uplift-modeling.ipynb

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    "# Supercharging customer touchpoints with uplift modeling\n",
    "\n",
    "Stephen Klosterman\n",
    "\n",
    "7/8/2020\n",
    "\n",
    "<img src=\"images/space-shuttle-774_1280.jpg\" alt=\"Lift off, from https://pixabay.com/photos/space-shuttle-rocket-lift-off-774/\" style=\"width: 400px;\"/>\n",
    "\n",
    "Image from [Pixabay](https://pixabay.com/photos/space-shuttle-rocket-lift-off-774/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this post I will introduce the concept of uplift modeling and make a case for why it's an important part of the data scientist's toolbox of methods to increase business value. Then I'll show a simple way to build an uplift model and demonstrate a few uplift model evaluation metrics, using synthetic data in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Introduction](#Intro)\n",
    "- [Data for uplift modeling: experiments are key](#Experiments)\n",
    "- [Mechanics of the model](#Mechanics)\n",
    "- [Example dataset](#Example)\n",
    "- [Analyze experimental results](#Analyze)\n",
    "- [Build an uplift model](#Build)\n",
    "- [Model evaluation](#Model-evaluation)\n",
    "    - [Quantile metrics](#quantile-metrics)\n",
    "        - [Get score quantiles](#get-quantiles)\n",
    "        - [Uplift quantile chart](#quantile-chart)\n",
    "        - [Calibration of uplift](#calibration)\n",
    "    - [Cumulative metrics](#cumulative-metrics)\n",
    "        - [Cumulative gain chart](#Cumulative-gain)\n",
    "        - [Cumulative gain curve](#gain-curve)\n",
    "- [Conclusion](#conclusion)\n",
    "- [References](#references)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction <a class=\"anchor\" id=\"Intro\"></a>\n",
    "\n",
    "Of the myriad ways that machine learning can create value for businesses, uplift modeling is one of the lesser known, compared to methods such as supervised classification or regression. But for many use cases, it may be the most effective modeling technique. In any situation where there is a costly action a business can selectively take for different customers, in hopes of influencing their behavior, uplift modeling should be a strong candidate for determining strategy. This is because uplift modeling aims to find the subset of customers that would be most influenced by the action. Identifying this segment is important for maximizing the return on investment in a business strategy.\n",
    "\n",
    "For example, in offering a coupon, a business is taking a potential revenue hit: if a customer buys and uses the coupon, revenue will be decreased by the value of the coupon. But, if the coupon persuades the customer to make a purchase, when they otherwise wouldn’t have, then it may still be worth it. These kinds of customers are called “persuadables” in the terminology of uplift modeling, which breaks things down into customer behavior with and without “treatment”, where treatment in this example is receiving a coupon.\n",
    "\n",
    "<img src=\"https://storage.googleapis.com/wf-blogs-engineering-media/2018/10/e45e2d97-confmatrix_alt.png\" alt=\"Drawing\" style=\"width: 400px;\"/>\n",
    "\n",
    "Image from Yi and Frost (2018)\n",
    "\n",
    "The goal of uplift modeling, also known as net lift or incremental response modeling, is to identify the \"persuadables\", not waste efforts on \"sure things\" and \"lost causes\", and avoid bothering \"sleeping dogs\", or those who would react negatively to the treatment, if they exist. Uplift modeling has found application in many domains including marketing, the classic use case illustrated here, as well as <a href=\"https://blogs.oracle.com/datascience/data-driven-debt-collection-using-machine-learning-and-predictive-analytics\" target=\"_blank\">debt collection</a> and <a href=\"https://go.forrester.com/blogs/13-06-27-how_the_obama_campaign_used_predictive_analytics_to_influence_voters/\" target=\"_blank\">political campaigns</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data for uplift modeling: experiments are key <a class=\"anchor\" id=\"Experiments\"></a>\n",
    "Now that we know the goal of uplift modeling, how do we get there? A typical starting point for building an uplift model is a dataset from a randomized, controlled experiment: we need a representative sample of all different kinds of customers in both a treatment group, as well as a control group that didn't receive treatment. If the proportion of customers making a purchase is significantly higher in the treatment group than the control group, we know that the promotion is “working” in the sense that it encourages a purchase on average across all customers. This is called the average treatment effect (ATE). Quantifying the ATE is the typical outcome of an A/B test.\n",
    "\n",
    "However, it may be that only a portion of customers within the treatment group are responsible for most of the ATE we observe. As an extreme example, maybe half of the customers in the treatment group were responsible for the entire ATE, whereas the promotion had no effect on the other half. If we had some way to identify the persuadable segment of customers ahead of time, who would more readily respond to treatment, then we would be able to concentrate our resources on them, and not waste time on those for whom the treatment would have little or no effect. We may need to find other promotions to engage the non-responders. In the process of determining variable treatment effects from person to person, conditional on the different traits these people have, we’re looking for the individual treatment effect (ITE), also called the conditional average treatment effect (CATE). This is where machine learning and predictive modeling come into the picture.\n",
    "\n",
    "# Mechanics of the model <a class=\"anchor\" id=\"Mechanics\"></a>\n",
    "A classic technique for structuring an uplift model is to predict, for an individual customer, their likelihood of purchase if they are treated, and also the likelihood if they are not treated. These two probabilities are then subtracted to obtain the uplift: how much more likely is a purchase if treatment is given? This can be accomplished in two ways, where in both cases the binary response variable of the model is whether or not the customer made a purchase after the treatment:\n",
    "- Lumping the treatment and control groups together into one data set and training a single model where treatment is a binary feature. In the inference phase, the model is used to make two predictions for each instance, the first with treatment = 1 and the second with treatment = 0. This is called the \"S-Learner\" approach since it uses a Single model.\n",
    "- Training separate models for the treatment and control groups. In the inference phase, the treatment and control models are both used to obtain predictions for each instance. This is called the \"T-Learner\" approach since it uses Two models.\n",
    "\n",
    "The two approaches are summarized in the following schematic:\n",
    "\n",
    "<img src=\"images/s-t-learner.svg\" alt=\"S- and T-Learner differences\" style=\"width: 800px;\"/>\n",
    "\n",
    "These approaches are widely documented in the literature on uplift modeling and causal inference (Lee et al. 2013, Gutierrez and Gerardy 2016). They have the advantage of being relatively simple and intuitive, and can be implemented using binary classification modeling techniques that many data scientists are familiar with, as well as specialized packages in enterprise software such as SAS (Lee et al. 2013). At the same time, causal inference is an active area of research within machine learning and other approaches may achieve better model performance. Different approaches include tree-based models designed to target uplift (reviewed in Gutierrez and Gerardy 2016), target variable transformation (Yi and Frost 2018), and other more recent innovations such as the X-Learner (Kunzel et al. 2019).\n",
    "\n",
    "In all varieties, uplift modeling faces a fundamental challenge. The goal is to predict, for an individual customer, their likelihood of purchase if treated, and also the likelihood if not treated, to calculate the uplift. But in reality, we never observe the outcome for someone who was both treated and not treated, because this is impossible! Someone is either treated, or not. In mathematical modeling, it’s typically a problem if we can’t observe all the outcomes we’re interested in. This challenge illustrates the counterfactual nature of uplift modeling, and the importance of randomized experiments to understand the CATE across all types of customers.\n",
    "\n",
    "<img src=\"images/fork-2115485_960_720.jpg\" alt=\"The choice of treating or not treating, from https://pixabay.com/images/id-2115485/\" style=\"width: 400px;\"/>\n",
    "\n",
    "Image from [Pixabay](https://pixabay.com/images/id-2115485/)\n",
    "\n",
    "Gutierrez and Gerardy (2016) summarize this challenge and point the way forward:\n",
    "\n",
    ">Estimating customer uplift is both a Causal Inference and a Machine Learning problem. It is a causal inference problem because one needs to estimate the difference between two outcomes that are mutually exclusive for an individual (either person *i* receives a promotional e-mail or does not receive it). To overcome this counter-factual nature, uplift modeling crucially relies on randomized experiments, i.e. the random assignment of customers to either receive the treatment (the treatment group) or not (the control group). Uplift modeling is also a machine learning problem as one needs to train different models and select the one that yields the most reliable uplift prediction according to some performance metrics. This requires sensible cross-validation strategies along with potential feature engineering.\n",
    "\n",
    "Let's explore these concepts using an example dataset, by building an S-Learner model and evaluating it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# load packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from statsmodels.stats.proportion import proportions_ztest\n",
    "\n",
    "import sklearn as sk\n",
    "from sklearn.metrics import auc\n",
    "import xgboost as xgb\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example dataset <a class=\"anchor\" id=\"Example\"></a>\n",
    "The most straightforward way to build an uplift model is to start with data from a randomized controlled experiment. This way, both the treatment and control groups should have a representative sample of the population of customers. Outside of designed experiments, quasi-experimental data may be available if a natural control group exists as part of a business's normal operations. Treatment and control groups can also be approximated by a technique known as propensity score matching, available in the CausalML package that also offers a suite of uplift modeling tools (CausalML).\n",
    "\n",
    "Here we use synthetic data from a recent publication (Zhao et al. 2020), which are publicly available [here](https://zenodo.org/record/3653141#.XwEVz5NKjYU). These data simulate a designed experiment with an even split between treatment and control groups. We load only the first 10,000 rows from this dataset, which is the first of \"100 trials (replicates with different random seeds)\". The dataset is constructed so that some features are predictive of the outcome, some are uninformative, and some are predictive of the treatment effect specifically.\n",
    "\n",
    "The columns we're interested in are `treatment_group_key`, which identifies whether or not the customer received treatment, `conversion` which is 1 if the customer made a purchase and 0 if not, and the 36 synthetic features which all start with `x`. In real data, the features may correspond to such things as customer purchase history, demographics, and other quantities a data scientist may engineer with the hypothesis that they would be useful in modeling uplift.\n",
    "\n",
    "Let's load the data and briefly explore it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/uplift_synthetic_data_100trials.csv', nrows=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 43 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   Unnamed: 0                  10000 non-null  int64  \n",
      " 1   trial_id                    10000 non-null  int64  \n",
      " 2   treatment_group_key         10000 non-null  object \n",
      " 3   conversion                  10000 non-null  int64  \n",
      " 4   control_conversion_prob     10000 non-null  float64\n",
      " 5   treatment1_conversion_prob  10000 non-null  float64\n",
      " 6   treatment1_true_effect      10000 non-null  float64\n",
      " 7   x1_informative              10000 non-null  float64\n",
      " 8   x2_informative              10000 non-null  float64\n",
      " 9   x3_informative              10000 non-null  float64\n",
      " 10  x4_informative              10000 non-null  float64\n",
      " 11  x5_informative              10000 non-null  float64\n",
      " 12  x6_informative              10000 non-null  float64\n",
      " 13  x7_informative              10000 non-null  float64\n",
      " 14  x8_informative              10000 non-null  float64\n",
      " 15  x9_informative              10000 non-null  float64\n",
      " 16  x10_informative             10000 non-null  float64\n",
      " 17  x11_irrelevant              10000 non-null  float64\n",
      " 18  x12_irrelevant              10000 non-null  float64\n",
      " 19  x13_irrelevant              10000 non-null  float64\n",
      " 20  x14_irrelevant              10000 non-null  float64\n",
      " 21  x15_irrelevant              10000 non-null  float64\n",
      " 22  x16_irrelevant              10000 non-null  float64\n",
      " 23  x17_irrelevant              10000 non-null  float64\n",
      " 24  x18_irrelevant              10000 non-null  float64\n",
      " 25  x19_irrelevant              10000 non-null  float64\n",
      " 26  x20_irrelevant              10000 non-null  float64\n",
      " 27  x21_irrelevant              10000 non-null  float64\n",
      " 28  x22_irrelevant              10000 non-null  float64\n",
      " 29  x23_irrelevant              10000 non-null  float64\n",
      " 30  x24_irrelevant              10000 non-null  float64\n",
      " 31  x25_irrelevant              10000 non-null  float64\n",
      " 32  x26_irrelevant              10000 non-null  float64\n",
      " 33  x27_irrelevant              10000 non-null  float64\n",
      " 34  x28_irrelevant              10000 non-null  float64\n",
      " 35  x29_irrelevant              10000 non-null  float64\n",
      " 36  x30_irrelevant              10000 non-null  float64\n",
      " 37  x31_uplift_increase         10000 non-null  float64\n",
      " 38  x32_uplift_increase         10000 non-null  float64\n",
      " 39  x33_uplift_increase         10000 non-null  float64\n",
      " 40  x34_uplift_increase         10000 non-null  float64\n",
      " 41  x35_uplift_increase         10000 non-null  float64\n",
      " 42  x36_uplift_increase         10000 non-null  float64\n",
      "dtypes: float64(39), int64(3), object(1)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "   Unnamed: 0  trial_id treatment_group_key  conversion  \\\n",
       "0           0         0             control           1   \n",
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       "3           3         0          treatment1           1   \n",
       "4           4         0             control           0   \n",
       "\n",
       "   control_conversion_prob  treatment1_conversion_prob  \\\n",
       "0                 0.516606                    0.572609   \n",
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       "3                 0.801968                    0.858532   \n",
       "4                 0.063552                    0.060142   \n",
       "\n",
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       "\n",
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       "\n",
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       "0            -1.158216            -1.336279            -0.708056  \n",
       "1             0.478074             0.531477             0.402723  \n",
       "2             2.029204            -0.280445             0.873970  \n",
       "3            -0.364984            -1.509045            -1.335023  \n",
       "4             0.478717            -0.680588            -2.730850  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of these 10,000 records, how many are in the treatment group, and how many are in the control group?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "control       5000\n",
       "treatment1    5000\n",
       "Name: treatment_group_key, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['treatment_group_key'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a 50/50 split. Let's encode the treatment variable as a binary 0/1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['treatment_group_key'] = df['treatment_group_key'].map(arg={'control':0, 'treatment1':1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze experimental results <a class=\"anchor\" id=\"Analyze\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What was the overall conversion rate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3191"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['conversion'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's the conversion rate in the treatment group versus the control group?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">conversion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment_group_key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.2670</td>\n",
       "      <td>1335</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3712</td>\n",
       "      <td>1856</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    conversion            \n",
       "                          mean   sum count\n",
       "treatment_group_key                       \n",
       "0                       0.2670  1335  5000\n",
       "1                       0.3712  1856  5000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_results_df = \\\n",
    "df.groupby('treatment_group_key').agg({'conversion':['mean', 'sum', 'count']})\n",
    "exp_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1042"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(exp_results_df.loc[1,('conversion', 'mean')]\n",
    " - exp_results_df.loc[0,('conversion', 'mean')]).round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a substantially higher conversion rate in the treatment group (37%) than the control group (27%), indicating the treatment is effective at encouraging conversion: the ATE is positive and is about 10%.\n",
    "\n",
    "Often in real data the difference is not so large and a significance test is usually conducted to determine the result of the A/B test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-11.177190529878043, 5.273302441543889e-29)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportions_ztest(count=exp_results_df[('conversion', 'sum')],\n",
    "                  nobs=exp_results_df[('conversion', 'count')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is the second quantity returned from the proportion test and is much smaller than 0.05, or pretty much any other threshold used to decide significance. So we know there's a significant ATE. This is the typical starting point for uplift modeling. If we were to have observed that the treatment did not increase conversion rate, while theoretically it may be possible to find a persuadable segment of customers using uplift modeling, in practice it may not be a worthwhile endeavor. This likely depends on the specific problem at hand.\n",
    "\n",
    "However in our case, having observed a substantial treatment effect, we proceed to use uplift modeling to find the CATE, and see if we can identify those persuadables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build an uplift model <a class=\"anchor\" id=\"Build\"></a>\n",
    "Here I'll use an `XGBClassifier` to train an S-Learner; that is, a single model including all the features, where the treatment indicator is also a feature. I'll split the data into training and validation sets (80/20 split), for early stopping. I'll also use the validation set to illustrate model evaluation metrics. In a real project, a held out test set should be reserved from this process, where the evaluation metrics on the test set would be used for final evaluation of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, valid = sk.model_selection.train_test_split(df, test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8000, 43) (2000, 43)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape, valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the features as a list. This includes the treatment column and all features, which are the 8th column onward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['treatment_group_key'] + df.columns.tolist()[7:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['treatment_group_key', 'x1_informative', 'x2_informative', 'x3_informative', 'x4_informative', 'x5_informative', 'x6_informative', 'x7_informative', 'x8_informative', 'x9_informative', 'x10_informative', 'x11_irrelevant', 'x12_irrelevant', 'x13_irrelevant', 'x14_irrelevant', 'x15_irrelevant', 'x16_irrelevant', 'x17_irrelevant', 'x18_irrelevant', 'x19_irrelevant', 'x20_irrelevant', 'x21_irrelevant', 'x22_irrelevant', 'x23_irrelevant', 'x24_irrelevant', 'x25_irrelevant', 'x26_irrelevant', 'x27_irrelevant', 'x28_irrelevant', 'x29_irrelevant', 'x30_irrelevant', 'x31_uplift_increase', 'x32_uplift_increase', 'x33_uplift_increase', 'x34_uplift_increase', 'x35_uplift_increase', 'x36_uplift_increase']\n"
     ]
    }
   ],
   "source": [
    "print(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assemble the training and validation sets for training the XGBoost classifer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train[features]\n",
    "y_train = train['conversion']\n",
    "X_valid = valid[features]\n",
    "y_valid = valid['conversion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_set = [(X_train, y_train), (X_valid, y_valid)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's time to instantiate and train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train an xgboost model\n",
    "model = xgb.XGBClassifier(learning_rate = 0.1,\n",
    "                          max_depth = 6,\n",
    "                          min_child_weight = 100,\n",
    "                          objective = 'binary:logistic',\n",
    "                          seed = 42,\n",
    "                          gamma = 0.1,\n",
    "                          silent = True,\n",
    "                          n_jobs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-auc:0.693049\tvalidation_1-auc:0.648941\n",
      "Multiple eval metrics have been passed: 'validation_1-auc' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-auc hasn't improved in 30 rounds.\n",
      "[1]\tvalidation_0-auc:0.718238\tvalidation_1-auc:0.656877\n",
      "[2]\tvalidation_0-auc:0.72416\tvalidation_1-auc:0.667244\n",
      "[3]\tvalidation_0-auc:0.727643\tvalidation_1-auc:0.669992\n",
      "[4]\tvalidation_0-auc:0.740204\tvalidation_1-auc:0.683216\n",
      "[5]\tvalidation_0-auc:0.74519\tvalidation_1-auc:0.686937\n",
      "[6]\tvalidation_0-auc:0.750754\tvalidation_1-auc:0.68907\n",
      "[7]\tvalidation_0-auc:0.753028\tvalidation_1-auc:0.690978\n",
      "[8]\tvalidation_0-auc:0.754925\tvalidation_1-auc:0.693401\n",
      "[9]\tvalidation_0-auc:0.758175\tvalidation_1-auc:0.69438\n",
      "[10]\tvalidation_0-auc:0.761099\tvalidation_1-auc:0.698729\n",
      "[11]\tvalidation_0-auc:0.762835\tvalidation_1-auc:0.696962\n",
      "[12]\tvalidation_0-auc:0.765661\tvalidation_1-auc:0.699823\n",
      "[13]\tvalidation_0-auc:0.770165\tvalidation_1-auc:0.706171\n",
      "[14]\tvalidation_0-auc:0.771473\tvalidation_1-auc:0.708292\n",
      "[15]\tvalidation_0-auc:0.773125\tvalidation_1-auc:0.710378\n",
      "[16]\tvalidation_0-auc:0.7752\tvalidation_1-auc:0.713611\n",
      "[17]\tvalidation_0-auc:0.777698\tvalidation_1-auc:0.716286\n",
      "[18]\tvalidation_0-auc:0.782048\tvalidation_1-auc:0.719247\n",
      "[19]\tvalidation_0-auc:0.783954\tvalidation_1-auc:0.721004\n",
      "[20]\tvalidation_0-auc:0.78624\tvalidation_1-auc:0.722997\n",
      "[21]\tvalidation_0-auc:0.787257\tvalidation_1-auc:0.725396\n",
      "[22]\tvalidation_0-auc:0.788996\tvalidation_1-auc:0.727244\n",
      "[23]\tvalidation_0-auc:0.790373\tvalidation_1-auc:0.727887\n",
      "[24]\tvalidation_0-auc:0.791979\tvalidation_1-auc:0.72852\n",
      "[25]\tvalidation_0-auc:0.793945\tvalidation_1-auc:0.730209\n",
      "[26]\tvalidation_0-auc:0.795331\tvalidation_1-auc:0.730727\n",
      "[27]\tvalidation_0-auc:0.796117\tvalidation_1-auc:0.731838\n",
      "[28]\tvalidation_0-auc:0.797467\tvalidation_1-auc:0.732765\n",
      "[29]\tvalidation_0-auc:0.798673\tvalidation_1-auc:0.734276\n",
      "[30]\tvalidation_0-auc:0.802214\tvalidation_1-auc:0.736881\n",
      "[31]\tvalidation_0-auc:0.802427\tvalidation_1-auc:0.737526\n",
      "[32]\tvalidation_0-auc:0.80369\tvalidation_1-auc:0.738862\n",
      "[33]\tvalidation_0-auc:0.805193\tvalidation_1-auc:0.73956\n",
      "[34]\tvalidation_0-auc:0.805578\tvalidation_1-auc:0.739617\n",
      "[35]\tvalidation_0-auc:0.806775\tvalidation_1-auc:0.740481\n",
      "[36]\tvalidation_0-auc:0.807079\tvalidation_1-auc:0.74101\n",
      "[37]\tvalidation_0-auc:0.808331\tvalidation_1-auc:0.742266\n",
      "[38]\tvalidation_0-auc:0.809984\tvalidation_1-auc:0.742684\n",
      "[39]\tvalidation_0-auc:0.811586\tvalidation_1-auc:0.743021\n",
      "[40]\tvalidation_0-auc:0.813437\tvalidation_1-auc:0.74377\n",
      "[41]\tvalidation_0-auc:0.814849\tvalidation_1-auc:0.745597\n",
      "[42]\tvalidation_0-auc:0.815733\tvalidation_1-auc:0.744883\n",
      "[43]\tvalidation_0-auc:0.816716\tvalidation_1-auc:0.745424\n",
      "[44]\tvalidation_0-auc:0.816905\tvalidation_1-auc:0.746245\n",
      "[45]\tvalidation_0-auc:0.818499\tvalidation_1-auc:0.747361\n",
      "[46]\tvalidation_0-auc:0.819439\tvalidation_1-auc:0.74811\n",
      "[47]\tvalidation_0-auc:0.820066\tvalidation_1-auc:0.747874\n",
      "[48]\tvalidation_0-auc:0.821155\tvalidation_1-auc:0.749222\n",
      "[49]\tvalidation_0-auc:0.82218\tvalidation_1-auc:0.75073\n",
      "[50]\tvalidation_0-auc:0.822849\tvalidation_1-auc:0.750854\n",
      "[51]\tvalidation_0-auc:0.823657\tvalidation_1-auc:0.750679\n",
      "[52]\tvalidation_0-auc:0.824743\tvalidation_1-auc:0.752018\n",
      "[53]\tvalidation_0-auc:0.825836\tvalidation_1-auc:0.752321\n",
      "[54]\tvalidation_0-auc:0.827034\tvalidation_1-auc:0.753869\n",
      "[55]\tvalidation_0-auc:0.827867\tvalidation_1-auc:0.753692\n",
      "[56]\tvalidation_0-auc:0.827731\tvalidation_1-auc:0.753742\n",
      "[57]\tvalidation_0-auc:0.828141\tvalidation_1-auc:0.753722\n",
      "[58]\tvalidation_0-auc:0.829511\tvalidation_1-auc:0.754337\n",
      "[59]\tvalidation_0-auc:0.829656\tvalidation_1-auc:0.754605\n",
      "[60]\tvalidation_0-auc:0.830575\tvalidation_1-auc:0.755371\n",
      "[61]\tvalidation_0-auc:0.83132\tvalidation_1-auc:0.756317\n",
      "[62]\tvalidation_0-auc:0.832247\tvalidation_1-auc:0.75723\n",
      "[63]\tvalidation_0-auc:0.832878\tvalidation_1-auc:0.75758\n",
      "[64]\tvalidation_0-auc:0.833463\tvalidation_1-auc:0.758079\n",
      "[65]\tvalidation_0-auc:0.834538\tvalidation_1-auc:0.757783\n",
      "[66]\tvalidation_0-auc:0.834467\tvalidation_1-auc:0.758043\n",
      "[67]\tvalidation_0-auc:0.835228\tvalidation_1-auc:0.758661\n",
      "[68]\tvalidation_0-auc:0.835746\tvalidation_1-auc:0.758963\n",
      "[69]\tvalidation_0-auc:0.83573\tvalidation_1-auc:0.758734\n",
      "[70]\tvalidation_0-auc:0.836281\tvalidation_1-auc:0.758539\n",
      "[71]\tvalidation_0-auc:0.837114\tvalidation_1-auc:0.759222\n",
      "[72]\tvalidation_0-auc:0.837185\tvalidation_1-auc:0.75868\n",
      "[73]\tvalidation_0-auc:0.837783\tvalidation_1-auc:0.758597\n",
      "[74]\tvalidation_0-auc:0.838315\tvalidation_1-auc:0.759544\n",
      "[75]\tvalidation_0-auc:0.83917\tvalidation_1-auc:0.760712\n",
      "[76]\tvalidation_0-auc:0.840081\tvalidation_1-auc:0.760552\n",
      "[77]\tvalidation_0-auc:0.840632\tvalidation_1-auc:0.761159\n",
      "[78]\tvalidation_0-auc:0.841354\tvalidation_1-auc:0.761663\n",
      "[79]\tvalidation_0-auc:0.84202\tvalidation_1-auc:0.761367\n",
      "[80]\tvalidation_0-auc:0.842633\tvalidation_1-auc:0.762303\n",
      "[81]\tvalidation_0-auc:0.843594\tvalidation_1-auc:0.762493\n",
      "[82]\tvalidation_0-auc:0.843723\tvalidation_1-auc:0.762829\n",
      "[83]\tvalidation_0-auc:0.844181\tvalidation_1-auc:0.763018\n",
      "[84]\tvalidation_0-auc:0.844654\tvalidation_1-auc:0.762929\n",
      "[85]\tvalidation_0-auc:0.84542\tvalidation_1-auc:0.762047\n",
      "[86]\tvalidation_0-auc:0.845686\tvalidation_1-auc:0.761959\n",
      "[87]\tvalidation_0-auc:0.846356\tvalidation_1-auc:0.761893\n",
      "[88]\tvalidation_0-auc:0.847198\tvalidation_1-auc:0.762377\n",
      "[89]\tvalidation_0-auc:0.847798\tvalidation_1-auc:0.762263\n",
      "[90]\tvalidation_0-auc:0.848321\tvalidation_1-auc:0.762386\n",
      "[91]\tvalidation_0-auc:0.848562\tvalidation_1-auc:0.763269\n",
      "[92]\tvalidation_0-auc:0.849109\tvalidation_1-auc:0.763219\n",
      "[93]\tvalidation_0-auc:0.849812\tvalidation_1-auc:0.76328\n",
      "[94]\tvalidation_0-auc:0.850679\tvalidation_1-auc:0.763148\n",
      "[95]\tvalidation_0-auc:0.850797\tvalidation_1-auc:0.763245\n",
      "[96]\tvalidation_0-auc:0.851011\tvalidation_1-auc:0.763394\n",
      "[97]\tvalidation_0-auc:0.851525\tvalidation_1-auc:0.763237\n",
      "[98]\tvalidation_0-auc:0.851664\tvalidation_1-auc:0.763091\n",
      "[99]\tvalidation_0-auc:0.852237\tvalidation_1-auc:0.762969\n",
      "CPU times: user 6.7 s, sys: 87.8 ms, total: 6.79 s\n",
      "Wall time: 3.48 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0.1,\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=100, missing=None, n_estimators=100, n_jobs=2,\n",
       "              nthread=None, objective='binary:logistic', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=42,\n",
       "              silent=True, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(X_train, y_train, eval_set=eval_set,\\\n",
    "          eval_metric=\"auc\", verbose=True, early_stopping_rounds=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training process completes and we can see the model has a fairly high validation AUC. The training AUC is even higher than this, meaning technically the model is overfit. Normally I'd do a hyperparameter search, but I found the values used here to provide sensible results for the purpose of illustrating uplift modeling with this dataset.\n",
    "\n",
    "As a practical side note, I've found that in some cases, when using a T-Learner (not shown here), that overfitting to the training set can cause unexpected results when calculating uplift. In my experience the issue could be remedied by decreasing `max_depth` or increasing `min_child_weight` in the `XGBClassifier`, in other words decreasing the amount of overfitting.\n",
    "\n",
    "Another point to consider in model building is feature selection, which I've omitted here. In the context of uplift modeling, one could use the uplift model evaluation metrics introduced below on a validation set as a way to select features, for example by recursive feature elimination. Feature selection for uplift models is also the topic of recent research, including the paper that is the source of the dataset used here (Zhao et al. 2020)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model evaluation <a class=\"anchor\" id=\"Model-evaluation\"></a>\n",
    "Now, we have our uplift model. The model building for an S-Learner is pretty simple, if you are already familiar with binary classification. To actually calculate the uplift for a given data set, with this approach we need to score the model twice, once with treatment = 1 and again with treatment = 0, then subtract these to get the uplift. Here we do this for the validation set, then plot a histogram of the uplift scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_valid_0 = X_valid.copy(); X_valid_0['treatment_group_key'] = 0\n",
    "X_valid_1 = X_valid.copy(); X_valid_1['treatment_group_key'] = 1\n",
    "Uplift = model.predict_proba(X_valid_1)[:,1]\\\n",
    "    - model.predict_proba(X_valid_0)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of observations in validation set')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.dpi'] = 200\n",
    "mpl.rcParams['figure.figsize'] = (6,4)\n",
    "plt.hist(Uplift, bins=50)\n",
    "plt.xlabel('Uplift score')\n",
    "plt.ylabel('Number of observations in validation set')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of uplift is mostly positive, which makes sense since we know from our analysis of the experiment that the treatment encourages conversion on average. However some instances have negative uplift, meaning treatment actually __discourages__ conversion for some people. In other words there appears to be a sleeping dog effect in these data.\n",
    "\n",
    "The main questions now are: should we trust these results? How do we know how good this model is? Metrics for uplift model evaluation are more complex than typical metrics used in supervised learning, such as the ROC AUC for classification tasks or RMSE for regression. Generally speaking, uplift evaluation metrics make a comparison of the conversion rate between the treatment and control groups, for different ranges of the predicted uplift score. For those with high uplift scores, we'd expect to see a larger difference between treatment and control, while those with lower uplift scores should have a smaller difference, or even a larger conversion rate in the control group in the case of sleeping dogs (i.e. negative difference)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantile metrics <a class=\"anchor\" id=\"quantile-metrics\"></a>\n",
    "A popular way to evaluate an uplift model is with a quantile chart. This will give a quick visual impression of whether the model is \"working\", in the sense of sloping true uplift. To create the quantile chart, we start with the uplift predictions for our validation set, and bin the instances into quantiles based on these scores. The number of quantiles depends on how much data we have, although 10 is a pretty typical number in practice (deciles). Then, within each bin, we'll find the difference in conversion rate for those who were in the treatment group and those who were in the control group. If the model is working well, we should see a larger positive difference in the highest decile, decreasing to a small or negative difference in the lowest decile (i.e. treatment rate similar to control rate, or lower than control rate). In other words, as predicted uplift increases, the true uplift from control to treatment group should increase as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get score quantiles <a class=\"anchor\" id=\"get-quantiles\"></a>\n",
    "Create a new `DataFrame` from the validation data, to add the uplift scores and quantiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Uplift.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 43)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_w_score = valid.copy()\n",
    "valid_w_score['Uplift score'] = Uplift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>trial_id</th>\n",
       "      <th>treatment_group_key</th>\n",
       "      <th>conversion</th>\n",
       "      <th>control_conversion_prob</th>\n",
       "      <th>treatment1_conversion_prob</th>\n",
       "      <th>treatment1_true_effect</th>\n",
       "      <th>x1_informative</th>\n",
       "      <th>x2_informative</th>\n",
       "      <th>x3_informative</th>\n",
       "      <th>...</th>\n",
       "      <th>x28_irrelevant</th>\n",
       "      <th>x29_irrelevant</th>\n",
       "      <th>x30_irrelevant</th>\n",
       "      <th>x31_uplift_increase</th>\n",
       "      <th>x32_uplift_increase</th>\n",
       "      <th>x33_uplift_increase</th>\n",
       "      <th>x34_uplift_increase</th>\n",
       "      <th>x35_uplift_increase</th>\n",
       "      <th>x36_uplift_increase</th>\n",
       "      <th>Uplift score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>-1.592787</td>\n",
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       "      <td>0.816324</td>\n",
       "      <td>0.408081</td>\n",
       "      <td>0.694069</td>\n",
       "      <td>-0.288068</td>\n",
       "      <td>-0.588280</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190106</td>\n",
       "      <td>1.643789</td>\n",
       "      <td>1.558527</td>\n",
       "      <td>0.684027</td>\n",
       "      <td>0.367873</td>\n",
       "      <td>-0.744745</td>\n",
       "      <td>0.378264</td>\n",
       "      <td>0.532618</td>\n",
       "      <td>0.103382</td>\n",
       "      <td>0.288655</td>\n",
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       "    <tr>\n",
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       "      <td>0.055446</td>\n",
       "      <td>0.019384</td>\n",
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       "      <td>0.239213</td>\n",
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       "      <td>-0.185026</td>\n",
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       "      <td>-0.391099</td>\n",
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       "      <td>-0.370168</td>\n",
       "      <td>-0.690919</td>\n",
       "      <td>0.383968</td>\n",
       "      <td>0.745777</td>\n",
       "      <td>0.693021</td>\n",
       "      <td>-0.860461</td>\n",
       "      <td>1.262036</td>\n",
       "      <td>0.042706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  trial_id  treatment_group_key  conversion  \\\n",
       "6252        6252         0                    0           1   \n",
       "4684        4684         0                    1           0   \n",
       "1731        1731         0                    1           0   \n",
       "4742        4742         0                    1           0   \n",
       "4521        4521         0                    0           1   \n",
       "\n",
       "      control_conversion_prob  treatment1_conversion_prob  \\\n",
       "6252                 0.091233                    0.043904   \n",
       "4684                 0.101494                    0.069031   \n",
       "1731                 0.408242                    0.816324   \n",
       "4742                 0.036061                    0.055446   \n",
       "4521                 0.206175                    0.125444   \n",
       "\n",
       "      treatment1_true_effect  x1_informative  x2_informative  x3_informative  \\\n",
       "6252               -0.047329        0.128796        0.582004        2.027088   \n",
       "4684               -0.032464       -0.249103       -0.032255        0.030591   \n",
       "1731                0.408081        0.694069       -0.288068       -0.588280   \n",
       "4742                0.019384       -1.012592        0.239213       -0.402686   \n",
       "4521               -0.080731       -1.564519       -0.809688        0.859528   \n",
       "\n",
       "      ...  x28_irrelevant  x29_irrelevant  x30_irrelevant  \\\n",
       "6252  ...        0.971923       -1.332471        0.218589   \n",
       "4684  ...        0.015858       -0.160939        0.211841   \n",
       "1731  ...        0.190106        1.643789        1.558527   \n",
       "4742  ...       -0.491389       -0.947252       -0.185026   \n",
       "4521  ...        1.053866        1.378201       -0.370168   \n",
       "\n",
       "      x31_uplift_increase  x32_uplift_increase  x33_uplift_increase  \\\n",
       "6252             0.700103             0.074845             0.580532   \n",
       "4684             0.447512            -0.269382            -0.328579   \n",
       "1731             0.684027             0.367873            -0.744745   \n",
       "4742             0.085944            -0.661352            -0.770008   \n",
       "4521            -0.690919             0.383968             0.745777   \n",
       "\n",
       "      x34_uplift_increase  x35_uplift_increase  x36_uplift_increase  \\\n",
       "6252            -1.966506            -0.322667            -1.311465   \n",
       "4684            -0.297928            -0.473154            -1.592787   \n",
       "1731             0.378264             0.532618             0.103382   \n",
       "4742             0.860812            -0.749852            -0.391099   \n",
       "4521             0.693021            -0.860461             1.262036   \n",
       "\n",
       "      Uplift score  \n",
       "6252      0.009574  \n",
       "4684     -0.003638  \n",
       "1731      0.288655  \n",
       "4742      0.131196  \n",
       "4521      0.042706  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_w_score.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that the treatment and control groups are approximately balanced, overall for the validation set (they should be since we used a random training/validation split but it's always good to check):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1011\n",
       "1     989\n",
       "Name: treatment_group_key, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_w_score['treatment_group_key'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, using the entire validation set (treatment and control groups together), make labels for the uplift score quantiles. We'll check that the treatment and control groups are balanced within the quantiles, since we'll be splitting the data by quantile and treatment to create the chart. Pandas has a convenient function to produce a series of labels according to which quantile an observation in the input series belongs to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_quantiles, score_quantile_bins = \\\n",
    "pd.qcut(x=valid_w_score['Uplift score'],\n",
    "        q=10,\n",
    "        retbins=True,\n",
    "        duplicates='drop')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this function we get a column indicating which quantile each instance belongs to, represented by the bin edges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6252    (-0.00339, 0.0186]\n",
       "4684    (-0.114, -0.00339]\n",
       "1731        (0.201, 0.398]\n",
       "4742        (0.121, 0.148]\n",
       "4521      (0.0391, 0.0548]\n",
       "Name: Uplift score, dtype: category\n",
       "Categories (10, interval[float64]): [(-0.114, -0.00339] < (-0.00339, 0.0186] < (0.0186, 0.0391] < (0.0391, 0.0548] ... (0.0941, 0.121] < (0.121, 0.148] < (0.148, 0.201] < (0.201, 0.398]]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_quantiles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also get a list of all bin edges in `score_quantile_bins`, but we don't need it here. Now let's add the score quantile to the dataframe so we can use it for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment_group_key</th>\n",
       "      <th>conversion</th>\n",
       "      <th>Uplift score</th>\n",
       "      <th>Quantile bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6252</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009574</td>\n",
       "      <td>(-0.00339, 0.0186]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.003638</td>\n",
       "      <td>(-0.114, -0.00339]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.288655</td>\n",
       "      <td>(0.201, 0.398]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4742</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.131196</td>\n",
       "      <td>(0.121, 0.148]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4521</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.042706</td>\n",
       "      <td>(0.0391, 0.0548]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6340</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.113626</td>\n",
       "      <td>(0.0941, 0.121]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.018831</td>\n",
       "      <td>(-0.114, -0.00339]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5202</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.036599</td>\n",
       "      <td>(-0.114, -0.00339]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6363</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.113048</td>\n",
       "      <td>(0.0941, 0.121]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.074832</td>\n",
       "      <td>(0.0726, 0.0941]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      treatment_group_key  conversion  Uplift score        Quantile bin\n",
       "6252                    0           1      0.009574  (-0.00339, 0.0186]\n",
       "4684                    1           0     -0.003638  (-0.114, -0.00339]\n",
       "1731                    1           0      0.288655      (0.201, 0.398]\n",
       "4742                    1           0      0.131196      (0.121, 0.148]\n",
       "4521                    0           1      0.042706    (0.0391, 0.0548]\n",
       "6340                    0           1      0.113626     (0.0941, 0.121]\n",
       "576                     1           0     -0.018831  (-0.114, -0.00339]\n",
       "5202                    1           0     -0.036599  (-0.114, -0.00339]\n",
       "6363                    1           1      0.113048     (0.0941, 0.121]\n",
       "439                     0           1      0.074832    (0.0726, 0.0941]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_w_score['Quantile bin'] = score_quantiles\n",
    "valid_w_score[[\n",
    "    'treatment_group_key', 'conversion', 'Uplift score', 'Quantile bin']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that the number of treated and control observations within quantile bins are similar, using groupby/count and some multiindex magic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>treatment_group_key</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile bin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(-0.114, -0.00339]</th>\n",
       "      <td>93</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(-0.00339, 0.0186]</th>\n",
       "      <td>110</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0186, 0.0391]</th>\n",
       "      <td>99</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0391, 0.0548]</th>\n",
       "      <td>105</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0548, 0.0726]</th>\n",
       "      <td>112</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0726, 0.0941]</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0941, 0.121]</th>\n",
       "      <td>102</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.121, 0.148]</th>\n",
       "      <td>107</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.148, 0.201]</th>\n",
       "      <td>89</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.201, 0.398]</th>\n",
       "      <td>94</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "treatment_group_key    0    1\n",
       "Quantile bin                 \n",
       "(-0.114, -0.00339]    93  107\n",
       "(-0.00339, 0.0186]   110   90\n",
       "(0.0186, 0.0391]      99  101\n",
       "(0.0391, 0.0548]     105   95\n",
       "(0.0548, 0.0726]     112   88\n",
       "(0.0726, 0.0941]     100  100\n",
       "(0.0941, 0.121]      102   98\n",
       "(0.121, 0.148]       107   93\n",
       "(0.148, 0.201]        89  111\n",
       "(0.201, 0.398]        94  106"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_by_quantile_and_treatment = valid_w_score.groupby(\n",
    "    ['Quantile bin', 'treatment_group_key'])['treatment_group_key'].count()\n",
    "count_by_quantile_and_treatment = count_by_quantile_and_treatment.unstack(-1)\n",
    "count_by_quantile_and_treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of observations')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d6rf3CNXiKO+lutcADzIySlGSJEmSJHXBQuFw+j5wELA4sCPwvVaBmXl6ROxG9dzAlYAjmoRdDOzYamXaMXgDVSEJqufNndAmtmFXYO7oDzNzXkS8mWra9MrAvuVVdwuwQ2bOH318zado/UzA7cur4a9As0LhErX2nW3O1VZm3h8R2wCnAy8APlBedfcB78zMP3V7nmJV4KPl1cpVwLsy8/I2Mc8BPlRezcwFdi8jJlvZGfgpVTF0DeBrLeJuB96RmVe36UuSJEmSJHXg1OMhlJk3AyeXzXeOIf5o4JVUz5K7gWpE151Uowh3BzYex4IjEy4zfw+8BDiQ6hl6D5TX5eWz9UrMRGs8O+8JRhZo6UpmXkc1Im9fqudM3kM12u9q4KvASzPz1NY9jMlLqUYk/hz4X+AOqtzvA/5CVWB+K/CyzGy3EvFvgX2AM6ieNfgQ1fd/DfBt4NWZuWtmPtIumcy8G9gK2IFqwZMbqUZNPk5VHDyX6vtYJzN/Pf7LlSRJkiRJdeEjvYZTme57IfAksNZ4nweoziLiXKpRicdk5nsHnI6ontNYmvtn5pw+9z0TmAew6u5zmTZjhX52L0mSJGmcbjp4m0GnIE2o+fPnM2vWrMbmrA4zJ8fEEYVDKjMvohrxtQiw34DTWehExGLAhlSF2C8NOJ2hFRFLRcR6jdeg85EkSZIkaSqzUDjc9qUqZO0aEasNOpmFzAZUz4D8fpk2rMFYn2rKeeMlSZIkSZJacDGTIZaZl5fVe9cCVqNanVZ9kJm/BWLQeUiSJEmSJI2VzyiUpD6oP6Nw3rx5zJw5c8AZSZIkSZIWZj6jUJIkSZIkSdKEsFAoSZIkSZIkyUKhJEmSJEmSJAuFkiRJkiRJkrBQKEmSJEmSJAkLhZIkSZIkSZKwUChJkiRJkiQJC4WSJEmSJEmSsFAoSZIkSZIkCQuFkiRJkiRJkrBQKEmSJEmSJAkLhZIkSZIkSZKwUChJkiRJkiQJC4WSJEmSJEmSsFAoSZIkSZIkCQuFkiRJkiRJkrBQKEmSJEmSJAkLhZIkSZIkSZKwUChJkiRJkiQJC4WSJEmSJEmSsFAoSZIkSZIkCQuFkiRJkiRJkrBQKEmSJEmSJAkLhZIkSZIkSZKwUChJkiRJkiQJC4WSJEmSJEmSsFAoSZIkSZIkCQuFkiRJkiRJkrBQKEmSJEmSJAmYNugEJGmhc/i6MMN/h5EkSZKkKWnOvYPOYMryb7KSJEmSJEmSLBRKkiRJkiRJslAoSZIkSZIkCQuFkiRJkiRJkrBQOPQiYtGIuDoiMiLeNuh8pH6LiDnl9z36tcWgc5MkSZIkaSqxUKiPAGsDVwE/bhcYES+OiG9FxHUR8XBE3B4Rv4mID0ZEzytoR8S0iHhF6e8/I+LPEfFErbCzeo/97zGqUDR7DMesGBGfjYjfRcRdEfF4RNwTEZdFxJcj4vm95DReEbF8ROwfEf8TEfdGxH2lvX9ELN+H/qdFxOsi4tCI+G25x41r/kNEHBYRa46zvw+W38nt5XdzXfkdvWgMxz+n5POZiPh5RPy9dv/O7eliJUmSJEnS00RmDjoHDUhELA3cCKwA/Gtm/qBN7PuAo4DFWoRcBGybmXf2kM/ngTltQtbIzJu67Pt5VMXQGbWPd83MuW2O2Qr4IdCuAPcQ8P7MPLGbvMYjItYHTgZWaRHyd2D7zLy0y/5XpPqOOhUcHwM+mZlf79Df8sBpwIYtQh4F9sjM77bp40Zg9Ra7z8vMLTrkSkQ8F3hu2VwfaJxvy8w8t9PxYxURM4F5APP2WpqZM/x3GEmSJEmakubcO+gM+mL+/PnMmjWrsTkrM+f32qd/kx1uu1MVCecBP2oVFBFbA9+hKhLeCuxJVfx5I3BSCdsIOCkievlNRa39CFXx8foe+qs7kqpIeNuYEolYA/gFI0Wzk4GdgQ2AHYC5QAJLAsdFxKv7lGerfFYFTqEqEj4BHAJsVl6HlM+eB5xaYruxGCPX+ydgf+BNwCuB1wKHUt2X6cDXIuIDbfJdhOq30SgSnkT1e9mQ6vdzWznfd8rvq2VXtfatwKnjuyTIzNsy84rMvIKqMC5JkiRJkproebqoFkylkPPhsnliZj7VIm4aVZHtWcB9wMaZWS/enRkRRwF7UBWt3gV8r8u0LgR2Ay4B/pyZT0TEXGDMU12biYjtgR2B24EvA18Zw2EfpyoCAhyamZ8ctf/kiPgD8A1gEWA/YLte8uzgi8BKpf2OzKxPE/9tRFxKVexdCTgAeG8X50jgV8D/y8yLmuw/JyJ+CpwDLAEcEhEnZub9TWLfTfV7APhmZn6otu/iiDgDuIyqeHtERLwoM59o0s+RVMW9SzLzbwAR4TBoSZIkSZImgCMKh9frgNVK+/g2cTsCa5X2QaOKhA37AHfX2l3JzLMy89uZ+YcWRaNxi4hlqIpNAJ8A7hrjoRs30gIObBFzZK2/13SV4BhExEpUBViAs0YVCQEon51VNt9TjhmXzLw5M1/fokjYiPk98M2y+WzgX1qENn4Hd9PkN5GZ1wEHlc0XANu3ON9hmfnTRpFQkiRJkiRNHAuFw2uX8n5tZl7eJm6HWntus4DMfIiRqcvrRcQLek+vbw4CZgLnZuZ4RjpOL+93ZuZ9zQKyesDnDWWz1bMb+2E7qlGLAMe0iZtb3hdhYkc3nlNrP2O0Z7n/jYVKflh+H83MrbXf0p/UJEmSJElStywUDq8ty3vL0WPFpuX96sy8pU3cebX2Jl1n1UcRsSHVcxgfK+/jcU15Xz4iZrSJ+6dR8RNh01r7vJZRk3cP6kXRZlPWx5Rv+T01vrcp8ZuRJEmSJGmYWSgcQmV11tXL5iVt4pamGo0H8JcO3db3r9t1cn0SEYsCR1P9xg/NzE75j/btRlfAp1uc40PAcqPiJ0Lj+7y3XbE2M/9B9RzJ+jETYfNau9n3um6H/TTZPysiluopqwkWETPbvYCVB52jJEmSJEm9cDGT4VR/nt4f28TNZGTV2U5LbM+rtWe1jJo8+wAvoZoa/MXxHpyZZ0XEl4F9gX3LdNoTgL9RrTy8AzC7hJ8A/Fcfcm6l8X2OZZnzecCLmaB7EBGrALuWzTt4+jTkhvq5x/q7Carf29U9JTix5nUOkSRJkiRpwWWhcDjNrLVvaxO3TK39QIc+H6y1lx53Rn0UEWsBnyubH8rMh7vpJzM/FRFnU61o/Bae+Ry9PwIHZ+aPnnFwfzXuQ6d7ACP3oe/3ICIC+FYtnwNafLcL5O+mnzZ65EimTV9h0GlIkiRJE+6mg7cZdAqS+shC4XBasda+u2UULF5rP9ahz0dr7SXGnVF/fYsq9x9n5pnddlJGz70H2KhFyEuAd0bEn7uY2jwejfvQ6R7AyH2YiHvwaUYWSTmHkdWkR1tQfzeddBqluTJtpvJLkiRJkjTVWSgcTsvV2u0KhY/U2tNbRlXqC1x0NYKvHyJiNrAV1bP6PtZDP+sCv6QafXkn8HHgFOBWqu/v9cCBVIWzzSJiu8z8bU/Jt/YIsCSd7wGM3Ie+3oOIeCdwQNm8CXhHZjZbyASe+bt5pEUcTJHfzVhkZttp1NWAS0mSJEmSFlwuZjKc6oWbdqO47q+1O00LrS9EMZYpsn0XESsCh5XNz2Xm33vo7jiqIuFDwKaZ+R+ZOT8zH8/MWzPzOODVVIXD5wAnRsRibfrrReM+jGVqbuM+9O0eRMQ2wDFUzxG8FXhdhxWwF6jfjSRJkiRJqjiicDjdXmsvx9MLO3X1EVQzW8Q01KdlDmrRh/cDywP3AHdGxNubxGxYb0dEo2h6dmbeBhARLwNeWT4/ITOvanayzPx7RBxBNbJwVeANwMm9X8YzzAdWovM9gJH70Jd7EBFbAD8BFqUaffr6zLyuw2Gjfzd3tIlt5JuMbbEWSZIkSZI0QSwUDqd6oXBZ4K/NgjLzgYiYR1XMWadDn/X9TQtrk6Axou85wPFjiN+tvAC2ZGRhl3VrMX/o0MdltfY6TEyh8EqqwuWzI2LlVqP5yjMVZ5TNnu9BRGxANd16carRfm/MzD+PMd+GdYA/tYlt/G7mZeaDbeIkSZIkSdIEc+rxcLq81l67Q+z55f2FEbFym7jNa+0Luspq6nii1u5UTF+0xXH9dH6tvXnLqD7eg4h4KXAm1dThR4A3Z+bvx3j4mPItv6fG729B/81IkiRJkrTAs1A4nC5lZOGI9TvE/rzWnt0sICKWBHYpm1dm5jU9ZdelzJyTmdHuBexaO2TX2r5za5/fWGtv2uG09ULYjS2jevMLoLFwyK5t4maX96fKMV2JiLWpFnJZFngc2GnU99NWuf+NEY27lN9HM7Nr7Z+NP1NJkiRJktRPFgqHUGY+BlxcNjfoEP4z4PrS3i8i1mwScyhVUanRfoaImB0RWV5zxpnyZPsjcHNp7xQRWzULioh/ZmTq8kPA2U1ier7uMtX4hLK5dUTs3OQ8bwW2LpvHNZueHBGr13I5t9m5ImI14NdUz0R8kmp149O7SLuxqMxywCFNzrMmsF/ZvB4LhZIkSZIkDZzPKBxep1GNhtsgIpbJzKYLmmTm4xGxJ9Wz6mYAF0TEgVSFxmWBfwd2KuHnU60W3JWIWBoYXQRbq9beOSLqC2P8KTPbPf+uK5n5VETsB3wPWAQ4IyK+TfUd3EZV/Ho98BGgMVruoMy8p9+51HyGarGUFalWWH4VcGrZty2wd2nfDny2mxNExPJURcLGAiNfAf4SEeu1OezuzLy5yefHAu8FNgY+VKYZH021IMoGwOeofk9PAR/JzKbTtiPi5cDLW5x75YiYPeqzn2SmqydLkiRJktQFC4XD6/vAQVQLVexIVRRrKjNPj4jdgCOpRpod0STsYmDHzHyyh5xWAI5ps3/0aMX9ab9QRtcy87iIWAn4EtVzCD9cXs8IBb4OfLFFV0vU2nf2kM+8iHgz1VTwlYF9y6vuFmCHzOx29eCXAC+obX+yvNo5liZT0jPzyYjYATidanr7TowUlBseAz6cmWe06X8H4PMt9r2QZ/5ezqVaeEWSJEmSJI2TU4+HVBkF1lih951jiD+aauXdo4EbqBa4uJNqFOHuwMaZeUfrHhY8mXkYVfHscKrVje+lmo57P3AF8C1g/czcKzOzRTevLu93A3N7zOf3JZ8Dy/kfKK/Ly2frjWPBkQlXfg+vAfag+p3cSfW7uYHqd/TP5XclSZIkSZKmgGhd39DCLiI2Ai6kKn6tlZk3DTajhU9E3AQ8H/h8Zn5hwOkMvYjYAjinbG45nkVaxtD3TGAewKq7z2XajBX61bUkSZI0Zd108DaDTkEaWvPnz2fWrMbTw5jVwwzD/+OIwiGWmRcBZ1A9h2+/DuEap7IwyPOpRiJ+fcDpDK2IeG5ErFeetbjGoPORJEmSJGmqslCofalGFO5aClvqn83L+zcy896BZjLc9qCann058N0B5yJJkiRJ0pTlYiZDLjMvLyvHrgWsBvxtsBktPDLzOHpYBVoLros+vRUzZ84cdBqSJEmSJI2LhUKRmccPOgdpomTmHGDOgNOQJEmSJGnKc+qxJEmSJEmSJAuFkiRJkiRJkiwUSpIkSZIkScJCoSRJkiRJkiQsFEqSJEmSJEnCQqEkSZIkSZIkLBRKkiRJkiRJwkKhJEmSJEmSJCwUSpIkSZIkScJCoSRJkiRJkiQsFEqSJEmSJEnCQqEkSZIkSZIkLBRKkiRJkiRJwkKhJEmSJEmSJCwUSpIkSZIkScJCoSRJkiRJkiQsFEqSJEmSJEnCQqEkSZIkSZIkLBRKkiRJkiRJwkKhJEmSJEmSJCwUSpIkSZIkScJCoSRJkiRJkiQsFEqSJEmSJEnCQqEkSZIkSZIkLBRKkiRJkiRJwkKhJEmSJEmSJCwUSpIkSZIkScJCoSRJkiRJkiRg2qATkKSFzuHrwgz/HUaSJEmaUHPuHXQG0kLHv8lKkiRJkiRJslAoSZIkSZIkyUKhJEmSJEmSJCwUSpIkSZIkScJC4dCLiEUj4uqIyIh426DzkfotIuaW3/fo1+qDzk2SJEmSpKnEQqE+AqwNXAX8uF1gRLw4Ir4VEddFxMMRcXtE/CYiPhgRPa+gHRHTIuIVpb//jIg/R8QT4y3sRMQLI2KviPh5RNxYcn2otEWdQYsAACAASURBVH8YEdtERIyhn7Ui4l8j4qsRcUHpo5HL7B4vtysRsXxE7B8R/xMR90bEfaW9f0Qs36dzTNh1R8RLI+LxWn9zx3DM9Ih4X0ScGRH/iIhHI+KBUuD+bkRs1EtOkiRJkiSp0nNxRwuuiFga2K9sfiEzn2oT+z7gKGCx2seLA5uW1+yI2DYz7+whpc8Ac3o4nog4FnhPi92rl9cuwFkR8fbMvKdFP5sD5/aSS79FxPrAycAqo3a9tLzeHxHbZ+alPZxjwq47Ip4FfIdx/LkTEbOA04CXjNo1narAvTawa0R8Fdg7M7NJN58BDivt7YEDx5m6JEmSJElDwRGFw213YAVgHvCjVkERsTVVgWcx4FZgT2BD4I3ASSVsI+CkUgzqVn2U3yPARcD14+xj1fJ+F1XO7wBeA2wAfBC4uuzfGjilTb71XJ4C/he4eJy59E1ErAqcQlUkfAI4BNisvA4pnz0POLXEdn2qWrvf1/1hqt/NbWNKpBqlWi8S/hmYDbwaeD3wBeDBsm8v4BPN+snMmzPzisy8Ari52+QlSZIkSVrYOaJwSEXEIlSFG4ATW40mLMWaI6mKyvcBG2dmvXh3ZkQcBexBVbR6F/C9LtO6ENgNuAT4c2Y+UaamrjmOPuZTFQSPzcxHR+27JCKOB84CNimvdwLHNennZmCfkstlmflAmXa7wThy6acvAiuV9jsysz5N/LcRcSlVsXcl4ADgvV2eZ0KuOyJmUo3ky9L/sWM4bHtGioQXAptm5pO1/b+KiF+UfYsC+0XEVzPziV5ylSRJkiRpWDmicHi9DlittI9vE7cjsFZpHzSqSNiwD3B3rd2VzDwrM7+dmX/ottiTmbMz8ztNioSN/Q9RjaRs2LlF3LWZeVhmnpeZD3STS79ExEpUBViAs0YVCQEon51VNt9Tjhm3Cbzuo4BlgLnAb8Z4zMa19kGjioQAZOZlwKllc1lgnR5ylCRJkiRpqFkoHF67lPdrM/PyNnE71NpzmwWU4ltj6vJ6EfGC3tObOGUK6h1lczyjFQdlO2CR0j6mTdzc8r5IOWZKiIidqfK5E/jkOA6dXmvf0CauXrxerGWUJEmSJElqy0Lh8NqyvF/UIW7T8n51Zt7SJu68WnuTrrOaPI0iVMsFXKaQTWvt81pGTcF7EBHPBr5RNj+ZmXe0ix/lmlr7n9rENYq9CVw7jv4lSZIkSVKNhcIhVJ4Xt3rZvKRN3NLAzLL5lw7d1vev23VykyAiXgHMKJudrmsqaHyf97Yr1mbmP6ieI1k/ZtC+TLUAy/m0Hw3ZzImMXM++5bmaT1Pu5TZl8weZed/omH6JiJntXsDKE3VuSZIkSZImg4uZDKfX1Np/bBM3k5FVcOd36HNerT2rm6Qm0adr7ZarPU8hje+z0z2A6j68mClwDyJiY+ADwOPAbpmZ4zk+M28vC6mcQPW8wksi4mtUIw2XLp/tTTU69E/Ax/uXfVPzOodIkiRJkrTgslA4nGbW2re1iVum1u60sMWDtfbS485okkTETowsYHIZ8NMBpjNWjfswlsVFGvdhoPcgIqYD36EqNB+emf/bTT+Z+bOIeBVVEfC9PHO15FuBzwPfycwHRx8/KBs9ciTTpq8w6DQkSZKkhdunTht0BurSTQdv0zlIA2GhcDitWGvf3TIKFq+1H+vQZ32V4SXGndEkiIh1GJn++jDwnvGOchuQxn3odA9g5D4M+h58CngR8FfgC912EhGLAu8A3szI6Na6lYB/pRplONH/l9BplObKtJnKL0mSJEnSVGehcDgtV2u3KxQ+UmtPbxlVqa82+/C4M5pgEfE84Ayq0XkJvC8zrxxsVmP2CLAkne8BjNyHgd2DiHghI9O7P1xWxe6mn6WA04HNgCeBQ6gKvTdQFU83BP4f1cItp0TEXpn59R7Tbykz2079jmhWx5QkSZIkacHhYibDqV4AbDfy7P5au9NU1qVq7bFMkZ00EbEc8EtGFnD5aGaeOLiMxq1xH8YynbhxHwZyD6Kqln2bqmD5s8w8tYfu9qcqEkJV2N03M/+SmY9l5n2Z+Suq1bvPoUxxjoiX9pK/JEmSJEnDzBGFw+n2Wns5nl4QrKuPoJrZIqahPi1zyiz6EBHLAGdSLfAB8LnMPGKAKXVjPtUU2073AEbuw6DuwUbA5qX9u4h4e5OY+tT3NWoxV2TmFfB/Bcddy+fXZOboZxMCkJlPRMTnqFZVflY5Zq8er0GSJEmSpKFkoXA41QuFy1I9R+4ZMvOBiJhHVXxap0Of9f1X9ZZef0TEEsApwPrlo0Mz88ABptStK4FXAs+OiJUz85ZmQRGxCjCjbA7qHtSnoB86hvjNGBk1uD9wRWmvxMgU+XYrc0O1KE1Dp9+pJEmSJElqwanHw+nyWnvtDrHnl/cXRsTKbeI2r7Uv6CqrPiqLYPyUkby+lZmfHGBKvTi/1t68ZdQUuwc9eqLW7vQPGou2OE6SJEmSJI2DhcLhdCkji12s3y4Q+HmtPbtZQEQsCexSNq/MzGt6yq5HEbEI8H3gjeWj44A9BpdRz34BPFXau7aJm13enyrHTLrMPDczo90LWKN2yLG1fXNqn98F3Ffar46IdsXCeoH0xv5ciSRJkiRJw8dC4RDKzMeAi8vmBh3CfwZcX9r7RcSaTWIOpZrC3Gg/Q0TMjogsrznjTHnMyrPtjgZ2Lh/9FNg1M3Oiztkhn56vu0w1PqFsbh0RO4+OiYi3AluXzeOaTU+OiNVruZzbTS6TJTOfAk4rm88DPtMsLiKWBb5c+6iXxVMkSZIkSRpqPqNweJ1GNRJrg4hYJjObLmiSmY9HxJ5Uz/qbAVwQEQdSFRqXBf4d2KmEn081eq8rEbE0IwW+hrVq7Z0j4o7a9p8y80+j4g9jZNTdFcCXgHWr+mFzjQU0muSzM09faXiTentUn7dk5pktT9K7zwBvoFoI5MSIeBUjRbFtgb1L+3bgs72caApd9xeA7YElgTkR8UrgWOAGYHGqhVM+BqxW4v87M385QblIkiRJkrTQs1A4vL4PHERVcNkR+F6rwMw8PSJ2A46kWmSi2arBFwM7ZuaTPeS0AnBMm/2jRyvuD4wuFO5Ua6/H0xe6aKVVFfEw4Pkt9r2vvBrOo1pdebQlau07x5BLU5k5LyLeTDUVfGVg3/KquwXYITPnjz5+nPpx3T3LzL9ExPbAiVS/jTeXVzNnA2+diDwkSZIkSRoWTj0eUpl5M3By2XznGOKPplp592iqEV2PUBW+zgd2BzbOzDta9zC0Xl3e7wbm9tJRZv4eeAlwINVoyQfK6/Ly2XolZqGRmb+mWsl4X+BcqhGTj1M9Y/NG4EfADsC/ZObdA0pTkiRJkqSFQgzo0W2aAiJiI+BC4Elgrcy8abAZLXwi4iaq0Xmfz8wvDDidoRcRsxkZtbpGP3/zETETmAew6u5zmTZjhX51LUmSJEkLlZsO3mbQKSwU5s+fz6xZsxqbs/oww9ARhcMsMy8CzgAWAfYbcDoLnYhYjapIeC/w9QGnM7QiYtWIWC8i1gNWHXQ+kiRJkiRNVRYKtS/ViMJdS2FL/bN5ef9GZt470EyG2xeppmc3pmhLkiRJkqQmXMxkyGXm5WU65lpUq8f+bbAZLTwy8zh6WAVakiRJkiRpMvmMQknqg/ozCufNm8fMmTMHnJEkSZIkaWHmMwolSZIkSZIkTQgLhZIkSZIkSZIsFEqSJEmSJEmyUChJkiRJkiQJC4WSJEmSJEmSsFAoSZIkSZIkCQuFkiRJkiRJkrBQKEmSJEmSJAkLhZIkSZIkSZKwUChJkiRJkiQJC4WSJEmSJEmSsFAoSZIkSZIkCQuFkiRJkiRJkrBQKEmSJEmSJAkLhZIkSZIkSZKwUChJkiRJkiQJC4WSJEmSJEmSsFAoSZIkSZIkCQuFkiRJkiRJkrBQKEmSJEmSJAkLhZIkSZIkSZKwUChJkiRJkiQJC4WSJEmSJEmSsFAoSZIkSZIkCQuFkiRJkiRJkrBQKEmSJEmSJAkLhZIkSZIkSZKwUChJkiRJkiQJC4WSJEmSJEmSgGmDTkCSFjqHrwsz/HcYSZIkaajNuXfQGUjj5t9kJUmSJEmSJFkolCRJkiRJkmShUJIkSZIkSRIWChdYEbFoRFwdERkRbxt0PtJUFRHnlv9OnvYadF6SJEmSJE01FgoXXB8B1gauAn7cLjAiXhwR34qI6yLi4Yi4PSJ+ExEfjIi+LmgTEW+PiLMi4h8R8UhE3BQRx0XERuPoY9uI+ElEzI+IRyPijoi4MCL2jogle8jtkFHFoi3axD4rIl4UEbMj4psRcUnJpeOxEykiVouIwyLiqoh4MCLuioiLI+ITvXw3tf77ct0lz90j4oeloP1g+T3Mj4iTI+Jfx/Lbi4hZEbFTRBwcEWdHxH21XOb0er2SJEmSJGmEqx4vgCJiaWC/svmFzHyqTez7gKOAxWofLw5sWl6zI2LbzLyzx5wWpypYbjtq1/PL6x0RMSczD2jTxzLACcCbR+1avrw2Aj4YEdtl5l/Gmd/LgL3Gcci7gbnjOcdEi4htqL6fZ9c+XhJYv7zeHxFvyswbejhNz9cdEV8APgtEk92rltd2wMcjYqfM/FuLfp4P3NRLLsWuwFKlvQewex/6lCRJkiRpoeOIwgXT7sAKwDzgR62CImJr4DtURcJbgT2BDYE3AieVsI2AkyKi19/CfzFSJDwH2AHYAHgfcD3Vb+0LEfH+FrkG8ENGioSXAe8CXgVsCRwGPAq8ADgjIpYfa2Ll2o6mKozfNtbDau3HgT8Cl4/1nP1WCp0/oioSPgB8BngNsBXVtQG8EDitFJK7PlWt3e11P6/08yBwPFWhbhOqe/lu4JIS9yrg123yreeSwHXAb8aZC5l5Y2ZekZlXMPb7L0mSJEnS0LFQuICJiEWAD5fNE1uNJizTOo+kusf3ARtn5hGZeXFmnpmZOwHfLOGbURXlus1pc+AdZfMU4HWZeXJmXpKZ36UqRjZGjR0SEc9p0s1OVAVMgF8Br8nMEzLzssw8NzP3oSpEPgmsDnx+HCnuSTXi7i9UBc2xuBL4KPBqYEZm/jMjxdVB+BrV6MEngNdn5pcy88LMPDszPwB8ssStA3y8h/P047rvBPYFVsnMd2fm3My8oNzL40vfjQL3C2g90vN+qpGJWwPLZ+YLGN99lyRJkiRJ42ChcMHzOmC10j6+TdyOwFqlfVBmXt8kZh/g7lq7W40i1ZPAHpn5ZH1nZt5BVTgCWJZqlOFo/1ZrfygzHxsdkJm/Bn5QNj8YEct2SiwiZgGN6c67A8/ot5lSUP1GZl6UmY+M5ZiJEhHrA1uUzf/KzAubhH2F6nmVAB+LiEW7OVc/rjsz983MQzLz/hb7n6SaAty4Fzu3iLszM7+Ymb/MzLubxUiSJEmSpP6xULjg2aW8X5uZ7aaE7lBrz20WkJkPMTKya72IeMF4kynTRrcqm7/KzPktQk+iGtkI8JYm+9cv79dl5rVtTnlmeZ9O9Zy7Tr4JLA0cm5nnjiF+Kqrfy2OaBZSRpd8rm8syUlicksozMf9cNtccZC6SJEmSJKlioXDBs2V5v6hD3Kbl/erMvKVN3Hm19iZd5LMBIwulnNcqqIwQbOS8QZMRb8uV91s7nK++f/N2gRGxC9V05bvobcTkoDXu5YNUz25spdd7Odkav5uWi/FIkiRJkqTJY6FwARIRM6mezwcjC0I0i1samFk2O60OXN+/bhdp1Y8Z67mmUT2bru7B8v5s2qvvf1GroPIcxK+XzX0z8/YO/U5lje/4usx8ok1cr/dy0kTEcxnJcVwrWEuSJEmSpIkxbdAJaFxeU2v/sU3cTEZWjG01FbhhXq09q4uc6seM91xX1ravolrkYt2IWLFNYW+zWnu1FjEAhwArA79j7AuYTDkRsTjVCtfQ4fvNzLsj4kFgKbq7l5NpH0b+/Gm5cvdUUgr17aw8KYlIkiRJkjRBLBQuWOqFitvaxC1Taz/Qoc8Ha+2lx51R/851MlWhcBHgQOCDow8uz1DctcW563GbAO+nWiF4t8zMDnlNZeP5fqH6jpeiu3s5KSJiQ+BjZXM+I6tvT3XzOodIkiRJkrTgslC4YFmx1m63CuzitXanVX4frbWXGHdG/TvXfwAfpiqGfiAilgQOpZqWugzwJqpRgkuX80xvlm9ETAe+QzWi8qsdFnxZEIzn+4WR77ibeznhImIl4CdUf/Yk8G9lUZ2FykaPHMm06St0DpQkSZK08PrUaQM79U0HbzOwc2vBZqFwwbJcrd2uUPhIrT29Q5+L1doPjzujPp0rM++LiO2A06mmcL6rvEb7JtX04/WA+5vs/zTVs+/+BuzfIZ8FwXi+Xxj5jru5lxMqIpYBTmNkZOynM/PsAaY0Xp2mc69Mm2eHSpIkSZI01VkoXLDUi0ZL0LxQxqjPO01BXarWHsvU1gk7V2b+MSJeBuwHvA1Ypbb7cuDQzDwuIu4onz2tWBoR65RjAT6SmfWpzguq8Xy/MPIdd3MvJ0x51uLJwCvLR4dn5sEDTGncMrPtMyIjot1uSZIkSZKmPAuFC5b6Ah/L0bpQWC9odFqAoT5KqptnsI0+16W9nCszbwP2AvYqK+M+B7g9M+8GiIhVgOVL+JWjDt+LatTdDcCSEfH2JqdYr9Z+bUQ0FqA4ZSoWFjPzkVIYXYEO9zIilmWkUDhlnqcXEdOoFizZsnz0n5m59wBTkiRJkiRJTVgoXLDUC4XLAn9tFpSZD0TEPKrC3Dod+qzvv6qLnOrFurGe6wnguk4dl6Lh6EVbNq21fz9qX2Pa7T8BJ3bqH/hcrb0GT19sZSq5iuq614qIaZn5RIu4Xu9l30XEs4DjgDeXj35Ik4VqJEmSJEnS4D1r0AloXOoLc6zdIfb88v7C2qi5ZjavtS/oIqdLGFlkY/NWQWWRkY0ax2TmWBbmaOYdtfaPu+xjQdO4l0sxMnW3mV7v5UT4NtAY2Xkq8O7MfGqA+UiSJEmSpBYsFC5YLmVkkYr1O8T+vNae3SygrCy8S9m8MjOvGW9CmXk/8N9l818iotX02LcAM0r7Z+M9D0BEbAhsVzb/OzP/MiqX2ZkZ7V48fYGTLWv7buomp0lSv5e7NgsoI/feUzbvAc6Z6KQ6iYjDgfeXzf8Gds7MxweYkiRJkiRJasNC4QKkjMK7uGxu0CH8Z8D1pb1fRKzZJOZQqinMjfYzRMTsiMjymtPiXIeV92nAURGxyKg+VgC+XDbvAf6zxblWa9E/EbEW8BMgqEYw7tkqdiqJiDm17292N31k5sXAb8vm+yLi1U3C9qZa7Rng680KchGxRS2Xud3kMlblt7JX2fwdsH1mPjqR55QkSZIkSb3xGYULntOopphuEBHLlBF9z5CZj0fEnsApVCP5LoiIA6kKjcsC/w7sVMLPp3qOXFcy8+yI+AHVFNPtgF9FxNeAvwMvAT4DNIqAn2osTNLENyPi+cD3qEZP3gM8F9ia6rl2SwIJ7JaZoxcy6bsmhb2X19pviIjVa9vXZeb5TJyPUk0nXgL4ZUR8iWrU4BJU3/sHStw1wFd6OVGv1x0RHwE+XzZvBj4JrNFhVeCrWxQ33wDUp87Xn8P48lG5PpCZP2l3EkmSJEmS1JqFwgXP94GDgMWBHamKak1l5ukRsRtwJLAScESTsIuBHTPzyR7zei9VQfJNVKvbbjlq/1PAAZn57Q79rAcc0mLfXcCHM3MsC5X0wzFt9u07avtYRp4lWLdErX1nt4lk5h8j4m3A8VTf85eahF0DbNOqeDwOvV73TrX2qk32N7MGcFOTzz9F62dfbl9eDX+lGnUqSZIkSZK6MKlTjyNi3Yj4akRcGhF3RcTjEfFkh1erFV6HUmbeDJxcNt85hvijqRbAOBq4AXiEqmB1PrA7sHFm3tGHvB7OzG1KTr+iWq34MWAeVXFzk8yc06Gbg4DDqRZIuQV4nGql54uATwPrTGKRsF8a04SvoRoN2rXMPAV4KfDV0t9DVKMuL6Uq4L0iMzuuJi1JkiRJktRMZObknCji41SFoGlUz5kbq8zMRTqHDY+I2Ai4EHgSWGuKL8QxtCJiMeBeYDHg3zKz5ehPTY7y7MTPA5TFbfrZ90yqwjir7j6XaTNW6Gf3kiRJkjRmNx28zaBT0CSYP38+s2bNamzOysz5vfY5KVOPy3PGGgteJNUIscuoppI+NRk5LEwy86KIOAN4I7Af1fP7NPVsRFUkvB44YcC5DK2IWANYqmw+d5C5SJIkSZI0lU3WMwo/Vt7vBrbLzAsm6bwLs32B1wO7RsQXM/Nvg05Iz7BZef9SH54Bqe4dQ+vnHEqSJEmSpGKyCoWvohpJ+AWLhP2RmZeXFV/XolpR2ELhFJOZBwAHDDoPSZIkSZKksZisQuGS5X0sq59qjDLz+EHnIE11mbnFZJ/zok9vxcyZMyf7tJIkSZIk9WSyVj2+ubxPn6TzSZIkSZIkSRqHySoUnlLeN56k80mSJEmSJEkah8kqFB5GtZDJ3hGx8iSdU5IkSZIkSdIYTUqhMDP/DmwPLAL8LiLeNBnnlSRJkiRJkjQ2k7KYSUScXZp3AWsDp0TEPcC1wEMdDs/M3Goi85MkSZIkSZKG3WSterwFkLXtAJYFNmhzTJa4bBMjSZIkSZIkqQ8mq1D4Gyz4SZIkSZLG4KmnnuKB/8/efYdJUWV9HP8eZoAh5wwCoggKIqIgJsA1Ys6JJazoyqqra1zkdUUX1zUhrpjBATOroq6iYgJMIGAEBUEFREVBQPKAA+f9o6pnmplOM/T0EH6f56mnq7pu1T3dNaBzuPeetWtZvXo1mzZtYvPmzeUdkohIWmVlZVGpUiVq1qxJ9erVqVAhU2VEEstIotDde2aiHxEREREREdmxrVmzhh9//BF3jTURkZ1Xfn4+GzduZM2aNZgZzZo1o0aNGuUdVsZGFIqIiIiIiIgkFCtJaGZkZWWVY1QiIum3efPmgr/r3J0ff/xxu0gWKlEoIiIiIiIi5W7Lli1bJQmrV69O3bp1qVq1KmZWztGJiKSXu7N+/XpWrFjB2rVrC5KFbdu2LddpyNvHBGgRERERERHZpUV+UYYgSdi8eXOqVaumJKGI7JTMjGrVqtG8eXOqV68OBMnDtWvXlmtcaR1RaGa7Rfbd/ftY75dG9L1ERERERERk57N69eqC/bp16ypBKCK7BDOjbt26BQnC1atXU7NmzXKLJ91TjxeEr17k3gtitE1V0XuJiIiIiIjITmbTpk1A8Etz1apVyzkaEZHMiSyx4O4FfxeWl3Qn4OL9k4/+KUhERERERETi2rx5MwBZWVkaTSgiu5RI0ab8/PyCvwvLS7oThQNK+L6IiIiIiIiIiIhsB9KaKHT3sSV5X0RERERERERERLYPqnosIiIiIiIiIiIiShSKiIiIiIiIiIhIOVUTNrOKwP5AB6Bu+PYKYDbwibv/Xh5xiYiIiIiIiIiI7KoyOqLQzKqb2W3AEuBD4GHg3+H2cPjeEjO7zcxqZDI2ERERERERERHZvowZMwYzw8xYuHBheYez08tYotDM2gNfAlcTjCK0OFvdsM0sM9srU/GJiIiIiIiIiIjsyjKSKDSz2sBbQAuCZOBs4BqgB9AOaB/uXw3MCtvsBrxlZrUyEaOIiIiIiIjsmHb2EUetWrXCzOjfv395h7JdWrhwYcHzHzNmTHmHI7JDy9SIwuuAJoADNwCd3P0ud3/P3ee5+9fh/nBgP+D/wuuahteKiIiIiIiIiIhIGcpUovAUgiThOHe/xd09XkMP/AsYRzCy8NQMxSgiIiIiIiIiIrLLylSisGX4OrYE14wpcq2IiIiIiIiIiIiUkUwlCteEr0tLcE2k7do0xyIiIiIiIiI7gcmTJ2NmDBgwoOC91q1bF6xXF9kmT54MQP/+/TEzWrVqBcCSJUu47rrr2GeffahRo8ZWbaOtXLmSYcOG0b17d+rXr0/lypVp2rQpJ598MuPHj08Y47p16xg3bhwDBw5kv/32o1atWlSsWJEGDRrQo0cP7rzzTtaujf1rb8+ePTEzFi1aBMDYsWOLfbaePXsWtI+1Vt/48eM5+uijadiwIdWqVaNTp07ce++9/P777wXXuTtPPfUUPXv2pGHDhlStWpX999+fBx98kAQTAgusX7+eESNG0KtXLxo1akSlSpVo2LAhRx99NLm5uWzevDnutUXXX5w7dy4XXnghrVq1onLlyjRq1IhTTz2VadOmxbzezGjdunXB8YABA4p9R0OHDk36GVL166+/cs0119C2bVuqVKlCo0aNOOqoo3jhhReA5OtlFv28H3/8Mf3796d169ZUrlwZMyt2zZYtW3jiiSfo3bs3jRs3plKlSjRo0IBevXpx//33s2nTprjxFv2Zj6ekcc+YMYNzzz2XFi1akJOTQ4sWLejfvz9z5sxJ2E9ZWbx4Me3atcPMqF69Om+++WbMdnPmzOGyyy5jn332oWbNmlStWpU2bdrwpz/9ic8++yzmNSeddBJmRr169RJ+1wC///47DRo0wMzo3bv3Nn+ucuHuZb4RFDLZDJxVgmvOArYAb2ciRm3atGnblg1oTrDEgi9evNhFREREpGTmzZvnX331lc+bNy/layZNmuSR/wdLtE2aNMnd3fv16+eAt2zZ0qdOner169eP2zZiwoQJXrt27YT3P/74433NmjUxY+zRo0fS+Fq3bu1z5swp1bU9evQoaL9gwYKC93Nzc33QoEFxrzvttNM8Pz/f8/Ly/Iwzzojb7sILL0z4DKZPn+7NmjVLGGPXrl39559/jnl9y5YtHfB+/fr5888/71WrVo15j6ysLH/mmWeKXZ/K87/xxhsTfoZUffbZZ96gQYO4/Vx00UWem5tbcLxgwYKEn/eBBx7w7OzsYveJtnz5cj/kkEMSfr727dv7woULY8Yc/TOfSEniHj16dMy4Aa9cuXLM57QtksU2d+5cb9Gif4BN9AAAIABJREFUhQNep04dnzp1asz73HjjjZ6VlRX3ezQzv/nmm4td99JLLxW0efbZZxPG+vzzz6fctqjS/B24ePHi6M/Q3NPxu206bpK0EzgzTPp9CFRIoX0FYCpBcvHsTMSoTZs2bduyoUShiIiIyDYpzS/Ja9eu9VmzZvmwYcMKflmeOHGiz5o1a6tt7dq17l6YNKlXr543bdrUq1ev7kOGDPHJkyf79OnTffTo0T537tyC+7/xxhsFiYVWrVr5bbfd5pMnT/ZPPvnEX375Ze/Tp89WibdYDjnkEO/YsaMPGTLEX3jhBf/oo4982rRpPm7cOD/nnHO8QoUKDvhee+3lGzZs2Ora7777zmfNmuVNmzZ1wE8++eRin+27774raB+dKOzWrZsD3rt3bx8/frx//PHH/uKLLxa8D/gjjzzil112mQN+3nnn+SuvvOIff/yxP/PMM96uXbuCdq+99lrMz/bFF194tWrVHPCGDRv6jTfe6G+99ZZ/+umnPnHiRL/kkksKEkrdunXzTZs2FbtHJAHVuXNnz8nJ8datW/vIkSN92rRpPnXqVB86dKjn5OQ44DVr1vSlS5dudf2sWbN84sSJBbEOGzas2Hf0yy+/pPYDlcCKFSu8cePGBf2cf/75/tprr/nMmTP9mWee8e7du2/1vSdLuO29996elZXlrVq18pEjR/rUqVP9/fff91tvvbWgbX5+fsF9I0nhZ5991mfOnOn/+9///JRTTik416ZNm5jJ6nQnCjt16uQVK1b0pk2b+r333usfffSRT5kyxa+77jqvXLmyA56dne0fffRRSb/iUsU2c+bMgoR/kyZNfNasWTHvMXjw4IJ7HHrooT569GifNm2az5w505944omtntv999+/1bX5+fkFfwZ79+6dMNYTTjjBAa9fv75v3LixRJ9zl0oUevBL9OgwWfg/oHGCdo2AF8O2ozMVnzZt2rRty6ZEoYiIiMi2Kc0vyRHJkhwRkaQJ4NWrV/fPPvssbtu1a9d6o0aNHPCjjz7a161bF7Pdww8/XHDPt956K+bnSuTNN98sSBaOGjUqZpvo0VyJRCcKAb/iiiuKtVm3bp23atWqIJlhZj5ixIhi7ZYsWeI1atRwwE866aRi57ds2eL77rtvQfJo2bJlMWN67bXXEn6+yGcDvEuXLv7bb78Va/PEE08UtBk+fHjCz52bmxszjm3117/+taCPO++8s9j5/Px8P/nkk7f6/hMl3ADv2LGjr1y5Mm6fI0eOLGjbt29f37JlS7E2119/fUGba6+9ttj5dCcKI/dasmRJsTbvvPNOQWL4gAMOSNhfScSLbdKkSQU/o23atNkqaR7tww8/dDNzwIcOHRqzTX5+vp977rkFCelVq1ZtdT7yPWdlZfmPP/4Y8x5Lliwp+Pyx/uwls70kCrNJIzPrm+D0FKADcALwnZm9AcwgWIvQCRKEBwJHA5XDc1PMrK+7P5bOOEVEytTw9lAzU0vAioiIiOwkut4KddpAjfrw06clu3blosL9X76ESitjt1u/omD32ov/SKcGW+L2lZv7DL/88gs5OZV5/I5rqfrb1/Bb8XYXHn8Aozp3YPqns8m9/y7+0L7uVuf3rEbCz3Pk3vU46egevPj6JF58ZiwXHLd/8UabNxXGn+i7+eWngt0WTRtz+xXnFmtfFeh32tHcNPxhfv31Vw7avyOXn3l4sXaNgVOP6cFjz73Ce+9OLnZ+wpvv8sUXXwDw2J2Dqb9pMfy0uFhIx+7biDOO/wP/fflNch8aWfzzbS5c8+3Rf19DrXXfwbqtm5zXsz3XNm7ATz8v4703X+ZvZ/eM+7lZuajkPz9J5OVtZOyYRwHYv2M7rjynV7E+soCHbrqUiRNfJy9vYxhXjJ/FqM97302XU3v9Algfu9/77rsPgPr16zNy5MiY6xfefPPNjB8/nrlz5/LII49w8803U7ly5dJ90BTdddddNG7cuNj7vXr14sILL+SBBx5g5syZzJgxgwMPPLBMYnjppZc455xzyMvLo2PHjrzxxhsxYwK47bbbcHe6du3KjTfeGLNNVlYW9957L8899xyrV69m/PjxBesxAlxwwQXceuutbN68mccff5zrrruu2D0ee+wx8vPzAfjTn/607R+ynKT7N9kxQG6C7QCCpGAOcCJwM/Ag8FC4f2J4jrBtLvBommMUERERERGRXdz5px2X8PxLE6cA0OOg/WlYv27Ctod36wzA1I9nJe132fKVzP/ue2bP/aZga1CvNgCffzU/ldBTctpxvahYsWLMc/u237Ng/+yTjo57j057twVg5W+r+W3Vmq3OvfRG8P3s1aYV+4bt4jn8oCA5OOPzL+MWNunYfo+49zEzOu+zFwDfLfoxYV9l4eNZc1i1Oig40/eME2Im7AAaNajHMT26p3TPFk0bc1i3GEnh0E8/LysoDHLWWWdRo0aNmO2ysrIKivmsXLmSTz75JKX+S6tOnTqcfPLJcc9HJ8jeeuutMolh7NixnH766eTl5dG9e3emTJkSN0m4ceNGJk6cCMCZZ56Z8L716tVjn332AWDq1Klbndt9993p1asXALm5uTGvjxQQOuCAA+jYsWPKn2d7k9YRhaHYf2JK1i7Ve4iIiIiIiIiUSPVqVdm9ZfOEbWZ+8RUAEydPxZrFT+hE+3nprzHf/2DGZ/xn9NO89d50Vvy2Ku71v66IMWSxlNru3jLuudo1a6TWrlb1gv01a9dRu1bhdTM/D76fr79dmPL3s2nT76z4bTUN6tUpdq5dm9YxrihUt3atII51cYbflaHZc78p2O+yb/uEbQ/Yd29emjg56T33bb9H4j6/LuyzW7duCdtGn589ezbdu6eWrCyNzp07k50dP5W03377UalSJTZt2sTs2bPT3v8999zDPffcg7tzzDHHMH78eKpWrRq3/ezZs8nLywPgmmuu4Zprrkmpn59//rnYewMHDuSdd97h66+/ZurUqVt9z9OmTStI7O7Iowkh/YnCxH+yRURERERERMpZdKIslt9//73YCLpUrN+QV+y9oXc9yE3DH07p+g15xa8vrapVcuKeq1ChQmrtrLDd5i1btjq3dPmKos1Tsn7DBqB4ojBRHFAYc7wRiWVpZdTPQsN6iUeXRkaHJlOnVs2E51f8trpgv1GjRgnbRo+mW7GidM8lVQ0bNkx4Pjs7m7p16/Lzzz+XSSwjRowAoEGDBjz//PMJk4QAS5cuLVU/69cXT0ifdtpp1KtXj+XLl5Obm7tVovDRR4PJsFWqVOHcc88tVZ/bi7QmCt19UfJWIiIiIiIiIuUnKyvxKlybNxcmxc468ShuuOLCUvXz9nsfFSQJd2/ZnKv//EcO7bofuzVrTPVqVcnKygLgH3c8wD9HPFKqPspL5Ds65MD9ePDf16d8XdNGDcoqpB1K5NmnIt5U5wgPiitmRLJYoGzjOf3003n++edZtmwZffr04dlnn004wjE6sTx8+HCOOuqolPqpXr16sfcqV65Mnz59uOeeexg3bhwjRoygatWqbNiwgXHjxgFBMrF27dSSxdursph6LAmYWUVgNtAWOMfdx5VzSCI7NTNbCBSdT7HI3VtlPhoRERER2RHk5FSmapUc1m/I47fVa+jQLvE00XgeeeoFAGrXqsHU/42Ju9bhylWrY76/PatXpxa/LFvOsuUrS/397CjqRE25Xrp8BW3bxJ+uvWx5eqaP161dOOIw1jTYaL/88kvhdXW3/hmLjMTcUmREaFHr1q1LeD5WX7Hk5+ezcuXKmLGkw5133kmTJk0YOXIkL774Iueeey5PP/103GRhvXr1toqtQ4cO29T/wIEDueeeewoKnvTp04fnn3+e1auDP8M7+rRjSH8xE0nuMoIk4Rzg2UQNzWwfM3vQzL4xsw1mtszM3jWzP5tZuitWn2NmE81siZnlmdlCM3vczA5Kct1QM/MSbkNj3MfM7FAzu9nM3g7j2GRmq83sSzO738w6leJzHWlmY8LvcJ2ZrTKzeWb2nJkNMrPi/0xQBsxsNzO708zmhHGsMLPpZna1mSUeK13yvo41s/Fm9oOZbQxfx5vZsSW4x6Fm9oSZLQh/9laa2SdmdqOZ1Ut+h7j3/UuRn4X+SdrvYWbnmtndZvaBma1P9VoRERER2XWkMsqppDp3aAfABzM+D6fLltyX874D4IhDDkxYECWy3l88ZfH5tlXnDkFxkXnfLWLRDz8laV22yvrr2WevNgX7yZ5VZG3LbdVhr8Lk60cffZSw7fTp0wuvK5IIixRB+e23xAnMr7/+OqW4Pvvss4LKvrF8/vnnbNq0KWYs6XLvvfcyaNAgAJ577jn69OkTd0p6hw4dCpKIb7zxxjb33aFDh4I1ISNFTSLTjlu3bl1Q8GRHpkRhBoVJqcHh4c3uHjelb2YXAB8DfwbaEFSDrg8cRlAp+r1tSdhE9ZNjZi8DTwNHA42BygQjsPoAH5jZDdvaTxGx/gZaCLwH3AAcEcZREagB7A0MAj41s9sshf9KmlkdM3sReBPoR/AdVgVqAnsCpwP3A2X+T19mdjzwBXAV0C6Mow5wIHAH8ImZ7Z6GfszMHgJeA04FmgGVwtdTgdfM7KFE35+ZVTSzRwiexflAK4KfvdpAZ2AoMNvMDilFfE2BW0vQvgcwH3gKuAI4GKhS0n4Jfq47httLpbheRERERLZzOZUrFexv3LgpLfc86ejDAVi3fgP3jflvqe4RSajEWrsw4rPZXzPtk8TVkiOfb+Om9Hy2dDjpqB4F+7ffP7YcI4GcypUL9sviOzpg372pVTMYY/L48xPiTq39ZdlyJk6ZGvNcSTVt3ID27YPCKc8++yxr1sReM3Pz5s0F1Xbr1KnD/vtvXVimdeuglMSaNWviJgM3bdrE888/n1JcK1as4OWXX457PpI0AzjyyCNTumdp3HfffVx00UUAjBs3jr59+8YcNVmjRo2C5N3bb7+dlqrQF14YLEUwadIkJk+ezOTJkwEYMGDAdpnULyklCjNrEEGybzEQ9780ZnYM8DBBwu4X4K9AN+A4YHzY7CBgvJlt6zMcDZwQ7k8CTgG6AhcA3xL8jNxsZgPjXH8/hUmYeNt+QGQs/WrgxRj3aRa+fgPcBpwEHECQGP0HsJKgGva1wC2JPpCZ1SJIEEZqtk8A/gh0Bw4lSICNAH5IdJ90CEdB/heoBawFhhAkvP4ARBYh2QuYkIbRjcOAi8L9T4FzCZ7lueEx4fl/JrjHvUDkWc8P2x9I8L39A1hFkMR92cxKmmQdSZCoTXU12ei/YbcAXwLT47SNy93nuftsd58NpK+MnIiIiIhsN5o0ql+w/+2i9Pxv/sV/PIP6dYO1xm644wFee+eDhO0/mPEZ7077eKv39my9GwDvT/+M72LEtWz5Svr89f+SxtKkYfD50vXZ0uH04/9A+z2DJNQDjz3H6Kdj/ZpXaPbcb3j5jSllEku9OrWoVKkiUDbfUU5OZfqeEfza/MmsuQx/6IlibbZs2cKfr7uFvLyNaev3kksuAWDZsmVcdtllMROUN910E199FYxivPDCC6kclTQF6NGjMKF71113Fbve3bn88sv56afUR4VeeeWVMacgT5kyhYcfDtbk7NKlCwceeGDK9ywpM+PBBx/kggsuAOCpp56if//+MZOFQ4YMAYLPetZZZ7FgwYK49928eTNPPPEES5YsidvmnHPOoUaNGrg75557Lu5OhQoV6N+//7Z9qO2E1ijMEDPLAi4ND5+ON5ownFI8kiBBtxo4xN2/jWryupndB/wFOJxg1N9jpYypB3BeePgycKq7R8brzjCz/xGMatwNuN3MnnP3rRIt7r6UJIkfMzuOIEEE8Ky7xxq3Px24CXjDi//t976ZPQVMBRoA15jZKHf/Lk6X9wJdgHygT4x1ID8AnjKzK4HUV5AtnREEIwjzgaPdPfqfl94xs/nA7QQjDa8Ebi5NJ2HS7trwcCZweNT3HHmWUwiSr9eZWW6RnyvM7ACCEawQjIA8zN2jF0v5wMxeAKYRjIgcTpDQTSW+kwlGNS4jSAQX/y9UcT8C1wAzgI/dfW043bhrKn2KiIiIyK6jc4d25ORUJi9vIzfccT/Z2Vm0at6UChWCf3tu1rghVZJU1S2qZo3qPH3frRz3x8vYuHETJ/S7nNN7/4HTex9Bm1YtAFjyyzI+njWHF16bxBdz5nPvsGs5/KAuBffoe8YJvPzmu6xdt54ep1/IdZf0o0vH9rjDhx9/zvCHn+Dnpcvp3mVfpn78RdxYDj6gE5M+nMmMz77k3yNzOa7XIVSrGnyeKjk5NGuSuBJtWcjKymLcA//m4JMHsHbdegZefTPPvvIm551yHHu1aUnFitks/XUFn87+mlfeeo8PZ37OVX/+Iyce3SP5zUsoOzubAzvtwwczPuPRZ/5H5w7t2G+ftlQMp5zWrV2LunVqbVMfQ6/8M8++8hY/L/2Vq/95N59+OZc/nn48DerV4ZsFi7ln9NN8OPNzunbuwPRPZwPbPiX64osv5sknn2Tq1KmMHTuWRYsWcckll7D77ruzZMkSHn30UcaPD8YStWnThhtuKD4ZsHPnzhx00EFMmzaNRx55hE2bNtGvXz9q1arF/PnzefDBB5k8eTLdu3dn6tTkoyE7derEV199RZcuXRg8eDBdu3Zl48aNvPrqq9x9993k5+eTnZ3Nfffdt20fPgVmxiOPPMKWLVvIzc3l8ccfJzs7m9GjR281sq9Hjx4MGTKEW265hW+//ZZ9992XCy+8kKOOOorGjRuTl5fHokWL+PDDD3nuuedYsmQJc+bMoUmTJjH7rVatGmeffTajRo0qWD/yyCOPpEWLFmX+mTNBicLMOYog4QZQ/J8fCp1K4XTYW4smc0LXEIwSqxPulypRSGFiaTPwl6gkIQDu/quZXUcwLbkOwSjDVBI8RfWN2o8Zq7sfnOgG7v6tmd1MkATMJhgteHfRdmZ2KMHoQYBhiYrFhAnJ+IsrbCMzOxDoGR6OLpIkjLgLGAC0B64ws1vd/fdSdPc3Cv88X1Y0Gevu683sMoJkazbBVN7LityjX9T+VUWShJH7zDazEQQjI080s33c/ctEgZlZDYLkN8DVpDiS2d3nA3em0lZEREREdm01qlfjr386h9vvH8sns+ZyzHmXbHV+0rMP0/PgA0p83yMP78bEJ0dy/mX/x89Lf+XZV97k2VfejNu+ZpFKqWeccCQDzj6J3HH/44clv3DZ/92+1fmsrCzuHnoVK1etTpgoHNT3TB547DlW/LaKwbfey+Bb7y0416N7FyY/Vz4Vkzu235MPXnyUMy66lvkLvmfi5KlMnBw/2VSzRrUyi2XwpQM4sf8VLF/5G+ddsnUV5huvvIihV128TfevW6cWrz8xkqPOHcSy5St5cvxrPDn+ta3a9D/rRA7r1rkgUZhTZHRfSWVlZfHKK69w0kkn8cEHH2w1zTVa+/btee2112JW6oVgLb0ePXqwdOlSxo4dy9ixW08Vv/LKK+nYsWNKicL99tuPSy+9lEGDBnHppZcWO1+pUiXGjh1bsI5fWTMzRo0axebNm3nsscfIzc0lKyuLhx9+eKtk4bBhw6hduzZDhgxh7dq13H333dx9d7GUQsFnKDoys6iBAwcyatSoguPIyMadgaYeZ85Z4et8d0+0AMUpUftjYjVw9/UUTl3uYGZ7ljSYcJrrH8LDN9093vjs8RROGz6tFP3UpHAK8AKCte9Ka1LUfps4bSJ/U62ldEnNdIp+lrmxGoQjSyPJ0zoUJhZTFq45GPmO57r7tDh9TaNwfchTYqxVGBkXngdMTtDl61H7Z6QQ4q1Ac2Cyu5c2qS0iIiIiktC/r/8rj9xxA4d160zd2rXIykrP5KEjDu3Ktx++xMhbruPYXgfTpFF9KlWqSE5OZVo0bczRPQ7ilusuYe6U8fQ984Ri1z86fCiP/+efHNatMzWqV6Ny5Uq0bN6EP55+PB++lMvlA8+L0evWmjVpyPQJj3HBuaewR6sW5ORsWwIqnfbduy1fTX6OsSNu5pRje9GiaWNycipTqVJFmjSqT8/uB/B/lw/k49ef5B9/uyj5DUvp+CMP4+1xD3LyMT1p2rgBFSumf1xUp32Cz3rVn//Inq13o3LlStSvW5teBx/AU/f9i9y7b2L1msLqwbVqbHvtzLp16/Luu+/y+OOPc+yxx9KoUSMqVqxIvXr16NmzJyNHjuSzzz6jZcv4lZjbtWvHJ598wqBBg2jZsiWVKlWiQYMGHHvssUyYMCHmlOREBg4cyHvvvcdZZ51F06ZNqVSpEs2aNaNv3758+umnnHPOOdv6sUukQoUK5Obmcv755wMwatQoBg0aVGyq9tVXX823337LkCFD6NatG/Xq1SMrK4vq1avTtm1bzjjjDB566CF++umngrUd4+nWrRtt27YFgmd08sknJ2y/I7F4i3BKepnZAoLCEI+7e98E7b4HWgBfu3u7BO3OJSjyAPAnd4+ZiEpw/RHA2+HhYHf/d4K2EwkKQuQDVUsy4i0syhJJs9/s7jeWJM4i99qfYCo0wL3u/tci5ysRrKGXQzDF+azw/WyCNRAd+NndM7ICsJm9S7DG4jqgtrvHHL1oZt2BD8PDEn9HYSGUyMjTh9w97j+VhcVOIv913t3dF0Sdm0dQ6OVHd2+e4B57AvPCw0nufkSCtt0IPls+0Mnd54bThyM/rwPcfUyCj1f0fqW+Nrx+DMHIyUXu3qok16Zw7+YE64+y+G/VaV5T/w4jIiIiUhLzu95Kfp02ZNeoz5519f9SsmMaePXNjH76RZo3acTima8lvyCRpp3TE1QatGrVikWLFtGvX7+C4im7slWrVhVMW77sssv4z3/+s833nD9/fsHU7T33TG082A8//BA95blFgkFgKdPfvhkQJhBahYczErSrTjDyCmBukttGn29firCir0m1r2yCRFJJJJ12XALRi1nEirkTQZIQYKqZNTazXIICFguBRcAqM3vVzBJOdU6TyHf8TbwkYag8nmWsviL/9FUzSWXp6MU99o7XyMwqEhRsqQDc4e7JYtuumVnzRBtBkRcREREREdlFbdiQx0sTJwNw0P4dyzcYKVNPPfUUeXlBNfOdadoxKFGYKdFJqU/jtgqShJEETbIs8OKo/dKsmBl9TZn0ZWYtCUbUAXwQZ73FVO9VlWBdPYBNwEsxmkUnrXKAWUB/oFqR948D3jOzKygjZpZDUOEakny/7r6SwiRdeT7LOeFrDSDRP10dHrXfKBzJGcs1BFWvvyNJpeodxOIkW9x/BBARERERkR3ftwsXx6w8DEG13EGDb+XXFUH9z34xpqHLziE/P58RI0YAcNBBB9GpU6dyjii9VMwkM6KncSaqEFwjan9tknuui9ovzcIHmeirL4WJz7GJGqbgNgqLwdzn7j/GaFM3av9GoDLwCjAUmE0wEu504N8EVZiHm9nX7r6N48FjKsn3C8F3XI3yfZYvERTJARhmZicUrc5tZvWBq2L0v7xIuz2ASMmtS+JUut5pHZQ3kuxK9ZM3FBEREZECQ7bUoZXnUM+r8MWW2NVGRcrTDXffwezPP+GYk06j435dqFu/ARvzNjBvzpeMf/ox5sz6HIBuh/agxRF9+WLLtpU93jcdQUtarFixghUrVrB8+XLuuusu5s0LVuP6+9//Xs6RpV/GE4Vm1ougymt3gql6VYB93f2rqDaHEYxEWu3uiSoE7ygaRO2vTNAuJ2o/2Tp6G6P2q5Q4osz01Sd8zaOw+EqJmdn5FBYpmUNQcTeW6JGDlYGXgVOikl1LgQfMbBYwhWBE7e1m9rqnf7HOkny/UPgdl+ezfI5gxGtnglGXE8xsKPA5UAnoBdwONA37qRTnPgAPUrhW5Osxzu+Iko32bIxGFYqIiIiIbHd+//13Fn47v1TXNtutJVWrFv6q+d38r3ngrlvjtt/vwG7cfv+jJF7Nadcye/bsUl3XvHlzateuneZoSmf48OHccsvWE+VOOumknaqISUTGEoXh1NGxFFbOjfypiZWg2QyMBNzMPnL30v2J3n5Ej3RLlCjMi9qPN50zIrrMVWlGa5VpX2Z2ENA2PHzJ3VeVILbo+/QERoeHK4EzEoxOyytyfE3REXEA7v6+mY0nqNjbIdwSVaIujZJ8v1D4HZfbs3T3zWZ2KvAGwbM7NtyKepng747jwuM10SfDgiN/IKiWXWbTuzMt2aKw+h8BEREREZHt09Kfl3DGUYeU6tpR/32ZA7sfCsAFl/6Nlru3Yep7k1ny42JWLl9Ofv7v1Kpdl3323Y9jTjyVY08+nQoVtMpbtI4dS7deY25uLv37909vMNsoOzubVq1acd555+2UowkhsyMKxwG9CRKE04F3gatjNXT3D8NRXx0onCq6I4tO5FShSGIlSvT7yaagRo+eS2Vqa6b72uYiJmZ2APA/guTWOqB39MjTGKI/0wJ3/zpB24kEiUKAA0l/orAk3y8Ufsfl+izdfVH4vV9JUB04uib8d8C9wH8I/gxDkNRfHWlgZg2AO8PDG9z9p1Q+gIiIiIiIyPauVZs9GXjZVQy8rOhqTDu/hQsXlncI5WrYsGEMGzasvMPIiIwkCsNRSscTjB68yN1Hhe/HTBSGxhNMP+7Bjp8oXBa1X5f4icLoEUvN47SJiJ4GuThuq/iK9jUzXX2FxS3ODg9/IUjKlYiZ7QO8TrD+3UaCKcTTklwWHVtJino0LGl8ybh7npn9SlDQJOGzNLM6FCbw0vEsE0n6LN19DXATcJOZ1QPqAb+5+9Iw3iwKKybPLTJte2CkPbDczM6J0UW36H0ziyTS34n0ISIiIiIiki7NWuzG54sTTe6TspT+lb6kLGVqRGG/8PWJSJIwBR+Hr+0TttoxRCcK6wCLYjVy97VmtpggmdMuyT2jz8+J2yq+6JF5qfaVD3yTwr1PoHC69ZPuvrkkgZlZG+BNgoRTPnC2u7+e/htKAAAgAElEQVSVwqVfRu1nJWkbfT6/JPGVwByCqs97mFm2u8frpzyeZUp9uftyihQqAboAVcP9j4qci0xtrg2ksr7oxeEGwRqIShSKiIiIiIiIlJNMTZw/kGA04bgSXLMkfG2QsNWOIXpaa9u4rQLvh697mVnjBO16RO1/UIqYZlBY+KJHvEbh6MCDIte4eyqFOUo97djMmgNvA02ALUA/d38plWvdfRHwfXjYJknz6POxKiinQ+RZViNIrsWzrc9yARCZ4hv3WYYOD19/BBaWoi+A86L2S12kRkRERERERES2L5lKFNYLX0uTkNkZVgGdSWHhiAOTtH0xar9/rAZhYZizwsOv3H1eSQMKp5e+HR4eGSboYjkNqBnuv5DsvuFU1d7h4efu/nmqMZlZQ+AtoGX41sXu/lSq14eeD18bmdnBCdqdFrX/Xgn7SFX0sxwQq4GZVaAwsfobMKmknYRTfyPJ1HZhIZlYfR1E4YjCl0pT6dnMWgIXhYfzCUZ+Rscy1N0t0cbW38WAqHOTSxqPiIiIiIiIiKRPppJwkTX5aiZstbXIiK+i0x53OOEovEjxh65Jmr8AfBvuDw6n4RZ1B8EU5sh+MWbW38w83IbG6StSdCIbuC9cey76HvWB28LD34BUpo2fC1QM91MeTWhmtQnWMtwrfOtv7v5IqtdHGUFh8Zj/mFm1og3MrA/QMzycEKuarZkNjfr++pciDtx9OoVJyAvMrHuMZldROL3+Hnf/PUYsPaNiGROnuxEUTqG+18yqFLlHFYJCJITtRsS6iZk1tzjle8NE7v8ICvIA/CVWVWkRERERERER2TFlao3C+QQFDLqS+uit08PXlEekbecmEEwL7WpmNcIRfcW4++9m9lfgZYLE6gdmNowg0VgHuJDC7+Z94PHSBuTu75jZM8A5wEnAm2Y2gmAaa0dgCLBb2Pzv7p7K6q+R0XGbgSdTicPMKhN8P/uFbz0JvGVmHRJcts7dFxR9092/N7N/ALcTTPedbma3A7OBWgQjCSNr4q0G/pZKjNvgcoLpxFWAN8zsXwSjBqsQfO+R0XnzgLtK24m7zzOzO4G/AwcQ/NzcRpB0bgNcB3QOm9/h7vPj3OrvQG8zGwt8CPxKsN5kD+AvFI4OHpriupHbxMzOYOtKzodG7xfJaf7s7q+XdUwiIiIiIiIiO6tMJQpfJVjn7i9mdp+75yVqbGbHEiTDHHglA/FlwlPArUAOcCoJRtu5+6tmdjEwEmhE4UiwaNOBU0taKCSGPxEkJHsTFJPoVeT8FuCf7v5QshuZ2V4UTq2e6O6/pBhDEyB6mvD54ZbIFApHBW7F3e8ws7oEybG9gTExmi0lqKQcL2EWPSKv1KNa3f1TMzuboLBHTeBfMZrNA46PlzwugSEEFZz/RJAUfCZGm9HA/yW5T2tgaJxzG4Ah7n53KWMsqTspnIpe1AXhFjGFoFK2iIiIiIiIiJRCpqYejySYutoKGB+uY1eMmeWY2VXA+DC2n4HcDMVYptz9RwrXkUuWBCOcdtsFeAT4jmA67XKCUYSDgEPc/dc0xLXB3Y8PY3qTIIG2CVhMkNw81N2Hpni7UhcxSTd3HwwcQjDiciGwEVhFUMTlBqCtu09NcIvINOF5BKMdtyWWl4F9gbvD+60n+PMwk3Ckn7unUk06WT9b3P0C4HiCn7WfCJ7lT+Fxb3cfmGS68EMEycwPCNYU3QSsAD4FhgHtM5gkFBEREREREZEMslLUMyhdR2a9CZIVFQiSXlOAYwlGDf4XqE2Q2KkGGPA7cMzOVOAgLCYxlWBa7h7uvrB8I5JYwqnQq4DKBFWXyzXpKekTrvHYD1jk7q3SfO/mBAl2mg0aQ3bN+um8vYiIiMhOb8ihdWhVN4d6NaqQXadJeYcjUu72bV67vEOQDJo/fz75+flkZ2ez5557pnTNDz/8QIsWLSKHLWLVYCipjFUUdvdXCaa3LiOY1hlJEkJQwfdogrXIjGBdtN47U5IQwN2nAa8BWcDgcg5H4juIIEn4LSmusyjbLzNra2YdwjUv9V9aERERERERkTgyligEcPc3gd2By4C3CEZtWbhtIJjueB3Qxt3fzmRsGXQdwYjCAWa2W7LGUi4OD1//lYY1IKX8vQHMCreTyzkWERERERERke1WpoqZFHD39cB94YaZZQNZ7r4x07GUB3efZWb9gT0IKgp/X74RSVHu/k/gn+Udh+y4pl3/B5o3b17eYYiIiIjsULaadqcplyIi5SLjicKi3D0fyC/vODLJ3Z8o7xhEdhXpXotQREREREREZGeV0anHIiIiIiIiIiIisn1SolBEREREREREZDvz/fffc/XVV9O+fXuqVatG3bp16dq1K3feeSfr168v7/BkJ5XWqcdm9k467xdyd/9DGdxXRERERERERGS7M2HCBM4//3xWrVpV8N769euZMWMGM2bMYNSoUbz66qvsvvvu5Ril7IzSvUZhT8AJqhhvq8h9PA33EhEREREREZEy1urvE8o7hDKz8N/HZ6Sfzz//nLPOOov169dTvXp1Bg8eTK9evdiwYQPPPPMMjzzyCF9//TXHH388M2bMoHr16hmJS3YN6U4UvosSeyIiIiIiIiIipXLFFVewfv16srOzeeONN+jevXvBuSOOOII999yTa6+9lrlz5zJ8+HD+8Y9/lGO0srNJa6LQ3Xum834iIiIiIiIiIruKGTNmMHnyZAAuuOCCrZKEEVdddRW5ubnMmTOHESNGMHjwYCpWrJjhSGVnpWImIiIiIiIiIiLbgRdffLFgf8CAATHbVKhQgb59+wKwcuXKgsSiSDooUSgiIiIiIiIish147733AKhWrRpdunSJ265Hjx4F+++//36ZxyW7DiUKRURERERERES2A3PmzAFgjz32IDs7/mpx7dq1K3aNSDooUSgiIiIiIiIiUs7y8vL49ddfAWjevHnCtnXq1KFatWoALF68uMxjk11HWouZmNnmcNfdPTvG+6Wx1b1ERERERERERHY2a9asKdivXr160vbVqlVj3bp1rF27tizDkl1MuhNwVsL3RURERERERER2eXl5eQX7lSpVStq+cuXKAGzYsKHMYpJdT7oThTeV8H0RERERERERkV1eTk5Owf6mTZuStt+4cSMAVapUKbOYZNeT1kShu8dMCMZ7X0REREREREREoEaNGgX7qUwnXrduHZDaNGWRVKmYiYiIiIiIiIhIOcvJyaF+/foA/PDDDwnbrly5siBR2KJFizKPTXYdGUkUmtnh4ZbyeFgzy4lcV5axiYiIiIiIiIhsD9q3bw/AN998Q35+ftx2c+fOLXaNSDpkakThZOAdoHUJrmkWdZ2IiIiIiIiIyE7t0EMPBYJpxR9//HHcdlOmTCnYP+SQQ8o8Ltl1ZHLqcWkrH6tisoiIiIiIiIjs9E455ZSC/dzc3JhttmzZwmOPPQZA7dq16dWrV0Zik13D9rxGYSS2zeUahYiIiIiIiIhIBnTt2pXDDjsMgNGjRzN16tRibe666y7mzJkDwOWXX07FihUzGqPs3NJa9TjNWoWvq8ozCBERERERERGRTLnnnns45JBD2LBhA0cffTTXX389vXr1YsOGDTzzzDM8/PDDALRt25arrrqqnKOVnU2ZJArNbLc4p5qYWbIa35WBNsA/AQe+TGdsIiIiIiIiIiLbq86dOzNu3Dj69OnD6tWruf7664u1adu2LRMmTKBGjRrlEKHszMpqROGCGO8Z8EYp7vXYNsYiIiIiIiIiIrLDOPHEE/niiy+45557mDBhAj/88AOVKlVijz324Mwzz+TSSy+latWq5R2m7ITM3dN/U7MtabhNHvAfd/97Gu4lIlKmzKw5sBhg8eLFNG/evJwjEhEREdmxzJ8/n/z8fLKzs9lzzz3LOxwRkYwqzd+BP/zwAy1atIgctnD3H7Y1jrIaUTigyHEuwTTiG4AfE1znBAnCJcCn7p5smrKIiIiIiIiIiIikQZkkCt19bPSxmUVqer/o7l+VRZ8iIiIiIiIiIiJSepmqetwrfI21dqGIiIiIiIiIiIiUs4wkCt19Sib6ERERERERERERkdKpUN4BiIiIiIiIiIiISPnL1NTjAmbWCTgM2B2oAWQlucTd/YIyD0xERERERERERGQXlrFEoZntBTwKHFSSywgqIStRKCI7juHtoaYGbIuIiIiUSNdboU4bqFEffvq0vKORiKadyzsCEcmgjCQKzawZ8C5QnyD5B7AWWAlsyUQMIiIiIiIiIiIiEl+mRhQOARoQjA4cBdzp7vMy1LeIiIiIiIiIiIgkkalE4bEEScLH3P2iDPUpIiIiIiIiIiIiKcrUIlpNw9fHMtTfdsvMKprZ12bmZnZ2eccjsrML/6wV3SaXd1wiIiIiIiIi25tMJQpXhq+/Zai/7dllQFtgDvBsooZmto+ZPWhm35jZBjNbZmbvmtmfzSyto0HN7Bwzm2hmS8wsz8wWmtnjZpa0+IyZTY6TjCm2lTK2/xa5T6sUrtnbzO41s1lmttrMNoXf3yQz+5uZ1ShNLKW1IzxLMxua6nOM2obGuI+Z2aFmdrOZvR3GsSl8Dl+a2f1h9fOSfq4jzWxM+B2uM7NVZjbPzJ4zs0FmVr2k9xQRERERERGRQpmaejwT6E2QINtly1eFiYzB4eHN7h63kIuZXQDcB1SOejsHOCzc+pvZCe6+fBtjyiFIWJ5Q5FTLcDvPzIa6+z+3pZ/SMrPjgTNLeM1VwL8p/vNdH+gZbpeb2Unu/kUawkwWz878LL+O8d5CYLcY71cE9g63i83sDuDv7p4wgWxmdYBc4OQYp2sCewKnA1OBz2K06Ri1nwsckKg/ERERERERkV1VphKF/wGOBy4CxmWoz+3RIIJk1WLgv/EamdkxwMMEIz5/AW4BPgLqAhcCpwEHAePNrFeihGMKRlOYWJoE3AP8RJBcuR5oA9xsZkvcfVSSe80EBmxDLFsJE6v3h4dLgYYpXHMWcGd4uIkgQfcW8CvBZ/kLcChB4ux1M2vv7qvSFXOMeHakZ3k/8FySPrIIKpjXBFYDL8Zo0yx8/QZ4HvggjKMK0Av4G1AHuBbYHMYWk5nVAt4EuoRvTQCeCe+dRfAcDwTOiHcPd58ddb91ST6fiIiIiIiIyC4rI4lCd3/TzG4HrjWzB4C/uvvvmeh7e2FmWcCl4eHT8RJC4TTUkQSJpdXAIe7+bVST183sPoKE1+FAH0q59qOZ9QDOCw9fBk51983h8Qwz+x/wMcHosNvN7Dl3TzR9fF10UiYNhoV9vw38APRL4ZobovZPc/cJUcfTgafN7HmCBF0T4AJgeHrC3dqO9izdfSlBQjZRP8cRJAkBnnX3DTGaTQduAt6IMVrwfTN7imD0XwPgGjMb5e7fxenyXoIkYT7Qx92L/kPDB8BTZnYlQeJQREREREREREopI2sUmllf4CvgQ4JRhQvMbISZXWxmfZNtmYgxA46icDrmEwnanQrsEe7fWiSxFHENhes+XrMNMV0bvm4G/hKVWALA3X8FrgsP6xAk1TLCzA4gSKxuJEikpXJNTaBDePhJkSRhtJui9g8udZDJ7YzPMvrPY8ykprsf7O4T400pDr+Hm8PDbGJPKcbMDgX+GB4Oi5EkjL6nu3t+suBFREREREREJL5MTT0eA0QnDZoQFPVIhbNzVEs+K3yd7+6zErQ7JWp/TKwG7r7ezP4L/BnoYGZ7uvv8kgQTTuv9Q3j4prv/EKfpeILRcDUJRuHdVZJ+SiMcifcIwQixYe4+z8xSubRS1H68EWoA0Qm7ynFbbbud6lmGidhIUm8B8F5Jri9iUtR+mzhtIiNw15KBnzsRERERERGRXV2mqh4D2DZsO4Ne4eu0JO0OC1+/dvefE7SbErV/aCni6UphkmxKvEbuvonCmLuaWcVS9FVSVwL7AfOBW1O9KBw1tyI83D1B0+jE1LwSR5e6ne1ZnkmwziDA48mKkCQRnaAtNg3fzCpRmJR8zd3Xhu9nm1lLM9stbCMiIiIiIiIiaZKpRGHrbdgSJXx2CGbWHGgVHs5I0K460Dw8nJvkttHn25cirOhrUu0rm6DCbDztzGyGma0xszwz+8HMXgqnkKeUlDKz1sCN4eFf3H1jKtdFeTh83T9cTy+WyDqGm4FkBVpKZSd4lrEknXZcAj2i9mPF3ImgMjTAVDNrbGa5wG8EVZUXAavM7FUzK8vp4yIiIiIiIiK7jEwVM1mUiX62Y9GJjE8TtGtO4QjKeNNHIxZH7bcoRUzR15S0r6/itGsUbhHNwu0k4DozO8Pd5yTp60GgKkHBl7eStI3lFuAA4EjgBTMbSVAM5VeCpPMggiTVZoKiOsniKa0d/VluxcxaUjhC8oM46y2mxMyqAleEh5uAl2I02ztqPweYRVAxnCLvHwccY2ZXufuI0saUijDhn0jjsuxfREREREREpKxlao3CXV10giFRVdkaUftrk9xzXdR+9RJHlN6+thAk414FPgeWh/ffn2DtvfYEiZ9JZtbV3b+P1YmZ9QGOBlYRTD8uMXdfG44k7A/8Hbgq3KKNB253949K00eKdtRnGU9fChOfY0twXSy3UVjY5z53/zFGm7pR+zcSTFV+BRgKzAZqAacD/yZYc3G4mX3t7q9tY2yJLE7eREREREREpPSWLl3K9OnTmT59OjNmzGDGjBksX74cgH79+jFmzJjyDVB2ekoUZkaDqP2VcVsVTrWEYKRVItFTcqvEbZWZvk5z999ivP+emd1PUJikH8FowxEEhTS2YmZ1geHh4fVJ1vRL5gDgXOJPWz8S+MXM5rj76m3oJ5Ed9VnG0yd8zQP+W4LrtmJm51NYpGQOMCRO02pR+5WBl4FT3D2ynuFS4AEzm0WwLmMF4HYze30b105Mi4PyRpJdqegASBERERFJZMiWOrTyHOp5Fb7Y0qS8w5GIH2L9qhffvqNallEg5e+LgWU/WbJTi0Zxz61ct4kvSvg8pHT2bV67vEMoN5ksZrIrix4dlShRmBe1n6xQQ3QxiA0ljiiNfcVJEkbO/Q4MpHAdulPNrFmMpsMJEqozCKYfl4qZnQFMBo4gmK56KlCP4DO2Aa4HKhJMQf7QzMpquugO+SxjMbODgLbh4UvuvqoEsUXfpycwOjxcCZzh7vFiyCtyfE1UkrCAu79PMEIUoEO4lZUWSbYDy7BvERERERHZxTRu2ozuhx9R3mHILibjIwrNrBdwCkGxgvoEo5oSVTZ2d2+T4PyOIDrpUQVYE6dd9PvJpoVGj7hKNt20XPty93wzGw3cEb7VA3gqct7MjiAYcbgZ+HOshFAqzKwRMIYgGfYlcLC7R0+1/Q641cymA28C+wD3ElTzTbed6VlucxETMzsA+B/Bs1kH9Hb3ROsjRn+mBe7+dYK2E4Ezwv0DCRLEaefuCdd/NNtZCrSLiIiIiEh5+fMV17JPp8506LQ/9Ro05MfF39P74E7lHZbsQjKWKDSzhsAzFFY7jfdbtRc5V+7TCNNgWdR+XeInCqMTEckKJ0QXsCjN2mlF+5pZhn3B1kUzio4ovC58nQnsZWZ7xbi+ddT+iWa2DMDdn4l6/xwKE2H/KpIkLODub5vZ2wRTkE8zszrunmikZ2nsFM/SzCoBZ4eHvxAk5UrEzPYBXidYS3EjwRTiaUkui46tJAVaGpY0PhERERERke3FX64aXN4hyC4uI4lCM6sIvAbsR5AE/BT4CTieIBH4BFCHoPhF0/C9TwiKFuwMohOFdYCYCxuEhTgWEyRz2iW5Z/T50lTujU7cpdpXPvBNKfqCxKNGI9NhuwFPp3Cv/0TtRycK20ftf5LkHh8TJAorEEyrTWthk53oWZ5A4dT5J919c0kCM7M2BKM364V9np1iNesvo/azkrSNPp9fkvhEREREREREpFCm1ijsD3QO9we4exeCirQAuHs/dz/J3ZsTrCm3hKBK7ivuPiBDMZal6KmQbeO2Crwfvu6VZP28HlH7H5QiphkUFr7oEa9ROKLsoMg17p6sWEY8e0ft/1TKeyQTnSRKlgSvGOe6dNoZnmWppx2bWXOCathNCCpj93P3l1K51t0XAZHq2MmWHog+H6uCsoiIiIiIiIikIFOJwtPD19fdfWyihmEioQdB4mOMme1Z1sFlwEwKC0ckK3jwYtR+/1gNzKwqcFZ4+JW7zytpQO6+hiCJA3BkmNSJ5TSgZrj/Qkn7ATCzbOBPUW+9WySWnu5uiTYg+uemddT70RZE7R+WJKzDI90DC1P/NCWyQz9LM6sH9A4PP3f3z1ONKVxq4C0gUvLsYnd/KsElsTwfvjYys4MTtIuuov1eCfsQERERERERkVCmEoWdKJxiXIwVqQLg7t8C9xCsN3d5mUdXxsKRW9PDw65Jmr8AfBvuDw6nbhZ1B8EU5sh+MWbW38w83IbG6evO8DUbuM/MtpriaWb1gdvCw9+AUTH66WVmceuGh9POR1E45fVldy/tOofJTKBwTcshcaorY2YXAQeEh9PcfXmMNmOivr+epYxnh3qWMZxL4cjLlEcThj8PE4HIWpN/c/dHUr0+yggKCwH9x8yqFW1gZn2AnuHhhGQFR0REREREREQkvkwlCiNrnEWP+Iqe9lg1xjWREVJHlUlEmTchfO1qZjXiNXL334G/EkzVrAl8YGaXmllXMzvGzJ4D/hI2fx94vLQBufs7FK7xdxLwppmdZGYHmNkAYBqwW3j+73EKfvQDFpvZk2Z2oZkdbmb7mdmhZnY58HnYBmApZZj4dfe5QG542Az41MyuN7PDwphONLMngYfCNpuB68swnh3tWRYVmXa8GXgylTjMrDLBz/p+4VtPAm+ZWYcEW+tY93L374F/hIddgOlm1s/MupjZEWY2kqDKNcBq4G+pxCgiIiIiIiIisWWq6vGmsK/o5ODqqP1mQNEpl3lR53YGTwG3AjkE6zDGHaHl7q+a2cXASKARcG+MZtOBU0taXCKGPxEksXoDvcIt2hbgn+7+UNELo1QHzgu3eGYB57j7ggRt0uEvBCNRzwYaALfEabcOuMjdJ8c5XyVqv9iIw1TtgM8SgLDydGSa/ER3/yXFGJoA0dOEzw+3RKZQOCpwK+5+h5nVJaiMvTeFicFoSwkqKc9PMUYRERERERERiSFTIwojRQkaRd4IEw9rwsNuMa7ZJ9K0DOPKGHf/EYgUckiWOCGcqtkFeAT4jiBxupxg5Nkg4BB3/zUNcW1w9+PDmN4kSLpsAhYTJDcPdfehCW5xG8FIrv8SVKn+BfgdWEsw7XYccCbQ2d2/ineTdHH3je5+DnAEQTJ2HkFSMB9YAUwF/gm0S7JmXqTox9vuPitBu1Ri2lGeZbRSFzFJN3cfDBxCMOJyIbARWEVQxOUGoK27Ty23AEVERERERER2EpkaUfgJwRp1nYHXot5/FzgeuNzM/uvuGwHMrBZwLUGSsMyTSxl0B0HhhT+YWSt3X5iosbvPBi4qTUfuPobYo6/itX+KIJlU0n7mAHMI1pMrM+7enzgFQeK0nwRMKk1fZtaKwmm6N5fmHjHi2e6fZZF7DAGGlOK6hUDRIjPbLEwEKhkoIiIiIiIiUoYyNaLwbYLkwfFF3n8wfO0MzDKzO8zsPoJpqpHiF+U6mimd3H0aQaI0CxhczuFIfD3C1ynu/m7ClrLdi14PkWBauoiIiIiIiIjEkKlE4YsE04+bR1d+dfcJwKMEScQ9gCuBi4HmYZM3gAcyFGOm/D979x6/+Vznf/zxMmOcxpQQZUaKxIZ+1SY2kXRWodok7Tai424t2wG1ZcJGqFWxnchESiclKZKwyxK1aik504zkmMM4xJjX74/3+3J95nIdvudrmMf9drtu1+dzfd7X+/O+Pp/PuJnnvA/7UhaH2CMi1h9UWEOxbX2fkN6EGrpLG6+/HVBWkiRJkqTl1pQMPc7MO4ENehzbKyIuAPaizEs4HbiK0pPwc5m5ZCraOFUy89KImEsJRtenPX+jlhGZuSew57DbIUmSJEmSNJWmao7CvjLzWODYYbdjqmTmN4bdBml5kZkTPmfiIBd+dAdmz549uKAkSZIecdVVV7F48WKmT5/OM2c/cdjNkR5liyE8l7MW3/nI9hqrzRhKG7R8maqhx5IkSZIkSZKWYctEj0JJkiRJkqTl3XnnncfVV1/9yP5tt932yPbVV1/N/Pnzlyo/d+7cKWqZlhcGhZIkSZIkScuAY445hq9//etdj51//vmcf/75S31mUKiJNiVBYURcO46vZ2ZuOLiYJEmSJEmSpLGaqh6FG4yibALRsS9JkiRJkpZ18+4adgse0+bPn/+o4cXSVJqqoLB7v9mlrQY8C9icEg5eAlw6mY2SJEmSJEmSVExJUJiZe4y0bERsBnwNeDZwSGZ+f9IaJkmSJEmSJAmAFYbdgE6ZeRnwUuAm4PiI2GTITZIkSZIkSZIe95a5oBAgMxcBnwVWAT485OZIkiRJkiRJj3vLZFBYXVzfdxhqKyRJkiRJkqTlwLIcFLZWPl5nqK2QJEmSJEmSlgPLclD4yvru2uqSJEmSJEnSJFsmg8KIeAuwP5DAeUNujiRJkiRJkvS4N30qThIRXxtBsRWANYDnAU+lDD1eDBw6iU2TJEmSJEmSxBQFhcBcSu/AkWjNTXg3sFdm/mpSWiRJkiRJkiTpEVMVFP6RwUHhEuAe4DrgXOAbmXnbZDdMkiRJkiRJ0hQFhZm5wVScR5IkSZL02DRt2jQWL17Mww8/TGYSEYO/JEmPA5nJww8/DJT/Fg7TMrmYiSRJkiRp+TJjxgyg/IX5vvvuG3JrJGnq3HfffWSWgbit/xYOi0GhJEmSJGnoZs2a9cj2HXfc8chfmiXp8SwzueOOOx7Zb/63cBimao7CR0TE2sBawBOBvwK3ZeYfp7odkiRJkqRlx8yZM4kIMpNFixaxcOFCnvSkJ7Hqqqs6DFnS406r9/Qdd9zBokWLAIgIZs6cOdR2TT0VyHsAACAASURBVElQGBE7AHsC2wDrdTl+P3Ae8B3KIiYPTkW7JEmSJEnLhhVWWIH11luPG2+88ZGwcNGiRUTE0OfskqSJ1pqPtSUiWG+99VhhheEO/p3UoDAiNgFOAJ7X/LhL0VWBl9fXQRHxz5n5gx51zsrMuye8sZIkSZKkoVp99dWXCguh9LpZvHjxkFsmSZOnFRKuvvrqw27K5AWFEfE64CRgZdrh4H3Ab4GbgXuBmcA6wBaUsBDgKcD3IuLgzDygo84PALOAgyer3ZIkSZKk4Vl99dXZeOONWbRoEXfffTcPPvjgI6uBStLjxbRp05gxYwazZs1i5syZQ+9J2DIpQWFEbAV8mxISApwGfB44KzOXdCm/AqU34fuB11CCxX+LiD9n5hdrmUOBDwOfnIw2S5IkSZKWDSussAKzZs0a+qT+krS8mfC4soZ+X6aEhH8FdsvM12Xmmd1CQoDMXJKZZ2Tma4G3Ag9SwsIjImKDiJhPCQkBXPpKkiRJkiRJmmCT0aPwDcDmlEDvHzPzu6P5cmaeFBFLaA9b/l/gCZTg8GLgSxPbXEmSJEmSJEmTMQB6p/p+1mhDwpbM/A5wFiUcfGJ9PxnYLjNvnZBWSpIkSZIkSXrEZASFW1J6E35rnPV8s74ncHhmvikzHxhnnZIkSZIkSZK6mIygcJ36fsU46/lDayMz9x1nXZIkSZIkSZL6mIw5Cles7w+Os56H6ru9CCU9tnx2U5i1bCxtL0mSJKmHeXcNuwXSMmcy/iZ7S31/2jjraX3fOQklSZIkSZKkSTYZQeHv6/vrxlnP6+v778ZZjyRJkiRJkqQBJiMoPI2ySvFuEbHZWCqIiC2A3SgLmfx4AtsmSZIkSZIkqYvJCAq/AdxOmf/w1Ih4+mi+HBHPAH5Uv397rU+SJEmSJEnSJJrwoDAz7wb2p/QqXB/4TUTsExGr9/teRKweEf8KXFK/l8D+mXnPRLdxmCJixYi4IiIyInYddnukx7uIuL7+eWu+rh92uyRJkiRJWtZMyrKcmXkM8BlKWDgTOAK4KSJ+EhEHR8Q/R8QeEfH+iPj3iPgJcBNwONAKFD+TmcdORvuG7P3AxsDlwHf7FYyIZ0fElyLi6oi4PyJujYj/ioh3R8SErlgdEW+JiDMi4qaIeKCGKydExFYj+O6OETEvIk6LiMsj4raIeCgi/hIRv46Iz0TEs0bYjpUj4n0RcVb9vQ9GxI217hEFqxGxUUTsFhH/ERHnR8R9jYBo7kjqmGgRsWZEfDIifhsRd0XE3XX7kxGx5gSfa+t6766v9/KmiDg9It4ygu/O6xKq9Xq9ZAxte19HHXNH8J21I+LfIuJ/IuKO+mzdWZ+tT0fEeBdOkiRJkiRJQGTm5FUe8U+U8G/l+lG/k0V9vx/4YGZ+adIaNiQRMRO4DlgL2C0zT+pTdk/gaGClHkUuBF6bmbePs00rUwLL1/YosgSYl5kH9fj+dOChEZzqIeATmXlon7Y8CzgF6Bcqng68KTPv7VHHdsA5fb6/R2bOH9jaCRQRL6D8rqf0KPInYKfM/NUEnOsTwAH0/keAU4E3Z+YDPb4/r35/JLbPzHNG0banUgLyWY2P+96PiNgB+DbQL0y9D9grM7/Vo46NgRl192BgJ+CGzNxgpG0fiYiYDSwAWLDPTGbPmpR/h5EkSZI0UebdNewWSOOycOFC5syZ09qdk5kLx1vnpP5NNjOPBjYBPgfcQQkDe71uB/4D2OTxGBJW76WEhAuA7/QqFBGvBL5CCQlvBj4AvBB4NXByLbYVcHJEjPceHks7JDwb2BnYEtgTuIbyjBwYEXv1qeMuShD2UeAtwHbACyiBzH/U4ysCh0TEe7pVEBFrA2fSDglb4eXz6nur9+WrgK6BUKuqxvYSyqrZF/UpP6kiYj1KOPcUYDFwGLBtfR1WP3sq8ONadjzn2gv4JOWeXUO5h1tS7unZtdjrgGNGWOXmA14Xj7KJR1FCwltGUjjK/KY/oh0SngK8ifZvmk/5x4dVgRMiYutu9WTmlZl5WWZeBtw5yjZLkiRJkrTcmNDhq91k5h+BfYB9IuLZwHMoYdlM4B7gNuD/MvN3k92WYYqIacA/191vZeaSHuWmUwKVFYC7gRdl5jWNIqdHxNHA+yhh09uA48fYpu2At9bdU4FdMvPhun9xRPwI+DVlzsjDIuJ7mblU0JKZiyNizcb3Ov0oIr5Q61mDEjp+tUv5A4BWDP7JzJzXOHYJcFpEfBL4BPC6iHhDZp7Mo90IfJgSYv06MxfV4a1b9rkUk+nfgXXq9lszsznc/L8j4leU0Hgd4CDgHWM5SUQ8kdJ7F+CPwFaZeVvj+I+BH1CCwt0j4iuZ+V/96qzB2oSIiJ2AXYBbgU9TpiYY5F8pISDA4Zn5kY7jp0TE/wKfB6ZR5kZ9/cS0WJIkSZKk5c+Ujo3LzN9l5jcz8/OZ+anM/EJmfuvxHhJWL6cEbtB/JeddgI3q9iEdIWHLh4G/NLbHqhW8PAy8rzO8q0HTvnV3DUoPtUfpExK2jl9HGT4KsDall+kjaoi6e929gRKYdXMgJQSDEgp1O9dVmXlEZp6bmYv6tWuyRcQ6lCAX4IyOkBCA+tkZdfcf63fG4p3AE+v2vs2QsJ7nYUq43LpX43luRiXKQkZH1d0PUXoXj8SL6ntShgx3c1Sjvr8bUwMlSZIkSRIwxUHhcu7N9f2qzLy0T7mdG9vzuxXIzPtoD13eLCKeOdrG1PkSd6i7Z/YZx34ypWcjwBtGe56G5pyCK3cceybtkOvMXsFj/fzMuvu3EbHBONozFV5P6ekGcFyfcvPr+zTG3iOu9dzcTXt4+lLqPf553X15fQamwiHAbOCczBxN79fWvIK319XUHyXLJKvX1t1e83lKkiRJkqQRMCicOtvX9wsHlHtxfb8iM//cp9y5je1txtCeLWkHK+f2KpSZD9Ju85YRseJoTxQRq1DmK4Qyb+CVHUWe1Ni+eUB1zePbjrYtU+zFje2e15hx3suImEF7aPUF9Z4NOtdKlHkkJ1VEvJAyN+eD9X00Ws/JmhExq0+5Z3SUlyRJkiRJY2BQOAXqaqgb1N2eC0DUHl6z6+4fBlTbPL7pGJrV/M5IzzWd0vtvoIhYMSLWj4i3AP9Dezj1cZl5T0fxZm/DJwyounn8b0bSliFqXeO7+oW+mXkT7V6bY7mXz6Q93+iEPTcRcWZE3B4RD0bELRFxTkTsFxFrjKRRNVT+KuW/M4dn5qC2dfpyqyrKQjndzvFPtIPmL3crM1EiYna/F7DuZJ5fkiRJkqTJNumLmQhYeu60S/qUm0171d5BS1ovaGzP6Vmqt+Z3Rnuu33crVIcCX9ennp8DH+zy+dXAQ5SVkQf1EmweX79nqWVD6xqPZHnyBcCzmfp72c/LGttrU1az3g7YNyLmZuYpA77/YcrqyNdSFnUZlcw8IyI+TZknc986xP5EyjyVT6EMt55bi59IWcF7Mi0YXESSJEmSpMcug8KpMbuxfUufcqs3tgctxNHshTeWueam8ly3U1Z8/m63+Qcz896IOAt4FbBFROyWmd/qLBcRu1GCp5bVO8ssY1rtG8miKq1rvCzcy0uBHwIXAX+iBLjPoiw48wrKfJLfj4jXZeZPu1UQERsBH6+7/5SZ9w9oV1eZuV9E/IKyeM0bePQ8mZcAh2bmdx715SHa6oGjmD5jrWE3Q5IkSVI/+5027BY8Zl1/6I7DboImiUHh1Fi7sf2XnqWWXuSj3zxzAH9tbK8y6hZNzrlupB3kTQfWo4R/ewL/CTwjIg6pC1B0OoDSg2068PWI2BA4HriJ0nvsH4FP1La2FrkYy++eSq1rPOj6QvsaD/teHpmZ87p8/kvg+Ih4N/AlysIrx0TERj1CwC/Vdn03M08f0KaeIqJ177fqUWRzYPeI+L8xDG0erUE9MNelz9QCkiRJkiQt65yjcGo0F+voFxQ+0Nie0bNU0VzhdSy9tSb8XJn5UGZeVl+/yczTMvP9lJAnKcNPuw4PzcyLKIHig5QebAcBN9T9G+r+Ciw9dLlzrsNlTesaD7q+0L7GQ72XmXlnvy9n5peBY+ruU+myEnZEzKWsqH03sPeA9vQUEZtSejXuTukJ+T5KWDeDEsr9I6XH4+uBCyLixT2qmhCZubDfC+i3+JAkSZIkScs8g8Kp0Qxy+vUYawZfg4agrtbYHsnQ1qGdKzP/D/i3urtHRLyiR7njKav3frejfUuAs4AXAec0Pu8Xui4LWr9hJMOJW9d4mb6XVXPRkO2aByJibeCIuvvxzPzTGM8BcAJl2P59wIsz84s1lHsoM2/OzBOArSkrYT8R+FZErNSnPkmSJEmS1IdDj6fGrY3tJ9G7J1xzIYrZPcq0NIdBjmWRhc5z/WoSzwVwCmX4McCbgJ91K5SZvwXeHBHTKEOOVwb+lJn3AUTEWxvFuy6qsgxZCKzD4HsJ7Ws8EfdyJOcZ67lg6eu+XsexvYA1gTuB2+uq151e2NyOiFaQ/ovMvAUgIp4DPL9+fmJmXt6tIZn5p4j4AnAw7aHugxZZkSRJkiRJXRgUTo1mULgGZSjto2TmoohYQAlzNhlQZ/N41xBlgGbYM9JzLaasUDwWzWvwtEGF66In3Vbw3aax/csxtmWq/J4Sdj0hItbNzK5DU+s8fLPq7lju5ZXAw5R5Ayf7uYH2ytzdtHr0PRH4xgjqek99AWxPe7GfTRtl/ndAHb9ubG+CQaEkSZIkSWPi0OOpcWlje+MBZc+r78+KiHX7lGsO+Tx/DG26mPbCF9v1KhQRM2gvJHFxZo5kYY5umj3PxjTktbblTXX3RuB/xtiWqXJeY7vnNWac97Lek4vq7tb1Og0611/p34u0n79pbI9naHE/ixvbg/5BY8Ue35MkSZIkSaNgUDg1fkV74YgXDCj7w8b23G4FImJV4M119/eZeeVoG5SZ91Dm/QN4WUT0GrL6Btq93X4w2vM0/H1j+9Kepfr7F9orSH+p9jpclv2IMr8iwB59ys2t70vqd8ai9dzMossCIwD1Hr+s7p5Vn4GxeHdj+9zmgcycl5nR78XS12KPxrFzGp9f19getEhJM2i9rmcpSZIkSZLUl0HhFOjo8bXlgOI/AK6p2/tHxIZdyhxOGcLc2n6UiJgbEVlf83qcq7XoxHTg6DovYLOOtYBP1907aa922yyzcx0621NEbAt8ou4uBr7Vo9z6fep4HWXVZICrGm2fFCO8fn3VocYn1t1XRsSbOstExN8Dr6y7J3QbnhwRGzTack6P0x0D3FW3D42INTvqmEaZI7J1jx91/SJi84jYqN9vioh3U1anhrLK73jC434uofQaBXhjROzQoz3Poz10+T7gF5PUHkmSJEmSHveco3DqnEbp+bRlRKzeqzdXZj4UER8ATqX0Djs/Ig6mBI1rAO8E3liLn0dZGXZMMvMXEXES8Bbg9cCZEXEkZTjp5sDHgFZ4t19mdltleGfg2xFxGqWH4u8ooeJKwIbA6yi9H1uh9EGZeUWPJl0WERdQVj3+HWVo9AaU3oi71jJ/AXbNzAe61gDUQK65+m9zXsNtIpaaYu/PmXl6r7omwMcoC2ysTVmV92+BH9djrwU+WLdvpb0y9Khl5h0RsS/wJcockL+MiH+n9N58KrA3ZQ5AgG9l5tldqnk+cExEnA38tH73dsp/JzYB3ga8vJZ9GHh3Zt471jYP+D1LImJ/4HhKuPnTiPgy5c/FLZRFgV4BvB9YtX7tkMy8czLaI0mSJEnS8sCgcOp8EziEsorvLpQApKvM/ElEvAc4irJq7he6FLsI2GUCht++gxJIvoYSJG3fcXwJJdz7cp86ZlB+0y59ytwPfDwzP9OnzAqU8OcVPY7/HnhbZl7Spw4oveV6LZiyJ+0ecVCGznYLCldpbN8+4Hw9ZeaC2hvyh8C6wL711fRnYOfM7LZ4y2jO9eWIeCrwcUpI+7UuxX5Cuee9TKMMT35ZnzK3A3tm5liHSY9IZp4QEesAn6LMQ/jP9fWoosDnaPc4lSRJkiRJY2BQOEUy88aIOIUyf9zu9AkKa/mv1t51HwB2oPQKu5eyUu2JwDGZOe6FGzLzfmDHiHgrZa6851BWrL0Z+G/gqMy8oE8VH6KETy8FnkcJw55MCRjvoPQM/AVwfGbeNKA5e1FCwi2Bp1B6Bd4K/B/wPcrQ3IdG/yvHZOv6/hdg/ngqysxfRsTmlDkWd6b0koQyn94pwJGZOeYwsuNcB0TEGcA/Ueb2W4fSw/O3wHGZ2XXYd/UTSoi6NfDc+t01Kasc31HrOB2Yn5l3T0R7B8nMIyLiVOBdlB65G1Gei/soq4efR/mz8OvetUiSJEmSpJGIzBx2G5YbEbEVcAFl2OZGmXn9cFukXiLiekqvxAMy88AhN0cTJCLmA28HbsjMDSa47tnAAoD13juf6bPWmsjqJUmSJGmZcf2hOw67CQIWLlzInDlzWrtzxjtSEVzMZEpl5oWUud+mAfsPuTnqoS6q8jTK4iCfG3JzNE4RsXFEbBYRm1F6y0qSJEmSpC4MCqfevpQehXv0W+VXQ7Vdff98Zt7Vt6QeC35GWZjlUmCnIbdFkiRJkqRllnMUTrHMvDQi5lLmWlsf+ONwW6ROmXkC41hNWrrwozswe/bsYTdDkiRJkqRRMSgcgsz8xrDbIC0vJnouQkmSJEmSHq8ceixJkiRJkiTJoFCSJEmSJEmSQaEkSZIkSZIkDAolSZIkSZIkYVAoSZIkSZIkCYNCSZIkSZIkSRgUSpIkSZIkScKgUJIkSZIkSRIGhZIkSZIkSZIwKJQkSZIkSZKEQaEkSZIkSZIkDAolSZIkSZIkYVAoSZIkSZIkCYNCSZIkSZIkSRgUSpIkSZIkScKgUJIkSZIkSRIGhZIkSZIkSZIwKJQkSZIkSZKEQaEkSZIkSZIkDAolSZIkSZIkYVAoSZIkSZIkCYNCSZIkSZIkSRgUSpIkSZIkScKgUJIkSZIkSRIGhZIkSZIkSZIwKJQkSZIkSZKEQaEkSZIkSZIkDAolSZIkSZIkAdOH3QBJetz57KYwy3+HkSRJkjSJ5t017Bbocci/yUqSJEmSJEkyKJQkSZIkSZJkUChJkiRJkiQJg0JJkiRJkiRJGBROuYhYMSKuiIiMiF2H3R7p8S4izql/3pZ6DbtdkiRJkiQtawwKp977gY2By4Hv9isYEc+OiC9FxNURcX9E3BoR/xUR746ICV2xOiLeEhFnRMRNEfFARFwfESdExFYj+O7MiNg2Ij4UEd+JiOsagcz1o2zHzIjYOyLOrr/3wYi4OyIujYijIuLZo6zvZRExv17DeyPiroi4MiK+FxHvjYiZo6lvrCJi/Yg4IiIur+24IyIuqtds1Qk+16si4uSIWBgRf63vJ0fEq0bw3e0iYv+I+EFE/C4ibq734K56D74YEc8fYTumRcTuEXFaRPy51vPnem/fNZJnOCLmRMQbI+LQiPhFfRZaz9a8kbRDkiRJkiSNTGTasWaq1FDqOmAtYLfMPKlP2T2Bo4GVehS5EHhtZt4+zjatTAksX9ujyBJgXmYe1KeOs4GX9Dh8Q2ZuMMK2bAH8CHhan2KLgf0y8zMD6loDOA7YacBpn5uZvxlJ+8YqInYETgSe0KPIFcBrMvPacZ4ngC8B7+pT7CvAe7LHH/yIWAisN+BUCRwF7J2ZS3rU8xTgZKBf0PwryjN8c486ngZc3+f7n8zMeQPaSkQ8HVit7r4PeC9AZsag745GRMwGFgAs2Gcms2f57zCSJEmSJtG8u4bdAg3ZwoULmTNnTmt3TmYuHG+d/k12ar2XEhIuAL7Tq1BEvJIS6KwE3Ax8AHgh8GpK+AIlgDk5IsZ7D4+lHRKeDewMbAnsCVxDeUYOjIi9+tTRDFz+ApwJLBpNIyLiCcDptEPC/wJ2o/zuHYEjgYeA6cAREfHmAXWdSTskPA34B2BrYBtg91rfuP8ADRIRz6Hc6ydQrsnHgL8DdgC+Wos9CzhtAno3Hkw7JLyEcv22rO+X1M/fBfQMfYF7gTOAeZTr9FLgbynPyIGU5zEoPWM/1a2CiFiFcs1bIeHPgTcBzwdeQXm2l9R6fxwRvcLw5nOVwNWU52JUMvO6zLwsMy8Dbhnt9yVJkiRJWl7Yo3CKRMQ04FpgfeCwzNy3R7nplGHJGwF3A8/LzGs6yhxN6RkF8PbMPH6MbdoOOKfungrskpkPN46vBfy6tvkvwDMy884u9byLEoJdlJlX18+up4R+I+pRGBEfBI6ou9/NzEcFgRHxeuCUuntpZm7Ro67jKcHgYuBtmfntHuUCmJaZiwe1b6wavS0XA9tm5gUdxz8MHFZ3D8jMA8d4no0oz810Sk+9bTPz/sbxVYFzKeHcYmCTzueqlpve73pExJrARcAzKMHtupl5R0eZDwGH193jgD07ezBGxB7A1+ruBzPzsz3O9R7gYuDizPxLRLyEEmjDCHsUdtQ5DzgA7FEoSZIk6THOHoXLPXsUPra9nBK4AXyjT7ldKCEhwCHdwhzgw5TgrrU9Vh+p7w8D72uGhACZeRvQCjTXoPQyfJTM/EpmfrMVEo7RixrbXXu8ZeaPaPeM2zwiVu8sExHbUEJCgIN7hYS1vpzkkPAFtIdkH9sZElafoQR8AHtHxIpjPN0+lJAQ4P3NkBAgM++j9AKkltu7WyWDrkcd6v6VursipZdmp7fX93uBfboNc87M44Dz6+5HuvWMzczbM/PfM/NnmfmXzuOSJEmSJGliGRROnVYPuasy89I+5XZubM/vVqCGPq2hy5tFxDNH25g6zHWHuntmn9T5ZErPRoA3jPY8ozCjsd1vrr5mcDqjy/F/ru+LKCHcMDXv5XHdCtQ5/lo9Qteg91yPPdWeka1h1n/IzAt7nOtCynyIADvX743FvY3tlTvasgqwWd29IDP7/RPX6fV9HeDFY2yLJEmSJEmaIAaFU2f7+t41xGloBSZXZOaf+5Q7t7G9zRjasyXthVLO7VUoMx+k3eYtx9HjbZArG9vP6FNuw/p+R+dCLhExg3Zg9tPMXFQ/nx4RT6srD3cLFydL617eSxnC3ct47+XTaS9A0vNedhyfDWww2hPVnn/NYeF/6CiyRmO76yIlPY5vN9q2SJIkSZKkiWVQOAXq3GUb1N2L+5SbSQlw4NEBTKfm8U3H0Kzmd0Z6runAqHsvjtAxlCHQUBb8eJS6evBz6+6XuxR5Du0ebhdExLoRcRxwJ2X13BuAuyLiJxHxdxPV8D5a1/jqAUN6h3EvR3yuiJgWEetFxGuBX9AOQM/KzN91FG/2Nuy1ynO3438zkrYMU0TM7vcC1h12GyVJkiRJGo/pg4toAjRDqUt6liohYWs46KAJKBc0tuf0LNVb8zujPdfvx3C+vjLz9xHxL8DngV0j4smUMPBaykrRL6M9rPjndF9xtxk2rQxcWr9Lx+evBl4ZER/MzCMn7le0RcTKjXP3vb51kY57gdWY+nvZU0T0W+noN8Dczg8z866IuAl4CrBVRMyovVK72baxvX6PMsuSBYOLSJIkSZL02GVQODVmN7Zv6VOuuTjHogF1NntuzRx1i6b2XCOSmUdHxK8oi6y8gfZw7ZZrgEOB+T166D2psX0AZWj1j4F5wGWUHmxvrHXMAj4bEVdk5k8n8ndUo7m+UK7xaiz79/I+4EPAcZn5QI8yp1BWK14L+CBwSGeBuujMjo2PHrUwzWPZVg8cxfQZnRm1JEmSJLVdf+iOgwtJU8ygcGqs3djut3prc2GIXr2wWv7a2F5l1C2a2nONSF3F+B8pvQe7eQawG/A7oNsKwqs1tlcCTgV2rguGQAlpvxgRl1Lm6lsBOCwiTu+2Mu84jeb6QvsaL0v3cvP6Po2y4Mj2lADwcGDjiPhIZj7U5XuHAm+lhLH/XnuH/idl+PealLD2EErv2cWU/w5N2nM1gQb19lyXPlMLSJIkSZK0rHOOwqnR7OnWLyhs9tAatOjGSo3t+0fdoqk910ARsS4l/HsfJTjan7JwyQxKuLQLJSB8KXB2RLypSzWdPdw+3AgJH5GZ51FWc4ayQu9mnWUmwGiuL7Sv8TJzLzPzsvr6bWb+LDP3B7agBK57A6dFxLQu37sB+HvgHkoYuDdlsZoHgZuAoyg9CPdrtP2eAe0eusxc2O8F9Ft8SJIkSZKkZZ5B4dRoBjn9ek41w5JBw0KbvedGMrR1mOcaiaOAZwMJvDYzD83MazPzocy8IzN/CGwNXE4Ju+ZHxDoddTR/03WZeUWf853R2H7BBLS/02iuL7Sv8TJ9LzNzAfBPdfflwJ49yv0M+H/AscDtHYcvBF4DHN1ob78AXZIkSZIkTQGDwqlxa2P7ST1LLb0QxeyepYrmMMixLLIwlefqKyLWoPQYBPh5Zp7drVxmLgL+ve6uBrylo0izbaNZ1OPJI2zqiNX5+26ru32vb/39rQDvsXAvf0a7N2K3np0A1KB3L8rQ+6cCzwKekJlb13khn9MoPuEL5EiSJEmSpNExKJwazaBwjV6FahDWCm82GVBn8/jlY2hTM5gZ6bkWA1eP4VyDPIv2s/i/A8r+urHd2e7fNbYfNSS2Q/N4t4VRJkLrvmwUEf3mAx3GvRzrucjMh2n3AHzaCMpnZt6UmVdm5t2NQ9s0tn85lrZIkiRJkqSJY1A4NS5tbG88oOx59f1Zdd6+XrZrbJ8/hjZdTHvhi+16FYqIGcBWre9k5kgW5hitZlA3aIGdFXt8rzU33h/r7oYD6mkev3FA2bFq3cvVgOf3KTfee3kd8KcudXWzbX2/kbK4yKjVZ6K1pO94hqK/tb7fT1l4RpIkSZIkDZFB4dT4Fe2hmoPmw/thY3tutwIRsSrw5rr7+8y8crQNysx7gLPq7ssioteQ1TdQVq8F+MFozzNC11PmJgR48YCyzSDsui7Hv1/f14mIv+tTzxsa2/894Jxj1byXe3QrEBErUFZ6BrgT6Drsup+647HO7AAAIABJREFUYvMpdXeTiNiqW7n6eatH4SnjWOl5J9qLplzar2AvEfH3tIcefzMz7xxjWyRJkiRJ0gQxKJwCtRfeRXV3ywHFfwBcU7f3j4huPeMOpz2E+fBulUTE3IjI+prX41xH1PfpwNGdK9hGxFrAp+vuncAxA9o+Jpl5G2WBC4AtI+Lt3cpFxNOAj7W+BpzWpdiRtBeP+XxErNZZICLeBryk7p5WV6ztLDOvcf3mjvS3NGXmRbRDyD0jYusuxT4IbFq3P5eZD3Vpy0sabZnf43RH0u5h+YWIWGrRnLr/hbq7uJbvPM/LImKjfr8pIv4G+HzjoxN6lFu/Tx0vpP0s3QF8tN85JUmSJEnS1Bg0zFMT5zRKb7gtI2L12qPvUTLzoYj4AGUo5izg/Ig4mBI0rgG8E3hjLX4ePYKakcjMX0TESZRFQV4PnBkRR1KGsW5OCeVagc9+mdl1ZdoaLm3T8XFrNduZXYK20zPzzx2ffRT4OWXuwOMiYgfgO5SFOlanXLu9gTVr+a91W9U4M/8YEZ8ADqMM970oIg4DLgOeQOlJ+J5a/G5gn26/aQL9C2U48SrAzyLiU5Reg6tQrvu7arkrgc+M9SSZeWVEHAHsB/wt5bn5NCV03hDYF3huLX54Zl7VpZptgNMj4izKqtD/R1mxeDplLsJXAP8ArFzLH5eZZ3WpB+C0iLgH+CbwW+BeYD3Kcza31vlX4K2ZeUuv3xURrwKaQ/Cbcyz+v45na1Fmfq9XXZIkSZIkqT+DwqnzTeAQSsiyC3B8r4KZ+ZOIeA9wFLAO7Z5gTRcBu9SFJcbjHZRA8jXA9vXVtAQ4KDO/3KeObYDjehxbs8ux7YGlgsLMPKf2JPwyZU6/f6ivbk4C3terMZl5eEQ8iRKO/Q0wv0uxW4CdewRmUIK8ltt7nWuQzLwkInYFvkG5zp/qUuxKYMde4fEofIyygvM7KKHgSV3KHAv8W586plECwVf0KfMw8Flg/z5lAti6vrpZCMztEzS27EfveRd3qq+WGwCDQkmSJEmSxsihx1MkM2+kPY/c7iMo/1VKj7ivAtdShtPeTulF+F7gRXXI7njbdX9m7ljbdCYlQHuQsvryN4FtMnPeeM8zwracSOkxdhBwAWVY6mJKb7QrgK8DL83M3QYtqpKZ+wMvovS4vJ7Se+0uyiIuHwc2zswL+lTRCriupPsQ5xHLzFOBLYD/qPXdRxnK/StqT7/MHPdq0pm5JDP3BHakPGt/otzLP9X912TmXpm5pEcVn6UEb5+jXP/rKXNr/hW4GTgXOBB4VmZ+ZEBI/SHgPym9CW8FHqKEw+dQelluOoKQUJIkSZIkTaEY+3oGGq26mMQFlB5ZG2Xm9cNtkbqJiJUooeJKwNszs2fvTz221Pk6DwDIzJjgumdTAnbWe+98ps9aa8A3JEmSJC3Prj90x2E3QY9xCxcuZM6cOa3dOd3WYBgtexROocy8EPgpZXhnv2GbGq6tKCHhNcCJQ26Lxikinh4Rm0XEZpSh2ZIkSZIkqQuDwqm3L6VH4R79VobVUG1b3z81AXNAaviOAy6tr/cOuS2SJEmSJC2zXMxkimXmpXWl1o0oKwr/cbgtUqfMPIgyT6IkSZIkSdJywzkKJWkCNOcoXLBgAbNnzx5yiyRJkiRJj2fOUShJkiRJkiRpUhgUSpIkSZIkSTIolCRJkiRJkmRQKEmSJEmSJAmDQkmSJEmSJEkYFEqSJEmSJEnCoFCSJEmSJEkSBoWSJEmSJEmSMCiUJEmSJEmShEGhJEmSJEmSJAwKJUmSJEmSJGFQKEmSJEmSJAmDQkmSJEmSJEkYFEqSJEmSJEnCoFCSJEmSJEkSBoWSJEmSJEmSMCiUJEmSJEmShEGhJEmSJEmSJAwKJUmSJEmSJGFQKEmSJEmSJAmDQkmSJEmSJEkYFEqSJEmSJEnCoFCSJEmSJEkSBoWSJEmSJEmSMCiUJEmSJEmShEGhJEmSJEmSJAwKJUmSJEmSJGFQKEmSJEmSJAmDQkmSJEmSJEnA9GE3QJIedz67Kczy32EkSZIk5t017BZIGgX/JitJkiRJkiTJoFCSJEmSJEmSQaEkSZIkSZIkDAqJiBUj4oqIyIjYddjtkTSxIuL6+ue7+bp+2O2SJEmSJGlZs9wHhcD7gY2By4HvTkSFEfHsiPhSRFwdEfdHxK0R8V8R8e6ImNAFZCLiLRFxRkTcFBEP1FDkhIjYahR1rBoRH46IiyLijohYFBGXR8QREbH+gO/Oqm34TEScW3/zXRHxYETcEhHnRMRHImLNAfU8LSLeGRFfre24ISLuq9dvQUT8KCL+ISJWHOFv2iIivhIRV0bEvRFxd0T8LiIOG/SbJkNErF+v5+W1PXfU3/mhiFh1gs/1qog4OSIWRsRf6/vJEfGqEXx3pYjYKiLeX5+jKyJiSStgG2U7ZkTEnhFxen0+/1qfrSsi4mujeUZrfVtGxH/Wa3h3reuaiDgtIv41ItYeTX2SJEmSJGlpkTmqv/s/rkTETOA6YC1gt8w8aQLq3BM4GlipR5ELgddm5u3jPM/KlGDztT2KLAHmZeZBA+rZEDgNeFaPIncBb83Mn/T4/suAM0fQ5NuAt2XmGT3qORj42AjquRTYKTOv61UgIj4JfByIHkXuBuZm5g9GcL5xi4gdgROBJ/QocgXwmsy8dpznCeBLwLv6FPsK8J7s8Qc/Io4D5vb6cmb2uqad9cyhPFebDyj6H8AHe7Wn1rUScBSwJ73vKcAumfnDLt/fGJhRdw8GdgJuyMwNBrRtVCJiNrAAYME+M5ntqseSJEmSqx5Lk2jhwoXMmTOntTsnMxeOt87l/W+y76WEhAuA74y3soh4JSWIWQm4GfgA8ELg1cDJtdhWwMkRMd5rfyztkPBsYGdgS0qYcg3l3h4YEXv1ae9M4Me0Q8KvAjsAf0cJ7RZRwq3vRsQWfdqyADge+BfgDcDWwIuAXSlh5sOU6/yjPvUsAX5LCVnfWX/bC4CX1v3/qeU2B87s1QsvIvYDPkEJlG4C/pVyzbeq238GZgHfioi/6/ObJkREPIfybD2Bcj0/Rrm+O1CuN5Trf1q9H+NxMO2Q8BJgN8ozsVvdpx7vFx43g7h7gHMp12zEaq/ZZkj4f5TwcWvgFcCBwL312D7Ah/rUNQP4AbBXbdt/U56HbSj3dFfgU8BVverIzCsz87LMvAy4czS/RZIkSZKk5cly26MwIqYB1wLrA4dl5r7jrG86ZfjyRpQea8/LzGs6yhwNvK/uvj0zjx/jubYDzqm7p1J6Uj3cOL4W8GvKb/sL8IzMfFRAEhHzgAPq7kcy8/CO41sD/wVMB87OzJd2qWNa89w92rszJewBODkz39ilzPTMXDygniMpYSTABzLzCx3H16OEpCsBfwJekJl/6lLmIuCplGDyeZm5pN95xyMizgZeAiwGts3MCzqOfxg4rO4ekJkHjvE8G1Gev+nAr+q57m8cX5US+v1tbcsmnc9nLbcrsApwMXB5Zi6JiHOA7WBkPQoj4o3A9+ruBcCLO5+RiHh+PbYi5Rl9crf7HxEHUnqHAnwoMz/T57wrZuZDA9o2H3g79iiUJEmSpoY9CqVJY4/CifVySpAG8I0JqG8XSkgIcEi3EAb4MCUUaW2P1Ufq+8PA+zpDmMy8DWgFn2tQehkupc711wrdLgceFcDUUOvYurt9DXc6y/QNCWuZHwJ/qLvb9ijTNySsDmlsd6vnLbSHfB/QGRLW89xIOxx9DqW356SIiBdQQkKAYztDwuozlOsPsPdI52DsYh9KSAjw/mZICJCZ91Hm46SW27tbJZn57cycn5m/G0eA+qLG9iHdnpHM/DWlNyuUZ3STzjIR8Qxgv7o7v19IWOvsGxJKkiRJkqT+lueg8M31/arMvHQC6tu5sT2/W4Ea1rSGOG8WEc8c7Unq8NQd6u6ZfdLikyk9G6EMB+70EuCJdfvrfUKh+Y3tbvWMVGuo6coTUEevel7Q2P5pn3pOb2y/aRztGaT5TBzXrUC97q2epWvQDhZHrM5NuFPd/UNmXtjjXBdS5kME2Ll+bzLMaGz3m3exGaZ3m9PzXZQeh0kZrixJkiRJkibR8hwUbl/fu4YqY/Di+n5FZvab0+3cxvY2YzjPlrRDlXN7FcrMB2n/ti279FR7cWO7Zz2UYaytgG4s7SUiNgX+X939Q7+yA+zW2O5Wz5Ma2zf3qad5bLtxtGeQ1jW+lzIUvJfxPhNPB9brUle/c80GNhjDuUbiysb2M/qU27C+J93nGPz7+v6r1uI1EbFCRMyOiKdHxCrjb6okSZIkSWpZLoPCOpfYBnX34gmobyYleIHBQVjz+KZjOF3zOyM913Sgs/fiiOqpQ4JbPb9G3N6IWDUinhkR/0pZbGVaPfS5kdZR61kjIp4bEZ+lLHQC8CBldd9OzR6HvVYY7jy2Qa+FUSZA63pdPWBo9TCeibGeayS+Rbs36751PtClRMRzgR3r7kmZeXfH8bVph4wXRMSsOkflbZR5AK8F7o6Ic+uq0pIkSZIkaZymDy7yuNRc7faSnqVGbjbt1WIHTRy5oLE9p2ep3prfGe25ft+lnnu7LXTSpZ4tgLUjYqXM/Gu3QhExlx5DbKsjgBMHnKu54EQ391MWguk2B+TltIf7bkd7pelOzfkNg3L/ruxRdkwiYmXKSs8w4D5l5l8i4l5gNab+mZhwmXlrfRZOpMxXeHEN+a4EZtbPPkgZovwbymrUnf6msX0/8L+0eyC2TKfcy20j4j8ys1s9E6b+A0M/607m+SVJkiRJmmzLZY9C2r3/AG6ZgPpWb2wvGlC22ett5hDP1apnUB2D6hmJ3wBbZeaHc3zLbH8b2DQzv9vj+CmN7U/UsG4p9bNPdHy8eme5CTCa+wTtazzMZ2LCZOYPKCssH0sZdv51yirHZwLzgPsoAeE2PYbqN4eR700JCf+HEgCvWo/vDtxUy+wTEe+Z8B+ytAUDXuPunSxJkiRJ0jAtrz0K125s/6VnqZFrBlIPDijb7I03ljnWJupcrXoG1TGonqYfUuY0bJXbkLJozC7AiRGxd2b+uNeXGz5G6X0IMIvSm/GdwK7AehHxjsx81Jx2mfnLiPgR8HrKisbnRsRHKQEVwNbAp+qxB2kvujEZc92N5j5B+xoP85mYMHVOzLcCr6Pd27ZpHcqck1cCp3U5vlpjeyXKHI87ZOYD9bP7gW9GxMWUXsGrAQdGxNc7V3wehq0eOIrpM9YaXFCSJEl6vNuv2//uS5Pr+kOdoWqsltegsNlbqWdQGBFPZ+nAoumWzGz1Rnyg8fmMboUbmqu7jiXQmKhzteoZVMegeh5RhzA3hzFfDJwUEf9A6VF2SkTsmZnz+50sM28Ebmx89D8R8VXKHIXvBn4ZEdtn5m+7fP3tlBWPt6Is/PLzLmUuBi4D9qj79/RrzxiN5j5B+xoP85mYEBGxGvATyrDgh4HDKEPSr6WEmi+k9OrcBjg1IvbJzM65Kx/o2P9YIyR8RGZeFRFfBD5E+QeAlwGnTuDPaRo0VHtd7FUoSZIkSXoMW16HHjcDh369qo4DLu3xel+jXDNoGjScsxk8jmRIaqeJOlernpEMPx1XmzPzBOC7lOftqIhYYwx1PAx8gDLEcw3giz3K3UkZnroPS8/JCPBn4CDKasSzGp9PRK/STqO5T9C+xsN8JibKJ2nPA7lnZu6bmX/IzAcz8+7MPJOy6vjZlN6Gn42ILTrqaP6mB2vZXs5obL9gnG3vKTMX9ntRni9JkiRJkh6zlteg8NbG9pN6lhq55gISgxY8aPZKWtCz1OSfq1XPahHxxBHWc2uvhUxGoDV/4GrAq8dSQWY+CJxed7eOiKf2KpeZR2bms4EnAhsDTwWempmfqL+hFUzdw+AFQMbS1gcoK/TCgPtUg9NWgDfMZ2LcIiJo99S8MjO/3q1cXQX643V3hcZ3urXt5nrve2mWffIomitJkiRJkhoMCkvvtK4y8yWZGT1e8xrlFtEOKzYZcO7m8ctH23CW7iU30nMtBq4eSz0RMZ32arNjaW9L85o/barqycy7MvOqzLyptZBKRKwDbFSLXJyZS8bRnn5a12ujeh17GcYzMdZzDbIO7fB90Iriv25sd7b7KuChuj1tQD3N44sHlJUkSZIkST0sr0HhpY3tjSeozvPq+7MiYt0+5bZrbJ8/hvNcTHvBiu16FYqIGZR5+qCEYZ09ss5rbPesh7Jybau321ja27JeY3s8Q14nop7daC+w8Z1xtGWQ1jVeDXh+n3LjfSauA/7Upa5uWkOCbwSuH8O5BmkGdYPmQF2xx/fIzIdoL0KzTp33sJcNG9s39iwlSZIkSZL6Wl6Dwl/RXshhouY0+2Fje263AhGxKmUVYIDfZ+aVoz1JZt4DnFV3XxYRvYaavoH2PHw/6HL8HOCuuv32OmS0m7mN7W71jNTfN7Yv7VmqjxoWtYYt3w9cM4Y6ZgH71t07gW+OpS0j1HwmOofWttqzAvCPjfb0m4uvq9pTsjW0e5OI2Kpbufp5q+feKa0elhPsDuDuur31gJ6UzVDzui7Hv1/fpwE79annDY3t/x7YQkmSJEmS1NVyGRTW3nUX1d0tJ6jaH9AOrvaPiA27lDmc9lDnw7tVEhFzIyLra16Pcx1R36cDR0fEUkMzI2It4NN1907gmM4K6jX4fN3dlLJqbGdbtgb2rLvnZuajVnSt7V25RztbZfYBXlN3r2fp3oxExFoR8cYBdawMfI32HHTfz8z7upR7SkSs2Pl5PbY6JXxq9fj8UA1eu5Wd17gPc/u1rZfMvIh2cLVnvZ6dPki5/gCfqz3pOtvykkZb5vc43ZG0e+V9ISKWWqSn7n+h7i6u5SdcHcZ9Wt19KvCxbuXqvIyfbnz04y7Fvga0Vhb/VB0y3lnPS4B/qLuXMb5er5IkSZIkLdcGDQ18PDuN0qNpy4hYvVdgNFKZ+VBEfAA4ldKT7/yIOJgSSK4BvBNohWHnASeM41y/iIiTgLcArwfOjIgjKcNPN6eEM+vX4vtlZq9VfQ8HdqUMvz4sIjYCTqL01tse+CjlGbkf2LtHHfOAz0TE9+vvuoYyJHj12pbdgRfVsg8C76wLWTTNBL4XEVdTgryLKENI/wqsRQlz9wSeUcvfSLtXYKfdgQ9FxNeBc4GbKPdjK8pK1a3rclxmHtujjon0L5TwahXgZxHxKUqvwVUo9+9dtdyVwGfGepLMvDIijgD2owwXPz8iPk25HxtSrtdza/HDM/OqbvXUYfOv6vh43cbxuR3HzsvMzvkvD6T0AFwVmBcRzwe+DlwLrEy5F3vTvhdnZebPuvymRfXP1Lco81FeHBGHUp6PlSm9S/eh9DhcDLxnknpJSpIkSZK0XFieg8JvAodQAoddgOPHW2Fm/iQi3gMcRVnU4Qtdil0E7JKZD4/zdO+gBGCvoYR623ccXwIclJlf7tPeeyJiR+AnwDMpodW7OordDeyemb/p05YnUYLQd/YpsxB4R2b+vE+ZjegdALZcALwtM//Up8w6wEfqq9NiSiD30QHnafbIu31A2Z4y85KI2BX4BuV+fapLsSuBHccbVlMC4idTno3nUkLfTscC/9anjk2A4/oc7zy2Bx0L5WTmHyJiJ0rAtxbwuvrq5hcsPSx9KZn57dpD9rOUFZuP7lJsEeWZsDehJEmSJEnjsNwGhZl5Y0ScQpnfbHcmICis9X41Ii4APgDsQBl+eS9lhdkTgWO69Kgby3nuB3aMiLdS5hF8DvBE4GbKcNejMvOC3jU8Us/VEfFc4J8ogc1GwAzKKs4/oQyHvaFPFTsAL6MElZtSQro1gQdqW35DGVb6nW5Dhas/Ai+sdWwHPL3WszolBPojZV7J7wJnDOg1djIl/H0ppSfdkyk9ExcCPwOOzczf9fl+S2uY8JW0h9KOSWaeGhFbUHoX7gjMpvSuvJrym47qc21Gc54llCHO36cEvi+gBHW3URbB+XJm/nS85xlhW34eEZtQeoK+Gng25flcDPy5tuebwI8G9QLMzKMj4hzKM/pyyoI2D1N6KJ4OHJmZN03ST5EkSZIkabkRy/NIvbq4wwWU0GGjzLx+uC3SsiAiVqIs9LIS8PbMnJAQWcNX53h8O3BDZm4wwXXPpgTsrPfe+UyftdZEVi9JkiRJGqHrD91x2E2YEgsXLmTOnDmt3TmZuXC8dS6Xi5m0ZOaFwE8pc5ztP+TmaNmxFSUkvIbSC1SPYRGxcURsFhGbUXo1SpIkSZKkLpbroLDal9KjcI+IWH9QYS0Xtq3vn5qAuSQ1fD8DLq2vnYbcFkmSJEmSllnL7RyFLZl5aV3JdSPKKqx/HG6LNGyZeRBw0LDbIUmSJEmSNJWW+6AQIDO/Mew2SJocEz0X4Uhc+NEdmD179lSfVpIkSZKkcXHosSRJkiRJkiSDQkmSJEmSJEkGhZIkSZIkSZIwKJQkSZIkSZKEQaEkSZIkSZIkDAolSZIkSZIkYVAoSZIkSZIkCYNCSZIkSZIkSRgUSpIkSZIkScKgUJIkSZIkSRIGhZIkSZIkSZIwKJQkSZIkSZKEQaEkSZIkSZIkDAolSZIkSZIkYVAoSZIkSZIkCYNCSZIkSZIkSRgUSpIkSZIkScKgUJIkSZIkSRIGhZIkSZIkSZIwKJQkSZIkSZKEQaEkSZIkSZIkDAolSZIkSZIkYVAoSZIkSZIkCYNC/f/27jtMsqJc/Pj3ZZcoIggqklwUFBSuKOGCSlKMoBcQETNJBRWzoqKyYMAcfkZMIKigCIgCXlQkelEBRVFRBFllSYIkyen9/VHV9tneTjPTu7278/08z3n6nD516lTPqZndeeetKkmSJEmSJAkDhZIkSZIkSZIwUChJkiRJkiQJA4WSJEmSJEmSMFAoSZIkSZIkCQOFkiRJkiRJkoCZ426AJC1xPrUhrOTfYSRJkiSpq9m3jLsF6sHfZCVJkiRJkiQZKJQkSZIkSZJkoFCSJEmSJEkSBgolSZIkSZIkYaBwoIhYOiL+EhEZES8ed3skTUxEnFm/f+fZxt0uSZIkSZIWNQYKBzsAeCxwCXDcKCqMiCdExJcj4rKIuDMiro+IsyPitREx5ZWoI2JmRDyp1ve1iPh9RNzXCJLMGrKelSPimRFxUET8ICKubtRx5hTbuFREnLeoB24iYtWIOCQifhcRt0TErXX/kIhYdcT32ioijo6IORFxV0RcExH/GxF7TLCePSLitHr9XbW+oyNiyyGu3TEiZkfEKRFxSUTcEBH3RsRNEXFhRHwyIh43oI6Vahs+GRFn1X5+S0TcExH/rIG7dw779YuIR0TEB+v9b67fM3+LiCOH+UySJEmSJGk4kblIxmcWCRGxInAFsBrwksw8dgR17gN8AVi2R5FfAjtl5r+mcI+Dgdl9iqybmXOGqOcKYFaP02dl5nYTbVuj7jcAn2u+l5kx2foWhIjYHDgJeGSPIlcD/5OZF4zgXu8HDqZ38P5HwO6ZeVefOpajBLN36lHkAWB2Zn6gx/UzgXuHaO69wPsz8yM96tkB+OkQ9dwAvDwzT+tVICJeABwNrNSjyAPAYZn53j51rAs8qB6+DtgfRt/fImIt4EqAK9+yImut5N9hJEmSJKmr2beMuwVLhLlz57L22mu3DtfOzLlTrdPfZPvbnxIkvBL43lQri4hnA1+hBAmvA94I/DfwXOCEWmxL4ISImMqzaQZA7qIEHy+fYj3XASdPoU3tSiPWBD4EJCVYtMipbfwRJUh4H/AxYJu6fay+twZwci07lXvtCxxC+X68HNgH2ALYGTijFns+8LUBVX2ddpDwjHr9FrW+y2v9h9b79XILJTj6HmAPYFtgc+B/gE/X80sDh0XEfn3quRI4CngTsCuwFfBU4MWUYOb9lO+tH0bEf3WrICK2Br5PCRLeDXwC2B7YDHg5cGH9TAdFxFt7NSQzr8jMP2TmH4B/9mmzJEmSJEnTmhmFPUTEDOBvwDrAxzLzwCnWN5MyfHk94FbgyZl5eUeZL1AyngBelZlHTfJez6ZkAp4P/D4z74uII4FX1SLDZhS+nZJReX5m/qO+1+owk84ojIgTKUGsbwCPoQSjFqmMwo6v1+6ZeVzH+RfRDh4fkZl7T/I+K1O+xisD/wA2zcwbGudnACdSAoUA22bm2V3q2RY4sx7+CNglM+9vnF+NElhbB7gJeHRm3tylnhnN67qcX7fWswpwPfDIzvKD6qhldq6fC+CEzHxhx/kAfgdsTAkqPjszT+8oswwleP1M4E5g/cy8asB9Z1MyN80olCRJkqRxMaNwJMwoXLieSQmqAHxrBPXtQgkSQhkq2S3D7x2UIE5rf1Iy87TMPDwzf5OZ902hnk9k5vGtIOEoRMSulCDhDcA7R1XvKEXEIygZawCndQYJAep7rSGzr6zXTMarKUFCgAObQcJ6n/spweNW4K1Xv2h9Le8HXtcZqKv1toLdq1CyDOczKMCXmVcA362HDwM2mGgdtcwPgD/Xw226FHkyJUgIcGxnkLDWcQ/w+nq4PPDmQfeVJEmSJEm9GSjsbff6+tfMvHgE9e3c2D+yW4HMvIN2ltpGEbH+CO67yIiIlYD/Vw/fMZV5GBewFwAz6v4RfcodWV9n1Gsmo9UvbqU9/Hwe9S8CP6uHz6xzZ/5HPX5GPfxpn78gnFDvA2U48GTd3thfbgT1dKtj88b+j3tVkJl/pT2sfrcptEWSJEmSpGnPQGFv29fXX46ovq3r618y89o+5c5q7D9tRPdeVHwEWBM4OzOPHHNb+tm6sX9Wz1JTfFZ16OwW9fC8miE36F7LMm8QjVrHsh3l5lPrb/XnLSJi6Ym1GCJiecp8hVAWErl0onXUejYENqmHf+5S5KGN/esGVNc6Pysi1ulbUpIkSZIk9WSgsIs619isenj+COpbEVirHnYLijQ1z2841XsvKiJiK+C1lBVz9x9zcwZpfd1v6RfUzcxraGfoTeZ1kuxCAAAgAElEQVRZrQ/MrPtT6Rcb9ijXr56Z9f4DRcTSEbFOROwB/B/tIfRHZOa/h6mj1rNCRKxfFx45g3bW5me7FG9mLT5kQNXN848ftj0TFRFr9duA1RfUvSVJkiRJWhhmDi4yLT2lsf/bEdS3Fu0VhAdNLHllY3/tnqUWIzVz7SuUwPRHM/NPY27SIK2v+zCTgF4JPIHJPavmNVPpF1Opp+uziIhZlEVWevkZ8LYB9yIi9qT/8O1PAN/u8v4ljf1tgeN71P9w5p0ncUFmFF45uIgkSZIkSYsvA4XdrdXY/+cI6ntwY/+2AWWbmVQr9iy1eDkQ2IgSePrAmNsyjNbzGvSsoP28JvOsRtUvFmb/+hfwBuC4YRYt6eMiYL/M/FWP8+cAN1KGIO8VEZ+r8xF2+gDtzESY92sxNlve9XlmLrPauJshSZIkLdHmfGTHcTdBWuIYKOzuYY39m3qWGl5zsYZ+89AB3N3YX34E9x6ruiDLQfXwDZl55zjbM6TW8xr0rKD9vCbzrEbVLxZE/7qK9qrDMylzSz6HslryF4FHR8RhmZkD7vcD4ILG/R5DWShoF+DbEfHmzDy586LMvDMiPgh8ihLQPCsiDgROBf5NySJ8J/AyymdeZojPNFWDskZXZwRTFUiSJEmSNC4GCrtrLqTQM1AYEesCD+px+p+Z2cpGvKvx/jLdCjcs29hfHIJqgxxOCWQdn5mnjqsRdYjqw3ucvj0zm8Ns7wJWYPCzgvbzmsyzGlW/GHn/ysx7gT803roIOCUivkqZX/BDlLkK9+53s8y8Gbi58db5wLER8Qrgm8BJEbFPt8VtMvPTEfFYYD/gkcBRXW7xL+AztDNVh54zcaL6rCYNQET0Oy1JkiRJ0iLPxUy6awZe+mUoHQFc3GN7XaNcM3gxaLhnM/A4zNDXRVZE7E1ZPfrfwJvG3JzX0ftZdc6h13pewwzNbT2vyTyrUfWLhda/MvP3wHvr4V4R8ayJ1lHrORo4jvIz6PMRsUqPcvsDOwNnA/c1Tt1BCRxuzLyByFFkAEuSJEmSNC2ZUdjd9Y39hzL1LKVmJtJaPUsVzeGNi/viCQfW17OArXtkXP0ny6+uqgslw+9HC7ht/cwFHsHgZwXt5zWZZzWqftFZzwX0Nor+dRJl+DHAbsBPplDP7pTg5XOB73QrlJknUTIPl6cM730AuLpmPRIR/9UovqgvlCNJkiRJ0iLLQGF3zUDhKsDfuxXKzO2GqSwzb4uIKylBmg0GFG+ev6RnqcVDa5jrTnUb5Jj6+ndgpIHCzJwNzB6y+J+ATYGHRMTqmXltt0IR8UhgpXo4mWd1KXA/ZTGOqfSLP/Uo16+e+4DLBjWwh+b3x6MmWceE66nzW3Zbiflp9fUu4HdTaI8kSZIkSdOaQ4+7u7ix/9gR1XlufX1cRKzep9y2jf1fjOjemphzG/vb9iw1xWeVmfcAv66HW0VEv/kFW/e6m/kzBs+nvYhJz/bW+rdsXVPvPxlrNvanMjx+yvVExCbAhvXwB60sQ0mSJEmSNHEGCru7gPZCD5uPqM4fNPb37FYgIlagDMUE+FNmXjqie49FZs7KzOi3UYYlt8q33p81vlYD8EPK8FaAvfqU27O+PlCvmYxWv1gJ2LVbgYhYC9ihHp6emfMMha/Hp9fDHWr5bnalnQF54iTbC/Cixv7FPUstnHo+1Nj/Ys9SkiRJkiRpIAOFXXRkem0xompPBC6v+++OiMd0KfNxylDn1v58ImLPiMi6zR5R2xZ5C/Nz16HG366Hz46I3bq050XAs+vh0d2GJ0fErEabz+xxu68Bt9T9j0TEqh11zKAEwGbUtz7Ro57W+zOBL9TrmvWsBny0Ht5c79vZ3p3rcOqeImIb4P318D7aw8WbZfaMiOUG1PMW4Hn1cA7zZnG2yqwUESv3uD4i4sONOr6Vmef0u6ckSZIkSerPOQp7O4UyjHOLiHhwZxbXRGXmvRHxRsrceysBv4iID1ICkqsArwZeWIufCxw92XtFxIqURSaa1mvs7xYRNzSOL8rMi7rUswmwSY/brB4Re3a89/3MXKxXam44CHgO8DDgmIjYDDi5ntsJeFvdv572KsATlpk3RsSBwJcp8/T9KiI+RMmwWwN4M2XlaIBjMvOMHvX8PCKOBfYAXgD8NCI+A1xNWRn4IGCdWvxdmdltdeCdge9GxCmUDMU/UoKKywKPAZ5PyXht/YHhA5n5ly71zAY+GRHHU/ry5ZShxQ+ubXkZ8NRa9h7g1Zl5X5d6HgucHRHfA04F/kb5mfUEYF/aw6h/CxzQ7esiSZIkSZKGZ6Cwt+8AhwHLAbsAR021wsw8NSL2Az5PWVX3c12K/RrYJTPvn8KtVgOO6HO+M1vxEGC+QCElcHRwjzoe1+UeZzK1Oev6Wb6x/68FdI//yMwrI+L5lKHBq1NWcD6wo9i1wM6ZObfz+gne6/CIWAN4HyUg940uxU4F9h5Q1d6UIPTzKMHF7TvOP0AJ7h3ep45lKP19lz5l7gTel5mf7FPmoZTg96v7lJkL7J2ZP+tTZnngVXXr5mTgVZl5c586JEmSJEnSEAwU9pCZV0XESZR53V7GCAKFtd6vRsR5wBuBZ1Cyxm6nrGT7beBrPbKrprut6utNwJEL44aZ+auI2Bh4EyVoOqueugI4CfhMZo4kaJmZB0fEacDrga0pgeSbKav4HpGZ8w3x7VLHncCOEfFSyvyJTwRWBq4DzgE+n5nn9ani7ZSA5NOBJ1MCpA+nBBhvpGQY/hw4KjOv6VPPMyhzKm5PWWjkEcCqlFWJr6MEpU8GvpeZd/Sp5y/AG2pdG9V6ZlACtP9HGW58Wp/rJUmSJEnSBERmjrsNi6yI2BI4D7gfWC8z54y3RdNXRMyhDM09ODMPHXNztJiq81seDGXxnBHXvRZwJcCa+x/JzJVWG2X1kiRJkjrM+ciO426CNFZz585l7bXXbh2uPdURj+BiJn1l5i+BH1OymN495uZMWxGxDiVIeAvw2TE3R4uZiFg3IjaKiI0oGZKSJEmSJKkLA4WDHUjJKNyrBqy08G1bX/9fZt7St6Q0vyMoi8NcDOw/5rZIkiRJkrTIco7CATLz4rq673qUVWP/Md4WTT+ZeTRTWAVaWth++Z5nsNZaa427GZIkSZIkTYiBwiFk5rfG3QZJk5OZ2427DZIkSZIkLQ4ceixJkiRJkiTJQKEkSZIkSZIkA4WSJEmSJEmSMFAoSZIkSZIkCQOFkiRJkiRJkjBQKEmSJEmSJAkDhZIkSZIkSZIwUChJkiRJkiQJA4WSJEmSJEmSMFAoSZIkSZIkCQOFkiRJkiRJkoCZ426AJC0hZrR2rrnmmnG2Q5IkSZI0DXT87jmjV7mJiMwcRT2SNK1FxGbA+eNuhyRJkiRpWto8My+YaiUOPZak0Xj4uBsgSZIkSdJUOPRYkkbjz439LYGrxtUQTSur085k3Ry4doxt0fRhv9M42O80DvY7jYt9T8OaATys7l88igoNFErSaNzT2L8qM+eOrSWaNiKieXit/U4Lg/1O42C/0zjY7zQu9j1N0N9HWZlDjyVJkiRJkiQZKJQkSZIkSZJkoFCSJEmSJEkSBgolSZIkSZIkYaBQkiRJkiRJEgYKJUmSJEmSJGGgUJIkSZIkSRIQmTnuNkiSJEmSJEkaMzMKJUmSJEmSJBkolCRJkiRJkmSgUJIkSZIkSRIGCiVJkiRJkiRhoFCSJEmSJEkSBgolSZIkSZIkYaBQkiRJkiRJEgYKJUmSJEmSJGGgUJIkSZIkSRIGCiVJkiRJkiRhoFCSpiwi1omIT0TEJRFxe0TcGBG/joi3R8QK426fFg8R8eSIeE9E/DgiroyIuyPitoi4NCKOjIitJ1jfcyLihIiYW+uaW4+fs6A+g5YsEfGxiMjGtt0Q19jvNGERsVpEvDMifhER19a+c3VE/CoiPh4RWw1Rx1YRcXREzImIuyLimoj434jYY2F8Bi1eImKZiNin9pFrGv/m/iUivhERWw5Zjz/zprmIeHhE7BQRh9b/w93Q+HfzyEnUN+U+FREzI+K1EXF2RFwfEXdGxGUR8eWIePxE26TpJzJz3G2QpMVWROwIfBt4SI8ifwGel5l/W3it0uImIs4Cthmi6NHAvpl5T5+6Avgy8Jo+9XwF2C/9T4B6iIgnAhcAMxtvb5+ZZ/Yob7/TpETEi4AvAav2KXZSZu7cp473AwfTOwniR8DumXnXpBuqJUZErA2cAmw8oOingbd1+5nlzzy1RES/5/vNzNxzyHpG0qciYlVK//7vHkXuBl6Xmd8Ypl2answolKRJqr9If48SJLwNOAh4CvAM4Ku12OOAUyJixbE0UouLNevr1cBngd2ALYCtgLcCV9XzrwCOHFDXB2n/J/O3wEtqXS+px9TzHxhBu7UEioilKD/DZgL/HPIy+50mLCJeCRxLCRL+EzgEeCawKbAj8Ebgp8C9ferYt163FHA5sA+l7+0MnFGLPR/42gL5EFqsRMRM5g0S/h7Yk/Lv7bOAQ4Hb67m3AG/vUZU/89TNlcBPJnntlPtURMwATqAdJDwBeG49fiPl5+yywFci4tmTbKemATMKJWmSIuIMYDvgPmCbzDyv4/w7gI/Vw4Mz89CF20ItLiLiZOAo4PjMvL/L+dWAXwCPrW9tk5nndCm3HnAJJcBzQS13Z+P8CsBZwGaUfrtBZl4+4o+jxVxEvJmSSfNn4ETg3fVU14xC+50mIyI2pPzyuyxwDvD8zLylR9llumVSR8TKwBXAysA/gE0z84bG+RmUPvz8+ta2mXn2SD+IFisR8ULg+/XwPGDrzn93I2LTem5p4Cbg4Zl5X+O8P/P0HxFxCHA+cH5mXhcRsyg/l2DIjMJR9amI2BM4oh5+MTNf3+U+FwIrAX8FHt/s21KLGYWSNAkRsTklSAjw9c4gYfVJyj/6AG+OiKUXRtu0+MnMnTLze92ChPX8DcDbGm/t1qOqt9AeKnpA8z+ZtZ47gAPq4UzgzZNvtZZEdUheK1thf6DnMPcG+50m43OUIOENwK69goQAfaZbeDUlSAhwYDNIWK+7H3gd0PrZ+o4ptVhLgqc29g/r9u9uZl4InFwPVwE26Cjizzz9R2YenJknZ+Z1U6hmVH2q9TPuJrr8vMvMy4DD6uH6wP9Moc1aghkolKTJac6VdES3Apn5ACVLDMp/NLdbwG3Sku3Mxv5jOk/WuW1a/+H7c2b+slsl9f2/1MOd63VSyxeBFSlZEGcOKmy/02RExAaUaToAPt8Z4JuA1r/Ft1KG2M0nM+cCP6uHz3QqkGlvmcZ+v/mjm9lay7Z2/JmnURtVn4qI9YHWQiXfrcHFbo5s7O86qUZriWegUJImp7UC7e2UFP5ezmrsP23BNUfTQPOXmwe6nF+X9lyHZ3U539Q6vxYwa2rN0pIiInYHdgJuZPjMK/udJuNFjf3jWjsRsUpErF8n4+8rIpahzN8FcF6/RZ5o971lgc0n2lgtUS5t7D+6T7nWH+SSMkSzxZ95GrVR9amtu5SbT2ZeS/v7wN9N1JWBQkmanA3r62UD5vb4c5drpMnYtrH/5y7nNxxwnh7n7ZdqzfX22Xp4YGZeP+Sl9jtNxpb19Rbgkoh4WUT8jhKkvhS4ISL+FhEH98kAXJ/2UD37noZ1DCUDFeDAOo/lPCLiSZTFdACOzcxbG6f9madRG1Wfmkw9a0fEgwaU1TRkoFCSJigilgNWq4dz+5XNzJtor5639oJsl5ZcdRXadzXe+l6XYs3+1bdfUlbl63adpq+PAasD/wd8fQLX2e80Ga3hcXMocxV+C/ivjjLrArOB8yJijS512Pc0YfWPIHsCd1LmKzw/Il4ZEVtGxA4RcTAlG2sZ4CLgrR1V2O80aqPqU5OpJyjZidI8DBRK0sQ9uLF/2xDlW4FC50XSZL2F9hC7EzPzgi5lJtIvb2/s2y+nuYh4GrAvZSXF/TIzJ3C5/U6T8dD6ugHweuBmYD/g4cBylOHBP65lNgKOq38wabLvaVIy80TK6rFfBzYBvklZ5finlOD0HZQA4dPqMM0m+51GbVR9yr6pkTFQKEkTt1xjf5gVQe+ur8svgLZoCRcR2wIfqYf/pKxE281E+uXdjX375TRW53n7CiWr4NOZefEEq7DfaTJaQ92WpaxI/NzMPDwzr8/Mu+sfQ3aiHSx8CvNPum/f06RExNLAS4HnU372dXoE8BK6L0Jnv9OojapP2Tc1MgYKJWni7mrsL9OzVFtrtbw7F0BbtASLiCcAJ1Lm4bob2D0zr+tRfCL9ctnGvv1yensPZV6jfwCHTOJ6+50mo9lvjuu2ymdmPsC8i+q8pE8d9j0Npc7H9jPgIGBVyrQLG1L6yEOAZwHnUrJafxQRb+qown6nURtVn7JvamQMFErSxP27sT9Mun4rc2KYYcoSABGxLvATYBVKxs1LMrPfangT6ZfNiavtl9NURGwAvLseHpCZt/cr34P9TpPR7Dc/7lUoM/8IXFUPO1crtu9pMg4Btqn7+2TmgZn558y8JzNvzcyfAtsDZ1CyDT8VEc35M+13GrVR9Sn7pkZm5uAikqSmzLwrIm6gLGjSdwLgiFiF9j/GV/YrK7XUift/BqwBJLB3nVOpn+bE1YMmpm5OeG2/nL7eQsk6+BuwQkTs0aXMRo39p0fE6nX/RzWwaL/TZFxJWTwHhpt0f03K/IVN9j1NSEQEsFc9vDQzv9mtXGbeFxHvo2QWLlWveUs9bb/TqI2qT3XWc8MQ9SSDfwZrGjJQKEmTcwmwNbBeRMzMzPt6lNug4xqpr4hYjTKh+qPrWwdk5lFDXPqnxv4GPUvNf95+OX21hh49GjhmiPLva+yvS5kM3X6nyfgj7QzBGQPKts53/jt7KSXbegb2PQ3nEbQX0vntgLIXNvab/cefeRq1UfWpznouGqKeKyc5mkBLOIceS9LknFtfHwRs2qfcto39Xyy45mhJEBEPAU4DHl/feldmfmHIy68Arq772/YrSHvY1VXAnIm0Uepgv9NknN3Yf8yAsq0/mlzVfDMz7wF+XQ+3qgvz9NLqm3cD3VaN1/TQDDYPSphZusd1/szTqI2qT53b2O9ZTx0Z8Nh66O8m6spAoSRNzg8a+3t1KxARSwGvrIc3U+a7kbqKiBWAU4An17c+lJkfHfb6zEzgpHq4QURs2eM+W9L+S/JJ9TpNQ5m5Z2ZGv415FzjZvnFuTq3DfqfJ+CFwb93vXM34P+qq76vWw3O6FGn9W7xSr3oiYi1gh3p4emb+u1s5TQs3ArfW/a0iol+wsBlouaK14888jdqo+lRmXko7y3D3+v/KbvZs7A+a1kbTlIFCSZqEzPw17V9a9omIrboUextlJT2Az2bmvV3KSNRMmBOBp9a3PpuZ751EVZ+hnfnwuYhYvuM+ywOfq4f31fLSVNnvNCGZ+S/ga/Xwmd3mx4yIBzNvXzm8S1VfA26p+x+JiFWbJyNiBvBF2sOXPzGVdmvxVlfSPqUerkFZ+Xg+dX7p5h/qTu4o4s88jdqo+lTrZ9xDKSt6zyMiHkN7EbPLMVCoHsI/bkjS5ETEkygp+8tTVgz7MCVrcHlgD+A1teilwGZmMaiXiDiedjbMz4E3UyaY7uWe+pfjbnUdBryrHv6W8svO5ZThfQcCT6rnDsvM90yx6VrCRcRs4OB6uH1mntmjnP1OExIRD6MMA16H8ovvl4ETKBlfG1P6TSt75kuZ+boe9by2Xgulz30IuJgSCHozZQVbgGMy86Wj/yRanNTV3i8EWtlWPwK+SVnUaTlgS0q/WaeePz0zd+hSjz/zBEBEPA1Yr/HWasDH6/4vaP9RBIDMPLJHPVPuU/WPI2fR/sPz8cBXgZuALShzDT8ceADYKTN7rjqv6c1AoSRNQUQ8H/gWZdhTN5cCO2bmZQuvVVrcRMRE/zH+e2bO6lHXUpT/FO7d5/qvA6+p2RVSTxMIFNrvNGERsSFlGPJ6fYp9A9ivX1Z+RBxC+QU4ehQ5FXhhZt412bZqyRERO1AWcFptQNGfA7tl5k1d6vBnngCIiCOBVw1bvk7r0a2ekfSpuijeqbQXjOp0D/CGzPzqcC3WdGSgUJKmKCIeBbwJ2BFYi/IP8GXAccDnM/OOMTZPi4FRBgobdT6PktW6OeWXoRuA84HD/QuyhjVsoLBR3n6nCYmIBwH7A7sB6wMrAv+kZOIcnplDze8bEU8BXg9sTVnd9mbgd8ARmTnMqt6aRuow9X2A5wJPAFamZLZeS/mZ9R3gh4PmFvRnnkYVKGzUN+U+VefffDXwUso0SA+iLJhyOmV6mz8O215NTwYKJUmSJEmSJLmYiSRJkiRJkiQDhZIkSZIkSZIwUChJkiRJkiQJA4WSJEmSJEmSMFAoSZIkSZIkCQOFkiRJkiRJkjBQKEmSJEmSJAkDhZIkSZIkSZIwUChJkiRJkiQJA4WSJEmSJEmSMFAoSZIkSZIkCQOFkiRJkiRJkjBQKEmSJEmSJAkDhZIkSZIkSZIwUChJkiRJkiQJA4WSJEmSJEmSMFAoSZIkSZIkCQOFkiRJmqYiIus2e9xtWZRFxCsi4uyIuCkiHqhfs4sWchtmt57XwryvhhcR2zW+p7Ybd3skSZNjoFCSJGma6fiFPiPi2CGuOdJAzfQTER8DjgK2BlYGYrwtkiRJC5KBQkmSJO0eERuPuxFatETE2sBb6+EvgZ2AJwIbAy8cV7u08ETErMYfFPYcd3skSQvezHE3QJIkSWMXwCHAruNuiBYp2wMz6v6+mfnHcTZGi7bMPBMzTiVpsWdGoSRJ0vR2Q33dJSKePNaWaFGzZmP/0rG1QpIkLTQGCiVJkqa3zwJ31/1DxtkQLXKWbe1k5r3jbIgkSVo4DBRKkiRNb3OBr9T9nSLivydTSUTMqfOYHTmgXGtRlDldzs03H1pE7BoRP4mIf0bE7RHxu4g4ICKWblwXEfHSiDizlrsjIn4TEftFxNBDISNih4j4YURcExF3RcTfIuLzEbHWkNc/LiL+X0T8MSJuiYg7ax1H9MvW7FwtNiKWioi9I+KMiLiurjR85LCfo6PuWRHx6dqmf9evzV8j4vBe81K2niVwcOO97NhmTaItS0XEyyPi1Ii4NiLuiYjr6+d8XUQsM4G6Vo6IQ+rnui0ibqzP/2VDXPv0iDgmIq6oz+iO+pl/GRGfiIinD7h+gT7niNi2UW7fIT7POxvlN+4498j6tf1+fe63R8TdEXFVRJwUES+OiK6/E9Y+cEXjrSO69IPZvT5fn/auGBHviojz6nO7OyLm1jbuNOCznlnrP7MerxkRn4qIy+pz+FdEnBYRzx1Qz4yI2LOWbfXFm+vX6PSIeE9EPL5fHZK0xMpMNzc3Nzc3Nze3abQB2wFZtz2BRwJ31OPTelxzZOuaHufn1PNHDrh3q545Xc7N6mjXFxvHndvxlPnzlgWO61PuK33a0iozmxIU61XHLcA2Az7X+4B7+9TxAHDIEM/jOcBPu1zf9+vao95XAnf1adN9wLv7PMt+26wJtuWhwLkD6vwT8Kge189ulFsXuKxPPccBM3vU86khPtsN43zOlHn+/l6Pzxjia3tRLXtxx/szgPuH+Lw/AVbs8/3Rb5vd4/Nt16OtTwKuGlDn8cByPa4/s5Y5E3gaZeqEXvW8vUcdKwJnD/HZvj/R7zk3Nze3JWEzo1CSJGmay8xrgC/Vw2dFxNPG2Z5qP2B/4FTKIiubAjsDv6rndwX2Aj4O7AZ8h7Iq76bAHsCfa7lXR8RzBtxrR0og6i/APsDmwA7A4ZTAz0rAyRHxqG4XR8ShwKGUhQL/D9gX2ArYDHgZcB4l+PP+iDhgQFs+Wu/9w8bnfh7w4wHXdbZpR0rQaVngNsqw8q1ru95GCbDMAD4cEft3XP4sysrGX2q8t3HHdtUE2jIDOBl4an3rLOBFlK/PC4Af1Pc3BE6PiBUHVPldSrDwy5Sv1eaU59aaR3E3SkCwsx07AW+ph7+n9K/tKMGr7Sh97njaQ/E7r18ozzkzEzimltsm+mS01qy3J9bDb3Werq8/B95BCU5uWj/r3rW9AM8EvtCl+o2BZzeO38v8/eCLfT5jZ1vXBE4H1qAE4o6o9W9GCWr/rhbdFfjmgOoeCZxICYS+ixI03IKySvfNtcxhEfGELtfOpnwvQOmXL6H0zU0pX6MDgXNqGyVp+hl3pNLNzc3Nzc3NzW3hbnRkFNb3HkYJKCXw8y7XHNm6pkedcxgi843hMwoT+HSXMitQhkMmcD0lkPemLuVWB26t5U7q0ZbmvS6ke1bVKxpljutyfnPaWVsf6HGfpYCja5lbgZX7PI8EDp3i812aMqQ8gX8Dm3Qp8yjg6lrmdmC1LmVm93vmE2jP6xuf7ZtAdCnzoUaZj/ZrS91e0qXMg2ln190PbNxx/qhW3+v2rBvlHjru50wJwvXNjOv4uj0ArNNxLoD1BjybQxrXrz/ge3LPAXU1P992Xc43M3/36XJ+WUpQs1XmuV3KnNk4PwdYs0uZp9XPk8Bnu5z/Bz2+nwf1Azc3N7fpsJlRKEmSJDLzeuDz9XD7iNh+nO0BrgTe2flmZt5BO9toNeBXmfnZLuWupWQcQTt7qJ/XZOZtXeo5mnY2384R8ciOIgdSAkQXAu/vVnFmPgAcQMlUezAl462XS5n6ojK70F6x+EOZeVGXNv2dkmUGJfi61xTv2c/r6+sNwBsys1um1vuZNwt02S5lWk7OzGM638zMfwOvqYdLUTIEm1avr7/p9qwb9dzY5e2F+pwz82Lg4nrYb97Fl9TXczLzHx11ZGZe1udaKBmSN1CCii8YUHbS6vfNLvXwtMz8emeZzLybkul4X33rDQOqPSAz58tszcxzaWced/veb/WDc/pV3qMfSNISz0ChJEmSWj5OyUAD+MA4GwKckL1X2v19Y/+7fepoDWVcJSJW7lPu4sy8sM/5b9TXmZSsKQCiLKjSWjTh+27J8LEAAAppSURBVD0CYABk5s20Az9b9bnXdzPz/j7nh7FD67a0297NcZT5F5vXjFRErEEZUgzwvRrMm0/9zEfUw1WAnouCNMp1q+fXwB/rYednuqa+bhMRj+nX7qYxPufWUOJNImLDzpMR8RTKEGyAbw+oq7WYzBp1MZaNImIjyrOZW4s8sc/lU7U9Zag7wHxBwpbMnEOZuxFguzpsvZubgVP63K/1/fzoLuda/eDFEbFCnzokaVoyUChJkiQAMvNfwGfq4VMj4tn9yi9gl/Y5d3Njf9hyD+5T7vwBbfl1Y3+jxv7jKdl4UOZD61wRdp6NMhcbtDOauvl9n3PDarVxTmb+s1ehzLwH+G3HNaPWrPdXPUvNf75fe4Z9XuvHvCspH1VfVwX+EBHHRsReEbHegPrG9ZyPoT1PXreswtZ79wDf71ZBFC+PiDMoUwtcRcncvLixbVKLrzZEmyZrMv1gBboH+gD+WjM4e2llA3b7vm9lJD8FuCLKyua7RMTDBrRLkqYFA4WSJElq+hTtANuhY2zHHX3ONQMEw5brlZkE0DOYVl3X2H9oY//hA67rpV8W002TrLOp1cbr+pYqru24ZtSa9Q5qz7WN/X7tGfZ5BSU7EYDMPJ0ynPVOYDngxZSMy79GxNyI+HJEdMuqG8tzzswrKavzAry0eS4iZgK718MfdxsmGxHLUbLujqZkwi4/4JaDzk/FqPtBv+97aH/vd/t99wOU556UZ/t64ATguoi4OCIOiYhHDKhfkpZYM8fdAEmSJC06MvPmiPgUJUi4RUTslJknj7tdC9hkVzdtBh/fAfzvkNfd3ufcVIcdNw3zuWJwkZEZ1J5h2zLpejLzCxFxHCXw9kzKarcPoczp+FrgNRHx4cx8b+OycT7nbwPbAutGxFaZ2Vqp+Fm0MwB7DTs+iPaQ6bMoKxv/hhKIu7OVkRcRZ1Pm8luYfaGfBdqOOqXBPhHxScocj0+nZIEuQ8l83Ah4a0S8PDNPWpBtkaRFkYFCSZIkdfoM8CbKEM1DI6LfXGAt/TJ4mh40lYYtIIOyh5rnm5lb/2rsL52Zfxhdk6ak1cZ+Q19bWp9tQS3c0Kx3UHt6fZ27lbuyz/lWBmDSJXOvDsf+DPCZiFiKMvR2V0pm2crAQRFxfiNINM7nfBzwOcqKwC8DWoHC1rDjfwPzBfIjIoB96+G5wNP7DNVdpcf7o9R8no+grDzcy7D9YEoy80/A+4D3RcTylKDxS4FXAisCx0TEYzLzmj7VSNISx6HHkiRJmkddcOLj9fBJwM5DXNZapGJQ0OFxk23XArT5BM43g0R/pMwPByXDa1HRauOsiOg5bLYu0vGkjmsWVFsA/ntA2S16XNdp2Of11zoPY0+Z+UBm/qZmED6jcWr3xv7YnnNdHOXUVpsiYmZdgON/6nvHZ+adXS59KO3A7Pd6BQkjYkX6f09ONtu202T6wR3AFSO6f1+ZeWdm/iwz96a9GvjywE4L4/6StCgxUChJkqRuPk97LrhDGDwcsPUL/ZNrNtN86iqrG4+meSO1cUQ8qc/5vevr/cCZrTcz8w7g9Hq4XURswaLhZ/U1aLe9m90ow26b14xUZl4NXFIPXxQRXReVqavb7lkPb6IMke3lVb1ORMRmtBfOmNBnyszf0M5AXK3x/rifc2to8cMow6V3pp2Z22vYcXPkWL+5EvcBlu5z/q7G/rJ9yg1yJu3h1vv0KhQR61A+I8CZmXnfFO45Wac39hfkAi+StEgyUChJkqT5ZObtwEfr4cbA8wZcclZ9XYMy79c8aoDoGyNr4Oh9JSLmGxYdES+l/dl/0GUY4odoZ10dGxGP6XWDiJgRES+NiLVG0uLeTgSurvvv6bZAR0SsDXyiHt4BHLEA2/OF+vow4HM9AskHU1YXBvhqZt7dp74XRMTunW/W7Liv1MMHgMM7zr+4DjHtqgYZWxmxnZls43zOJ9NeYOhltIcdXwuc0eOa6xvX7NGx+nOrnZsDHxxw73/Rzqbs+ZkHqQHjE+vhsyNivgB2beM3aAcuPz/Z+/USEQ+NiBf0+mNG1cwaXSgZjZK0KHGOQkmSJPXyJeDtwCMZnFnzLWA2sBLw9YhYDziNElzZDHgrZcGI39Ie7rqouIDSxgsi4qPAxZRMu90oC1xAGVr99s4LM/MXEXEoJdC1LnBRRHwd+AlwDSULaxawVa1vDUrgde6C+jCZeW9EvAb4EfBg4NyI+DglU+o+4CnAu2jP5ff2zLxhQbUH+DIluLUVJRvwURHxBeBvlL61N2WOQIDLKavS9nMB8J2I2Bb4PnAr8F/AgbSH0X4hM3/fcd1HgS9HxEmU1YQvpSw4sirwNOCAWu5+4KvNC8f5nDPz7og4npKJtzNl0Q2AYzKz66IomflARHybMu/iJsA5EfFp4DJK334e8DrgNkpQ+bE96rkvIs6nzN+3d0T8FrgIuLcWubHbiss9vIUyvHsV4GsR8VTgWMo8hBtQvr82qWW/l5k/HrLeiVgJOAmYExEnAL8C/k75vngk8HzaczvOpXwPSdK0YqBQkiRJXWXmnRHxYcpiCoPKXh8R+wLHAMtRhisf0ihyF2WRgB1Z9AKFp9TtYLpn1t0KvCAz53S7ODNnR8TNwEcoiyC8qW7d3MO8wzkXiMw8JSL2omTVrcj8zwNKQOx9mfmlBdyW+yNiJ+CHlIDTdnXrdAnw3My8bUCVu1OCnq+rW6fjKYHpblamBCt7DV++C3htZl7YeWLMz/nblEDhgzre6+cgytd7E8q8f8d0nL8ReCFlhfOugcLqMErAbFXgOx3nDqH8gWCgzJwbEc+gZEiuQQkQdxsafwJ9hpePyCx69xGAqyjf8/1WrpakJZJDjyVJktTPV+m/wux/ZOZxlGy1EylDH++p134T2KyeXyRl5mzgOZSA4XWUts8Bvgg8ITPP6nlxuf4zlKGZHwB+CdxAyVK6nZK5djywH7BmZl62QD7E/G36JiVT67OUINztwJ2UrL2vAk/KzMMWUltuBLYBXgH8L+VrfC9laOuZwBuATTLz70PUdQWwKfBhyue6A7iFkiX48szcrcfcdttQssW+S8kavZ7yjG6lzIn4ceDxmXlUn3uP6zmfxbzZiZd2C2Z2tPUWSqDwfZTPexclg/ASyrDzJ2bm2YNunJmnUDIBT6JkH97b/4q+df2WkvX5bko2382U77WrKQHCF2TmCzNzQQXT/04JnL4D+DHwl9qG+yjP8ixKZuOGta2SNO1E5qgWspIkSZIkSZK0uDKjUJIkSZIkSZKBQkmSJEmSJEkGCiVJkiRJkiRhoFCSJEmSJEkSBgolSZIkSZIkYaBQkiRJkiRJEgYKJUmSJEmSJGGgUJIkSZIkSRIGCiVJkiRJkiRhoFCSJEmSJEkSBgolSZIkSZIkYaBQkiRJkiRJEgYKJUmSJEmSJGGgUJIkSZIkSRIGCiVJkiRJkiRhoFCSJEmSJEkSBgolSZIkSZIkYaBQkiRJkiRJEgYKJUmSJEmSJGGgUJIkSZIkSRIGCiVJkiRJkiRhoFCSJEmSJEkSBgolSZIkSZIkYaBQkiRJkiRJEvD/AQVwnkdzl2plAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_by_quantile_and_treatment.plot.barh()\n",
    "plt.xlabel('Number of observations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without being too precise about it, it doesn't appear that the score quantiles are unbalanced in terms of the proportion of treatment and control; they are similar in each bin. This is expected, as we are working with data from a randomized experiment, however again it's good to check such assumptions.\n",
    "\n",
    "### Uplift quantile chart <a class=\"anchor\" id=\"quantile-chart\"></a>\n",
    "\n",
    "On to the uplift quantile chart. We'll start by creating a mask we can use for the treatment group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_treatment_mask = valid_w_score['treatment_group_key'] == 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we get the conversion rates within uplift score quantiles, separately for treatment and control groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "treatment_by_quantile = valid_w_score[validation_treatment_mask]\\\n",
    "    .groupby('Quantile bin')['conversion'].mean()\n",
    "control_by_quantile = valid_w_score[~validation_treatment_mask]\\\n",
    "    .groupby('Quantile bin')['conversion'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we calculate their difference, which is the true uplift within each score quantile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quantile bin\n",
       "(-0.114, -0.00339]   -0.017486\n",
       "(-0.00339, 0.0186]    0.034343\n",
       "(0.0186, 0.0391]     -0.004600\n",
       "(0.0391, 0.0548]      0.021554\n",
       "(0.0548, 0.0726]      0.133929\n",
       "Name: conversion, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_uplift_by_quantile = treatment_by_quantile - control_by_quantile\n",
    "true_uplift_by_quantile.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have all the information needed to plot an uplift quantile chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'True uplift')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_uplift_by_quantile.plot.barh()\n",
    "plt.xlabel('True uplift')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The uplift quantile chart shows that, for the most part, true uplift increases from lower score bins to higher ones, which is what we'd expect to see if the model is working. So it appears our model can effectively segment out customers who more readily respond to treatment. In a real project, it would be important to repeat this analysis on a held-out test set, to confirm the model works with data that was not used at all for model training, since technically this validation set was used for early stopping in the model fitting process. However good performance on the validation set is a good sign and as long as the test set has similar characteristics to the training and validation sets, we'd expect to see similar performance there.\n",
    "\n",
    "What can we learn from the quantile chart? From analysis of the experiment we know the ATE is about 10%. The quantile chart we created with the validation set tells us that by targeting the top decile of uplift scores, we may achieve a treatment effect of over 35%, a noticeable increase. The next few deciles appear to have larger treatment effects than the ATE as well. Depending on how expensive the treatment is to apply, it may make sense to target a limited portion of the population using this information.\n",
    "\n",
    "We can also see that the sleeping dog effect has some support from observations of the true uplift. The bottom score decile, which consists entirely of negative scores, in fact has negative true uplift. So it appears that targeting the bottom 10% of the population, by uplift score, actually has a negative impact on the business.\n",
    "\n",
    "### Calibration of uplift <a class=\"anchor\" id=\"calibration\"></a>\n",
    "\n",
    "While the uplift quantile chart provides a qualitative snapshot telling us whether the model is effective at segmenting customers or not, we can go a step further in this direction and ask how accurate the model is at predicting uplift. This is the process of calibration, for which we'll need the average predicted uplift within the score quantiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quantile bin\n",
       "(-0.114, -0.00339]   -0.035133\n",
       "(-0.00339, 0.0186]    0.008385\n",
       "(0.0186, 0.0391]      0.029582\n",
       "(0.0391, 0.0548]      0.047033\n",
       "(0.0548, 0.0726]      0.063634\n",
       "Name: Uplift score, dtype: float32"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_uplift_by_quantile = valid_w_score\\\n",
    "    .groupby(['Quantile bin'])['Uplift score'].mean()\n",
    "predicted_uplift_by_quantile.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll put this together in a `DataFrame` with the true uplift that we calculated above, to create a scatter plot. If the uplift predictions are accurate, a scatter plot of predicted and true uplift should lie close to the one-one line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1c84c990>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_true_uplift = pd.DataFrame({'Predicted Uplift':predicted_uplift_by_quantile,\n",
    "                                 'True Uplift':true_uplift_by_quantile})\n",
    "\n",
    "min_on_plot = pred_true_uplift.min().min()\n",
    "max_on_plot = pred_true_uplift.max().max()\n",
    "\n",
    "ax = plt.axes()\n",
    "ax.plot([min_on_plot, max_on_plot], [min_on_plot, max_on_plot],\n",
    "        linestyle='--', color='tab:orange', label='One-one line')\n",
    "pred_true_uplift.plot.scatter(x='Predicted Uplift', y='True Uplift',\n",
    "                              ax=ax, label='Model performance')\n",
    "\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qualitatively, we can see from the calibration plot that mean predicted uplift is close to true uplift, by quantile. This calibration could be made more precise by calculating some sort of metric, perhaps MAE (mean absolute error), as a measure of model goodness of fit.\n",
    "\n",
    "There are a few other ways these quantile-based analyses could be extended:\n",
    "- The predictions for treatment = 1 and treatment = 0, that were used to calculate uplift, could be separately calibrated against the conversion rates by deciles of those scores, in the treatment and control groups respectively. This would be the calibration of predicted probability of conversion for these groups.\n",
    "- Error bars could be included on all plots. For predicted probability of conversion, one could calculate the standard error of the mean within each bin of the treatment and control groups separately, while for true conversion rate one could use the normal approximation to the binomial. Then when subtracting the means of treatment and control to get uplift, the variances based on these calculations would be added, and the standard error of uplift could be calculated within each bin.\n",
    "- In a business situation, the cost of treatment and the expected revenue of conversion should be known. The uplift quantile chart could be extended to represent uplift in revenue, which could be balanced against the cost of treatment to assess profitability of a business strategy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cumulative metrics <a class=\"anchor\" id=\"cumulative-metrics\"></a>\n",
    "### Cumulative gain chart <a class=\"anchor\" id=\"Cumulative-gain\"></a>\n",
    "When using uplift scores in practice, a common approach is to rank customers in descending order, according to their uplift score. We can extend the quantile chart idea to calculate how many additional customers (\"incremental customers\") we can obtain by targeting a particular fraction of the population, in this way.\n",
    "\n",
    "This idea underlies the cumulative gain chart. The formula for cumulative gain is given by Gutierrez and Gerardy (2016) as:\n",
    "\n",
    "$$ \\left( \\frac{Y^T}{N^T} - \\frac{Y^C}{N^C} \\right) \\left( N^T + N^C \\right)$$\n",
    "\n",
    "where $Y^T$ is the cumulative sum of conversions in each bin of the treatment group, starting with the highest score bin and proceeding down, and $N^T$ is the cumulative number of customers found in the same way; $Y^C$ and $N^C$ are similar cumulative sums for the control group. Cumulative gain effectively measures the cumulative uplift in probability of conversion, starting with the highest bin, and multiplies by the number of total customers in both treatment and control groups, to estimate the number of additional conversions that would occur if that number of customers were targeted.\n",
    "\n",
    "To get the data for the cumulative gain chart, we will need to calculate the amount of customers in each score quantile bin, both in the treatment and control groups (we visualized this above but will recalculate it here) and also the sum of converted customers. Here we'll flip the result upside down with `.iloc[::-1]` to simulate the strategy of targeting the customers with highest uplift scores first, and proceeding down from there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "treatment_count_by_quantile = valid_w_score[validation_treatment_mask]\\\n",
    "    .groupby('Quantile bin').agg({'conversion':['sum', 'count']}).iloc[::-1]\n",
    "\n",
    "control_count_by_quantile = valid_w_score[~validation_treatment_mask]\\\n",
    "    .groupby('Quantile bin').agg({'conversion':['sum', 'count']}).iloc[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is how the treatment group looks, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">conversion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile bin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0.201, 0.398]</th>\n",
       "      <td>57</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.148, 0.201]</th>\n",
       "      <td>57</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.121, 0.148]</th>\n",
       "      <td>41</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0941, 0.121]</th>\n",
       "      <td>38</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0726, 0.0941]</th>\n",
       "      <td>37</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 conversion      \n",
       "                        sum count\n",
       "Quantile bin                     \n",
       "(0.201, 0.398]           57   106\n",
       "(0.148, 0.201]           57   111\n",
       "(0.121, 0.148]           41    93\n",
       "(0.0941, 0.121]          38    98\n",
       "(0.0726, 0.0941]         37   100"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatment_count_by_quantile.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the cumulative sums of conversions and total customers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">conversion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile bin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0.201, 0.398]</th>\n",
       "      <td>57</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.148, 0.201]</th>\n",
       "      <td>114</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.121, 0.148]</th>\n",
       "      <td>155</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0941, 0.121]</th>\n",
       "      <td>193</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0726, 0.0941]</th>\n",
       "      <td>230</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 conversion      \n",
       "                        sum count\n",
       "Quantile bin                     \n",
       "(0.201, 0.398]           57   106\n",
       "(0.148, 0.201]          114   217\n",
       "(0.121, 0.148]          155   310\n",
       "(0.0941, 0.121]         193   408\n",
       "(0.0726, 0.0941]        230   508"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatment_count_by_quantile.cumsum().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting all this together into the formula for cumulative gain shown above, we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumulative_gain = \\\n",
    "    ((treatment_count_by_quantile[('conversion','sum')].cumsum()\n",
    "      / treatment_count_by_quantile[('conversion','count')].cumsum())\n",
    "     -\n",
    "     (control_count_by_quantile[('conversion','sum')].cumsum()\n",
    "      / control_count_by_quantile[('conversion','count')].cumsum())) \\\n",
    "    * (treatment_count_by_quantile[('conversion','count')].cumsum()\n",
    "       + control_count_by_quantile[('conversion','count')].cumsum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now examine and plot the cumulative gain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quantile bin\n",
       "(0.201, 0.398]         74.0\n",
       "(0.148, 0.201]        125.0\n",
       "(0.121, 0.148]        149.0\n",
       "(0.0941, 0.121]       172.0\n",
       "(0.0726, 0.0941]      209.0\n",
       "(0.0548, 0.0726]      237.0\n",
       "(0.0391, 0.0548]      242.0\n",
       "(0.0186, 0.0391]      240.0\n",
       "(-0.00339, 0.0186]    249.0\n",
       "(-0.114, -0.00339]    248.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_gain.round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Cumulative gain in converted customers')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_gain.plot.barh()\n",
    "plt.xlabel('Cumulative gain in converted customers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cumulative gain gives another way to look at the potential impact of an uplift model-guided strategy. If we offer the treatment to every customer, we'll increase the number of converted customers by 248. However we can achieve a gain of 149 customers, about 60% of the maximum possible, by only offering treatment to the top 30% of customers (top 3 deciles), by uplift score. This is because as we move down the list, we're targeting customers with lower predicted individual treatment effect. The cumulative number of conversions may even go down from bin to bin, in the lower-valued bins of the chart, as we actually lose customers by targeting sleeping dogs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cumulative gain curve <a class=\"anchor\" id=\"gain-curve\"></a>\n",
    "The various charts above are all informative and intuitive ways to understand the performance of an uplift model, however they don't immediately lead to model performance metrics, by which different modeling approaches might be compared. In order to make this leap, the idea of cumulative gain can be generalized to a curve, in a similar way to how the receiver operating characteristic (ROC) curve is used to evaluate binary classifiers. To get an ROC curve, true positive rates and false positive rates are calculated as the threshold for positive classification is successively raised to include more and more instances in a data set, until all are included. By comparison, a cumulative gain curve measures the cumulative gain in conversions, as defined above, in the targeted population as more and more people are targeted according to descending uplift score, until all are targeted.\n",
    "\n",
    "The gain curve is defined as\n",
    "\n",
    "$$ f(t) = \\left( \\frac{Y_t^T}{N_t^T} - \\frac{Y_t^C}{N_t^C} \\right) \\left( N_t^T + N_t^C \\right)$$\n",
    "\n",
    "where $t$ is the index of the customer, starting from the highest uplift score and proceeding down, and the other variables are defined similarly to the previous equation.\n",
    "\n",
    "The gain curve calculation is available as part of the CausalML package, where it is called the uplift curve (CausalML). It can also be calculated fairly quickly in pandas. The first step is sorting on uplift score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment_group_key</th>\n",
       "      <th>conversion</th>\n",
       "      <th>Uplift score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.397679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.375838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.372153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.364114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.361814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.356630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.349603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.348355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.345382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.340867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   treatment_group_key  conversion  Uplift score\n",
       "0                    1           0      0.397679\n",
       "1                    0           0      0.375838\n",
       "2                    0           0      0.372153\n",
       "3                    1           1      0.364114\n",
       "4                    1           1      0.361814\n",
       "5                    0           0      0.356630\n",
       "6                    0           0      0.349603\n",
       "7                    1           1      0.348355\n",
       "8                    0           1      0.345382\n",
       "9                    0           0      0.340867"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_valid = valid_w_score.sort_values('Uplift score', ascending=False)\\\n",
    ".reset_index(drop=True)\n",
    "\n",
    "sorted_valid[['treatment_group_key', 'conversion', 'Uplift score']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On to this `DataFrame`, we add a few columns which will make the calculation of the curve easier, following the notation in the equations above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_valid['Y_T'] = \\\n",
    "    (sorted_valid['conversion'] * sorted_valid['treatment_group_key']).cumsum()\n",
    "sorted_valid['Y_C'] = \\\n",
    "    (sorted_valid['conversion'] * (sorted_valid['treatment_group_key']==0)).cumsum()\n",
    "sorted_valid['N_T'] = sorted_valid['treatment_group_key'].cumsum()\n",
    "sorted_valid['N_C'] = (sorted_valid['treatment_group_key']==0).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment_group_key</th>\n",
       "      <th>conversion</th>\n",
       "      <th>Uplift score</th>\n",
       "      <th>Y_T</th>\n",
       "      <th>Y_C</th>\n",
       "      <th>N_T</th>\n",
       "      <th>N_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.397679</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.375838</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.372153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.364114</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.361814</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.356630</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.349603</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.348355</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.345382</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.340867</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   treatment_group_key  conversion  Uplift score  Y_T  Y_C  N_T  N_C\n",
       "0                    1           0      0.397679    0    0    1    0\n",
       "1                    0           0      0.375838    0    0    1    1\n",
       "2                    0           0      0.372153    0    0    1    2\n",
       "3                    1           1      0.364114    1    0    2    2\n",
       "4                    1           1      0.361814    2    0    3    2\n",
       "5                    0           0      0.356630    2    0    3    3\n",
       "6                    0           0      0.349603    2    0    3    4\n",
       "7                    1           1      0.348355    3    0    4    4\n",
       "8                    0           1      0.345382    3    1    4    5\n",
       "9                    0           0      0.340867    3    1    4    6"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_valid[['treatment_group_key', 'conversion', 'Uplift score',\n",
    "              'Y_T', 'Y_C', 'N_T', 'N_C']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the calculation of the gain curve can be done as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_valid['Gain curve'] = (\n",
    "    (sorted_valid['Y_T']/sorted_valid['N_T'])\n",
    "    -\n",
    "    (sorted_valid['Y_C']/sorted_valid['N_C'])\n",
    "    ) * (sorted_valid['N_T'] + sorted_valid['N_C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine the gain curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Gain in conversion')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_valid['Gain curve'].plot()\n",
    "plt.xlabel('Number of customers treated')\n",
    "plt.ylabel('Gain in conversion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The gain curve looks fairly similar to how the gain chart above would look (if it were turned on its side), just more continuous, and largely tells the same story. One advantage of the curve, however, is that similar to the ROC curve, we can calculate an area under the curve, with the interpretation that larger areas mean a more performant model: we would like to be able to gain as many customers as possible, by targeting a few as possible. If we had perfect knowledge of who would respond positively to treatment, we would treat only those who would, and the gain curve as plotted above would have a slope of one initially, before leveling off and potentially declining if there were sleeping dogs. This would lead to a gain curve with a steep initial slope, that stayed high as long as possible, resulting in a large area under the curve.\n",
    "\n",
    "Before calculating an AUC, it can be beneficial to normalize the data. As shown, the gain curve has units of customers on both the x- and y-axes. This can be good for visualizing things in terms of real-world quantities. However, if we wanted to assess performance on validation and test sets, for example, the areas under these curves may not be comparable as these datasets may have different numbers of observations. We can remedy this by scaling the curve so that both the x- and y-axes have a maximum of 1.\n",
    "\n",
    "The scaled x-axis represents the fraction of the population targeted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0005 0.001  0.0015]\n",
      "[0.999  0.9995 1.    ]\n"
     ]
    }
   ],
   "source": [
    "gain_x = sorted_valid.index.values + 1\n",
    "gain_x = gain_x/gain_x.max()\n",
    "print(gain_x[:3])\n",
    "print(gain_x[-3:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the scaled y-axis is the fraction of gain from treating the entire population:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nan  0.  0.]\n",
      "[1.00802087 1.00534686 1.        ]\n"
     ]
    }
   ],
   "source": [
    "gain_y = (\n",
    "    sorted_valid['Gain curve']\n",
    "    /\n",
    "    sorted_valid['Gain curve'].tail(1).values\n",
    "    ).values\n",
    "\n",
    "print(gain_y[:3])\n",
    "print(gain_y[-3:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the first entry in the normalized gain curve is `NaN`; there will always be at least one of these at the beginning because either $N^T_t$ or $N^C_t$ will be zero for at least the first observation, leading to a divide by zero error. So we'll drop entries from both the x and y vectors here to get rid of `NaN`s, which shouldn't be an issue if the data set is large enough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.         0.00805023]\n",
      "[1.00802087 1.00534686 1.        ]\n"
     ]
    }
   ],
   "source": [
    "nan_mask = np.isnan(gain_y)\n",
    "gain_x = gain_x[~nan_mask]\n",
    "gain_y = gain_y[~nan_mask]\n",
    "print(gain_y[:3])\n",
    "print(gain_y[-3:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the normalized gain curve, along with a computed AUC. To this, we'll add a one-one line. Similar to the interpretation of a one-one line on an ROC curve, here this corresponds to the gain curve we'd theoretically expect by treating customers at random: the fraction of the gain we would get by treating all customers increases according to the fraction treated and the ATE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.size'] = 8\n",
    "gain_auc = auc(gain_x, gain_y)\n",
    "\n",
    "ax = plt.axes()\n",
    "ax.plot(gain_x, gain_y,\n",
    "        label='Normalized gain curve, AUC {}'.format(gain_auc.round(2)))\n",
    "ax.plot([0, 1], [0, 1],\n",
    "        '--', color='tab:orange',\n",
    "        label='Random treatment')\n",
    "ax.set_aspect('equal')\n",
    "ax.legend()\n",
    "ax.set_xlabel('Fraction of population treated')\n",
    "ax.set_ylabel('Fraction of gain in converted customers')\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that, unlike an ROC curve, the gain curve can actually exceed 1.0 on the y-axis. This is because we may be able to gain more customers than the number we'd get by treating everyone, if we can avoid some sleeping dogs.\n",
    "\n",
    "The AUC calculated here gives a general way to compare uplift model performance across different models and data sets, such as the training, validation, and testing sets for a given application. Normalized gain curves can be plotted together and have their AUCs compared in the same way ROC AUCs are compared for supervised classification problems. The interested reader may wish to develop a T-Learner model and compare with the S-Learner results shown here as an exercise.\n",
    "\n",
    "As a final step here we'll save the validation set and the trained model, in case we want to do further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('data/validation_set_and_model_7_8_20.pkl', 'wb') as fname:\n",
    "#     pickle.dump([valid_w_score, model], fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion <a class=\"anchor\" id=\"conclusion\"></a>\n",
    "The goal of uplift modeling is to create predictive models of the individual treatment effect. Such models allow data scientists to segment populations into groups that are more likely to respond to treatment, and those that are less so. With this goal, a variety of modeling techniques have been developed; uplift modeling continues to receive active research interest. The evaluation of uplift models is not as straightforward as that of supervised classification or regression models because it requires separate consideration, and comparison, of treatment and control groups. However, open source Python packages (CausalML, Pylift) have been created to facilitate uplift model development and evaluation. Several useful uplift evaluation techniques, some of which are available in those packages, were demonstrated here using Python and pandas.\n",
    "\n",
    "---\n",
    "\n",
    "Thanks to Pierre Gutierrez and Robert Yi for your input and feedback"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References <a class=\"anchor\" id=\"references\"></a>\n",
    "\n",
    "[CausalML: a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research. Accessed 7/5/2020.](https://causalml.readthedocs.io/en/latest/about.html)\n",
    "\n",
    "[Gutierrez, Pierre and Jean-Yves Gerardy, 2016. Causal Inference and Uplift Modeling: A review of the literature. JMLR: Workshop and Conference Proceedings 67:1-13.](http://proceedings.mlr.press/v67/gutierrez17a/gutierrez17a.pdf)\n",
    "\n",
    "[Kunzel, Soren R. et al., 2019. Metalearners for estimating heterogeneous treatment effects using maching learning. PNAS March 5, 2019 115 (10) 4156-4165](https://www.pnas.org/content/116/10/4156)\n",
    "\n",
    "[Lee, Taiyeong et al., 2013 Incremental Response Modeling Using SAS Enterprise Miner. SAS Global Forum 2013: Data Mining and Text Analytics.](https://support.sas.com/resources/papers/proceedings13/096-2013.pdf)\n",
    "\n",
    "[Pylift: an uplift library that provides, primarily, (1) fast uplift modeling implementations and (2) evaluation tools. Accessed 7/5/2020.](https://pylift.readthedocs.io/en/latest/)\n",
    "\n",
    "[Yi, Robert and Will Frost, 2018. Pylift: A Fast Python Package for Uplift Modeling. Accessed 7/5/2020.](https://tech.wayfair.com/data-science/2018/10/pylift-a-fast-python-package-for-uplift-modeling)\n",
    "\n",
    "[Zhao, Zhenyu et al., 2020. Feature Selection Methods for Uplift Modeling.](https://arxiv.org/abs/2005.03447)"
   ]
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