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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Introduction\n",
+    "\n",
+    "## Estimators Library\n",
+    "\n",
+    "In contextual bandits, a learning algorithm repeatedly observes a context, takes an action, and observes a reward for the chosen action. An example is content personalization: the context describes a user, actions are candidate stories, and the reward measures how much the user liked the recommended story. In essence, the algorithm is a policy that picks the best action given a context.\n",
+    "\n",
+    "Given different policies, the metric of interest is their reward. One way to measure the reward is to deploy such policy online and let it choose actions (for example, recommend stories to users). However, such online evaluation can be costly for two reasons: It exposes users to an untested, experimental policy; and it doesn't scale to evaluating multiple target policies.\n",
+    "\n",
+    "The alternative is off-policy evaluation: Given data logs collected by using a logging policy, off-policy evaluation can estimate the expected rewards for different target policies and provide confidence intervals around such estimates.\n",
+    "\n",
+    "The estimators library collects estimators to perform such off-policy evaluation. \n",
+    "\n",
+    "This notebook is a concise walkthrough of the estimators library.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by importing from our estimator library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "from vowpalwabbit import pyvw\n",
+    "import argparse, os, gzip\n",
+    "\n",
+    "#Estimator library\n",
+    "import cressieread\n",
+    "import ips_snips\n",
+    "import mle\n",
+    "import ds_parse\n",
+    "import dr\n",
+    "import policy\n",
+    "import evaluation\n",
+    "import softening\n",
+    "\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.gaussian_process.kernels import RBF\n",
+    "from scipy import spatial\n",
+    "\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "sns.set_palette(sns.color_palette(\"colorblind\", 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Logging Data\n",
+    "\n",
+    "As in any machine learning/data science project, the first step is to load and massage your data in order to extract the appropriate features to use in your succeeding steps.\n",
+    "\n",
+    "We are working with data from a logging policy, which we expect to be provided in  ```dsjson``` format. From this data, we then  extract our ```x```, ```a```,```r```, and ```p```. In this walkthrough, the data has been generated in a similar way with the <a href=\"https://vowpalwabbit.org/tutorials/cb_simulation.html\">Simulating Content Personalization with Contextual Bandits tutorial</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the input file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"_label_cost\": 0, \"c_user\": 0, \"c_time_of_day\": 0, \"a\": [1, 2, 3, 4, 5, 6, 7], \"_label_Action\": 2, \"_label_probability\": 0.14285714285714285, \"ctr\": -0.0}\r",
+      "\r\n",
+      "{\"_label_cost\": -1, \"c_user\": 0, \"c_time_of_day\": 1, \"a\": [1, 2, 3, 4, 5, 6, 7], \"_label_Action\": 3, \"_label_probability\": 0.14285714285714285, \"ctr\": 0.5}\r",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head -2 ../data/cpwcb.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we have a convenience function called  ```parse_file``` from imported ```ds_parse``` module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#?ds_parse.parse_file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Parsing file... Processing: ../data/cpwcb.json\n",
+      "Progress: [##################################################] 100.0%"
+     ]
+    }
+   ],
+   "source": [
+    "#data_file = \"../data/cb_data.json\"\n",
+    "log_data_file = \"../data/cpwcb.json\"\n",
+    "log_data_parsed = \"../data/parsed_data.dat\"\n",
+    "\n",
+    "ds_parse.parse_file(log_data_file, log_data_parsed, True) #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see how the parsed data looks by printing a few lines in the resulting file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'cost': 0, 'p': 0.14285714285714285, 'a_vec': [1, 2, 3, 4, 5, 6, 7], 'a': 2, 'num_a': 7, 'c_user': 0, 'c_time_of_day': 0, 'r': 0, 'XNamespace': 'c_user: 0 c_time_of_day: 0', 'skipLearn': 0}\r",
+      "\r\n",
+      "{'cost': -1, 'p': 0.14285714285714285, 'a_vec': [1, 2, 3, 4, 5, 6, 7], 'a': 3, 'num_a': 7, 'c_user': 0, 'c_time_of_day': 1, 'r': 1, 'XNamespace': 'c_user: 0 c_time_of_day: 1', 'skipLearn': 0}\r",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head -2 ../data/parsed_data.dat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Estimators Description"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Estimators implemented in estimators library\n",
+    "- ```ips``` - IPS\n",
+    "- ```snips``` - SNIPS\n",
+    "- ```mle``` - MLE\n",
+    "- ```cressieread``` - CRESSIE READ\n",
+    "- ```dr_batch``` - DOUBLY ROBUST in Batch Mode\n",
+    "- ```dr_online``` - DOUBLY ROBUST in Online Mode \n",
+    "- ```dr_seq_batch``` - DOUBLY ROBUST SEQUENTIAL in Batch Mode\n",
+    "- ```dr_seq_online``` - DOUBLY ROBUST SEQUENTIAL in Online Mode\n",
+    "\n",
+    "- ```pseudo_inverse``` - PSEUDO INVERSE*\n",
+    "\n",
+    "### Description of implemented modules\n",
+    "- ```Evaluation.py``` - provides interface for interacting with the implemented estimators. It provides for evaluating the expected reward, comparing performance of estimators and other related tasks\n",
+    "- ```softening.py``` - provides functionalities related to softening and transforming a Classification dataset to a CB dataset\n",
+    "- ```policy.py``` - provides capabilities for invoking policies including custom VW policies\n",
+    "\n",
+    "Current notebook provides usage examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. Evaluation and Comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "List containing the Policies that we may want to evaluate as well as the estimators to be used.\n",
+    "\n",
+    "Policies and estimators are provided as strings as specified"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "policies = ['Constant', 'UniformRandom']\n",
+    "estimators = ['snips','ips', 'dr_batch', 'dr_online', 'mle','cressieread']#'dr_seq_batch', 'dr_seq_batch']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#New plotting function with bigger figure size and with legends\n",
+    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
+    "def plot_expectedr(title, expectedr, label):\n",
+    "    plt.plot(range(1,len(expectedr)+1), expectedr, label=label)\n",
+    "    plt.grid()\n",
+    "    plt.xlabel('samples', fontsize=14)\n",
+    "    plt.ylabel('expected reward', fontsize=14)\n",
+    "    plt.title(title)\n",
+    "    plt.ylim([0, 1])\n",
+    "    plt.xlim([0, len(expectedr)])\n",
+    "    plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.07),\n",
+    "          fancybox=True, shadow=True, ncol=2)\n",
+    "    #plt.legend(loc=\"upper right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage Scenarios\n",
+    "\n",
+    "## 4.1. Evaluation\n",
+    "#### 4.1.1. Evaluating the estimated expected reward of one target policy for one estimator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#?evaluation.compute_estimate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tExpected reward:  0.1432369325175987\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "estimated_rewards = evaluation.compute_estimate('UniformRandom', 'snips', log_data_parsed)\n",
+    "print(\"\\tExpected reward: \", estimated_rewards[-1])\n",
+    "plot_expectedr(\"Estimated expected reward over samples for Policy: 'Constant', Estimator:'snips'\", estimated_rewards, \"Policy: 'Constant', Estimator:'snips'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.1.2 Evaluating the estimated expected reward of one target policy and one estimator for dr-based estimators\n",
+    "\n",
+    "For this, we have implemented the reward predictor for DR-based methods using Vowpal Wabbit\n",
+    "\n",
+    "##### dr_online"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tExpected reward:  0.24600809676490898\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "estimated_rewards = evaluation.compute_estimate_dr('Constant', 'dr_online', log_data_parsed)\n",
+    "print(\"\\tExpected reward: \", estimated_rewards[-1])\n",
+    "plot_expectedr(\"Estimated expected reward over samples for Policy: 'Constant', Estimator:'dr_online'\", estimated_rewards, \"Policy: 'Constant', Estimator:'dr_online'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### dr_batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tExpected reward:  0.2458137307657858\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "estimated_rewards = evaluation.compute_estimate_dr('Constant', 'dr_batch', log_data_parsed)\n",
+    "print(\"\\tExpected reward: \", estimated_rewards[-1])\n",
+    "plot_expectedr(\"Estimated expected reward over samples for Policy: 'Constant', Estimator:'dr_batch'\", estimated_rewards, \"Policy: 'Constant', Estimator:'dr_batch'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Support for scikit-learn for the reward predictor in DR-based methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We First need to a Pandas Dataframe for the data.\n",
+    "\n",
+    "We have another utility function ```create_json``` for generating a json file from the parsed log data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#?ds_parse.create_json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_data_json = \"../data/parsed_data.json\"\n",
+    "ds_parse.create_json(log_data_parsed, log_data_json)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Loading the file into a Pandas Dataframe to have a look:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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 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>XNamespace</th>\n",
+       "      <th>a</th>\n",
+       "      <th>a_vec</th>\n",
+       "      <th>c_time_of_day</th>\n",
+       "      <th>c_user</th>\n",
+       "      <th>cost</th>\n",
+       "      <th>num_a</th>\n",
+       "      <th>p</th>\n",
+       "      <th>r</th>\n",
+       "      <th>skipLearn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>c_user: 0 c_time_of_day: 1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>[1, 2, 3, 4, 5, 6, 7]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>c_user: 0 c_time_of_day: 1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>[1, 2, 3, 4, 5, 6, 7]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>c_user: 1 c_time_of_day: 1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[1, 2, 3, 4, 5, 6, 7]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>c_user: 1 c_time_of_day: 0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[1, 2, 3, 4, 5, 6, 7]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.028571</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>c_user: 1 c_time_of_day: 0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[1, 2, 3, 4, 5, 6, 7]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.028571</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   XNamespace  a                  a_vec  c_time_of_day  \\\n",
+       "0  c_user: 0 c_time_of_day: 1  7  [1, 2, 3, 4, 5, 6, 7]              1   \n",
+       "1  c_user: 0 c_time_of_day: 1  7  [1, 2, 3, 4, 5, 6, 7]              1   \n",
+       "2  c_user: 1 c_time_of_day: 1  1  [1, 2, 3, 4, 5, 6, 7]              1   \n",
+       "3  c_user: 1 c_time_of_day: 0  2  [1, 2, 3, 4, 5, 6, 7]              0   \n",
+       "4  c_user: 1 c_time_of_day: 0  5  [1, 2, 3, 4, 5, 6, 7]              0   \n",
+       "\n",
+       "   c_user  cost  num_a         p  r  skipLearn  \n",
+       "0       0     0      7  0.142857  0          0  \n",
+       "1       0     0      7  0.142857  0          0  \n",
+       "2       1    -1      7  0.142857  1          0  \n",
+       "3       1    -1      7  0.028571  1          0  \n",
+       "4       1     0      7  0.028571  0          0  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the JSON file into a data frame\n",
+    "df = pd.read_json(log_data_json, orient='columns')\n",
+    "\n",
+    "# View the first rows\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To train doubly robust based methods with scikit-learn, you need to ensure that \n",
+    "1. You provide a dataframe as the data file\n",
+    "2. The argument ```clf``` to the ```compute_estimate_dr``` method, supports <a href=\"https://scikit-learn.org/stable/modules/computing.html#incremental-learning\">scikit-learn's incremental learning</a>. The default is ```SGDClassifier```.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### dr_batch with scikit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tExpected reward:  [0.1698362]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "clf = linear_model.Perceptron(tol=1e-3, random_state=0)\n",
+    "\n",
+    "estimated_rewards = evaluation.compute_estimate_dr('Constant', 'dr_batch', df, scikit=True, clf=clf)\n",
+    "print(\"\\tExpected reward: \", estimated_rewards[-1])\n",
+    "plot_expectedr(\"Estimated expected reward over samples for Policy: 'Constant', Estimator:'dr_batch' with scikit\", estimated_rewards, \"Policy: 'Constant', Estimator:'dr_batch' with scikit\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### dr_online with scikit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tExpected reward:  [0.17130511]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "clf = linear_model.Perceptron(tol=1e-3, random_state=0)\n",
+    "\n",
+    "estimated_rewards = evaluation.compute_estimate_dr('Constant', 'dr_online', df, scikit=True, clf=clf, load_pretrained_model=False)\n",
+    "print(\"\\tExpected reward: \", estimated_rewards[-1])\n",
+    "plot_expectedr(\"Estimated expected reward over samples for Policy: 'Constant', Estimator:'dr_batch'\", estimated_rewards, \"Policy: 'Constant', Estimator:'dr_batch'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.2. Support for Custom VW Policies \n",
+    "\n",
+    "In this section, we add support for custom VW generated policies.\n",
+    "\n",
+    "Here, you can learn a custom policy using ```--cb_explore```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tExpected reward:  0.9546195457259233\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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JJ5nZCvmHbT3OuQfyhpe6PuWukROdmycyVvonew78G/kqzk3OuYcmsfxri+6dzpEk59wf5EvHvyVpn5l9z8yaJjHf4mO63R18CclQ8D93//Qa89WVO4Nt+FqNfe8kjT7PF+/3A863AS5Hwb1WsJ+LrxHlXDPGSldEhcfK3rzfgxp9/RzLWNMtk/TnRcfny/T8zvdVj4aYVcg5t1e++kDuAnSLmd3hnNs0xYt6h3x1rPPkb5qb5UvTcnXBXdH4O+Sf3K4cY357dLA6meTbyYxlh6TbnXPnjzPOtyX9RtKrzexlzrn8Twcsyf0w/xKLxfJPNNMTpHGH/FPFMV8WUTTuF5xzXygeYGZ/ImmRmVneRXWpxr/Il9qeX3DOjXWzfLv8E8tT5C+Kt8s/FTxDB28GJ9qHxcs9IL+NlsiXnubSPZbd8iViknwbiWDaXUGvX0j6NzNbLOnP5J/u59ZtWP5Jb3qMeRdvj8KB/ib4wmD/vlnSdebbrLxZE6/zZC3J+71Ufr2LjZv/x0pv0Q10KbvlL4D5lsrfOI/MfoJ5jDfvWWbWmBf0LdXB/TeWHcF0Lc65Um/Au1rSB+SvQfc6326wLM45Z77d2M/kL/Dbxxj1FkmXmVn9ONtwUD7gyJkv//BHwfp+XP6hzWpJt5nZg865WzV6e06UzyfFObfdzO6Uf4j1GvkAMN+98vn2EvngWc65XjPbHfTb7ZzbcgjLLb4R+1DwJ0kyswUaf7+2Fm3vpRr/XNkufyO8eqw8YGavlT8v3CofhP9F0SjF56JSx145+yd/n050rRrLeyT9t3Ouf6wRnHMF2zRQ6gHSeG2jyznfFCy2RDoSZnatfB47QaPbHJa6Pt2g8s7NEym5zw7hHHiifMn40BjDJ805d7mky81srnwJ9SfkS4gP9Rw6ivm2d7+UL2n7lXMuFZzTxrp3kg6e5/Pvj/L3+2TSl7vXyqXHVJinJjO/4uvPUvl7hH3y91WHww75Er6S7clRGiV8VcjM/jy4gZb8jayTr5Yl+YNw3Jc2TEKj/Im/Q/6m6YtFw4uX9YCkXvMNi2uDkpY1ZpZ7Ocu1kj5lZq1B+j8yzrJ/I+l4M3u3+UbmUTM73Q6+EOTd8m2wLpb0UUlXW+Fr5k8zszebbyD8t8F63FdGGn8g6fNmttK8k+1gw/fi9f2+pA+Z2ZnBuPVm9joza5S/YUtL+qj5FwS8WZN/c+B3gu2Ve1FOs5n9ed7w2+UvKOudc0n5KhIfkL+RORCMM9E+LBA8rfxv+dKVOjM7Sf4mZyzXSnqdmZ1rvjTm48Hy7gnmdyBI15VBup4O+u+RdLN8MNhkvmH4sUGgXBYze5eZzQme5uduTjOTXecy/bWZLTazWfIlAT8vMc64eWuc9E7kRvlj4R1BXnqbpJPkj5HnxTm3Q35ffcn8izJOln8KP26JXLD/bpL07eB4jprZK/JGuV6+jevfaHQwU066hoOfsXFG+5H8jcEvzb/gKWT+hUOfDoIIybfbekewLy6QL/2WNPLCpeOCm6Fe+X0x1nl03Hx+iK6W9GH5UvmC7Z1Xgv9/5ds959wV9Dssb+csY79K0j+bf3HWyyW9XuO8rTLI69+X9PXgBltmtsjMXh38ni3ph/LnrfdIekPevsv5x+BctFq+TXepY2+y+2ei68Ao5l808+fyVf+erx9I+jvzL1KxIB/m31SXc77Jt09SmwUv08hzjfw18k8lFX+zcK789SkaXFdOlK8t87zPzRpjnx3COTAqvx8LmH+ZysWTSE9uutOD63VUvqpxQofn3ikm39bugKS0mb1GhZ+XKLW/firps2Y2JzguPqfR+6xc/ytpdd490EdVWDtqMn4q6WNmdkxwj/VF+Tbah/owoBw/lj8XvDo4NuPmX1xzuALMqkDAV9lyb4nL/f1P0P90SfebWb/8E7m/yXvae6l88NNtwVstn4dr5Ivvd0laLx8w5fuhfJWRbjO7PggW3iD/4oct8k93fyD/pFrydfe3BcNu1jhvOQuevr9KvmrXbvmi/69IqjGzpfIvArjIOdfvnPuJ/M3R1/Nm8Sv5Ngy5l1282TmXKiON/y5/83Cz/E3gD+XbOklF2zaoYvJB+SoiXQradgTpT8o/wbw4GPY2+UCqbM65/wnW+WfmqyY+KV8akHNPkLbczd96+QtY/s3gRPuwlA/LP5HeK39zc+U4adwo6V2SrpDflm+Qf/1/Mm+0n8iXtv2kaPKL5C+M6+W30XWaXJWNCyQ9FRwH35RvN5DQoa3zRH4inyc2B3+jvj9ZRt4aK73jcv47fK+Xv4ntkK/S/XrnXPvzXKecC+Wr0+2W9D/y7WR/X8Z075Zvy7FB/iUlf5uX5iH5J9zHqCjfm6+GPuHbHeUfZI15DQuCwvOC5f9e/nh9QL7a1P3BaH8jv09yVcLz3zi5Ur6UsF/+Ac233cFvbn1J/uar28z+rsx8PlnXyb8Y5dbgJrvY7fI35fk1F+4M+o0Z8AUB703PI11j7lf5c0KXfF75L/m2ZhtGzaHQ38ufG+8LzmO3yLcDknxb9F85524M8vn7Jf3A8t4uKb8dNsmXAH7NOXdz8QImu3/KOFZLeZP8i01um2B9J+Sc+4X8S3h+It/e9Xr5F7TkTHi+KZrfBvkb881Bnl0Y9L9bvk3mI0HV2Xz3yx8D7UFa3uIOfvPz+Z6bx9pnkz0HhlX0pl7zn2Np0/jn9bcV3Tv1Bw8cmuQfQHTJXyM6JH0tmKbgfmYS6zpKcP/yUfl7iS75mjY35A0vtb8uk7+PeVz+xXGPaIL9HqzXy0ssv13+4cSXg3VcqaCmwCH4T/l7tTvkj5WExn9Y/7wFDyLfKP+w44D8g71PiJhmXLk3jgFHDfMfZj7OOfeumU4LKp/5DwJ/wDl3y0ynpZKY/0zK8Yd6HJqvvvhJ59yhPuVGBTP/+YMt8m+dPZylCVXN/KddfuKc+0Fev4vlz2kvm+JlLdcU7TPz7X/Xy6fzxrz+L5P01865C8ecGDgKEQ0DAKZVUBXt/fIlOIfqHyT9k5k9PDWpAo4uQRXVUzVxldAjipn9nXwJ8+3ypYQjnHN3EewBo01bwGdm/2n+o4wlG3AH9dQvN/9h38et8COjAIAqYP7D7Tsk3eScO+S2Zs65a5xzK51zp01d6nA4BNV0i6vQ9ZdZdRdlCKrqltrGJavvmtnV8tVn/9YVvoX3iOec+5pzbr5z7sK89rw4TKzwzbL5f9+Z6bShfNNWpdN8w+5++W96rSkx/LXy9X5fK/8NnG86586clsQBAAAAQBWathK+4Elu5zijvFE+GHTOufsktZh/BTQAAAAA4BAcSd/hW6TCDz3uDPqNejuZmV0i/60hxePx05YuHe8zYMDUyWazCoVo+orpQ57DdCK/YTqR3zDdqjnPPfPMM+3OuTmlhh1JAV+pjx6XrG/qnPuegsb+q1atchs3bjyc6QJGrFu3TmvXrp3pZOAoQp7DdCK/YTqR3zDdqjnPmdm2sYYdSSHuTklL8roXy3/LBwAAAABwCI6kgO8GSRcFb+s8S1LPGB+bBQAAAACUYdqqdJrZTyWtlTTbzHZK+idJUUlyzn1H0o3yb+jcJGlQ0nunK20AAAAAUI2mLeCb6EOYzn8f4q+nKTkAAAAAUPWOpCqdAAAAAIApRMAHAAAAAFWKgA8AAAAAqhQBHwAAAABUKQI+AAAAAKhSBHwAAAAAUKUI+AAAAACgShHwAQAAAECVOuoDvuF0Rrt6hmY6GQAAAAAw5SIznYCZ1DOU0vx/vlmpTFbpf33DTCcHAAAAAKbUUVvCNzCc1ut+eL+G01llnTSUyjzvefYPp/XNOzfr7Cvu0v3buqYglQAAAABw6I7KgC+TdXrHfz2i+7Z1aeXseklS/adu1LpN7XqufUBvveYhbdjfN2q6bNaVnF/3UEqX/m6jll12iz72q6d077YuveSKu/RXv3y8YLy9vQndvaVzwvRt7hhQXyItSdrfN6zrHtutrsHkZFcTAAAAwFHuqKzS+U+/26hfr9+ny9+0Rk/u7dOz7QOSpP95cq+uuGuLJOnVq+bqhLmNkqSOgaTefNWD2tQxoO2fPV/hkEmSkumsrrhri75467PqGkrpjavn6SMvW6HX//B+JdJZfefebfrq60/Sk3v7dMVdW/TTP+6SJL33jCX6wBlLNb8xrisf3K4rH9yh81bO1pr5TfrRwzv1+J5ehUz6kxVtuu25DknSe168WFe+/ZRDXud0JquHd/bozi2dOmNJixY01Wj5rDpFw0dlzA8AAAAcFY66gO+2Te364q3P6r1nLNFfn71c92/v1tbOQd38zAFdcdcWRcOmVMbpg794TG970UI9ubdPb/vRQ9rRnZAk3f5ch165crbWbWrXX/33E9qwv18XrJqjL772RL1oUbMkadfnztdbf/Swbn22XU2fuUmS1ByPaFZdVJ2DKV35wA5d+cAOSZKZ5Jx09UM7JUlnLm2RJGWdtLdvWJ869zh96dZNuvqhnfr5o7v16/efoXNXzplwPZ1z2rC/X7/beEC3bWrX7Zs71BuUGua7/E1r9OGXHTPmfAaG03pyb59Wz29UQ81Rl10AAACAinZU3cEnUhn9xXWP67jZ9br8jWtkZjprWau+8abVOumr6yRJ93305Trt63dIkj75m/X6wf3btbg5rrecvEDXPb5H7/v5o3rdSfP0H/ds1TGz6nTD+87Q60+aV7Cc1rqY/v1PV+uF/3a7JOlrbzhJl5y1TJL06/X79NmbNmhH95A+c95Kve+Mpbpna6ee2NOnd5+2WKvmNmhzx4CG01mdOM+XMO7qTuiah3cqkc7q2sd2jxnwJVIZ3fpsu37z9D79dsN+bevybx89bna93vrChTp35Wzt6klow/5+/eD+7ZKkj17/pK57fLdWz2/S5y9YpXTG6Y7NHbpjS6fu2tyhx/f0KuukmkhI+y59lZri0andKUcZ55x6E2k1xSMys5lODgAAAKqcOVe6XVqlWLVqldu4cWNZ4375D8/q0zdu0O8uOUvnH38waMpmnS6/a4ve/qKFmt8U1zt+/LB+9uhuSdLrTpyray48Rf3JjJZddsvINH/7ihW67IJVqouNHTP/cVePTprXoJpIuKB/NuvkpJGqoeUYSmVU/6kbJUk7/vE87epJ6GeP7tbPH92ls5a2KhoO6cYN+9Q/nFF9LKzzVs7WBSfM1QWr5mrZrLqS8/zLXz6u7967reSwumhYZy1r1UuWt+oLtzwryVcxvejFi3XPti5tbh/Q61fPU20krA+9dLmcc9raOaT7tndpIJnWCxc064EdXXp4Z48e2N6lrZ1DuuLP1qgpHtEju3p0z5YufeX1J+qMpa2jlp3OZBUOmcxMmayb1HY63NatW6e1a9eOO07nYFKbOwa1uWNQWzoHtbVrUNs6B7Wta0jbu4c0kPQvCFreWqtFzXHdu61L5xw3W8e21ctM2tk9pOc6BtU1lNK8hhp9+GXLVR+LaHPHgE5f0qIVbfXqSaS0o3tIO7p9AH/6khY1xMJ6w+p56h5Ka0f3kIbTWS1qjmtXT0I7e4YUCZma41Ht7k1oT++wVs9vVFtdVE7S7p6EdvcmtLt3WE/t7dXnLzhBx8+p157e4ZHxd/cmtKsnofe8eLHWLGgque7JdFZ7+xI6MJDUqjkNaqiJKJt16hhMqmMwqSXNtepJpLWgqYaAt0zl5DlgqpDfMJ3Ib5hu1ZznzOxh59yLSw6r9oBvX9+wZtVF1T+c1oov3qqzl8/Sbz5w5rjzvOnpfXrdDx/Qn62Zr2svevFIwPHh/35C375n66iAcbq8678e0U+CdoCSFAuHlMxkJUlzG2J605oF+rMXzNfaY9tGBZljSaazempfn972o4e1vLVWr1w5W2uPna3TFjePtO/buL9fJ371tjHnEQ37QKJ9YPSLZeY2xLS/f+wXzjzysVeobzitR3b16NFdPXp0d68e290rSZpdH1P7QFLhkGn1vEZ95y0n67jZdWqri8nMNJzOaOP+AT25t1dP7u3Tfdu6dNriFvUNp7V+X5/u2tKpt79ooW59tl1vPnmBnt7Xp7CZ3nHqYmWd0/p9fRpOZ3Xygib9n5MXaE5DzUi6uodSkqTGmoi2dw3p2fZ+bWof1A0x2pAAACAASURBVLpHNyhZN0sb9vfrmQMD+uuzl6uhJqLN7QN6rmNQmzsHR6bNmVUX1TGz6rS0pVZLW2v1h03temJPnxY3x7WzJzFq3KUttXo02AZHsrpoWC21Ua2e36A9vcPa05tQx2Dhus+pj6lzKKXMGC88etOa+TrQP6wd3Qkf4DbWqCEWVnM8qj19CT1zYEBnLm3Rzh4fbC5tqdX27iGtnteop/b16TPnrVRPIq1d3UM67/g5qomE1JNI6U2rF2heY0zxSFihkCmZ9sdJLDK6zapzTgPJjBpqInLOHXGBaDVfnGbKcDqj3kRavYm0ehIp9STSioZNIfPnslQm64cPp9WbSKk3kdau3oSObavX3r6EFjXHdcaSVjXWRBQO2ci0uXH7htPqT2bUVhdVTyKtZa21WtFWr0zWKeucouGQhtMZhc1P15/00/QNp9WXyKh3OKW+4bT29yd1wpwGvWR5qyIh00Ayo4FkRq21UT9NIq15jTWKRw+e751zyjr/QDGTdRpIpjWQzKitLqZYJDRuHnfO6fd/WKdTz3qpmuPRimnjfSQetygP5zdMt2rOc0dtwNcxkNScf/qdPnnOcaqLhnXpzRv1yMdeMdLWbizpTFZ/3NWrFy9pLriIpDNZZZwrO5iaasPpjE74ym1a3BzXe89Yqje/YIEe3dUjJ+kVK9oOa0nYA9u79PieXp29fJbmNdbo/m1dikfDOvc792pJS1znHjdHZyxt0bLWWt23vVtr5jfqzKUtWtJSq0zW6a4tndp4oF+nLmrRCxY06i1XP6QbN+wvWMa8xhqdsrBJv914QMfPqdfK2fX636f3l0zPmvmN2rC/X+kSgURrbVRdRUFXOS4+fYk2tQ/omQP9I0FqflAtSTUhaeXcRrUPJLW3b1iSD3iXt9bp2LY6rWir14q2Oq1oq9Mxs/xfOdVgu4dSioZM9UXtJB/Y3qXOwZSWtNRqw/4+Pba7V3MaarSkJa6lLbVa3FyrbV1DOjAwrB89vFPLWuu0uDmupnhEG/b369i2ei1uiWs4nVXfcFqLm+OaVRfTcx0D2tM7rFg4pEXNcS1simthc43a6mL61t1btfFAv+/XFNfC5rgWNNaorT6m/3pkl254aq+e2NOrnkRa8xtrtLSlVguaajS/Ka4FjXEtaKrRb9bvU99wWse01Wl+Y43mN8b1xJ5eLW2t1a+e3KuHd/YoGjatmFWnhU1xrdvcoVVzGrRhf79WtNVpUVNc6/f1aUGQhoFkWh2DKcUjIe3rH9ae3uGRbZRrdzuW5nhEPUXtV09b3Kx01ulAf1IHBobHnP6MJS1a3BLX/Ma4zPw55dHdvVo1p16nLm7RgYGkEqmMzl4+S2cfM0u10ZDaB5La35/U0pZaLWmJq30gqba6mOqDgHIgmVHHQFJmUjwSVvtAUk5SPBJS52BKJ85rKGgv+7tbb9Pq087ypaQDKXUGpaU7exJqjkcVDkm7ehJ616mLR7obayJqrYuNWp90JquuoZS6hlJKpLIaTmc1nMkokfL9o2HTC+Y3jYzTOZhU16D/3T2U0ob9/fqTY9t0xtIWHehPamFTXP3JtOY21MgkxaNhdQ4m1VIb1XA6q67BlLoTKXUNprSvf1jLWmu1szuhFW116kmk1T2U0rPtA3rLyQvUk0ipe8j36xpKaX5jjbqHUuoYTGogmVFNJKQFjXFlnVP3kA/SOgeT6hxMKRySXrCgSX3DPohrH0iqZyilxS216gnG7R1Oj/zOP6YrRa69dynLW2uVyjr1D6dH8vp440v+4VI665TOOP8/6z9RVOy42fXqGEiqayilumhYs+tjSqQzWtFWr437+3X8nHqls059wbIP9A9rcbN/MLOstVbprFPHQFKnLm5WIpVV77DfP2994UINJNPqD4LjvkRae/uGlcxkNasuqvmNcfUP+2C1P5ke2eenLW7WUMoHvv3DaXUOpUat58rZ9eodTmtf37BqoyEtbvbpaKvzDyYj4ZCWt9aObCeTBf81cs3f1jWoFyxoUv9wWl1DKSXTWbXWRUeC7oFkRr2JlPb3J3Xm0hb1D2fUnUjppHkNOm52g593kJ72gaQa4xEd11avrqGUnHOqi4WVyTod21avgWRGg6mMBpMZ7e1LaHFLrToGklo529f6GExllMo4xcIhvWLFLGWyvtZPLBJS1jkN5k3fO5yWyTfD6B/OaCiVUSRsOnFugyKhkELm1zlkpmg4pKUttco6p2Qmq1Qm9z+rRDqrwWRG0bBfi6FUdmQZ+/qGdcLcBmWdU1t9TINJv5z+ZEYt8YgfL5Xx0yQzah/w54rGeFixcEixcEjxSEgNNRHd+9Af9ZIXn6LFzbWaXR9TOpvVUCqrrPPrOxjs63gkpIxzGgrm6/9nFAmZlrXWqaXWX2eH037ZiVRWTlLIpPmNcdVGQ0plnFKZrFJZ/98/gJGyzslMIw9gwiH/1xALqzYaVtb5mkYzde+HqUXAV6HGC/i+d982fei6x3X+8bP16K5enb6kZcLSPUyfT9/4tOKRsE5b3KxTFzdrQVO85Hh9ibS+e9823betU79ev0/1sYheuqxVJy9s0gsWNGnN/EatmtOg7d1Dqo+FNb/RVxdMZ7KKhEP+BjQe1WAqo4d2dGsgmdFJ8xoVj4a0cX+/PvvbDbpna5fa6qI6aV6jjp/boL7gZvSFC5u0Mgg+j5tdr2ceuU/nnHOOJGlH95AyWaclLbVHVLXTo0VvIqWaSEg1kbCe2NOroVRGddGwfvroLnUNprSpfUAttVHNa6xRx0BSITNd/+ReRcKmE+Y0aF5jjWY3xDSnvkaP7e7RouZaPbGnV0ta4vrN+v2jgoKW2uio0tvJCJmCkp3ygo3FzXF1DCY1lDr04KQ2GlIindXy1jp1DiZHBb6VKhYOqaU2UlB7wEya21Cj5nhEzxwY0KLmuGbVRdUcj6o5HlFTPKqmeERNNRE110bVWBMJ+kfUMZBSfSys3b0Jza6PqbEmEozrp6mPhdU5mFJdLKzdPQndtbVTJimZyco5aTiT1dyGmJpq/HyjYdOBgaTmNsT0XPug/vfpfTplUbOSmaz6h9Oa3xTXgf6k6mJhtQZpaYpH1FiT+wursSaih3f26MEd3aqLhUeGbekc9DfPNRH94rHdymSdlrbWqiEYPpz2y1jQFFdDTVgNMT/N3r6Els+q0zP7B7SwOa5o2BQJmSKhkCIjv027tm9VbNZCbe4YUDTsb8qf2NOr2fUxHehPak9fQsfPaVDPUErprNOCpho11kTUUBPR0/v6dWxbnXqH00plslrQFNdQKqOtnUNqrY2qoSasGzfsVzwSGpmmIRZRQ40PBDbs71fHYFInzWtUQyyshhq/7RtiEf12434tafFBQUPM96+LhVUTCem59kENpnywtGJWvRpqwtrWNaSdPUM6Zlad1u/r14lzGxSPhvTU3n4tn+WDPeckJ19CmPstSRsP9CseCamlNqq6aFjPtg/omFl1qo+FVR8suz4W1pbOQaUyTq11UT17YEB7+hJqrY0q6/w8JY2q9YDKlbvM5x6MhEOmsJnCISkSCgXdUiKd1dKWWjn5YDKTzT1UOfg7998/+PPHWSoIunNBaa5GQNZJ9TF/jDj5BwnRsAXnwajmN9aoPhYZGb+tPqa2+pjC5o/p1rqoIiFTQyyiSNgOLls+8A+Fgv9mioVNDTV+Xpmg+VFO7i4n91DkYPcYw0v0r4+FFQ2FRoLoaNiUSGWVSPtAPpHOBN1ZOefUFPe1KCKhkMIm1UT8udDJKRLyDy1y0zsnNcUjioRsJKjPbc/cPjOTnn7iMa0++YVKpP1Dz9z08SCgT2d94Y4kza6LKRwypbNOqYx/MJb7ncpkR/XPPURwcprfGFdLbUTpjFMqeKiWDh6qDKezB5efzoykYziTVTKdVSKVUTKTVTwalnNBmoL9lsrk/vt8kh757/TkJ885OgO+V3/vXv3+mXYtb63V1q4h3fqhl+ic42ZPcwpRCRKpTEG1qLFU85MhFMqdGxPp7MiFJde/fSAZlKaZdnQP6fon92p/UII1uz6mHd0JPb6nV8fPqdem9kE9sadXaxY0qq0uprbgArK3L6FlrXUySd2JlJY012rdc+1a91yHTl7QpLa6mGbVR9W7b6devOYEtdVF/fT1Mc2q8yVozkl1sbCufWy3BpL+aXdvIq2N+/u1sDmu3z9zQCtm1WlWXVStdX66WXUxtdZGVRv1wXJNxD9tzzqnh3b0aHYw/9a6qFpr/e+W2qhqo2E9uqtHG/b3q60+pi2dg2qri2pHd0LhoNrs3MaYdnb7oKmlNqrWWj+f+pgvyWyJR7W/P6nWuqha4j4AuHNLp1pr/TJa4lFFw6a+4fTI9Ga+dCEXKDfHI6OO1SOtrW8l4xw39TJB6WuuSnkqk1U0HNKe3oRccDNfF5Qm9Q2nVR8Lj5SQR8Km+lhEWee07rkOpTMuGNc/PAqHTHVRP219LDxyvqqLhpUJgo2uoBQ0nfXnjKzz55zOweTIsRMNhRSLhBQNznXxaEjRUEhDqYxqo+GRZdZGw+oZSmswldaB/qSa4hHVxcJKpv35si4WVl3e+HVRvy5DqazSWX8DPpzJqn0gqZBJdz/0qE46abWebR+Qk4J18esWCYVUFwuPNM3JbaPaiO8/kMwok3Xa1+9rabjgBXN1sbDikZDMpHTGaXv3kJzzQVI0HPL/gwcdITOZVBCcZbJOGefUMZBSNrgO7OsfVm00HAR4JudcsH01Mn4667SvN6Gs00iJcSh08GFK7nfYTJGwD2CcpM6glkcuTbk05paVDV72Jh0suU9nnBJpX9q6t294JICSpJ09CQ2lMiPBB45s4ZCpJjjmaiKh4HdYIVNwHIx+MBcNh4L/fljuAd4vLz7j6Av4+ofTmvWPvx2p8nfi3AY9+Ym11PPH88LNEKYbeQ7TifyG6UR+O7xSmay6g3b03YmUQnYw6JQ0UqU1GwSwPUNpZZwbCTZzz9FykUIuZBj5Hww52J0b7kZNl8k69QRpMJMGkhmFQ6baiA9w4hH/QCEeDSke8Q8KkpmDJWe5dUhlfOmkn943jYhHQ3JO6hhMBtWVfVCfC/Jz6zuYyujRxx7XGae+KFiOX1YkKGmMBNV5IyFTT8K3pc4FV7nAKxdcHQy6CgP0cMiUzjj/8rxMtjAwy3ugUhP2AV5kCttKj1els2o/y3Drs+0F7bs+eNYygj0AAAAcFaLh0MgL6eaP0WzmaBPfG9HalYe5tl9UOml+4+FdxiRVxiu4DsFNG/arLq/az9tftHAGUwMAAAAA069qA747NnfoFStmjXTzZAMAAADA0aYqA76OgaQ27O/Xy1e0aeXsen3jjatnOkkAAAAAMO2qsg3fwzu7JUlnLm3Vp85dOcOpAQAAAICZUZUlfH/c1StJetGiphlOCQAAAADMnKoM+B7b3aOlLbWaVReb6aQAAAAAwIypyoDvyb19OnkBpXsAAAAAjm5VF/ClMlltPNCv1QuOrO9fAAAAAMB0q7qAb3PHoFIZpxPnNsx0UgAAAABgRlVdwPds+4AkaeXs+hlOCQAAAADMrCoM+PolSSvnEPABAAAAOLpVXcC3pXNIjTURtfGGTgAAAABHuaoL+HZ0DWpZa63MbKaTAgAAAAAzquoCvu3dQ1raUjvTyQAAAACAGVd1Ad+2riEtaSXgAwAAAICqCvgGk2l1Dqa0hBI+AAAAAKiugG9377AkaVFTfIZTAgAAAAAzr7oCvp6EJGlhMwEfAAAAAFRXwNcbBHyU8AEAAABAdQV8BwaSkqS5DXyDDwAAAACqKuDrHPQBX2ttdIZTAgAAAAAzr8oCvpSa4xFFwlW1WgAAAABwSKoqMuoaTGpWHdU5AQAAAECqsoCvYzClWXVU5wQAAAAAqcoCvs7BJAEfAAAAAASqLOBLUaUTAAAAAAJVFfB1DaV4QycAAAAABKom4HPOqXsopVaqdAIAAACApCoK+AaSGaWzTi1xAj4AAAAAkKoo4OseSkmSWqjSCQAAAACSCPgAAAAAoGpVT8CXIOADAAAAgHzVE/ANpSVJzfHIDKcEAAAAAI4MVRPw9Q37gK+JgA8AAAAAJFVhwNcQI+ADAAAAAKmKAr7+IOBrrCHgAwAAAACpigK+kRI+Aj4AAAAAkFRlAV9dNKxwyGY6KQAAAABwRKiqgK+RF7YAAAAAwIiqCfj6hzNqiIVnOhkAAAAAcMSoooAvTfs9AAAAAMhTNQHfQDKjekr4AAAAAGBEFQV8ab7BBwAAAAB5qijgo4QPAAAAAPIR8AEAAABAlaqigC+tel7aAgAAAAAjqibg6x+mhA8AAAAA8lVFwOec01A6o7ooAR8AAAAA5FRFwDeczso5qY4SPgAAAAAYURUB31AqI0mKRwj4AAAAACCnSgK+rCSpNloVqwMAAAAAU6IqIqRcCV8tbfgAAAAAYAQBHwAAAABUqSoJ+HJVOgn4AAAAACCnSgK+XAlfVawOAAAAAEyJqoiQhtK8pRMAAAAAilVFwJfgLZ0AAAAAMEpVREjDaR/w1VDCBwAAAAAjqiLgS2V9wBcL2wynBAAAAACOHFUR8CXTuYCvKlYHAAAAAKZEVURIyUwQ8EWqYnUAAAAAYEpURYSUzDhJlPABAAAAQL6qiJByVTqjtOEDAAAAgBHVEfBlaMMHAAAAAMWqIkLKBXxRAj4AAAAAGFEVEVIq4xQOmcIhqnQCAAAAQE5VBHzJTJZv8AEAAABAkSoK+KpiVQAAAABgylRFlJRME/ABAAAAQLGqiJKSGcdH1wEAAACgSFVESalMVlFe2AIAAAAABaoi4EtmspTwAQAAAECRqoiSfAlfVawKAAAAAEyZqoiSkmmnWIQqnQAAAACQryoCvlSWt3QCAAAAQLGqiJKS6ayiBHwAAAAAUKAqoqRU1vGWTgAAAAAoUh0BX4YSPgAAAAAoNq1RkpldYGYbzWyTmf1DieFLzew2M/ujmT1uZq8tZ76pjKMNHwAAAAAUmbYoyczCkr4l6TWSTpJ0oZmdVDTaZyVd65w7RdLbJX27nHknM1lFw1TpBAAAAIB801ksdoakTc65zc65pKSfSXpj0ThOUlPwu1nS7nJmTJVOAAAAABgtMo3LWiRpR173TklnFo1zqaSbzewjkuolnVdqRmZ2iaRLJGnOnDmKDgyqs31Y69atm+o0AwX6+/vJZ5hW5DlMJ/IbphP5DdPtaM1z0xnwlapz6Yq6L5R0lXPu38zsJZJ+ZGZrnHPZgomc+56k70nSqlWrXCJWo8UL52jt2hcdloQDOevWrdPatWtnOhk4ipDnMJ3Ib5hO5DdMt6M1z01nPcidkpbkdS/W6Cqb75d0rSQ55+6VFJc0e6IZJzOONnwAAAAAUGQ6A74HJa00s2PMLCb/UpYbisbZLulcSTKzE+UDvgMTzTiVySoaog0fAAAAAOSbtijJOZeW9GFJv5P0tPzbOJ8ys38xsz8NRvu4pA+a2WOSfirpYudccbXPUToHU5TwAQAAAECR6WzDJ+fcjZJuLOr3ubzf6yWdfSjz7hxMPb/EAQAAAECVqZp6kIua4zOdBAAAAAA4olRNwMd3+AAAAACgUNVESZEQbfgAAAAAIF/FB3y5N7oQ8AEAAABAocoP+IKIj4APAAAAAApVfMCXE+GzDAAAAABQoOIDvoNVOit+VQAAAABgSlVNlESVTgAAAAAoVPEBH234AAAAAKC0yg/4gv9hAj4AAAAAKFDxAV8OJXwAAAAAUKjiAz6+wwcAAAAApVV8wJfDWzoBAAAAoFDFR0kjL23hO3wAAAAAUKDyA77gP1U6AQAAAKBQxQd8OQR8AAAAAFCo4gM+PssAAAAAAKVVfMCXzTXiAwAAAAAUqPiAj3I9AAAAACit4gO+nNpIeKaTAAAAAABHlKoJ+PgMHwAAAAAUqvgwKdeCL2RU7gQAAACAfBUf8OUQ8AEAAABAoaoJ+Aj3AAAAAKBQ1QR8lPABAAAAQKGKD/hG2vBV/JoAAAAAwNSq/DApiPgo3wMAAACAQpUf8AWo0gkAAAAAhQj4AAAAAKBKVXzAd/A7fDOaDAAAAAA44lR8wJdjlPABAAAAQIGqCfgo4QMAAACAQlUU8BHxAQAAAEC+ig/4aMMHAAAAAKVVfMCXQxs+AAAAAChUNQEfJXwAAAAAUKjyA76gTidt+AAAAACgUMUHfLThAwAAAIDSKj7gy6ENHwAAAAAUqpqAjxI+AAAAAChURQEfER8AAAAA5Kv4gC/Xho9wDwAAAAAKVXzAl0MJHwAAAAAUqvyAL/dZhspfEwAAAACYUlUTJlHCBwAAAACFKj7gow0fAAAAAJRW8QFfDiV8AAAAAFCIgA8AAAAAqlTFB3y5Kp18eB0AAAAAClV8wJdDAR8AAAAAFKqagI8qnQAAAABQiIAPAAAAAKpUxQd8I59lIN4DAAAAgAIVH/DlIj5K+AAAAACgUOUHfAHe0gkAAAAAhaom4DNK+AAAAACgQMUHfE603wMAAACAUio+4JNovwcAAAAApVRJwDfTKQAAAACAI0+VBHxEfAAAAABQrOIDPieJcA8AAAAARqv4gE+ihA8AAAAASqmOgK8q1gIAAAAAplblh0qOEj4AAAAAKKXiAz7a8AEAAABAaRUf8EmU8AEAAABAKVUS8M10CgAAAADgyFPxAZ+TZJTwAQAAAMAoFR/wSZTwAQAAAEApVRLwEfEBAAAAQLGKD/hSWSc304kAAAAAgCNQxQd8EZP29Q3PdDIAAAAA4IhT8QGfJC1tqZ3pJAAAAADAEacqAj5e2gIAAAAAo1V8wMdnGQAAAACgtIoP+CRK+AAAAACglKoI+CjhAwAAAIDRqiPgm+kEAAAAAMARqCoCPqp0AgAAAMBoFR/w8dIWAAAAACit4gM+iRI+AAAAACilKgI+oxUfAAAAAIxSFQFfqCrWAgAAAACmVuWHSo4SPgAAAAAopeIDPv/SlplOBQAAAAAceSo+4JN4aQsAAAAAlFIVAR9VOgEAAABgtKoI+CjhAwAAAIDRqiLg48PrAAAAADBaxQd8TqJCJwAAAACUUPEBnySFqNMJAAAAAKNURcBHuAcAAAAAo1VFwEcBHwAAAACMVvEBn//wOhEfAAAAABSr+IBPokonAAAAAJRS+QGfk0KU8AEAAADAKJUf8Eki3gMAAACA0aoi4KOEDwAAAABGq/iAz7+0ZaZTAQAAAABHnooP+CRe2gIAAAAApUTGGmBmF5U7E+fcNeWMZ2YXSPqmpLCkHzjnvlxinLdKulS+8O4x59w7JpovVToBAAAAYLQxAz5J3yrqjkmKSsoG3SFJKUnDkiYM+MwsHMzzfEk7JT1oZjc459bnjbNS0qckne2c6zKzueWsBPEeAAAAAIw2ZpVO51xj7k/S2yU9LunlkuLB38slPSppwhK4wBmSNjnnNjvnkpJ+JumNReN8UNK3nHNdQRr2l7USRHwAAAAAMMp4JXz5vibpfc65e/P63W1mfyvpKkm/KWMeiyTtyOveKenMonGOlyQzu1u+2uelzrnfFs/IzC6RdIkkReYeo87ODq1bt668NQGeh/7+fvIaphV5DtOJ/IbpRH7DdDta81y5Ad9ySQMl+g9KWlrmPEoVw7kS6Vkpaa2kxZLuNLM1zrnugomc+56k70lSfNFKN7utTWvXFseOwNRbt26d1q5dO9PJwFGEPIfpRH7DdCK/YbodrXmu3Ld03i/pcjNblOsR/P66pPvKnMdOSUvyuhdL2l1inF8551LOuS2SNsoHgOOiSicAAAAAjFZuwPcBSW2StprZVjPbKmmrpLny7e7K8aCklWZ2jJnF5NsF3lA0zvWSzpEkM5stX8Vz80QzJt4DAAAAgNHKqtLpnNtkZifLv2HzBPnqmesl3eKcK66WOdY80mb2YUm/k2+f95/OuafM7F8kPeScuyEY9iozWy8pI+kTzrmO8WdMCR8AAAAAlDJhwGdmUUl3SbrIOXezpJsPdWHOuRsl3VjU73N5v52k/xv8lTdP8eF1AAAAAChlwiqdzrmUpGM0+gUrRwwK+AAAAABgtHLb8F2t8tvqTTuqdAIAAADAaOV+lqFe0jvN7HxJD6voEw3OuY9OdcImg3APAAAAAEYrN+A7UdIjwe8VRcNmvKpnKETIBwAAAADFyn1L5zmHOyGHipe2AAAAAEBp5bbhO6LRhg8AAAAARiu3SqfM7BxJF0paKimWP8w598opTtekEO8BAAAAwGhllfCZ2cWSbpLUKGmtpAOSWiWdKv8B9hlFvAcAAAAAo5VbpfPvJH3YOXehpJSkTznnTpH0Y0n9hytx5TKK+AAAAABglHIDvhWSbgl+D0tqCH7/P0kXT3GaJoWXtgAAAABAaeUGfB3y1TklaZekNcHvNkm1U52oyaKADwAAAABGK/elLXdKepWkJyRdK+ny4CPs50r6/WFKW3mcZJTxAQAAAMAo5QZ8H5YUD35/SVJa0tnywd9lhyFdk0O8BwAAAACjlPvh9c6831lJXzlsKToEVOkEAAAAgNHK/SzDd83s7Wa24HAn6FBQpRMAAAAARiu3SmeDpH+VtNDMNklal/tzzu05PEkrHyV8AAAAADBaWSV8zrl3OueWSDpBPvCrl6/WudPMNhzG9E2cNtGEDwAAAABKKbeEL+c5SbMkzZE0V9ICSTVTnajJooQPAAAAAEYrtw3fJ8zsRkndkn4qaZWkn0g6zjl3zGFMX1lowwcAAAAAo5VbwvcVSQckfV7SVc65A4cvSZNHCR8AAAAAjFZWCZ/8R9e/L+mNkrab2RNmdoWZvdnM2g5f8spDvAcAAAAAo5X7Hb5bJN0iSWZWK//R9XdK+pl8vBU9XAksh1HEBwAAAACjlP3SFjObJ2lt8HeOpOMl7ZP/PAMAAAAA4AhTVsBnZuvlX9SyX9Ltkr4h/w2+Gf0kQw7lewAAAAAwWrklfJfrCArw8jnxVOG6JQAAIABJREFU0hYAAAAAKKXcNnzfyf0OqnYecM5lD1uqJok2fAAAAADw/9u79/AoqnTf4783QRFFPCoKgiiogEau4SKKRAIOCGgAgdky6AAD4+iWo3szOMrxhqjH8X4bBXW8oI7CCFvMUdxeEBAFQUBkRkflIs4AKoqKXEQhec8fXendpLuTCiQdqHw/z1NPuldXV79dWenkl1VVK1nYefhqmdntZrZF0npJTYP228zs36uwvlCIewAAAACQLOy0DBMknSfpQkk/JbQvljSickuqOAb4AAAAACBZ2HP4hkr6jbvPM7PEQzn/rtjVOqsVeQ8AAAAAkoUd4Wsk6fMU7bVUgakdqoRzDh8AAAAApBI28H0oKS9F+y8lLa28cirOxQgfAAAAAKQSdnTuRknPmFkTSdmShpjZyZJ+JalfVRUXFgN8AAAAAJAs1Aifu/8/xUbzekkqlnSDpOaSznP3N6quvHCMMT4AAAAASFLuCJ+Z1VIs6C1y97OqvqSKY4QPAAAAAJKVO8Ln7rsk/ZekQ6u+nD1D3gMAAACAZGEv2vKBpJOqspC9wVU6AQAAACBZRSZev8vMBphZEzM7InGpwvpCIe8BAAAAQLKwV+l8Ofj6X4rNhFDCgvvZlVlURTAtAwAAAACkFjbw5VdpFXuJET4AAAAASBYq8Ln7vKouZG8wLQMAAAAAJAt7Dt8+jRE+AAAAAEgWjcBX3QUAAAAAwD4oGoGPIT4AAAAASBKNwFfdBQAAAADAPigagY/EBwAAAABJ0l6l08zmaPc599Jy9x6VVhEAAAAAoFKUNS3D3xNuZ0saJulLSYuCts6SjpH0TNWUFh7TMgAAAABAsrSBz93/d8ltM7tH0hRJV7i7J7Tfq33gFDoO6QQAAACAZGHP4fu1pD8lhr3AQ5IuqtySKo7ABwAAAADJwgY+k9Q6RXuqtowj7wEAAABAsrLO4Uv0uKQ/m1lzSe8GbV0k/UHSE1VRWEUwDx8AAAAAJAsb+P4gaaOkKyT936DtC0l/lHRXFdRVIcQ9AAAAAEgWKvC5e7Gk2yXdbmb1grYfqrKwimCADwAAAACSVWjidTPrKKmPpKLg/iFmFnaUsMowLQMAAAAAJAsV1sysgaRCSZ0Um4y9uaQ1ku6WtEOxQz2rzaJ/fledLw8AAAAA+6SwI3z3KDbp+pGStie0Py+pV2UXVVFf/LCjuksAAAAAgH1O2MMxe0rq6e7flboi5mpJx1V6VRXEVToBAAAAIFnYEb46kn5O0X6UYod0ViviHgAAAAAkCxv43pI0IuG+m1m2pKskza7sogAAAAAAe68i8/DNM7NOkmorNvfeqZIOk9S1imoLjSM6AQAAACBZqBE+d/9IUmtJCyS9JukgxS7Y0t7dV1ddeeGQ9wAAAAAgWdhpGY6T9C93vyHVY+7+z0qvrAK4aAsAAAAAJAt7Dt9nil2gZTdmdmTwWLUi7gEAAABAsrCBzxSbcL20utoXrtJJ4gMAAACAJGUe0mlm9wc3XdKtZpY46Xq2pM6SlldRbaEZY3wAAAAAkKS8c/haB19N0inafS6+nyUtk3RnFdQFAAAAANhLZQY+d8+XJDN7QtIV7v5DRqoCAAAAAOy1sOfw/R9J9Uo3mtmxZtagckuquE3bfy5/JQAAAACoYcIGvqck9UnR3lvS05VXzp5pcGjt6i4BAAAAAPY5YQNfJ0lvpWifL6lj5ZWzZ7hkCwAAAAAkCxv4aklKNYx2UJr2jGJaBgAAAABIFjbwLZJ0aYr2yyS9V3nl7BmmZQAAAACAZOVNy1DiGklvmllbSbODth6S2ks6uyoKAwAAAADsnVAjfO7+rqTTJX0m6XxJg4Lbp7v7gqorLxwO6QQAAACAZGFH+OTuH0gaVoW17DHyHgAAAAAkC3sOn8ysgZmNM7OHzKx+0NbVzJpVXXmha6vuEgAAAABgnxMq8JlZB0mfKDbCN1r/Mwn7LyTdUjWlhUfcAwAAAIBkYUf47pR0n7u3l/RTQvurkrpWelUVxAAfAAAAACQLG/g6SJqSov0LSQ0qrxwAAAAAQGUJG/h+lHR4ivaTJW2svHL2DPPwAQAAAECysIHvRUk3mFnt4L6bWVNJt0maUQV1VQiHdAIAAABAsrCBb5ykIyR9LelgSW9LWiXpe0nXVk1p4RH4AAAAACBZqHn43P0HSWeaWQ9JuYoFxWXu/kZVFhcWeQ8AAAAAkoWeeF2S3P1NSW9WUS17jHn4AAAAACBZRSZeH2Bmb5nZN8Ey38wGVmVxYRH3AAAAACBZ2InXfy9pmmKTr/8hWD6W9KyZjau68gAAAAAAeyrsIZ3jJI1x90cT2h43s8WSJio2MXu14YhOAAAAAEgW9pDOupLmpGifEzxWrZiHDwAAAACShQ18MyUNTtE+SFJh5ZUDAAAAAKgsYQ/pXCXpajPLl7QwaOsSLHeb2diSFd397sotsXxZDPABAAAAQJKwgW+EpO8ktQiWEt9JGplw3yVlPPAxLQMAAAAAJAs78Xqzqi5kb5D3AAAAACBZ2GkZDivjsRMrrxwAAAAAQGUJe9GWFWaWV7rRzH4j6f3KLaniGOADAAAAgGRhA99zkt4ws/9rZtlmdriZzZB0r6T/qLrywuEcPgAAAABIFvYcvqvN7L8lPSWpt6QGktZJynX3VVVYHwAAAABgD4Ud4ZOktyW9Iqm9pKMl3byvhD2mZQAAAACAZGEv2tJC0mJJv5CUL+kmSTPM7B4zO7AK6wuFvAcAAAAAycKO8C2T9JGkdu4+z91vktRNUj9JS6qqOAAAAADAngsb+C5x9wvd/YeSBndfLClXsZG/UMzsHDP7xMxWmdnVZaw32MzczDqG3TYAAAAAYHehAp+7P5Omfau7jw6zDTPLlvSgpD6SciQNNbOcFOsdKulySYvCbDd4TthVAQAAAKDGCH3RFjPrY2YvmdlHZtYkaBttZj1DbqKzpFXuvsbdf5Y0VVL/FOvdJOl2STtC1xZ2RQAAAACoQUJNy2BmwyRNlvRnST0lHRA8lC3pD5Jmh9hMY0n/Sri/TtJppV6nvaQm7v6SmY0ro56LJV0sSWpwkr786kvNnTs3zFsB9srWrVvpa8go+hwyif6GTKK/IdNqap8LFfgUC3W/dfepZpZ4COe7kiaG3EaqgTiPP2iWJekeSSPK25C7PyLpEUmyhs29UcNj1L17u5BlAHtu7ty56t69e3WXgRqEPodMor8hk+hvyLSa2ufCHtLZXNLCFO1bJdULuY11kpok3D9W0oaE+4dKaiVprpmtldRFUmGoC7dwTCcAAAAAJAkb+DZIapGiPU/S6pDbeE9SczNrFszdd4GkwpIH3X2zu9d396bu3lSx0cMCd2faBwAAAADYA2ED3yOS7jezrsH9JmY2XLGLq0wKswF33yVpjKRXJf1D0l/d/UMzm2hmBRWsezdcpBMAAAAAkoU6h8/dbzezwyS9LukgSXMk/STpTnd/MOyLufssSbNKtV2fZt3uYbdrHNMJAAAAAEnCXrRF7n6Nmd2i2Bx6WZI+cvetVVZZBTDCBwAAAADJQgc+SXL37ZI4pw4AAAAA9gOhJ17fl2UxwgcAAAAASSIR+Lo1O7K6SwAAAACAfU4kAl/XpkdUdwkAAAAAsM+JROADAAAAACQj8AEAAABARBH4AAAAACCiCHwAAAAAEFEEPgAAAACIqEgEPmMePgAAAABIEonABwAAAABIRuADAAAAgIgi8AEAAABARBH4AAAAACCiCHwAAAAAEFEEPgAAAACIqEgEPmZlAAAAAIBkkQh8AAAAAIBkBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIikTgM+ZlAAAAAIAkkQh8AAAAAIBkBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIikTgM+ZlAAAAAIAkkQh8AAAAAIBkBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIikTgY1IGAAAAAEgWicAHAAAAAEhG4AMAAACAiCLwAQAAAEBEEfgAAAAAIKIIfAAAAAAQUQQ+AAAAAIioSAQ+Y14GAAAAAEgSicAHAAAAAEhG4AMAAACAiCLwAQAAAEBEEfgAAAAAIKIIfAAAAAAQUQQ+AAAAAIioSAQ+E/MyAAAAAEBpkQh8AAAAAIBkBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIikTgM2ZlAAAAAIAkkQh8AAAAAIBkBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAERWJwMdFOgEAAAAgWSQCHwAAAAAgGYEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgoiIR+MyYmAEAAAAASotE4AMAAAAAJCPwAQAAAEBEEfgAAAAAIKIIfAAAAAAQUQQ+AAAAAIgoAh8AAAAARFQkAh+TMgAAAABAskgEPgAAAABAMgIfAAAAAEQUgQ8AAAAAIorABwAAAAARFYnA59VdAAAAAADsg6IR+JzIBwAAAAClRSLwmTExAwAAAACUFo3AV90FAAAAAMA+KBKBDwAAAACQjMAHAAAAABFF4AMAAACAiCLwAQAAAEBEEfgAAAAAIKIIfAAAAAAQUZEIfEy7DgAAAADJIhH4AAAAAADJCHwAAAAAEFEEPgAAAACIKAIfAAAAAEQUgQ8AAAAAIiqjgc/MzjGzT8xslZldneLxsWb2kZmtMLPZZnZ8JusDAAAAgCjJWOAzs2xJD0rqIylH0lAzyym12vuSOrp7G0nTJd2eqfoAAAAAIGoyOcLXWdIqd1/j7j9Lmiqpf+IK7j7H3bcHd9+VdGwG6wMAAACASKmVwddqLOlfCffXSTqtjPVHSXol1QNmdrGkiyVJDU7SO++8oyMO4nREVL2tW7dq7ty51V0GahD6HDKJ/oZMor8h02pqn8tk4LMUbZ5yRbMLJXWUdFaqx939EUmPSJI1bO4De+fLLNXmgco1d+5cde/evbrLQA1Cn0Mm0d+QSfQ3ZFpN7XOZDHzrJDVJuH+spA2lVzKzsyVdI+ksd/+pvI0ekCXCHgAAAACkkMnjIN+T1NzMmpnZgZIukFSYuIKZtZf0sKQCd9+YwdoAAAAAIHIyFvjcfZekMZJelfQPSX919w/NbKKZFQSr3SGprqTnzWy5mRWm2RwAAAAAoByZPKRT7j5L0qxSbdcn3D47k/UAAAAAQJRxaUsAAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACJqvw98Vt0FAAAAAMA+ar8PfAAAAACA1Ah8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAAAAQERlNPCZ2Tlm9omZrTKzq1M8XtvMpgWPLzKzppmsDwAAAACiJGOBz8yyJT0oqY+kHElDzSyn1GqjJH3n7idJukfSbZmqDwAAAACiJpMjfJ0lrXL3Ne7+s6SpkvqXWqe/pCnB7emSepqZZbBGAAAAAIiMWhl8rcaS/pVwf52k09Kt4+67zGyzpCMlfZO4kpldLOni4O5PZvb3KqkYSFZfpfojUMXoc8gk+hsyif6GTItynzs+3QOZDHypRup8D9aRuz8i6RFJMrMl7t5x78sDykd/Q6bR55BJ9DdkEv0NmVZT+1wmD+lcJ6lJwv1jJW1It46Z1ZJ0mKRvM1IdAAAAAERMJgPfe5Kam1kzMztQ0gWSCkutUyhpeHB7sKQ33T1phA8AAAAAUL6MHdIZnJM3RtKrkrIlPe7uH5rZRElL3L1Q0mOSnjazVYqN7F0QYtOPVFnRQDL6GzKNPodMor8hk+hvyLQa2eeMATQAAAAAiKaMTrwOAAAAAMgcAh8AAAAARNR+HfjM7Bwz+8TMVpnZ1dVdD/YvZrbWzP5mZsvNbEnQdoSZvW5mK4OvhwftZmb3B31thZnlJmxneLD+SjMbntDeIdj+quC5qaYdQUSZ2eNmtjFxntBM9K90r4FoS9PfJpjZ+uAzbrmZ9U14bHzQdz4xs94J7Sl/rwYXXFsU9KtpwcXXZGa1g/urgsebZuYdozqZWRMzm2Nm/zCzD83siqCdzzhUiTL6HJ9zYbj7frkoduGX1ZJOkHSgpA8k5VR3XSz7zyJpraT6pdpul3R1cPtqSbcFt/tKekWxuSK7SFoUtB8haU3w9fDg9uHBY4slnR485xVJfar7PbNktH/lScqV9PeEtirvX+legyXaS5r+NkHSuBTr5gS/M2tLahb8Ls0u6/eqpL9KuiC4PVnSpcHtf5c0Obh9gaRp1b0vWDLS346RlBvcPlTSp0G/4jOOJdN9js+5EMv+PMLXWdIqd1/j7j9LmiqpfzXXhP1ff0lTgttTJA1IaH/KY96V9L/M7BhJvSW97u7fuvt3kl6XdE7wWD13X+ixT4inEraFGsDd31LyPKKZ6F/pXgMRlqa/pdNf0lR3/8ndP5O0SrHfqSl/rwYjKz0kTQ+eX7rvlvS36ZJ6cjRD9Ln7F+6+LLi9RdI/JDUWn3GoImX0uXT4nEuwPwe+xpL+lXB/ncr+xgOluaTXzGypmV0ctDVw9y+k2IeLpKOD9nT9raz2dSnaUbNlon+lew3UTGOCQ+geTzj0raL97UhJ37v7rlLtu20reHxzsD5qiODwtvaSFonPOGRAqT4n8TlXrv058KVK1swxgYro6u65kvpIuszM8spYN11/q2g7kAr9C1VhkqQTJbWT9IWku4L2yuxv9MUazMzqSpoh6T/c/YeyVk3RxmccKixFn+NzLoT9OfCtk9Qk4f6xkjZUUy3YD7n7huDrRkkvKDbM/1VwKImCrxuD1dP1t7Laj03RjpotE/0r3WughnH3r9y9yN2LJT2q2GecVPH+9o1ih+DVKtW+27aCxw9T+ENLsR8zswMU+8P7L+7+X0Ezn3GoMqn6HJ9z4ezPge89Sc2DK+ocqNhJlIXVXBP2E2Z2iJkdWnJbUi9Jf1esD5VcJWy4pBeD24WSfh1caayLpM3BoSSvSuplZocHhxH0kvRq8NgWM+sSHOf964RtoebKRP9K9xqoYUr+KA4MVOwzTor1kQuCK881k9RcsQtkpPy9GpxDNUfS4OD5pftuSX8bLOnNYH1EWPC585ikf7j73QkP8RmHKpGuz/E5F1J1XzVmbxbFrvr0qWJX27mmuuth2X8Wxa7O9EGwfFjSfxQ7Jnu2pJXB1yOCdpP0YNDX/iapY8K2fqPYycCrJI1MaO+o2AfPakl/kmTV/b5ZMtrHnlPs8JKdiv13cFQm+le612CJ9pKmvz0d9KcViv3BckzC+tcEfecTJVxBON3v1eAzc3HQD5+XVDtoPyi4vyp4/ITq3hcsGelvZyp2SNsKScuDpS+fcSzV0Of4nAuxlPzwAAAAAAAiZn8+pBMAAAAAUAYCHwAAAABEFIEPAAAAACKKwAcAAAAAEUXgAwAAAICIIvABAJABZtbUzNzMOlZ3LQCAmoPABwAAAAARReADAAAAgIgi8AEAIsfM8szsXTPbamabzWyRmbUysyPN7DkzW2dmP5rZh2Y2stRz55rZJDO7y8y+NbOvzewKM6ttZg+a2fdm9k8zuyjhOSWHa/7KzN42sx1m9rGZ9Sqnzhwze9nMtpjZxqC2hgmPtzaz2Wb2Q7DOB2aWX/l7DAAQVQQ+AECkmFktSS9KeltSW0mnSbpPUpGkgyQtk3SupFOD9ofNrGepzQyTtCV47h8l3StppqRPJXWUNEXSn82sUann3S7pfkntJL0u6UUza5ymzmMkvSXp75I6SzpbUl1JhWZW8vv5WUlfBI+3lzRB0o6K7A8AQM1m7l7dNQAAUGnM7AhJmyR1d/d5IdafKmmru48O7s+VVNvdTw/um6SNkha6e0HQdoCkbZJ+5e7TzayppM8kXevutwTrZEn6WNJf3f3ahHU6ufsSM5soqau790yo5XBJ30o6zd0Xm9kPkv63u0/Zy90CAKihGOEDAESKu38r6UlJrwaHS441syaSZGbZZnaNma0ws01mtlXS+ZKOK7WZFQnbc8UC398S2nZK+k7S0aWetzBhnWJJiyTlpCm1g6S84LDTrUEt/woeOzH4erdiI4lvBnWfHHI3AAAgicAHAIggdx+p2OGYb0kqkPSpmfWWNE7S7yXdIamnYodezpR0YKlN7Cy9yTRte/N7NEvSy0ENiUtzSS8F72OCYoFxpqQzJK0ws9/sxWsCAGqYWtVdAAAAVcHdP5D0gaTbzOwVScMlHSrp/7n701L8cM0Wkr6vpJftIunNhG13ljQ9zbrLJP1S0ufBiGG697FS0kpJ95vZJEmjJT1eSfUCACKOET4AQKSYWTMz+6OZnWFmxwdXtWwj6SPFLrrS08zODA6P/JOkZpX48pea2WAza6nYhV6OlzQpzboPSjpM0jQzO83MTjCzs83sETM71MzqBFcF7R5cBfQ0SWcG7wMAgFAY4QMARM12xUbtnpdUX9JXkv4i6TbFroLZTNIrkn5U7Fy/vyj9eXYVdbWksZJyJX0uaaC7r0u1ortvMLOukm6V9N+KXUH0n5Jek/RTsNrhil0RtKFiF6J5SbHDUgEACIWrdAIAsJdKX4GzeqsBAOB/cEgnAAAAAEQUgQ8AAAAAIopDOgEAAAAgohjhAwAAAICIIvABAAAAQEQR+AAAAAAgogh8AAAAABBRBD4AAAAAiCgCHwAAAABEVK3qLgDAvm/p0qXHZmVlvVZcXHyyJKvuegAA+wXPysr6uLi4uFeHDh3WVXcxQE1F4ANQrqysrNcaNmzYvEGDBpaVxYEBAIDyFRcX25dfftnin//854KCgoLcwsLCb6q7JqAm4i83AOUqLi4+uUGDBrUIewCAsLKystSwYcPsWrVqNZE0vqCg4IjqrgmoifjrDUAYjOwBACosKytLZiZJR0o6pZrLAWok/oIDAABAVXNJB1V3EUBNROADAAAAgIgi8AHYL2RnZ6tdu3Zq1aqVhgwZou3bt5e5ft26dSVJGzZs0ODBg6ukplmzZunkk0/WNddcI0lau3atunfvHn988eLFysvLU8uWLXXyySdr9OjR5dadyr333rtHzytt27ZtOvLII7V58+bd2gcMGKBp06apfv36+u677yRJX3zxhcxMb7/9dny9o446Sps2bdKECRP05JNPJm3/ySef1IQJE/a4vqZNm+qbbzJ7TYe5c+fq3HPPrfTtfvLJJ2rXrl18qVevnu69995K2XZF9tPMmTP10UcfVcrrprNkyRJdfvnle/Tcpk2bxm+PHTtWp5xyiubMmRPquXPnztVhhx22235+44030q5f+ueob9+++v777/eo7kTLly/XrFmz9no7ifsi0YgRIzR9+vS93n46iT8DhYWF+uMf/7jH20r3HgBUL67SCaBC/uPFv+uD9T9U6jbbNq6ne/u3KnOdOnXqaPny5ZKkYcOGafLkyRo7dmy5227UqFGV/bH06KOP6u6771bfvn2THvvqq680ZMgQTZ06VaeffrrcXTNmzNCWLVt08MEHV+h17r33Xl144YUVfl5phxxyiHr16qWZM2dq+PDhkqTNmzfr7bff1rPPPqunnnpKCxcuVN++fbVgwQK1b99eCxYs0JlnnqlPPvlE9evX15FHHrlXNezvdu3apVq1yv/V2bJly3h/LSoqUuPGjTVw4MCqLi/JzJkzde655yonJ6fKXqNjx47q2LHjXm/n7rvvVufOnfX4448rPz8/1HO6deuml156KdS6pX+OKiOkSbHAt2TJkpSfA+mE7UdlKSoqUnZ29l5to7SCggIVFBRU6jYBVD9G+ADsd7p166ZVq1ZJiv2R2KpVK7Vq1SrlCMratWvVqlUsTBYVFWncuHFq3bq12rRpowceeECzZ8/e7Q/x119/Xeeff36oOr7//nsdffTR8fvZ2dk64ojYRegefPBBDR8+XKeffrokycw0ePBgNWjQQBMmTNCdd94Zf16rVq20du1abdu2Tf369VPbtm3VqlUrTZt2fL69AAAPkElEQVQ2Tffff782bNig/Pz8+B/Bzz33nFq3bq1WrVrpqquuim+nbt26uuqqq9ShQwedffbZWrx4sbp3764TTjhBhYWFkqShQ4dq6tSp8ee88MILOuecc3TwwQera9euWrBggSRpwYIFGjt2rBYuXBi/f8YZZ8Rfp06dOkn7o06dOvGR1bKk+j6UuOOOO9S5c2d17tw5/j1O5euvv9agQYPUqVMnderUSe+8844k6fLLL9fEiRMlSa+++qry8vJUXFysESNG6JJLLlG3bt3UokWLlCHh22+/1YABA9SmTRt16dJFK1askCRNmDBBF198sXr16qVf//rXKioq0pVXXqlOnTqpTZs2evjhh8t8v7Nnz9aJJ56o448/vtx9U5n7acGCBSosLNSVV16pdu3aafXq1crNzY0/vnLlSnXo0EFSbGTmqquuStpmuv2cKHGEaMKECbrooovUo0cPNW/eXI8++qik2IhxXl5efJR+/vz5kmKjxokaNmy416NuYX+OSkZK165dGx+Bb9WqlYYNG6Y33nhDXbt2VfPmzbV48WJJsRH7M844Q+3bt9cZZ5yhTz75RD///LOuv/56TZs2Te3atdO0adNC96PSSvaFu2vMmDHKyclRv379tHHjxvg6TZs21cSJE3XmmWfq+eefT/n+ly9fri5duqhNmzYaOHBgfNS+e/fu8e9xixYt4t+DRE8++aTGjBkjKTayePnll+uMM87QCSecsNs/zu644454/7/hhhuS3gOAfYy7s7CwsJS5LFmyxKvbIYcc4u7uO3fu9IKCAn/ooYd8yZIl3qpVK9+6datv2bLFc3JyfNmyZbut/9lnn/mpp57q7u4PPfSQn3/++b5z5053d9+0aZMXFxd7y5YtfePGje7uPnToUC8sLHR391GjRvl7772XtqZu3br50qVLUz42cOBAnzlzZsrHbrjhBr/jjjvi90899VT/7LPPfPr06T569Oh4+/fff+/u7scff7x//fXX7u6+fv16b9KkiW/cuNF37tzp+fn5/sILL7i7uySfNWuWu7sPGDDAf/GLX/jPP//sy5cv97Zt27q7+08//eRHHXWUf/PNN+7u3rt3b3/ppZfc3X3OnDmen5/v7u5nnnmmb9myxTt06ODu7qNHj/bHHnss6b1MmjTJJ02alHYfpZLq+1DyPm+++WZ3d58yZYr369cv7TaGDh3q8+fPd3f3zz//3E8++WR3d9+2bZvn5OT4m2++6S1atPBVq1a5u/vw4cO9d+/eXlRU5J9++qk3btzYf/zxR58zZ078dcaMGeMTJkxwd/fZs2fH99kNN9zgubm5vn37dnd3f/jhh/2mm25yd/cdO3Z4hw4dfM2aNWlrHTlypD/wwAMV2keVtZ+GDx/uzz//fPx+9+7d/f3333d39/Hjx/v9999f5jbT7edEifvwhhtu8DZt2vj27dv966+/9mOPPdbXr1/vd955Z3z7u3bt8h9++CFlvfPmzfM+ffqE2T0+Z84cr1evnrdt2za+rFq1KtTPUeL9zz77zLOzs33FihVeVFTkubm5PnLkSC8uLvaZM2d6//793d198+bN8e/F66+/7ueff767uz/xxBN+2WWXxbcbth+tX78+5XudMWOGn3322b5r1y5fv369H3bYYfHv4fHHH++33XZbmfuldevWPnfuXHd3v+666/yKK65wd/ezzjrLx44d6+7uL7/8svfs2TO+H0u+f4nvZfjw4T548GAvKiryDz/80E888UR3d3/11Vf9t7/9rRcXF3tRUZH369fP582bV2ZNS5Ys8fPOO++J8847r6fvA7/TWFhq2sIhnQD2Cz/++KPatWsnKTbCN2rUKE2aNEkDBw7UIYccIkk6//zzNX/+fLVv3z7lNt544w1dcskl8UOpSkbjLrroIj3zzDMaOXKkFi5cqKeeekqS9Oc//zltPdu2bdPq1avVqFGjSnuPrVu31rhx43TVVVfp3HPPVbdu3ZLWee+999S9e/f4f9KHDRumt956SwMGDNCBBx6oc845J76t2rVr64ADDlDr1q21du1aSdKBBx6ogoICTZ8+XYMGDdLy5cvVq1cvSVLnzp31/vvva9u2bdq5c6fq1q2rE044QatWrdKCBQv0+9//PqmeSy65pMLvM933QYqNQJZ8/c///M8yt5F4btoPP/ygLVu26NBDD9Wjjz6qvLw83XPPPTrxxBPj6/zyl79UVlaWmjdvrhNOOEEff/zxbtt8++23NWPGDElSjx49tGnTpvj5jgUFBfFRzddee00rVqyIj3hs3rxZK1euVLNmzZLq/Pnnn1VYWKhbb701/A5KeI97u59KGz16tJ544gndfffdmjZtWnz0Kt02y9rP6fTv31916tRRnTp1lJ+fr8WLF6tTp076zW9+o507d2rAgAHxn+XSGjdurE8//VQ7duzQQQeVf0HHVId0loyMlvVzVFqzZs3UunVrSdKpp56qnj17ysx2+9nZvHmzhg8frpUrV8rMtHPnzpTbCtuPGjVqlPKw0rfeektDhw5Vdna2GjVqpB49euz2+L/927+lfR+bN2/W999/r7POOkuSNHz4cA0ZMiT+eMnRCx06dIi/r7IMGDBAWVlZysnJ0VdffSUp1v9fe+21+Ofs1q1btXLlSuXl5ZW7PQDVg8AHYL+QeA5fCXev0DbcvWQ+qN2MHDlS5513ng466CANGTKk3HNrFi1apL59+6pXr15q2LBhynVOPfVULV26VP379096rFatWiouLo7f37FjhySpRYsWWrp0qWbNmqXx48erV69euv7665PeQzoHHHBA/P1lZWWpdu3a8du7du2Krzd06FDdfPPNcnf1799fBxxwgCTp4IMP1kknnaTHH388fuhfly5dNGvWLG3cuFEtW7Ysc7+UKCoqih8qWFBQoPbt2+vGG2+UFAvR6b4PknZrT7eOJBUXF2vhwoUpDy3929/+piOPPFIbNmxIu+1U91Pt25J1Sv6pULLeAw88oN69e6etr8Qrr7yi3NxcNWjQIOmxTOyn0gYNGqQbb7xRPXr0UIcOHXY7JzPVNsvaz+mk2s95eXl666239PLLL+uiiy7SlVdemfKwxhNPPFE5OTk67rjjNHv27HgIq4gwP0ellfysSOl/dq677jrl5+frhRdeSLpAU6Kw/agsZX1Pw24jlZL3lZ2dvdtnQnnrS//zvtxd48eP1+9+97s9rgNAZnEOH4D9Vl5enmbOnKnt27dr27ZteuGFF8r8b36vXr00efLk+B863377raTYf9obNWqkm2++WSNGjCj3dU877TRt2LBB7777rlavXp1ynTFjxmjKlClatGhRvO2ZZ57Rl19+qaZNm2rZsmWSpGXLlumzzz6TFLui6MEHH6wLL7xQ48aNi69z6KGHasuWLfHXnjdvnr755hsVFRXpueeei/83P6z8/HytXLlSDz74YHxUp0TXrl117733xs89PP3003XfffepS5cuoYNFdna2li9fruXLl2vixIkaOHBg/H7Hjh3Tfh8kadq0afGvJTWk0qtXL/3pT3+K3y/5Z8Dnn3+uu+66S++//75eeeWV3fb/888/r+LiYq1evVpr1qxJCrB5eXn6y1/+Iil2Xlr9+vVVr169pNfu3bu3Jk2aFB/h+fTTT7Vt27aUdT733HNJ+ziT+ymx70jSQQcdpN69e+vSSy/VyJEjd1s31TbT7eeyvPjii9qxY4c2bdqkuXPnqlOnTvr888919NFH67e//a1GjRoV79ulffDBB1qzZo3Wr18fD3svvPCCxo8fX+7rlgjzc7QnNm/erMaNG0vSblepLb3dsP0onby8PE2dOlVFRUX64osvQl+1VJIOO+wwHX744fHz855++ukKfz6Up3fv3nr88ce1detWSdL69et3O88QwL6HwAdgv5Wbm6sRI0aoc+fOOu200zR69Oi0h3NKscPZjjvuOLVp00Zt27bVs88+G39s2LBhatKkyW5XMxw9erSWLFmSclu1a9fW8ccfH78gQmkNGjTQ1KlTNW7cOLVs2VKnnHKK5s+fr3r16mnQoEH69ttv1a5dO02aNEktWrSQFBuZ6ty5s9q1a6dbbrlF1157rSTp4osvVp8+fZSfn69jjjlGt956q/Lz89W2bVvl5uamHEUsS1ZWlgYNGqRNmzYlHYbVtWtXrVmzJv4Hf25urtatWxe/YEtpkydP1uTJkyv0+mV9H3766Seddtppuu+++3TPPfek3cb999+vJUuWqE2bNsrJydHkyZPl7ho1apTuvPNONWrUSI899phGjx4dH0Ft2bKlzjrrLPXp00eTJ09OOmRwwoQJ8W1effXVmjJlStr6c3JylJubq1atWul3v/tdytGS7du3V+giQKleZ2/30wUXXKA77rhD7du3j/9zYtiwYTKz+KG8ZW0z1X4uT+fOndWvXz916dJF1113nRo1aqS5c+eqXbt2at++vWbMmKErrrgi5XO/++47NW3aND7qLEmrV69OG5jmz5+/27QM06dPD/VztCf+8Ic/aPz48eratauKiori7fn5+froo4/iF20J2482bNiQ8sqeAwcOVPPmzdW6dWtdeumlFQ5sU6ZM0ZVXXqk2bdpo+fLl5Y5uVlSvXr30q1/9Sqeffrpat26twYMH71WQBlD1rKKHRAGoeZYuXeolh55F1ZgxY9S+fXuNGjUq9HP69u2rMWPGVOhy7KgeI0aM0LnnnltlczLuT+68805t3rxZN910U7ytadOmWrJkierXr79X254wYYLq1q2rcePG7dHz//rXv2rGjBnx0UZJuvDCC3XPPfdwBcj92NKlS3XjjTc+KemZwsLC2dVdD1DTcA4fgBqvQ4cOOuSQQ3TXXXdV6HmXXHKJrr32Wr3zzju65ZZbqqg6oPIMHDhQq1ev1ptvvlndpSQZO3as5s2blzTx9zPPPFNNFQFANDDCB6BcNWGED/umW265JWm+sSFDhuiaa66ppor2Teynmueyyy5LmhfxiiuuSDo3c1/ACB9QvQh8AMq1dOnS4vbt21tWFqf9AgDCKy4u1rJlyzRx4sQnReADqgV/vQEoV1ZW1sdffvllUeJUAgAAlKW4uFhffPFF8Y4dO76RZJIYZQCqAefwAShXcXFxrw0bNszZsGHDSRWZ8wsAUHO5u3bs2PHtU0899bSkoyT9UN01ATURgQ9AuTp06LCuoKCghaQhkvpKKn/GXgAAYg6XtFDSB9VdCFATcQ4fgNAKCgpMUiNJdau7FgDAfmOHpHWFhYVF5a4JoNIR+AAAAAAgorhoCwAAAABEFIEPAAAAACKKwAcAAAAAEfX/ARdxw2TnXx+fAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "policy_vw_string='--cb_explore 7 --cb_type ips'\n",
+    "\n",
+    "estimated_rewards = evaluation.compute_estimate({'CustomVW':policy_vw_string}, 'snips', log_data_parsed)\n",
+    "print(\"\\tExpected reward: \", estimated_rewards[-1])\n",
+    "plot_expectedr(\"Estimated expected reward over samples for Policy: {'CustomVW':'--cb_explore 7 --cb_type ips'}, Estimator:'dr_online'\", estimated_rewards, \"Policy: {'CustomVW':'--cb_explore 7 --cb_type ips'}, Estimator:'dr_online'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tExpected reward:  0.9556278620509878\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "policy_vw_string='--cb_explore 7 --cb_type ips'\n",
+    "\n",
+    "estimated_rewards = evaluation.compute_estimate_dr({'CustomVW':policy_vw_string}, 'dr_online', log_data_parsed)\n",
+    "print(\"\\tExpected reward: \", estimated_rewards[-1])\n",
+    "plot_expectedr(\"Estimated expected reward over samples for Policy: {'CustomVW':'--cb_explore 7 --cb_type ips'}, Estimator:'dr_online'\", estimated_rewards, \"Policy: {'CustomVW':'--cb_explore 7 --cb_type ips'}, Estimator:'dr_online'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tExpected reward:  0.8232294257263666\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "policy_vw_string='--cb_explore 7 --epsilon 0.2'\n",
+    "\n",
+    "estimated_rewards = evaluation.compute_estimate_dr({'CustomVW':policy_vw_string}, 'dr_online', log_data_parsed)\n",
+    "print(\"\\tExpected reward: \", estimated_rewards[-1])\n",
+    "plot_expectedr(\"Estimated expected reward over samples for Policy: {'CustomVW':'--cb_explore 7 --epsilon 0.2'}, Estimator:'dr_online'\", estimated_rewards, \"Policy: {'CustomVW':'--cb_explore 7 --epsilon 0.2'}, Estimator:'dr_online'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.3. Comparison of Policies and Estimators \n",
+    "\n",
+    "#### 4.3.1. Comparing the the estimated expected reward of one target policy over a set of estimators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_comparison(results):\n",
+    "    plt.figure(1)\n",
+    "    plt.grid()\n",
+    "    for res in results:\n",
+    "        plot_expectedr('Comparison of performance over different policies and estimators', res['estimates'], \"Policy: \"+str(res['policy'])+\", Estimator: \"+str(res['estimator']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#?evaluation.compare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tPolicy:  Constant\n",
+      "\t\tsnips:\t 0.2501682532525464\n",
+      "\t\tips:\t 0.25169017253762\n",
+      "\t\tdr_batch:\t 0.2458137307657858\n",
+      "\t\tdr_online:\t 0.24600809676490898\n",
+      "\tBest result: \n",
+      "\t\tpolicy: Constant \n",
+      "\t\testimator: ips \n",
+      "\t\testimate: 0.25169017253762\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "estimators = ['snips','ips', 'dr_batch', 'dr_online']#, 'mle','cressieread','dr_seq_batch', 'dr_seq_batch']\n",
+    "\n",
+    "best, results = evaluation.compare('Constant', estimators, log_data_parsed)\n",
+    "\n",
+    "visualize_comparison(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.3.2. Comparing the estimated expected reward of a set of target policies using one estimator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tPolicy:  Constant\n",
+      "\t\tdr_online:\t 0.24600809676490898\n",
+      "\tPolicy:  UniformRandom\n",
+      "\t\tdr_online:\t 0.14172350553392965\n",
+      "\tBest result: \n",
+      "\t\tpolicy: Constant \n",
+      "\t\testimator: dr_online \n",
+      "\t\testimate: 0.24600809676490898\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "policies = ['Constant', 'UniformRandom']\n",
+    "\n",
+    "best, results = evaluation.compare(policies, 'dr_online', log_data_parsed)\n",
+    "\n",
+    "visualize_comparison(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.3.3. Comparing the estimated expected reward of a set of target policies over a set of estimators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tPolicy:  Constant\n",
+      "\t\tsnips:\t 0.2501682532525464\n",
+      "\t\tips:\t 0.25169017253762\n",
+      "\t\tdr_batch:\t 0.2458137307657858\n",
+      "\t\tdr_online:\t 0.24600809676490898\n",
+      "\tPolicy:  UniformRandom\n",
+      "\t\tsnips:\t 0.14478352554786503\n",
+      "\t\tips:\t 0.14279318327096793\n",
+      "\t\tdr_batch:\t 0.14600336602577066\n",
+      "\t\tdr_online:\t 0.15215173370175333\n",
+      "\tPolicy:  {'CustomVW': '--cb_explore 7 --cb_type ips'}\n",
+      "\t\tsnips:\t 0.956243162769107\n",
+      "\t\tips:\t 0.9576677851573627\n",
+      "\t\tdr_batch:\t 0.9565441238330799\n",
+      "\t\tdr_online:\t 0.9570781569038105\n",
+      "\tPolicy:  {'CustomVW': '--cb_explore 7 --cb_type dm'}\n",
+      "\t\tsnips:\t 0.9579884720826138\n",
+      "\t\tips:\t 0.9549494176878724\n",
+      "\t\tdr_batch:\t 0.956398538863531\n",
+      "\t\tdr_online:\t 0.9548387794743484\n",
+      "\tBest result: \n",
+      "\t\tpolicy: {'CustomVW': '--cb_explore 7 --cb_type dm'} \n",
+      "\t\testimator: snips \n",
+      "\t\testimate: 0.9579884720826138\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "policies = ['Constant', 'UniformRandom', {'CustomVW':'--cb_explore 7 --cb_type ips'}, {'CustomVW':'--cb_explore 7 --cb_type dm'}]\n",
+    "estimators = ['snips','ips', 'dr_batch', 'dr_online']#, 'mle','cressieread','dr_seq_batch', 'dr_seq_batch']\n",
+    "\n",
+    "best, results = evaluation.compare(policies, estimators, log_data_parsed)\n",
+    "\n",
+    "visualize_comparison(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 5. Generating a Contextual Bandit Dataset\n",
+    "\n",
+    "Generating a random logging policy and target policy to use for evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.1. Training our classifier model  to generate a deterministic policy π\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yeast_data = pd.read_csv('../data/yeast.data', delim_whitespace=True, header=None, names=['Sequence_Name', 'mcg', 'gvh', 'alm', 'mit', 'erl', 'pox', 'vac', 'nuc', 'Target'])  \n",
+    "X = yeast_data[['mcg', 'gvh', 'alm', 'mit', 'erl', 'pox', 'vac', 'nuc']]\n",
+    "Y = yeast_data[['Target']].astype(\"category\")\n",
+    "\n",
+    "test_size=0.3\n",
+    "\n",
+    "clf=RandomForestClassifier(max_depth=5, n_estimators=10)\n",
+    "\n",
+    "acc, action_num, act_gtruth, act, X_test=softening.train_target(X, Y, test_size, clf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model Accuracy: 0.5695\n",
+      "Action Number: 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Model Accuracy: %.4f'%acc)\n",
+    "print('Action Number: %d'%action_num)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2. Softening our deterministic policy π\n",
+    "\n",
+    "Softening is a method for transforming a deterministic policy into a stochastic policy\n",
+    "\n",
+    "We support 3 different softening methods[1]:\n",
+    "- Neutral\n",
+    "- Friendly\n",
+    "- Adversarial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First instantiate the CBPolicy class from the ```softening``` module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cb_policy = softening.CBPolicy(action_num)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next Soften by passing your desired softening parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2.1. Comparing softening methods\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Neutral"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soften_params = {'method':'neutral'\n",
+    "                }\n",
+    "act_prob_neutral = cb_policy.get_soften_action(act, soften_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assessing similarity of softened actions with actions from deterministic policy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6192216697359445"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1-spatial.distance.cosine(act_prob_neutral['action'][0], act)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Computing the neutral softening reward:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neutral_r = softening.get_reward(act_prob_neutral['action'][0], act)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Friendly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soften_params = {'method':'friendly',\n",
+    "                 'alpha': 0.7,\n",
+    "                 'beta':0.2\n",
+    "                }\n",
+    "act_prob_friendly = cb_policy.get_soften_action(act, soften_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assessing similarity of softened actions with actions from deterministic policy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8602646160794079"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1-spatial.distance.cosine(act_prob_friendly['action'][0], act)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Computing the friendly truth reward:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "friendly_r = softening.get_reward(act_prob_friendly['action'][0], act)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Adversarial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soften_params = {'method':'adversarial',\n",
+    "                 'alpha': 0.5,\n",
+    "                 'beta':0.2\n",
+    "                }\n",
+    "act_prob_adv = cb_policy.get_soften_action(act, soften_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assessing similarity of softened actions with actions from deterministic policy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6241456323671213"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1-spatial.distance.cosine(act_prob_adv['action'][0], act)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Computing the adversarial softening reward:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adversarial_r = softening.get_reward(act_prob_adv['action'][0], act)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Putting it all together"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To sum it up we need ```(x, a, r, p)``` for off-policy evaluation. \n",
+    "Taking Friendly softening as our softening technique, we have these as follows:\n",
+    "\n",
+    "```x```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mcg</th>\n",
+       "      <th>gvh</th>\n",
+       "      <th>alm</th>\n",
+       "      <th>mit</th>\n",
+       "      <th>erl</th>\n",
+       "      <th>pox</th>\n",
+       "      <th>vac</th>\n",
+       "      <th>nuc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>0.62</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>0.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.49</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.49</td>\n",
+       "      <td>0.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.46</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>0.47</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.55</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>0.47</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>0.51</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mcg   gvh   alm   mit  erl  pox   vac   nuc\n",
+       "0  0.42  0.31  0.62  0.12  0.5  0.0  0.40  0.22\n",
+       "1  0.69  0.50  0.49  0.42  0.5  0.0  0.49  0.22\n",
+       "2  0.46  0.38  0.47  0.22  0.5  0.0  0.52  0.27\n",
+       "3  0.55  0.59  0.47  0.64  0.5  0.0  0.54  0.33\n",
+       "4  0.41  0.48  0.54  0.24  0.5  0.0  0.37  0.51"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```a```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 6, 6, 6])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "act_prob_friendly['action'][0][:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```p```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.71743938, 0.64966372, 0.0242057 , 0.78724364])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "act_prob_friendly['prob'][:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "action pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[0.7174393792990699,\n",
+       "  0.03139562452232557,\n",
+       "  0.03139562452232557,\n",
+       "  0.03139562452232557,\n",
+       "  0.03139562452232557,\n",
+       "  0.03139562452232557,\n",
+       "  0.03139562452232557,\n",
+       "  0.03139562452232557,\n",
+       "  0.03139562452232557,\n",
+       "  0.03139562452232557],\n",
+       " [0.03892625379120813,\n",
+       "  0.03892625379120813,\n",
+       "  0.03892625379120813,\n",
+       "  0.03892625379120813,\n",
+       "  0.03892625379120813,\n",
+       "  0.03892625379120813,\n",
+       "  0.6496637158791269,\n",
+       "  0.03892625379120813,\n",
+       "  0.03892625379120813,\n",
+       "  0.03892625379120813]]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "act_prob_friendly['prob_list'][:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```r```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 0, 1])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "friendly_r[:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.3. Bringing it all together\n",
+    "#### Training, softening and generating a CB dataset from a classification dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yeast_data = pd.read_csv('../data/yeast.data', delim_whitespace=True, header=None, names=['Sequence_Name', 'mcg', 'gvh', 'alm', 'mit', 'erl', 'pox', 'vac', 'nuc', 'Target'])  \n",
+    "X = yeast_data[['mcg', 'gvh', 'alm', 'mit', 'erl', 'pox', 'vac', 'nuc']]\n",
+    "Y = yeast_data[['Target']].astype(\"category\")\n",
+    "\n",
+    "test_size=0.3\n",
+    "\n",
+    "clf=RandomForestClassifier(max_depth=5, n_estimators=10)\n",
+    "#clf=SVC(kernel=\"linear\", C=0.025)\n",
+    "#clf=SVC(gamma=2, C=1)\n",
+    "#clf=GaussianProcessClassifier(1.0 * RBF(1.0))\n",
+    "#clf=DecisionTreeClassifier(max_depth=5)\n",
+    "\n",
+    "accuracy, action_num, act_gtruth, act_deterministic, X_test=softening.train_target(X, Y, test_size, clf)\n",
+    "\n",
+    "N=10000 #number of samples\n",
+    "\n",
+    "soften_params_behaviour = {'method':'friendly',\n",
+    "                           'alpha': 0.9,\n",
+    "                           'beta':0.0\n",
+    "                          }\n",
+    "\n",
+    "cb_policy = softening.CBPolicy(action_num)\n",
+    "\n",
+    "#Generating Behaviour Data\n",
+    "out_behaviour_json = \"../data/cb_behaviour_data.json\"\n",
+    "X_context, a_det, a_gtruth = softening.classification_to_cb_behaviour(X_test, action_num, act_gtruth, act_deterministic, cb_policy, soften_params_behaviour, out_behaviour_json, N)\n",
+    "\n",
+    "soften_params_evaluation1={'method':'neutral'}\n",
+    "\n",
+    "soften_params_evaluation2={'method':'friendly',\n",
+    "                           'alpha': 0.7,\n",
+    "                           'beta':0.2\n",
+    "                          }\n",
+    "\n",
+    "soften_params_evaluation3={'method':'adversarial',\n",
+    "                           'alpha': 0.5,\n",
+    "                           'beta':0.2\n",
+    "                           }\n",
+    "\n",
+    "#Generating Evaluation Data ---Preserving context used in generating behaviour data\n",
+    "out_evaluation_json1 = \"../data/cb_evaluation_data1.json\"\n",
+    "r1 = softening.classification_to_cb_evaluation(X_context, action_num, np.array(a_gtruth, dtype=int), np.array(a_det, dtype=int), cb_policy, soften_params_evaluation1, out_evaluation_json1, N)\n",
+    "\n",
+    "out_evaluation_json2 = \"../data/cb_evaluation_data2.json\"\n",
+    "r2 = softening.classification_to_cb_evaluation(X_context, action_num, np.array(a_gtruth, dtype=int), np.array(a_det, dtype=int), cb_policy, soften_params_evaluation2, out_evaluation_json2, N)\n",
+    "\n",
+    "out_evaluation_json3 = \"../data/cb_evaluation_data3.json\"\n",
+    "r3 = softening.classification_to_cb_evaluation(X_context, action_num, np.array(a_gtruth, dtype=int), np.array(a_det, dtype=int), cb_policy, soften_params_evaluation3, out_evaluation_json3, N)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Parsing file... Processing: ../data/cb_behaviour_data.json\n",
+      "Progress: [##################################################] 100.0%\n",
+      "Parsing file... Processing: ../data/cb_evaluation_data1.json\n",
+      "Progress: [##################################################] 100.0%\n",
+      "Parsing file... Processing: ../data/cb_evaluation_data2.json\n",
+      "Progress: [##################################################] 100.0%\n",
+      "Parsing file... Processing: ../data/cb_evaluation_data3.json\n",
+      "Progress: [##################################################] 100.0%"
+     ]
+    }
+   ],
+   "source": [
+    "behaviour_parsed = \"../data/cb_behaviour_parsed.dat\"\n",
+    "ds_parse.parse_file(out_behaviour_json, behaviour_parsed)#\n",
+    "\n",
+    "evaluation_parsed1 = \"../data/cb_evaluation_parsed1.dat\"\n",
+    "ds_parse.parse_file(out_evaluation_json1, evaluation_parsed1)#\n",
+    "\n",
+    "evaluation_parsed2 = \"../data/cb_evaluation_parsed2.dat\"\n",
+    "ds_parse.parse_file(out_evaluation_json2, evaluation_parsed2)#\n",
+    "\n",
+    "evaluation_parsed3 = \"../data/cb_evaluation_parsed3.dat\"\n",
+    "ds_parse.parse_file(out_evaluation_json3, evaluation_parsed3)#"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.4. Demonstrating how to compute the expected reward on this generated data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'p': 0.9, 'probas': [0.9, 0.011111111111111108, 0.011111111111111108, 0.011111111111111108, 0.011111111111111108, 0.011111111111111108, 0.011111111111111108, 0.011111111111111108, 0.011111111111111108, 0.011111111111111108], 'a_vec': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'a': 1, 'num_a': 10, 'r': 1, 'cost': -1, 'XNamespace': 'mcg:0.56 gvh:0.62 alm:0.53 mit:0.24 erl:0.5 pox:0.0 vac:0.57 nuc:0.22', 'skipLearn': 0}\r",
+      "\r\n",
+      "{'p': 0.9, 'probas': [0.011111111111111108, 0.011111111111111108, 0.011111111111111108, 0.011111111111111108, 0.011111111111111108, 0.011111111111111108, 0.9, 0.011111111111111108, 0.011111111111111108, 0.011111111111111108], 'a_vec': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'a': 7, 'num_a': 10, 'r': 0, 'cost': 0, 'XNamespace': 'mcg:0.36 gvh:0.46 alm:0.62 mit:0.46 erl:0.5 pox:0.0 vac:0.51 nuc:0.33', 'skipLearn': 0}\r",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head -2 ../data/cb_behaviour_parsed.dat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'p': 0.1, 'probas': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], 'a_vec': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'a': 6, 'num_a': 10, 'r': 0, 'cost': 0, 'XNamespace': 'mcg:0.56 gvh:0.62 alm:0.53 mit:0.24 erl:0.5 pox:0.0 vac:0.57 nuc:0.22', 'skipLearn': 0}\r",
+      "\r\n",
+      "{'p': 0.1, 'probas': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], 'a_vec': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'a': 1, 'num_a': 10, 'r': 1, 'cost': -1, 'XNamespace': 'mcg:0.36 gvh:0.46 alm:0.62 mit:0.46 erl:0.5 pox:0.0 vac:0.51 nuc:0.33', 'skipLearn': 0}\r",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head -2 ../data/cb_evaluation_parsed1.dat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ground truth rewards "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reward 1:  0.0991\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Reward 1: \",np.mean(r1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tExpected reward:  -0.6405332628667229\n"
+     ]
+    }
+   ],
+   "source": [
+    "estimated_rewards = evaluation.compute_estimate_dr({'CustomCB': evaluation_parsed3}, 'dr_online', behaviour_parsed)\n",
+    "print(\"\\tExpected reward: \", estimated_rewards[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.5. Comparing Estimators on this generated data\n",
+    "\n",
+    "Because you have access to the ground truths, you can compare using MSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ground truth reward:  0.0991\n",
+      "\tPolicy:  {'CustomCB': '../data/cb_evaluation_parsed1.dat'}\n",
+      "\t\tsnips mean of estimated expected rewards:\t 0.0901958525345405\n",
+      "\t\tips mean of estimated expected rewards:\t 0.08698888888888004\n",
+      "\t\tdr_batch mean of estimated expected rewards:\t 0.10803843027174694\n",
+      "\t\tdr_online mean of estimated expected rewards:\t 0.10446797525966314\n",
+      "\tBest result: \n",
+      "\t\tpolicy: {'CustomCB': '../data/cb_evaluation_parsed1.dat'} \n",
+      "\t\testimator: dr_online \n",
+      "\t\tmean expected reward: 0.10446797525966314\n"
+     ]
+    }
+   ],
+   "source": [
+    "policy = {'CustomCB': evaluation_parsed1}\n",
+    "r_gtruth = np.mean(r1)\n",
+    "estimators = ['snips','ips', 'dr_batch', 'dr_online']\n",
+    "runs_per_estimator = 10\n",
+    "print(\"Ground truth reward: \",np.mean(r1))\n",
+    "\n",
+    "best, results = evaluation.compare_mean_rewards(policy, estimators, behaviour_parsed, r_gtruth, runs_per_estimator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/action_value_model.py b/action_value_model.py
new file mode 100644
index 0000000..811a7ab
--- /dev/null
+++ b/action_value_model.py
@@ -0,0 +1,73 @@
+class ActionValueModel:
+    r"""
+    An abstract class representing an action value model for a fixed policy
+    \pi, a.k.a. Q_\pi, in a Reinforcement Learning (RL) setting.
+
+    All other action value models should subclass it. All subclasses should
+    override ``q``, that returns the Q value prediction for an Observation
+    and an action Q(obs.state, action) ; and ``update'', which updates the
+    Q value model given two subsequent observations.
+    """
+
+    def __init__(self, horizon, action_num, policy):
+        r"""
+        Initializes variables to store episode data and all the Q models.
+
+        Arguments:
+            horizon: RL horizon (fixed horizon setting), a.k.a H.
+            actions_num: number of possible actions (indexing starts at 1)
+            policy: a Policy object for which we want to learn Q.
+        """
+        self.horizon = horizon
+        self.action_num = action_num
+        self.policy = policy
+
+    def q(self, index, observation, action):
+        r"""
+        Tge prediction for Q_\pi at index, for the state action pair
+        (observation, action).
+
+        Arguments:
+            index: the number of steps already passed in the episode,
+                   in 1 ... H
+            obseravtion: an Observation, where obs.state is s_i in Q_\pi(s_i, a_i)
+            action: the action a in Q_\pi(s_i, a_i)
+
+        Returns the predicted cumulative reward for the last t observations of
+        the episode, playing a then \pi
+        """
+        raise NotImplementedError
+
+    def v(self, index, observation):
+        r"""
+        Integrates Q_\pi over all action and proabs for observation at index
+        to return the cumulative reward to the end of the episode, playing \pi.
+
+        Arguments:
+            index: the number of steps already passed in the episode,
+                   in 1 ... H
+            obseravtion: an Observation, where obs.state is s_i in V_\pi(s_i)
+
+        Returns the predicted cumulative reward for the last t observations of
+        the episode
+        """
+        if index > self.horizon:
+            return 0
+
+        v = 0
+        for action, p in enumerate(self.policy.get_action_probas(index, observation)):
+            v += p * self.q(index, observation, action+1)
+        return v
+
+    def update(self, index, observation, next_observation):
+        r"""
+        Updates Q_\pi at index, with a new observation and its next state.
+
+        Arguments:
+            index: the number of steps already passed in the episode,
+                   in 1 ... H
+            obseravtion: an Observation of state, action, proba, reward
+            next_observation: an Observation used to get the next state after
+                observation in Q learning
+        """
+        raise NotImplementedError
diff --git a/basic-usage.py b/basic-usage.py
index 31a2d4a..737fcb0 100644
--- a/basic-usage.py
+++ b/basic-usage.py
@@ -3,9 +3,29 @@
 import ips_snips
 import mle
 import ds_parse
-
-
-def compute_estimates(log_fp):
+import dr
+import policy
+from vowpalwabbit import pyvw
+import os
+
+def compute_estimates(log_fp, dr_mode, pol):
+    """
+        Using a logging policy, computes estimates of expected rewards for different target policies as well as confidence intervals around such estimates, and reports the results
+
+        Parameters
+        ----------
+        log_fp : file(json)
+            Logging policy
+        dr_mode: string
+            The mode of the doubly robust estimator, can be {batch, online}
+        pol : string
+            target policy to evaluate
+            
+        Returns
+        -------
+        None
+        """
+    
     # Init estimators
     online = ips_snips.Estimator()
     baseline1 = ips_snips.Estimator()
@@ -17,10 +37,39 @@ def compute_estimates(log_fp):
     baseline1_cressieread = cressieread.Estimator()
     baselineR_cressieread = cressieread.Estimator()
 
-    print('Processing: {}'.format(log_fp))
+    dre_batch = dr.Estimator()
+    baseline1_dre_batch = dr.Estimator()
+    baselineR_dre_batch = dr.Estimator()
+
+    dre_online = dr.Estimator()
+    baseline1_dre_online = dr.Estimator()
+    baselineR_dre_online = dr.Estimator()
+
+    reward_estimator_model = pyvw.vw(quiet=True)
+
+    if(pol=='constant'):
+        policy_pi = policy.Constant(10, 5)
+    elif(pol=='uniformrandom'):
+        policy_pi = policy.UniformRandom(10)
+
+    lines_count = sum(1 for line in open(log_fp))
+    train_ratio = 0.5
+    train_size = int(lines_count * train_ratio)
+
+    if(dr_mode=='batch'):
+        print("Batch train size: ", train_size)
+
+    load_pretrained_model = False
+    train_data_str = ""
+    train_data_file = open("xar.dat","w+")
+    train_data_batch = []
+
+    print('\n Computing estimates... Processing: {}'.format(log_fp))
     bytes_count = 0
     tot_bytes = os.path.getsize(log_fp)
     evts = 0
+    idxx = 0
+
     for i,x in enumerate(gzip.open(log_fp, 'rb') if log_fp.endswith('.gz') else open(log_fp, 'rb')):
         # display progress
         bytes_count += len(x)
@@ -32,7 +81,8 @@ def compute_estimates(log_fp):
 
         # parse dsjson file
         if x.startswith(b'{"_label_cost":') and x.strip().endswith(b'}'):
-            data = ds_parse.json_cooked(x)
+
+            data = ds_parse.ds_json_parse(x)
 
             if data['skipLearn']:
                 continue
@@ -51,44 +101,97 @@ def compute_estimates(log_fp):
             online_cressieread.add_example(data['p'], r, data['p'])
             baseline1_cressieread.add_example(data['p'], r, 1 if data['a'] == 1 else 0)
             baselineR_cressieread.add_example(data['p'], r, 1/data['num_a'])
+            
+            if(dr_mode == 'online'):
+                #construct training example (x, a, r)
+                train_example = str(data['r']) + " |ANamespace Action:" + str(data['a']) + " |SharedNamespace c_user:" + str(data['c_user']) + " c_time_of_day:" + str(data['c_time_of_day'])
+                
+                #At the beginning, you don't have a model to predict with, so you can only learn a reward estimator
+                if(idxx==0):
+                    reward_estimator_model = dre_online.reward_estimator(train_example, reward_estimator_model, dr_mode, load_pretrained_model)
+                    
+                #Now you have a reward estimator to predict with
+                elif(idxx>1):
+                    
+                    x = " |SharedNamespace c_user:" + str(data['c_user']) + " c_time_of_day:" + str(data['c_time_of_day'])
+                    
+                    #compute estimate
+                    dre_online.add_example(x, data['a'], data['a_vec'], data['p'], data['r'], data['p'], policy_pi.get_action_probas(0, 0), reward_estimator_model)
+                    baseline1_dre_online.add_example(x, data['a'], data['a_vec'], data['p'], data['r'], 1 if data['a'] == 1 else 0, policy_pi.get_action_probas(0, 0), reward_estimator_model)
+                    baselineR_dre_online.add_example(x, data['a'], data['a_vec'], data['p'], data['r'], 1/data['num_a'], policy_pi.get_action_probas(0, 0), reward_estimator_model)
+
+                    #Update reward estimator
+                    reward_estimator_model = dre_online.reward_estimator(train_example, reward_estimator_model, dr_mode, load_pretrained_model)
+
+            elif(dr_mode == 'batch'):
+                
+                #accumulate training examples
+                if(idxx < train_size):
+                    train_example = str(data['r']) + " |ANamespace Action:" + str(data['a']) + " |SharedNamespace c_user:" + str(data['c_user']) + " c_time_of_day:" + str(data['c_time_of_day'])
+                    
+                    train_data_batch.append(train_example)
+                    train_data_file.write(train_example + "\r\n")
+                
+                #use batch training examples to train a reward estimator
+                else:
+                    if(idxx == train_size):
+                        train_data_file.close()
+                        reward_estimator_model = dre_batch.reward_estimator(train_data_batch, reward_estimator_model, dr_mode, load_pretrained_model)
+                    
+                    #use reward estimator to compute estimate
+                    x = " |SharedNamespace c_user:" + str(data['c_user']) + " c_time_of_day:" + str(data['c_time_of_day'])
+                    dre_batch.add_example(x, data['a'], data['a_vec'], data['p'], data['r'], data['p'], policy_pi.get_action_probas(0, 0), reward_estimator_model)
+                    baseline1_dre_batch.add_example(x, data['a'], data['a_vec'], data['p'], data['r'], 1 if data['a'] == 1 else 0, policy_pi.get_action_probas(0, 0), reward_estimator_model)
+                    baselineR_dre_batch.add_example(x, data['a'], data['a_vec'], data['p'], data['r'], 1/data['num_a'], policy_pi.get_action_probas(0, 0), reward_estimator_model)
 
             evts += 1
+            idxx += 1
 
     if log_fp.endswith('.gz'):
         len_text = ds_parse.update_progress(i+1)
     else:
         len_text = ds_parse.update_progress(bytes_count,tot_bytes)
 
+    
     print('\nProcessed {} events out of {} lines'.format(evts,i+1))
 
-    print('online_ips:',online.get_estimate('ips'))
+    print('\nonline_ips:',online.get_estimate('ips'))
 
     print('baseline1_ips:', baseline1.get_estimate('ips'))
     print('baseline1 gaussian ci:', baseline1.get_interval('gaussian'))
     print('baseline1 clopper pearson ci:', baseline1.get_interval('clopper-pearson'))
 
-    print('baselineR_ips:',baselineR.get_estimate('ips'))
+    print('\nbaselineR_ips:',baselineR.get_estimate('ips'))
     print('baselineR gaussian ci:', baselineR.get_interval('gaussian'))
     print('baselineR clopper pearson ci:', baselineR.get_interval('clopper-pearson'))
 
-
-    print('online_snips:',online.get_estimate('snips'))
+    print('\nonline_snips:',online.get_estimate('snips'))
     print('baseline1_snips:',baseline1.get_estimate('snips'))
     print('baselineR_snips:',baselineR.get_estimate('snips'))
 
-    print('online_mle:',online_mle.get_estimate())
+    print('\nonline_mle:',online_mle.get_estimate())
     print('baseline1_mle:',baseline1_mle.get_estimate())
     print('baselineR_mle:',baselineR_mle.get_estimate())
 
-    print('online_cressieread:',online_cressieread.get_estimate())
+    print('\nonline_cressieread:',online_cressieread.get_estimate())
     print('baseline1_cressieread:',baseline1_cressieread.get_estimate())
     print('baselineR_cressieread:',baselineR_cressieread.get_estimate())
 
-if __name__ == '__main__':
+    if(dr_mode=='online'):
+        print('\ndre_online:', dre_online.get_estimate())
+        print('baseline1_dre_online:', baseline1_dre_online.get_estimate())
+        print('baselineR_dre_online:', baselineR_dre_online.get_estimate())
+    elif(dr_mode=='batch'):
+        print('\ndre_batch:', dre_batch.get_estimate())
+        print('baseline1_dre_batch:', baseline1_dre_batch.get_estimate())
+        print('baselineR_dre_batch:', baselineR_dre_batch.get_estimate())
 
+if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('-l','--log_fp', help="data file path (.json or .json.gz format - each line is a dsjson)", required=True)
-
+    parser.add_argument('-m','--mode', help="Doubly Robust estimator mode", required=True)
+    parser.add_argument('-p','--policy', help="Policy to evaluate one of {constant, uniformrandom}", required=True)
+    
     args = parser.parse_args()
 
-    compute_estimates(args.log_fp)
+    compute_estimates(args.log_fp, args.mode, args.policy)
diff --git a/dr.py b/dr.py
new file mode 100644
index 0000000..e8d15e4
--- /dev/null
+++ b/dr.py
@@ -0,0 +1,225 @@
+import os
+import ds_parse
+from vowpalwabbit import pyvw
+
+
+class Estimator:
+    """
+    A class for the Doubly Robust Estimator
+
+
+    Attributes
+    ----------
+    data: dict
+        dictionary containing various attributes
+    data['v_hat']: float
+        Doubly Robust Estimator estimate
+    data['N']: int
+        total number of samples in bin
+
+    Methods
+    -------
+    add_example(self, x , a, a_vec, p_log, r, p_pred, policy_probas, reward_estimator_model, count=1):
+        Implements the doubly robust estimator equation and computes the summation in the equation
+    def get_estimate(self):
+        Computes the mean of the estimated value from the doubly robust estimator equation
+    reward_estimator(self, train_data, reward_estimator_model, mode='batch', load_model=False):
+        Trains a learner to use in the estimation of a reward
+    """
+    def __init__(self):
+        """
+        Constructs attributes of the Doubly Robust Estimator
+
+        Parameters
+        ----------
+            data: dict
+                dictionary containing various attributes
+            data['v_hat']: float
+                Doubly Robust Estimator estimate
+            data['N']: int
+                total number of samples in bin
+        """
+        
+        self.data = {'v_hat':0.,'N':0}
+
+    def add_example(self, x , a, a_vec, p_log, r, p_pred, policy_probas, reward_estimator_model, count=1):
+        """
+        Implements the doubly robust estimator equation and computes the summation in the equation
+
+        Parameters
+        ----------
+        x : str
+            X example from logging policy in vw format to pass to the reward estimator model to predict a reward
+        a : int
+            Action from logging policy
+        a_vec : list(int)
+            List of actions in logging policy
+        p_log : float
+            Probability of choosing an action in the given a context. From logging policy
+        r : int
+            Reward from logging policy
+        p_pred : float
+            More info to be displayed (default is None)
+        policy_probas : list(float)
+            List of probabilities of choosing the actions in the target policy being evaluated
+        reward_estimator_model : vw model (pyvw.vw)
+            A vw model that estimates a reward for a given context and reward, (x, a) --> r
+            
+        Returns
+        -------
+        None
+        """
+        self.data['N'] += count
+
+        r_hat = sum(policy_probas[i] * reward_estimator_model.predict("|ANamespace Action:" + str(ac)+ x ) for i, ac in enumerate(a_vec))
+        self.data['v_hat'] += r_hat
+
+        p_over_p = p_pred/p_log
+        value_bias_correction = r - reward_estimator_model.predict("|ANamespace Action:" + str(a) + x )
+
+        self.data['v_hat'] += (p_over_p * value_bias_correction)
+
+    def get_estimate(self):
+        """
+        Trains a learner to use in the estimation of a reward
+
+        Parameters
+        ----------
+        None
+            
+        Returns
+        -------
+        float: policy value
+        """
+        v_hat = 0
+        if(self.data['N'])>0:
+            v_hat = self.data['v_hat'] / self.data['N']
+        return v_hat
+
+    def reward_estimator(self, train_data, reward_estimator_model, mode='batch', load_model=False):
+        """
+        Computes the mean of the estimated value from the doubly robust estimator equation
+
+        Parameters
+        ----------
+        train_data : str
+            (x, a, r) data from the logging policy to train the model
+            if mode=batch, train_data is a list of training examples
+            if mode=online, train_data is one training example
+                    
+        reward_estimator_model : vw model (pyvw.vw)
+            A vw model that is trained from the training example(s)
+        mode : float
+            The mode of the doubly robust estimator, can be {batch, online}
+        load_model : bool
+            indicator variable, if true then do not train model afresh, otherwise train afresh
+            
+        Returns
+        -------
+        vowpal wabbit model
+        """
+        if(mode=='batch'):
+            if (not load_model):
+                for example in train_data:
+                    reward_estimator_model.learn(example)
+                    
+                reward_estimator_model.save("../models/reward_estimator_batch.model")
+
+            else:
+                reward_estimator_model = pyvw.vw("-i ../models/reward_estimator_batch.model")# --quiet
+
+        elif(mode=='online'):
+            if (not load_model):
+                reward_estimator_model.learn(train_data)
+
+            else:
+                pass
+
+        return reward_estimator_model
+
+    def reward_estimator_scikit(self, train_data, clf, classes, mode='batch', load_model=False):
+        """
+        Computes the mean of the estimated value from the doubly robust estimator equation
+
+        Parameters
+        ----------
+        train_data : str
+            (x, a, r) data from the logging policy to train the model
+            if mode=batch, train_data is a list of training examples
+            if mode=online, train_data is one training example
+                    
+        reward_estimator_model : vw model (pyvw.vw)
+            A vw model that is trained from the training example(s)
+        mode : float
+            The mode of the doubly robust estimator, can be {batch, online}
+        load_model : bool
+            indicator variable, if true then do not train model afresh, otherwise train afresh
+            
+        Returns
+        -------
+        vowpal wabbit model
+        """
+        
+        X = train_data[['a','c_time_of_day','c_user']]
+        Y = train_data[['r']]
+        X = X.astype({"a": int, "c_time_of_day": int, "c_user": int})
+        Y = Y.astype({"r": int})
+                
+        if(mode=='batch'):
+            if (not load_model):
+                reward_estimator_model = clf.fit(X, Y.values.ravel())
+                
+            else:
+                pass
+            
+        elif(mode=='online'):
+            if (not load_model):
+                reward_estimator_model = clf.partial_fit(X, Y.values.ravel(), classes)
+            else:
+                pass
+
+        return reward_estimator_model
+    
+    def add_example_scikit(self, x , a, a_vec, p_log, r, p_pred, policy_probas, reward_estimator_model, count=1):
+            """
+            Implements the doubly robust estimator equation and computes the summation in the equation
+
+            Parameters
+            ----------
+            x : str
+                X example from logging policy in vw format to pass to the reward estimator model to predict a reward
+            a : int
+                Action from logging policy
+            a_vec : list(int)
+                List of actions in logging policy
+            p_log : float
+                Probability of choosing an action in the given a context. From logging policy
+            r : int
+                Reward from logging policy
+            p_pred : float
+                More info to be displayed (default is None)
+            policy_probas : list(float)
+                List of probabilities of choosing the actions in the target policy being evaluated
+            reward_estimator_model : vw model (pyvw.vw)
+                A vw model that estimates a reward for a given context and reward, (x, a) --> r
+
+            Returns
+            -------
+            None
+            """
+            self.data['N'] += count
+            
+            x = x.reset_index(drop=True)
+            r_hat=0
+            
+            for i, ac in enumerate(a_vec):
+                x['a']=list([a_vec[i]])
+                r_hat = policy_probas[i] * reward_estimator_model.predict(x)
+                self.data['v_hat'] += r_hat
+
+            p_over_p = p_pred/p_log
+            
+            x['a']=list([a])
+            value_bias_correction = r - reward_estimator_model.predict(x)
+
+            self.data['v_hat'] += (p_over_p * value_bias_correction)
\ No newline at end of file
diff --git a/dr_sequential.py b/dr_sequential.py
new file mode 100644
index 0000000..8e0b2cf
--- /dev/null
+++ b/dr_sequential.py
@@ -0,0 +1,34 @@
+import os
+import ds_parse
+from vowpalwabbit import pyvw
+
+class Estimator:
+    def __init__(self):
+        ############################### Aggregates quantities ######################################
+        #
+        # 'tot':   DRE sum value
+        # 'N':   total number of samples in bin from log (IPS = n/N)
+        #
+        #################################################################################################
+
+        self.data = {'tot':0.,'N':0}
+
+    def add_example(self, x_feat_string, a, a_vec, p_log, r, p_pred, policy_probas, reward_estimator_model, count=1):
+        self.data['N'] += count
+
+        r_hat_xk_pi = sum(policy_probas[i] * reward_estimator_model.predict("|ANamespace Action:" + str(ac)+ x_feat_string) for i, ac in enumerate(a_vec))
+        self.data['tot'] += r_hat_xk_pi
+
+        p_over_p = p_pred/p_log
+        rk_minus_r_hat = r - reward_estimator_model.predict("|ANamespace Action:" + str(a) + x_feat_string)
+
+        self.data['tot'] += (p_over_p * rk_minus_r_hat)
+
+    def get_estimate(self):
+        est = 0
+
+        return est
+
+    def reward_estimator(self, train_data, reward_estimator_model, mode='batch', load_model=False):
+
+        return reward_estimator_model
diff --git a/ds_parse.py b/ds_parse.py
index c7b45fe..7210a57 100644
--- a/ds_parse.py
+++ b/ds_parse.py
@@ -1,4 +1,7 @@
-import sys
+import sys, os
+import json
+import ast
+import re
 
 def update_progress(current, total=None, prefix=''):
     if total:
@@ -35,5 +38,139 @@ def json_cooked(x):
     data['a'] = int(data['a_vec'][0])
     data['num_a'] = len(data['a_vec'])
     data['skipLearn'] = b'"_skipLearn":true' in x[ind2+34:ind3] # len('"_label_Action":1,"_labelIndex":0,') = 34
+
+    return data
+
+def ds_json_parse(x, cp=False):
+    """
+        Parses a line from a logging policy file based on expected structure
+
+        Parameters
+        ----------
+        x : str
+             line from logging policy in a pre-determined format
+        cp : bool
+            indicator variable, if true then consider it as a content personalization with contextual bandits example
+            
+        Returns
+        -------
+        data : dict
+        """
+    
+    data = {}
+    
+    try:
+        dat_json = json.loads(x.strip())
+        if cp:
+            data['cost'] = dat_json['_label_cost']
+            data['p'] = dat_json['_label_probability']
+            data['a_vec'] = dat_json['a']
+            data['a'] = int(dat_json['_label_Action'])
+            data['num_a'] = len(list(dat_json['a']))
+            data['c_user'] = dat_json['c_user']
+            data['c_time_of_day'] = dat_json['c_time_of_day']
+            data['r'] = 0 if float(data['cost']) == 0.0 else -int(data['cost'])
+            data['XNamespace'] = str("c_user: "+str(dat_json['c_user'])+" c_time_of_day: "+str(dat_json['c_time_of_day']))
+            data['skipLearn'] = 0
+        else:
+            data['p'] = dat_json['p']
+            data['probas'] = dat_json['probas']
+            data['a_vec'] = list(range(1,int(dat_json['num_a'])+1))
+            data['a'] = 1+int(dat_json['a'])
+            data['num_a'] = dat_json['num_a']
+            data['r'] = int(dat_json['r'])
+            data['cost'] = -int(dat_json['r'])
+            str_xns = re.sub('[{"" }]', '', str(dat_json['XNamespace']))
+            data['XNamespace'] = re.sub('[,]', ' ', str_xns)
+            data['skipLearn'] = 0
+
+    except:
+
+        data['skipLearn'] = 1
+        print(data)
+    return data
+
+def parse_file(log_fp, file_out, cp=False):
+    """
+        Parses data from a logging policy file and creates a file containing the format to use in our pipeline
+
+        Parameters
+        ----------
+        log_fp : str
+             Path to the file containing the logging policy data
+        file_out : str
+            Path to the file containing the reformated logging policy data
+            
+        Returns
+        -------
+        None
+        """
+    # Init estimators
+    print('\nParsing file... Processing: {}'.format(log_fp))
+    evts = 0
+    bytes_count = 0
+    tot_bytes = os.path.getsize(log_fp)
+    
+    idxx = 0
+    data_file = open(file_out,"w+")
+
+    for i,x in enumerate(gzip.open(log_fp, 'rb') if log_fp.endswith('.gz') else open(log_fp, 'rb')):
+        # display progress
+        bytes_count += len(x)
+        if (i+1) % 10000 == 0:
+            if log_fp.endswith('.gz'):
+                update_progress(i+1)
+            else:
+                update_progress(bytes_count,tot_bytes)
+
+        # parse dsjson file
+        if x.startswith(b'{"_label_cost":') and x.strip().endswith(b'}'):
+
+            data = ds_json_parse(x, cp)
+
+            if data['skipLearn']:
+                continue
+            data_file.write(str(data) + "\r\n")
+            
+            evts += 1
+            idxx += 1
+        
+        if x.startswith(b'{"XNamespace":') and x.strip().endswith(b'}'):
+            data = ds_json_parse(x, cp)
+            #data= ast.literal_eval(x)
+
+            if data['skipLearn']:
+                continue
+            data_file.write(str(data) + "\r\n")
+            
+            evts += 1
+            idxx += 1
+
+    if log_fp.endswith('.gz'):
+        len_text = update_progress(i+1)
+    else:
+        len_text = update_progress(bytes_count,tot_bytes)
+    
+    data_file.close()
+    
+def create_json(_fin, _fout):
+    """
+        Creates a well formated JSON file for the logging policy data
+
+        Parameters
+        ----------
+        _fin : str
+             Path to the file containing the reformated logging policy data
+        _fout : str
+            Path to the file containing the logging policy data in JSON format
             
-    return data
\ No newline at end of file
+        Returns
+        -------
+        None
+        """
+    data = []
+    for i, inst in enumerate(open(_fin)):
+        data.append(ast.literal_eval(inst))
+    
+    with open(_fout, 'w') as fout:
+        json.dump(data , fout)
\ No newline at end of file
diff --git a/evaluation.py b/evaluation.py
new file mode 100644
index 0000000..cf22543
--- /dev/null
+++ b/evaluation.py
@@ -0,0 +1,583 @@
+from vowpalwabbit import pyvw
+
+import cressieread
+import ips_snips
+import mle
+import ds_parse
+import dr
+import policy
+
+import ast
+import numpy as np
+import pandas as pd
+import random
+
+from sklearn.metrics import mean_squared_error
+from sklearn import linear_model
+
+def compute_estimate(pol, est, data_file, evaluation_data=None):
+    """
+        Given data logs collected by using a logging policy, computes the expected reward for a provided target policy using an         estimator provided
+
+        Parameters
+        ----------
+        pol : str
+            target policy to evaluate
+        est : str
+            Estimator to use
+        data_file : file
+            Logged behavioural data
+        evaluation_data : file
+            Logged evaluation data
+
+        Returns
+        -------
+        estimate: list(float)
+            The computed expected rewards
+        """
+
+    a_pred = 1 
+    p_pred=0
+
+    num_samples = 0
+    estimates = []
+    logging_rewards = []
+    
+    pol_type=None
+    cb_vw=None
+    policy_pi=None
+    vw_string=None
+    evaluation_data=None
+    
+    behaviour_data=open(data_file,'r').readlines()
+    
+    if isinstance(pol, dict):
+        if 'CustomVW' in pol:
+            vw_string=pol['CustomVW']
+            pol_type='CustomVW'
+        if 'CustomCB' in pol:
+            evaluation_data = open(pol['CustomCB'],'r').readlines()
+            pol_type='CustomCB'
+            
+    if isinstance(pol, str):
+        pol_type=pol
+        
+    if vw_string is not None:
+        cb_vw=pyvw.vw(vw_string)
+        policy_pi = policy.VWPolicy(cb_vw)
+
+    # Init estimators
+    if(est=='ips' or est=='snips'):
+        estimator = ips_snips.Estimator()
+    else:
+        estimator = eval(est).Estimator()
+
+    for i, data in enumerate(behaviour_data):
+        data = ast.literal_eval(data)
+    
+        if pol_type=='CustomCB':
+            probas = ast.literal_eval(evaluation_data[i])['probas']
+            p_pred = ast.literal_eval(evaluation_data[i])['p']
+            
+        else:
+            if vw_string is None:
+                probas, _, p_pred =  compute_p_pred(pol_type, data)
+            else:
+                probas, _, p_pred =  compute_p_pred(pol_type, data, policy_pi=policy_pi)
+
+        # Update estimators with tuple (p_log, r, p_pred)
+        estimator.add_example(data['p'], data['r'], p_pred)
+
+        logging_rewards.append(float(data['r']))
+
+        if(est=='ips' or est=='snips'):
+            estimates.append(estimator.get_estimate(est))
+        else:
+            estimates.append(estimator.get_estimate())
+
+    return estimates
+
+def compute_estimate_dr(pol, est, data_file, scikit=False, clf=None, evaluation_data=None, load_pretrained_model=False):
+    """
+        Given data logs collected by using a logging policy, computes the expected reward for a provided target policy using an         estimator provided. Computes for doubly-roboust based estimators
+
+        Parameters
+        ----------
+        pol : str
+            target policy to evaluate
+        est : str
+            Estimator to use
+        data_file : file
+            Logged data
+        scikit: bool
+            flag, if true then train using scikit, otherwise use vw
+        evaluation_data: dict
+            for CustomCB policies, this is the data used in the evaluation policy
+        clf: linear_model
+            if scikit is used, this is a model instantiated from the linear_model module
+        load_pretrained_model: bool
+            flag, if true then do not train model afresh, otherwise train afresh
+        Returns
+        -------
+        
+        estimates: list
+            The list of computed expected rewards for all the samples
+        
+        """
+
+
+    if not scikit:
+        estimates = []
+
+        reward_estimator_model = pyvw.vw(quiet=True)
+
+        est, mode = dr_mode(est)
+
+        # Init estimators
+        estimator = eval(est).Estimator()
+
+        train_ratio = 0.5
+        train_size = int(sum(1 for line in open(data_file)) * train_ratio)
+
+        idxx = 0
+        train_data_batch = []
+        train_data_batch_str = ""
+
+        pol_type=None
+        cb_vw=None
+        policy_pi=None
+        vw_string=None
+        evaluation_data=None
+        probas=None
+        p_pred=None
+    
+        behaviour_data=open(data_file,'r').readlines()
+
+        if isinstance(pol, dict):
+            if 'CustomVW' in pol:
+                vw_string=pol['CustomVW']
+                pol_type='CustomVW'
+            if 'CustomCB' in pol:
+                evaluation_data = open(pol['CustomCB'],'r').readlines()
+                pol_type='CustomCB'
+        
+        if isinstance(pol, str):
+            pol_type=pol
+        
+        if vw_string is not None:
+            cb_vw=pyvw.vw(vw_string)
+            policy_pi = policy.VWPolicy(cb_vw)
+       
+        for i, data in enumerate(behaviour_data):
+            data = ast.literal_eval(data)
+            
+            if pol_type=='CustomCB':
+                probas = ast.literal_eval(evaluation_data[i])['probas']
+                p_pred = ast.literal_eval(evaluation_data[i])['p']
+            else:
+                if vw_string is None:
+                    probas, _, p_pred =  compute_p_pred(pol_type, data)
+                else:
+                    probas, _, p_pred =  compute_p_pred(pol_type, data, policy_pi=policy_pi)
+
+            #logging_rewards.append(float(data['r']))
+            if(mode == 'online'):
+                #construct training example (x, a, r)
+                train_example = str(data['r']) + " |ANamespace Action:" + str(data['a']) + " |XNamespace " + str(data['XNamespace'])
+                #print(train_example)
+
+                #At the beginning, you don't have a model to predict with, so you can only learn a reward estimator
+                if(idxx==0):
+                    reward_estimator_model = estimator.reward_estimator(train_example, reward_estimator_model, 'online', load_pretrained_model)
+                    estimates.append(0)
+
+                #Now you have a reward estimator to predict with
+                elif(idxx>0):
+                    x = " |XNamespace " + str(data['XNamespace'])
+
+                    #compute estimate
+                    estimator.add_example(x, data['a'], data['a_vec'], data['p'], data['r'], p_pred, probas, reward_estimator_model)
+                    estimates.append(estimator.get_estimate())
+
+                    #Update reward estimator
+                    reward_estimator_model = estimator.reward_estimator(train_example, reward_estimator_model, 'online', load_pretrained_model)
+            elif(mode == 'seq_online'):
+                pass
+            
+            elif(mode == 'batch'):
+                #first half, train on second half data
+                if(idxx < train_size):
+                    if(idxx == 0):
+                        for tr_exs in behaviour_data[train_size:]:
+                            tr_exs = ast.literal_eval(tr_exs)
+                            train_example=str(tr_exs['r']) + " |ANamespace Action:" + str(tr_exs['a']) + " |XNamespace " + str(tr_exs['XNamespace'])
+                            train_data_batch.append(train_example)
+                            
+                        reward_estimator_model = estimator.reward_estimator(train_data_batch, reward_estimator_model, 'batch')
+                    
+                    #use reward estimator to compute estimate
+                    x = " |XNamespace " + str(data['XNamespace'])
+                    estimator.add_example(x, data['a'], data['a_vec'], data['p'], data['r'], p_pred, probas, reward_estimator_model)
+                    estimates.append(estimator.get_estimate())
+                    
+                #second half, train on first half data
+                else:
+                    if(idxx == train_size):
+                        reward_estimator_model = pyvw.vw(quiet=True)
+                        train_data_batch=[]
+                        for tr_exs in behaviour_data[0:train_size]:
+                            tr_exs = ast.literal_eval(tr_exs)
+                            train_example=str(tr_exs['r']) + " |ANamespace Action:" + str(tr_exs['a']) + " |XNamespace " + str(tr_exs['XNamespace'])
+                            train_data_batch.append(train_example)
+                            
+                        reward_estimator_model = estimator.reward_estimator(train_data_batch, reward_estimator_model, 'batch')
+                    
+                    x = " |XNamespace " + str(data['XNamespace'])
+                    estimator.add_example(x, data['a'], data['a_vec'], data['p'], data['r'], p_pred, probas, reward_estimator_model)
+                    estimates.append(estimator.get_estimate())
+            
+            elif(mode == 'seq_batch'):
+                pass
+            
+            idxx += 1
+
+        return estimates
+
+    else:
+        return compute_estimate_dr_scikit(pol, est, data_file, clf, load_pretrained_model=False)
+
+
+def compute_estimate_dr_scikit(pol, est, train_data, clf=None, behaviour_data=None, load_pretrained_model=False):
+    """
+        Given data logs collected by using a logging policy, computes the expected reward for a provided target policy using an         estimator provided. Computes for doubly-roboust based estimators
+
+        Parameters
+        ----------
+        pol : str
+            target policy to evaluate
+        est : str
+            Estimator to use
+        train_data : pd.DataFrame
+            Logged data
+        clf: linear_model
+            if scikit is used, this is a model instantiated from the linear_model module
+        load_pretrained_model: bool
+            flag, if true then do not train model afresh, otherwise train afresh
+        Returns
+        -------
+        
+        estimates: list
+            The list of computed expected rewards for all the samples
+        
+        """
+
+    estimates = []
+
+    reward_estimator_model = pyvw.vw(quiet=True)
+
+    if clf is None:
+        clf=linear_model.SGDClassifier(max_iter = 1000, tol=1e-3, penalty = "elasticnet")
+
+    est, mode = dr_mode(est)
+
+    # Init estimators
+    estimator = eval(est).Estimator()
+
+    train_ratio = 0.5
+    train_size = int(train_data.shape[0] * train_ratio)
+
+    idxx = 0
+    train_data_batch = []
+
+    data_batch = pd.DataFrame(columns =list(train_data.columns))
+    train_data = train_data.sample(frac=1).reset_index(drop=True)
+    classes=np.unique(train_data[['r']])
+    
+    pol_type=None
+    cb_vw=None
+    policy_pi=None
+    vw_string=None
+    evaluation_data=None
+
+    if isinstance(pol, str):
+        pol_type=pol
+            
+    if vw_string is not None:
+        cb_vw=pyvw.vw(vw_string)
+        policy_pi = policy.VWPolicy(cb_vw)
+
+    for i in range(0, train_data.shape[0]):
+        data = train_data.iloc[[i]]
+        if vw_string is None:
+            probas, _, p_pred =  compute_p_pred(pol_type, data)
+        else:
+            probas, _, p_pred =  compute_p_pred(pol_type, data, policy_pi=policy_pi)
+
+        if(mode == 'online'):
+            #At the beginning, you don't have a model to predict with, so you can only learn a reward estimator
+            if(idxx==0):
+                reward_estimator_model = estimator.reward_estimator_scikit(data, clf, classes, 'online', load_pretrained_model)
+                estimates.append(0)
+
+            #Now you have a reward estimator to predict with
+            elif(idxx>0):
+                x = data[['c_time_of_day','c_user']]
+
+                #compute estimate
+                estimator.add_example_scikit(x, int(data['a']), list(map(int, list(data['a_vec'])[0])), float(data['p']), int(data['r']), p_pred, probas, reward_estimator_model)
+                estimates.append(estimator.get_estimate())
+
+                #Update reward estimator
+                reward_estimator_model = estimator.reward_estimator_scikit(data, clf, classes, 'online',  load_pretrained_model)
+
+        elif(mode == 'batch'):
+            
+            if(idxx < train_size):
+                if(idxx == 0):
+                    for j in range(train_size, train_data.shape[0]):
+                        tr_exs = train_data.iloc[[j]]
+                        data_batch = data_batch.append(tr_exs)
+                    reward_estimator_model = estimator.reward_estimator_scikit(data_batch, clf, classes, 'batch',  load_pretrained_model)
+                #use reward estimator to compute estimate
+                x = data[['c_time_of_day','c_user']]
+
+                #compute estimate
+                estimator.add_example_scikit(x, int(data['a']), list(map(int, list(data['a_vec'])[0])) , float(data['p']), int(data['r']), p_pred, probas, reward_estimator_model)
+
+                estimates.append(estimator.get_estimate())
+                            
+            #use batch training examples to train a reward estimator
+            else:
+                if(idxx == train_size):
+                    for j in range(0, train_size):
+                        tr_exs = train_data.iloc[[j]]
+                        data_batch = data_batch.append(tr_exs)
+                    reward_estimator_model = estimator.reward_estimator_scikit(data_batch, clf, classes, 'batch',  load_pretrained_model)
+
+                #use reward estimator to compute estimate
+                x = data[['c_time_of_day','c_user']]
+
+                #compute estimate
+                estimator.add_example_scikit(x, int(data['a']), list(map(int, list(data['a_vec'])[0])) , float(data['p']), int(data['r']), p_pred, probas, reward_estimator_model)
+
+                estimates.append(estimator.get_estimate())
+
+        idxx += 1
+
+    return estimates
+
+def compute_p_pred(pol_type, data, policy_pi=None):
+    """
+    Computes the probabilities for predicting actions
+
+    Parameters
+    ----------
+    pol_type : str
+        target policy type used in evaluation
+    num_a : int
+        number of actions in this setting
+    a : int
+        the action selected from the logged data
+
+    Returns
+    -------
+    policy_pi: Policy
+        An instance of the Policy class being used
+    p_pred: list(float)
+        list of predicted action probabilitis
+
+    """
+    
+    a_const_pred = 2 
+    
+    if(pol_type=='Constant'):
+        policy_pi = policy.Constant(int(data['num_a']), a_const_pred)
+        probas, action, p_pred = policy_pi.get_action(0, 0)
+        p_pred = 1 if int(data['a']) == action else 0
+        
+    elif(pol_type=='UniformRandom'):
+        policy_pi = policy.UniformRandom(int(data['num_a']))
+        probas, action, p_pred = policy_pi.get_action(0, 0)
+        p_pred = 1 if int(data['a']) == action else 0
+    
+    elif(pol_type=='CustomVW'):
+        probas, action, p_pred = policy_pi.get_action(data)
+        policy_pi.learn_cb_policy(data)
+        p_pred = 1 if int(data['a']) == action else 0
+        
+    return probas, action, p_pred
+
+def compare(policies, estimators, log_data_parsed):
+    """
+        Given data log collected by using a logging policy, computes the expected reward for a provided set of target policies using a set of estimators provided. Reports the computed values and reports the best policy-estimator combination on that dataset
+
+        Parameters
+        ----------
+        policies : str or list(str)
+            target policies to evaluate
+        estimators : str or list(str)
+            Estimators to use
+        log_data_parsed : file
+            Logged data
+
+        Returns
+        -------
+        None
+        """
+    best={}
+
+
+    if not isinstance(policies, list): policies = [policies]
+    if not isinstance(estimators, list): estimators = [estimators]
+
+    results = []
+    for i, pol in enumerate(policies):
+        print("\tPolicy: ", str(policies[i]))
+        result = {}
+        for j, est in enumerate(estimators):
+            if not check_if_dr(est):
+                estimates = compute_estimate(pol, est, log_data_parsed)
+                print("\t\t"+est+":\t", estimates[-1])
+            else:
+                estimates = compute_estimate_dr(pol, est, log_data_parsed)
+                print("\t\t"+est+":\t", estimates[-1])
+
+            result['policy']=str(pol)
+            result['estimator']=est
+            result['estimate']=estimates[-1]
+            result['estimates']=estimates
+
+            results.append(result.copy())
+
+            if(i==0 and j==0):
+                best = result.copy()
+            else:
+                if(result['estimate']>best['estimate']):
+                    best = result.copy()
+
+    print("\tBest result: \n\t\tpolicy: {} \n\t\testimator: {} \n\t\testimate: {}".format(best['policy'], best['estimator'], best['estimate']))
+
+    return best, results
+
+
+def check_if_dr(est):
+    """
+        Checks if a policy is doubly-robust based
+
+        Parameters
+        ----------
+        est : str
+            Estimator to use
+
+        Returns
+        -------
+        bool: True or False
+        """
+    return est.startswith('dr')
+
+def dr_mode(est):
+    """
+        {dr_online, dr_batch, dr_seq_online, dr_seq_batch}
+        For a doubly-robust based estimator, extracts the type{dr, dr_seq} and mode{online, batch} of the estimator
+
+        Parameters
+        ----------
+        est : str
+            Estimator to use
+
+        Returns
+        -------
+        str: estimator type{dr, dr_seq}
+        str: estimator mode{online, batch}
+        """
+    dr_list = est.split('_')
+    if(len(dr_list)==2):
+        return dr_list[0], dr_list[1]
+    else:
+        return dr_list[0]+"_"+dr_list[1], dr_list[2]
+
+def compute_running_mse(estimates, g_truth_rewards):
+    """
+        Given logging rewards and estimated rewards, compute windowed RMSE. w=30
+
+        Parameters
+        ----------
+        estimates: list(float)
+        logging_rewards:list(float)
+
+        Returns
+        -------
+        RMSE:list(float)
+        """
+    estimates = np.array(estimates)
+    g_truth_rewards = np.array(g_truth_rewards)
+
+    MSE=np.zeros(len(g_truth_rewards))
+    w=30
+    for i in(range(0, len(g_truth_rewards))):
+        if(i<=(len(g_truth_rewards)-w)):
+            MSE[i]= mean_squared_error(g_truth_rewards[i:i+w], estimates[i:i+w])
+
+    return MSE
+
+def compare_mean_rewards(policy, estimators, log_data_parsed, r_gtruth, N):
+    """
+        Given data log collected by using a logging policy, computes the expected reward for a provided set of target policies using a set of estimators provided. Reports the computed values and reports the best policy-estimator combination on that dataset
+
+        Parameters
+        ----------
+        policy : str
+            target policy to evaluate
+        estimators : str or list(str)
+            Estimators to use
+        log_data_parsed : file
+            Logged data
+        r_gtruth: float
+            Ground truth r
+        Returns
+        -------
+        best: dict
+            best result
+        results: list(dict)
+            evaluation results
+        """
+    best={}
+
+    if not isinstance(estimators, list): estimators = [estimators]
+
+    results = []
+    print("\tPolicy: ", str(policy))
+    
+    for j, est in enumerate(estimators):
+        result = {}
+        g_truth_r = []
+        expected_r = []
+        
+        for n in range(N):
+            if not check_if_dr(est):
+                estimates = compute_estimate(policy, est, log_data_parsed)
+            else:
+                estimates = compute_estimate_dr(policy, est, log_data_parsed)
+
+            expected_r.append(estimates[-1])
+            g_truth_r.append(r_gtruth)
+        
+        abs_diff=np.abs(np.mean(expected_r)-r_gtruth)
+        print("\t\t"+est+" mean of estimated expected rewards:\t", np.mean(expected_r))
+        
+        result['policy']=str(policy)
+        result['estimator']=est
+        result['abs_diff']=abs_diff
+        result['mean_expected_r']=np.mean(expected_r)
+
+        results.append(result.copy())
+
+        if(j==0):
+            best = result.copy()
+        else:
+            if(result['abs_diff']<best['abs_diff']):
+                best = result.copy()
+
+    print("\tBest result: \n\t\tpolicy: {} \n\t\testimator: {} \n\t\tmean expected reward: {}".format(best['policy'], best['estimator'], best['mean_expected_r']))
+
+    return best, results
\ No newline at end of file
diff --git a/ips_snips.py b/ips_snips.py
index e8d41ce..0a53f17 100644
--- a/ips_snips.py
+++ b/ips_snips.py
@@ -35,7 +35,10 @@ def get_estimate(self, type):
         if type == 'ips':
             return self.data['n']/self.data['N']
         elif type == 'snips':
-            return self.data['n']/self.data['d']
+            if self.data['d']==0:
+                return 0.0
+            else:
+                return self.data['n']/self.data['d']
         else:
             raise('Error: Incorrect estimator type {}. Supported options are ips or snips'.format(type))
 
diff --git a/mle.py b/mle.py
index 74d7009..b1d5cab 100644
--- a/mle.py
+++ b/mle.py
@@ -25,8 +25,7 @@ def add_example(self, p_log, r, p_pred, count=1):
             self.wmin = min(self.wmin, w)
 
     def graddualobjective(self, n, beta):
-       return fsum(c * (w - 1)/((w - 1) * beta + n)
-                  for c, w, _ in self.data)
+        return fsum(c * (w - 1)/((w - 1) * beta + n) for c, w, _ in self.data)
 
     def get_estimate(self, rmin=0, rmax=1):
         from scipy.optimize import brentq
diff --git a/policy.py b/policy.py
new file mode 100644
index 0000000..c634c02
--- /dev/null
+++ b/policy.py
@@ -0,0 +1,178 @@
+import numpy.random
+from vowpalwabbit import pyvw
+import numpy as np
+
+class Policy:
+    r"""
+    An abstract class representing a Reinforcement Learning (RL) policy,
+    mapping a state to a probability for each possible action.  A policy can be
+    stateless or stateful.
+    All other policies should subclass it. All subclasses should
+    override ``get_action_probas``, that returns the probilities associated
+    with each possible action.
+    Non stateless policies should cache their results in
+    Observation.target_probas.
+    """
+
+    def __init__(self, stateless=True):
+        r"""
+        Arguments:
+            stateless: is the policy stateless? When it's not, some estimators
+                may run it to cache the results in an Observation
+        """
+        self.stateless = stateless
+
+    def get_action_probas(self, index, observation):
+        r"""
+        Returns the probabilities associated with each possible action.
+        Arguments:
+            index: index in the trace (in 1 ... H)
+            observation: an Observation
+        Returns:
+            A list of probabilities (ps where len(ps) = action number,
+            sum(ps) == 1, and p >=0 for p in ps)
+        """
+        raise NotImplementedError
+
+    def get_action(self, index, observation):
+        r"""
+        Returns a chosen action and its associated probility.
+        Arguments:
+            index: index in the trace (in 1 ... H).
+            observation: an Observation.
+        Returns:
+            (a, p): a tuple of the chosen action in (1 ... action_num) and its
+            probability.
+        """
+        p = self.get_action_probas(index, observation)
+        a = numpy.random.choice(len(p), p=p)
+        return (p, a+1, p[a])
+
+class Constant(Policy):
+    r"""
+    A constant policy that always returns the same action with probability 1.
+    """
+    def __init__(self, action_num, action):
+        r"""
+        Arguments:
+            action_num: the total number of actions
+            action: the action returned with probability 1, indexed in
+                1 ... action_num
+        """
+        super().__init__(stateless=True)
+
+        self.action_num = action_num
+        self.action = action
+
+        self.ps = [0] * action_num
+        self.ps[action-1] = 1.0
+
+    def get_action_probas(self, index, observation):
+        return self.ps
+
+class UniformRandom(Policy):
+    r"""
+    A policy that returns a subset of the actions uniformly at random.
+    """
+    def __init__(self, action_num, possible_actions=None):
+        r"""
+        Arguments:
+            action_num: the total number of actions
+            action: the actions returned uniformly at random, indexed in
+                1 ...  action_num
+        """
+        super().__init__(stateless=True)
+        self.action_num = action_num
+
+        if possible_actions == None:
+            possible_actions = [a+1 for a in range(action_num)]
+        self.possible_actions = possible_actions
+
+        p = 1. / len(self.possible_actions)
+        self.ps = [0] * action_num
+        for a in self.possible_actions:
+            self.ps[a-1] = p
+
+    def get_action_probas(self, index, observation):
+        return self.ps
+
+class RoundRobin(Policy):
+    r"""
+    A policy that choses each action in turn with prba 1.
+    This is an example of a stateful policy.
+    """
+    def __init__(self, action_num):
+        r"""
+        Arguments:
+            action_num: the total number of actions
+        """
+        super().__init__(stateless=False)
+        self.action_num = action_num
+        self.next_action = 0
+
+    def get_action_probas(self, index, observation):
+        r"""
+        Example of stateful policies, get_action_probas should cache the
+        results in the observation's target_probas field.
+        """
+        if observation.target_probas is not None:
+            # The probaes have been cached, return them.
+            return observation.target_probas
+
+        # Compute probas
+        p = [0.] * self.action_num
+        p[self.next_action] = 1.0
+        # Cache result
+        observation.target_probas = p
+
+        # Update state
+        self.next_action = (self.next_action + 1) % self.action_num
+
+        return p
+
+class ReturnTargetProbas(Policy):
+    r"""
+    A policy that just returns observation.target_probas.
+    It assumnes they exist in Observation.
+    """
+    def __init__(self):
+        super().__init__(stateless=True)
+
+    def get_action_probas(self, index, observation):
+        return observation.target_probas
+
+#class CustomPolicy(Policy):
+#    pass
+    
+class VWPolicy(Policy):
+    r"""
+    A policy that instantiates a VW policy and returns the action probabilities.
+    """
+    def __init__(self, vw_model):
+        super().__init__(stateless=True)
+        self.vw_model=vw_model 
+        
+    def learn_cb_policy(self, data):
+        self.vw_model.learn(str(data['a'])+":"+str(data['cost'])+":"+str(data['p'])+" | "+data['XNamespace'])
+
+    def get_action_probas(self, data):
+        target_probas=self.vw_model.predict("| "+ data['XNamespace'])
+        return target_probas
+                                            
+    def get_action(self, data):
+        r"""
+        Returns a chosen action and its associated probility.
+        Arguments:
+            data: 
+            
+        Returns:
+            (p, a, p[a]): a tuple of the chosen action in (1 ... action_num) and its
+            probability.
+        """
+        p = self.get_action_probas(data)
+        #normalizing p to ensure it always sums to 1
+        c=np.array(p)
+        p=c/sum(p)
+                
+        a = np.random.choice(len(p), p=p)
+        return (p, a+1, p[a])                                        
diff --git a/softening.py b/softening.py
new file mode 100644
index 0000000..6235951
--- /dev/null
+++ b/softening.py
@@ -0,0 +1,305 @@
+from sklearn.model_selection import train_test_split
+import numpy as np
+import pandas as pd
+import json
+
+class CBPolicy(object):
+    def __init__(self, act_num):
+        self.act_num=act_num
+        
+    def friendly_soften(self, action, soften_params):
+        alpha = soften_params['alpha']
+        beta = soften_params['beta']
+        
+        soft_action = np.copy(action)
+        
+        probs = np.zeros(len(action)) #p
+        probas = np.ones((len(action), self.act_num)) #probas
+        
+        for i in range(len(action)):
+            u = np.random.uniform(-0.5, 0.5)
+            explore_prob = alpha + beta * u
+            
+            probas[i] = (1-explore_prob) / (self.act_num - 1) * probas[i]
+            
+            probs[i] = probas[i][soft_action[i]] = explore_prob
+            
+            #probas[i] = probas[i] / np.sum(probas[i])
+            
+            #probs[i]=probas[i][soft_action[i]] #explore_prob
+            
+            if np.random.uniform(0,1) > explore_prob:
+                a_soft = int(np.random.randint(self.act_num, size=1))
+                
+                while(a_soft == action[i]):
+                    a_soft = int(np.random.randint(self.act_num, size=1))
+                    
+                soft_action[i] = a_soft
+                probs[i] = probas[i][soft_action[i]]
+                #probs_lists[i][soft_action[i]]=   (1-explore_prob)/(self.act_num-1)
+        return soft_action, probs, probas
+    
+    def adversarial_soften(self, action, soften_params):
+        alpha = soften_params['alpha']
+        beta = soften_params['beta']
+        
+        soft_action = np.copy(action)
+        probs = np.zeros(len(action))
+        
+        probas = np.ones((len(action), self.act_num))
+        
+        for i in range(len(action)):
+            u = np.random.uniform(-0.5, 0.5)
+            explore_prob = alpha+beta*u
+            
+            probas[i] = (1-explore_prob) / (self.act_num) * probas[i]
+            a_soft = int(np.random.randint(self.act_num, size=1))
+            
+            while(a_soft==soft_action[i]):
+                a_soft = int(np.random.randint(self.act_num, size=1))
+            
+            soft_action[i]=a_soft
+            probas[i] = probas[i]/np.sum(probas[i])
+            probs[i] = probas[i][soft_action[i]] = explore_prob # explore_prob
+
+            if np.random.uniform(0, 1) > explore_prob:
+                soft_action[i] = int(np.random.randint(self.act_num, size=1))
+                probs[i]=probas[i][soft_action[i]] #(1-explore_prob)/(self.act_num)
+                
+        return soft_action, probs, probas
+    
+    def neutral_soften(self, action, soften_params):
+        soft_action = np.random.randint(self.act_num, size=len(action))        
+        probs = np.ones(len(action))*1/self.act_num
+        #probs=probs/sum(probs)
+        
+        return soft_action, probs,  np.ones((len(action), self.act_num))*1/self.act_num
+    
+    def get_soften_action(self, action, soften_params):
+        if soften_params != None:
+            soft_action = []
+            if soften_params['method'] == "friendly":
+                soft_action, probs, prob_list = self.friendly_soften(action, soften_params)
+            elif soften_params['method'] == "adversarial":
+                soft_action, probs, prob_list = self.adversarial_soften(action, soften_params)
+            elif soften_params['method'] == "neutral":
+                soft_action, probs, prob_list = self.neutral_soften(action, soften_params)
+            return {"action": [soft_action], "prob": probs, "prob_list": prob_list.tolist()}
+        else:
+            probs = []
+            return {"action": action, "prob": probs, "prob_list": probs}
+        
+
+def train_target(X, Y, test_size, clf):
+    """
+        Trains a classifier and returns training model accuracy, the number of actions, the actual actions from the test data,         deterministic actions and the testing data X.
+
+        Parameters
+        ----------
+        X : Pandas Dataframe
+            Covariate data
+        Y : str
+            Target Data
+        test_size : float
+            Testing proportion from data
+        clf : sklearn model
+            sklearn model
+            
+        Returns
+        -------
+        acc: float
+            Model Accuracy
+        num_actions:
+            Number of actions equal to the number of classes
+        act_gtruth: numpy array
+            The actual actions from training data
+        act_pred:
+            the actions predicted from classifer(deterministic policy)
+        X_test:
+            The covariate proportion in testing data
+        """
+    Y_cat_map = dict( enumerate( Y['Target'].cat.categories ) )
+    Y_cat_inv_map = {v: k for k, v in Y_cat_map.items()}
+    num_actions = len(Y_cat_map)
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, 
+                                                        Y, 
+                                                        test_size=test_size,
+                                                        random_state=1)
+
+    clf.fit(X_train, np.ravel(y_train))
+    acc=clf.score(X_test, y_test) #Compute accuracy
+    
+    y_pred = clf.predict(X_test) #Compute the y_pred
+    
+    #creating action arrays
+    act_pred = np.array((pd.Series(y_pred)).map(Y_cat_inv_map))
+    act_gtruth = np.array((pd.Series(y_test.Target)).map(Y_cat_inv_map))
+    
+    return acc, num_actions, act_gtruth, act_pred, X_test.reset_index(drop=True)
+
+def get_reward(act_pred, act_test):
+    """
+        Compares two lists or arrays and returns an array containing rewards
+
+        Parameters
+        ----------
+        act_pred : array(int)
+            Action array predicted from softened policy
+        act_test : array(int)
+            Action array from the ground policy
+
+        Returns
+        -------
+        array(int) : array containing rewards
+        """
+    return np.array((np.array(act_pred)==np.array(act_test)), dtype='int64')
+
+def classification_to_cb_behaviour(X_context, action_num, act_gtruth, act_deterministic, cb_policy, soften_params, out_file_json, N=5000):
+    """
+        Trains a deterministic policy, softens and creates a cb dataset from a classification dataset
+
+        Parameters
+        ----------
+        X : Pandas Dataframe
+            Covariate data
+        Y : Pandas Dataframe
+            Target Data
+        test_size : float
+            Testing proportion from data
+        clf : sklearn model
+            sklearn model
+        soften_params:dict
+            Softening parameters
+        out_behaviour_json:file path
+            A file path to the generated data for behaviour policy
+        out_evaluation_json:file path
+            A file path to the generated data for evaluation policy
+            
+        Returns
+        -------
+        df : Pandas Dataframe
+            Covariate data to use for evaluation policy
+            Number of actions
+        act_det: list
+            Actions from the deterministic policy (totalling to N datapoints to use in evaluation policy)
+        act_gtruth: list
+            Actual actions in the data (totalling to N datapoints to use in evaluation policy)
+        """
+    num_a = action_num*(np.ones(N, dtype='int64'))
+    #print("Deterministic policy ground truth accuracy(G_truth reward): ", accuracy)
+   
+    df = pd.DataFrame()
+    r=np.zeros(N)
+    a_det=np.zeros(N)
+    a_gtruth=np.zeros(N)
+    a=np.zeros(N)
+    p=np.zeros(N)
+    probas=np.zeros((N, action_num))
+    
+    for i in range(N):
+        idx=np.random.randint(X_context.shape[0])
+        df=df.append(X_context.iloc[[idx]])
+        a_det[i]=act_deterministic[idx]
+        a_gtruth[i]=act_gtruth[idx]
+        act_prob=cb_policy.get_soften_action([act_deterministic[idx]], soften_params)
+        r[i]=get_reward(act_gtruth[idx], act_prob['action'][0])[0]
+        a[i]=act_prob['action'][0][0]
+        p[i]=act_prob['prob'][0]
+        probas[i]=act_prob['prob_list'][0]
+            
+    df=df.reset_index(drop=True)
+    class2cb_json(df, a.tolist(), r.tolist(), p.tolist(), probas.tolist(), num_a.tolist(), out_file_json)
+    #print("action_num behaviour: ", action_num)
+    return df, a_det, a_gtruth
+
+def classification_to_cb_evaluation(X_context, action_num, act_gtruth, act_deterministic, cb_policy, soften_params, out_file_json, N=5000):
+    """
+        Trains a deterministic policy, softens and creates a cb dataset from a classification dataset
+
+        Parameters
+        ----------
+        X_context : Pandas Dataframe
+            Covariate data
+        action_num : int
+            Number of actions
+        act_deterministic: list
+            Actions from the deterministic policy
+        act_gtruth: list
+            Actual actions in the data 
+        cb_policy : CBPolicy
+            CBPolicy object
+        clf : sklearn model
+            sklearn model
+        soften_params:dict
+            Softening parameters
+        out_file_json:file path
+            A file path to the generated dat
+        N: int
+            Number of data points
+        Returns
+        -------
+        None
+        """
+    assert N==X_context.shape[0]
+    num_a = action_num*(np.ones(N, dtype='int64'))
+    #print("Deterministic policy ground truth accuracy(G_truth reward): ", accuracy)
+    
+    #cb_policy = CBPolicy(action_num)
+
+    act_prob=cb_policy.get_soften_action(np.array(act_deterministic, dtype='int64'), soften_params)
+    r=get_reward(np.array(act_gtruth, dtype='int64'), act_prob['action'][0])
+    a=np.array(act_prob['action'][0], dtype='int64')
+    p=act_prob['prob']
+    probas=act_prob['prob_list']
+    #print("Action Num", action_num)
+    class2cb_json(X_context, a.tolist(), r.tolist(), p.tolist(), probas, num_a.tolist(), out_file_json)
+    return r
+    
+def class2cb_json(X_df, a, r, p, probas, num_a, out_file_json):
+    """
+        Formatting function from Pandas Dataframes to write to a JSON text file
+
+        Parameters
+        ----------
+        X_df : Pandas Dataframesoftened 
+            Covariate data
+        a : list or np array
+            Actions from behaviour policy
+        r : list or np array
+            rewards from behaviour policy
+        p : list or np array
+            probs from behaviour policy
+        num_a : list or np array
+            number of actions
+        out_file_json:file path
+            A file path to the generated data
+            
+        Returns
+        -------
+        None
+        """
+    context_list=[]
+    context={}
+    for i, row in X_df.iterrows():
+        #context="{"
+        context={}
+        for j, column in row.iteritems():
+            context[j]=column
+        context_list.append(json.dumps(context))
+    
+    parsed_data = pd.DataFrame(list(zip(context_list, a, r, p, probas, num_a)), 
+               columns =['XNamespace', 'a', 'r', 'p', 'probas', 'num_a'])
+    
+    parsed_data_list=[]
+    for i, row in parsed_data.iterrows():
+        context={}
+        for j, column in row.iteritems():
+            context[j]=column
+        parsed_data_list.append(json.dumps(context))
+        
+    with open (out_file_json,"w")as fp:
+        for line in parsed_data_list:
+            #y = json.dumps(x)
+            fp.write(line+"\n")
+    fp.close()
\ No newline at end of file
diff --git a/tests/test_dr.py b/tests/test_dr.py
new file mode 100644
index 0000000..e7bb6d7
--- /dev/null
+++ b/tests/test_dr.py
@@ -0,0 +1,10 @@
+import unittest
+import sys
+sys.path.insert(0,'..')
+import dr
+
+class TestDR(unittest.TestCase):
+    pass
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/tests/test_ds_parse.py b/tests/test_ds_parse.py
new file mode 100644
index 0000000..485862e
--- /dev/null
+++ b/tests/test_ds_parse.py
@@ -0,0 +1,10 @@
+import unittest
+import sys
+sys.path.insert(0,'..')
+import ds_parse
+
+class TestDSParse(unittest.TestCase):
+    pass
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file