792bFhAkRthXZa6qtrW3y90uXLpFVq1axu7CjR4+ScePGUeHqxtCChReU+/fv4/fff0diYiI++OADbN68GTU1NQAaZpFlZ2cDaBjeyMyDkkUqlT7L01UpMTExcHJygr29PbZu3drk799++y1cXV3h4eGB0aNHIycnh/2buro6vLy84OXlhaCgoGd52s8EDocDKyurTq1lOnnypNz4nxMnTrR4vKrG/6ipqaG4uBgzZ87E+vXrER0dLfc+HjlyJCZPnoyrV69iypQpiIiIwMaNG+Hg4NCh56V0HnQYzQtKTU0NEhMTcf78eZibm6O8vBx6enrYsmULuFwukpKSIJVK8fvvv8PS0hKxsbHw9fVl7cTPqljT0tISubm57O95eXmwtLRUeIyVlRXEYjF4PB5MTU0V3tfc3BwhISE4f/48rKys4Ofnh6CgILi6urLHeXt7IzExEbq6uti1axdWr16NP/74AwCgo6ODu3fvdvJVP98UFxezQy3Nzc1RXFzc4vFRUVH45JNP5G4LCwvDpk2bWi2vIDL2+MePH2PZsmVYtGgRNDU1MW/ePPz555947bXX2ONGjBiB//73vygqKsKgQYNUcLWUzoTuwF5Q3N3dMXXqVERERODs2bMwMTHBjRs3sG3bNgQHB+P06dNYsGABjh49CkIIwsPD8d///hcAcPbsWZw9e7bFXZiiXVt7kB3sWV9fj6ioqCY7n6CgIBw6dAhAw2o9ICAAHA4HQUFBiIqKglAoRFZWFtLS0gAA9vb2sLW1hZaWFoKDg3Hy5Em5xxs1ahS72h82bBjy8vJUci0vEmPGjIG7u3uTn8avNYfDabEYurCwEA8ePEBgYCB7W0REBJKTk1k7v6LOHMx7k8PhsC26hEIh3njjDQwYMAA7d+7E7Nmz8dprr7HHMejo6FDx6il0cQiT0gXIOqwiIyNJQEAAuXjxItmxYwfx8fEhs2bNIt7e3sTR0ZEEBwcTQhqMF35+fiQ2NpZ4eHiQ48ePK3xs2VxUY8RicbtyaqdPnyYODg7E1taWfPHFF4QQQtavX09OnjxJCGnIZ0ybNo3Y2dkRPz8/kpGRwd73iy++ILa2tsTR0ZFER0crlVOTZcmSJeTzzz9nf1dXVyc+Pj5k6NChzb4GlJZpizX+u+++I4sWLWr275cvXyZvvvlms3/fv38/ee211wiXyyX//PMPGTFiBBk+fDg5f/48IaThs/Dw4cN2Xgmlq6EC9gJSWFhI3n33XRIeHk6OHTtG5syZQ65fv05WrlxJvvzyS/a4goICtqYqLy+PvPLKKyQwMJBcunSJECIvSCUlJeSbb74hw4YNI4MHDyZz5sxhk+7MfxUhFos71TDQmLYI2OHDh8nQoUNZizYhDa8DIQ3utoEDB5L09HRy5swZ4ujoSOzs7BTWqf3yyy+kT58+rMty37597N8OHjxI7O3tib29PTl48KCqLrNbo4w1nmHo0KHs+42BET+pVEqWLVtG1qxZo/C+oaGhZPTo0SQuLo4Q0uCKHT9+PPs6i8ViMnHiRPLhhx8+0/cgRXVQAXsBEYvF5MqVKyQ8PJxMmjSJxMTEEKlUSqKjo8ncuXPJlStXSH19PeHxeKxAbd++nfTt25d89NFH7GMQ8j+X4rZt28i4ceNIVVUVqa6uJrGxsaSuro78+++/JCgoiDg7O5Ply5cTPp/f4rlJJBJy8+ZNcuDAAZKTk6Pya29czKrIlk9Ig0PT2dmZFBcXN/tYISEhJCoqitja2pKMjAwiFAoV1qn98ssvCkWSy+WSQYMGES6XS8rLy8mgQYM6zbrenVCmvIIQQrKyskj//v2biMuoUaOIu7s7cXNzY8srGh8jEAjIf/7zH1bsmEXI6dOnyZIlS0hgYCDx8vIiGzZs6MxLpXQyVMAocmzdupWMHTuWODk5kS+//JIIhUKyevVqMnnyZJKYmEjGjh1LsrOz2eOZL44333yTREZGyj3WjRs3yKxZs8jevXuJVColc+bMYWt0UlNTyerVq8nMmTNJZGQku0vLysoi48ePJ++99x7Ztm0b4fF4ZPbs2WTDhg0kOzu7w1/wIpGIDBo0iGRmZrKC0ziEdOfOHWJra9vEPq2oYPb3339vVRCbE7DIyEjy7rvvsr+/++67TV7DnsKff/5JXF1dCYfDIQkJCc0e19xuNTMzk/j7+xM7Ozsyffp0uTB3S8iGpIuLi8m///7L/j58+HCyZ88eQsj/FlzMf5OSkkh6erryF0jpllATB0WONWvW4Ny5c0hOTsbixYshFArB5/Nx5MgR+Pj4wMnJCVFRUezxTPI7MzMT9vb2ABqS5QBw7do1eHt7Y+LEieBwODAyMkJZWRkyMjLw008/wd/fH8uXL0dMTAzu3r2Lu3fv4q233kJGRgbWrVuHBQsW4MCBA1BTU0NAQADu3buHb775BlwuF0CDw4y0sZWnhoYGduzYgcDAQLi4uGD69Olwc3PDhg0bcOrUKQANHfmrq6vx9ttvy9nlnzx5Al9fX3h6emLUqFEIDQ2FlpYWBgwYwD6+lZUV8vPzmzzvX3/9BQ8PD0ybNo11Rubn5yt1356Au7s7/v77b7z66qvNHiORSLBkyRKcOXMGjx8/xpEjR/D48WMADe+75cuXIz09Hb1798bPP/+s1PMy77+9e/fi1Vdfxb59+7BkyRIUFBRg/fr1OHbsGNLS0qCurs46SoGGmWt2dnYdvGpKV0Nt9JRmYYb57dq1ixWKsLAw3Lp1iz2Gw+GAEIIZM2bgwoULcHV1hZ6eHgBAU1MTAoEApqamABpGXlhYWOC3337DuXPncO/ePQQHB6OgoAAxMTHYtGkTTExMYGZmhmPHjiEuLg7R0dGws7ODra0tDA0NWTFknluWqqoqpKSkYNCgQexzKmL8+PEYP3683G2bNm1i///ChQsK7zdixAg8ePBA7rZjx441/wL+PxMnTsTMmTOhra2NPXv2ICQkBJcuXWr1fj0JFxeXVo+Jj49nHaAAWAeoi4sLLl26hMjISAANtWHh4eHNzu2SSCTskM+6ujpcu3YNN2/exL1793Du3DmEhIRgwIABmDFjBl566SXMmTMHpqamEAqFKmtATOke0B0YRSkYsTA3N8eUKVPkdj4cDgcLFy5EYmIi/P39MWPGDFRXV8PHxwc3b96EpqYmKisrcfLkSTg5OUEkEmHZsmX44osvwOPxYG1tDSsrKwANwwF/+uknLFu2DBERERg+fDj75b9gwQJ8//33ABpGXZw7dw5JSUly51JSUoKMjAwADVZqiUTSqUXXytSpmZqasnVKCxcuxO3bt1u8b2uF1suXL2eLqR0dHWFsbMz+rTsXWje34+RyuTA2NmZrDFvbiTLi9dtvv2HmzJnw9/fHjh078N133+HLL7/E559/jhs3buDevXv47LPP8Pfff2P58uU4f/48vL29O/ciKc8UugOjtIvGux9LS0v89ddfAICCggLo6+vD29sbdnZ2sLOzg5OTE2bNmoWhQ4fi0aNHOH36NN59912MGDGCfQwulws+nw9TU1NIpVJkZmZCS0sLK1euBNBQfM2MtWA6OeTn56OyshJnzpwBIQQODg5sKLO5YmupVNpq/ZGyyNapWVpaIioqit1JMBQWFrKFu6dOnWJ3K4GBgfj000/Z8SDnzp3DF198gWHDhrVYaL19+3b2/3/88UckJSWxv3dmoXVLY08mTZrUKc+piPr6eixZsgRVVVXYsmULjI2NweVycePGDZw/fx46OjqIjIzEwYMHYWtrCw8PD/b1pzxfUAGjqJz+/fsDaAhB7tixA0KhEIWFhewuKzAwEGfPnsWECRPg5OSEV199FZMmTUJiYiIGDhwIQ0ND1NfXIysrC7179wbQMMtJJBLB0tISJ0+eRHZ2Nm7cuAEAKC0tRd++fXHgwAHExMTg0KFD6NWrFzZu3AgOhwM/Pz8EBgayK3dZYSMynRpqamrY8KeyyObUJBIJ5s+fz+bUfH19ERQUhB9++AGnTp2ChoYGTExMcPDgQQCAiYkJ1q9fDz8/PwANs6bS09MVhtlkBUyWI0eOYOPGjW065/bSXGhVWZrbcZqamqKyshJisRgaGhoKd7GyaGlpQV1dHfHx8XBxcQEhBMbGxuDz+fjmm2/g7OwMPT09LFiwAB4eHh06Z0r3hoYQKZ2OtrY2bGxsoKGhAUIILC0tsW/fPnzwwQfQ0dFhJ+DGxMSwX1wCgQDp6elwcnICANy9exf9+vWDjo4O0tLS8NZbbwFoMIz07dsXAoEAfD4fHh4e4HA4WLRoEXx9fWFtbY3IyEhcvXoVQEPO6urVq7h//z5EIpHcLmz79u0oKytr8/WNHz8eqampyMjIQFhYGICGnBoTwouIiMCjR49w7949XL58Gc7Ozux958+fj/T0dKSnp2PevHltMnbk5OQgKysLAQEB7G11dXXw9fXFsGHDWu0x+KxprqsKh8PBqFGj2HzioUOHWt3Rbdu2DSYmJti/fz84HA7U1NSwbds23L59G/v27cO3336LiRMnPovLonQlXWV/pFAaU1RUxFr0i4uLyTvvvMN2HV+1ahX5+OOPCSENdvPG9Tu5ublk1apV5K+//iK3bt0iWlpaZM6cOeT48ePk66+/JiNHjiSEEDJ79mwyatQoMmnSJOLk5EQuXLhAJBIJ2bt3L7GxsXmGV6uYthRab926lXz44YdytykqtH4W/P3338TS0pJoaWmRfv36saUF+fn5ZNy4cexxirqqMOcrO/ZEtni8Oc6dO0e8vb3lur/weDwVXhWlu0NDiJRug5mZGfv//fr1w6FDhyAWiwE0hJ+Y0OT48eNx7NgxnDt3DoMGDYKDgwNKS0tRVVUFDw8P/PLLL5gzZw7mzZuHqKgoJCQkYNy4cSgsLAQhBJMnT8bSpUuxZ88e7NixA6NHj0Z0dDQqKyvxyiuvYN68eZg/f36XvAbKmEIYoqKisHPnzib3BwBbW1uMHDkSSUlJz8QuPmXKFEyZMqXJ7f3790d0dDT7uyIHKNBwvvHx8W16zrFjxyImJgYhISG4ePEiAMDQ0LCNZ07pyVABo3RbOBwONDU1AQDLli1jb3/zzTfx9OlT7N69G8XFxTh9+jQKCwtRWloKY2NjmJqaQiwWY8SIEXImkf/+97+wsLBga5VycnJYUfTy8sLgwYOxadMmFBQUPMOrlEcZUwgAJCcno6KiAsOHD2dvq6iogK6uLrS1tVFWVoYbN26wdU/PK2FhYTh79mxXnwali6A5MEqPQ0NDAx999BH+/vtv3LhxA8bGxnB3d8esWbNgamqK9957DxkZGZg7dy727duH48ePA2hwLOrp6bGidf/+fQwbNgwAkJCQAE9PTwD/M6F0BcoUWgMNu6/g4GC5HJ6iQuvmzB/PCyYmJpg5c2ZXnwali+AQ0sZWBhRKDyA7Oxvnz59HXFwcrK2tsWHDBnzyyScwNjbGhg0bAABDhgzBwYMH4eDgAF9fX1y6dAlmZmZyzkQKhdJ9oQJGeWHIzs6GQCCAq6srsrOzERgYiKSkJPB4PLzyyitITk5mi2kpFEr3h4YQKS8MNjY2cHV1BSEENjY2SEhIgK6uLnr16oUxY8ZgxIgRbI0WhULp/tAdGIUiQ3uKmSkUStdABYxCoVAoPRIaQqRQKBRKj4QKGIVCoVB6JFTAKBQKhdIjoQJGoVAolB4JFTAKhUKh9EiogFEoFAqlR0IFjEKhUCg9EipgFAqFQumRUAGjUCgUSo+EChiFQqFQeiRUwCgUCoXSI6ECRqFQKJQeCRUwCoVCofRIqIBRKBQKpUdCBYxCoVAoPRIqYBQKhULpkVABo1AoFEqPhAoYhUKhUHokVMAoFAqF0iOhAkahUCiUHgkVMAqFQqH0SKiAUSgUCqVHQgWMQqFQKD0SKmAUCoVC6ZFQAaNQKBRKj4QKGIVCoVB6JP8H7BuVmhx77EMAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LrpRmOpVg9Fv"
+      },
+      "source": [
+        "**3-d heatmaps**\n",
+        "\n",
+        "We now show to generate a 3d-heatmap of unfairness using the `generate_heatmap` method. The $X-Y$ axes in the plot represent the coefficients of the linear threshold function that defines a protected subgroup with respect to the first two sensitive attributes. Which $2$ attributes are considered sensitive can be overwritten with the `col_index` argument. The $Z$-axes is the $\\gamma$-disparity (FP) of the corresponding subgroup defined by the linear threshold function. This is important because it allows us to (1) visualize convergence as the heatmap flattens and (2) brute force check the fairness in low-dimensions without relying on a heuristic auditor. See the [the rich subgroup fairness empirical paper](https://arxiv.org/abs/1808.08166) for a discussion of these plots. Note that in the below plot no group has a $\\gamma$-disparity of greater than $.005$, which we would expect since the set of linear threshold functions on two attributes is a subset of the set of linear threshold functions on all protected attributes, and the final model is $\\gamma$-fair."
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "# output heatmap (brute force)\n",
-    "# replace None with the relative path if you want to save the plot\n",
-    "fair_model.heatmapflag = True\n",
-    "fair_model.heatmap_path  = 'heatmap'\n",
-    "fair_model.generate_heatmap(data_set, dataset_yhat.labels)\n",
-    "Image(filename='{}.png'.format(fair_model.heatmap_path)) \n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**black-box auditing**\n",
-    "\n",
-    "We now show to audit any black box classifier with respect to rich subgroup fairness under either FP or FN rate. Note the below auditing procedure would work for any set of (soft) predictions $\\hat{y}$, and need make no assumptions about the structure of the predictor. We note that as expected the disparity of the group found is the same as the disparity printed out in the last iteration of the `fit` method.\n",
-    "   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    }
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.004955178356713431\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "gerry_metric = BinaryLabelDatasetMetric(data_set)\n",
-    "gamma_disparity = gerry_metric.rich_subgroup(array_to_tuple(dataset_yhat.labels), 'FP')\n",
-    "print(gamma_disparity)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**pareto curves**\n",
-    "\n",
-    "The `FairFictPlay` algorithm implemented in the `fit` method converges given access to perfect oracles for solving cost-sensitive classification (CSC) problems. A cost-sensitive classification problem over a hypothesis class $\\mathcal{H}$ is $$\\min_{h}\\sum_{i = 1}^{n}(1-h(x_i))c_0 + h(x_i)c_1$$\n",
-    "By default in this package, and in the companion [empirical](https://arxiv.org/abs/1808.08166) and [theory](https://arxiv.org/pdf/1711.05144.pdf) papers, the hypothesis class of the learner and the of the subgroups are hyperplanes. The corresponding heuristic oracle for solving the CSC problem first forms two regression problems $(x_i, c_0)$ and $(x_i, c_1)$. Then in the case of hyperplanes, trains two regressions $r_i: \\mathcal{X} \\to R$ which predict the costs of classifying a given point $x$ $0,1$ respectively. Finally the binary classifier output by the oracle is defined as $\\hat{r}(x) = \\arg\\min_{j \\in \\{0,1\\}}r_j(x)$. But of course if we are interesting in different hypothesis classes for the learner, we simply need different regressors. In this package in addition to linear regression, we've added support for regression trees, kernelized ridge regression, and support vector regression. Below we trace out Pareto curves of $\\gamma$-disparity vs. error for each of these different heuristic oracles. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    }
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "pycharm": {
+          "is_executing": true
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 497
+        },
+        "id": "4kKzNgmRg9Fw",
+        "outputId": "f8f0f0a7-babf-4a1c-f92f-d3f46f127e92"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "\n",
+        "# output heatmap (brute force)\n",
+        "# replace None with the relative path if you want to save the plot\n",
+        "fair_model.heatmapflag = True\n",
+        "fair_model.heatmap_path  = 'heatmap'\n",
+        "fair_model.generate_heatmap(data_set, dataset_yhat.labels)\n",
+        "Image(filename='{}.png'.format(fair_model.heatmap_path))\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Curr Predictor: Linear\n",
-      "Curr Predictor: SVR\n",
-      "Curr Predictor: Tree\n",
-      "Curr Predictor: Kernel\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OYsi5G4Pg9Fw"
+      },
+      "source": [
+        "**black-box auditing**\n",
+        "\n",
+        "We now show to audit any black box classifier with respect to rich subgroup fairness under either FP or FN rate. Note the below auditing procedure would work for any set of (soft) predictions $\\hat{y}$, and need make no assumptions about the structure of the predictor. We note that as expected the disparity of the group found is the same as the disparity printed out in the last iteration of the `fit` method.\n",
+        "   "
+      ]
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "pycharm": {
+          "is_executing": true
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lBcmHN2zg9Fw",
+        "outputId": "a72d2611-a00a-41b8-d617-04a8aa7a58e7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.004363030060120244\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "gerry_metric = BinaryLabelDatasetMetric(data_set)\n",
+        "gamma_disparity = gerry_metric.rich_subgroup(array_to_tuple(dataset_yhat.labels), 'FP')\n",
+        "print(gamma_disparity)\n"
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# set to 50 iterations for fast running of notebook - set >= 1000 when running real experiments\n",
-    "pareto_iters = 50\n",
-    "def multiple_classifiers_pareto(dataset, gamma_list=[0.002, 0.005, 0.01, 0.02, 0.05, 0.1], save_results=False, iters=pareto_iters):\n",
-    "\n",
-    "    ln_predictor = linear_model.LinearRegression()\n",
-    "    svm_predictor = svm.LinearSVR()\n",
-    "    tree_predictor = tree.DecisionTreeRegressor(max_depth=3)\n",
-    "    kernel_predictor = KernelRidge(alpha=1.0, gamma=1.0, kernel='rbf')\n",
-    "    predictor_dict = {'Linear': {'predictor': ln_predictor, 'iters': iters},\n",
-    "                      'SVR': {'predictor': svm_predictor, 'iters': iters},\n",
-    "                      'Tree': {'predictor': tree_predictor, 'iters': iters},\n",
-    "                      'Kernel': {'predictor': kernel_predictor, 'iters': iters}}\n",
-    "\n",
-    "    results_dict = {}\n",
-    "\n",
-    "    for pred in predictor_dict:\n",
-    "        print('Curr Predictor: {}'.format(pred))\n",
-    "        predictor = predictor_dict[pred]['predictor']\n",
-    "        max_iters = predictor_dict[pred]['iters']\n",
-    "        fair_clf = GerryFairClassifier(C=100, printflag=True, gamma=1, predictor=predictor, max_iters=max_iters)\n",
-    "        fair_clf.printflag = False\n",
-    "        fair_clf.max_iters=max_iters\n",
-    "        errors, fp_violations, fn_violations = fair_clf.pareto(dataset, gamma_list)\n",
-    "        results_dict[pred] = {'errors': errors, 'fp_violations': fp_violations, 'fn_violations': fn_violations}\n",
-    "        plt.plot(errors, fp_violations, label=pred)\n",
-    "\n",
-    "    if save_results:\n",
-    "        pickle.dump(results_dict, open('results_dict_' + str(gamma_list) + '_gammas' + str(gamma_list) + '.pkl', 'wb'))\n",
-    "\n",
-    "    plt.xlabel('Error')\n",
-    "    plt.ylabel('Unfairness')\n",
-    "    plt.legend()\n",
-    "    plt.title('Error vs. Unfairness\\n(Adult Dataset)')\n",
-    "    plt.savefig('gerryfair_pareto.png')\n",
-    "    plt.close()\n",
-    "multiple_classifiers_pareto(data_set)\n",
-    "Image(filename='gerryfair_pareto.png') "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A natural question one might ask is, suppose we fix a statistical fairness definition for rich subgroup fairness like equality of false positive rates, `FP`. Does learning a classifier that is fair with respect to `FP` increase or decrease fairness with respect to false negative rates `FN`? One could see this relationship going in either direction - and indeed we submit that it is dataset dependent. In some cases, if enforcing `FP` fairness pushes the classifier towards the constant classifier, then it will also satisify `FN` rate fairness, since the constant classifier is perfectly fair. However, if the hypothesis class is sufficiently rich, then one would expect that ceteris paribus since we are optimizing for error in addition to `FP` rate fairness, the algorithm would increase `FN` rate unfairness in order to decrease error. Below we trace the FN vs. FP rate tradeoff across a range of input $\\gamma$, where the classifier is optimized only for `FP` rate fairness. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    }
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pHEF-qTag9Fw"
+      },
+      "source": [
+        "**pareto curves**\n",
+        "\n",
+        "The `FairFictPlay` algorithm implemented in the `fit` method converges given access to perfect oracles for solving cost-sensitive classification (CSC) problems. A cost-sensitive classification problem over a hypothesis class $\\mathcal{H}$ is $$\\min_{h}\\sum_{i = 1}^{n}(1-h(x_i))c_0 + h(x_i)c_1$$\n",
+        "By default in this package, and in the companion [empirical](https://arxiv.org/abs/1808.08166) and [theory](https://arxiv.org/pdf/1711.05144.pdf) papers, the hypothesis class of the learner and the of the subgroups are hyperplanes. The corresponding heuristic oracle for solving the CSC problem first forms two regression problems $(x_i, c_0)$ and $(x_i, c_1)$. Then in the case of hyperplanes, trains two regressions $r_i: \\mathcal{X} \\to R$ which predict the costs of classifying a given point $x$ $0,1$ respectively. Finally the binary classifier output by the oracle is defined as $\\hat{r}(x) = \\arg\\min_{j \\in \\{0,1\\}}r_j(x)$. But of course if we are interesting in different hypothesis classes for the learner, we simply need different regressors. In this package in addition to linear regression, we've added support for regression trees, kernelized ridge regression, and support vector regression. Below we trace out Pareto curves of $\\gamma$-disparity vs. error for each of these different heuristic oracles."
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "gamma: 0.001  gamma: 0.002  gamma: 0.003  gamma: 0.004  gamma: 0.005  gamma: 0.0075  gamma: 0.01  gamma: 0.02  gamma: 0.03  gamma: 0.05  "
-     ]
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "pycharm": {
+          "is_executing": true
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 568
+        },
+        "id": "2KbOJzJfg9Fw",
+        "outputId": "dbf3a216-2c8e-4711-c601-771810249fd5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Curr Predictor: Linear\n",
+            "Curr Predictor: SVR\n",
+            "Curr Predictor: Tree\n",
+            "Curr Predictor: Kernel\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ],
+      "source": [
+        "# set to 50 iterations for fast running of notebook - set >= 1000 when running real experiments\n",
+        "pareto_iters = 50\n",
+        "def multiple_classifiers_pareto(dataset, gamma_list=[0.002, 0.005, 0.01, 0.02, 0.05, 0.1], save_results=False, iters=pareto_iters):\n",
+        "\n",
+        "    ln_predictor = linear_model.LinearRegression()\n",
+        "    svm_predictor = svm.LinearSVR()\n",
+        "    tree_predictor = tree.DecisionTreeRegressor(max_depth=3)\n",
+        "    kernel_predictor = KernelRidge(alpha=1.0, gamma=1.0, kernel='rbf')\n",
+        "    predictor_dict = {'Linear': {'predictor': ln_predictor, 'iters': iters},\n",
+        "                      'SVR': {'predictor': svm_predictor, 'iters': iters},\n",
+        "                      'Tree': {'predictor': tree_predictor, 'iters': iters},\n",
+        "                      'Kernel': {'predictor': kernel_predictor, 'iters': iters}}\n",
+        "\n",
+        "    results_dict = {}\n",
+        "\n",
+        "    for pred in predictor_dict:\n",
+        "        print('Curr Predictor: {}'.format(pred))\n",
+        "        predictor = predictor_dict[pred]['predictor']\n",
+        "        max_iters = predictor_dict[pred]['iters']\n",
+        "        fair_clf = GerryFairClassifier(C=100, printflag=True, gamma=1, predictor=predictor, max_iters=max_iters)\n",
+        "        fair_clf.printflag = False\n",
+        "        fair_clf.max_iters=max_iters\n",
+        "        errors, fp_violations, fn_violations = fair_clf.pareto(dataset, gamma_list)\n",
+        "        results_dict[pred] = {'errors': errors, 'fp_violations': fp_violations, 'fn_violations': fn_violations}\n",
+        "        plt.plot(errors, fp_violations, label=pred)\n",
+        "\n",
+        "    if save_results:\n",
+        "        pickle.dump(results_dict, open('results_dict_' + str(gamma_list) + '_gammas' + str(gamma_list) + '.pkl', 'wb'))\n",
+        "\n",
+        "    plt.xlabel('Error')\n",
+        "    plt.ylabel('Unfairness')\n",
+        "    plt.legend()\n",
+        "    plt.title('Error vs. Unfairness\\n(Adult Dataset)')\n",
+        "    plt.savefig('gerryfair_pareto.png')\n",
+        "    plt.close()\n",
+        "multiple_classifiers_pareto(data_set)\n",
+        "Image(filename='gerryfair_pareto.png')"
+      ]
     },
     {
-     "data": {
-      "image/png": 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MC+7OqL4eONrI0latTVGULnXE2xmmP7RqAUtNTUWj0eDv7w9ATEwMSUlJzQpYUlISK1asAGDmzJksXrwYRVGwsfnP8jw1NTVd6i+SaH3Jpy7y7tdn+e0QH2KG9DJ2nF/Fw96a6MheREf2orpOx4GcpqWt9mQVs+3ERdRmKob4uehPNfZylaWt7pa1tTVlZWVdZkuVX/YDs7Y27WuurVrAioqK8PHx0T/29vbm8OHDN22jVqtxdHSkrKwMNzc3Dh8+zLx58zh//jzr16/XH32pVCoeeOABVCoVCxcuZMGCBa0ZW3QyOSXX+N1nPzLQx4kVWuPclNpaulmaMy64O+OCu9PYqHC8sPznSSDFrNyWwcptGQR2t2dscNPCwwO9nWRpqzvg7e1NYWEhpaWlxo7Sbn7ZkdmUdahJHFFRUaSnp5OZmcncuXOZOHEi1tbWHDhwAC8vL0pKShg3bhz9+vXjvvvuu+H98fHxxMfHA3Spv4jiP67VNrBwfRrWFub86+HBWKk7z9JOZmYqBvdyZnAvZ16c0I+8y1VNN09nFvPeN+dYve8s7vZWjA1qKmbDNW6ytFULWVhYmPTOxF1VqxYwLy8vCgoK9I8LCwvx8vIy2Mbb25uGhgYqKipwdXVt1iYoKAg7OztOnTpFRESEvg8PDw+mTZtGamqqwQK2YMEC/dGZTPboehRF4fnPfiSv7Drr5w/B06mbsSO1KV83Wx6/15/H7/Wn/HodX58uJSWzmC9/vEhCagHWFmbc28edcUHdGd3PA3f7jjOJRYjW0KoFLDIykuzsbHJzc/Hy8iIxMZFPP/20WRutVsu6desYNmwYGzduZMyYMahUKnJzc/Hx8UGtVnP+/HmysrLw9fWlqqqKxsZG7O3tqaqqYteuXSxfvrw1Y4tO4r1vzpGcfok/TA7inoCuNeHHycaSqYO8mDrIi9oGHYfP/fTz0lZNpxtVKhjk48S44B6MC/YgwN2uS1zrEZ1bqxYwtVpNXFwc48ePR6fTMW/ePEJCQli+fDkRERFotVrmz5/PnDlz0Gg0uLi4kJiYCMCBAweIjY3FwsICMzMz3n33Xdzc3Dh37hzTpk0DoKGhgdmzZzNhwoTWjC06gf3Zpfx5ZxZTBvRk/oiufSrISm3OfX3dua+vO3/UhpBxsZLdGSWkZF7ireQs3krOwtfVRr9OY0RvZ6PeHyfEnZKlpITJK/jpOtq4A3jYW7Nl0T3YWHaoS7sdysWKanZnlrA7o5iDZ8uo0zXi2M2CMf08GBfcnfv6umMnS1t1aab03Sl/U4VJq6nX8eQnR2hoVHhvTrgUr9vo6diNOUN7M2dob67VNrD/TCkpGcXsPV3ClmNFWJqbMTTAlXE/7z7d2a8jCtMm/7cLk6UoCsu2nOJUUSVr50bg53bjatzi5uys1EwM7cnE0J406Bo5cv4/S1u9kpTOK0nphHg6MC646X6zEE8HuW4mOhQpYMJkfXw4n01HC3nm/j7cH9Td2HFMmtrcjCh/V6L8Xfn9pCDOllbpJ4H8Y082f9+dTU9Ha/11s6H+Lp3qFgVhmqSACZN05PxPrPwyndGB7jxzfx9jx+lUVCoVGg87NB52/NfIAC5fq23afTqjmI1HCll/6Dy2lub6pa1GB3rgZCO7T4v2JwVMmJySqzU8+fFRPJ268ffoQbLyRBtzs7NiVoQPsyJ8qKnX8f3Zy6RklLA7s5ivTl7C3ExFRG9n/alGXzmVK9qJzEIUJqVe18jsNYc4WVTBlqeGE9TTwdiRuqzGRoUTRRXszmhaDSTrUtPW9RoPO30xC/Nxwlx+wTAppvTdKUdgwqS8vj2TH/Ku8I+YMCleRmZmpiLMx4kwHyeeHx9IwU/X9Utbrfn2HP/6+ixudpaM6de0tNW9fdzpZinXzUTrkQImTMaWY4V8+H0e84b78VCY1+3fINqVj4sNjw3347HhflRcr+frMyXszixhx8lLfJZWiJXajHv7uDE2qDtjgjzwsDftldCF8UkBEyYh/UIFL28+yRA/F16e1M/YccRtONpY8FCYFw+FeVHX0MgPeT+R8vOyVrszSwAI83HSn2rs212WthK/nlwDEx1e+fU6How7QF1DI9uevlcWpTVhiqKQdemq/rrZj4UVAPi4dGNsUNO2MZG+LljI0lZGY0rfnXIEJjo0XaPCM4nHuVRRw4aFw6R4mTiVSkVQTweCejrw9P19KK6sYU9m04zGTw7n88F3eThYqxn983WzkYHuOFjL7tPCMClgokP7x+4zfHOmlNen9WdwL2djxxGtrLuDNbOjejE7qhdVtQ3sz77M7sxi9maVkHT8AmozFUP9XRkX3J37gzzwdpbdp8V/SAETHVZKRjHv7M1hVoQ3s4f0MnYc0cZsrdRM6N+DCf17oGtUOJZ/hZSfl7Z69Yt0Xv0inaCeDowL8mBscHf6ezrKPYBdnFwDEx3SudJrPBT3HX7utny2cJjsLNzFnS29xp7MYnZnlJB2/icaFejuYMX9P183G+bvKn9HWokpfXfKEZjocKpqG1i4/ggWajP+9f/C5YtJEOBuR4C7HQvuC+Cnqjr2ZZWQklHM1mNFfHo4HxtLc+7r487Y4O6M6eeBi60sbdUVSAETHYqiKLy48QRnS6+xfn4UXrKdh/g/XGwtmRHuzYxwb2rqdRw8V6af1ZicfgkzFYT3dtbPavR3tzN2ZNFG5BSi6FDivz3LG19l8fLEfiwcGWDsOMKEKIrCqaJK/XWzzIuVAPi72zLu51X0B/dylqWtbsOUvjtb/WaL5ORkAgMD0Wg0xMbG3vB6bW0t0dHRaDQaoqKiyMvLAyA1NZWwsDDCwsIYOHAgW7ZsaXGfonP4PucysTuymBTagwX3+Rs7jjAxKpWKUG9HnhvXlx3P3MuBpaP5ozYEL6du/Pu7XH7z3kEiX9/N7z77keRTl6iqbTB2ZHGXWvUITKfT0bdvX1JSUvD29iYyMpKEhASCg4P1bd59911OnDjBe++9R2JiIlu2bGHDhg1cv34dS0tL1Go1Fy9eZODAgVy4cAGVSnXbPg0xpd8iBBSVV/PgPw/gamvJlkXDZVt70aoqa+r59ufdp/dllVBZ04Cl2ozhAa6M/Xk1kO4OsrQVmNZ3p8EjsPDwcFavXs2VK1d+VWepqaloNBr8/f2xtLQkJiaGpKSkZm2SkpKYO3cuADNnzmTPnj0oioKNjQ1qddOXVk1NjX5ZmZb0KUxbTb2OJz8+Qn1DI+/NCZfiJVqdg7UFUwZ48o+YQRx5ZRyfPhHF/4vqTU7pNZZtOUXUG3vQxh3gn3uyybxYSSe9stLpGCxgGzZs4MKFC0RGRhITE8POnTtb9AdaVFSEj4+P/rG3tzdFRUU3baNWq3F0dKSsrAyAw4cPExISQmhoKO+99x5qtbpFfQrTpSgKy5NOcaKwgr/MGkiAXHAXbczC3Ix7AtxY/mAw374wml3P3scL4wMxN1Pxl5QzTPzHfka8tY8VX6RzIPsydQ2Nxo4sbsLgr7oajYbXX3+dVatWsW3bNubNm4e5uTmPPfYYzzzzDC4uLm0SJioqivT0dDIzM5k7dy4TJ078Ve+Pj48nPj4egNLS0raIKFpZQmoBn6UV8vQYDQ+E9DB2HNHFqFQq+na3p293exaN1lBytYa9Py9tlZCaz4ff52FvpdbvPj0q0APHbrK0VUdx03M1J06c4IMPPuCrr75ixowZPPzwwxw4cIAxY8Zw/Phxg+/x8vKioKBA/7iwsBAvLy+Dbby9vWloaKCiogJXV9dmbYKCgrCzs+PUqVMt6vMXCxYsYMGCBUDTeVzRsR3Nv8KrX5xiZF93/ntsX2PHEQIPe2tihvQiZkgvqut0HMi5zO6MYvZkFbPtxEXUZiqG+Lnop+j7uMjSVsZksICFh4fj5OTE/PnziY2NxcqqaQHVqKgovvvuu5t2FhkZSXZ2Nrm5uXh5eZGYmMinn37arI1Wq2XdunUMGzaMjRs3MmbMGFQqFbm5ufj4+KBWqzl//jxZWVn4+vri5OR02z6F6Sm9WstTHx+lh6M1/4gJk6nNosPpZmnOuOCmQqVrVDheUN60YWdGMSu3ZbByWwaB3e0ZG+zBuOAeDPCSpa3am8EC9vnnn+Pv33wac25uLn5+fmzevPnmnanVxMXFMX78eHQ6HfPmzSMkJITly5cTERGBVqtl/vz5zJkzB41Gg4uLC4mJiQAcOHCA2NhYLCwsMDMz491338XNzQ3AYJ/CdDXoGln86VHKq+vY/ORwnGxk1QTRsZmbqQjv7Ux4b2eWTuhH3uUq/e7T731zjtX7zuJub8XYoKZV9Idr3GQFmXZgcBr94MGDOXr0aLPnwsPDOXLkSLsFu1umNBW0q3ltWwb/cyCXv0UPZNogb2PHEeKuXKmqa9p9OqOEr0+XUFWnw9rCjHv7NF03G9PPAzc709kGyJS+O5sdgWVlZZGenk5FRUWzI63KykpqamraPZzofL748QL/cyCXR+/xleIlOgVnW0umDfJm2iBvaht0HD73k/5UY0pGMSoVDO71y9JWHgS4y+7TraVZATt9+jTbtm2jvLycL7/8Uv+8vb09a9asafdwonPJulTJ0o0nGOLrwrLJQcaOI0Srs1Kbc19fd+7r684ftSGkX6jUn2p8KzmLt5Kz8HW10U8CCe/tjFp2n75jBk8hHjx4kGHDhhkjT6sxpcPgrqCiuh5t3AGq63RsWzICD3tZ9UB0LRfKq9nz8yr6B89epl6n4GRjwZjApv3N7uvr3iFu4jel785mP60//elPvPjii3z66ackJCTc0Pidd95pt2Ci82hsVHh2w3EulFeTuGCYFC/RJXk6dWPO0N7MGdqbqzX1TbtPZxSz93QJm48VYWluxtAAV/2GnT0dZSeG22lWwIKCmk7ryD1UojW9szebvVklrJran/DezsaOI4TR2VtbMCm0J5NCe9Kga+TI+SukZBSTklnMK0npvJKUTn8vB8YGNa3TGOLpINfNDLjhFKJOp2Pp0qW8/fbbxsrUKkzpMLgz25NZzPx1acwM9+bPMwfI/4RC3IKiKJwtvUZKRtNqIEfzr6Ao4Olord99OsrfBSt1203RN6XvzhtOuJqbm9/yZmUhWirvchX/veE4/b0ceG1qfyleQtyGSqVC42GPxsOeJ0cFcPlaLXt/vm72+ZEC1h86j52VmpF93Rkb7MHoQI8ufR+lwSuGYWFhaLVafvOb32Bra6t/fvr06e0WTJi263UNLFx/BHMzFf96OFxu6hTiDrjZWTErwodZET7U1Ov4Lufyz7MaS9h+8iLmZioiejvrVwzp7Wp7+047EYMFrKamBldXV/bu3at/TqVSSQETLaIoCks3nSS75Crr5g2R9eKEaAXWFubcH9Sd+4O683qjwomiCv29Zq9tz+S17Zn08bBjbHB3fhPujX8X2NnBYAH74IMP2juH6ETWHsjlyx8v8OKEQO7t427sOEJ0OmZmKsJ8nAjzceL58YHkl13X328W/+05wns5d90CVlNTw9q1a0lPT2+2Ase///3vdgsmTNPBs2W8uSOL8SHdeXJkgLHjCNEl9HK1Yd4IP+aN8KPiej3Wll3j5miDo5wzZw6XLl1i586djBw5ksLCQuzt7ds7mzAxFyuqWfzpUXxdbXj7NwNl0oYQRuBoY9GmsxQ7EoMFLCcnh1WrVmFra8vcuXPZvn07hw8fbu9swoTUNuj4r4+PUtvQyPtzIrC3lk3/hBBty2ABs7Bo+vJxcnLi1KlTVFRUUFJS0q7BhGlZ8UUGPxaU8/ZvBqLx6Pzn3oUQxmfwGtiCBQu4cuUKq1atQqvVcu3aNVatWtXe2YSJ2PBDPgmp+Tw1KoAJ/XsYO44QooswWMAef/xxAEaOHMm5c+faNZAwLT8WlPNKUjr39nHjdw8EGjuOEKILMXgKsaysjKeffprBgwcTHh7Of//3f1NWVtbe2UQHV3atlic/PoK7nRXvxAzCXLZTF0K0I4MFLCYmBg8PDzZt2sTGjRtxc3MjOjq6RR0mJycTGBiIRqMhNjb2htdra2uJjo5Go9EQFRVFXl4eACkpKYSHhxMaGkp4eHizm6hHjRpFYGAgYWFhhIWFyfW4DqBB18jTCccoq6rj/TnhONt23eVshIxcVY0AACAASURBVBBGohgQEhJyw3P9+/c31LSZhoYGxd/fXzl79qxSW1urDBgwQElPT2/WZvXq1crChQsVRVGUhIQEZdasWYqiKMrRo0eVoqIiRVEU5eTJk4qnp6f+PSNHjlR++OGH237+/xYeHv6r2otf543tGUrvpduUz9MKjB1FCNGKTOm70+AR2AMPPEBiYiKNjY00Njby2WefMX78+NsWw9TUVDQaDf7+/lhaWhITE0NSUlKzNklJScydOxeAmTNnsmfPHhRFYdCgQXh6egIQEhJCdXU1tbW1d1ufRRvYfuIi7397jkeG9WZmuLex4wghuiiDBWzNmjXMnj0bKysrfSF6//33sbe3x8HB4aadFRUV4ePjo3/s7e1NUVHRTduo1WocHR1vuL62adMmBg8ejJWVlf65xx57jLCwMFatWoVy4ybSop2cKb7KCxt/JLy3M3+YHGzsOEKILszgLMSrV6+2dw699PR0li5dyq5du/TPffLJJ3h5eXH16lVmzJjB+vXreeSRR254b3x8PPHx8QCUlpa2W+auorKmnoXrj2Brpebdhwdjqe4ay9UIITomg99A3333HVVVVQB8/PHHPPfcc+Tn59+2My8vLwoKCvSPCwsL8fLyummbhoYGKioqcHV11befNm0aH330EQEBAc3eA2Bvb8/s2bNJTU01+PkLFiwgLS2NtLQ03N1lEdnW1Nio8NyGHyn46TrvPjyY7g7Wxo4khOjiDBawJ598EhsbG3788Uf+8pe/EBAQwJw5c27bWWRkJNnZ2eTm5lJXV0diYiJarbZZG61Wy7p16wDYuHEjY8aMQaVSUV5ezuTJk4mNjWX48OH69g0NDVy+fBmA+vp6tm3bRv/+/e94wOLXq6iuZ8WX6ezOLOaVKcFE+roYO5IQQhguYGq1GpVKRVJSEosXL2bRokUtOq2oVquJi4tj/PjxBAUFMWvWLEJCQli+fDlffPEFAPPnz6esrAyNRsNf//pX/VT7uLg4cnJyWLlyZbPp8rW1tYwfP54BAwYQFhaGl5cXTzzxRCv+CMSt1DU0MvCPu/jo4HmmD/LikWG9jR1JCCEAUCkGZkSMHDmSCRMm8MEHH/Dtt9/i4eHBwIEDOXnypDEy3pGIiAjS0tKMHcPkvbMnm7+mnAEgc+UEull2jVWuheiqTOm70+AR2IYNG7CysmLt2rX06NGDwsJCXnjhhfbOJozsTPFV/rk3mwcHepL75iQpXkKIDsXgEVhnYEq/RXREukaFGf/6nvNlVex+biSudla3f5MQwuSZ0ndnsyOwESNGAOjv9/rln9vd/yU6nw+/z+N4QTkrtCFSvIQQHVKz+8AOHDgAGPc+MGF8+WXXeXvnae7v54F2oKex4wghhEEGb2Q+efIkWVlZAAQHBxMSEtKuoYTxKIrCS5tPYG6m4rVp/VGpZIV5IUTH1KyAVVRU8NBDD5Gfn8/AgQNRFIWTJ0/Sq1cvkpKS5DRiF/BZWgHfny3j9Wn96enYzdhxhBDipppdA3vllVeIiIggJyeHLVu2sHXrVrKzs4mMjGTZsmXGyijaSXFlDa9tz2Sovwu/jexl7DhCCHFLzY7Adu/ezYkTJzAz+09dMzMz44033iA0NLTdw4n2oygKy7acoq6hkdjpAzCTzSmFEB1csyMwS0tL1OobL4up1epmK8OLzmfbiYvszizmdw/0xdfN1thxhBDitppVq5qaGo4dO3bDdiWKosjeXJ3YT1V1rPginYHejswb7mfsOEII0SLNCljPnj157rnnDDbs0aNHuwQS7W/Vtgwqquv55Iko1OayRYoQwjQ0K2D79u0zVg5hJPuySthyrIgl9/ehXw+ZZSqEMB3y63YXdrWmnt9vOUnf7nYsGh1w+zcIIUQHIgWsC3srOYviyhremjEAK7Us1CuEMC1SwLqoQ+fK+PhQPvOG+zGol7Ox4wghxK9msIApisLHH3/MypUrAcjPzyc1NbVdg4m2U1Ov46VNJ+jlYsNzD/Q1dhwhhLgjBgvYU089xcGDB0lISACaVqdftGhRuwYTbedvKWfIK7tO7PRQbCwNLocphBAdnsECdvjwYVavXo21tTUAzs7O1NXVtajD5ORkAgMD0Wg0xMbG3vB6bW0t0dHRaDQaoqKiyMvLAyAlJYXw8HBCQ0MJDw9n7969+vccOXKE0NBQNBoNS5YsueE+NdFyJwrLWbP/HL8d4sM9GjdjxxFCiDtmsIBZWFig0+n0K5GXlpY2W17qZnQ6HYsWLWLHjh1kZGSQkJBARkZGszZr167F2dmZnJwcnn32WZYuXQqAm5sbX375JSdPnmTdunXMmTNH/54nn3ySNWvWkJ2dTXZ2NsnJyXc84K6srqGRFzeewN3eipcmBhk7jhBC3BWDVWnJkiVMmzaNkpISli1bxogRI/j9739/285SU1PRaDT4+/tjaWlJTEwMSUlJzdokJSUxd+5cAGbOnMmePXtQFIVBgwbh6dm091RISAjV1dXU1tZy8eJFKisrGTp0KCqVikceeYStW7fe7bi7pPe+OUvWpau8NjUUx24Wxo4jhBB3xeAFkIcffpjw8HB9cdm6dStBQbf/jb2oqAgfHx/9Y29vbw4fPnzTNmq1GkdHR8rKynBz+8/prE2bNjF48GCsrKwoKirC29u7WZ9FRUW/bpSC7OKr/HNvNg8O9GRccHdjxxFCiLtmsIAtWbKEmJgYo0zcSE9PZ+nSpezatetXvzc+Pp74+Hig6bSnaKJrVHhx0wnsrNSseDDY2HGEEKJVGDyFGB4ezmuvvUZAQADPP/88aWlpLerMy8uLgoIC/ePCwkK8vLxu2qahoYGKigpcXV317adNm8ZHH31EQECAvn1hYeEt+/zFggULSEtLIy0tDXd39xZl7go+/D6PY/nlrNCG4GonuwoIIToHgwVs7ty5fPXVV/zwww8EBgaydOlS+vTpc9vOIiMjyc7OJjc3l7q6OhITE9Fqtc3aaLVa1q1bB8DGjRsZM2YMKpWK8vJyJk+eTGxsLMOHD9e379mzJw4ODhw6dAhFUfjoo4946KGH7mbMXUp+2XXe3nmaMf080A70NHYcIYRoNbecWpiTk0NWVhbnz5+nX79+t+1MrVYTFxfH+PHjCQoKYtasWYSEhLB8+XK++OILAObPn09ZWRkajYa//vWv+qn2cXFx5OTksHLlSsLCwggLC6OkpASAd999l8cffxyNRkNAQAATJ06823F3CYqi8PKWE5ibqXhtan/9rFIhhOgMVIqBm6pefPFFtmzZQkBAANHR0UybNg0nJydj5LtjERERLT712Vlt+CGfpZtO8vq0/jwc1dvYcYQQJsCUvjsNTuIICAjg4MGDzWYGCtNSXFnDa9szifJz4beRvYwdRwghWl2zApaVlUW/fv2IjIwkPz+f/Pz8Zo0HDx7cruHEnVEUhT9sPUVdQyOxMwZgZianDoUQnU+zAvbXv/6V+Ph4fve7393QUKVSNVveSXRc209eJCWjmN9P6oefm62x4wghRJtoVsB+uYdqx44d+nUQf1FTU9N+qcQdu1JVx6tJ6QzwdmTecD9jxxFCiDZjcBbiPffc06LnRMezclsGFdX1vDVjAGpz2e5NCNF5NTsCu3TpEkVFRVRXV3Ps2DH9qu+VlZVcv37dKAFFy+3LKmHLsSKW3N+HoJ4Oxo4jhBBtqlkB27lzJx9++CGFhYU899xz+uft7e1544032j2caLmrNfX8fstJ+njYsWh0gLHjCCFEm2tWwObOncvcuXPZtGkTM2bMMFYmcQfeSs7iUmUNm5+8Byu1ubHjCCFEmzN4H9iMGTPYvn076enpzSZvLF++vN2CiZY7fK6Mjw/lM3+EH4N6ORs7jhBCtAuDV/n/67/+iw0bNvDPf/4TRVH4/PPPOX/+fHtnEy1QU6/jpc0n6eViw+8e6GvsOEII0W4MFrDvv/+ejz76CGdnZ1599VUOHjzImTNn2jubaIG/7T5D7uUqYqeHYmNp8IBaCCE6JYMFrFu3bgDY2Nhw4cIFLCwsuHjxYrsGE7d3orCcNd+eIybSh3s0suyXEKJrMfgr+5QpUygvL+eFF15g8ODBqFQqHn/88fbOJm6hrqGRFzeewN3eipcn3X63bCGE6GwMFrBXXnkFaJrMMWXKFGpqanB0dGzXYOLW3v/mLFmXrrLmkQgcu1kYO44QQrQ7gwVs8+bNNzzn6OhIaGgoHh4ebR5K3Fp28VX+uTeHKQN6Mi64u7HjCCGEURgsYGvXruXgwYOMHj0agK+//prw8HByc3NZvnw5c+bMadeQ4j90jQovbjqBrZU5K7Qhxo4jhBBGY7CANTQ0kJmZSffuTb/dFxcX88gjj3D48GHuu+8+KWBGtO77PI7ll/P36DDc7KyMHUcIIYzG4CzEgoICffEC8PDwoKCgABcXFyws5HqLseSXXefPO08zOtCdh8I8jR1HCCGMymABGzVqFFOmTGHdunWsW7cOrVbLqFGjqKqqwsnJ6ZYdJicnExgYiEajITY29obXa2triY6ORqPREBUVRV5eHgBlZWWMHj0aOzs7Fi9efEOewMBAwsLCCAsLo6Sk5A6Ha7oUReHlLScwN1Px+rRQVCrZpFII0bUZPIW4evVqNm/ezIEDB4CmNRJnzJiBSqVi3759N+1Mp9OxaNEiUlJS8Pb2JjIyEq1WS3BwsL7N2rVrcXZ2Jicnh8TERJYuXcqGDRuwtrZm1apVnDp1ilOnTt3Q9yeffEJERMTdjtdkfZ5WyHc5Zbw2tT+eTt2MHUcIIYzOYAFTqVRERETg6OjI2LFjuX79OteuXcPe3v6WnaWmpqLRaPD39wcgJiaGpKSkZgUsKSmJFStWADBz5kwWL16MoijY2toyYsQIcnJyWmlonUdxZQ2rtmcwxM+F2UN6GTuOEEJ0CAZPIa5Zs4aZM2eycOFCAIqKipg6deptOysqKsLHx0f/2Nvbm6Kiopu2UavVODo6UlZWdtu+H3vsMcLCwli1apV+n7KuQFEU/rD1FHUNjbw1YwBmZnLqUAgh4CYFbPXq1Xz33Xc4ODRtitinTx+jXnf65JNPOHnyJPv372f//v2sX7/eYLv4+HgiIiKIiIigtLS0nVO2ja9OXiIlo5jnxvXFz83W2HGEEKLDMFjArKyssLS01D9uaGho0aQBLy8vCgoK9I8LCwvx8vK6aZuGhgYqKipwdXW9bb/QtLHm7NmzSU1NNdhuwYIFpKWlkZaWhru7+23zdnRXqup49YtThHo5Mn+En7HjCCFEh2KwgI0cOZI33niD6upqUlJS+M1vfsODDz54284iIyPJzs4mNzeXuro6EhMT0Wq1zdpotVrWrVsHwMaNGxkzZswti2NDQwOXL18GoL6+nm3bttG/f/8WD9CUrdqWQfn1ev40cwBqc4N/VEII0WUZnMQRGxvL2rVrCQ0N5f3332fSpEktWsxXrVYTFxfH+PHj0el0zJs3j5CQEJYvX05ERARarZb58+czZ84cNBoNLi4uJCYm6t/v6+tLZWUldXV1bN26lV27dtG7d2/Gjx9PfX09Op2OsWPH8sQTT7TeT6CD2pdVwuZjRSwZoyGop4Ox4wghRIejUjrpjIiIiAjS0tKMHeOOXK2pZ/zfvsXWSs22JSOwUpsbO5IQooswpe/OZkdgo0ePvunpPJVKxZ49e9olVFf3p+TTXKysYdOT90jxEkKIm2hWwN5+++0bGhw6dIg//elPsgp9Ozl8roz1h84zb7gfg3s5GzuOEEJ0WM0KWHh4uP6/v/nmG1atWkVNTQ3vvfceEydObPdwXU1NvY6XNp/Ex6Ubz4/va+w4QgjRod0wiWPnzp289tprWFlZsWzZMv2WKqLt/X13NrmXq/jk8ShsLA3OrxFCCPGzZt+SkZGRlJaW8sILLzBs2DAAjh49qn998ODB7ZuuCzlZWMGa/eeIjvBhuMbN2HGEEKLDa1bAbG1tsbOzY+PGjWzatKnZkk0qlYq9e/e2e8CuoF7XyAsbf8TV1pLfTw4ydhwhhDAJzQrY119/baQYXdt7X58l69JV4ueE49hN9lsTQoiWkOUdjCy7+Cr/3JvD5AE9eSCkh7HjCCGEyZACZkS6RoWlm05gY2XOH7Uhxo4jhBAmRQqYEa37Po+j+eW8+mAwbnZWxo4jhBAmxWABUxSFjz/+mJUrVwKQn59/0xXgxZ0p+Ok6f955mtGB7kwN87r9G4QQQjRjsIA99dRTHDx4kISEBKBpG5NFixa1a7DOTFEUXt58EjMVvD4ttEVb1QghhGjO4N2yhw8f5ujRowwaNAgAZ2dn6urq2jVYZ/Z5WiEHci6zamp/PJ26GTuOEEKYJINHYBYWFuh0Ov2RQWlpKWZmcrmsNRRX1rBqewZD/Fx4eEgvY8cRQgiTZbAqLVmyhGnTplFSUsKyZcsYMWIEv//979s7W6ejKAqvbD1FXUMjsdNDMTOTU4dCCHGnDJ5CfPjhhwkPD2fPnj0oisLWrVsJCpIVIu7WVycvsSujmJcm9sPf3c7YcYQQwqQZPAI7e/Ysfn5+LFq0iP79+5OSkkJ5eXl7Z+tUrlTV8eoXpwj1cuTxEX7GjiOEECbPYAGbMWMG5ubm5OTksHDhQgoKCpg9e3aLOkxOTiYwMBCNRkNsbOwNr9fW1hIdHY1GoyEqKoq8vDwAysrKGD16NHZ2dixevLjZe44cOUJoaCgajYYlS5ZgiptIr9qWQfn1et6aMQC1uVxPFEKIu2Xwm9TMzAy1Ws3mzZtZvHgxf/7zn7l48eJtO9PpdCxatIgdO3aQkZFBQkICGRkZzdqsXbsWZ2dncnJyePbZZ1m6dCkA1tbWrFq1yuCmmk8++SRr1qwhOzub7OxskpOT72SsRrPvdAmbjxXx1KgAgj0djB1HCCE6hZvOQkxISOCjjz5iypQpANTX19+2s9TUVDQaDf7+/lhaWhITE0NSUlKzNklJScydOxeAmTNn6q+z2draMmLECKytrZu1v3jxIpWVlQwdOhSVSsUjjzzC1q1b72iwxnCttoFlm0+i8bBj0RiNseMIIUSnYbCAffDBBxw8eJBly5bh5+dHbm4uc+bMuW1nRUVF+Pj46B97e3tTVFR00zZqtRpHR0fKyspu2ae3t/ct++zI3tqRxcXKGt6aMQArtbmx4wghRKdhcBZicHAw77zzjv6xn5+f/lRfRxYfH098fDzQdO+asaXm/sT6Q+eZN9yP8N7Oxo4jhBCdSrMCFhp662WNTpw4ccvOvLy8KCgo0D8uLCzEy8vLYBtvb28aGhqoqKjA1dX1ln0WFhbess9fLFiwgAULFgAQERFxy6xtraZex9JNJ/B27sbz4/saNYsQQnRGzQrYtm3b7qqzyMhIsrOzyc3NxcvLi8TERD799NNmbbRaLevWrWPYsGFs3LiRMWPG3LJo9uzZEwcHBw4dOkRUVBQfffQRTz/99F3lbA+7MorJvVzF2rkR2FgaPNAVQghxF5p9s/bu3fvuOlOriYuLY/z48eh0OubNm0dISAjLly8nIiICrVbL/PnzmTNnDhqNBhcXFxITE/Xv9/X1pbKykrq6OrZu3cquXbsIDg7m3Xff5dFHH6W6upqJEycyceLEu8rZHjYfLcTT0ZrRgR7GjiKEEJ2SSjFwU9WhQ4d4+umnyczMpK6uDp1Oh62tLZWVlcbIeEciIiJIS0szymeXXK1h6Bt7eHJUAC+M72eUDEIIcSeM+d35axmchbh48WISEhLo06cP1dXV/M///I9sp/IrfHH8Ao0KTBvkffvGQggh7shNl4TQaDTodDrMzc157LHHTO7mYWPadLSIgT5OaDxkvUMhhGgrBmcX2NjYUFdXR1hYGC+++CI9e/aksbGxvbOZpIwLlWRerGTlQyHGjiKEEJ2awSOw9evX09jYSFxcHLa2thQUFLBp06b2zmaSthwrxMJcxZQBnsaOIoQQnVqzI7D8/Hx69eqln41obW3Nq6++apRgpqhB18iWYxcYHeiBi62lseMIIUSn1uwIbOrUqfr/njFjRruHMXX7cy5z+Vot0wfL5A0hhGhrzQrY/55Rf+7cuXYPY+o2Hy3CycaC0f3cjR1FCCE6vWYF7H+viHGr1THEjSpr6tmVfokHB3jKor1CCNEOml0D+/HHH3FwcEBRFKqrq3FwaNq7SlEUVCqVSd3I3N52nLxIbUMjM8Ll9KEQQrSHZgVMp9MZK4fJ23S0CH93WwZ6Oxo7ihBCdAmyt30rKPjpOqm5PzFjsLecehVCiHYiBawVbDnWtMHm1EGGt3kRQgjR+qSA3SVFUdh8tJBh/q54OXUzdhwhhOgypIDdpaP5V8gru870wXL0JYQQ7UkK2F3adLQIawszJob2NHYUIYToUqSA3YWaeh3bfrzAhJAe2FnJrstCCNGepIDdhb1ZJVTWNMi9X0IIYQStXsCSk5MJDAxEo9EQGxt7w+u1tbVER0ej0WiIiooiLy9P/9qbb76JRqMhMDCQnTt36p/39fUlNDSUsLAwIiIiWjvyHdt8tJDuDlbcE+Bm7ChCCNHltOp5L51Ox6JFi0hJScHb25vIyEi0Wi3BwcH6NmvXrsXZ2ZmcnBwSExNZunQpGzZsICMjg8TERNLT07lw4QJjx47lzJkzmJs3Lcu0b98+3Nw6TqG4fK2Wr0+XMv9eP8zN5N4vIYRob616BJaamopGo8Hf3x9LS0tiYmJISkpq1iYpKYm5c+cCMHPmTPbs2YOiKCQlJRETE4OVlRV+fn5oNBpSU1NbM16r+vLHCzQ0KkwfJKcPhRDCGFq1gBUVFeHj46N/7O3tTVFR0U3bqNVqHB0dKSsru+V7VSoVDzzwAOHh4cTHx7dm5Du2+WgR/b0cCOxhb+woQgjRJZnE1LkDBw7g5eVFSUkJ48aNo1+/ftx33303tIuPj9cXuNLS0jbLc6b4KieLKlg+Jfj2jYUQQrSJVj0C8/LyoqCgQP+4sLAQLy+vm7ZpaGigoqICV1fXW773l397eHgwbdq0m55aXLBgAWlpaaSlpeHu3nZ7cm06Woi5mQptmGebfYYQQohba9UCFhkZSXZ2Nrm5udTV1ZGYmIhWq23WRqvVsm7dOgA2btzImDFjUKlUaLVaEhMTqa2tJTc3l+zsbIYMGUJVVRVXr14FoKqqil27dtG/f//WjP2r6BoVth4rYlRfd9zsrIyWQwghurpWPYWoVquJi4tj/Pjx6HQ65s2bR0hICMuXLyciIgKtVsv8+fOZM2cOGo0GFxcXEhMTAQgJCWHWrFkEBwejVqtZvXo15ubmFBcXM23aNKDpiG327NlMmDChNWP/Kt+fvUxxZS3Lp8jkDSGEMCaVoiiKsUO0hYiICNLS0lq932c3HGdPZjGpy8ZibSE7LwshOpe2+u5sC7ISx69wrbaB5FOXmDLQU4qXEEIYmRSwXyH51CWq63XMkJXnhRDC6KSA/QqbjxbS29WGwb2cjR1FCCG6PClgLVRUXs3Bc2VMH+SNSiVLRwkhhLFJAWuhrceKUBSYNkhOHwohREcgBawFFEVh09FChvi60MvVxthxhBBCIAWsRX4srOBcaRXTZfKGEEJ0GFLAWmDz0UKs1GZMGtDT2FGEEEL8TArYbdQ1NPLFjxd4IKQHDtYWxo4jhBDiZ1LAbmPf6RLKr9fL6UMhhOhgpIDdxuajhbjZWXGvpuPsBi2EEEIK2C1dqapjb1YJU8M8UZvLj0oIIToS+Va+hS9PXKBepzB9sKw8L4QQHY0UsFvYdLSIfj3sCfZ0MHYUIYQQ/4cUsJvIKbnGjwXlzJCjLyGE6JCkgN3ElmOFmKngoTBPY0cRQghhgBQwAxobFbYcLeLePu54OFgbO44QQggDWr2AJScnExgYiEajITY29obXa2triY6ORqPREBUVRV5env61N998E41GQ2BgIDt37mxxn63tUG4ZFypqmBEupw+FEKKjatUCptPpWLRoETt27CAjI4OEhAQyMjKatVm7di3Ozs7k5OTw7LPPsnTpUgAyMjJITEwkPT2d5ORknnrqKXQ6XYv6bG2bjxZhb6XmgeDubfo5Qggh7lyrFrDU1FQ0Gg3+/v5YWloSExNDUlJSszZJSUnMnTsXgJkzZ7Jnzx4URSEpKYmYmBisrKzw8/NDo9GQmpraoj5b0/W6BnacvMik0J5YW5i32ecIIYS4O61awIqKivDx8dE/9vb2pqio6KZt1Go1jo6OlJWV3fS9LemzNe1Mv0RVnU6WjhJCiA5ObewArSk+Pp74+HgASktL76iPSxW1aDzsiPR1ac1oQgghWlmrHoF5eXlRUFCgf1xYWIiXl9dN2zQ0NFBRUYGrq+tN39uSPn+xYMEC0tLSSEtLw93d/Y7G8OSoAHb+932Ymanu6P1CCCHaR6sWsMjISLKzs8nNzaWuro7ExES0Wm2zNlqtlnXr1gGwceNGxowZg0qlQqvVkpiYSG1tLbm5uWRnZzNkyJAW9dnazKV4CSFEh9eqpxDVajVxcXGMHz8enU7HvHnzCAkJYfny5URERKDVapk/fz5z5sxBo9Hg4uJCYmIiACEhIcyaNYvg4GDUajWrV6/G3LxpEoWhPoUQQnRtKkVRFGOHaAsRERGkpaUZO4YQQpgUU/rulJU4hBBCmCQpYEIIIUySFDAhhBAmSQqYEEIIkyQFTAghhEnqtLMQ3dzc8PX1vaP3lpaW3vGN0B2VjMk0yJhMQ2cb0/8eT15eHpcvXzZyopbptAXsbpjSNNKWkjGZBhmTaehsYzLV8cgpRCGEECZJCpgQQgiTZL5ixYoVxg7REYWHhxs7QquTMZkGGZNp6GxjMsXxyDUwIYQQJklOIQohhDBJXaKAJScnExgYiEajITY29obXa2triY6ORqPREBUVRV5env61N998E41GQ2BgIDt37mxxn22tLcbk6+tLaGgoYWFhREREtMcw9O50PGVlZYwePRo7OzsWL17c+moWqgAAC4RJREFU7D1HjhwhNDQUjUbDkiVLaO+TDW0xplGjRhEYGEhYWBhhYWGUlJS0x1D07nRMKSkphIeHExoaSnh4OHv37tW/x1T/nG41JlP9c0pNTdVnHjhwIFu2bGlxn0ahdHINDQ2Kv7+/cvbsWaW2tlYZMGCAkp6e3qzN6tWrlYULFyqKoigJCQnKrFmzFEVRlPT0dGXAgAFKTU2Ncu7cOcXf319paGhoUZ+mNiZFUZTevXsrpaWl7TaOX9zNeK5du6bs379f+de//qUsWrSo2XsiIyOVgwcPKo2NjcqECROUr776qn0GpLTdmEaOHKn88MMP7TOI/+NuxnT06FGlqKhIURRFOXnypOLp6al/j6n+Od1qTKb651RVVaXU19criqIoFy5cUNzd3ZX6+nqjf+fdTKc/AktNTUWj0eDv74+lpSUxMTEkJSU1a5OUlMTcuXMBmDlzJnv27EFRFJKSkoiJicHKygo/Pz80Gg2pqakt6tPUxmRMdzMeW1tbRowYgbW1dbP2Fy9epLKykqFDh6JSqXjkkUfYunWrSY/J2O5mTIMGDcLT0xNo2vuvurqa2tpak/5zutmYjO1uxmRjY8P/b+/+Y6Ku/wCOP/mxgsA519KxwxXHMDjuPnecIFPsbDlhOfpxIItJEzRaVgyXy35s1nLQ2tLpH+nEWoMgamc4fzTWVqO5frBScofepWZHUGctnSVxhnXI+/sHu88X8lCMn2evx+Yfd5/P5/V+vfxs997n837zfsfGDm0TefnyZaKiosYcczrc9B3Y2bNnmT9/vv45OTmZs2fPjnpObGwss2fP5sKFC6NeO5aYk2kyagKIiooiPz+fhQsX8uabb05BJVfn+s+cwp0zvJ5rxUxOTr5mzMk0GTWFrF27FpvNRk1NzZS+bpuomvbt24fdbufWW2+9ae7T8JpCIvU+ff3112RmZmKxWKirqyM2Nnbaf/NGM6E7MovI9sUXX2AwGDh37hwrVqwgPT0dh8Mx3WmJYZqbmzEYDPT19VFcXExTUxNr1qyZ7rTGzOv18vzzz/Pxxx9PdyoTJlxNkXyfcnNz8Xq9nDx5kvLycu6///7pTmlUN/0TmMFg4KefftI/+/1+DAbDqOcMDAzQ29vL7bffPuq1Y4k5mSajptA1AHPnzsXpdE7Zq8Xx1HOtmH6//5oxJ9Nk1BS6BmDWrFmsXr16Sl//jrcmv9+P0+mksbGR1NRU/fxIvk/hagpdA5F5n0IyMjJITEzE4/FM+2/eqKZt9G2KBINBlZKSorq6uvTBR4/HM+KcnTt3jhjQLCkpUUop5fF4Rkx4SElJUQMDA2OKGWk1BQIB9ccffyilhiYRLF68WH300Uczvp6Q+vr6607iaG1tndxChpmMmoLBoD7J5u+//1bFxcVq9+7dk1zJ/42npt9//11pmqb27dt3VdxIvU+j1RTJ96mrq0ufxNHd3a2SkpLU+fPnp/03bzQ3fQemlFKtra0qLS1NGY1GVVtbq5RS6qWXXlIHDx5USinV39+vVq1apVJTU1VOTo7y+Xz6tbW1tcpoNKoFCxaMmB0VLuZUmuiafD6f0jRNaZqmTCbTlNc0nnruvPNONWfOHJWQkKAMBoM+O+ro0aMqMzNTGY1G9fTTT6vBwcGIrikQCCi73a4sFosymUyqurpan0E602uqqalRt912m7Jarfq/X3/9VSkVufdptJoi+T41NjYqk8mkrFarysrKUvv3779mzOkmK3EIIYSISDf9GJgQQoibk3RgQgghIpJ0YEIIISKSdGBCCCEiknRgQgghIpJ0YGJGiYmJ0VfDttlsI1bR/6fu7m7MZvO42wytHG61WsnLy+P06dM3HKOuro7GxkYAGhoa+Pnnn/VjlZWVfPvttxOWp6ZppKenU1VVxcWLF/XjS5YsGXcb13Lo0CF9FfIDBw5MSE1CjMt0z+MXYriEhIQxn/vDDz+ozMzMcbc5fOXwPXv2qAceeGDC4k2k4XH/+usvtXHjRuVwOCa8nXBCf9waUl5erj744IMpaVuI0cgTmJjxuru7ueeee7Db7djtdtrb2686x+v1smjRImw2G5qmcebMGQDeffdd/fsnnniCK1euXLMth8PB999/D0BbWxtZWVlYLBbWrVunrzT+wgsvYDKZ0DSNZ599FoBXXnmFbdu20dLSQkdHB2VlZdhsNvr7+7n33nvp6Oigrq6OTZs26W01NDTo+33daJ633HILr7/+Oj/++COdnZ0AJCYmAkMr8TscDmw2G2azmc8//1w//swzz5CZmcny5cs5f/48AG+99RY5OTlYrVaKi4v5888/AaioqGD9+vXk5uby3HPP6fm2t7dz6NAhNm3ahM1mw+fzYbfb9dzOnDkz4rMQk0U6MDGj9Pf3668PnU4nMLQ24yeffMKxY8dwuVxUV1dfdV1dXR0bNmzA7XbT0dFBcnIyJ0+exOVy8eWXX+J2u4mJiaG5ufma7X/44YdYLBYuX75MRUUFLpeLEydOMDAwwO7du7lw4QL79+/H6/Vy/PhxNm/ePOL6VatWkZ2dTXNzM263m/j4eP1YcXHxiA0CXS4XpaWl/ypPGHrdarVaOXXq1Ijv33vvPQoKCnC73XR2dmKz2QC4dOkS2dnZeL1eli1bxpYtWwAoKiri6NGjdHZ2kpGRwdtvv63H8vv9tLe3s337dv27JUuW8OCDD7J161bcbjepqanMnj0bt9sNQH19PWvXrr1u/kKMl6xGL2aU+Ph4/YcwJBgMUlVVpf+4f/fdd1ddt3jxYl599VX8fj9FRUWkpaXR1tbGN998Q05ODjDUOc6dOzdsu2VlZcTHx3PXXXfxxhtvcPr0aVJSUliwYAEA5eXl7Nq1i6qqKuLi4njssccoLCyksLBwzLXdcccdGI1GvvrqK9LS0jh16hR5eXns2rVrzHn+kwqzkE5OTg7r1q0jGAzy8MMP6x1YdHQ0jzzyCACPPvooRUVFAHg8HjZv3szFixcJBAIUFBTosUpKSoiJibluHpWVldTX17N9+3ZcLte07zEn/hukAxMz3o4dO5g3bx6dnZ0MDg6G3ehx9erV5Obm0traysqVK9mzZw9KKcrLy3nttdeu20ZzczPZ2dn6599++y3sebGxsRw5coS2tjZaWlrYuXPniK3kr6e0tJS9e/eSnp6O0+kkKirqhvIc7sqVK5w4cYKMjIwR3zscDj777DNaW1upqKhg48aNYbfyCG1WWFFRwYEDB7BarTQ0NHD48GH9nISEhDHlUlxczJYtW7jvvvtYuHDhdVfVF2IiyCtEMeP19vaSlJREdHQ0TU1NYceHurq6MBqNVFdX89BDD3H8+HGWL19OS0sL586dA4Y6pZ6enjG1effdd9Pd3a2PhzU1NbFs2TICgQC9vb2sXLmSHTt26ONPw82aNYu+vr6wcZ1OJwcPHuT999+ntLQU4F/lGQwGefHFF5k/fz6apo041tPTw7x583j88ceprKzk2LFjAAwODtLS0gIMvWZcunQpAH19fSQlJREMBsf06jJcjXFxcRQUFPDkk0/K60MxZaQDEzPeU089xTvvvKOP94R7Kti7dy9msxmbzYbH42HNmjWYTCZqa2vJz89H0zRWrFjBL7/8MqY24+LiqK+vp6SkBIvFQnR0NOvXr6evr4/CwkI0TWPp0qUjxoZCQpMfQpM4hpszZw4ZGRn09PSwaNEigBvKs6ysDE3TMJvNXLp0Key27ocPH8ZqtZKVlYXL5WLDhg3A0NPUkSNHMJvNfPrpp7z88ssA1NTUkJubS15eHunp6WP6/yktLWXr1q1kZWXh8/n03KKjo8nPzx9TDCHGS1ajF+I/IjExkUAgMGnxt23bRm9vLzU1NZPWhhDDyRiYEGLcnE4nPp/vhsYDhRgveQITQggRkWQMTAghRESSDkwIIUREkg5MCCFERJIOTAghRET6H/2w3GtQFERzAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vuZlaXsgg9Fw"
+      },
+      "source": [
+        "A natural question one might ask is, suppose we fix a statistical fairness definition for rich subgroup fairness like equality of false positive rates, `FP`. Does learning a classifier that is fair with respect to `FP` increase or decrease fairness with respect to false negative rates `FN`? One could see this relationship going in either direction - and indeed we submit that it is dataset dependent. In some cases, if enforcing `FP` fairness pushes the classifier towards the constant classifier, then it will also satisify `FN` rate fairness, since the constant classifier is perfectly fair. However, if the hypothesis class is sufficiently rich, then one would expect that ceteris paribus since we are optimizing for error in addition to `FP` rate fairness, the algorithm would increase `FN` rate unfairness in order to decrease error. Below we trace the FN vs. FP rate tradeoff across a range of input $\\gamma$, where the classifier is optimized only for `FP` rate fairness."
       ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "pycharm": {
+          "is_executing": true
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 515
+        },
+        "id": "BmyF8-u9g9Fw",
+        "outputId": "bd958e75-a38d-43c1-bccd-f14ee0ef9fed"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "gamma: 0.001  gamma: 0.002  gamma: 0.003  gamma: 0.004  gamma: 0.005  gamma: 0.0075  gamma: 0.01  gamma: 0.02  gamma: 0.03  gamma: 0.05  "
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "def fp_vs_fn(dataset, gamma_list, iters):\n",
+        "    fp_auditor = Auditor(dataset, 'FP')\n",
+        "    fn_auditor = Auditor(dataset, 'FN')\n",
+        "    fp_violations = []\n",
+        "    fn_violations = []\n",
+        "    for g in gamma_list:\n",
+        "        print('gamma: {} '.format(g), end =\" \")\n",
+        "        fair_model = GerryFairClassifier(C=100, printflag=False, gamma=g, max_iters=iters)\n",
+        "        fair_model.gamma=g\n",
+        "        fair_model.fit(dataset)\n",
+        "        predictions = array_to_tuple((fair_model.predict(dataset)).labels)\n",
+        "        _, fp_diff = fp_auditor.audit(predictions)\n",
+        "        _, fn_diff = fn_auditor.audit(predictions)\n",
+        "        fp_violations.append(fp_diff)\n",
+        "        fn_violations.append(fn_diff)\n",
+        "\n",
+        "    plt.plot(fp_violations, fn_violations, label='adult')\n",
+        "    plt.xlabel('False Positive Disparity')\n",
+        "    plt.ylabel('False Negative Disparity')\n",
+        "    plt.legend()\n",
+        "    plt.title('FP vs FN Unfairness')\n",
+        "    plt.savefig('gerryfair_fp_fn.png')\n",
+        "    plt.close()\n",
+        "\n",
+        "gamma_list = [0.001, 0.002, 0.003, 0.004, 0.005, 0.0075, 0.01, 0.02, 0.03, 0.05]\n",
+        "fp_vs_fn(data_set, gamma_list, pareto_iters)\n",
+        "Image(filename='gerryfair_fp_fn.png')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "pycharm": {
+          "is_executing": true
+        },
+        "id": "NDHXlmO0g9Fx"
+      },
+      "outputs": [],
+      "source": []
     }
-   ],
-   "source": [
-    "def fp_vs_fn(dataset, gamma_list, iters):\n",
-    "    fp_auditor = Auditor(dataset, 'FP')\n",
-    "    fn_auditor = Auditor(dataset, 'FN')\n",
-    "    fp_violations = []\n",
-    "    fn_violations = []\n",
-    "    for g in gamma_list:\n",
-    "        print('gamma: {} '.format(g), end =\" \")\n",
-    "        fair_model = GerryFairClassifier(C=100, printflag=False, gamma=g, max_iters=iters)\n",
-    "        fair_model.gamma=g\n",
-    "        fair_model.fit(dataset)\n",
-    "        predictions = array_to_tuple((fair_model.predict(dataset)).labels)\n",
-    "        _, fp_diff = fp_auditor.audit(predictions)\n",
-    "        _, fn_diff = fn_auditor.audit(predictions)\n",
-    "        fp_violations.append(fp_diff)\n",
-    "        fn_violations.append(fn_diff)\n",
-    "\n",
-    "    plt.plot(fp_violations, fn_violations, label='adult')\n",
-    "    plt.xlabel('False Positive Disparity')\n",
-    "    plt.ylabel('False Negative Disparity')\n",
-    "    plt.legend()\n",
-    "    plt.title('FP vs FN Unfairness')\n",
-    "    plt.savefig('gerryfair_fp_fn.png')\n",
-    "    plt.close()\n",
-    "\n",
-    "gamma_list = [0.001, 0.002, 0.003, 0.004, 0.005, 0.0075, 0.01, 0.02, 0.03, 0.05]\n",
-    "fp_vs_fn(data_set, gamma_list, pareto_iters)\n",
-    "Image(filename='gerryfair_fp_fn.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.9"
+    },
     "pycharm": {
-     "is_executing": true
+      "stem_cell": {
+        "cell_type": "raw",
+        "metadata": {
+          "collapsed": false
+        },
+        "source": []
+      }
+    },
+    "colab": {
+      "provenance": []
     }
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
   },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "metadata": {
-     "collapsed": false
-    },
-    "source": []
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
diff --git a/examples/demo_json_explainers.ipynb b/examples/demo_json_explainers.ipynb
index da23274c..c7879213 100644
--- a/examples/demo_json_explainers.ipynb
+++ b/examples/demo_json_explainers.ipynb
@@ -1,263 +1,444 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Load all necessary packages\n",
-    "import sys\n",
-    "sys.path.append(\"../\")\n",
-    "from collections import OrderedDict\n",
-    "import json\n",
-    "from pprint import pprint\n",
-    "from aif360.datasets import GermanDataset\n",
-    "from aif360.metrics import BinaryLabelDatasetMetric\n",
-    "from aif360.explainers import MetricTextExplainer, MetricJSONExplainer\n",
-    "from IPython.display import JSON, display_json"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Load dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "gd = GermanDataset()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Create metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "priv = [{'sex': 1}]\n",
-    "unpriv = [{'sex': 0}]\n",
-    "bldm = BinaryLabelDatasetMetric(gd, unprivileged_groups=unpriv, privileged_groups=priv)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Create explainers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "text_expl = MetricTextExplainer(bldm)\n",
-    "json_expl = MetricJSONExplainer(bldm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Text explanations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of positive-outcome instances: 700.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(text_expl.num_positives())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+      },
+      "source": [
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Trusted-AI/AIF360/blob/master/examples/demo_json_explainers.ipynb)\n"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean difference (mean label value on privileged instances - mean label value on unprivileged instances): -0.0748013090229\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(text_expl.mean_difference())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "source": [
+        "!pip install aif360"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zZR2TMbZ0x_l",
+        "outputId": "2f3e2fb5-33b8-45a7-bc79-c2c781eba163"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting aif360\n",
+            "  Downloading aif360-0.5.0-py3-none-any.whl (214 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m214.1/214.1 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.23.5)\n",
+            "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.11.2)\n",
+            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.5.3)\n",
+            "Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.2.2)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from aif360) (3.7.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->aif360) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->aif360) (2023.3.post1)\n",
+            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->aif360) (1.3.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->aif360) (3.2.0)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (1.1.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (0.11.0)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (4.42.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (23.1)\n",
+            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (3.1.1)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.24.0->aif360) (1.16.0)\n",
+            "Installing collected packages: aif360\n",
+            "Successfully installed aif360-0.5.0\n"
+          ]
+        }
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Disparate impact (probability of favorable outcome for unprivileged instances / probability of favorable outcome for privileged instances): 0.896567328205\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(text_expl.disparate_impact())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### JSON Explanations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def format_json(json_str):\n",
-    "    return json.dumps(json.loads(json_str, object_pairs_hook=OrderedDict), indent=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "collapsed": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VwiyXRmd0uqW",
+        "outputId": "b6ada560-3bb5-4dfc-ec8d-3600bb677fa5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:No module named 'tempeh': LawSchoolGPADataset will be unavailable. To install, run:\n",
+            "pip install 'aif360[LawSchoolGPA]'\n",
+            "WARNING:root:No module named 'fairlearn': ExponentiatedGradientReduction will be unavailable. To install, run:\n",
+            "pip install 'aif360[Reductions]'\n",
+            "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n",
+            "pip install 'aif360[Reductions]'\n",
+            "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n",
+            "pip install 'aif360[Reductions]'\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load all necessary packages\n",
+        "import sys\n",
+        "sys.path.append(\"../\")\n",
+        "from collections import OrderedDict\n",
+        "import json\n",
+        "from pprint import pprint\n",
+        "from aif360.datasets import GermanDataset\n",
+        "from aif360.metrics import BinaryLabelDatasetMetric\n",
+        "from aif360.explainers import MetricTextExplainer, MetricJSONExplainer\n",
+        "from IPython.display import JSON, display_json"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\n",
-      "  \"metric\": \"num_positives\", \n",
-      "  \"message\": \"Number of positive-outcome instances: 700.0\", \n",
-      "  \"numPositives\": 700.0, \n",
-      "  \"description\": \"Computed as the number of positive instances for the given (privileged or unprivileged) group.\", \n",
-      "  \"ideal\": \"The ideal value of this metric lies in the total number of positive instances made available\"\n",
-      "}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(format_json(json_expl.num_positives()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tLoVob4l0uqX"
+      },
+      "source": [
+        "##### Load dataset"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\n",
-      "  \"metric\": \"mean_difference\", \n",
-      "  \"message\": \"Mean difference (mean label value on privileged instances - mean label value on unprivileged instances): -0.0748013090229\", \n",
-      "  \"numPositivesUnprivileged\": 201.0, \n",
-      "  \"numInstancesUnprivileged\": 310.0, \n",
-      "  \"numPositivesPrivileged\": 499.0, \n",
-      "  \"numInstancesPrivileged\": 690.0, \n",
-      "  \"description\": \"Computed as the difference of the rate of favorable outcomes received by the unprivileged group to the privileged group.\", \n",
-      "  \"ideal\": \"The ideal value of this metric is 0.0\"\n",
-      "}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(format_json(json_expl.mean_difference()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import urllib\n",
+        "import aif360\n",
+        "\n",
+        "# Obtain the location where it is installed\n",
+        "LIB_PATH = aif360.__file__.rsplit(\"aif360\", 1)[0]\n",
+        "\n",
+        "# check if the data got download properly\n",
+        "def check_data_or_download(destn, files, data_source_directory):\n",
+        "    check = all(item in os.listdir(destn) for item in files)\n",
+        "    if check:\n",
+        "        print(\"Adult dataset is available for us\")\n",
+        "    else:\n",
+        "        print(\"Some files are missing. Downloading now.\")\n",
+        "        for data_file in files:\n",
+        "            _ = urllib.request.urlretrieve(data_source_directory + data_file,\n",
+        "                                           os.path.join(destn, data_file))\n",
+        "\n",
+        "# Download adult dataset\n",
+        "data_source_directory = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/\"\n",
+        "destn = os.path.join(LIB_PATH, \"aif360\", \"data\", \"raw\", \"adult\")\n",
+        "files = [\"adult.data\", \"adult.test\", \"adult.names\"]\n",
+        "\n",
+        "check_data_or_download(destn, files, data_source_directory)\n",
+        "\n",
+        "\n",
+        "# Download german dataset\n",
+        "data_source_directory = \"https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/\"\n",
+        "destn = os.path.join(LIB_PATH, \"aif360\", \"data\", \"raw\", \"german\")\n",
+        "files = [\"german.data\", \"german.doc\"]\n",
+        "\n",
+        "check_data_or_download(destn, files, data_source_directory)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pXyLozkz1AXQ",
+        "outputId": "1527abee-dd12-492b-eb1f-fcc551a09f29"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Adult dataset is available for us\n",
+            "Some files are missing. Downloading now.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "collapsed": true,
+        "id": "lssX4aJy0uqX"
+      },
+      "outputs": [],
+      "source": [
+        "gd = GermanDataset()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9AwYJJX10uqX"
+      },
+      "source": [
+        "##### Create metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "collapsed": true,
+        "id": "SbbDkw8j0uqX"
+      },
+      "outputs": [],
+      "source": [
+        "priv = [{'sex': 1}]\n",
+        "unpriv = [{'sex': 0}]\n",
+        "bldm = BinaryLabelDatasetMetric(gd, unprivileged_groups=unpriv, privileged_groups=priv)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d9ilIGpQ0uqX"
+      },
+      "source": [
+        "##### Create explainers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "collapsed": true,
+        "id": "wDLSltK00uqY"
+      },
+      "outputs": [],
+      "source": [
+        "text_expl = MetricTextExplainer(bldm)\n",
+        "json_expl = MetricJSONExplainer(bldm)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xqKmK4Q20uqY"
+      },
+      "source": [
+        "##### Text explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OF_1QgqF0uqY",
+        "outputId": "70207a1c-2107-4ce9-a63a-0bb42b5079a0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of positive-outcome instances: 700.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(text_expl.num_positives())"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\n",
-      "  \"metric\": \"disparate_impact\", \n",
-      "  \"message\": \"Disparate impact (probability of favorable outcome for unprivileged instances / probability of favorable outcome for privileged instances): 0.896567328205\", \n",
-      "  \"numPositivePredictionsUnprivileged\": 201.0, \n",
-      "  \"numUnprivileged\": 310.0, \n",
-      "  \"numPositivePredictionsPrivileged\": 499.0, \n",
-      "  \"numPrivileged\": 690.0, \n",
-      "  \"description\": \"Computed as the ratio of likelihood of favorable outcome for the unprivileged group to that of the privileged group.\", \n",
-      "  \"ideal\": \"The ideal value of this metric is 1.0\"\n",
-      "}\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0eeYcg-B0uqY",
+        "outputId": "48e9502a-972f-4d0a-9c94-6f00eef0a4a9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean difference (mean label value on unprivileged instances - mean label value on privileged instances): -0.07480130902290782\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(text_expl.mean_difference())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JgYsTkkC0uqZ",
+        "outputId": "cca8e89f-ba98-43cc-a849-8661e9c53b60"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Disparate impact (probability of favorable outcome for unprivileged instances / probability of favorable outcome for privileged instances): 0.8965673282047968\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(text_expl.disparate_impact())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E48C_xQA0uqZ"
+      },
+      "source": [
+        "##### JSON Explanations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "collapsed": true,
+        "id": "sOcjvBtl0uqZ"
+      },
+      "outputs": [],
+      "source": [
+        "def format_json(json_str):\n",
+        "    return json.dumps(json.loads(json_str, object_pairs_hook=OrderedDict), indent=2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "M3D3rFq80uqZ",
+        "outputId": "eb5dbff9-ff26-4960-d49f-933ffa9c916c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{\n",
+            "  \"metric\": \"Number Of Positives\",\n",
+            "  \"message\": \"Number of positive-outcome instances: 700.0\",\n",
+            "  \"numPositives\": 700.0,\n",
+            "  \"description\": \"Computed as the number of positive instances for the given (privileged or unprivileged) group.\",\n",
+            "  \"ideal\": \"The ideal value of this metric lies in the total number of positive instances made available\"\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(format_json(json_expl.num_positives()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LDJv2LR80uqa",
+        "outputId": "e22ff853-dbee-4fa3-c817-199b27f5a219"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{\n",
+            "  \"metric\": \"Mean Difference\",\n",
+            "  \"message\": \"Mean difference (mean label value on unprivileged instances - mean label value on privileged instances): -0.07480130902290782\",\n",
+            "  \"numPositivesUnprivileged\": 201.0,\n",
+            "  \"numInstancesUnprivileged\": 310.0,\n",
+            "  \"numPositivesPrivileged\": 499.0,\n",
+            "  \"numInstancesPrivileged\": 690.0,\n",
+            "  \"description\": \"Computed as the difference of the rate of favorable outcomes received by the unprivileged group to the privileged group.\",\n",
+            "  \"ideal\": \"The ideal value of this metric is 0.0\"\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(format_json(json_expl.mean_difference()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2YhmXYW_0uqa",
+        "outputId": "8db89352-db50-44bc-f99a-cbf3ffd7b909"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{\n",
+            "  \"metric\": \"Disparate Impact\",\n",
+            "  \"message\": \"Disparate impact (probability of favorable outcome for unprivileged instances / probability of favorable outcome for privileged instances): 0.8965673282047968\",\n",
+            "  \"numPositivePredictionsUnprivileged\": 201.0,\n",
+            "  \"numUnprivileged\": 310.0,\n",
+            "  \"numPositivePredictionsPrivileged\": 499.0,\n",
+            "  \"numPrivileged\": 690.0,\n",
+            "  \"description\": \"Computed as the ratio of rate of favorable outcome for the unprivileged group to that of the privileged group.\",\n",
+            "  \"ideal\": \"The ideal value of this metric is 1.0 A value < 1 implies higher benefit for the privileged group and a value >1 implies a higher benefit for the unprivileged group.\"\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(format_json(json_expl.disparate_impact()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": true,
+        "id": "wmbtgejP0uqa"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 2",
+      "language": "python",
+      "name": "python2"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 2
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython2",
+      "version": "2.7.11"
+    },
+    "colab": {
+      "provenance": []
     }
-   ],
-   "source": [
-    "print(format_json(json_expl.disparate_impact()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
diff --git a/examples/demo_lfr.ipynb b/examples/demo_lfr.ipynb
index 134a2729..7170cf05 100644
--- a/examples/demo_lfr.ipynb
+++ b/examples/demo_lfr.ipynb
@@ -1,473 +1,770 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### This notebook demonstrates the use of the learning fair representations algorithm for bias mitigation\n",
-    "Learning fair representations [1] is a pre-processing technique that finds a latent representation which encodes the data well but obfuscates information about protected attributes. We will see how to use this algorithm for learning representations that encourage individual fairness and apply them on the Adult dataset.\n",
-    "\n",
-    "References:\n",
-    "\n",
-    "[1] R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork,  \"Learning Fair Representations.\" \n",
-    "International Conference on Machine Learning, 2013."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "# Load all necessary packages\n",
-    "import sys\n",
-    "sys.path.append(\"../\")\n",
-    "from aif360.datasets import BinaryLabelDataset\n",
-    "from aif360.datasets import AdultDataset\n",
-    "from aif360.metrics import BinaryLabelDatasetMetric\n",
-    "from aif360.metrics import ClassificationMetric\n",
-    "from aif360.metrics.utils import compute_boolean_conditioning_vector\n",
-    "\n",
-    "from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions import load_preproc_data_adult\n",
-    "from aif360.algorithms.preprocessing.lfr import LFR\n",
-    "\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.metrics import classification_report\n",
-    "\n",
-    "from IPython.display import Markdown, display\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "from common_utils import compute_metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Load dataset and set options"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the dataset and split into train and test\n",
-    "dataset_orig = load_preproc_data_adult()\n",
-    "dataset_orig_train, dataset_orig_test = dataset_orig.split([0.7], shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Clean up training data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+      },
+      "source": [
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Trusted-AI/AIF360/blob/master/examples/demo_lfr.ipynb)\n"
+      ]
+    },
     {
-     "data": {
-      "text/markdown": [
-       "#### Training Dataset shape"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pSHxIvnmJMlH"
+      },
+      "source": [
+        "#### This notebook demonstrates the use of the learning fair representations algorithm for bias mitigation\n",
+        "Learning fair representations [1] is a pre-processing technique that finds a latent representation which encodes the data well but obfuscates information about protected attributes. We will see how to use this algorithm for learning representations that encourage individual fairness and apply them on the Adult dataset.\n",
+        "\n",
+        "References:\n",
+        "\n",
+        "[1] R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork,  \"Learning Fair Representations.\"\n",
+        "International Conference on Machine Learning, 2013."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install aif360"
       ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
+      "metadata": {
+        "id": "rbjwJ83JKYOE",
+        "outputId": "9bda5dcb-ad33-4763-c650-fa8dc87391a7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: aif360 in /usr/local/lib/python3.10/dist-packages (0.5.0)\n",
+            "Requirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.23.5)\n",
+            "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.11.2)\n",
+            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.5.3)\n",
+            "Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from aif360) (1.2.2)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from aif360) (3.7.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->aif360) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->aif360) (2023.3.post1)\n",
+            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->aif360) (1.3.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->aif360) (3.2.0)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (1.1.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (0.11.0)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (4.42.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (23.1)\n",
+            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->aif360) (3.1.1)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.24.0->aif360) (1.16.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "WRq6KHByJMlH"
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "# Load all necessary packages\n",
+        "import sys\n",
+        "sys.path.append(\"../\")\n",
+        "from aif360.datasets import BinaryLabelDataset\n",
+        "from aif360.datasets import AdultDataset\n",
+        "from aif360.metrics import BinaryLabelDatasetMetric\n",
+        "from aif360.metrics import ClassificationMetric\n",
+        "from aif360.metrics.utils import compute_boolean_conditioning_vector\n",
+        "\n",
+        "from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions import load_preproc_data_adult\n",
+        "from aif360.algorithms.preprocessing.lfr import LFR\n",
+        "\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "from sklearn.metrics import classification_report\n",
+        "\n",
+        "from IPython.display import Markdown, display\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(34189, 18)\n"
-     ]
+      "cell_type": "code",
+      "source": [
+        "# Metrics function\n",
+        "from collections import OrderedDict\n",
+        "\n",
+        "def compute_metrics(dataset_true, dataset_pred,\n",
+        "                    unprivileged_groups, privileged_groups,\n",
+        "                    disp = True):\n",
+        "    \"\"\" Compute the key metrics \"\"\"\n",
+        "    classified_metric_pred = ClassificationMetric(dataset_true,\n",
+        "                                                 dataset_pred,\n",
+        "                                                 unprivileged_groups=unprivileged_groups,\n",
+        "                                                 privileged_groups=privileged_groups)\n",
+        "    metrics = OrderedDict()\n",
+        "    metrics[\"Balanced accuracy\"] = 0.5*(classified_metric_pred.true_positive_rate()+\n",
+        "                                             classified_metric_pred.true_negative_rate())\n",
+        "    metrics[\"Statistical parity difference\"] = classified_metric_pred.statistical_parity_difference()\n",
+        "    metrics[\"Disparate impact\"] = classified_metric_pred.disparate_impact()\n",
+        "    metrics[\"Average odds difference\"] = classified_metric_pred.average_odds_difference()\n",
+        "    metrics[\"Equal opportunity difference\"] = classified_metric_pred.equal_opportunity_difference()\n",
+        "    metrics[\"Theil index\"] = classified_metric_pred.theil_index()\n",
+        "\n",
+        "    if disp:\n",
+        "        for k in metrics:\n",
+        "            print(\"%s = %.4f\" % (k, metrics[k]))\n",
+        "\n",
+        "    return metrics"
+      ],
+      "metadata": {
+        "id": "HHWJ4YOUKepn"
+      },
+      "execution_count": 25,
+      "outputs": []
     },
     {
-     "data": {
-      "text/markdown": [
-       "#### Favorable and unfavorable labels"
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import urllib\n",
+        "import aif360\n",
+        "\n",
+        "# Obtain the location where it is installed\n",
+        "LIB_PATH = aif360.__file__.rsplit(\"aif360\", 1)[0]\n",
+        "\n",
+        "# check if the data got download properly\n",
+        "def check_data_or_download(destn, files, data_source_directory):\n",
+        "    check = all(item in os.listdir(destn) for item in files)\n",
+        "    if check:\n",
+        "        print(\"Adult dataset is available for us\")\n",
+        "    else:\n",
+        "        print(\"Some files are missing. Downloading now.\")\n",
+        "        for data_file in files:\n",
+        "            _ = urllib.request.urlretrieve(data_source_directory + data_file,\n",
+        "                                           os.path.join(destn, data_file))\n",
+        "\n",
+        "# Download adult dataset\n",
+        "data_source_directory = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/\"\n",
+        "destn = os.path.join(LIB_PATH, \"aif360\", \"data\", \"raw\", \"adult\")\n",
+        "files = [\"adult.data\", \"adult.test\", \"adult.names\"]\n",
+        "\n",
+        "check_data_or_download(destn, files, data_source_directory)"
       ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
+      "metadata": {
+        "id": "4sOyTMUcKiL8",
+        "outputId": "9d748ad2-0eab-476f-8fdf-4bf6d68cfa2a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Adult dataset is available for us\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UHNPTDPEJMlI"
+      },
+      "source": [
+        "#### Load dataset and set options"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "xuQMtYfZJMlI"
+      },
+      "outputs": [],
+      "source": [
+        "# Get the dataset and split into train and test\n",
+        "dataset_orig = load_preproc_data_adult()\n",
+        "dataset_orig_train, dataset_orig_test = dataset_orig.split([0.7], shuffle=True)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0 0.0\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z08sQ76nJMlI"
+      },
+      "source": [
+        "#### Clean up training data"
+      ]
     },
     {
-     "data": {
-      "text/markdown": [
-       "#### Protected attribute names"
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 326
+        },
+        "id": "lQr_A68uJMlI",
+        "outputId": "accac0d7-7459-4867-ee62-7cc4ba965865"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "#### Training Dataset shape"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(34189, 18)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "#### Favorable and unfavorable labels"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1.0 0.0\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "#### Protected attribute names"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "['sex', 'race']\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "#### Privileged and unprivileged protected attribute values"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[array([1.]), array([1.])] [array([0.]), array([0.])]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "#### Dataset feature names"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "['race', 'sex', 'Age (decade)=10', 'Age (decade)=20', 'Age (decade)=30', 'Age (decade)=40', 'Age (decade)=50', 'Age (decade)=60', 'Age (decade)=>=70', 'Education Years=6', 'Education Years=7', 'Education Years=8', 'Education Years=9', 'Education Years=10', 'Education Years=11', 'Education Years=12', 'Education Years=<6', 'Education Years=>12']\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
+      "source": [
+        "# print out some labels, names, etc.\n",
+        "display(Markdown(\"#### Training Dataset shape\"))\n",
+        "print(dataset_orig_train.features.shape)\n",
+        "display(Markdown(\"#### Favorable and unfavorable labels\"))\n",
+        "print(dataset_orig_train.favorable_label, dataset_orig_train.unfavorable_label)\n",
+        "display(Markdown(\"#### Protected attribute names\"))\n",
+        "print(dataset_orig_train.protected_attribute_names)\n",
+        "display(Markdown(\"#### Privileged and unprivileged protected attribute values\"))\n",
+        "print(dataset_orig_train.privileged_protected_attributes,\n",
+        "      dataset_orig_train.unprivileged_protected_attributes)\n",
+        "display(Markdown(\"#### Dataset feature names\"))\n",
+        "print(dataset_orig_train.feature_names)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['sex', 'race']\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iPxd_eWiJMlJ"
+      },
+      "source": [
+        "#### Metric for original training data"
+      ]
     },
     {
-     "data": {
-      "text/markdown": [
-       "#### Privileged and unprivileged protected attribute values"
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 133
+        },
+        "id": "el-vJ8mRJMlJ",
+        "outputId": "0ff531f0-b57d-4c10-f359-aee5f0342dd5"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "#### Original training dataset"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Difference in mean outcomes between unprivileged and privileged groups = -0.190862\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "#### Original test dataset"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Difference in mean outcomes between unprivileged and privileged groups = -0.203005\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
+      "source": [
+        "# Metric for the original dataset\n",
+        "privileged_groups = [{'sex': 1.0}]\n",
+        "unprivileged_groups = [{'sex': 0.0}]\n",
+        "\n",
+        "metric_orig_train = BinaryLabelDatasetMetric(dataset_orig_train,\n",
+        "                                             unprivileged_groups=unprivileged_groups,\n",
+        "                                             privileged_groups=privileged_groups)\n",
+        "display(Markdown(\"#### Original training dataset\"))\n",
+        "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_train.mean_difference())\n",
+        "metric_orig_test = BinaryLabelDatasetMetric(dataset_orig_test,\n",
+        "                                             unprivileged_groups=unprivileged_groups,\n",
+        "                                             privileged_groups=privileged_groups)\n",
+        "display(Markdown(\"#### Original test dataset\"))\n",
+        "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_test.mean_difference())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AdAihiWeJMlJ"
+      },
+      "source": [
+        "#### Train with and transform the original training data"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[array([1.]), array([1.])] [array([0.]), array([0.])]\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "6tTVHlcxJMlJ"
+      },
+      "outputs": [],
+      "source": [
+        "scale_orig = StandardScaler()\n",
+        "dataset_orig_train.features = scale_orig.fit_transform(dataset_orig_train.features)\n",
+        "dataset_orig_test.features = scale_orig.transform(dataset_orig_test.features)"
+      ]
     },
     {
-     "data": {
-      "text/markdown": [
-       "#### Dataset feature names"
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8xJrpt6-JMlJ",
+        "outputId": "1d083ae3-e916-49ff-f9cb-76b5b646bcd8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "step: 0, loss: 0.8962825492184127, L_x: 2.5714339164470723,  L_y: 0.620754999471439,  L_z: 0.009192079051133246\n",
+            "step: 250, loss: 0.8480866071999373, L_x: 2.5685650112390204,  L_y: 0.5738304854931037,  L_z: 0.00869981029146582\n",
+            "step: 500, loss: 0.812413520785976, L_x: 2.5559943149989834,  L_y: 0.5435687192438329,  L_z: 0.006622685021122375\n",
+            "step: 750, loss: 0.8068090347952472, L_x: 2.5485767929820033,  L_y: 0.540429587756876,  L_z: 0.005760883870085431\n",
+            "step: 1000, loss: 0.7697788013653353, L_x: 2.4301546591529353,  L_y: 0.5097902430672545,  L_z: 0.008486546191393631\n",
+            "step: 1250, loss: 0.7488916175381501, L_x: 2.319045335882304,  L_y: 0.4899897467945588,  L_z: 0.013498668577680478\n",
+            "step: 1500, loss: 0.7292348172986615, L_x: 2.180622957659463,  L_y: 0.47458348428043823,  L_z: 0.018294518626138442\n",
+            "step: 1750, loss: 0.6732503970062083, L_x: 1.8022392527041993,  L_y: 0.4457481793715224,  L_z: 0.023639146182132982\n",
+            "step: 2000, loss: 0.6551453071296062, L_x: 1.7369379199412447,  L_y: 0.44547346510191477,  L_z: 0.017989025016783476\n",
+            "step: 2250, loss: 0.6515877755909989, L_x: 1.729866141423706,  L_y: 0.44581105955253014,  L_z: 0.016395050948049064\n",
+            "step: 2500, loss: 0.642317154312171, L_x: 1.7102692235915193,  L_y: 0.4432637228700424,  L_z: 0.01401325454148834\n",
+            "step: 2750, loss: 0.6408832286060121, L_x: 1.692429573709087,  L_y: 0.4444873357962501,  L_z: 0.013576467719426591\n",
+            "step: 3000, loss: 0.6393149303295383, L_x: 1.6822172127482986,  L_y: 0.44394928162497177,  L_z: 0.013571963714868329\n",
+            "step: 3250, loss: 0.6364420807364426, L_x: 1.681350214471769,  L_y: 0.44296918237940514,  L_z: 0.01266893845493026\n",
+            "step: 3500, loss: 0.6376425149993452, L_x: 1.6755513830998394,  L_y: 0.4442870991810238,  L_z: 0.012900138754168733\n",
+            "step: 3750, loss: 0.6355475233628246, L_x: 1.6793840623156662,  L_y: 0.44315958605303135,  L_z: 0.012224765539113263\n",
+            "step: 4000, loss: 0.6432096538091823, L_x: 1.6669782170813154,  L_y: 0.4465226779831983,  L_z: 0.014994577058926217\n",
+            "step: 4250, loss: 0.6354345060723801, L_x: 1.6787641405659137,  L_y: 0.4432519397770607,  L_z: 0.012153076119363986\n",
+            "step: 4500, loss: 0.6379459397795982, L_x: 1.6680215898648223,  L_y: 0.4444484635599675,  L_z: 0.013347658616574281\n",
+            "step: 4750, loss: 0.6354297833077306, L_x: 1.6771828167354075,  L_y: 0.4433828100247568,  L_z: 0.012164345804716512\n",
+            "step: 5000, loss: 0.6344102371530024, L_x: 1.667823494652633,  L_y: 0.44373122467523646,  L_z: 0.011948331506251294\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
+      "source": [
+        "# Input recontruction quality - Ax\n",
+        "# Fairness constraint - Az\n",
+        "# Output prediction error - Ay\n",
+        "\n",
+        "privileged_groups = [{'sex': 1}]\n",
+        "unprivileged_groups = [{'sex': 0}]\n",
+        "\n",
+        "TR = LFR(unprivileged_groups=unprivileged_groups,\n",
+        "         privileged_groups=privileged_groups,\n",
+        "         k=10, Ax=0.1, Ay=1.0, Az=2.0,\n",
+        "         verbose=1\n",
+        "        )\n",
+        "TR = TR.fit(dataset_orig_train, maxiter=5000, maxfun=5000)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['race', 'sex', 'Age (decade)=10', 'Age (decade)=20', 'Age (decade)=30', 'Age (decade)=40', 'Age (decade)=50', 'Age (decade)=60', 'Age (decade)=>=70', 'Education Years=6', 'Education Years=7', 'Education Years=8', 'Education Years=9', 'Education Years=10', 'Education Years=11', 'Education Years=12', 'Education Years=<6', 'Education Years=>12']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# print out some labels, names, etc.\n",
-    "display(Markdown(\"#### Training Dataset shape\"))\n",
-    "print(dataset_orig_train.features.shape)\n",
-    "display(Markdown(\"#### Favorable and unfavorable labels\"))\n",
-    "print(dataset_orig_train.favorable_label, dataset_orig_train.unfavorable_label)\n",
-    "display(Markdown(\"#### Protected attribute names\"))\n",
-    "print(dataset_orig_train.protected_attribute_names)\n",
-    "display(Markdown(\"#### Privileged and unprivileged protected attribute values\"))\n",
-    "print(dataset_orig_train.privileged_protected_attributes, \n",
-    "      dataset_orig_train.unprivileged_protected_attributes)\n",
-    "display(Markdown(\"#### Dataset feature names\"))\n",
-    "print(dataset_orig_train.feature_names)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Metric for original training data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "pzDX-rNMJMlJ"
+      },
+      "outputs": [],
+      "source": [
+        "# Transform training data and align features\n",
+        "dataset_transf_train = TR.transform(dataset_orig_train)\n",
+        "dataset_transf_test = TR.transform(dataset_orig_test)"
+      ]
+    },
     {
-     "data": {
-      "text/markdown": [
-       "#### Original training dataset"
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dlc9Q8C1JMlJ",
+        "outputId": "92a8abce-1ad4-4961-e8e8-3d0ae80f70ac"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "         0.0       0.81      0.95      0.87     11142\n",
+            "         1.0       0.62      0.28      0.39      3511\n",
+            "\n",
+            "    accuracy                           0.79     14653\n",
+            "   macro avg       0.72      0.61      0.63     14653\n",
+            "weighted avg       0.76      0.79      0.76     14653\n",
+            "\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
+      "source": [
+        "print(classification_report(dataset_orig_test.labels, dataset_transf_test.labels))"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Difference in mean outcomes between unprivileged and privileged groups = -0.193139\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 133
+        },
+        "id": "kqDqTcxQJMlJ",
+        "outputId": "7a7e747b-eca0-43d9-84f5-8beeee2588e2"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "#### Transformed training dataset"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Difference in mean outcomes between unprivileged and privileged groups = -0.062725\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "#### Transformed test dataset"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Difference in mean outcomes between unprivileged and privileged groups = -0.057548\n"
+          ]
+        }
+      ],
+      "source": [
+        "metric_transf_train = BinaryLabelDatasetMetric(dataset_transf_train,\n",
+        "                                             unprivileged_groups=unprivileged_groups,\n",
+        "                                             privileged_groups=privileged_groups)\n",
+        "display(Markdown(\"#### Transformed training dataset\"))\n",
+        "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_transf_train.mean_difference())\n",
+        "metric_transf_test = BinaryLabelDatasetMetric(dataset_transf_test,\n",
+        "                                             unprivileged_groups=unprivileged_groups,\n",
+        "                                             privileged_groups=privileged_groups)\n",
+        "display(Markdown(\"#### Transformed test dataset\"))\n",
+        "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_transf_test.mean_difference())\n"
+      ]
     },
     {
-     "data": {
-      "text/markdown": [
-       "#### Original test dataset"
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 93
+        },
+        "id": "cRKBWIcFJMlJ",
+        "outputId": "4218db37-dfe5-420a-ca08-2913294736d1"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "#### Predictions from transformed testing data"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/aif360/metrics/dataset_metric.py:82: RuntimeWarning: invalid value encountered in double_scalars\n",
+            "  return metric_fun(privileged=False) / metric_fun(privileged=True)\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
+      "source": [
+        "display(Markdown(\"#### Predictions from transformed testing data\"))\n",
+        "bal_acc_arr_transf = []\n",
+        "disp_imp_arr_transf = []\n",
+        "\n",
+        "class_thresh_arr = np.linspace(0.01, 0.99, 100)\n",
+        "\n",
+        "dataset_transf_test_new = dataset_orig_test.copy(deepcopy=True)\n",
+        "dataset_transf_test_new.scores = dataset_transf_test.scores\n",
+        "\n",
+        "\n",
+        "for thresh in class_thresh_arr:\n",
+        "\n",
+        "    fav_inds = dataset_transf_test_new.scores > thresh\n",
+        "    dataset_transf_test_new.labels[fav_inds] = 1.0\n",
+        "    dataset_transf_test_new.labels[~fav_inds] = 0.0\n",
+        "\n",
+        "    metric_test_aft = compute_metrics(dataset_orig_test, dataset_transf_test_new,\n",
+        "                                      unprivileged_groups, privileged_groups,\n",
+        "                                      disp = False)\n",
+        "\n",
+        "    bal_acc_arr_transf.append(metric_test_aft[\"Balanced accuracy\"])\n",
+        "    disp_imp_arr_transf.append(metric_test_aft[\"Disparate impact\"])"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Difference in mean outcomes between unprivileged and privileged groups = -0.197697\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Metric for the original dataset\n",
-    "privileged_groups = [{'sex': 1.0}]\n",
-    "unprivileged_groups = [{'sex': 0.0}]\n",
-    "\n",
-    "metric_orig_train = BinaryLabelDatasetMetric(dataset_orig_train, \n",
-    "                                             unprivileged_groups=unprivileged_groups,\n",
-    "                                             privileged_groups=privileged_groups)\n",
-    "display(Markdown(\"#### Original training dataset\"))\n",
-    "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_train.mean_difference())\n",
-    "metric_orig_test = BinaryLabelDatasetMetric(dataset_orig_test, \n",
-    "                                             unprivileged_groups=unprivileged_groups,\n",
-    "                                             privileged_groups=privileged_groups)\n",
-    "display(Markdown(\"#### Original test dataset\"))\n",
-    "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_test.mean_difference())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Train with and transform the original training data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scale_orig = StandardScaler()\n",
-    "dataset_orig_train.features = scale_orig.fit_transform(dataset_orig_train.features)\n",
-    "dataset_orig_test.features = scale_orig.transform(dataset_orig_test.features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 630
+        },
+        "id": "V9_kLfaeJMlK",
+        "outputId": "5233f9a8-68e8-48d1-ed0d-c8b526ceba12"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x700 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, ax1 = plt.subplots(figsize=(10,7))\n",
+        "ax1.plot(class_thresh_arr, bal_acc_arr_transf)\n",
+        "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n",
+        "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n",
+        "ax1.xaxis.set_tick_params(labelsize=14)\n",
+        "ax1.yaxis.set_tick_params(labelsize=14)\n",
+        "\n",
+        "\n",
+        "ax2 = ax1.twinx()\n",
+        "ax2.plot(class_thresh_arr, np.abs(1.0-np.array(disp_imp_arr_transf)), color='r')\n",
+        "ax2.set_ylabel('abs(1-disparate impact)', color='r', fontsize=16, fontweight='bold')\n",
+        "ax2.yaxis.set_tick_params(labelsize=14)\n",
+        "ax2.grid(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c1rFal8YJMlK"
+      },
+      "source": [
+        "abs(1-disparate impact) must be small (close to 0) for classifier predictions to be fair."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 128
+        },
+        "id": "5qvd6Oo6JMlK",
+        "outputId": "b1ee8132-1e15-4431-9861-fee0ef882872"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "#### Individual fairness metrics"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Consistency of labels in transformed training dataset= 1.000000\n",
+            "Consistency of labels in original training dataset= 0.714183\n",
+            "Consistency of labels in transformed test dataset= 1.000000\n",
+            "Consistency of labels in original test dataset= 0.752337\n"
+          ]
+        }
+      ],
+      "source": [
+        "display(Markdown(\"#### Individual fairness metrics\"))\n",
+        "print(\"Consistency of labels in transformed training dataset= %f\" %metric_transf_train.consistency())\n",
+        "print(\"Consistency of labels in original training dataset= %f\" %metric_orig_train.consistency())\n",
+        "print(\"Consistency of labels in transformed test dataset= %f\" %metric_transf_test.consistency())\n",
+        "print(\"Consistency of labels in original test dataset= %f\" %metric_orig_test.consistency())"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "step: 0, loss: 1.0939550595829053, L_x: 2.531834521858599,  L_y: 0.8200826015334493,  L_z: 0.010344502931797964\n",
-      "step: 250, loss: 0.9162820270109503, L_x: 2.529109218043187,  L_y: 0.6432961063010657,  L_z: 0.010037499452782905\n",
-      "step: 500, loss: 0.8207071510514392, L_x: 2.5204911168067197,  L_y: 0.5500397646035967,  L_z: 0.00930913738358528\n",
-      "step: 750, loss: 0.8102771268166408, L_x: 2.511873834704061,  L_y: 0.5427956868742799,  L_z: 0.008147028235977415\n",
-      "step: 1000, loss: 0.7996570283329768, L_x: 2.480828451323288,  L_y: 0.5399446552800813,  L_z: 0.00581476396028337\n",
-      "step: 1250, loss: 0.7844631169970814, L_x: 2.4242508289183613,  L_y: 0.5304307199052671,  L_z: 0.005803657099989009\n",
-      "step: 1500, loss: 0.7653305722023572, L_x: 2.3297047767431986,  L_y: 0.5176248867874912,  L_z: 0.007367603870273078\n",
-      "step: 1750, loss: 0.7154304631442515, L_x: 2.085955877234543,  L_y: 0.48081670080967953,  L_z: 0.013009087305558827\n",
-      "step: 2000, loss: 0.6906420918886886, L_x: 1.896344106091722,  L_y: 0.4646651544564373,  L_z: 0.018171263411539594\n",
-      "step: 2250, loss: 0.6783680937630076, L_x: 1.7895665853948028,  L_y: 0.4587714378849466,  L_z: 0.020319998669290275\n",
-      "step: 2500, loss: 0.6725576747654705, L_x: 1.742061633693402,  L_y: 0.4577729094336143,  L_z: 0.020289300981257967\n",
-      "step: 2750, loss: 0.6694103860159343, L_x: 1.7548885984309939,  L_y: 0.4545867175857845,  L_z: 0.019667404293525217\n",
-      "step: 3000, loss: 0.6658207636894926, L_x: 1.7515234617350093,  L_y: 0.4539151313299769,  L_z: 0.018376643093007367\n",
-      "step: 3250, loss: 0.6481415219979564, L_x: 1.7252276686316934,  L_y: 0.4491717858033674,  L_z: 0.013223484665709846\n",
-      "step: 3500, loss: 0.645366243737316, L_x: 1.7196207136719521,  L_y: 0.4482843307446003,  L_z: 0.012559920812760247\n",
-      "step: 3750, loss: 0.6425278186287126, L_x: 1.7117758355776211,  L_y: 0.4473063883366716,  L_z: 0.012021923367139413\n",
-      "step: 4000, loss: 0.6419409673076768, L_x: 1.7092609385556714,  L_y: 0.44744616781598634,  L_z: 0.011784352818061686\n",
-      "step: 4250, loss: 0.6377801462539607, L_x: 1.6917081956472533,  L_y: 0.4496335370425122,  L_z: 0.009487894823361622\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "xO0pRb_OJMlL"
+      },
+      "outputs": [],
+      "source": [
+        "def check_algorithm_success():\n",
+        "    \"\"\"Transformed dataset consistency should be greater than original dataset.\"\"\"\n",
+        "    assert metric_transf_test.consistency() > metric_orig_test.consistency(), \"Transformed dataset consistency should be greater than original dataset.\"\n",
+        "\n",
+        "check_algorithm_success()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "w5rLKCqTJMlL"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.10"
+    },
+    "colab": {
+      "provenance": []
     }
-   ],
-   "source": [
-    "# Input recontruction quality - Ax\n",
-    "# Fairness constraint - Az\n",
-    "# Output prediction error - Ay\n",
-    "\n",
-    "privileged_groups = [{'sex': 1}]\n",
-    "unprivileged_groups = [{'sex': 0}]\n",
-    "    \n",
-    "TR = LFR(unprivileged_groups=unprivileged_groups,\n",
-    "         privileged_groups=privileged_groups,\n",
-    "         k=10, Ax=0.1, Ay=1.0, Az=2.0,\n",
-    "         verbose=1\n",
-    "        )\n",
-    "TR = TR.fit(dataset_orig_train, maxiter=5000, maxfun=5000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Transform training data and align features\n",
-    "dataset_transf_train = TR.transform(dataset_orig_train)\n",
-    "dataset_transf_test = TR.transform(dataset_orig_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(classification_report(dataset_orig_test.labels, dataset_transf_test.labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric_transf_train = BinaryLabelDatasetMetric(dataset_transf_train, \n",
-    "                                             unprivileged_groups=unprivileged_groups,\n",
-    "                                             privileged_groups=privileged_groups)\n",
-    "display(Markdown(\"#### Transformed training dataset\"))\n",
-    "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_transf_train.mean_difference())\n",
-    "metric_transf_test = BinaryLabelDatasetMetric(dataset_transf_test, \n",
-    "                                             unprivileged_groups=unprivileged_groups,\n",
-    "                                             privileged_groups=privileged_groups)\n",
-    "display(Markdown(\"#### Transformed test dataset\"))\n",
-    "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_transf_test.mean_difference())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from common_utils import compute_metrics\n",
-    "\n",
-    "display(Markdown(\"#### Predictions from transformed testing data\"))\n",
-    "bal_acc_arr_transf = []\n",
-    "disp_imp_arr_transf = []\n",
-    "\n",
-    "class_thresh_arr = np.linspace(0.01, 0.99, 100)\n",
-    "\n",
-    "dataset_transf_test_new = dataset_orig_test.copy(deepcopy=True)\n",
-    "dataset_transf_test_new.scores = dataset_transf_test.scores\n",
-    "\n",
-    "\n",
-    "for thresh in class_thresh_arr:\n",
-    "    \n",
-    "    fav_inds = dataset_transf_test_new.scores > thresh\n",
-    "    dataset_transf_test_new.labels[fav_inds] = 1.0\n",
-    "    dataset_transf_test_new.labels[~fav_inds] = 0.0\n",
-    "    \n",
-    "    metric_test_aft = compute_metrics(dataset_orig_test, dataset_transf_test_new, \n",
-    "                                      unprivileged_groups, privileged_groups,\n",
-    "                                      disp = False)\n",
-    "\n",
-    "    bal_acc_arr_transf.append(metric_test_aft[\"Balanced accuracy\"])\n",
-    "    disp_imp_arr_transf.append(metric_test_aft[\"Disparate impact\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, ax1 = plt.subplots(figsize=(10,7))\n",
-    "ax1.plot(class_thresh_arr, bal_acc_arr_transf)\n",
-    "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n",
-    "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n",
-    "ax1.xaxis.set_tick_params(labelsize=14)\n",
-    "ax1.yaxis.set_tick_params(labelsize=14)\n",
-    "\n",
-    "\n",
-    "ax2 = ax1.twinx()\n",
-    "ax2.plot(class_thresh_arr, np.abs(1.0-np.array(disp_imp_arr_transf)), color='r')\n",
-    "ax2.set_ylabel('abs(1-disparate impact)', color='r', fontsize=16, fontweight='bold')\n",
-    "ax2.yaxis.set_tick_params(labelsize=14)\n",
-    "ax2.grid(True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "abs(1-disparate impact) must be small (close to 0) for classifier predictions to be fair."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "display(Markdown(\"#### Individual fairness metrics\"))\n",
-    "print(\"Consistency of labels in transformed training dataset= %f\" %metric_transf_train.consistency())\n",
-    "print(\"Consistency of labels in original training dataset= %f\" %metric_orig_train.consistency())\n",
-    "print(\"Consistency of labels in transformed test dataset= %f\" %metric_transf_test.consistency())\n",
-    "print(\"Consistency of labels in original test dataset= %f\" %metric_orig_test.consistency())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def check_algorithm_success():\n",
-    "    \"\"\"Transformed dataset consistency should be greater than original dataset.\"\"\"\n",
-    "    assert metric_transf_test.consistency() > metric_orig_test.consistency(), \"Transformed dataset consistency should be greater than original dataset.\"\n",
-    "\n",
-    "check_algorithm_success()    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 0
 }