scikit-XCS User Guide.ipynb

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   "source": [
    "# scikit-XCS Complete User Guide\n",
    "\n",
    "Author: Robert Zhang - University of Pennsylvania, B.S.E Computer Science, B.S.E. Economics (SEAS '22, WH '22)\n",
    "\n",
    "Advisor: Ryan Urbanowicz, PhD - University of Pennsylvania, Department of Biostatics, Epidemiology, and Informatics & Institue for Biomedical Informatics (IBI)\n",
    "\n",
    "Special thanks to Will N. Browne (Victoria University of Wellingston), for his valuable input in the compilation of the scikit-XCS algorithm\n",
    "\n",
    "Date: 04/30/2020\n",
    "\n",
    "Package Requirements: (Python 3)\n",
    "<ul>\n",
    "    <li>pandas</li>\n",
    "    <li>numpy</li>\n",
    "    <li>scikit-learn</li>\n",
    "</ul>\n",
    "\n",
    "Notebook Requirements:\n",
    "<ul>\n",
    "    <li>scikit-XCS</li>\n",
    "    <li>scikit-learn</li>\n",
    "    <li>pandas</li>\n",
    "    <li>mathplotlib</li>\n",
    "</ul>\n",
    "\n",
    "## Introduction\n",
    "This notebook presents a complete user guide to the core sklearn-compatible scikit-XCS package as well as auxilliary helper objects included in the package. XCS is the most popular and best studied learning classifier system algorithm to date. In general, Learning Classifier Systems (LCSs) are a classification of Rule Based Machine Learning Algorithms that have been shown to perform well on problems involving high amounts of heterogeneity and epistasis. Well designed LCSs are also highly human interpretable. LCS variants have been shown to adeptly handle supervised and reinforced, classification and regression, online and offline learning problems, as well as missing or unbalanced data. These characteristics of versatility and interpretability give LCSs a wide range of potential applications, notably those in biomedicine. This package is still under active development and we encourage you to check back on this repository for updates.\n",
    "\n",
    "This version of scikit-XCS is suitable for **single step, classification** problems. It has not yet been developed for multi-step reinforcement learning problems nor regression problems. Within these bounds however, scikit-XCS can be applied to almost any supervised classification data set and supports:\n",
    "\n",
    "<ul>\n",
    "    <li>Feature sets that are discrete/categorical, continuous-valued or a mix of both</li>\n",
    "    <li>Data with missing values</li>\n",
    "    <li>Binary Classification Problems (Binary Endpoints)</li>\n",
    "    <li>Multi-class Classification Problems (Multi-class Endpoints)</li>\n",
    "</ul>\n",
    "\n",
    "## Notebook Organization\n",
    "\n",
    "**Part 1: Loading Data**\n",
    "<ul>\n",
    "    <li> Dataset format requirements for scikit-XCS</li>\n",
    "    <li> Method 1: Loading data using pandas</li>\n",
    "    <li> Method 2: Loading data using StringEnumerator (additional data loading/transformation tool provided by scikit-XCS package)</li>\n",
    "</ul>\n",
    "\n",
    "**Part 2: Initializing XCS Estimator Object**\n",
    "<ul>\n",
    "    <li> Basic Initialization</li>\n",
    "    <li> Overview of all scikit-XCS initialization parameters and default values</li>\n",
    "</ul>\n",
    "\n",
    "**Part 3: Model Training and Testing**\n",
    "<ul>\n",
    "    <li> model.fit(X,y) and how it works</li>\n",
    "    <li> model.predict(X), model.predict_proba(X)</li>\n",
    "    <li> model Cross Validation and Scoring</li>\n",
    "    <li> ROC/PRC curves and AUC</li>\n",
    "    <li> Auxilliary scikit-XCS methods\n",
    "</ul>\n",
    "\n",
    "**Part 4: Data Collection and Export**\n",
    "<ul>\n",
    "    <li> Exporting and Accessing Iteration Tracking Data </li>\n",
    "    <li> Exporting and Accessing Rule Population Data </li>\n",
    "</ul>\n",
    "\n",
    "**Part 5: Population Reboot**\n"
   ]
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   "metadata": {},
   "source": [
    "## Part 1: Loading Data\n",
    "\n",
    "### Dataset Format Requirement for scikit-XCS\n",
    "\n",
    "There are 4 requirements for the dataset:\n",
    "<ul>\n",
    "    <li>Data Attributes for all data instances are represented by an <b>nxm</b> numpy.ndarray, where <b>n</b> = # of instances and <b>m</b> = # of attributes for each instance</li>\n",
    "    <li>Data Actions for all data instances are represented by a 1 dimensional numpy.ndarray of length <b>n</b></li>\n",
    "    <li>Both the <b>nxm</b> attribute array and the length <b>n</b> action array are fully numeric. This means that every element in each array must be convertable to a float. String, boolean types are not permitted (of course, boolean datasets can be represented by binary 0s and 1s). It is important to mention that missing data in the attribute dataset is acceptable, if represented as a NaN type within the array. Missing data in the action array is not acceptable.</li>\n",
    "    <li>As mentioned aboved, the problem must be a classification problem. This version does not support regression problems.</li>\n",
    "</ul>\n",
    "\n",
    "The package includes automated parameter checking that will raise an Exception at the start of training if the dataset does not meet the above requirements.\n",
    "\n",
    "Below, we present 2 methods to load data of the correct format for the XCS model. The first is the commonly used pandas. The second is a custom, standalone method included with this package named StringEnumerator, that will make your life much easier in terms of ensuring data formatting requirements are met for the XCS model."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data Method 1: Using pandas\n",
    "\n",
    "There are many ways you can derive the attribute and phenotype array. One most common method is by using pandas via reading from a csv file, as the below code demonstrates with the a 6 bit multiplexer dataset:"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
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    {
     "name": "stdout",
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     "text": [
      "Data Features\n",
      "[[0 0 0 0 0 0]\n",
      " [0 0 0 0 0 1]\n",
      " [0 0 0 0 1 0]\n",
      " [0 0 0 0 1 1]\n",
      " [0 0 0 1 0 0]\n",
      " [0 0 0 1 0 1]\n",
      " [0 0 0 1 1 0]\n",
      " [0 0 0 1 1 1]\n",
      " [0 0 1 0 0 0]\n",
      " [0 0 1 0 0 1]\n",
      " [0 0 1 0 1 0]\n",
      " [0 0 1 0 1 1]\n",
      " [0 0 1 1 0 0]\n",
      " [0 0 1 1 0 1]\n",
      " [0 0 1 1 1 0]\n",
      " [0 0 1 1 1 1]\n",
      " [0 1 0 0 0 0]\n",
      " [0 1 0 0 0 1]\n",
      " [0 1 0 0 1 0]\n",
      " [0 1 0 0 1 1]\n",
      " [0 1 0 1 0 0]\n",
      " [0 1 0 1 0 1]\n",
      " [0 1 0 1 1 0]\n",
      " [0 1 0 1 1 1]\n",
      " [0 1 1 0 0 0]\n",
      " [0 1 1 0 0 1]\n",
      " [0 1 1 0 1 0]\n",
      " [0 1 1 0 1 1]\n",
      " [0 1 1 1 0 0]\n",
      " [0 1 1 1 0 1]\n",
      " [0 1 1 1 1 0]\n",
      " [0 1 1 1 1 1]\n",
      " [1 0 0 0 0 0]\n",
      " [1 0 0 0 0 1]\n",
      " [1 0 0 0 1 0]\n",
      " [1 0 0 0 1 1]\n",
      " [1 0 0 1 0 0]\n",
      " [1 0 0 1 0 1]\n",
      " [1 0 0 1 1 0]\n",
      " [1 0 0 1 1 1]\n",
      " [1 0 1 0 0 0]\n",
      " [1 0 1 0 0 1]\n",
      " [1 0 1 0 1 0]\n",
      " [1 0 1 0 1 1]\n",
      " [1 0 1 1 0 0]\n",
      " [1 0 1 1 0 1]\n",
      " [1 0 1 1 1 0]\n",
      " [1 0 1 1 1 1]\n",
      " [1 1 0 0 0 0]\n",
      " [1 1 0 0 0 1]\n",
      " [1 1 0 0 1 0]\n",
      " [1 1 0 0 1 1]\n",
      " [1 1 0 1 0 0]\n",
      " [1 1 0 1 0 1]\n",
      " [1 1 0 1 1 0]\n",
      " [1 1 0 1 1 1]\n",
      " [1 1 1 0 0 0]\n",
      " [1 1 1 0 0 1]\n",
      " [1 1 1 0 1 0]\n",
      " [1 1 1 0 1 1]\n",
      " [1 1 1 1 0 0]\n",
      " [1 1 1 1 0 1]\n",
      " [1 1 1 1 1 0]\n",
      " [1 1 1 1 1 1]]\n",
      "\n",
      "Data Actions\n",
      "[0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 0\n",
      " 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1]\n",
      "\n",
      "Data Headers\n",
      "['A_0' 'A_1' 'R_0' 'R_1' 'R_2' 'R_3']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#Read from CSV file\n",
    "data = pd.read_csv(\"test/DataSets/Real/Multiplexer6.csv\")\n",
    "\n",
    "#Specify the dataset's action label\n",
    "actionLabel = \"class\"\n",
    "\n",
    "#Derive the attribute and action array using the action label\n",
    "dataFeatures = data.drop(actionLabel,axis = 1).values\n",
    "dataActions = data[actionLabel].values\n",
    "\n",
    "#Optional: Retrieve the headers for each attribute as a length n array\n",
    "dataHeaders = data.drop(actionLabel,axis=1).columns.values\n",
    "\n",
    "print(\"Data Features\")\n",
    "print(dataFeatures)\n",
    "print(\"\\nData Actions\")\n",
    "print(dataActions)\n",
    "print(\"\\nData Headers\")\n",
    "print(dataHeaders)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is another code snippet that uses the same method, but with a dataset that contains missing values. Note that all missing values must be represented as a NaN type when used in the scikit-XCS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Features\n",
      "[[ 1. nan  1.  4.]\n",
      " [ 2.  0.  1. nan]\n",
      " [ 4. nan  1.  2.]\n",
      " [nan  1. nan  1.]\n",
      " [ 6. nan  1.  1.]]\n",
      "\n",
      "Data Actions\n",
      "[1 0 1 0 1]\n",
      "\n",
      "Data Headers\n",
      "['N1' 'N2' 'N3' 'N4']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#Read from CSV file\n",
    "data = pd.read_csv(\"test/DataSets/Tests/MissingFeatureData.csv\")\n",
    "\n",
    "#Specify the dataset's action label\n",
    "actionLabel = \"phenotype\"\n",
    "\n",
    "#Derive the attribute and action array using the action label\n",
    "dataFeatures = data.drop(actionLabel,axis = 1).values\n",
    "dataActions = data[actionLabel].values\n",
    "\n",
    "#Optional: Retrieve the headers for each attribute as a length n array\n",
    "dataHeaders = data.drop(actionLabel,axis=1).columns.values\n",
    "\n",
    "print(\"Data Features\")\n",
    "print(dataFeatures)\n",
    "print(\"\\nData Actions\")\n",
    "print(dataActions)\n",
    "print(\"\\nData Headers\")\n",
    "print(dataHeaders)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data Method 2: Using StringEnumerator\n",
    "\n",
    "Given the 4 requirements for the attribute and phenotype array, not all datasets necessarily satisfy those requirements. For example, a dataset may contain an attribute with values that are colors \"red\", \"green\", \"blue\". Since the scikit-XCS model can only work with numeric data, this dataset would not meet the requirements. Alternatively, a dataset may, for some reason, have missing phenotype data. Since the scikit-XCS model requires that the phenotype array is complete, this dataset would also not meet the requirements.\n",
    "\n",
    "To make things more convenient, included in this package is an auxilliary data type called **String Enumerator** that makes it easy to transform datasets that do not meet the requirements into datasets that do.\n",
    "\n",
    "For example, take the below dummy dataset:"
   ]
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   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Features\n",
      "[['male' 1.2 'young']\n",
      " ['female' 0.3 nan]\n",
      " ['female' -0.4 'old']\n",
      " [nan 0.0 'young']]\n",
      "\n",
      "Data Actions\n",
      "['china' nan 'china' 'russia']\n",
      "\n",
      "Data Headers\n",
      "['N1' 'N2' 'N3']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#Read from CSV file\n",
    "data = pd.read_csv(\"test/DataSets/Tests/StringData2.csv\")\n",
    "\n",
    "#Specify the dataset's action label\n",
    "actionLabel = \"phenotype\"\n",
    "\n",
    "#Derive the attribute and phenotype array using the action label\n",
    "dataFeatures = data.drop(actionLabel,axis = 1).values\n",
    "dataActions = data[actionLabel].values\n",
    "\n",
    "#Optional: Retrieve the headers for each attribute as a length n array\n",
    "dataHeaders = data.drop(actionLabel,axis=1).columns.values\n",
    "\n",
    "print(\"Data Features\")\n",
    "print(dataFeatures)\n",
    "print(\"\\nData Actions\")\n",
    "print(dataActions)\n",
    "print(\"\\nData Headers\")\n",
    "print(dataHeaders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dummy dataset does not come close to meeting the dataset requirements for training. There exist many string types in the attribute and phenotype array, as well as missing phenotype data. Aside from not meeting the requirements, the data headers and phenotype label are not too descriptive. Below StringEnumerator cleans this up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Features\n",
      "[[ 0.   1.2  1. ]\n",
      " [ 1.  -0.4  0. ]\n",
      " [ nan  0.   1. ]]\n",
      "\n",
      "Data Phenotypes\n",
      "[0. 0. 1.]\n",
      "\n",
      "Data Headers\n",
      "['Gender' 'Numeric' 'Age']\n",
      "\n",
      "Class Label\n",
      "Country\n"
     ]
    }
   ],
   "source": [
    "from skXCS import StringEnumerator\n",
    "\n",
    "#Initialize StringEnumerator object with csv filepath and class label.\n",
    "converter = StringEnumerator(\"test/DataSets/Tests/StringData2.csv\",\"phenotype\")\n",
    "\n",
    "#Change Header Names to be more descriptive using change_header_name(currentName,newName)\n",
    "converter.change_header_name(\"N1\",\"Gender\")\n",
    "converter.change_header_name(\"N2\",\"Numeric\")\n",
    "converter.change_header_name(\"N3\",\"Age\")\n",
    "\n",
    "#Change Phenotype Label to be more descriptive using change_class_name(newName)\n",
    "converter.change_class_name(\"Country\")\n",
    "\n",
    "'''\n",
    "Convert attributes to numeric data using either:\n",
    "-add_attribute_converter_random(headerName):   Given an attribute name, randomly assigns each unique attribute value an\n",
    "                                            integer value from 0 to n-1, where n = # of unique attribute values\n",
    "                                            \n",
    "-add_attribute_converter(headerName,array):   Given an attribute name, and an array of attribute values that will be\n",
    "                                            converted, converter assigns each attribute value an integer value from\n",
    "                                            0 to n-1 in the order of attribute values given in the array, where\n",
    "                                            n = length of array. This can be useful for discrete attributes where the\n",
    "                                            ordering of the attributes are important (ordinal values). For example,\n",
    "                                            an attribute may have values \"stage 1\", \"stage 2\", \"stage 3\", \"stage 4\"\n",
    "                                            to indicate stage of cancer, where the values are strings, but how they\n",
    "                                            are enumerated is crucial.\n",
    "'''\n",
    "converter.add_attribute_converter_random(\"Gender\")\n",
    "converter.add_attribute_converter(\"Age\",[\"old\",\"young\"])\n",
    "\n",
    "'''\n",
    "Convert phenotypes to numeric data using either\n",
    "-add_class_converter(array)\n",
    "-add_class_converter_random()\n",
    "Same functionality as the attribute converters\n",
    "'''\n",
    "converter.add_class_converter_random()\n",
    "\n",
    "'''\n",
    "Convert all attributes using convert_all_attributes(). Note: this is NOT a standalone method. This method\n",
    "just puts all of the changes made above \"into stone\". This method does not automatically convert anything for you. You\n",
    "MUST call this method if data transformation operations were invoked (any of the class/attributeConverter methods\n",
    "above), or you will not see any changes when you call get_params().\n",
    "'''\n",
    "converter.convert_all_attributes()\n",
    "\n",
    "#Get arrays using get_params()\n",
    "headers,classLabel,dataFeatures,dataPhenotypes = converter.get_params()\n",
    "\n",
    "print(\"Data Features\")\n",
    "print(dataFeatures)\n",
    "print(\"\\nData Phenotypes\")\n",
    "print(dataPhenotypes)\n",
    "print(\"\\nData Headers\")\n",
    "print(headers)\n",
    "print(\"\\nClass Label\")\n",
    "print(classLabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the StringEnumerator mapped all string attributes and phenotypes into numeric types, deleted the instance that was missing phenotype data, and made the data headers and class label more descriptive. Now, the data features and phenotypes are ready for training.\n",
    "\n",
    "The StringEnumerator has 2 additional methods not shown above that can also be powerful in data transformation:\n",
    "<ul>\n",
    "    <li>delete_attribute(headerName): deletes the specified attribute from all instances in the dataset</li>\n",
    "    <li>delete_all_instances_without_header_data(headerName): there may be situations where you only want to use data instances that does not have missing data for a specific attribute. This method deletes all instances who's specified attribute is missing</li>\n",
    "</ul>\n",
    "\n",
    "Even if the dataset satisfies all requirements, StringEnumerator can be used as an easy way to get data in the correct format without needing to use pandas, as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Features\n",
      "[[ 1. nan  1.  4.]\n",
      " [ 2.  0.  1. nan]\n",
      " [ 4. nan  1.  2.]\n",
      " [nan  1. nan  1.]\n",
      " [ 6. nan  1.  1.]]\n",
      "\n",
      "Data Actions\n",
      "[1. 0. 1. 0. 1.]\n",
      "\n",
      "Data Headers\n",
      "['N1' 'N2' 'N3' 'N4']\n"
     ]
    }
   ],
   "source": [
    "converter = StringEnumerator(\"test/DataSets/Tests/MissingFeatureData.csv\",\"phenotype\")\n",
    "headers,actionLabel,dataFeatures,dataActions = converter.get_params()\n",
    "\n",
    "print(\"Data Features\")\n",
    "print(dataFeatures)\n",
    "print(\"\\nData Actions\")\n",
    "print(dataActions)\n",
    "print(\"\\nData Headers\")\n",
    "print(headers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, StringEnumerator contains a print_invalid_attributes() method that will print out the names of any attribute with invalid typed data (including the phenotype). This can help you identify which attributes need to converted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALL INVALID ATTRIBUTES & THEIR DISTINCT VALUES\n",
      "N1: male\tfemale\t\n",
      "N3: young\told\t\n",
      "phenotype (the phenotype): china\trussia\t\n"
     ]
    }
   ],
   "source": [
    "converter = StringEnumerator(\"test/DataSets/Tests/StringData2.csv\",\"phenotype\")\n",
    "converter.print_invalid_attributes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All in all, StringEnumerator makes loading data in the correct format easy. It is intuitive to use, and if any invalid operation were done with it, built in checkers will raise contextually Exceptions to help you correct the error. We will be using it to load data for the rest of this user guide."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Initializing scikit-XCS Estimator Object\n",
    "\n",
    "### Basic Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skXCS import XCS\n",
    "\n",
    "model = XCS()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing the above will initialize an XCS estimator object using the default hyperparameters, that is now ready for training. However, in most cases, you would want to modify some hyperparameters to your needs. We list all of the tunable hyperparameters and their descriptions:\n",
    "\n",
    "### Overview of all scikit-XCS initialization parameters and default values\n",
    "\n",
    "| Parameter Name | Requirements | Description | Default Value |\n",
    "| :--- | :--- | :--- | :--- |\n",
    "| learning_iterations | non negative integer | The number of training cycles to run | 10000 |\n",
    "| N | non negative integer | Maximum micropopulation size | 1000 |\n",
    "| p_general | float from 0 - 1 | Probability of generalizing an allele during covering | 0.5 |\n",
    "| p_crossover | float from 0 - 1 | The probability of applying crossover in an offspring classifier | 0.8 |\n",
    "| p_mutation | float from 0 - 1 | The probability of mutating one allele and the action in an offspring classifier | 0.04 |\n",
    "| p_explore | float from 0 - 1 | Probability of doing an explore cycle instead of an exploit cycle | 0.5 |\n",
    "| alpha | float | The fall of rate in the fitness evaluation | 0.1 |\n",
    "| beta | float | Learning Rate for updating statistics | 0.2 |\n",
    "| delta | float | The fraction of the mean fitness of the population below which the fitness of a classifier may be considered in its vote for deletion | 0.1 |\n",
    "| e_0 | float | The error threshold under which accuracy of a classifier can be set to 1 | 10 |\n",
    "| nu | float | Power parameter for fitness evaluation | 5 |\n",
    "| theta_del | non negative integer | Specified the threshold over which the fitness of a classifier may be considered in its deletion probability | 20 |\n",
    "| theta_GA | nonnegative float | The threshold for the GA application in an action set | 25 |\n",
    "| theta_matching | non negative integer | Number of unique actions that must be represented in the match set (otherwise, covering) | the # of unique actions in dataset|\n",
    "| theta_select | float from 0 - 1 | The fraction of the action set to be included in tournament selection | 0.5 |\n",
    "| theta_sub | non negative integer | The experience of a classifier required to be a subsumer | 20 |\n",
    "| init_e | float | The initial prediction error value when generating a new classifier (e.g in covering) | 0 |\n",
    "| init_fitness | float | The initial prediction value when generating a new classifier (e.g in covering) | 0.01 |\n",
    "| init_prediction | float | The initial prediction value when generating a new classifier (e.g in covering) | 10 |\n",
    "| do_action_set_subsumption | boolean | Do subsumption in action set | False |\n",
    "| do_GA_subsumption | boolean | Do subsumption in GA between parents and offspring | True |\n",
    "| max_payoff | float | For single step problems, what the maximum reward for correctness | 1000 |\n",
    "| fitness_reduction | float | The reduction of the fitness when generating an offspring classifier | 0.1 |\n",
    "| prediction_error_reduction | float | The reduction of the prediction error when generating an offspring classifier | 0.25 |\n",
    "\n",
    "Below, for your reference, we also put together a table that shows how the training process may be effected, if one of the above attributes were changed:\n",
    "\n",
    "| Parameter Name | Impact on training when changed |\n",
    "| :--- | :--- |\n",
    "| learning_iterations | more iterations increases accuracy (up to a point - there exists diminishing returns), but also increases training time |\n",
    "| N | for larger feature spaces, a larger N is recommended. A larger N increases training time, but too small of an N will prevent the LCS from converging on a solution |\n",
    "| p_general | the more attributes in the data set, the higher p_general should be. Too high of a p_spec and the population initialization will be less suited to detecting complex patterns, but too low can run the risk of early overfitting |\n",
    "| p_crossover | Typically between 0.5 and 1. Recommended to use default parameter |\n",
    "| p_mutation | Typically between 0.01 and 0.05. Recommended to use default parameter |\n",
    "| p_explore | Recommended to use default parameter |\n",
    "| alpha | Recommended to use default parameter |\n",
    "| beta | Typically between 0.1 and 0.2. Recommended to use default parameter |\n",
    "| delta | Recommended to use default parameter |\n",
    "| e_0 | Typically about 1 percent of max_payoff |\n",
    "| nu | recommended to be 1 for data with any level of noise. Increasing nu in clean problems improves chances of converging on optimal solution. Default of 5 is common for clean problems |\n",
    "| theta_del | Recommended to use default parameter |\n",
    "| theta_GA | Typically between 25 to 50. Recommended to use default parameter|\n",
    "| theta_matching | Recommended to use default parameter |\n",
    "| theta_select | Recommended to use default parameter |\n",
    "| theta_sub | Recommended to use default parameter |\n",
    "| init_e | Recommended to use default parameter |\n",
    "| init_fitness | Recommended to use default parameter |\n",
    "| init_prediction | Recommended to use default parameter |\n",
    "| do_action_set_subsumption | Is stronger of the two subsumptions. Subsumption is useful in problems where this is a well-defined underlying target function. |\n",
    "| do_GA_subsumption | Is weaker of the two subsumptions. Subsumption is useful in problems where this is a well-defined underlying target function. |\n",
    "| max_payoff | Arbitrary, but 1000 is commonly used |\n",
    "| fitness_reduction | Recommended to use default parameter |\n",
    "| prediction_error_reduction | Recommended to use default parameter |\n",
    "\n",
    "There also exists a few hyperparameters related to the setup and evaluation of the training process:\n",
    "\n",
    "| Parameter Name | Requirements | Description | Default Value |\n",
    "| :--- | :--- | :--- | :--- |\n",
    "| discrete_attribute_limit | non negative integer OR \"c\" OR \"d\" | Multipurpose param. If it is a nonnegative integer, discrete_attribute_limit determines the threshold that determines if an attribute will be treated as a continuous or discrete attribute. For example, if discrete_attribute_limit == 10, if an attribute has more than 10 unique values in the dataset, the attribute will be continuous. If the attribute has 10 or less unique values, it will be discrete. Alternatively, discrete_attribute_limit can take the value of \"c\" or \"d\". See next param for this. | 10 |\n",
    "| specified_attributes | numpy.ndarray of nonnegative integers of attribute indices | If discrete_attribute_limit == \"c\", attributes specified by index in this param will be continuous and the rest will be discrete. If \"d\", attributes specified by index in this param will be discrete and the rest will be continuous | empty numpy ndarray |\n",
    "| random_state | integer OR None | Set a constant random seed value to some integer (in order to obtain reproducible results). Put None if none (pseudo-random algorithm runs) | None |\n",
    "| reboot_filename | str or None | File name of pickled model to be rebooted. None by default (no model to reboot) | None |\n",
    "\n",
    "\n",
    "These hyperparameters can be set during initialization. There exists built in parameter checking to ensure each specified parameter is valid. Below is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XCS(learning_iterations = 5000,nu = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Model Training and Testing\n",
    "\n",
    "### model.fit(X, y) and how it works\n",
    "Once the model is initialized with the hyperparameters you want, you can train the model by calling the fit(X,y) method, where X is the numpy.ndarray of attributes and y is the numpy.ndarray of phenotypes. Be sure both X and y satisfy the requirements specified in Part 1, or the parameter checker will raise an Exception. Below we demo the entire process of importing relevant packages, loading data, model initialization, and model training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XCS(N=1000, alpha=0.1, beta=0.2, delta=0.1, discrete_attribute_limit=10,\n",
       "    do_GA_subsumption=True, do_action_set_subsumption=False, e_0=10,\n",
       "    fitness_reduction=0.1, init_e=0, init_fitness=0.01, init_prediction=10,\n",
       "    learning_iterations=5000, max_payoff=1000, nu=5, p_crossover=0.8,\n",
       "    p_explore=0.5, p_general=0.5, p_mutation=0.04,\n",
       "    prediction_error_reduction=0.25, random_state=None, reboot_filename=None,\n",
       "    specified_attributes=array([], dtype=float64), theta_GA=25, theta_del=20,\n",
       "    theta_matching=2, theta_select=0.5, theta_sub=20)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skXCS import StringEnumerator\n",
    "from skXCS import XCS\n",
    "import numpy as np\n",
    "\n",
    "#Use StringEnumerator to gather data\n",
    "converter = StringEnumerator(\"test/DataSets/Real/Multiplexer11.csv\",\"class\")\n",
    "headers,actionLabel,dataFeatures,dataActions = converter.get_params()\n",
    "\n",
    "#Initialize and train model\n",
    "model = XCS(learning_iterations = 5000)\n",
    "trainedModel = model.fit(dataFeatures,dataActions)\n",
    "\n",
    "trainedModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit function returns a trained model. Below we will provide a high level overview of the scikit-XCS training algorithm. In compiling this algorithm, we aimed to make scikit-XCS as close to a 'modern' XCS as possible.\n",
    "\n",
    "<ul>\n",
    "    <li> Before training, scikit-XCS uses the training set to discern each attribute as continuous or discrete based on the <b>discrete_attribute_limit</b> and <b>specified_attributes</b> hyperparameters. It also counts the number of unique actions and determines if the problem is multiclass or binary. The training set is shuffled, and training begins. </li>\n",
    "    <li> At the start of each iteration, an unlabelled training instance is fetched from the environment. </li>\n",
    "    <li> Using the <b>p_explore</b> hyperparameter, scikit-XCS probabilistically determines if the learning iteration should be an 'explore' or 'exploit' iteration.</li>\n",
    "    <li> A match set is created. For multiclass problems, coverage is by default triggered until all actions are represented in [M] (unless the <b>theta_matching</b> hyperparameter was changed). For binary problem coverage, it is the same, but coverage also triggers (once) if there are less than 5 classifiers in [M] </li>\n",
    "    <li> If the iteration is 'explore', a random action in the match set is chosen. If the iteration is 'exploit', the classifiers in the match set each cast a weighted vote based on prediction and fitness to select the 'best action' </li>\n",
    "    <li> The chosen action is given to the environment for execution. Since it is a single step classification problem, the environment is very simple. If the chosen action matches with the correct action for that instance, the environment returns the maximum reward. If the chosen action does not match, a reward of 0 is returned. </li>\n",
    "    <li> Based on the reward, the experience, prediction, prediction error, and fitness of the classifiers in [A] are updated. If the <b>do_action_set_subsumption</b> hyperparameter is True, subsumption is done in [A] after this. </li>\n",
    "    <li> If the iteration is 'explore', GA can occur. GA then occurs if the average number of iterations since the last GA is greater than hyperparameter <b>theta_GA</b> for classifiers in [A]. Tournament selection is used to choose two parent classifiers. Mutation and uniform crossover are then applied to create offspring classifiers. If the <b>do_GA_subsumption</b> hyperparameter is True, subsumption is done between the offspring and the parents before the offspring are added back into [P]. </li>\n",
    "    <li> If the micropopulation (sum of numerosities) is greater than hyperparameter <b>N</b>, deletion occurs until it is. </li>\n",
    "    <li> Training ends when the number of iterations equals the hyperparameter <b>learning_iterations</b>.\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model.predict(X), model.predict_proba(X)\n",
    "\n",
    "Once the model has finished training, you can call the predict(X) method to return an numpy.ndarray of action predictions given an array of unlabelled instances. The predict method works by using the final rule population of the model to vote on the most probable action based on classifier fitness and numerosity. Before making a prediction, parameter checkers again ensure X is a valid input (fully numeric numpy.ndarray). We refer back to the trained model above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 1., 0., 1.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainedModel.predict(dataFeatures)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also call the predict_proba(X) method to return an nxm numpy.ndarray of phenotype probabilities, where n = number of data instances and m = number of distinct discrete phenotypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.74259694, 0.25740306],\n",
       "       [0.73320971, 0.26679029],\n",
       "       [0.74942598, 0.25057402],\n",
       "       ...,\n",
       "       [0.47434201, 0.52565799],\n",
       "       [0.74617255, 0.25382745],\n",
       "       [0.35748198, 0.64251802]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainedModel.predict_proba(dataFeatures)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation and Scoring\n",
    "You can score a set of predictions using the score(X,y) method, where X is the unlabelled test data and y is associated list of correct action values. By default, score(X,y) uses balanced accuracy to score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90478515625"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainedModel.score(dataFeatures,dataActions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use different scoring metrics to score the model. We will do this with a quick cross validation on the model. Below we will run a 3 fold CV on the trained model from above. Again, by default, balanced accuracy is used to score each partition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9365299857659789"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy as np\n",
    "\n",
    "#Shuffle Data (useful for cross validation)\n",
    "formatted = np.insert(dataFeatures,dataFeatures.shape[1],dataActions,1)\n",
    "np.random.shuffle(formatted)\n",
    "dataFeatures = np.delete(formatted,-1,axis=1)\n",
    "dataActions = formatted[:,-1]\n",
    "\n",
    "np.mean(cross_val_score(trainedModel,dataFeatures,dataActions,cv=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you prefer to use a scoring method other than balanced accuracy during CV (e.g. accuracy_score, precision_score, recall_score, f1_score), that can be specified as well via the standard scoring parameter. Be aware that some of these scoring methods only accept binary endpoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9540938530751774"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cross_val_score(trainedModel,dataFeatures,dataActions,cv=3,scoring=\"recall\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we demonstrate a scoring method that calls predict_prob(X):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9917937329101935"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cross_val_score(trainedModel,dataFeatures,dataActions,cv=3,scoring=\"roc_auc\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC/PRC curves\n",
    "\n",
    "As shown above, you can compute the AUC score of the ROC using the standard scikit scoring methods/metrics. Below, we show a means to graph the ROC and PRC, and compute the AUC for both curves (on training data). Again, be aware that ROC/PRC analysis only accepts binary endpoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRC AUC:0.9708239171515141\n",
      "ROC AUC:0.9712200164794922\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "probs = trainedModel.predict_proba(dataFeatures)\n",
    "fpr, tpr, thresholds = roc_curve(dataActions, probs[:, 1])\n",
    "prec, recall, thresholds = precision_recall_curve(dataActions, probs[:, 1])\n",
    "\n",
    "plt.plot(fpr,tpr,label=\"ROC\")\n",
    "plt.plot(recall,prec,label=\"PRC\")\n",
    "\n",
    "plt.xlabel('False Positive Rate/Recall')\n",
    "plt.ylabel('True Positive Rate/Precision')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(\"PRC AUC:\" + str(auc(recall, prec)))\n",
    "print(\"ROC AUC:\" + str(auc(fpr, tpr)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auxilliary scikit-XCS methods\n",
    "In addition to the standard sklearn methods you can use to access training metrics, scikit-XCS provides a few of its own.\n",
    "\n",
    "| Method Name | Description |\n",
    "| :--- | :--- |\n",
    "| model.get_final_training_accuracy() | Evaluates (in real time) the final training accuracy of the model |\n",
    "| model.get_final_instance_coverage() | Evaluates (in real time) the final instance coverage of the model |\n",
    "| model.get_final_attribute_specificity_list() | Returns a list of attribute specificities (which attributes are specified the most in the rule population) |\n",
    "| model.get_final_attribute_accuracy_list() | Returns a list of attribute specificities weighted by accuracy |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Accuracy: 0.90478515625\n",
      "Final Instance Coverage: 1.0\n",
      "Final Attribute Specificity List: [562, 662, 609, 341, 418, 475, 483, 434, 432, 456, 411]\n",
      "Final Attribute Accuracy List: [325.0001772560421, 349.0065781938231, 328.0098285616509, 138.015180836602, 186.0102264674025, 198.01367465651916, 192.00872651643598, 190.00495975152197, 181.00650950834168, 192.00017587542155, 181.0087662169749]\n"
     ]
    }
   ],
   "source": [
    "print(\"Final Training Accuracy: \"+str(trainedModel.get_final_training_accuracy()))\n",
    "print(\"Final Instance Coverage: \"+str(trainedModel.get_final_instance_coverage()))\n",
    "print(\"Final Attribute Specificity List: \"+str(trainedModel.get_final_attribute_specificity_list()))\n",
    "print(\"Final Attribute Accuracy List: \"+str(trainedModel.get_final_attribute_accuracy_list()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4: Data Collection and Export\n",
    "\n",
    "### Exporting and Accessing Iteration Tracking Data\n",
    "During training, scikit-XCS tracks a series of training statistics that may be of use. Aside from average rule population generality (which is tracked at least once an epoch), these following statistics are tracked every iteration:\n",
    "<ul>\n",
    "    <li>Approximate Training Accuracy (50 pt moving average)</li>\n",
    "    <li>Macropopulation size</li>\n",
    "    <li>Micropopulation size</li>\n",
    "    <li>Match set size</li>\n",
    "    <li>Action set size</li>\n",
    "    <li>Average iteration age of classifiers in action set</li>\n",
    "    <li>Number of classifiers subsumed in iteration</li>\n",
    "    <li>Number of crossover operations performed in iteration (GA operation)</li>\n",
    "    <li>Number of mutation operations performed in iteration (GA operation)</li>\n",
    "    <li>Number of covering operations performed in iteration</li>\n",
    "    <li>Number of macroclassifiers deleted in iteration</li>\n",
    "    <li>Total training time</li>\n",
    "    <li>Total matching time</li>\n",
    "    <li>Total deletion time</li>\n",
    "    <li>Total subsumption time</li>\n",
    "    <li>Total selection time</li>\n",
    "    <li>Total evaluation time</li>\n",
    "</ul>\n",
    "These statistics are not time intensive to compute, and thus can be tracked every iteration. \n",
    "\n",
    "Why is collecting this kind of iteration by iteration data important? Unlike many other machine learning models, as the XCS trains, it is easy to visualize its training process, such as how its accuracy or its macropopulation size changes over time. Thus, doing iteration tracking will allow you to export this training progess data (as you will see below) to aid in this visualization. Also, LCSs are stochastic algorithms with no clear stop criteria, so tracking these metrics will allow you to identify when training has stagnated, which can help in future hyperparameter optimization.\n",
    "\n",
    "To access this iteration tracking data, you can call the following method that exports the entire tracking data record from training into a local folder:\n",
    "\n",
    "| Method Name | Description |\n",
    "| :--- | :--- |\n",
    "| model.export_iteration_tracking_data(filename) | Exports all tracked data from training |\n",
    "\n",
    "The filename paramater is optional. By default, the program saves a CSV file named **iterationData.csv** into your local directory. However, you can set the filename param as an absolute path with the format **/filepath/filename.csv** which will save the data into any specified directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainedModel.export_iteration_tracking_data(\"defaultExportDir/iterationData.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The exported CSV is below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>Iteration</th>\n",
       "      <th>Accuracy (approx)</th>\n",
       "      <th>Average Population Generality</th>\n",
       "      <th>Macropopulation Size</th>\n",
       "      <th>Micropopulation Size</th>\n",
       "      <th>Match Set Size</th>\n",
       "      <th>Action Set Size</th>\n",
       "      <th>Average Iteration Age of Action Set Classifiers</th>\n",
       "      <th># Classifiers Subsumed in Iteration</th>\n",
       "      <th># Crossover Operations Performed in Iteration</th>\n",
       "      <th># Mutation Operations Performed in Iteration</th>\n",
       "      <th># Covering Operations Performed in Iteration</th>\n",
       "      <th># Deletion Operations Performed in Iteration</th>\n",
       "      <th>Total Global Time</th>\n",
       "      <th>Total Matching Time</th>\n",
       "      <th>Total Deletion Time</th>\n",
       "      <th>Total Subsumption Time</th>\n",
       "      <th>Total GA Time</th>\n",
       "      <th>Total Evaluation Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067317</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067510</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067634</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067745</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067911</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068090</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068205</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068312</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068414</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068564</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068680</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068769</td>\n",
       "      <td>0.000940</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068853</td>\n",
       "      <td>0.000991</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068953</td>\n",
       "      <td>0.001057</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069051</td>\n",
       "      <td>0.001119</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069149</td>\n",
       "      <td>0.001184</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069245</td>\n",
       "      <td>0.001242</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069347</td>\n",
       "      <td>0.001310</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069446</td>\n",
       "      <td>0.001368</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069547</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069645</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069734</td>\n",
       "      <td>0.001561</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069840</td>\n",
       "      <td>0.001632</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069935</td>\n",
       "      <td>0.001695</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.070039</td>\n",
       "      <td>0.001761</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.070150</td>\n",
       "      <td>0.001839</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.070247</td>\n",
       "      <td>0.001904</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.070357</td>\n",
       "      <td>0.001967</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.070463</td>\n",
       "      <td>0.002042</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.070587</td>\n",
       "      <td>0.002128</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4970</th>\n",
       "      <td>4970</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>775</td>\n",
       "      <td>1000</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>1152.214286</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.386313</td>\n",
       "      <td>6.010051</td>\n",
       "      <td>2.612359</td>\n",
       "      <td>1.322957</td>\n",
       "      <td>0.336911</td>\n",
       "      <td>0.037347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4971</th>\n",
       "      <td>4971</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>775</td>\n",
       "      <td>1000</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>869.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.387699</td>\n",
       "      <td>6.011306</td>\n",
       "      <td>2.612360</td>\n",
       "      <td>1.322957</td>\n",
       "      <td>0.336911</td>\n",
       "      <td>0.037353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4972</th>\n",
       "      <td>4972</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>775</td>\n",
       "      <td>1000</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>1180.833333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.389024</td>\n",
       "      <td>6.012506</td>\n",
       "      <td>2.612360</td>\n",
       "      <td>1.322957</td>\n",
       "      <td>0.336911</td>\n",
       "      <td>0.037359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4973</th>\n",
       "      <td>4973</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>774</td>\n",
       "      <td>1000</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>636.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.392859</td>\n",
       "      <td>6.013642</td>\n",
       "      <td>2.614110</td>\n",
       "      <td>1.323638</td>\n",
       "      <td>0.337043</td>\n",
       "      <td>0.037363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4974</th>\n",
       "      <td>4974</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>774</td>\n",
       "      <td>1000</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>1098.842105</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.396668</td>\n",
       "      <td>6.014769</td>\n",
       "      <td>2.615684</td>\n",
       "      <td>1.324439</td>\n",
       "      <td>0.337190</td>\n",
       "      <td>0.037369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4975</th>\n",
       "      <td>4975</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>774</td>\n",
       "      <td>1000</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>908.800000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.400657</td>\n",
       "      <td>6.015913</td>\n",
       "      <td>2.617640</td>\n",
       "      <td>1.325119</td>\n",
       "      <td>0.337277</td>\n",
       "      <td>0.037376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4976</th>\n",
       "      <td>4976</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>774</td>\n",
       "      <td>1000</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>1016.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.402044</td>\n",
       "      <td>6.017161</td>\n",
       "      <td>2.617641</td>\n",
       "      <td>1.325119</td>\n",
       "      <td>0.337277</td>\n",
       "      <td>0.037385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4977</th>\n",
       "      <td>4977</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>774</td>\n",
       "      <td>1000</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>1132.315789</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.403336</td>\n",
       "      <td>6.018337</td>\n",
       "      <td>2.617642</td>\n",
       "      <td>1.325119</td>\n",
       "      <td>0.337277</td>\n",
       "      <td>0.037390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4978</th>\n",
       "      <td>4978</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>774</td>\n",
       "      <td>1000</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>810.214286</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.407331</td>\n",
       "      <td>6.019507</td>\n",
       "      <td>2.619355</td>\n",
       "      <td>1.325953</td>\n",
       "      <td>0.337413</td>\n",
       "      <td>0.037394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4979</th>\n",
       "      <td>4979</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>774</td>\n",
       "      <td>1000</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.410968</td>\n",
       "      <td>6.020649</td>\n",
       "      <td>2.620830</td>\n",
       "      <td>1.326733</td>\n",
       "      <td>0.337518</td>\n",
       "      <td>0.037399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4980</th>\n",
       "      <td>4980</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>774</td>\n",
       "      <td>1000</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>910.100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.412290</td>\n",
       "      <td>6.021788</td>\n",
       "      <td>2.620831</td>\n",
       "      <td>1.326733</td>\n",
       "      <td>0.337596</td>\n",
       "      <td>0.037404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4981</th>\n",
       "      <td>4981</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>774</td>\n",
       "      <td>1000</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>698.100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.413604</td>\n",
       "      <td>6.022925</td>\n",
       "      <td>2.620832</td>\n",
       "      <td>1.326733</td>\n",
       "      <td>0.337672</td>\n",
       "      <td>0.037409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4982</th>\n",
       "      <td>4982</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>774</td>\n",
       "      <td>1000</td>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>746.772727</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.415007</td>\n",
       "      <td>6.024064</td>\n",
       "      <td>2.620833</td>\n",
       "      <td>1.326733</td>\n",
       "      <td>0.337770</td>\n",
       "      <td>0.037414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4983</th>\n",
       "      <td>4983</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>774</td>\n",
       "      <td>1000</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>822.857143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.416217</td>\n",
       "      <td>6.025156</td>\n",
       "      <td>2.620833</td>\n",
       "      <td>1.326733</td>\n",
       "      <td>0.337770</td>\n",
       "      <td>0.037418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4984</th>\n",
       "      <td>4984</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>775</td>\n",
       "      <td>1000</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>1834.181818</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.420123</td>\n",
       "      <td>6.026333</td>\n",
       "      <td>2.622453</td>\n",
       "      <td>1.327558</td>\n",
       "      <td>0.337900</td>\n",
       "      <td>0.037422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4985</th>\n",
       "      <td>4985</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>776</td>\n",
       "      <td>1000</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>789.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.423530</td>\n",
       "      <td>6.027459</td>\n",
       "      <td>2.623864</td>\n",
       "      <td>1.328187</td>\n",
       "      <td>0.338018</td>\n",
       "      <td>0.037427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4986</th>\n",
       "      <td>4986</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>776</td>\n",
       "      <td>1000</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>681.846154</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.425313</td>\n",
       "      <td>6.028958</td>\n",
       "      <td>2.623866</td>\n",
       "      <td>1.328187</td>\n",
       "      <td>0.338139</td>\n",
       "      <td>0.037434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4987</th>\n",
       "      <td>4987</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>776</td>\n",
       "      <td>1000</td>\n",
       "      <td>49</td>\n",
       "      <td>22</td>\n",
       "      <td>745.321429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.430466</td>\n",
       "      <td>6.030629</td>\n",
       "      <td>2.625876</td>\n",
       "      <td>1.329199</td>\n",
       "      <td>0.338320</td>\n",
       "      <td>0.037442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4988</th>\n",
       "      <td>4988</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>776</td>\n",
       "      <td>1000</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>1849.571429</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.431902</td>\n",
       "      <td>6.031885</td>\n",
       "      <td>2.625879</td>\n",
       "      <td>1.329199</td>\n",
       "      <td>0.338320</td>\n",
       "      <td>0.037451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4989</th>\n",
       "      <td>4989</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>776</td>\n",
       "      <td>1000</td>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>635.315789</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.436016</td>\n",
       "      <td>6.033068</td>\n",
       "      <td>2.627558</td>\n",
       "      <td>1.330135</td>\n",
       "      <td>0.338486</td>\n",
       "      <td>0.037457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4990</th>\n",
       "      <td>4990</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>776</td>\n",
       "      <td>1000</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>775.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.440039</td>\n",
       "      <td>6.034272</td>\n",
       "      <td>2.629207</td>\n",
       "      <td>1.331003</td>\n",
       "      <td>0.338628</td>\n",
       "      <td>0.037463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4991</th>\n",
       "      <td>4991</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>776</td>\n",
       "      <td>1000</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>2239.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.441464</td>\n",
       "      <td>6.035491</td>\n",
       "      <td>2.629208</td>\n",
       "      <td>1.331003</td>\n",
       "      <td>0.338722</td>\n",
       "      <td>0.037471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4992</th>\n",
       "      <td>4992</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>776</td>\n",
       "      <td>1000</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>969.785714</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.442798</td>\n",
       "      <td>6.036664</td>\n",
       "      <td>2.629209</td>\n",
       "      <td>1.331003</td>\n",
       "      <td>0.338722</td>\n",
       "      <td>0.037477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4993</th>\n",
       "      <td>4993</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>775</td>\n",
       "      <td>1000</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>1089.571429</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.446730</td>\n",
       "      <td>6.038069</td>\n",
       "      <td>2.630915</td>\n",
       "      <td>1.331536</td>\n",
       "      <td>0.338874</td>\n",
       "      <td>0.037483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4994</th>\n",
       "      <td>4994</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>775</td>\n",
       "      <td>1000</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>2300.476190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.448131</td>\n",
       "      <td>6.039310</td>\n",
       "      <td>2.630916</td>\n",
       "      <td>1.331536</td>\n",
       "      <td>0.338874</td>\n",
       "      <td>0.037493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>4995</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>775</td>\n",
       "      <td>1000</td>\n",
       "      <td>36</td>\n",
       "      <td>13</td>\n",
       "      <td>658.785714</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.449758</td>\n",
       "      <td>6.040759</td>\n",
       "      <td>2.630918</td>\n",
       "      <td>1.331536</td>\n",
       "      <td>0.338874</td>\n",
       "      <td>0.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>4996</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>775</td>\n",
       "      <td>1000</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>924.800000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.451217</td>\n",
       "      <td>6.042099</td>\n",
       "      <td>2.630920</td>\n",
       "      <td>1.331536</td>\n",
       "      <td>0.338874</td>\n",
       "      <td>0.037508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>4997</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>775</td>\n",
       "      <td>1000</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>871.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.452650</td>\n",
       "      <td>6.043398</td>\n",
       "      <td>2.630921</td>\n",
       "      <td>1.331536</td>\n",
       "      <td>0.338874</td>\n",
       "      <td>0.037513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>4998</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.526455</td>\n",
       "      <td>775</td>\n",
       "      <td>1000</td>\n",
       "      <td>48</td>\n",
       "      <td>27</td>\n",
       "      <td>1022.411765</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.454269</td>\n",
       "      <td>6.044658</td>\n",
       "      <td>2.630922</td>\n",
       "      <td>1.331536</td>\n",
       "      <td>0.339002</td>\n",
       "      <td>0.037517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>4999</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.519727</td>\n",
       "      <td>775</td>\n",
       "      <td>1000</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>843.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.455630</td>\n",
       "      <td>6.045904</td>\n",
       "      <td>2.630923</td>\n",
       "      <td>1.331536</td>\n",
       "      <td>0.339002</td>\n",
       "      <td>0.037522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Iteration  Accuracy (approx)  Average Population Generality  \\\n",
       "0             0           1.000000                       0.000000   \n",
       "1             1           1.000000                       0.000000   \n",
       "2             2           0.500000                       0.000000   \n",
       "3             3           0.333333                       0.000000   \n",
       "4             4           0.250000                       0.000000   \n",
       "5             5           0.200000                       0.000000   \n",
       "6             6           0.200000                       0.000000   \n",
       "7             7           0.200000                       0.000000   \n",
       "8             8           0.200000                       0.000000   \n",
       "9             9           0.200000                       0.000000   \n",
       "10           10           0.166667                       0.000000   \n",
       "11           11           0.166667                       0.000000   \n",
       "12           12           0.142857                       0.000000   \n",
       "13           13           0.142857                       0.000000   \n",
       "14           14           0.125000                       0.000000   \n",
       "15           15           0.111111                       0.000000   \n",
       "16           16           0.100000                       0.000000   \n",
       "17           17           0.181818                       0.000000   \n",
       "18           18           0.181818                       0.000000   \n",
       "19           19           0.181818                       0.000000   \n",
       "20           20           0.181818                       0.000000   \n",
       "21           21           0.166667                       0.000000   \n",
       "22           22           0.230769                       0.000000   \n",
       "23           23           0.230769                       0.000000   \n",
       "24           24           0.285714                       0.000000   \n",
       "25           25           0.333333                       0.000000   \n",
       "26           26           0.312500                       0.000000   \n",
       "27           27           0.312500                       0.000000   \n",
       "28           28           0.312500                       0.000000   \n",
       "29           29           0.312500                       0.000000   \n",
       "...         ...                ...                            ...   \n",
       "4970       4970           0.940000                       0.526455   \n",
       "4971       4971           0.920000                       0.526455   \n",
       "4972       4972           0.920000                       0.526455   \n",
       "4973       4973           0.920000                       0.526455   \n",
       "4974       4974           0.920000                       0.526455   \n",
       "4975       4975           0.920000                       0.526455   \n",
       "4976       4976           0.920000                       0.526455   \n",
       "4977       4977           0.920000                       0.526455   \n",
       "4978       4978           0.920000                       0.526455   \n",
       "4979       4979           0.920000                       0.526455   \n",
       "4980       4980           0.920000                       0.526455   \n",
       "4981       4981           0.920000                       0.526455   \n",
       "4982       4982           0.920000                       0.526455   \n",
       "4983       4983           0.920000                       0.526455   \n",
       "4984       4984           0.920000                       0.526455   \n",
       "4985       4985           0.920000                       0.526455   \n",
       "4986       4986           0.920000                       0.526455   \n",
       "4987       4987           0.920000                       0.526455   \n",
       "4988       4988           0.940000                       0.526455   \n",
       "4989       4989           0.940000                       0.526455   \n",
       "4990       4990           0.940000                       0.526455   \n",
       "4991       4991           0.940000                       0.526455   \n",
       "4992       4992           0.940000                       0.526455   \n",
       "4993       4993           0.940000                       0.526455   \n",
       "4994       4994           0.940000                       0.526455   \n",
       "4995       4995           0.940000                       0.526455   \n",
       "4996       4996           0.940000                       0.526455   \n",
       "4997       4997           0.940000                       0.526455   \n",
       "4998       4998           0.940000                       0.526455   \n",
       "4999       4999           0.940000                       0.519727   \n",
       "\n",
       "      Macropopulation Size  Micropopulation Size  Match Set Size  \\\n",
       "0                        2                     2               2   \n",
       "1                        4                     4               2   \n",
       "2                        6                     6               2   \n",
       "3                        7                     7               2   \n",
       "4                        8                     8               2   \n",
       "5                       10                    10               2   \n",
       "6                       12                    12               2   \n",
       "7                       13                    13               2   \n",
       "8                       14                    14               2   \n",
       "9                       16                    16               2   \n",
       "10                      18                    18               2   \n",
       "11                      19                    19               2   \n",
       "12                      20                    20               2   \n",
       "13                      21                    21               3   \n",
       "14                      23                    23               2   \n",
       "15                      25                    25               2   \n",
       "16                      26                    26               3   \n",
       "17                      27                    27               3   \n",
       "18                      28                    28               3   \n",
       "19                      30                    30               2   \n",
       "20                      31                    31               2   \n",
       "21                      32                    32               2   \n",
       "22                      33                    33               3   \n",
       "23                      34                    34               2   \n",
       "24                      35                    35               3   \n",
       "25                      36                    36               5   \n",
       "26                      37                    37               2   \n",
       "27                      37                    37               5   \n",
       "28                      38                    38               2   \n",
       "29                      39                    39               3   \n",
       "...                    ...                   ...             ...   \n",
       "4970                   775                  1000              27   \n",
       "4971                   775                  1000              20   \n",
       "4972                   775                  1000              28   \n",
       "4973                   774                  1000              22   \n",
       "4974                   774                  1000              29   \n",
       "4975                   774                  1000              22   \n",
       "4976                   774                  1000              25   \n",
       "4977                   774                  1000              25   \n",
       "4978                   774                  1000              36   \n",
       "4979                   774                  1000              23   \n",
       "4980                   774                  1000              22   \n",
       "4981                   774                  1000              18   \n",
       "4982                   774                  1000              33   \n",
       "4983                   774                  1000              29   \n",
       "4984                   775                  1000              33   \n",
       "4985                   776                  1000              21   \n",
       "4986                   776                  1000              20   \n",
       "4987                   776                  1000              49   \n",
       "4988                   776                  1000              33   \n",
       "4989                   776                  1000              28   \n",
       "4990                   776                  1000              29   \n",
       "4991                   776                  1000              22   \n",
       "4992                   776                  1000              22   \n",
       "4993                   775                  1000              21   \n",
       "4994                   775                  1000              26   \n",
       "4995                   775                  1000              36   \n",
       "4996                   775                  1000              19   \n",
       "4997                   775                  1000              25   \n",
       "4998                   775                  1000              48   \n",
       "4999                   775                  1000              21   \n",
       "\n",
       "      Action Set Size  Average Iteration Age of Action Set Classifiers  \\\n",
       "0                   1                                         0.000000   \n",
       "1                   1                                         0.000000   \n",
       "2                   1                                         0.000000   \n",
       "3                   1                                         3.000000   \n",
       "4                   1                                         3.000000   \n",
       "5                   1                                         0.000000   \n",
       "6                   1                                         0.000000   \n",
       "7                   1                                         6.000000   \n",
       "8                   1                                         2.000000   \n",
       "9                   1                                         0.000000   \n",
       "10                  1                                         0.000000   \n",
       "11                  1                                         0.000000   \n",
       "12                  1                                         0.000000   \n",
       "13                  1                                         3.000000   \n",
       "14                  1                                         0.000000   \n",
       "15                  1                                         0.000000   \n",
       "16                  2                                         6.500000   \n",
       "17                  1                                        16.000000   \n",
       "18                  2                                        10.000000   \n",
       "19                  1                                         0.000000   \n",
       "20                  1                                         0.000000   \n",
       "21                  1                                        20.000000   \n",
       "22                  1                                        21.000000   \n",
       "23                  1                                         0.000000   \n",
       "24                  2                                        12.000000   \n",
       "25                  1                                        19.000000   \n",
       "26                  1                                        25.000000   \n",
       "27                  3                                        10.666667   \n",
       "28                  1                                         0.000000   \n",
       "29                  2                                        14.000000   \n",
       "...               ...                                              ...   \n",
       "4970               13                                      1152.214286   \n",
       "4971               12                                       869.500000   \n",
       "4972               11                                      1180.833333   \n",
       "4973               13                                       636.666667   \n",
       "4974               16                                      1098.842105   \n",
       "4975                8                                       908.800000   \n",
       "4976                9                                      1016.500000   \n",
       "4977               11                                      1132.315789   \n",
       "4978               12                                       810.214286   \n",
       "4979               13                                      1493.000000   \n",
       "4980                8                                       910.100000   \n",
       "4981                8                                       698.100000   \n",
       "4982               20                                       746.772727   \n",
       "4983               12                                       822.857143   \n",
       "4984               16                                      1834.181818   \n",
       "4985               12                                       789.500000   \n",
       "4986               13                                       681.846154   \n",
       "4987               22                                       745.321429   \n",
       "4988               16                                      1849.571429   \n",
       "4989               13                                       635.315789   \n",
       "4990               14                                       775.200000   \n",
       "4991                6                                      2239.000000   \n",
       "4992               13                                       969.785714   \n",
       "4993                8                                      1089.571429   \n",
       "4994               15                                      2300.476190   \n",
       "4995               13                                       658.785714   \n",
       "4996                9                                       924.800000   \n",
       "4997               13                                       871.500000   \n",
       "4998               27                                      1022.411765   \n",
       "4999               11                                       843.500000   \n",
       "\n",
       "      # Classifiers Subsumed in Iteration  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "5                                       0   \n",
       "6                                       0   \n",
       "7                                       0   \n",
       "8                                       0   \n",
       "9                                       0   \n",
       "10                                      0   \n",
       "11                                      0   \n",
       "12                                      0   \n",
       "13                                      0   \n",
       "14                                      0   \n",
       "15                                      0   \n",
       "16                                      0   \n",
       "17                                      0   \n",
       "18                                      0   \n",
       "19                                      0   \n",
       "20                                      0   \n",
       "21                                      0   \n",
       "22                                      0   \n",
       "23                                      0   \n",
       "24                                      0   \n",
       "25                                      0   \n",
       "26                                      0   \n",
       "27                                      0   \n",
       "28                                      0   \n",
       "29                                      0   \n",
       "...                                   ...   \n",
       "4970                                    0   \n",
       "4971                                    0   \n",
       "4972                                    0   \n",
       "4973                                    0   \n",
       "4974                                    0   \n",
       "4975                                    0   \n",
       "4976                                    0   \n",
       "4977                                    0   \n",
       "4978                                    0   \n",
       "4979                                    0   \n",
       "4980                                    0   \n",
       "4981                                    0   \n",
       "4982                                    0   \n",
       "4983                                    0   \n",
       "4984                                    0   \n",
       "4985                                    0   \n",
       "4986                                    0   \n",
       "4987                                    0   \n",
       "4988                                    0   \n",
       "4989                                    0   \n",
       "4990                                    0   \n",
       "4991                                    0   \n",
       "4992                                    0   \n",
       "4993                                    1   \n",
       "4994                                    0   \n",
       "4995                                    0   \n",
       "4996                                    0   \n",
       "4997                                    0   \n",
       "4998                                    0   \n",
       "4999                                    0   \n",
       "\n",
       "      # Crossover Operations Performed in Iteration  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "5                                                 0   \n",
       "6                                                 0   \n",
       "7                                                 0   \n",
       "8                                                 0   \n",
       "9                                                 0   \n",
       "10                                                0   \n",
       "11                                                0   \n",
       "12                                                0   \n",
       "13                                                0   \n",
       "14                                                0   \n",
       "15                                                0   \n",
       "16                                                0   \n",
       "17                                                0   \n",
       "18                                                0   \n",
       "19                                                0   \n",
       "20                                                0   \n",
       "21                                                0   \n",
       "22                                                0   \n",
       "23                                                0   \n",
       "24                                                0   \n",
       "25                                                0   \n",
       "26                                                0   \n",
       "27                                                0   \n",
       "28                                                0   \n",
       "29                                                0   \n",
       "...                                             ...   \n",
       "4970                                              1   \n",
       "4971                                              0   \n",
       "4972                                              0   \n",
       "4973                                              1   \n",
       "4974                                              1   \n",
       "4975                                              0   \n",
       "4976                                              0   \n",
       "4977                                              0   \n",
       "4978                                              1   \n",
       "4979                                              0   \n",
       "4980                                              0   \n",
       "4981                                              0   \n",
       "4982                                              0   \n",
       "4983                                              0   \n",
       "4984                                              1   \n",
       "4985                                              1   \n",
       "4986                                              0   \n",
       "4987                                              1   \n",
       "4988                                              0   \n",
       "4989                                              1   \n",
       "4990                                              1   \n",
       "4991                                              0   \n",
       "4992                                              0   \n",
       "4993                                              0   \n",
       "4994                                              0   \n",
       "4995                                              0   \n",
       "4996                                              0   \n",
       "4997                                              0   \n",
       "4998                                              0   \n",
       "4999                                              0   \n",
       "\n",
       "      # Mutation Operations Performed in Iteration  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "5                                                0   \n",
       "6                                                0   \n",
       "7                                                0   \n",
       "8                                                0   \n",
       "9                                                0   \n",
       "10                                               0   \n",
       "11                                               0   \n",
       "12                                               0   \n",
       "13                                               0   \n",
       "14                                               0   \n",
       "15                                               0   \n",
       "16                                               0   \n",
       "17                                               0   \n",
       "18                                               0   \n",
       "19                                               0   \n",
       "20                                               0   \n",
       "21                                               0   \n",
       "22                                               0   \n",
       "23                                               0   \n",
       "24                                               0   \n",
       "25                                               0   \n",
       "26                                               0   \n",
       "27                                               0   \n",
       "28                                               0   \n",
       "29                                               0   \n",
       "...                                            ...   \n",
       "4970                                             1   \n",
       "4971                                             0   \n",
       "4972                                             0   \n",
       "4973                                             1   \n",
       "4974                                             1   \n",
       "4975                                             1   \n",
       "4976                                             0   \n",
       "4977                                             0   \n",
       "4978                                             1   \n",
       "4979                                             1   \n",
       "4980                                             0   \n",
       "4981                                             0   \n",
       "4982                                             0   \n",
       "4983                                             0   \n",
       "4984                                             0   \n",
       "4985                                             1   \n",
       "4986                                             0   \n",
       "4987                                             1   \n",
       "4988                                             0   \n",
       "4989                                             1   \n",
       "4990                                             1   \n",
       "4991                                             0   \n",
       "4992                                             0   \n",
       "4993                                             1   \n",
       "4994                                             0   \n",
       "4995                                             0   \n",
       "4996                                             0   \n",
       "4997                                             0   \n",
       "4998                                             0   \n",
       "4999                                             0   \n",
       "\n",
       "      # Covering Operations Performed in Iteration  \\\n",
       "0                                                2   \n",
       "1                                                2   \n",
       "2                                                2   \n",
       "3                                                1   \n",
       "4                                                1   \n",
       "5                                                2   \n",
       "6                                                2   \n",
       "7                                                1   \n",
       "8                                                1   \n",
       "9                                                2   \n",
       "10                                               2   \n",
       "11                                               1   \n",
       "12                                               1   \n",
       "13                                               1   \n",
       "14                                               2   \n",
       "15                                               2   \n",
       "16                                               1   \n",
       "17                                               1   \n",
       "18                                               1   \n",
       "19                                               2   \n",
       "20                                               1   \n",
       "21                                               1   \n",
       "22                                               1   \n",
       "23                                               1   \n",
       "24                                               1   \n",
       "25                                               1   \n",
       "26                                               1   \n",
       "27                                               0   \n",
       "28                                               1   \n",
       "29                                               1   \n",
       "...                                            ...   \n",
       "4970                                             0   \n",
       "4971                                             0   \n",
       "4972                                             0   \n",
       "4973                                             0   \n",
       "4974                                             0   \n",
       "4975                                             0   \n",
       "4976                                             0   \n",
       "4977                                             0   \n",
       "4978                                             0   \n",
       "4979                                             0   \n",
       "4980                                             0   \n",
       "4981                                             0   \n",
       "4982                                             0   \n",
       "4983                                             0   \n",
       "4984                                             0   \n",
       "4985                                             0   \n",
       "4986                                             0   \n",
       "4987                                             0   \n",
       "4988                                             0   \n",
       "4989                                             0   \n",
       "4990                                             0   \n",
       "4991                                             0   \n",
       "4992                                             0   \n",
       "4993                                             0   \n",
       "4994                                             0   \n",
       "4995                                             0   \n",
       "4996                                             0   \n",
       "4997                                             0   \n",
       "4998                                             0   \n",
       "4999                                             0   \n",
       "\n",
       "      # Deletion Operations Performed in Iteration  Total Global Time  \\\n",
       "0                                                0           0.067317   \n",
       "1                                                0           0.067510   \n",
       "2                                                0           0.067634   \n",
       "3                                                0           0.067745   \n",
       "4                                                0           0.067911   \n",
       "5                                                0           0.068090   \n",
       "6                                                0           0.068205   \n",
       "7                                                0           0.068312   \n",
       "8                                                0           0.068414   \n",
       "9                                                0           0.068564   \n",
       "10                                               0           0.068680   \n",
       "11                                               0           0.068769   \n",
       "12                                               0           0.068853   \n",
       "13                                               0           0.068953   \n",
       "14                                               0           0.069051   \n",
       "15                                               0           0.069149   \n",
       "16                                               0           0.069245   \n",
       "17                                               0           0.069347   \n",
       "18                                               0           0.069446   \n",
       "19                                               0           0.069547   \n",
       "20                                               0           0.069645   \n",
       "21                                               0           0.069734   \n",
       "22                                               0           0.069840   \n",
       "23                                               0           0.069935   \n",
       "24                                               0           0.070039   \n",
       "25                                               0           0.070150   \n",
       "26                                               0           0.070247   \n",
       "27                                               0           0.070357   \n",
       "28                                               0           0.070463   \n",
       "29                                               0           0.070587   \n",
       "...                                            ...                ...   \n",
       "4970                                             2          11.386313   \n",
       "4971                                             0          11.387699   \n",
       "4972                                             0          11.389024   \n",
       "4973                                             2          11.392859   \n",
       "4974                                             2          11.396668   \n",
       "4975                                             1          11.400657   \n",
       "4976                                             0          11.402044   \n",
       "4977                                             0          11.403336   \n",
       "4978                                             2          11.407331   \n",
       "4979                                             1          11.410968   \n",
       "4980                                             0          11.412290   \n",
       "4981                                             0          11.413604   \n",
       "4982                                             0          11.415007   \n",
       "4983                                             0          11.416217   \n",
       "4984                                             1          11.420123   \n",
       "4985                                             1          11.423530   \n",
       "4986                                             0          11.425313   \n",
       "4987                                             2          11.430466   \n",
       "4988                                             0          11.431902   \n",
       "4989                                             2          11.436016   \n",
       "4990                                             2          11.440039   \n",
       "4991                                             0          11.441464   \n",
       "4992                                             0          11.442798   \n",
       "4993                                             2          11.446730   \n",
       "4994                                             0          11.448131   \n",
       "4995                                             0          11.449758   \n",
       "4996                                             0          11.451217   \n",
       "4997                                             0          11.452650   \n",
       "4998                                             0          11.454269   \n",
       "4999                                             0          11.455630   \n",
       "\n",
       "      Total Matching Time  Total Deletion Time  Total Subsumption Time  \\\n",
       "0                0.000112             0.000002                0.000000   \n",
       "1                0.000197             0.000003                0.000000   \n",
       "2                0.000274             0.000004                0.000000   \n",
       "3                0.000326             0.000005                0.000000   \n",
       "4                0.000420             0.000006                0.000000   \n",
       "5                0.000526             0.000007                0.000000   \n",
       "6                0.000599             0.000008                0.000000   \n",
       "7                0.000649             0.000009                0.000000   \n",
       "8                0.000712             0.000010                0.000000   \n",
       "9                0.000811             0.000012                0.000000   \n",
       "10               0.000887             0.000013                0.000000   \n",
       "11               0.000940             0.000014                0.000000   \n",
       "12               0.000991             0.000015                0.000000   \n",
       "13               0.001057             0.000016                0.000000   \n",
       "14               0.001119             0.000017                0.000000   \n",
       "15               0.001184             0.000018                0.000000   \n",
       "16               0.001242             0.000019                0.000000   \n",
       "17               0.001310             0.000019                0.000000   \n",
       "18               0.001368             0.000020                0.000000   \n",
       "19               0.001437             0.000020                0.000000   \n",
       "20               0.001503             0.000021                0.000000   \n",
       "21               0.001561             0.000022                0.000000   \n",
       "22               0.001632             0.000022                0.000000   \n",
       "23               0.001695             0.000023                0.000000   \n",
       "24               0.001761             0.000023                0.000000   \n",
       "25               0.001839             0.000024                0.000000   \n",
       "26               0.001904             0.000025                0.000000   \n",
       "27               0.001967             0.000026                0.000000   \n",
       "28               0.002042             0.000027                0.000000   \n",
       "29               0.002128             0.000027                0.000000   \n",
       "...                   ...                  ...                     ...   \n",
       "4970             6.010051             2.612359                1.322957   \n",
       "4971             6.011306             2.612360                1.322957   \n",
       "4972             6.012506             2.612360                1.322957   \n",
       "4973             6.013642             2.614110                1.323638   \n",
       "4974             6.014769             2.615684                1.324439   \n",
       "4975             6.015913             2.617640                1.325119   \n",
       "4976             6.017161             2.617641                1.325119   \n",
       "4977             6.018337             2.617642                1.325119   \n",
       "4978             6.019507             2.619355                1.325953   \n",
       "4979             6.020649             2.620830                1.326733   \n",
       "4980             6.021788             2.620831                1.326733   \n",
       "4981             6.022925             2.620832                1.326733   \n",
       "4982             6.024064             2.620833                1.326733   \n",
       "4983             6.025156             2.620833                1.326733   \n",
       "4984             6.026333             2.622453                1.327558   \n",
       "4985             6.027459             2.623864                1.328187   \n",
       "4986             6.028958             2.623866                1.328187   \n",
       "4987             6.030629             2.625876                1.329199   \n",
       "4988             6.031885             2.625879                1.329199   \n",
       "4989             6.033068             2.627558                1.330135   \n",
       "4990             6.034272             2.629207                1.331003   \n",
       "4991             6.035491             2.629208                1.331003   \n",
       "4992             6.036664             2.629209                1.331003   \n",
       "4993             6.038069             2.630915                1.331536   \n",
       "4994             6.039310             2.630916                1.331536   \n",
       "4995             6.040759             2.630918                1.331536   \n",
       "4996             6.042099             2.630920                1.331536   \n",
       "4997             6.043398             2.630921                1.331536   \n",
       "4998             6.044658             2.630922                1.331536   \n",
       "4999             6.045904             2.630923                1.331536   \n",
       "\n",
       "      Total GA Time  Total Evaluation Time  \n",
       "0          0.000000               0.000000  \n",
       "1          0.000000               0.000020  \n",
       "2          0.000000               0.000025  \n",
       "3          0.000000               0.000032  \n",
       "4          0.000000               0.000038  \n",
       "5          0.000000               0.000044  \n",
       "6          0.000000               0.000049  \n",
       "7          0.000000               0.000052  \n",
       "8          0.000000               0.000055  \n",
       "9          0.000000               0.000061  \n",
       "10         0.000000               0.000066  \n",
       "11         0.000000               0.000071  \n",
       "12         0.000000               0.000074  \n",
       "13         0.000000               0.000077  \n",
       "14         0.000000               0.000080  \n",
       "15         0.000000               0.000083  \n",
       "16         0.000000               0.000087  \n",
       "17         0.000000               0.000090  \n",
       "18         0.000000               0.000093  \n",
       "19         0.000000               0.000097  \n",
       "20         0.000000               0.000099  \n",
       "21         0.000000               0.000102  \n",
       "22         0.000000               0.000108  \n",
       "23         0.000000               0.000113  \n",
       "24         0.000000               0.000117  \n",
       "25         0.000000               0.000121  \n",
       "26         0.000000               0.000124  \n",
       "27         0.000000               0.000127  \n",
       "28         0.000000               0.000130  \n",
       "29         0.000000               0.000133  \n",
       "...             ...                    ...  \n",
       "4970       0.336911               0.037347  \n",
       "4971       0.336911               0.037353  \n",
       "4972       0.336911               0.037359  \n",
       "4973       0.337043               0.037363  \n",
       "4974       0.337190               0.037369  \n",
       "4975       0.337277               0.037376  \n",
       "4976       0.337277               0.037385  \n",
       "4977       0.337277               0.037390  \n",
       "4978       0.337413               0.037394  \n",
       "4979       0.337518               0.037399  \n",
       "4980       0.337596               0.037404  \n",
       "4981       0.337672               0.037409  \n",
       "4982       0.337770               0.037414  \n",
       "4983       0.337770               0.037418  \n",
       "4984       0.337900               0.037422  \n",
       "4985       0.338018               0.037427  \n",
       "4986       0.338139               0.037434  \n",
       "4987       0.338320               0.037442  \n",
       "4988       0.338320               0.037451  \n",
       "4989       0.338486               0.037457  \n",
       "4990       0.338628               0.037463  \n",
       "4991       0.338722               0.037471  \n",
       "4992       0.338722               0.037477  \n",
       "4993       0.338874               0.037483  \n",
       "4994       0.338874               0.037493  \n",
       "4995       0.338874               0.037500  \n",
       "4996       0.338874               0.037508  \n",
       "4997       0.338874               0.037513  \n",
       "4998       0.339002               0.037517  \n",
       "4999       0.339002               0.037522  \n",
       "\n",
       "[5000 rows x 19 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iterationData = pd.read_csv(\"defaultExportDir/iterationData.csv\")\n",
    "display(iterationData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the data is exported to a CSV, you can plot trendlines for each statistic. We will use pandas and matplotlib as a demo below. Please note that for the average accuracy and average generality graphs, the first number of iterations is not as \"accurate\" as the rest of the iterations. The average accuracy moving average only begins moving at iteration 50, and average generality is not tracked until a few hundred iterations in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lkhAiVFGUSkBEIY8vkUjeMFIS4jn082LuXrqARaXK9Jo8ixoOhbOOf2bbNYKuJ1IyOQz7CE/q93LBctAKlH8drLwZe5OfLv3EyeCTNCrfiM+dPse5grPOTgYXtvDvAQYCcx7/Kc/5SyQSnSCE4MqxQ3htXk9mehpt+g/B0aMbBvk4zf4yqLJV+By4y4X999HLSsfm0T807l4by48Wofevk+0pmSn87PczG65uoIRBCSY0mcCH9T/UuRWELtM5t6LeyLVUFCUYmIZa8LcrijIUuA/01tX4EonkzSUlPo5DK3/i7qULVLN1wH3QMJ0v6wiV4K5fJGe2XiUhQVA+wofG1nFUnToaoxo1nmmbpcpiW+A2VvuvJjotmvdqv8eXjb/E3NhcpzE+QZdZPf1e8KN2uhqzsJk9ezZbtmxBX18fPT09Vq1aRdOmTV/Yfvr06ZiZmTFu3LhCjPK/rF+/no4dO1K5snrt8OOPP2bs2LE0aNAg332uW7eOn376CYBr165Rt25d9PX18fDwoHnz5ly7do2JEydqJX6JJCcigu6ye/5sUuJicfvoY5w6d0NPT19n42VmZHPzfBiX9t4iIUGFSUoEjlH/4DBxAKVa/3dT9mzIWeZ4z+Fu/F1cKrqwyGkRTuWdntOz7igelg1FwNmzZ9m3bx+XLl3C2NiYqKgoMjIyijqsPLF+/Xrs7Ow0wr9mzZoC9zl48GAGDx4MqJ0+jx8/jqWlpebn3bt3L/AYEklOCJUK38P7ObHpV0xKm9N7+g9UqqU7SxFVtooAz2DO77pFRqZCqcQH2MecpV43RyyHLkPvKesTgLDkMOZdmMeR+0eoUboGi90W07Z62yKxdZaWDfkkNDQUS0tLjY+NpaWlRkifWByDuoiKm5ub5j4/Pz+aNWtG7dq1Wb16taav1q1b4+joiJ2dHV5eXgCYmf3/qPiOHTsYNGgQAIMGDWLEiBG4urpiY2ODp6cnQ4YMoX79+po2T+7/8ssvNUZrkZGR7Nixg4sXL/Lhhx/i6OhIamoqbm5uPDkgt3XrVuzt7bGzs2PChAnP9DV58mQaNmyIq6sr4eHhef5drV+/ns8///ylYj98+DDNmjWjUaNGvP/++yQlJeV5PMmbR8jNG2ycMJpj61ZR3d6RAXN+0qnoP7gWzW8TT+D1x21Mw2/gErqFLp0NafnnYsqPHPGM6D9MfMjkU5Pp/GdnvIK9GOk4ku1dt9OuRtF5+ec641cUpSrQF2gFVAZSgQBgP3BQCJFzyalCYK73XG7E3NBqn/XK1mOCy4vPlnXs2JGZM2dSp04d2rdvT58+fWjTpk2u/fr7+3Pu3DmSk5NxcnKiS5cubN26lU6dOjF58mSys7NJSUnJtZ/Y2FjOnj3Lnj176N69O6dPn2bNmjU0adIEX19fHB0dSU5OxtnZmUWLFjFz5kxmzJjBsmXLWLZsGfPnz/9PcZeQkBAmTJiAj48PFhYWdOzYkV27dtGjRw+Sk5NxdXVl9uzZjB8/ntWrVzNlypTcf5H5iL1q1arMmjWLo0ePYmpqyty5c1m4cCHffvttvsaTFF9SExM488cW/I4coFQ5S97+/CvqtXTTmaA+Cozl/M7rhD5Io2RKGI4xJ7Ad3h3zLiP/M2ZSRhLrrq5j49WNAPSu05uBtgN1kp75suQo/IqirAOqAPuAuajTL0sAdQAPYLKiKBOFECd1HeirhpmZGT4+Pnh5eXH8+HH69OnDnDlznpm1Po933nkHExMTTExMcHd3x9vbmyZNmjBkyBAyMzPp0aMHjo65p5p169ZNY5VcoUKFZ2yUg4KCcHR0RE9Pjz59+gDQv39/3n333Rz7vHDhAm5ublhZWQHw4YcfcvLkSXr06IGRkZGmGEzjxo05cuRIrjHmN/bg4GCuXbtGixYtAMjIyKBZs2b5Hk9S/FBlZ+N7+ACnt20iIy2Vhh3epmXfATozVIt8mMip364Qcj8No/Q4aoWeoGG3+pT/ZCl6z7FRP3L/CN+d/Y7Y9Fg8rD0Y5zyOCqYvPK9a6OQ2418ghAh4zvUA4E9FUYyAIj9Wm9PMXJfo6+vj5uaGm5sb9vb2bNiwgUGDBmFgYKCpvZuWlvbMPf+eFSiKQuvWrTl58iT79+9n0KBBjB07lo8++uiZtv/u52mr5H/bKL/IjrkgsyBDQ0PN/QWxfIbcY9fX16dDhw5s3bo132NIii9ht29ycMUiYh49xLphI9oMGIpltRq535gPMtKy8Nl/h8tHgjHITKb2o3+wb1cDqx+nYvDUHtYTAqICWOyzmPNh57EtZ8vP7X/G1tJWJ7EVhBzX+F8g+k//PEMIcVu7Ib0eBAYGcuvWLc1rX19fajxO2bK2tsbHxwdAUzTlCbt37yYtLY3o6Gg8PT1p0qQJ9+/fp0KFCnzyySd8/PHHXLp0CYAKFSpw/fp1VCoVf/3110vHqFKpNEVetmzZQsuWLQFeaPvs4uLCiRMniIqKIjs7m61bt+Zp+UrbuLq6cvr0aW7fVr+1kpOTuXnzZqHHIXm1EELg/8/f/D59ApnpaXT/6hvenTRDZ6IfdCWKLd+c4NKRR5QPv0inKn60/W0qlb4e+4zoZ6my+OfBPww9NJR++/txLeYaXzt/zaa3N72Sog+5L/VcAXKyVXDQekSvCUlJSYwaNYq4uDgMDAyoVasWv/zyC6Aujzh06FCmTp36zMYugIODA+7u7kRFRTF16lQqV67Mhg0b+PHHHzE0NMTMzIyNG9VrgnPmzKFr165YWVnh7Oz80hucpqameHt7M2vWLMqXL8+2bdsA9Qbr8OHDMTEx4ezZs5r2lSpVYs6cObi7uyOEoEuXLrzzzjsF+C3lDysrK9avX0+/fv1IT08HYNasWc9UL5O8WaQkxHN41RLuXDxPdbuGdPliPCVL6ybnPTkunXM7b3DjQjSmyWG4pp+m3oSBmLo+m6qdrcpm/739LL+8nJDkEMqXLM8453G8V/s9zIx0X7ilIORoy6woypOP0s8e/7np8Z8fAgghCiUxW9oy5w8zM7NinQ0j3wPFHyEEt86f5ti6VaQmJtL6w8E06twNRU/7CYmqbBV+R+5zfvcdVCpB1ZCTuHa3xmrwQBSDZ+fI3qHefHfuO4ISgrAtZ8snDp/QpmobDPRerQz5fNkyCyHuP765gxDi6RMGExVFuQTIEzkSiUQnxIY+4ti6VQT5XcKqRk3e+2YmVjVq6mSssHvxeP7qS3RUNuWir9Kw7H1sFo7AuHZtTRshBGdDzvJrwK94h3lT1awq89vMp0ONDugpr1dmfF4/nhRFUVoIIU4/ftEceQbglac4z/YlxRdVdjb+/xzixMY16Bno4z7wExw7dUVPX/unb5Ni0zm9NYDb/vEYpcfTMPooDmPeo3TbL55p5xvhy+JLi/EJ96FCyQqMcx5H77q9MTHQbZUuXZFX4R8KrFUU5cmiWhwwRDchSSSSN5V7ly/iuelXYh49pLqdA50/H4eZhfbLECZEp3J+xw1uXY4BVTbWj47RsJUVlT6fh77Z/43UYtNiWe67nO2B2ylnUo6JLhPpXac3hvq6NXrTNXkSfiGED9DwifALIeJ1GpVEInmjECoV5/7cxpk/NlO2clW6jZ1EbZfmWj+IFReRwtltV7l7NQFFlU2VkJPUrZ6BzYrPnjFSe9obPykjiT51+/Bl4y8paajbCl2FRZ6EX1GUCsD3QGUhRGdFURoAzYQQv+o0OolEUux52i+/fks3On46GoPnHIoqCGlJmZz/K5Crp8PQy86kxqMT1LER1FgyHGObZ/cNAqICWHZ5GadDTtOofCOmuE6htkXtF/T8epLXpZ71wDpg8uPXN4FtgBR+iUSSb26eO8WR1cvJSE3FfdCnOHl01eosXwjBnUsReK6/QnoGVA49Q8N62VSbPgrDKs+WPIxLi2Pp5aVsv7md0kal+dr5a/o36P/abdzmhbwKv6UQYruiKJMAhBBZiqJk6zCu14Lw8HC+/PJLzp07h4WFBUZGRowfP56ePXtq2owZM4Y//viDhw8fovevFLRDhw5pjNBu375NlSpVMDExwcHBgdGjR7Nx40aWLFlSqM8kkRQGcWGhXNz3J35HDlKxVh06fToaSy375cdFpPDPSh/CQjIxTQqlqdElas8dRol/pQBnZGew+85ull5aSkJGAv3r9+dzp88xNTR9Qc+vP3kV/mRFUcrx+DCXoiiuwBu9zi+EoEePHgwcOJAtW7YAcP/+ffbs2aNp8+TEbbVq1Thx4gTu7u7P9NGpUyc6deoEgJub23+M0/5toiaRvO7ER4Rx6vdN3Dh9AkXRw8mjG637D9F6VayrJx5w8vebKJkZ1A8/TMN+TSnbb+Ez3yayVdl4PvRk8aXFBCUE4WjlyBTXKdQtqztXz1eFvAr/WNRlE99SFOU0YAX00llUrwHHjh3DyMiI4cOHa67VqFGDUaNGaV57enpia2tLnz592Lp163+EPyc8PT2ZP38++/btY/r06dy7d4+7d+/y4MEDFi1axLlz5zh48CBVqlRh7969GBoa4uPjw9ixY0lKSsLS0pL169dTqVIlrT63RJIfhBD4HTnI8fW/oKevT5N3euHk0ZVSZf/rd1MQ0lMyObHej1v+CVjE3qJpzQisF8xA3/z/p3xTMlM4FHSItQFrCUoIwrq0NcvbLadVlVZFZpP8XKJuwdnl4DYJSmnX4C2vwn8ZaAPUBRQgkFcojz/s++9Jv65dW2bj+vWo+M03L/z51atXadSoUY59bN26lX79+vHOO+/wzTffkJmZiWE+ZzZ37tzh+PHjXLt2jWbNmrFz507mzZtHz5492b9/P126dGHUqFHs3r0bKysrtm3bxuTJk1m7dm2+xpNItEVWRgb7l8zj9oVzWDs2puOno7Qu+NnZKq6fDuXCzuukpgpqhh2n+Ug3yniM0LRRCRW7bu9ihe8KwlPCqVWmFj+2+ZF21dthqPcKpWdG3YLzK8FnPegbQb0uUKqDVofIq/D/KoQYAlwFUBTFFPU3gGJTRrGgfPbZZ5w6dQojIyMuXLhARkYGBw4cYOHChZQqVYqmTZty6NAhjbXxy9K5c2cMDQ2xt7cnOzsbDw8PAOzt7QkKCiIwMJCAgAA6dFC/QbKzs+VsX1KkCCG4cfoEp37fREJkOC1696dpz95at1sIvR3HyfW+REWpKJXwkOYG/jRYOuaZ9MyTwSdZenkpN2Ju0KBcA75v+T1NKjZ5tWb48Y/g2Czw2wp6BuDUH9yngJmV1ofKq/A/UhRlhRBipKIoFqiLsKzWejT5JKeZua6wtbV9xnlz+fLlREVFadblDx06RFxcnMZrPiUlBRMTk3wL/9NWxk9bJD+xMhZCYGtr+4zpmkRSVEQ9COLEb2vVdgvWNvQaNosaDrnXmXipMYKTOP2bP8FBaRhmJNIw7hh2g9pj3nWB5sPlfsJ9ll1ext9Bf1OtVDW+b/k9XW20mzlUYOIfgdcC8N0MQgUtRkOzz8GsvM6GzOsBrqmKosxTFGUl0BiYI4TYmdt9xZm2bdvyzTff8PPPPzNihPrr5NOVs7Zu3cqaNWvo109dcz45OZmaNWuSkpJCyZLaPwRSt25dIiMjOXv2LM2aNSMzM5ObN29ia/tq2sJKiidCpeLy33s5uXkdBsbGuA/8BCcP7ZqqJcenc3pzALf9YzHITOGt0BM4tK9OxU+/R89UnYmTpcpiw9UNrPJfBcDwhsMZ5jDs1VrSiXsIp3+Cy5vUgu/QG1qPBwvd2Ew/TW62zE+XbDoPTAW8AaEoyrtCiD91GdyrjKIo7Nq1iy+//JJ58+ZhZWWlKROYkpLC33//zcqVKzXtTU1NadmyJXv37tVUxdImRkZG7Nixg9GjRxMfH09WVhZjxoyRwi8pNCKC7nJo5U9E3LuDTaMmdBoxRuvWycGBMRxe5kN6WjZVQ0/h1NyCSvOnYGBhoWkTlhzGJK9JXAy/iHs1dyY3nfxKVb8iIQROzofLvwEC7N+HNhMKRfCfkJst87oc7hWP1/11jrRlljwP+R54NUhPSeb0tt/wPbyfEqZmuA8aRr3mrbU6y89IzcJrkz83LsVRMiWcJiX9qTPlc4yq/v8Qlkqo2H17Nz9e+JEskcXkppN5p1bh15N4IYnh6vV+3iEAACAASURBVCWdSxtAlQWOH0Lrr6FMNZ0NmV9b5sE6i0gikbz23PI+w9E1K0hJiFfXve0zgBJm2itCIoTgjk84XpuukJKmUD30JE262VDh4++fWac/8+gMP3j/QFBCEI3KN2JG8xlYm1trLY4CEXFDvaRz5Q/1kk7DftDma7CwLrKQ8urVswH4QggR9/i1Bep6vNKhUyJ5A8lIS+XomhVc9zpOBZta9JwwjYpvadfPJvpREsd/8SE8PBvTpDBaGPjRYNlojKr/v8y3X6Qfq/1XcyL4BDXNazK31Vw8anq8GjYLETfg9GLw3wYGJcB5MDQdDuXeKurI8pzV4/BE9AGEELGKojjldENOKIryJfAx6pPAV4DBQoi0nO+SSCSvAhFBdzm4fCHRDx/g+l4/mvbsrdWTt2nJmVzcHYj/yTAMMlNoEOVJw49aY9Fznmb56GbsTZZfXs6xh8cwNzbnM8fPGGQ7iBIGJbQWR76JDYIT89RZOoYl1WLfahyYlivqyDTkVfj1FEWxEELEAiiKUvYl7n0GRVGqAKOBBkKIVEVRtgN9URvBSSSSVxShUnHl+BGOrf0ZY1Mzek74lppO2rMVUakEPntucvnwAzJV+lQMv4hLsxJUWfKdJlsnMzuTpZeXsv7qekoYlGCU0yj61+9f9HbJQsBDbzi3HK7vA0UPmo+GFmNeKcF/Ql7FewFwVlGUP1Cf3O0FzC7guCaKomQCJYGQAvQlkUh0THZWFn+vWMSN0yeoZutAty8nYlKqtNb6T03M4NCS8zx6mIll5BUaWIZT+4fBlKhXT9MmICqAmWdncj3mOr3q9GJMozGYG+um4HqeyUhRZ+dc2gDhAVCiDDT7DFxHQulX9wBlXvP4NyqK4gM8MZt5VwhxLT8DCiEeKYoyH3gApAKHhRCH89OXRCLRPekpKeyeP4uHV/1p0WcATXu8r7WMnSe2ySfW+5GRpqJB1D+4zhiAiYODpk1SRhIr/Faw5foWypUox0K3hXSooV0Lg5cmPQnO/Qzev0ByBFRyhC4LwaEPGGtvc1tX5Hm5RghxVVGUSKAEgKIo1YUQD152wMcbw+8ANVGXcPxDUZT+Qojf/tVuGDAMoPpTmzmvEvr6+pqTuQB9+/Zl4sSXrz//PGfOvOLp6YmRkRHNmzcHYOXKlZQsWZKPPvropft6wpUrVxgwYAAADx48wNzcHHNzcywtLdm4cSOjR49mx44d+e5f8vogVCoOLl9A8PUAPEZ+iW0b7bm0pCVncnSFN/fvpGOaFE7rMjept2Eq+qXV3ySyVFnsuLmDn/1+JiYthvfrvM8Xjb4o2ll+3AO48Kv60FVKNLzVTp2SWaNZ0cWUD/Ka1dMd9XJPZSACqAFcB/JzOqg9cE8IEfm47z+B5sAzwi+E+AX4BdR5/PkYR+eYmJjg6+tbpDF4enpiZmamEf6n3ULzi729vea5Bg0aRNeuXenV6/9mrFL03wyESsWR1cu4c/E87gM/0Zrox0em4nvwNre8w8jIENSOOEaTwS0o032mJkXTL9KP785+R2BsIM4VnFnebjl2lnZaGT9fhPrD2WUQ8Kc6JbOOB7QaC1VfT+v0vH5f+w5wBW4KIWqiNmc7l88xHwCuiqKUVNT/yu1Qf4gUC/7++2/ef/99zWtPT0+NP8+IESNwdnbG1taWadOmPfd+s6dyoHfs2MGgQYMA2Lt3L02bNsXJyYn27dsTHh5OUFAQK1euZNGiRTg6OuLl5cX06dOZP38+AL6+vri6uuLg4EDPnj2JjY0F1N8wJkyYgIuLC3Xq1MHLyyvPzxcUFISdnfp/wPXr19OjRw86dOiAtbU1y5YtY+HChTg5OeHq6kpMTAygdhb18PCgcePGtGrVihs3tOukKtENxzes5sqxwzTt2Runzt0L3F9qUgZHV/qweeoZrp4KpXSIH630TtB27Xgs3umGoigERAUw/Ohw+h/oT1x6HPPbzGdtp7VFJ/rRd2DHUFjVGm4cUKdkfuEH/ba8tqIPeV/qyRRCRCuKoqcoip4Q4riiKIvzM6AQ4ryiKDuAS0AWasvnX/LT1xO8tt8k6mFSQbr4D5bVzGjVu06ObVJTU3F0/L/x1KRJk3jvvfcYNmwYycnJmJqasm3bNvr27QvA7NmzKVu2LNnZ2bRr1w5/f38cnlrLzImWLVty7tw5FEVhzZo1zJs3jwULFjB8+HDMzMwYN24cAP/884/mno8++oilS5fSpk0bvv32W2bMmMHixep/tqysLLy9vTlw4AAzZszg6NGjL/X7eUJAQACXL18mLS2NWrVqMXfuXC5fvsyXX37Jxo0bGTNmDMOGDWPlypXUrl2b8+fPM3LkSI4dO5av8SS6RwjBuT9/5/Lfe3Fo70GLPgMKbGoWdieOA4u9SUuHKmGnsW9oTOWx72o2b7NV2awNWMsy32WUMS7DaKfR9K3Xl1JGpbTxSC9PchQc+w4ubQJDE2g+Clp9BSZliiYeLZNX4Y9TFMUMOAlsVhQlAkjO76BCiGnA86e8rxEvWurx8PBg79699OrVi/379zNv3jwAtm/fzi+//EJWVhahoaFcu3Ytz8IfHBxMnz59CA0NJSMjg5o1a+bYPj4+nri4ONq0aQPAwIEDn/km8u67ahumxo0bExQUlKcYnoe7uzulSpWiVKlSmJub061bN0C9XOTv709SUhJnzpx5Zuz09PR8jyfRLZkZ6ez/6UfuXDxHvRZtaDt4eIFEPyUhA589gQScCsMoNZbWpQKou2okhk9Zhj9MeMi0s9O4EHaBTtadmN5sOmZGRbRBmhgOJ+aqc/CzM8HlE2g5VuuFUIqavAr/O6gzcL4EPgTMgRm6CuplyW1mXtj07duXZcuWUbZsWZydnSlVqhT37t1j/vz5XLhwAQsLCwYNGkRa2n/PrD39P9nTPx81ahRjx46le/fueHp6Mn369ALF+MTmWV9fn6ysrAL3A2qL6Kfto7OyslCpVJQpU6bI90IkuSNUKg4uW8Adn/O0GTCUxm+/k+/sncyMbPwP3+XC/iCyVQoVIy7i2qkilT/9/zp+liqLNVfWsMp/Fcb6xnzX4jt61OqhzUfKO6mxaluF86vUgu/4gTot06p4lmHM67/qt0IIlRAiSwixQQixBJigy8BeZ9q0acOlS5dYvXq1ZpknISEBU1NTzM3NCQ8P5+DBg8+9t0KFCly/fl1Tr/cJ8fHxVKmiNqTasGGD5nqpUqVITEz8Tz/m5uZYWFho1u83bdqkmf0XJqVLl6ZmzZr88ccfwOMSfH5+hR6HJHdOblnPrfNnaNN/CM5de+Zb9B/djGXz+GOc2/eQMlE3aFfSi64L+lJl+GAURSElM4Xtgdv5YP8HLPddTofqHdjbY2/RiH5SJByeAovs4NQiqNcVPjsP3ZcUW9GHvM/4O/Bfoe/8nGtvFP9e4/fw8GDOnDno6+vTtWtX1q9frxHphg0b4uTkRL169ahWrRotWrR4bp9z5syha9euWFlZ4ezsTFKSeu9i+vTpvP/++1hYWNC2bVvu3bsHQLdu3ejVqxe7d+9m6dKlz/S1YcMGhg8fTkpKCjY2Nqxbl5PZqu7YvHkzI0aMYNasWWRmZtK3b18aNmxYJLFIns/RNcvxO3IQx05daNwlfwKcHJfOyXWXuHsjBZO0GJpygfoT+2HauDEA6dnpbLq2iY1XNxKbHouNuQ1zW83lbZu3tfkouSMEPLqkLm949S8Q2WD3HrT4Aira535/MSA3W+YRwEjABrjz5DJgBpwWQvTXeYRIW2bJ85HvAe0QeiuQLVO+omHHLrQb/OlLz/QzM7Lx2R2I77FHqLIFNcJO4NzLjvID+mqWde4n3Ocrz68IjA2kRZUWfOrwKY5WjoVbCUulgluH1Gv4IZfVPjqNB4HzELDUrsHcq0K+bJmBLcBB4Afg6ZNJiUKIGC3GJ5FIioD0lGQOLl+ImUVZWvYZ8FKiL4Tgrk8YJzYGkJqhj1WkH4510rGZO05TGCUlM4Xfrv/G+qvrMVAMWNp2KW7V3HT0NC9ApYKAnXDmJwi7AqWrQtdF6ll+iSK2fCgichP+TOCREKIfgKIodYG3gfvAG1t9SyIpDmSmpbFnwWziI8J4f8rsl/LRT03K4PCScwQ/yMIsKZTmxlep/8NQStRRJ1pkq7LZfWc3i30WE5seS+uqrZnoMpFqpXRXdOS53NgPJ39Uz/At60KPn9UVr/RfoRKMRUBuwv83MBS4pShKLeAssBnoqiiKixDi5f0JJBJJkZOSEM+fP0wn4t4dOo34gqoN8n5A6tb5ELx+CyAtXVA36jjOH7tj3vn/uf6+Eb7MPDeTW7G3aFS+Ecucl+Fglbe0Za0R6g+HvoEgLyj7FryzHBp+AFqsCvY6k5vwWwghbj3++0BgqxBilKIoRoAPzy7/SCSS1wBVdjYHls4n6sE9un/1DbWauObpvrTkTLzWX+bmlSRKJYbQqmwQDTZOQf/xN4XQpFCW+S5j3919lC9Znh9b/0hH646FWxQlNU49wz+/EkwswGMuNBn6xs/w/01uwv/0zm9b4EcAIUSGoigqnUUlkUh0glCpOLxqKff9L9P+45F5Fv3YsGT2zT1FYrIe1lGnaTmyNebtBwEQnx7Pr1d+ZcuNLaiEioENBjLMYVjhHsJKjVN76VxcCykx4NQfOsyEkmULL4bXiNyE3/+xhfIjoBZwGEBRlOJxblkiecM48dtarp44SvP3P6Rhh7ylUQZfj+TAkkuIjHSaG13E9ufPMKxQQbOOv+DiAhIyEuhm041RTqOoZFaIPvSpsXBuJVxYrRb8up2hzQSo7Jj7vW8wuQn/J8AXgDXQUQiR8vh6A2C+DuN65YmOjqZdO7VbYVhYGPr6+lhZWQHg7e2NkZHRM+1jYmLYvn17ru6ZWVlZWFpaEhenqXRJREQEHTt2fO5YPj4+uLm5vZTRmuTN5OFVf3wO7KZhh864vtc3T/fEhCbx95IL6Kck0rZ+BNZfT0PR1yc4MZjxJ8dzJeoKjco3YlLTSdQrWy/3DrVF7H21H/6lDZCZAnXfloL/EuQo/EKIVGDOc66fAc7oKqjXgXLlymlsCKZPn/6MUdrziImJYeXKlfmyTS5fvrxmrClTpmBpacmYMWM0P5eiL8mNxJgoDiydT5kKFWn1weA85c9H3I9n95xzkJ5Bm/qx1Jw4imxVNuuv/MpKv5UY6BnwQ6sf6FKzS+Hl46clgOcP6gIoQoBDb2j2OVQsQsvm15Acd10URdmrKEo3RVH+szOiKIqNoigzFUUZorvwXk/mzZuHnZ0ddnZ2mtO0EydOJDAwEEdHRyZOnEhCQgJt27alUaNGODg4sG/fvnyNlZWVRZky6pW3o0eP4u7uTvfu3bGxsWHKlCls3LiRJk2a4ODgoDFjCw8P591338XZ2RkXFxfOncuvw7bkdeHgsoWkJSXR7ctJGJfMvT5t5MNEds05B+mptH3rPrUmfMrd+LsMOTSExZcW07xyc3Z030FXm66FI/qqbPBZD0uc1DN9p/4wxh96rpSinw/ystQzFlisKEoM8KQClzXqk7zLhBC7dRphHji+/hci7t/Vap/la9jgPmjYS993/vx5Nm/ezIULF8jKysLFxQU3NzfmzJnD7du3NTP3zMxMdu3aRenSpYmIiKBFixYa3/6C4Ofnx/Xr1zE3N8fa2pqRI0dy4cIFFixYwLJly5g/fz6jR49m/PjxuLq6EhQURNeuXQkICCjw2JJXk0c3rvHwqj+t+w+hvLVNru1vnn7AsU030E9Ppm29MGzGj+bUo1NM8JqAgWLAzOYz6Vm7ZyFEjvrw1c2/4Z+ZEHkdqjeHTrOgSuPCGb+YkttSTxgwHhivKIo1UAm1S+fNp9b7JU9x6tQp3nvvPUxMTADo0aMHXl5emjX6JwghmDhxIqdOnUJPT4+HDx8SFRWlmb3nl6ZNm1KhgtpC1sbGhk6dOgFqm+SzZ88C6m8GgYGBmntiY2NJTU3VxCwpPqSnJHNs3SpMSpXGMQ+budeP3+HY7/cwT3hAy6Yqaowezu47u/n29LfUtqjNkrZLqGJWRfeBCwG3j8I/M9Snbcu+Bb3WQYMeMhdfC7xMzd0gIEhnkRSA/MzMi5qNGzcSHx/PpUuXMDAwoGrVqs+1aX5ZcrNJBvWHzvM2oCXFj8MrlxD54B7dxkzEsESJHNsGHL7FiZ0PsIi/jcfQOpTt0JZ9d/cx/cx0XCu5sth9MSUNc18mKhCqbPUM/+xyuH8aylSHnqvU9goyF19ryI9OLdOqVSv++usvUlNTSUpKYvfu3bRq1eo/9snx8fGUL18eAwMDjhw5wqNHjwotxvbt27N8+XLNa+mVX/wQQuCzfzc3z5+mWa9+1G7aPMf2ZzZe4sSfDykbH8jbw+0o26EtW65vYZLXJBpXaMwi90W6F/2H3rCyFfz+gTprp/M8+NwHGvaVoq9l8jzjl+QNFxcX+vXrR5MmTQB1nV17e7XVa+PGjbG3t6dLly6MHTuWbt26YW9vj4uLC7VrF5474PLlyxkxYgTr1q0jKysLd3f3Zz4IJK83WRkZHFyxiJtnvbBp7ILLO71ybB8SEMblM3FUiPGjw+hmlG7RlA1XNzD/4nyaV27OkrZLMNY3zrGPAhF7X52p47cVSlWC935VL+noS3nSFTnaMj/TUFFMgOpCiMBcG2sZacsseR7yPfBfMtJS2Tn7W0JuXqdl349weadXjo6bacmZ/D7+MFnJqbw/wgYc3+KrE19xPvQ8bau1ZU7rOZgY6GjvJzUOPOfAhTWgKOqKV63GgXERlV0shuTXlvnJzd1QH9gyAmoqiuIIzBRCdNdumBKJJL9kZ2Wx/6d5hN4KpOuYCdRt1irH9ikJGez+7jgpmQa4WYeh7+TBsCPDuBFzg6muU+lVp5dufHZCLsPZFXB9L2Sng9MA9eEr80LYNJYAeV/qmQ64AJ4AQghfRVFyrvYtkUgKDZUqm/1L5nH30gXaDR2Zq+hHP0pk9w+nSU8HZ+U8FcaN4LN/PuN69HUWui3Evbq79oN8eAEOT4aH58GoFDh9CI0GQqVCdu6U5Fn4M4UQ8f86qJG3NSIdIoQo3Ao+kleGvC5Rvil4blyjrpc7YCiOHXNO20xLzmT/nJNkpqpoY3WV2jPG0/PA+zxKesQPrX7QvugnhoPn9+oDWKUqqx0zHfu9sUVQXgXyKvxXFUX5ANBXFKU2MJoitmwoUaIE0dHRlCtXTor/G4YQgujoaErkkp74phDgeZTLB/fi1Lkbzl1zPliVkpDBn9OPkZRuSIty/tT/YQIr/VZyP+E+89vMp5N1J+0FlhACJ+fD5d/USzpNh0PbKWBcSntjSPJFXoV/FDAZSAe2AoeA73QVVF6oWrUqwcHBREZGFmUYkiKiRIkSVK1atajDKHJiQx9xfP0qqta3o/WHObunqLJV7J7+D4mJ4Gp0HofZE9l7dy8r/FbQ/a3udKzRMcf780xiGBz7Dvx+BxRw/ACajwbLWtrpX1Jg8iT8j0/pTn783yuBoaEhNWvKbQbJm0tMSDB/zZ2Bnr4BnT8bi4Fhzrnup3/1JibFGGfjizRcPJGVAWtY4beCJhWbMK3ZtIJ/c06KgNM/qT3xVVngPFSdqWNRo2D9SrROXrN6nIFvUHv0aO4RQshdGYmkCAi9Hchfc2aAotDj66mUtiqfY3u/PQH4X0qhStwlGv/8CQsuL2LTtU10s+nG9ObTMdIvwCnuxDA4MRcubwZVJtj1AreJUO6t/Pcp0Sl5XerZDHwNXAEKXHnrcSGXNYAd6k3iIUKIswXtVyJ5E4gOfsjO2d9ibGpGr8kzsaiUcxpkWkom5/Y9xDw5hI7fduPnoN/YdG0Tvev0ZorrlPzP9JMiwGshXPwVhEqdltl8lBT814C8Cn+kEGKPFsf9CfhbCNHrcf1eHZ8Fl0iKB5H377H9u8noGxrS+9vZmJevmGN7IQRnVp0iS88Y11ZmrE77mzVX1vBu7Xf5puk3+RP97Cy4vAmOfAsZSeD4IbQaC2Vzd/6U5B0hBNdCE6hV3gxjA32t9p1X4Z+mKMoa4B/UG7xPAvvzZQdUFMUcaA0MetxHBpDxsv1IJG8aaUlJ7PpxFgYGBvSZPjdX0Qfw2e7H9UBBlQRfwj0as+bELN6t/S7Tm03Pn+jf84K9X0DMHajRArr9BJaFZzdS3BFC4Bccz8EroRwMCONBTArrBjXBvV7OS3kvS16FfzBQDzDk/0s9Anhp4Qdqovb1X6coSkPAB/hCCJH8dCNFUYYBwwCqV6+ej2EkkuJDRmoKu36cSVJMNH2mz6FMxdzr2obciMT7WCSWCTep9UU9hp4eT60ytZjQZMLLi35KDPw9Cfx/hzI1oO8WdblDmUpdYFQqgc+DWA5cCeVQQBgh8WkY6Cm0qGXJSLe3cKym/RLnefLqURQlUAhRVysDqjeKzwEthBDnFUX5CUgQQkx90T3P8+qRSN4UEqOj+GveTKIeBNFl9HjqNmuZY3uhEpzb4stlr2iM06Lp0teKoakLSM5MZlvXbVQwrfByATzygW0DICkcWn4Jrb4CQ1m7oSBkZavwvhfDgYBQDl0NJzIxHSMDPVrXtqKzXUXa16+AecmCO5IWyKsHOKMoSgMhxLUCRwLBQLAQ4vzj1zuAiVroVyIpdmRmpLNj1hQSY6LpMX4qNk5NcmyflZnN3lmehIQrVIzxo1XvWhyvGcmD8w9Y7Lb45UX/1lHYOQSMzWHoEajSqABP82aTkaXizJ0oDl4J48j1cGKSMzAx1Me9nhUedpVoW688ZsaF40ia11FcAV9FUe6hXuNXAJGfdE4hRJiiKA8VRan72OmzHaCNDxSJpNhxZvtmYkKCeW/yd1g7OOXYNjtbxb5ZxwgJ16dBkhfNFw7hjLjND57TaFyhMW2rt325wS+uhf1fgVV96LcFLKzz/yBvKGmZ2XjdiuLglVCOXA8nMS0LM2MD2tUvT2e7irSpUx4TI+1u3OaFvAq/h5bHHQVsfpzRcxf1HoJEInmK4OsB+OzbhUM7j9xFP1PF3tnHeBSuj23aGVqtHEeIKobJ+yZrSibmeV0/KRIOTYIrf0DtjuqSh9IqOc+kZGThGRjJgSuhHL8RQXJGNuYmhnSyrUhnu4q0qGVJCcPCF/unyVH4FUUpLYRIABJzaveyCCF8gf+sO0kkEjUp8XEcWLaA0uXL07p/zlYMQgj2z/6HR2H62KacotXSL9gTeph5F+ahr6fP/DbzKW1UOm8D3zsJ2z+CjGRoMxFaj5PVr/JAYlomx25EcOBKKCduRpKWqaKcqRHdHavQ2a4izd4qh6H+q1PwMLcZ/xagK+rMG4F6iecJApCJuxKJDji/6w+SYqL5YNYCjEvmfMzlzFpvHobpUy/NmxbLx/Ct31z23NmDS0UXpjWbRvXSecyKu3lILfoW1tB7I1hpJZ+j2BKXksGRa+EcDAjj1K0oMrJVVChtTB/nanjYVcKlZln09V7NrKfchP8zACGENMWRSAqJxOgo/I/+TYNW7lR8K+cc+Vsn7+LrnUjFxGs0nz+AyZdncfDeQT6x/4SRjiMx0Mvjau6xWWonzUoNof+fYFpOC09S/IhKSufw1XAOBoRy9k40WSpBlTImfNSsBp3tK+JUzQK9V1Tsnya3d8VfgNzGl0gKiagHQexZ+D2KotC0Z+8XthNCcHG7P97HIimd/BC3Ua5MvDGPYw+PMdppNJ84fJL3QQMPwskfwe499YEsaZv8DGHxaRy6GsaBK6FcCIpBJcC6XEk+aW1DZ7uK2Fcxf+2s4XMT/tfraSSS1xRVdjYX9/3F2R1bMTIx4d2J01/owSOE4OjiU9wMzKRCXABuQxsyO/13jj08xvgm4+lfv3/eB76xH/4YrJ7pd18GRtI9BeBhTIpG7C89iAOgTgUzPm9bm852FalXsdRrJ/ZPk5vwV1EUZcmLfiiEGK3leCSSN460pCQOLJvPvcsXsWnsQsdhozAtY/HcttnZKo4tPc3NwEys471pP6cPiyO3c+T6Eb5q/BUDGgzI26AZyWqvnQtroJIjDPjrjRf9e1HJHAwI5eCVMK48igfAtnJpxnWsg4ddJWqVLz6ZTbkJfyrqjV2JRKIDQm8Fsu+nuSTFRNP+45E07PDisolCCP5ZeIJbdwQ28edot3goO2OP8dv13+hVpxcDbQfmbdDkKNjaD4IvgOtIaDcNDN+8amZCCG5FJHHwShgHA0K5EaZOXnSsVoZJnevR2a4S1csVzw/D3IQ/WgixoVAikUjeMEJvBbJ9xiRKlilD3xnzqFT7xVk0QgjOb/Lh1h3BW8kXabPsE74PXM7OWztpWaUl37jk0Wkz4gZsfh+SI9SZOw26a/GJXn2EEFwNSeDvgDAOBIRyNzIZRYEmNcrybdcGeNhVpHKZ4m9HkZvwS9dMiUQHxIWHsWfBbEqULk2/7+ZjZlH2hW2zs1Sc+Pks16+mUyneH9c5vRjuPY5LEZf4xP4TRjiOwFAvD7n2sUGw/m1Q9GHwAajSWHsP9AojhOB6aCJ7/UPY7x/Kg5gU9PUUXG3KMrhFTTrZVqB8qTfrG0+Owi+EcC2sQCSSNwWhUnHklyVkpqfTd8bcHEVfla3ir6lHCI81pEbseZrMeJvPrk4lICqAea3n0blm57wNGvcQNnQHVTZ8fOiNsFK+HZHEPv8Q9vqFcCcyGf3Hjpefub9FhwYVKWtagKpjrzmF4wgkkUg0XDl2mAcB/rT/+DMsq1vn2PbipvOExxrikHWeRj9/zKfnviQwJpD5bebToUaHvA2YkQIb34HUOPhoV7EW/eDYFPb6hbLHL4TroQkoCrhYq2f2b9tXeqPF/mmk8EskhcjNc6c4vmE1VerZ4tA+ZwuskKth+JxJoHzKHRyXDuWri1Pxi/RjodvCvIu+SqXO3om5Ax/tLpbumlFJ6Ry4Espu3xB87scC4FS9DN92bUAXh0pUKP1mLePkPPH7dgAAIABJREFUhTwLv6IoLYHaQoh1iqJYAWZCiHu6C00iKT5kZ2Vx6veNXNz3F5Vr16PLF1/nuBmblpzBoWUXMMr4X3v3HRbF9TVw/HuX3jtIFxS7YsGuscQSY0k0iTG9m2qSX7rJm6LppjcTTa/GxGg09t4roKiASlWq9F6W3b3vH7smKAqoIO1+nseH2dnZmXtwOAx37pxbQbeb2nH7tgdILkpmzpA59U/6UsKq/0HEDxB2HwSPbIhQmoXiiirWRZ9m+aE0difkojdIOns58Oz4zkwJ9cHftXWOxmko9Ur8QohXMRZV6wx8j3Emrl+AoY3XNEVpHQoyM1j12Xtkxp8gdOwERtx5PxaWVrV+ZufnWykz2BAWkswDZYuw0Fjw5ZgvGeIzpH4HLc2BlU9C7D8w9EkY89plx9HUKqr0bDmWxfJD6Ww+noVWZ8DPxYaHRgQzJdSXzu3UE8f1Vd8r/qlAHyASQEqZLoRQ32VFqUNWciKLX3segElPPk/nwcPr/EzyvpMcT9QQUBHDzwOOUXW6iiWTl+Bj71O/g8ZtgGUPQWURjJ0Lg2e12CkSdXoDuxJyWX4ojfXRpymp1OFub8WtAwKY0tuHPv7OLfoJ2qZS38SvlVJKIYQEEELYNWKbFKVVyM9MZ+nbr2JpY8stc9/D0aP2CbOllESvP8GupUnYledSfH0Z29J38L9+/6tf0pcSdnwAm18Hrx5w1z/g1a2Borly9AbJgeQ8Vh5OZ82RTHJLtThYm3Ntz3ZMCfVlULAr5s2oxHFLVN/E/4cQYgHgLIR4ALgX+LrxmqUoLVtOykmWvvMaer2eGS+/WWfSL8wqZf2HO8kqsMC56BSBo7U8XPwr4wLHcU/3esxTVFUBfz8E0cugx41w3ectal5cg0FyMKWAlYfTWX0kg9NFldhYmHF1V08mh/owsrMHVuZNO3lJa1KvxC+lfF8IMRYowtjP/4qUckOjtkxRWqjUmKMsmzcHCytrbnxxLm5+F66HL6XkxI5kdvwWi77KQA/dfvrPvZnbYp7HX+/PW8Pfqrsro7IE/rwL4jcayy8MfRI0zf+KWEpJVGohq0wPVqUXVmBprmFUZw8m9fLh6q6e2FqqgYeNob43d58CFqtkryi1O7j2H7b+9A1OXt7c+NLrOLp7XHBbvc7A+ve2kngS7IuzGTVI0O7Bp3g3fB7xBfG8N+I9rMxqvwlMVQUsmgEnd8GUz6DvnQ0cUcOSUnI4tZDVRzJYfTSDlLxyLMwEV4V48Ow1nRnT1QsHazXjV2Or769TB2C9ECIPWAz8KaU83XjNUpSWZ+/Sxexa/DPBfftzzaNPYWN/4fEPBoNkw7xNJJ4yo0PJfoa/OIUDjlnMWnkTqcWp3NfjPsYHjq/9gHlJsPxRY9KfugBCZzRwRA0nq6iCn/eeZNnBNFLzyzE3PUU7a3QI47u1w8lWJfsrqb5dPXOAOUKIXsDNwDYhRKqUckyjtk5RWoj48H3sWvwzXYeP4ppHnkSjqb0/eveCHSScMqNjWQRDPr+X92Lm81f4XwQ6BvLt+G/p365/7Qc89BusetpYd2fqQgi9uQGjaRi5JZWsiz7NqiPp7EnIRQLDQzx4/OoQxnXzwtlWPUXbVC62Ay0LyARygdrvVilKG5F9Kpm18z/EM6gD42bOqjPpp0WlcvhQJd5lx3H+vz5M3XArp8tOc0+Pe5jVZ1btBdd0Wtg0B/Z8Du2Hw9SvwMmvgSO6dHmlWtZFZ7LqcAZ7Eo0PVgW52/HIyI7c0M+PIHc1ILA5qG8f/yPAdMAD+BN4QEoZ05gNU5SW4HRiPH/MfRELKyumPDUbc8var2JzThaw/suDWGq1uNxsz8x9TxPgEMDPE36mt2fv2g9WVQ6L74D4DdD/frjmXTBr+pufZVodG2JOs+xgGjvictAbJO3dbHloRDATe/rQ1btlz1bVGtX3rPEHnpRSHmrMxihKS5IRf5zl77+Jla0dt7z+Hg5u7hfcVhokh1fGsGdVKmZVejoGxPNU/u90dunMt+O/xcGyjuchC1Phz7uNk6dM/gT63d2gsVwsg0GyJzGXvyJTWXc0k1KtHl9nG2ZeFcykXt5083ZUyb4ZqzXxCyEcpZRFwHum12fVj5VS5jVi2xSl2SorKmTZO3OwtLFhytMv1Zr0C7PLWP/eNrKKrHApSmbAFFfu16whyCaIr8d9XXfSP/QbrH4OkDD95yadPCWvVMuSiBQW7U8hKacUB2tzJof6MLWPL/3bu6LRqGTfEtR1xf8bMAnj9IuSsydfl0BwI7VLUZotg0HPtp++oaKkhOmvvFVraeWsE1n8/UEEeoOgF/vo9daNvJj4MYXphSwctxAnK6faDxbxA/zzBAQOg+s+A9em+ZGLTi/k6+2JrD6SiVZvoH97Fx6/uiMTenhjbaEerGpp6pqIZZLpa1BDH1gIYQaEA2lnjqMoLcG+ZX8Qs2MLA6dOrzXpp0WlsWr+YTTaMq4ZWkXRlIncsftpkguTebb/s3Ry6VT7gVIOwNrZEDwKblvSJP35ESfz+XRTHNtOZGNvZc4tA/y5dWCgKojWwtX35u4mKeXVda27SE8AsYDjZexDUa6oU0ej2PPnIjoNHMqwGRd+WColKoM186Mwryhi1BgL/uqWwQ9r38bN2o2vx33NQO+BtR+oIAV+vwXsvYwjd65w0o/PKub9dSdYG52Jm50lz47vzO2DAnGyUePtW4O6+vitAVvAXQjhwn9dPY6A76UeVAjhB0wE3gSeutT9KMqVlJeeyrJ35+Lq68eYmY+ddxuDQRK+KJIDOwqxqShm0FjBo7bfknI0hakdp/Js/2fr7tOvLIFFtxiHbt79Bzi0a4Rozi+toJxPNp5gSUQqtpbmPDW2E/cPD1KlE1qZuv43HwSeBHww9vOfSfxFwOeXcdyPgecwPhGsKM2eXlfFms8/wMzCnBtfev28T+VWlFWxau4GMgus8SyKpfcDQczK+5SiiiIWjl3IYJ/BdR8oKxZWPQNZ0XDrn+BRR3dQA8kr1fLFlnh+3nsSJNwzNIhHRnbAzb6OkhFKi1RXH/8nwCdCiFlSys8a4oBCiElAlpQyQggxspbtZgIzAQICLlzkSlEam8GgZ/Wn75OZEMeUp1/E3tWtxjZVWh1LX1hLQaU1PQnH/aVBPBr7BhW6ChaMXUBPj561H6QkGza9BlG/g6UdTPkcQhr/wfhKnZ5vdyYxf0sCZVodN/bz44kxnfB1bjmVPZWLV9+SDZ8JIXoA3QDraut/uoRjDgWmCCGuNe3LUQjxi5Ty9nOOuRBYCBAWFiYv4TiKctmklGz7+TtO7NvFiNvvJWRAzRmwDAbJurlrydfa0d/tBEm3+/N4+OM4WzmzYOwCurp1re0AcOAb2PYuVBRBv3tg5Gywq/nLpSFpdQb+PpjGF1vjOZlbxpiuXrwwoTMdPdUf4W3BxUy9OBJj4l8NTAB2Ahed+KWUs4HZpv2OBJ45N+krSnNgMOjZ+tM3HFzzD30mTCZs8rQa21SWV7FyzgYyC2zprDnKT6OOs+3Adob4DGHeVfNqH65ZWQx/PwKxK4zlF655B9r1aMSIjL/INsVm8ebqWJJySunu48iP9w5gRKcLVxFVWp/63rG5EQgFDkop7xFCeGGcc1dRWiW9Tsfm777i8Ka19JkwmVF3PlBjm7LiSlbPWU9WsTU9OcBfE5PYnr6b5/s/z61db0UjaqmJn5tgvIGbGw/j3oTBjzb69IjHMot4fWUMu+Jz6eBhx7d3hTG6i6d6wrYNqm/iL5dSGoQQOiGEI8Zibf6Xe3Ap5VZg6+XuR1EaUllhAcs/eIv04zEMuP4mhs24s0ZyNOgNLHtxHYWVNvS2PsKS6zPZnLKLlwe9zPTO02s/QGku/HKDcU7cO/+GoKsaMRooLK/i001xfL8rCQdrC16b3I3bBgVioaYvbLPqm/jDhRDOGKdbjABKgD2N1ipFaSLlxUX89n9PU1pQwMQnnqPLkJpJWUrJts+2UVBlT1/nw8wfHsGhlMM81/+5upN+xmFYOhOK0uHuleA/oJEiMfbj/7L3JJ9tjiO/rIpbBgTw3PjOuNipcshtXX1v7j5iWvxKCLEWcJRSHm68ZinKlZefkcaa+R9RnJvL9Fffxrfz+W/K7vl6FzHHJD4lh/l8/C6OFyYx76p5TAiacOGda8tg92ew/T2wdYVbFjVa0pdSsupIBvPWHudUXhlDO7oxe0JXevjWUR5CaTPqeoCrb23vSSkjG75JinLl5aWn8vsrz2HQ65nw2FMXTPqH/ozkYKQWr+JYvh+9jOxyLfOumsfogNEX3nnMclj3f1B4CrpPg4kfGJN/I9gRl837644TlVpIl3YO/HBPf0Z08lD9+MpZ6rri/6CW9yRQy9muKC1DZkIcf739KhqNhlvnzsPV5/wTmyTtiGPXpgLci09wcPJhTmkLWTR+0YWHa+qrYONrxklTPLvB3aug/bBGiSGnpJI5/8TwT1Q6vs42zLuhFzf088NMVctUzqOuB7hGXamGKEpTSD0WzYr338TS2pobXnwdV5/zVyLJTy1k08/HsasoIm7yUVZU7uaZsGcunPSLMmDZTEjaDgNmwvi3wKxx6twsP5TGayuiKanU8b8xnXhoZDBW5qpipnJh9R3Hf95qVJf4AJeiNAunjkax/P03sXVy4vrnXrlg0s9NyuWvd/Yh9RK6HmBx5TYe6f0Id3W/q+bGBj2Efwcb54BeC9fNhz63NXjbpZRsO5HNl1sT2JeUR58AZ+bd0IsQL/UAllK3+o7qqT7zszVwNRDJJTzApSjNQdqxGP6e9zqOHp5Mm/0aju7nn0I6JymX5e/sRlTp8ex1iJddNvJAzwd4qNdDNTcuSoe/HoCTOyF4JEz8ENw6NGi79QbJmqMZfLk1gej0IrydrHl1cjfuHNxedeso9VbfUT2zqr82De38vVFapCiNLDkqkr/nzcXRw4sb/+8N7F3Of6M1eV8ya789hplOT2CPEzzruoxRfqOY1WdWzZulxZnwzRgoL4DrvoDetzXoA1lSSrbH5fD26liOZRYT7G7HvBt6cX0fXyzN1Xh85eJcaq3VUqDBJ2dRlMYWt283q7/4AFcfP258+U1sHWsOcTToDez+ejeHD1ZgW5GLz8gsnjH7hd4evXln+Ds1k37mUfjzLmPSv3cNeIc2WHsNBsna6Ezmb43naFoRfi42fHpLHyb29FZX+Molq28f/z8YR/EAaDDW7PmjsRqlKI0hdudW1n35MZ5BHbjumf87b9LXVelZNnsNWSW2+JREUz6tjGdLf6efZz/mj5mPjfk5VStjVhgfyLKyh9v/atCkfzStkBeWHuZoWhFB7na8e0NPru/jq27cKpetvlf871db1gEnpZSpjdAeRWkUR7asZ/2Cz/Dr2p0pT7903nr6RVmlbHx/M1kldvSyOMTme7NZkrqSsYFjeWPoG2cnfYMBVj0FEd+DX3+YsQjsG6bQmZSSxQdSeGVFNC62Fnx0cyhTQn3VFb7SYOrbx78NwFSnx9y07CqlzGvEtinKZavSVrLt5++IWr8K/+69mPr8K1hYWdfYLjcpl2Xv7KHKYEFH9vL21dtJTU1nZq+ZPNb7sZrdO7s+Nib9QY/C1a+ARc19XqzC8iqWRqby675TxGeVMDzEnY9v7q0mQ1EaXH27emYCc4EKwIBxJi4JBDde0xTl0kkpOb5nB9t++Y6S3Bz6TZrKVbfdjUZTs5skeU8C674/gdDp6NP3FI86LsFdePD9Nd/Tz6tfzZ1vfQe2vg1dp8D4Ny/7Jm5sRhE/7k5m+aF0yqv0hPo78/5NoUzto67ylcZR366eZ4EeUsqcxmyMojSE0oJ81s7/iOSoSLyCQ7j2safx73b+GbCi/ghn16Z8bCvysBwQy8OOq/Cy8+K3ib/han3OaB8pYccHxqTf62a4/svLSvrhyXl8uOEEuxNysbbQcH1vX24fFKhq6iiNrr6JPwEoa8yGKMrlMhj0xO3bzZYfv6aytJRRdz1A7/GT0JjVvMqXUrL/+z2E7yvDtewkOaNiWGi2gYlBE3km7JmaSR9gzxew+XXocSNM+QzO89dDfZRW6pj7TwyLw1PwdLDihQldmNHfH2dbVTVTuTLqm/hnA7uFEPuAyjMrpZSPN0qrFOUiJUdFsu2X78g5lYybXwA3zJ6DR+D5Rxwb9AY2f7CZ44kavEqPsXfMPtaJQ9zf834e7/N4zf58KWH7+7DlDeh2HUz7GjQXP3Y+o7CcX/ae5Ld9pygor+LhkR2YNbojtpaXOqpaUS5Nfc+4BcBm4AjGPn5FaRbKS4rZsOAz4vbvxsmrHROfeI7Og4YhLpCYqyr1rJmzhpQ8WwLKDvLz+I0kiRzeHPwmUzpMqfmByhJYMQuilxq7dyZ/elFJX0pJ5Kl8vtuVzNqjmRikZGxXLx4a2YG+AS6XGraiXJb6Jn4LKeVTjdoSRblIyYcPsvaLDykvLmbozXcQNnka5hYXLoRWWlDO8pfXk1/lQLBhP++NW0WVOXw16qvz38TNPAp/3AH5yTDmNRj6ZL379Cuq9Kw6nMEPu5M5klaIo7U59w0L4o5Bgfi72l5SvIrSUOqb+NeYRvb8w9ldPWo4p3LFGfR6wlcuY+fvP+Hq48fU51/FK7hjrZ9JP57Dmg/3opXWBNvv5OWeS/Gw8ea7MV/S3qn92Rtry2DbO8Y+fVs3uOufepdTzivV8s2ORH4/kEJeqZaOnva8cX0PpvX1VV06SrNR3zPxFtPX2dXWqeGcyhVXXlzEyo/f5dTRKDr2H8yER/+Hpc2Fr6CllBxZEsHOjflYVZThHnKQ5/1W0q9dGB+P+hgnq3NG0OQnw6/TIec49LkDxswBO7c621Wp0/PtziS+3JJAqVbH2G5e3DW4PYM7uKlJUJRmp74PcKm6PEqTK8zKZMkbL1Ocm834h56gx6ixtW6ftDuBfX9Ek1thj2tpMpn99vCx6wEmBE1gzpA5Zz+JK6Xxoaxt84x18+9YBh3qN8/QjrhsXlkeTVJOKWO6evL8NV1UeWSlWVP1+JUW4XRSAn+99QpSr2f6q2/j0+kCE6AAxVklbHh/CxlFdliXV9DJJpZfx+0mQibyVL+nuKv7XWhEtRu0Jdmw/FGIWwddJsHYufUqp5xWUM5bq2NZdTiD9m62/HjvAEZ0apiyDYrSmFQ9fqXZy0yI48/XX8TK1p4bXp2Dm1/AeberLKsi6o9wDu3OxyDN6aw5SNzUYmaXrMTR0pHPBn/GSP+RZ38oeRf8dT+U58E178LAB+u8gVup0/PFlgQWbEsA4MkxITw0ogPWFqp4mtIyqHr8SrOWdjyWpW+/grW9AzPmzMPBzb3GNtoKHfu/20V0VDk6YYlLaRoyLIEXPTejLdIyLnAczw14Dnebap81GGDnh7DlLXBpD7esA5/edbYns7CCh3+N4OCpAiaH+vDChC74OtvU+TlFaU5UPX6l2Tq2axvrF36OvYsLN73yFg6uNZP+iY0xbPszCa2wwbMkAfdepcxvv4NjFclc638tD4c+XHPUTlE6/P0IJG6BHjfApI/Auu4yCXsScpm1KJJyrZ4vb+vLhJ7eDRSpolxZqh6/0uzodVVs/OZLjm5Zj3dIZyY/NbtG0teWV7Fm7lpS8+1wKM/DNySZBV32klh2Cm8zbxaMWcAQ3yE1d564FZY9BBWFMOlj6Hd3nV070emFfL45njVHMwl2t2PRA4PUzVulRbvi9fiFEP4Y7w14YfxlslBK+cml7EtpfSrLylg7/0PiD+xlwPU3MXT67TVq7cRtjmX74gQqsSbQsJ8/RmznoHkKXa278n7/97k64GrMNeec2voq2Pga7Pkc3DvDbX9Cu/MXbjtjf1Ie87fGs/V4NvZW5jxxdQgzrwrGzkqNx1datlrPYCFER8DrTD3+auuHCiGspJQJl3BMHfC0lDJSCOEARAghNkgpYy5hX0orkhl/gpWfzqMoO4tRd8+k74SzSyjo9Qa2fbCR2ERz7MsL0PgfYHbIJvwdA/i438eMDhh9/jHzybuMk6ZkH4Ow+4yjdqzsz9sGKSWbj2Xx5dYEwk/m42ZnybPjO3P7oECcbC78VLCitCR1Xbp8zNkPbZ1RZHpv8sUeUEqZAWSYlouFELGAL6ASfxuWfPggy+e9jo2TEze/9i6+nc8erlmWV8rKORvIrnTEu+wgfw5dR7R1Jg/0nMmDoQ9iZXbOZCVSQvwm2POZsXvHOdA4S1aXa897/EqdnuWH0vlmRyInTpfg62zDnCndmR7mj42lGq2jtC51JX4vKeWRc1dKKY8IIdpf7sFN++gD7DvPezOBmQABAecfvqe0fAaDnj1LFrF36WLc/QK46ZW3asyFm5ecy99v76LSYI2r+TrmjFpFB7dOfNP/GwZ4D6i50+SdsO5FyIgCBx8Y/TIMehgs7WpsWlRRxS97T/LDrmSyiivp0s6BD6eHMjnUBwuzi6/AqSgtQV2J37mW9y5rDJsQwh74C3hSSll07vtSyoXAQoCwsDB57vtKy1dRWsKazz8gMfIA3UeMYfS9D2JpffZplRqezOqF0Ui9oMTzd+Z3ieDu7vcwq88sLM2q1a+vKofYfyDiRzi5Exz9jBOl9LgRzGvWuc8uruTbnUn8uvckxZU6hoe488H0UIZ1dFclFpRWr67EHy6EeEBK+XX1lUKI+4GISz2oEMICY9L/VUq59FL3o7RcRdlZrPjwLbJPJjP63ofoPW7iWQnXoDcQ/sNuIvaVYqUt5XDgj+zplMNHwz5iTOCY/3akq4T9C2H3Z1By2tilM+4N6H8/WNS8NknJK+PrHYn8EZ5Cpc7AtT29eXhEBzXrldKm1JX4nwSWCSFu479EHwZYAlMv5YDC+NP9LRArpfzwUvahtGyZCXH8PW8uOq2WKU/PpkO/gWe9X5pbwopXN5Cnc8K1LIkV3X8ju6Mlv439jQ7OplIKUsLxNcYunfwkCBoBU7+CoJHnrZcfk17EV9sSWHUkA42A63v78vDIDgR7nP8mr6K0ZrUmfinlaWCIEGIU0MO0epWUcvNlHHMocAdwRAhxyLTuRSnl6svYp9JCJETsZ/Vn72Ft78hNL79Zo/xC0vYTbPkllkppg4v1Wt4fsYYuPr34ffQn/z15mxULa2cbH8By7wy3/wUdx9Q4VnZxJWujM1kZlc6+pDzsLM24b1gQ9w4Nop2T9ZUIV1GapfqWbNgCbGmIA0opdwKqE7WNKTidyfZfvyNu3248AoOY+sKrZz2Updcb2PjWWuLTrLGtLCfT6xcW9Ijjli6380TfJ7A2tzbOhrVpLhz4xjgc85p3of99xmqaJrklxmS/6nAGexNzMUjo4GFnHJI5MBAnWzUkU1HUkyhKo9JWlBP+z1L2L1+C0GgYOv12wqbccNZMWadj0tg8/wB5Okc8y/fyddhSpLcLv436jW5u3YwbndwNfz8MBacg7F4Y+eK/dfLzSrWsMyX7PYm56A2SYHc7HhvVkYm9fOjkZa9u2CpKNSrxK41CGgwc37OD7b/+QHFuNp0HD2fEnfeddZVvMEgOfLOdiPBKzHVgYf4nb4zcwfgO1/Jcf1NRNYPeONH51rfBOQDuXgWBQyip1LH6QAr/HE5nd4Ix2bd3s+WhEcFM7OlDV28HlewV5QJU4lcalJSShIj97F78M9mnkvEIaM/Ex5/Ft0u3s7YrLyhn9Zy1ZJY74VJ2gn96/kGCXwUfDf+U0QGmCVAyj8CqpyFln3Gi80kfkVAo+XlFNH9FpFJcqSPA1ZaZVwUzsac33X0cVbJXlHpQiV9pMCnRh9nx249kxB/HuZ031856hs5DhqPRnP3ka3rkSdZ+dYhy4YCNbjXvj1zL4KARzBvwAv4O/lCWBxtfhcifwdoJw/UL2GI5kh9+PsqOuBwszAQTe3pzx+BA+ga4qGSvKBdJJX7lsuWcSmbn4l9ICN+Lg5sHY2fOovuIqzEzP/v00un0HPp+OwcOaLGoqiLTfT6b+2QyZ+DbTAqehJASDi0yFlMry6Ei7EF+t7mFb9fnkZIXgZejFU+P7cSMAQF4OFidvzGKotRJJX7lkhWczmT3n78Su3MrltY2DJl+G2GTp2FheXZS1usMRP26m0O7cinXOOBYlszS7t9j0SOAX676lSCnIMhNMJZLTt1PuXsPFnq9yfy9dlTq0hgQ5MrsCV0Z281LlVFQlAagEr9y0Ypystn/9x8c2bwBjUZD2KSpDLjuRmwcHM/arqpSR9Sve4jaW0CFxlg3v9hzOV/338+dve9lVp9ZWGjLYO1s5L4F6Mys+crxaT5M7YP1aQum9fXlzsGBdPV2vEBLFEW5FCrxK/VWlJNF+D/LOLxxDVJCz9HjGDhteo1JUkpzS4n8aRfHY7VUamxxLMsi120b34ZF0tGrG5/0/oIR/iPg2GoMK2YhynJZphnD2yVTsbXy4aWJgdzUz1+NuVeURqISv1IrKSWnjkZxaN1KEsL3g4AeI8cwaNoMHD08z9r2dEwaB36J4FSuDVKY41yWQIbbVr7tH00379583Gs+w32HQ8EpCr69AeeUjZyQATxb9STuIQOZN6Q9I0I80GjUzVpFaUwq8SvnVZh1mtidW4nZvpn8jDRsHBzpf90NhI6dgKP7fwlfSknyzjgilx4ls9wZM50ZzhX72OG/md2DshnqN5yve/5IX8++FGalEPnTC3RJ+hELaeBDeSsFoffz6YguBLnXLJmsKErjUIlf+VdFSQkn9u4kZsdm0o4Z58Xx7dKdQdNuptOgYZhb/lfeuEqrI/qPfRzemUUxTlhoNVjrN7C860by2lsxLWQaL3a6CV8bb2L3rOTgL6/Ts2g7/YSe/ZYDyRr6Gg8OGqCmMVSUJqB+6tq4yrIyEiP3c2LvTpIOhqPX6XD18WPYjDvpMnQETp5eZ21fklNCxPc7OBGnR6uxxbaslCqrNSwJ3Y9HcCfu7/Yy4wLHkZdSHZN1AAAO3klEQVQUS8bi97BKW0l3mUsRdoR73Yjn6EcZ0CW0iaJVFAVU4m+TyouLiD+wl/gDezh55BD6qirsXFwJHTeRbsNH4RnU4ayHovQ6AwkbjhC7KZ60IgeksMCxNJ5Et01sCktiWMermdfla9rrXEjc9hupi+bQURePp9QQZR1Gcs+X6HX1DAbbqO4cRWkOVOJvI/Iz0kiMPEBCxH7SjkVj0Otx9PAkdOy1dBo4FJ9OXRDV6thrK3QkbjhM3I4k0gts0WmssNCaY63dzc72W0kZY860kBv4zq4P5eEbcdo1C4+qWDyAOLOO7Or4DMGj7qKfr5o2U1GaG5X4Wym9TkfasRgSI/eTGHmA/Iw0ANwD2hM2eRqdBg6tcWVflFlE/JpDJB7MIrvCEYPGHIsqCywrD5LqdJCd/RPp0WkIkxzuxjs2DveV3xGiOwFAvCaInQEP4z/sFkI6hRLSJFErilIfKvG3ImVFhSQfiiAh8gDJhyLQlpdhZm6Of49Q+kyYTHCf/mf12WsrdJzaHkPS7kTSMiSlwjj9oHWFAXPdDuLdjxDeN50eHQfTU9+XMcnutN+1lUDDTwAcN+/E7qDH8B08nY6dQunYJFErinKxVOJvwaSU5KaeIiF8H4mRB8iIO46UBuycXeg0aBjB/foT2LP3vxOYGwyStMhkkrbEkJZYRm6VI1JjjkZvjU1ZAlWWmzjmdYy0UA093UMJyQ9maip02b4EZ0qokmbEWvVgT+DNBA67mc6B6rpeUVoilfhbGINeT2bCCU4dieL43p3knEoGwCu4IwOn3UyHfgPwCuqA0GiQUpJ/MpeTW/eTciSLjCJbdGY2gDW2ZTloZCTJrseJ6XQab99AgsssuDbbmr4nTuB3fB8A2ThzwnEIdBpPx8HX0cvNo+mCVxSlQajE38wZDHpyTp0kJfowp45GkRp7FG15OQDeIZ0Zfe9DhPQfjL2rG9IgyYpNI/yrTaTH5ZNVYo3WzDiZuIXWArPKKLIcY4kOSMY8wJ6uZZLR+Vm8lp2MdfYRADJxI8OuG2n+d+PZazyBnfvgoQqjKUqrohJ/M6PX6chKSiAl5ggpMUdIPx6LtrwMABdvH7oOG0lAj1D8uvXE2t6RtP3xRH2/h4zkUnIrHNCZWQNmWFZqEFWxlNgkkOiVRLFfOT2qKhlTks4LlSVYxEGRtCXeohO73G9F4x9GQM+hBAd1pJ2qb68orZpK/E1Mr9NxOjGelJgjpMYcIe1YDFWVFQC4+QXQddgIfDt3w7drD6ztXMg4dJK0/YlE/7Ge7GJrqszsAFusy4sQhgjy7BJI8U5FehTSt7KAa8sL6KLVIjPMSDALJttxDLs69MO102A6dAmlr7Vl7Q1UFKXVEVLKpm5DncLCwmR4eHhTN6NB6LRaTifGkxp79N8r+uqJ3r97T/y79aRdx66UJBWQFpnE6cRCcgoEpTiCMHa7WJdnY9AnkeYUTYbvSdzscxhemU/vikq89XpOCl/S7bpR1a4PDh0GEtx9EE6O9k0ZuqIoV5gQIkJKGXbuenXF38iK83LIiDtO+oljpJ+IJSsxHr1OB4C7fyDdR47Br2sPbM1cyD2cTnpsFnt3n6JEU4bUmAMaLLTmWFYkozE7yWmHU5R4JOPhnEPfqgpmlFdQrnUmtbwLZR5TyAgagHn3oQR6ehHYtKEritJMqcTfQKoqK8hLTyMvLYW8tBSykhM5nRhPaUE+AGYWFngFh9D7msm4uPlhyBFkx+eTsU9L3J5C9GZawAyN3hUL7UkMmqMU2J1E5xyPs3Mm3kJLQCUYqtpRYBuCzmkitv69yO/SH38/fzxVv7yiKPWkEv9FKi8uIteU3PPSUshNSyUvLYWi7Kx/txFCg6uvH/7dQ7GzckWWmlN0Wk9RphnH013QmRsnGBEGV6wq0pAigjLbZAwu8bi5JNNFX4mD1oUKy2BKnAYjvHrgGBhKuw7d8HSyVZOLK4pyWZok8QshrgE+AcyAb6SU7zRFOy7EoNdTnJtDfnrqv4k9Ny2FvPRUyosK/93O3NIKF28fPHyD8PHtirZUoC3WUFlqgzbfleQSt3/75IWhCquKdOAQBpGCwSEZZ9d43MwtcRa+CIdOaDynYR/QC5+OvXFzdW2i6BVFae2ueOIXQpgBXwBjgVTggBBihZQypjGOp9fpqCwrpaKkhMqyEipLSqgoLam2rtT4uqSE0sICinKyKMnNRUrDv/uwsrPH3tENR0dv7Kz90FdYIqucMOBFab47xUX/TS4uDFVY6nKR+lTM5AGkdSpm9ulYuxbjZOODq0MXrLwG4uR/L+2Cu+Pk4NAYYSuKolxQU1zxDwDipZSJAEKI34HrgAZP/Ju++5JD61bVuo3QaBAac4TQIDBHgxWWZn5ohDNC44m0CMBg7kRpxX/dKxoqsZC5CEMuZhwDcjDYFYBjBWZeTni4dsTTrStO3iNw9w3Cw8VVdc8oitJsNEXi9wVSqr1OBQY2xoHitsdgZdELKWwRGjsMZnYIjS0IK4SwAmGNEOd8C6Qei6oizHT5CFmAQReJFMVI63I0DnqsvKxwbt+BAN+eePhchZunD1YW6laJoigtR7PNWEKImcBMgICAS6vpbuPsgD7fFdCCpghEHggdwtyAMJMISzC3NcPa2Q4HT3fc2wfRrkMIbu4+WFlZN2A0iqIozUdTJP40wL/aaz/TurNIKRcCC8H4ANelHOiuj9+6lI8piqK0ak1RfesAECKECBJCWAIzgBVN0A5FUZQ26Ypf8UspdUKIx4B1GIdzfieljL7S7VAURWmrmqSPX0q5GljdFMdWFEVp61ShdUVRlDZGJX5FUZQ2RiV+RVGUNkYlfkVRlDZGJX5FUZQ2pkXMwCWEyAZOXuLH3YGcBmxOS6BibhtUzG3D5cQcKKX0OHdli0j8l0MIEX6+qcdaMxVz26BibhsaI2bV1aMoitLGqMSvKIrSxrSFxL+wqRvQBFTMbYOKuW1o8JhbfR+/oiiKcra2cMWvKIqiVNOqE78Q4hohxHEhRLwQ4oWmbs/lEEJ8J4TIEkIcrbbOVQixQQgRZ/rqYlovhBCfmuI+LIToW+0zd5m2jxNC3NUUsdSHEMJfCLFFCBEjhIgWQjxhWt+aY7YWQuwXQkSZYp5jWh8khNhnim2xqZw5Qggr0+t40/vtq+1rtmn9cSHE+KaJqP6EEGZCiINCiJWm1606ZiFEshDiiBDikBAi3LTuyp3bUspW+Q9jyecEIBiwBKKAbk3drsuI5yqgL3C02rp5wAum5ReAd03L1wJrAAEMAvaZ1rsCiaavLqZll6aO7QLxegN9TcsOwAmgWyuPWQD2pmULYJ8plj+AGab1XwEPm5YfAb4yLc8AFpuWu5nOdysgyPRzYNbU8dUR+1PAb8BK0+tWHTOQDLifs+6Kndut+Yr/30ndpZRa4Myk7i2SlHI7kHfO6uuAH03LPwLXV1v/kzTaCzgLIbyB8cAGKWWelDIf2ABc0/itv3hSygwpZaRpuRiIxThfc2uOWUopS0wvLUz/JDAaWGJaf27MZ74XS4CrhRDCtP53KWWllDIJiMf489AsCSH8gInAN6bXglYe8wVcsXO7NSf+803q7ttEbWksXlLKDNNyJuBlWr5Q7C3ye2L6c74PxivgVh2zqcvjEJCF8Qc5ASiQUupMm1Rv/7+xmd4vBNxoYTEDHwPPAQbTazdaf8wSWC+EiBDG+cXhCp7bzXaydeXiSCmlEKLVDdESQtgDfwFPSimLjBd3Rq0xZimlHugthHAGlgFdmrhJjUoIMQnIklJGCCFGNnV7rqBhUso0IYQnsEEIcaz6m419brfmK/56Terewp02/cmH6WuWaf2FYm9R3xMhhAXGpP+rlHKpaXWrjvkMKWUBsAUYjPFP+zMXadXb/29spvedgFxaVsxDgSlCiGSM3bGjgU9o3TEjpUwzfc3C+At+AFfw3G7Nib8tTOq+AjhzJ/8uYHm19XeaRgMMAgpNf0KuA8YJIVxMIwbGmdY1O6Z+22+BWCnlh9Xeas0xe5iu9BFC2ABjMd7b2ALcaNrs3JjPfC9uBDZL412/FcAM0wiYICAE2H9lorg4UsrZUko/KWV7jD+jm6WUt9GKYxZC2AkhHM4sYzwnj3Ilz+2mvrvdmP8w3g0/gbGf9KWmbs9lxrIIyACqMPbl3Yexb3MTEAdsBFxN2wrgC1PcR4Cwavu5F+ONr3jgnqaOq5Z4h2HsBz0MHDL9u7aVx9wLOGiK+Sjwiml9MMYkFg/8CViZ1lubXseb3g+utq+XTN+L48CEpo6tnvGP5L9RPa02ZlNsUaZ/0Wdy05U8t9WTu4qiKG1Ma+7qURRFUc5DJX5FUZQ2RiV+RVGUNkYlfkVRlDZGJX5FUZQ2RiV+pU0RQpSYvrYXQtzawPt+8ZzXuxty/4rSUFTiV9qq9sBFJf5qT5JeyFmJX0o55CLbpChXhEr8Slv1DjDcVA/9f6biaO8JIQ6Yap4/CCCEGCmE2CGEWAHEmNb9bSquFX2mwJYQ4h3AxrS/X03rzvx1IUz7PmqqwX5ztX1vFUIsEUIcE0L8KqoXI1KURqKKtClt1QvAM1LKSQCmBF4opewvhLACdgkh1pu27Qv0kMZyvwD3SinzTGUVDggh/pJSviCEeExK2fs8x5oG9AZCAXfTZ7ab3usDdAfSgV0Ya9fsbPhwFeU/6opfUYzGYayHcghj+Wc3jPVeAPZXS/oAjwshooC9GItkhVC7YcAiKaVeSnka2Ab0r7bvVCmlAWNZivYNEo2i1EJd8SuKkQBmSSnPKnJlKhVces7rMcBgKWWZEGIrxvoxl6qy2rIe9TOpXAHqil9pq4oxTul4xjrgYVMpaIQQnUyVE8/lBOSbkn4XjFPhnVF15vPn2AHcbLqP4IFxGs1mWTlSaRvU1YXSVh0G9KYumx8w1oBvD0SabrBm89/Ud9WtBR4SQsRirAK5t9p7C4HDQohIaSwtfMYyjHX1ozBWHH1OSplp+sWhKFecqs6pKIrSxqiuHkVRlDZGJX5FUZQ2RiV+RVGUNkYlfkVRlDZGJX5FUZQ2RiV+RVGUNkYlfkVRlDZGJX5FUZQ25v8BbZX2Q8TUJ1IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def cumulativeFreq(freq):\n",
    "    a = []\n",
    "    c = []\n",
    "    for i in freq:\n",
    "        a.append(i+sum(c))\n",
    "        c.append(i)\n",
    "    return np.array(a)\n",
    "\n",
    "def movingAvg(a,threshold=300):\n",
    "    weights = np.repeat(1.0,threshold)/threshold\n",
    "    conv = np.convolve(a,weights,'valid')\n",
    "    return np.append(conv,np.full(threshold-1,conv[conv.size-1]),)\n",
    "\n",
    "dataTracking = pd.read_csv(\"defaultExportDir/iterationData.csv\")\n",
    "\n",
    "iterations = dataTracking[\"Iteration\"].values\n",
    "accuracy = dataTracking['Accuracy (approx)'].values\n",
    "generality = dataTracking['Average Population Generality'].values\n",
    "macroPop = dataTracking[\"Macropopulation Size\"].values\n",
    "microPop = dataTracking[\"Micropopulation Size\"].values\n",
    "mSize = dataTracking[\"Match Set Size\"].values\n",
    "aSize = dataTracking[\"Action Set Size\"].values\n",
    "experience = dataTracking[\"Average Iteration Age of Action Set Classifiers\"].values\n",
    "subsumption = dataTracking[\"# Classifiers Subsumed in Iteration\"].values\n",
    "crossover = dataTracking[\"# Crossover Operations Performed in Iteration\"].values\n",
    "mutation = dataTracking[\"# Mutation Operations Performed in Iteration\"].values\n",
    "covering = dataTracking[\"# Covering Operations Performed in Iteration\"].values\n",
    "deletion = dataTracking[\"# Deletion Operations Performed in Iteration\"].values\n",
    "\n",
    "gTime = dataTracking[\"Total Global Time\"].values\n",
    "mTime = dataTracking[\"Total Matching Time\"].values\n",
    "delTime = dataTracking[\"Total Deletion Time\"].values\n",
    "subTime = dataTracking[\"Total Subsumption Time\"].values\n",
    "selTime = dataTracking[\"Total GA Time\"].values\n",
    "evalTime = dataTracking[\"Total Evaluation Time\"].values\n",
    "\n",
    "plt.plot(iterations,accuracy,label=\"approx accuracy\")\n",
    "plt.plot(iterations,generality,label=\"avg generality\")\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('accuracy/generality')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(iterations,macroPop,label=\"macroPop Size\")\n",
    "plt.plot(iterations,microPop,label=\"microPop Size\")\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Macro/MicroPop Size')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(iterations,mSize,label=\"[M] size\")\n",
    "plt.plot(iterations,movingAvg(mSize),label=\"[M] size movingAvg\")\n",
    "plt.plot(iterations,aSize,label=\"[A] size\")\n",
    "plt.plot(iterations,movingAvg(aSize),label=\"[A] size movingAvg\")\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('[M]/[A] size per iteration')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(iterations,experience)\n",
    "plt.ylabel('Average [A] Classifier Age')\n",
    "plt.xlabel('Iteration')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(iterations,cumulativeFreq(subsumption),label=\"Subsumption Count\")\n",
    "plt.plot(iterations,cumulativeFreq(crossover),label=\"Crossover Count\")\n",
    "plt.plot(iterations,cumulativeFreq(mutation),label=\"Mutation Count\")\n",
    "plt.plot(iterations,cumulativeFreq(deletion),label=\"Deletion Count\")\n",
    "plt.plot(iterations,cumulativeFreq(covering),label=\"Covering Count\")\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Cumulative Operations Count Over Iterations')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(iterations,mTime,label=\"Matching Time\")\n",
    "plt.plot(iterations,delTime+mTime,label=\"Deletion Time\")\n",
    "plt.plot(iterations,subTime+delTime+mTime,label=\"Subsumption Time\")\n",
    "plt.plot(iterations,selTime+subTime+delTime+mTime,label=\"GA Time\")\n",
    "plt.plot(iterations,evalTime+selTime+subTime+delTime+mTime,label=\"Evaluation Time\")\n",
    "plt.plot(iterations,gTime,label=\"Total Time\")\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Cumulative Time (Stacked)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting and Accessing Rule Population Data\n",
    "Post-training, you also have the option to export the final rule population. This can be done be calling one of the following methods:\n",
    "\n",
    "| Method Name | Description |\n",
    "| :--- | :--- |\n",
    "| model.export_final_rule_population(filename,headerNames,classLabel) | Exports the entire final rule population with traditional rule representation|\n",
    "| model.export_final_rule_population_DCAL(filename,headerNames,classLabel) | Exports the entire final rule population with DCAL rule representation|\n",
    "\n",
    "The headerNames and classLabel params are optional. If they are not provided, default header and class names will be populated. But they make the exported csv more descriptive.\n",
    "\n",
    "The filename param is also optional. By default, the program saves a CSV file named **populationData.csv** into your local directory. However, you can set the filename param as an absolute path with the format **/filepath/filename.csv** which will save the data into any specified directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>A_0</th>\n",
       "      <th>A_1</th>\n",
       "      <th>A_2</th>\n",
       "      <th>R_0</th>\n",
       "      <th>R_1</th>\n",
       "      <th>R_2</th>\n",
       "      <th>R_3</th>\n",
       "      <th>R_4</th>\n",
       "      <th>R_5</th>\n",
       "      <th>R_6</th>\n",
       "      <th>...</th>\n",
       "      <th>Prediction Error</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Numerosity</th>\n",
       "      <th>Avg Action Set Size</th>\n",
       "      <th>TimeStamp GA</th>\n",
       "      <th>Iteration Initialized</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>Deletion Probability</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Match Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2</td>\n",
       "      <td>26.799250</td>\n",
       "      <td>4984</td>\n",
       "      <td>1</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000912</td>\n",
       "      <td>18</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>344.601095</td>\n",
       "      <td>2.057864e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>17.255268</td>\n",
       "      <td>4840</td>\n",
       "      <td>2</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>18</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>376.339606</td>\n",
       "      <td>1.324648e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>30.584710</td>\n",
       "      <td>3827</td>\n",
       "      <td>7</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>14</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>414.577197</td>\n",
       "      <td>8.165315e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>18.900757</td>\n",
       "      <td>4506</td>\n",
       "      <td>9</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>177.342172</td>\n",
       "      <td>5.700854e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>17.063032</td>\n",
       "      <td>4918</td>\n",
       "      <td>12</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.004933</td>\n",
       "      <td>38</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>200.827808</td>\n",
       "      <td>3.061123e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>22.304588</td>\n",
       "      <td>3701</td>\n",
       "      <td>14</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000380</td>\n",
       "      <td>19</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>210.145312</td>\n",
       "      <td>2.440065e-08</td>\n",
       "      <td>2</td>\n",
       "      <td>20.624682</td>\n",
       "      <td>4964</td>\n",
       "      <td>17</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000702</td>\n",
       "      <td>47</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>18.267373</td>\n",
       "      <td>4390</td>\n",
       "      <td>20</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>31</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7</td>\n",
       "      <td>30.349350</td>\n",
       "      <td>4928</td>\n",
       "      <td>33</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.003894</td>\n",
       "      <td>109</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>416.795366</td>\n",
       "      <td>7.950338e-10</td>\n",
       "      <td>2</td>\n",
       "      <td>21.125244</td>\n",
       "      <td>4703</td>\n",
       "      <td>36</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.007569</td>\n",
       "      <td>65</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>430.579314</td>\n",
       "      <td>6.756693e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>21.912203</td>\n",
       "      <td>4979</td>\n",
       "      <td>40</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>28</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>313.333333</td>\n",
       "      <td>3.311060e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2089</td>\n",
       "      <td>41</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>171.337319</td>\n",
       "      <td>6.772362e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>18.954739</td>\n",
       "      <td>4861</td>\n",
       "      <td>51</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>96</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>415.779676</td>\n",
       "      <td>8.047921e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>14.426694</td>\n",
       "      <td>4943</td>\n",
       "      <td>52</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>31</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>331.890671</td>\n",
       "      <td>2.483275e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>22.182422</td>\n",
       "      <td>4753</td>\n",
       "      <td>57</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.004363</td>\n",
       "      <td>22</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>125.399518</td>\n",
       "      <td>3.224933e-07</td>\n",
       "      <td>1</td>\n",
       "      <td>14.628833</td>\n",
       "      <td>4705</td>\n",
       "      <td>66</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>39</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>388.359389</td>\n",
       "      <td>1.131960e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>25.842298</td>\n",
       "      <td>4853</td>\n",
       "      <td>70</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>16</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>416.177323</td>\n",
       "      <td>8.009547e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>26.210666</td>\n",
       "      <td>4172</td>\n",
       "      <td>81</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>410.777296</td>\n",
       "      <td>8.550034e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>26.754301</td>\n",
       "      <td>4850</td>\n",
       "      <td>105</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>16</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>23.318400</td>\n",
       "      <td>4498</td>\n",
       "      <td>122</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>16.641343</td>\n",
       "      <td>4990</td>\n",
       "      <td>127</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>25</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>393.663135</td>\n",
       "      <td>1.057734e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>16.283290</td>\n",
       "      <td>4711</td>\n",
       "      <td>181</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>422.271453</td>\n",
       "      <td>7.448029e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>13.496705</td>\n",
       "      <td>4939</td>\n",
       "      <td>191</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.002556</td>\n",
       "      <td>92</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3</td>\n",
       "      <td>16.435600</td>\n",
       "      <td>4652</td>\n",
       "      <td>214</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000839</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3</td>\n",
       "      <td>21.390802</td>\n",
       "      <td>3991</td>\n",
       "      <td>246</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>51</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>142.306583</td>\n",
       "      <td>1.713464e-07</td>\n",
       "      <td>1</td>\n",
       "      <td>21.859878</td>\n",
       "      <td>3574</td>\n",
       "      <td>314</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000372</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>233.156886</td>\n",
       "      <td>1.451305e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>25.422952</td>\n",
       "      <td>4776</td>\n",
       "      <td>321</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>25</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>377.074965</td>\n",
       "      <td>1.311781e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>22.945948</td>\n",
       "      <td>4953</td>\n",
       "      <td>377</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>27</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3</td>\n",
       "      <td>24.303443</td>\n",
       "      <td>4946</td>\n",
       "      <td>493</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001241</td>\n",
       "      <td>26</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>332.800000</td>\n",
       "      <td>2.449534e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>26.817600</td>\n",
       "      <td>4813</td>\n",
       "      <td>504</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>8</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.024000e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>4975</td>\n",
       "      <td>4936</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>4953</td>\n",
       "      <td>4942</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>4974</td>\n",
       "      <td>4942</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>11.250000</td>\n",
       "      <td>4943</td>\n",
       "      <td>4943</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.024000e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>4981</td>\n",
       "      <td>4944</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>303.333333</td>\n",
       "      <td>3.894030e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>4990</td>\n",
       "      <td>4946</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.333333</td>\n",
       "      <td>4987</td>\n",
       "      <td>4947</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>22.166312</td>\n",
       "      <td>4947</td>\n",
       "      <td>4947</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>210.097305</td>\n",
       "      <td>2.442854e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>25.269023</td>\n",
       "      <td>4967</td>\n",
       "      <td>4967</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000430</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>210.097305</td>\n",
       "      <td>2.442854e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>23.109259</td>\n",
       "      <td>4967</td>\n",
       "      <td>4967</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>18.645345</td>\n",
       "      <td>4970</td>\n",
       "      <td>4970</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>21.980018</td>\n",
       "      <td>4970</td>\n",
       "      <td>4970</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>4973</td>\n",
       "      <td>4973</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.333333</td>\n",
       "      <td>4974</td>\n",
       "      <td>4974</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4974</td>\n",
       "      <td>4974</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4984</td>\n",
       "      <td>4975</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60.129542</td>\n",
       "      <td>1.272215e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>17.265691</td>\n",
       "      <td>4978</td>\n",
       "      <td>4978</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4978</td>\n",
       "      <td>4978</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>4979</td>\n",
       "      <td>4979</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>22.143493</td>\n",
       "      <td>4984</td>\n",
       "      <td>4984</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>28.494728</td>\n",
       "      <td>4984</td>\n",
       "      <td>4984</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>4987</td>\n",
       "      <td>4985</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>277.777778</td>\n",
       "      <td>6.046618e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>4990</td>\n",
       "      <td>4985</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4987</td>\n",
       "      <td>4987</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4998</td>\n",
       "      <td>4987</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4989</td>\n",
       "      <td>4989</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>16.184000</td>\n",
       "      <td>4989</td>\n",
       "      <td>4989</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>9.536743e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>4990</td>\n",
       "      <td>4990</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>9.536743e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>16.641343</td>\n",
       "      <td>4990</td>\n",
       "      <td>4990</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.274795</td>\n",
       "      <td>4993</td>\n",
       "      <td>4993</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>775 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     A_0  A_1  A_2  R_0  R_1  R_2  R_3  R_4  R_5  R_6  ... Prediction Error  \\\n",
       "0    1.0  0.0  0.0    #    #  0.0    #  1.0    #  1.0  ...         0.000000   \n",
       "1    0.0    #  1.0    #    #  1.0  0.0  1.0    #  1.0  ...       344.601095   \n",
       "2    0.0  0.0    #  1.0    #  1.0    #  0.0  1.0    #  ...       376.339606   \n",
       "3      #  1.0  0.0    #  1.0  1.0  1.0    #  0.0  0.0  ...       414.577197   \n",
       "4    0.0  0.0    #  0.0    #  0.0  1.0  1.0    #    #  ...       177.342172   \n",
       "5    0.0    #    #    #  1.0  1.0    #  0.0  0.0  0.0  ...       200.827808   \n",
       "6      #  0.0    #  1.0    #  1.0    #  1.0  1.0    #  ...       210.145312   \n",
       "7    1.0  0.0  1.0    #    #    #  1.0  0.0  1.0    #  ...         0.000000   \n",
       "8      #  1.0  0.0    #    #  0.0  0.0    #    #  0.0  ...         0.000000   \n",
       "9    1.0  1.0    #    #    #    #  1.0    #  0.0    #  ...       416.795366   \n",
       "10   1.0    #  1.0  1.0    #    #    #  0.0  0.0  1.0  ...       430.579314   \n",
       "11     #  1.0  1.0  0.0  0.0  1.0  1.0    #  0.0  0.0  ...       313.333333   \n",
       "12     #  1.0    #  1.0    #    #  0.0  1.0    #  0.0  ...       171.337319   \n",
       "13   0.0  0.0    #  0.0    #  0.0  1.0    #    #  0.0  ...       415.779676   \n",
       "14     #    #  1.0  1.0    #  1.0  0.0  1.0  0.0    #  ...       331.890671   \n",
       "15     #    #  0.0  0.0  1.0  1.0  0.0    #    #  0.0  ...       125.399518   \n",
       "16   0.0    #  1.0  0.0  1.0  0.0    #  0.0    #    #  ...       388.359389   \n",
       "17     #  0.0    #    #  0.0  1.0  1.0  1.0  0.0    #  ...       416.177323   \n",
       "18     #  0.0    #    #  0.0    #  1.0  1.0  0.0  1.0  ...       410.777296   \n",
       "19   0.0  1.0  1.0    #  1.0  0.0  0.0    #  1.0    #  ...         0.000000   \n",
       "20   0.0  0.0  1.0    #  1.0  0.0  0.0    #    #    #  ...         0.000000   \n",
       "21     #  0.0  0.0  0.0  1.0  1.0    #    #  0.0    #  ...       393.663135   \n",
       "22     #  1.0    #    #    #    #  1.0  1.0    #  0.0  ...       422.271453   \n",
       "23   1.0    #  0.0  0.0    #  1.0  0.0  0.0    #  0.0  ...         0.000000   \n",
       "24   1.0    #  1.0  0.0    #    #    #    #  0.0  0.0  ...         0.000000   \n",
       "25   0.0  0.0    #  0.0    #  0.0  1.0  1.0    #  1.0  ...       142.306583   \n",
       "26     #    #    #  0.0  1.0  1.0  1.0  1.0    #    #  ...       233.156886   \n",
       "27     #    #  1.0  0.0  1.0    #  1.0  0.0  0.0    #  ...       377.074965   \n",
       "28   0.0    #  1.0    #  0.0  0.0  0.0    #    #    #  ...         0.000000   \n",
       "29   1.0    #  0.0  0.0  1.0  0.0    #  1.0  1.0    #  ...       332.800000   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...              ...   \n",
       "745  0.0    #    #    #  0.0    #  0.0  1.0    #    #  ...       250.000000   \n",
       "746  1.0    #  1.0    #    #  1.0    #    #    #  0.0  ...         0.000000   \n",
       "747    #  1.0  1.0  1.0    #    #    #    #    #  0.0  ...         0.000000   \n",
       "748  0.0    #    #  0.0  0.0  0.0  1.0    #  1.0    #  ...         0.000000   \n",
       "749  0.0  1.0  1.0    #    #    #    #  1.0    #    #  ...       250.000000   \n",
       "750  0.0    #    #    #    #  0.0  0.0    #    #    #  ...       303.333333   \n",
       "751  1.0  0.0    #    #    #    #    #  0.0    #    #  ...         0.000000   \n",
       "752  1.0  0.0  0.0  0.0    #    #  1.0  0.0  1.0  1.0  ...         0.000000   \n",
       "753    #    #  0.0  0.0    #  0.0    #  0.0    #    #  ...       210.097305   \n",
       "754    #    #  0.0    #    #  0.0    #  0.0    #  0.0  ...       210.097305   \n",
       "755  1.0    #  1.0  1.0  0.0    #    #    #  1.0    #  ...         0.000000   \n",
       "756  1.0  0.0    #  1.0  0.0    #  1.0    #  1.0    #  ...         0.000000   \n",
       "757  0.0    #    #    #    #    #  0.0    #    #  0.0  ...         0.000000   \n",
       "758  0.0  1.0    #    #  0.0    #  1.0  0.0  1.0  0.0  ...         0.000000   \n",
       "759  0.0    #  1.0  1.0    #    #    #    #  1.0    #  ...         0.000000   \n",
       "760    #  0.0    #  1.0    #    #    #  1.0    #    #  ...         0.000000   \n",
       "761  0.0    #  1.0  1.0    #  1.0    #    #    #  0.0  ...        60.129542   \n",
       "762    #    #  1.0  1.0    #  1.0    #    #    #    #  ...         0.000000   \n",
       "763  1.0    #    #  1.0  0.0    #  1.0    #    #  1.0  ...         0.000000   \n",
       "764  1.0  0.0  0.0    #    #  0.0    #  1.0    #    #  ...         0.000000   \n",
       "765  1.0  0.0  0.0    #    #    #    #  1.0    #  1.0  ...         0.000000   \n",
       "766  1.0    #    #    #    #    #    #    #    #    #  ...         0.000000   \n",
       "767    #  0.0    #  0.0    #    #    #  0.0    #    #  ...       277.777778   \n",
       "768    #  0.0  0.0    #    #  0.0    #  0.0  0.0  1.0  ...         0.000000   \n",
       "769  1.0  0.0    #    #    #    #    #    #    #    #  ...         0.000000   \n",
       "770  0.0  0.0  1.0    #  1.0  0.0    #    #    #    #  ...         0.000000   \n",
       "771    #  0.0  1.0    #    #  0.0  0.0  0.0  0.0    #  ...         0.000000   \n",
       "772    #  0.0    #    #  1.0  0.0    #    #  0.0  1.0  ...       160.000000   \n",
       "773  0.0  0.0  1.0    #    #  0.0  0.0    #    #    #  ...       160.000000   \n",
       "774  0.0  1.0  0.0    #    #  1.0    #  1.0    #    #  ...         0.000000   \n",
       "\n",
       "         Accuracy  Numerosity  Avg Action Set Size  TimeStamp GA  \\\n",
       "0    1.000000e+00           2            26.799250          4984   \n",
       "1    2.057864e-09           1            17.255268          4840   \n",
       "2    1.324648e-09           1            30.584710          3827   \n",
       "3    8.165315e-10           1            18.900757          4506   \n",
       "4    5.700854e-08           1            17.063032          4918   \n",
       "5    3.061123e-08           1            22.304588          3701   \n",
       "6    2.440065e-08           2            20.624682          4964   \n",
       "7    1.000000e+00           1            18.267373          4390   \n",
       "8    1.000000e+00           7            30.349350          4928   \n",
       "9    7.950338e-10           2            21.125244          4703   \n",
       "10   6.756693e-10           1            21.912203          4979   \n",
       "11   3.311060e-09           1            23.000000          2089   \n",
       "12   6.772362e-08           1            18.954739          4861   \n",
       "13   8.047921e-10           1            14.426694          4943   \n",
       "14   2.483275e-09           1            22.182422          4753   \n",
       "15   3.224933e-07           1            14.628833          4705   \n",
       "16   1.131960e-09           1            25.842298          4853   \n",
       "17   8.009547e-10           1            26.210666          4172   \n",
       "18   8.550034e-10           1            26.754301          4850   \n",
       "19   1.000000e+00           1            23.318400          4498   \n",
       "20   1.000000e+00           1            16.641343          4990   \n",
       "21   1.057734e-09           1            16.283290          4711   \n",
       "22   7.448029e-10           1            13.496705          4939   \n",
       "23   1.000000e+00           3            16.435600          4652   \n",
       "24   1.000000e+00           3            21.390802          3991   \n",
       "25   1.713464e-07           1            21.859878          3574   \n",
       "26   1.451305e-08           1            25.422952          4776   \n",
       "27   1.311781e-09           1            22.945948          4953   \n",
       "28   1.000000e+00           3            24.303443          4946   \n",
       "29   2.449534e-09           1            26.817600          4813   \n",
       "..            ...         ...                  ...           ...   \n",
       "745  1.024000e-08           1            20.500000          4975   \n",
       "746  1.000000e+00           1            42.000000          4953   \n",
       "747  1.000000e+00           1            16.500000          4974   \n",
       "748  1.000000e+00           1            11.250000          4943   \n",
       "749  1.024000e-08           1            20.500000          4981   \n",
       "750  3.894030e-09           1            23.200000          4990   \n",
       "751  1.000000e+00           1            19.333333          4987   \n",
       "752  1.000000e+00           1            22.166312          4947   \n",
       "753  2.442854e-08           1            25.269023          4967   \n",
       "754  2.442854e-08           1            23.109259          4967   \n",
       "755  1.000000e+00           1            18.645345          4970   \n",
       "756  1.000000e+00           1            21.980018          4970   \n",
       "757  1.000000e+00           1            35.000000          4973   \n",
       "758  1.000000e+00           1            19.333333          4974   \n",
       "759  1.000000e+00           1            14.000000          4974   \n",
       "760  1.000000e+00           1            22.000000          4984   \n",
       "761  1.272215e-05           1            17.265691          4978   \n",
       "762  1.000000e+00           1            14.000000          4978   \n",
       "763  1.000000e+00           1            27.000000          4979   \n",
       "764  1.000000e+00           1            22.143493          4984   \n",
       "765  1.000000e+00           1            28.494728          4984   \n",
       "766  1.000000e+00           1            21.000000          4987   \n",
       "767  6.046618e-09           1            19.000000          4990   \n",
       "768  1.000000e+00           1            20.000000          4987   \n",
       "769  1.000000e+00           1            20.000000          4998   \n",
       "770  1.000000e+00           1            16.000000          4989   \n",
       "771  1.000000e+00           1            16.184000          4989   \n",
       "772  9.536743e-08           1            19.000000          4990   \n",
       "773  9.536743e-08           1            16.641343          4990   \n",
       "774  1.000000e+00           1            19.274795          4993   \n",
       "\n",
       "     Iteration Initialized  Specificity  Deletion Probability  Experience  \\\n",
       "0                        1     0.545455              0.000912          18   \n",
       "1                        2     0.636364              0.000294          18   \n",
       "2                        7     0.636364              0.000521          14   \n",
       "3                        9     0.636364              0.000322          19   \n",
       "4                       12     0.545455              0.004933          38   \n",
       "5                       14     0.636364              0.000380          19   \n",
       "6                       17     0.454545              0.000702          47   \n",
       "7                       20     0.636364              0.000329          31   \n",
       "8                       33     0.454545              0.003894         109   \n",
       "9                       36     0.454545              0.007569          65   \n",
       "10                      40     0.545455              0.000373          28   \n",
       "11                      41     0.818182              0.000391           5   \n",
       "12                      51     0.454545              0.000323          96   \n",
       "13                      52     0.545455              0.000246          31   \n",
       "14                      57     0.636364              0.004363          22   \n",
       "15                      66     0.545455              0.000249          39   \n",
       "16                      70     0.636364              0.000440          16   \n",
       "17                      81     0.636364              0.000446          16   \n",
       "18                     105     0.636364              0.000455          16   \n",
       "19                     122     0.636364              0.000397           8   \n",
       "20                     127     0.545455              0.000283          25   \n",
       "21                     181     0.636364              0.000277          21   \n",
       "22                     191     0.454545              0.002556          92   \n",
       "23                     214     0.636364              0.000839          22   \n",
       "24                     246     0.545455              0.001092          51   \n",
       "25                     314     0.636364              0.000372          16   \n",
       "26                     321     0.545455              0.000433          25   \n",
       "27                     377     0.545455              0.004615          27   \n",
       "28                     493     0.454545              0.001241          26   \n",
       "29                     504     0.636364              0.000456           8   \n",
       "..                     ...          ...                   ...         ...   \n",
       "745                   4936     0.363636              0.000349           2   \n",
       "746                   4942     0.454545              0.000715           1   \n",
       "747                   4942     0.363636              0.000281           2   \n",
       "748                   4943     0.636364              0.000191           0   \n",
       "749                   4944     0.363636              0.000349           2   \n",
       "750                   4946     0.272727              0.000395           5   \n",
       "751                   4947     0.272727              0.000374           3   \n",
       "752                   4947     0.818182              0.000377           0   \n",
       "753                   4967     0.363636              0.000430           0   \n",
       "754                   4967     0.454545              0.000393           0   \n",
       "755                   4970     0.545455              0.000317           0   \n",
       "756                   4970     0.636364              0.000374           0   \n",
       "757                   4973     0.272727              0.000596           1   \n",
       "758                   4974     0.636364              0.000329           0   \n",
       "759                   4974     0.454545              0.000238           1   \n",
       "760                   4975     0.272727              0.000374           2   \n",
       "761                   4978     0.454545              0.000294           0   \n",
       "762                   4978     0.272727              0.000238           2   \n",
       "763                   4979     0.545455              0.000460           0   \n",
       "764                   4984     0.454545              0.000377           0   \n",
       "765                   4984     0.545455              0.000485           0   \n",
       "766                   4985     0.090909              0.000477           3   \n",
       "767                   4985     0.272727              0.000366           3   \n",
       "768                   4987     0.545455              0.000340           0   \n",
       "769                   4987     0.181818              0.000368           3   \n",
       "770                   4989     0.454545              0.000272           0   \n",
       "771                   4989     0.545455              0.000275           0   \n",
       "772                   4990     0.454545              0.000323           0   \n",
       "773                   4990     0.545455              0.000283           0   \n",
       "774                   4993     0.454545              0.000328           0   \n",
       "\n",
       "     Match Count  \n",
       "0             79  \n",
       "1             39  \n",
       "2             38  \n",
       "3             40  \n",
       "4             79  \n",
       "5             38  \n",
       "6            159  \n",
       "7             41  \n",
       "8            153  \n",
       "9            151  \n",
       "10            81  \n",
       "11            11  \n",
       "12           157  \n",
       "13            78  \n",
       "14            41  \n",
       "15            76  \n",
       "16            40  \n",
       "17            36  \n",
       "18            35  \n",
       "19            38  \n",
       "20            77  \n",
       "21            40  \n",
       "22           149  \n",
       "23            34  \n",
       "24            75  \n",
       "25            36  \n",
       "26            75  \n",
       "27            72  \n",
       "28           139  \n",
       "29            36  \n",
       "..           ...  \n",
       "745            3  \n",
       "746            3  \n",
       "747            4  \n",
       "748            0  \n",
       "749            5  \n",
       "750            7  \n",
       "751            7  \n",
       "752            0  \n",
       "753            2  \n",
       "754            0  \n",
       "755            0  \n",
       "756            0  \n",
       "757            4  \n",
       "758            0  \n",
       "759            1  \n",
       "760            4  \n",
       "761            0  \n",
       "762            3  \n",
       "763            0  \n",
       "764            1  \n",
       "765            0  \n",
       "766            6  \n",
       "767            4  \n",
       "768            0  \n",
       "769            4  \n",
       "770            1  \n",
       "771            2  \n",
       "772            0  \n",
       "773            0  \n",
       "774            0  \n",
       "\n",
       "[775 rows x 24 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainedModel.export_final_rule_population(\"defaultExportDir/fileRulePopulation.csv\",headers,classLabel)\n",
    "\n",
    "populationData2 = pd.read_csv(\"defaultExportDir/fileRulePopulation.csv\")\n",
    "display(populationData2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the above final rule population exported in DCAL format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Specified Values</th>\n",
       "      <th>Specified Attribute Names</th>\n",
       "      <th>Country</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Prediction Error</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Numerosity</th>\n",
       "      <th>Avg Action Set Size</th>\n",
       "      <th>TimeStamp GA</th>\n",
       "      <th>Iteration Initialized</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>Deletion Probability</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Match Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_2, R_4, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.196456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2</td>\n",
       "      <td>26.799250</td>\n",
       "      <td>4984</td>\n",
       "      <td>1</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000912</td>\n",
       "      <td>18</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_2, R_3, R_4, R_6, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009850</td>\n",
       "      <td>259.152004</td>\n",
       "      <td>344.601095</td>\n",
       "      <td>2.057864e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>17.255268</td>\n",
       "      <td>4840</td>\n",
       "      <td>2</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>18</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_0, R_2, R_4, R_5, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014714</td>\n",
       "      <td>722.697523</td>\n",
       "      <td>376.339606</td>\n",
       "      <td>1.324648e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>30.584710</td>\n",
       "      <td>3827</td>\n",
       "      <td>7</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>14</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_1, A_2, R_1, R_2, R_3, R_5, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>624.236840</td>\n",
       "      <td>414.577197</td>\n",
       "      <td>8.165315e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>18.900757</td>\n",
       "      <td>4506</td>\n",
       "      <td>9</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_0, R_2, R_3, R_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003881</td>\n",
       "      <td>921.982842</td>\n",
       "      <td>177.342172</td>\n",
       "      <td>5.700854e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>17.063032</td>\n",
       "      <td>4918</td>\n",
       "      <td>12</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.004933</td>\n",
       "      <td>38</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, R_1, R_2, R_4, R_5, R_6, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.035367</td>\n",
       "      <td>880.007814</td>\n",
       "      <td>200.827808</td>\n",
       "      <td>3.061123e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>22.304588</td>\n",
       "      <td>3701</td>\n",
       "      <td>14</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000380</td>\n",
       "      <td>19</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_1, R_0, R_2, R_4, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.133690</td>\n",
       "      <td>125.517090</td>\n",
       "      <td>210.145312</td>\n",
       "      <td>2.440065e-08</td>\n",
       "      <td>2</td>\n",
       "      <td>20.624682</td>\n",
       "      <td>4964</td>\n",
       "      <td>17</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000702</td>\n",
       "      <td>47</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_3, R_4, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.348711</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>18.267373</td>\n",
       "      <td>4390</td>\n",
       "      <td>20</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>31</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_1, A_2, R_2, R_3, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.505995</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7</td>\n",
       "      <td>30.349350</td>\n",
       "      <td>4928</td>\n",
       "      <td>33</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.003894</td>\n",
       "      <td>109</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_3, R_5, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012524</td>\n",
       "      <td>490.003544</td>\n",
       "      <td>416.795366</td>\n",
       "      <td>7.950338e-10</td>\n",
       "      <td>2</td>\n",
       "      <td>21.125244</td>\n",
       "      <td>4703</td>\n",
       "      <td>36</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.007569</td>\n",
       "      <td>65</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_2, R_0, R_4, R_5, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010012</td>\n",
       "      <td>532.177355</td>\n",
       "      <td>430.579314</td>\n",
       "      <td>6.756693e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>21.912203</td>\n",
       "      <td>4979</td>\n",
       "      <td>40</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>28</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_1, A_2, R_0, R_1, R_2, R_3, R_5, R_6, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.144028</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>313.333333</td>\n",
       "      <td>3.311060e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2089</td>\n",
       "      <td>41</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 0.0</td>\n",
       "      <td>A_1, R_0, R_3, R_4, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140173</td>\n",
       "      <td>907.540128</td>\n",
       "      <td>171.337319</td>\n",
       "      <td>6.772362e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>18.954739</td>\n",
       "      <td>4861</td>\n",
       "      <td>51</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>96</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_0, R_2, R_3, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013874</td>\n",
       "      <td>485.189062</td>\n",
       "      <td>415.779676</td>\n",
       "      <td>8.047921e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>14.426694</td>\n",
       "      <td>4943</td>\n",
       "      <td>52</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>31</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_2, R_0, R_2, R_3, R_4, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005704</td>\n",
       "      <td>806.150571</td>\n",
       "      <td>331.890671</td>\n",
       "      <td>2.483275e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>22.182422</td>\n",
       "      <td>4753</td>\n",
       "      <td>57</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.004363</td>\n",
       "      <td>22</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_2, R_0, R_1, R_2, R_3, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018474</td>\n",
       "      <td>959.161948</td>\n",
       "      <td>125.399518</td>\n",
       "      <td>3.224933e-07</td>\n",
       "      <td>1</td>\n",
       "      <td>14.628833</td>\n",
       "      <td>4705</td>\n",
       "      <td>66</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>39</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_0, R_1, R_2, R_4, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.062935</td>\n",
       "      <td>618.223636</td>\n",
       "      <td>388.359389</td>\n",
       "      <td>1.131960e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>25.842298</td>\n",
       "      <td>4853</td>\n",
       "      <td>70</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>16</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_1, R_1, R_2, R_3, R_4, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004468</td>\n",
       "      <td>576.241222</td>\n",
       "      <td>416.177323</td>\n",
       "      <td>8.009547e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>26.210666</td>\n",
       "      <td>4172</td>\n",
       "      <td>81</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0</td>\n",
       "      <td>A_1, R_1, R_3, R_4, R_5, R_6, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.019688</td>\n",
       "      <td>584.629830</td>\n",
       "      <td>410.777296</td>\n",
       "      <td>8.550034e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>26.754301</td>\n",
       "      <td>4850</td>\n",
       "      <td>105</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>16</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_1, R_2, R_3, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.217416</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>23.318400</td>\n",
       "      <td>4498</td>\n",
       "      <td>122</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_1, R_2, R_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.600392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>16.641343</td>\n",
       "      <td>4990</td>\n",
       "      <td>127</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>25</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, R_0, R_1, R_2, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025555</td>\n",
       "      <td>348.627787</td>\n",
       "      <td>393.663135</td>\n",
       "      <td>1.057734e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>16.283290</td>\n",
       "      <td>4711</td>\n",
       "      <td>181</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_1, R_3, R_4, R_6, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005925</td>\n",
       "      <td>431.889107</td>\n",
       "      <td>422.271453</td>\n",
       "      <td>7.448029e-10</td>\n",
       "      <td>1</td>\n",
       "      <td>13.496705</td>\n",
       "      <td>4939</td>\n",
       "      <td>191</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.002556</td>\n",
       "      <td>92</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_0, R_2, R_3, R_4, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722976</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3</td>\n",
       "      <td>16.435600</td>\n",
       "      <td>4652</td>\n",
       "      <td>214</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000839</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0, 1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_0, R_5, R_6, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.647359</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3</td>\n",
       "      <td>21.390802</td>\n",
       "      <td>3991</td>\n",
       "      <td>246</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>51</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_0, R_2, R_3, R_4, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010095</td>\n",
       "      <td>948.460392</td>\n",
       "      <td>142.306583</td>\n",
       "      <td>1.713464e-07</td>\n",
       "      <td>1</td>\n",
       "      <td>21.859878</td>\n",
       "      <td>3574</td>\n",
       "      <td>314</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000372</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>R_0, R_1, R_2, R_3, R_4, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008152</td>\n",
       "      <td>227.383559</td>\n",
       "      <td>233.156886</td>\n",
       "      <td>1.451305e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>25.422952</td>\n",
       "      <td>4776</td>\n",
       "      <td>321</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>25</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0, 0.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_2, R_0, R_1, R_3, R_4, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005578</td>\n",
       "      <td>631.932628</td>\n",
       "      <td>377.074965</td>\n",
       "      <td>1.311781e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>22.945948</td>\n",
       "      <td>4953</td>\n",
       "      <td>377</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>27</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_1, R_2, R_3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.134303</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3</td>\n",
       "      <td>24.303443</td>\n",
       "      <td>4946</td>\n",
       "      <td>493</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001241</td>\n",
       "      <td>26</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, R_0, R_1, R_2, R_4, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115143</td>\n",
       "      <td>332.800000</td>\n",
       "      <td>332.800000</td>\n",
       "      <td>2.449534e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>26.817600</td>\n",
       "      <td>4813</td>\n",
       "      <td>504</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>8</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, R_1, R_3, R_4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013164</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.024000e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>4975</td>\n",
       "      <td>4936</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_2, R_6, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.026260</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>4953</td>\n",
       "      <td>4942</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_1, A_2, R_0, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.080056</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>4974</td>\n",
       "      <td>4942</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, R_0, R_1, R_2, R_3, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.041561</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>11.250000</td>\n",
       "      <td>4943</td>\n",
       "      <td>4943</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.080939</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.024000e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>4981</td>\n",
       "      <td>4944</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, R_2, R_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.081905</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>303.333333</td>\n",
       "      <td>3.894030e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>4990</td>\n",
       "      <td>4946</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.137798</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.333333</td>\n",
       "      <td>4987</td>\n",
       "      <td>4947</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_0, R_3, R_4, R_5, R_6, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.018571</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>22.166312</td>\n",
       "      <td>4947</td>\n",
       "      <td>4947</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_2, R_0, R_2, R_4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003876</td>\n",
       "      <td>202.683565</td>\n",
       "      <td>210.097305</td>\n",
       "      <td>2.442854e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>25.269023</td>\n",
       "      <td>4967</td>\n",
       "      <td>4967</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000430</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_2, R_2, R_4, R_6, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003876</td>\n",
       "      <td>202.683565</td>\n",
       "      <td>210.097305</td>\n",
       "      <td>2.442854e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>23.109259</td>\n",
       "      <td>4967</td>\n",
       "      <td>4967</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, R_0, R_1, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022311</td>\n",
       "      <td>879.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>18.645345</td>\n",
       "      <td>4970</td>\n",
       "      <td>4970</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_0, R_1, R_3, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022311</td>\n",
       "      <td>879.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>21.980018</td>\n",
       "      <td>4970</td>\n",
       "      <td>4970</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, R_3, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023854</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>4973</td>\n",
       "      <td>4973</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_1, R_3, R_4, R_5, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013658</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.333333</td>\n",
       "      <td>4974</td>\n",
       "      <td>4974</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, R_0, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.050927</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4974</td>\n",
       "      <td>4974</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>0.0, 1.0, 1.0</td>\n",
       "      <td>A_1, R_0, R_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.084449</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4984</td>\n",
       "      <td>4975</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_0, R_2, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.020960</td>\n",
       "      <td>909.262582</td>\n",
       "      <td>60.129542</td>\n",
       "      <td>1.272215e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>17.265691</td>\n",
       "      <td>4978</td>\n",
       "      <td>4978</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>1.0, 1.0, 1.0</td>\n",
       "      <td>A_2, R_0, R_2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.240074</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4978</td>\n",
       "      <td>4978</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, R_0, R_1, R_3, R_6, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022857</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>4979</td>\n",
       "      <td>4979</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_2, R_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012039</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>22.143493</td>\n",
       "      <td>4984</td>\n",
       "      <td>4984</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_4, R_6, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012039</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>28.494728</td>\n",
       "      <td>4984</td>\n",
       "      <td>4984</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1.0</td>\n",
       "      <td>A_0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.126135</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>4987</td>\n",
       "      <td>4985</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>0.0, 0.0, 0.0</td>\n",
       "      <td>A_1, R_0, R_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010135</td>\n",
       "      <td>666.666667</td>\n",
       "      <td>277.777778</td>\n",
       "      <td>6.046618e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>4990</td>\n",
       "      <td>4985</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, R_2, R_4, R_5, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017070</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4987</td>\n",
       "      <td>4987</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>1.0, 0.0</td>\n",
       "      <td>A_0, A_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.159387</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4998</td>\n",
       "      <td>4987</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_1, R_2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.026722</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4989</td>\n",
       "      <td>4989</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>0.0, 1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_1, A_2, R_2, R_3, R_4, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.026722</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>16.184000</td>\n",
       "      <td>4989</td>\n",
       "      <td>4989</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>0.0, 1.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_1, R_1, R_2, R_5, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.039177</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>9.536743e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>4990</td>\n",
       "      <td>4990</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_2, R_3, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.039177</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>9.536743e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>16.641343</td>\n",
       "      <td>4990</td>\n",
       "      <td>4990</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>0.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_2, R_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011852</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.274795</td>\n",
       "      <td>4993</td>\n",
       "      <td>4993</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>775 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Specified Values  \\\n",
       "0                   1.0, 0.0, 0.0, 0.0, 1.0, 1.0   \n",
       "1              0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0   \n",
       "2              0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0   \n",
       "3              1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0   \n",
       "4                   0.0, 0.0, 0.0, 0.0, 1.0, 1.0   \n",
       "5              0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0   \n",
       "6                        0.0, 1.0, 1.0, 1.0, 1.0   \n",
       "7              1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0   \n",
       "8                        1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "9                        1.0, 1.0, 1.0, 0.0, 1.0   \n",
       "10                  1.0, 1.0, 1.0, 0.0, 0.0, 1.0   \n",
       "11   1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0   \n",
       "12                       1.0, 1.0, 0.0, 1.0, 0.0   \n",
       "13                  0.0, 0.0, 0.0, 0.0, 1.0, 0.0   \n",
       "14             1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0   \n",
       "15                  0.0, 0.0, 1.0, 1.0, 0.0, 0.0   \n",
       "16             0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "17             0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0   \n",
       "18             0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0   \n",
       "19             0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0   \n",
       "20                  0.0, 0.0, 1.0, 1.0, 0.0, 0.0   \n",
       "21             0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0   \n",
       "22                       1.0, 1.0, 1.0, 0.0, 1.0   \n",
       "23             1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "24                  1.0, 1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "25             0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0   \n",
       "26                  0.0, 1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "27                  1.0, 0.0, 1.0, 1.0, 0.0, 0.0   \n",
       "28                       0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "29             1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0   \n",
       "..                                           ...   \n",
       "745                           0.0, 0.0, 0.0, 1.0   \n",
       "746                      1.0, 1.0, 1.0, 0.0, 0.0   \n",
       "747                           1.0, 1.0, 1.0, 0.0   \n",
       "748            0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0   \n",
       "749                           0.0, 1.0, 1.0, 1.0   \n",
       "750                                0.0, 0.0, 0.0   \n",
       "751                                1.0, 0.0, 0.0   \n",
       "752  1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0   \n",
       "753                           0.0, 0.0, 0.0, 0.0   \n",
       "754                      0.0, 0.0, 0.0, 0.0, 1.0   \n",
       "755                 1.0, 1.0, 1.0, 0.0, 1.0, 1.0   \n",
       "756            1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0   \n",
       "757                                0.0, 0.0, 0.0   \n",
       "758            0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0   \n",
       "759                      0.0, 1.0, 1.0, 1.0, 1.0   \n",
       "760                                0.0, 1.0, 1.0   \n",
       "761                      0.0, 1.0, 1.0, 1.0, 0.0   \n",
       "762                                1.0, 1.0, 1.0   \n",
       "763                 1.0, 1.0, 0.0, 1.0, 1.0, 1.0   \n",
       "764                      1.0, 0.0, 0.0, 0.0, 1.0   \n",
       "765                 1.0, 0.0, 0.0, 1.0, 1.0, 0.0   \n",
       "766                                          1.0   \n",
       "767                                0.0, 0.0, 0.0   \n",
       "768                 0.0, 0.0, 0.0, 0.0, 0.0, 1.0   \n",
       "769                                     1.0, 0.0   \n",
       "770                      0.0, 0.0, 1.0, 1.0, 0.0   \n",
       "771                 0.0, 1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "772                      0.0, 1.0, 0.0, 0.0, 1.0   \n",
       "773                 0.0, 0.0, 1.0, 0.0, 0.0, 1.0   \n",
       "774                      0.0, 1.0, 0.0, 1.0, 1.0   \n",
       "\n",
       "                       Specified Attribute Names  Country   Fitness  \\\n",
       "0                   A_0, A_1, A_2, R_2, R_4, R_6      0.0  0.196456   \n",
       "1              A_0, A_2, R_2, R_3, R_4, R_6, R_7      1.0  0.009850   \n",
       "2              A_0, A_1, R_0, R_2, R_4, R_5, R_7      0.0  0.014714   \n",
       "3              A_1, A_2, R_1, R_2, R_3, R_5, R_6      1.0  0.011609   \n",
       "4                   A_0, A_1, R_0, R_2, R_3, R_4      0.0  0.003881   \n",
       "5              A_0, R_1, R_2, R_4, R_5, R_6, R_7      1.0  0.035367   \n",
       "6                        A_1, R_0, R_2, R_4, R_5      0.0  0.133690   \n",
       "7              A_0, A_1, A_2, R_3, R_4, R_5, R_7      1.0  0.348711   \n",
       "8                        A_1, A_2, R_2, R_3, R_6      0.0  0.505995   \n",
       "9                        A_0, A_1, R_3, R_5, R_7      0.0  0.012524   \n",
       "10                  A_0, A_2, R_0, R_4, R_5, R_6      1.0  0.010012   \n",
       "11   A_1, A_2, R_0, R_1, R_2, R_3, R_5, R_6, R_7      0.0  0.144028   \n",
       "12                       A_1, R_0, R_3, R_4, R_6      0.0  0.140173   \n",
       "13                  A_0, A_1, R_0, R_2, R_3, R_6      1.0  0.013874   \n",
       "14             A_2, R_0, R_2, R_3, R_4, R_5, R_7      1.0  0.005704   \n",
       "15                  A_2, R_0, R_1, R_2, R_3, R_6      0.0  0.018474   \n",
       "16             A_0, A_2, R_0, R_1, R_2, R_4, R_7      0.0  0.062935   \n",
       "17             A_1, R_1, R_2, R_3, R_4, R_5, R_7      1.0  0.004468   \n",
       "18             A_1, R_1, R_3, R_4, R_5, R_6, R_7      1.0  0.019688   \n",
       "19             A_0, A_1, A_2, R_1, R_2, R_3, R_5      1.0  0.217416   \n",
       "20                  A_0, A_1, A_2, R_1, R_2, R_3      0.0  0.600392   \n",
       "21             A_1, A_2, R_0, R_1, R_2, R_5, R_7      1.0  0.025555   \n",
       "22                       A_1, R_3, R_4, R_6, R_7      1.0  0.005925   \n",
       "23             A_0, A_2, R_0, R_2, R_3, R_4, R_6      0.0  0.722976   \n",
       "24                  A_0, A_2, R_0, R_5, R_6, R_7      0.0  0.647359   \n",
       "25             A_0, A_1, R_0, R_2, R_3, R_4, R_6      0.0  0.010095   \n",
       "26                  R_0, R_1, R_2, R_3, R_4, R_7      0.0  0.008152   \n",
       "27                  A_2, R_0, R_1, R_3, R_4, R_5      1.0  0.005578   \n",
       "28                       A_0, A_2, R_1, R_2, R_3      1.0  0.134303   \n",
       "29             A_0, A_2, R_0, R_1, R_2, R_4, R_5      0.0  0.115143   \n",
       "..                                           ...      ...       ...   \n",
       "745                           A_0, R_1, R_3, R_4      1.0  0.013164   \n",
       "746                      A_0, A_2, R_2, R_6, R_7      1.0  0.026260   \n",
       "747                           A_1, A_2, R_0, R_6      1.0  0.080056   \n",
       "748            A_0, R_0, R_1, R_2, R_3, R_5, R_7      1.0  0.041561   \n",
       "749                           A_0, A_1, A_2, R_4      0.0  0.080939   \n",
       "750                                A_0, R_2, R_3      0.0  0.081905   \n",
       "751                                A_0, A_1, R_4      0.0  0.137798   \n",
       "752  A_0, A_1, A_2, R_0, R_3, R_4, R_5, R_6, R_7      1.0  0.018571   \n",
       "753                           A_2, R_0, R_2, R_4      1.0  0.003876   \n",
       "754                      A_2, R_2, R_4, R_6, R_7      1.0  0.003876   \n",
       "755                 A_0, A_2, R_0, R_1, R_5, R_7      1.0  0.022311   \n",
       "756            A_0, A_1, R_0, R_1, R_3, R_5, R_7      1.0  0.022311   \n",
       "757                                A_0, R_3, R_6      0.0  0.023854   \n",
       "758            A_0, A_1, R_1, R_3, R_4, R_5, R_6      0.0  0.013658   \n",
       "759                      A_0, A_2, R_0, R_5, R_7      1.0  0.050927   \n",
       "760                                A_1, R_0, R_4      0.0  0.084449   \n",
       "761                      A_0, A_2, R_0, R_2, R_6      1.0  0.020960   \n",
       "762                                A_2, R_0, R_2      1.0  0.240074   \n",
       "763                 A_0, R_0, R_1, R_3, R_6, R_7      0.0  0.022857   \n",
       "764                      A_0, A_1, A_2, R_2, R_4      0.0  0.012039   \n",
       "765                 A_0, A_1, A_2, R_4, R_6, R_7      0.0  0.012039   \n",
       "766                                          A_0      0.0  0.126135   \n",
       "767                                A_1, R_0, R_4      0.0  0.010135   \n",
       "768                 A_1, A_2, R_2, R_4, R_5, R_6      0.0  0.017070   \n",
       "769                                     A_0, A_1      1.0  0.159387   \n",
       "770                      A_0, A_1, A_2, R_1, R_2      1.0  0.026722   \n",
       "771                 A_1, A_2, R_2, R_3, R_4, R_5      1.0  0.026722   \n",
       "772                      A_1, R_1, R_2, R_5, R_6      0.0  0.039177   \n",
       "773                 A_0, A_1, A_2, R_2, R_3, R_7      0.0  0.039177   \n",
       "774                      A_0, A_1, A_2, R_2, R_4      0.0  0.011852   \n",
       "\n",
       "      Prediction  Prediction Error      Accuracy  Numerosity  \\\n",
       "0       0.000000          0.000000  1.000000e+00           2   \n",
       "1     259.152004        344.601095  2.057864e-09           1   \n",
       "2     722.697523        376.339606  1.324648e-09           1   \n",
       "3     624.236840        414.577197  8.165315e-10           1   \n",
       "4     921.982842        177.342172  5.700854e-08           1   \n",
       "5     880.007814        200.827808  3.061123e-08           1   \n",
       "6     125.517090        210.145312  2.440065e-08           2   \n",
       "7    1000.000000          0.000000  1.000000e+00           1   \n",
       "8    1000.000000          0.000000  1.000000e+00           7   \n",
       "9     490.003544        416.795366  7.950338e-10           2   \n",
       "10    532.177355        430.579314  6.756693e-10           1   \n",
       "11    600.000000        313.333333  3.311060e-09           1   \n",
       "12    907.540128        171.337319  6.772362e-08           1   \n",
       "13    485.189062        415.779676  8.047921e-10           1   \n",
       "14    806.150571        331.890671  2.483275e-09           1   \n",
       "15    959.161948        125.399518  3.224933e-07           1   \n",
       "16    618.223636        388.359389  1.131960e-09           1   \n",
       "17    576.241222        416.177323  8.009547e-10           1   \n",
       "18    584.629830        410.777296  8.550034e-10           1   \n",
       "19      0.000000          0.000000  1.000000e+00           1   \n",
       "20      0.000000          0.000000  1.000000e+00           1   \n",
       "21    348.627787        393.663135  1.057734e-09           1   \n",
       "22    431.889107        422.271453  7.448029e-10           1   \n",
       "23   1000.000000          0.000000  1.000000e+00           3   \n",
       "24   1000.000000          0.000000  1.000000e+00           3   \n",
       "25    948.460392        142.306583  1.713464e-07           1   \n",
       "26    227.383559        233.156886  1.451305e-08           1   \n",
       "27    631.932628        377.074965  1.311781e-09           1   \n",
       "28      0.000000          0.000000  1.000000e+00           3   \n",
       "29    332.800000        332.800000  2.449534e-09           1   \n",
       "..           ...               ...           ...         ...   \n",
       "745   500.000000        250.000000  1.024000e-08           1   \n",
       "746     0.000000          0.000000  1.000000e+00           1   \n",
       "747  1000.000000          0.000000  1.000000e+00           1   \n",
       "748     0.000000          0.000000  1.000000e+00           1   \n",
       "749   500.000000        250.000000  1.024000e-08           1   \n",
       "750   600.000000        303.333333  3.894030e-09           1   \n",
       "751  1000.000000          0.000000  1.000000e+00           1   \n",
       "752  1000.000000          0.000000  1.000000e+00           1   \n",
       "753   202.683565        210.097305  2.442854e-08           1   \n",
       "754   202.683565        210.097305  2.442854e-08           1   \n",
       "755   879.040000          0.000000  1.000000e+00           1   \n",
       "756   879.040000          0.000000  1.000000e+00           1   \n",
       "757  1000.000000          0.000000  1.000000e+00           1   \n",
       "758  1000.000000          0.000000  1.000000e+00           1   \n",
       "759  1000.000000          0.000000  1.000000e+00           1   \n",
       "760     0.000000          0.000000  1.000000e+00           1   \n",
       "761   909.262582         60.129542  1.272215e-05           1   \n",
       "762  1000.000000          0.000000  1.000000e+00           1   \n",
       "763  1000.000000          0.000000  1.000000e+00           1   \n",
       "764     0.000000          0.000000  1.000000e+00           1   \n",
       "765     0.000000          0.000000  1.000000e+00           1   \n",
       "766  1000.000000          0.000000  1.000000e+00           1   \n",
       "767   666.666667        277.777778  6.046618e-09           1   \n",
       "768  1000.000000          0.000000  1.000000e+00           1   \n",
       "769  1000.000000          0.000000  1.000000e+00           1   \n",
       "770  1000.000000          0.000000  1.000000e+00           1   \n",
       "771  1000.000000          0.000000  1.000000e+00           1   \n",
       "772   400.000000        160.000000  9.536743e-08           1   \n",
       "773   400.000000        160.000000  9.536743e-08           1   \n",
       "774  1000.000000          0.000000  1.000000e+00           1   \n",
       "\n",
       "     Avg Action Set Size  TimeStamp GA  Iteration Initialized  Specificity  \\\n",
       "0              26.799250          4984                      1     0.545455   \n",
       "1              17.255268          4840                      2     0.636364   \n",
       "2              30.584710          3827                      7     0.636364   \n",
       "3              18.900757          4506                      9     0.636364   \n",
       "4              17.063032          4918                     12     0.545455   \n",
       "5              22.304588          3701                     14     0.636364   \n",
       "6              20.624682          4964                     17     0.454545   \n",
       "7              18.267373          4390                     20     0.636364   \n",
       "8              30.349350          4928                     33     0.454545   \n",
       "9              21.125244          4703                     36     0.454545   \n",
       "10             21.912203          4979                     40     0.545455   \n",
       "11             23.000000          2089                     41     0.818182   \n",
       "12             18.954739          4861                     51     0.454545   \n",
       "13             14.426694          4943                     52     0.545455   \n",
       "14             22.182422          4753                     57     0.636364   \n",
       "15             14.628833          4705                     66     0.545455   \n",
       "16             25.842298          4853                     70     0.636364   \n",
       "17             26.210666          4172                     81     0.636364   \n",
       "18             26.754301          4850                    105     0.636364   \n",
       "19             23.318400          4498                    122     0.636364   \n",
       "20             16.641343          4990                    127     0.545455   \n",
       "21             16.283290          4711                    181     0.636364   \n",
       "22             13.496705          4939                    191     0.454545   \n",
       "23             16.435600          4652                    214     0.636364   \n",
       "24             21.390802          3991                    246     0.545455   \n",
       "25             21.859878          3574                    314     0.636364   \n",
       "26             25.422952          4776                    321     0.545455   \n",
       "27             22.945948          4953                    377     0.545455   \n",
       "28             24.303443          4946                    493     0.454545   \n",
       "29             26.817600          4813                    504     0.636364   \n",
       "..                   ...           ...                    ...          ...   \n",
       "745            20.500000          4975                   4936     0.363636   \n",
       "746            42.000000          4953                   4942     0.454545   \n",
       "747            16.500000          4974                   4942     0.363636   \n",
       "748            11.250000          4943                   4943     0.636364   \n",
       "749            20.500000          4981                   4944     0.363636   \n",
       "750            23.200000          4990                   4946     0.272727   \n",
       "751            19.333333          4987                   4947     0.272727   \n",
       "752            22.166312          4947                   4947     0.818182   \n",
       "753            25.269023          4967                   4967     0.363636   \n",
       "754            23.109259          4967                   4967     0.454545   \n",
       "755            18.645345          4970                   4970     0.545455   \n",
       "756            21.980018          4970                   4970     0.636364   \n",
       "757            35.000000          4973                   4973     0.272727   \n",
       "758            19.333333          4974                   4974     0.636364   \n",
       "759            14.000000          4974                   4974     0.454545   \n",
       "760            22.000000          4984                   4975     0.272727   \n",
       "761            17.265691          4978                   4978     0.454545   \n",
       "762            14.000000          4978                   4978     0.272727   \n",
       "763            27.000000          4979                   4979     0.545455   \n",
       "764            22.143493          4984                   4984     0.454545   \n",
       "765            28.494728          4984                   4984     0.545455   \n",
       "766            21.000000          4987                   4985     0.090909   \n",
       "767            19.000000          4990                   4985     0.272727   \n",
       "768            20.000000          4987                   4987     0.545455   \n",
       "769            20.000000          4998                   4987     0.181818   \n",
       "770            16.000000          4989                   4989     0.454545   \n",
       "771            16.184000          4989                   4989     0.545455   \n",
       "772            19.000000          4990                   4990     0.454545   \n",
       "773            16.641343          4990                   4990     0.545455   \n",
       "774            19.274795          4993                   4993     0.454545   \n",
       "\n",
       "     Deletion Probability  Experience  Match Count  \n",
       "0                0.000912          18           79  \n",
       "1                0.000294          18           39  \n",
       "2                0.000521          14           38  \n",
       "3                0.000322          19           40  \n",
       "4                0.004933          38           79  \n",
       "5                0.000380          19           38  \n",
       "6                0.000702          47          159  \n",
       "7                0.000329          31           41  \n",
       "8                0.003894         109          153  \n",
       "9                0.007569          65          151  \n",
       "10               0.000373          28           81  \n",
       "11               0.000391           5           11  \n",
       "12               0.000323          96          157  \n",
       "13               0.000246          31           78  \n",
       "14               0.004363          22           41  \n",
       "15               0.000249          39           76  \n",
       "16               0.000440          16           40  \n",
       "17               0.000446          16           36  \n",
       "18               0.000455          16           35  \n",
       "19               0.000397           8           38  \n",
       "20               0.000283          25           77  \n",
       "21               0.000277          21           40  \n",
       "22               0.002556          92          149  \n",
       "23               0.000839          22           34  \n",
       "24               0.001092          51           75  \n",
       "25               0.000372          16           36  \n",
       "26               0.000433          25           75  \n",
       "27               0.004615          27           72  \n",
       "28               0.001241          26          139  \n",
       "29               0.000456           8           36  \n",
       "..                    ...         ...          ...  \n",
       "745              0.000349           2            3  \n",
       "746              0.000715           1            3  \n",
       "747              0.000281           2            4  \n",
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       "763              0.000460           0            0  \n",
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       "766              0.000477           3            6  \n",
       "767              0.000366           3            4  \n",
       "768              0.000340           0            0  \n",
       "769              0.000368           3            4  \n",
       "770              0.000272           0            1  \n",
       "771              0.000275           0            2  \n",
       "772              0.000323           0            0  \n",
       "773              0.000283           0            0  \n",
       "774              0.000328           0            0  \n",
       "\n",
       "[775 rows x 15 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainedModel.export_final_rule_population_DCAL(\"defaultExportDir/popData2.csv\",headers,classLabel)\n",
    "\n",
    "populationData2 = pd.read_csv(\"defaultExportDir/popData2.csv\")\n",
    "display(populationData2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 5: Population Reboot\n",
    "The final feature to introduce is the ability to \"reboot\" past rule populations. In some cases, you might want to run training, pause training, and resume training at some point in the future. Alternatively, you might want to train the generated rule population on new datasets or with different hyperparameters. The population reboot feature allows you to \"save\" the current rule population into a txt file for future use. You can later initialize a new XCS estimator with this txt file. When you run fit, the new XCS estimator will effectively start off where the saved model left off, beginning with the saved rule population, as well as starting at the next sequential learning iteration and adding to the last model's training times. A demo of this is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.69189453125\n",
      "0.69189453125\n",
      "0.77392578125\n",
      "0.77392578125\n",
      "0.82861328125\n",
      "0.82861328125\n",
      "0.9013671875\n"
     ]
    }
   ],
   "source": [
    "converter = StringEnumerator(\"test/DataSets/Real/Multiplexer11.csv\",\"class\")\n",
    "headers,classLabel,dataFeatures,dataActions = converter.get_params()\n",
    "\n",
    "#Run 1\n",
    "model = XCS(learning_iterations=1000)\n",
    "model.fit(dataFeatures,dataActions)\n",
    "print(model.score(dataFeatures,dataActions))\n",
    "model.pickle_model(\"defaultExportDir/savedModel1\")\n",
    "\n",
    "#Run 2 w/ rebooted first model\n",
    "model2 = XCS(learning_iterations=1000,reboot_filename=\"defaultExportDir/savedModel1\")\n",
    "print(model2.score(dataFeatures,dataActions))\n",
    "model2.fit(dataFeatures,dataActions)\n",
    "print(model2.score(dataFeatures,dataActions))\n",
    "model2.pickle_model(\"defaultExportDir/savedModel2\")\n",
    "\n",
    "#Run 3 w/ rebooted second model\n",
    "model3 = XCS(learning_iterations=1000,reboot_filename=\"defaultExportDir/savedModel2\")\n",
    "print(model3.score(dataFeatures,dataActions))\n",
    "model3.fit(dataFeatures,dataActions)\n",
    "print(model3.score(dataFeatures,dataActions))\n",
    "model3.pickle_model(\"defaultExportDir/savedModel3\")\n",
    "\n",
    "#Run 4 w/ rebooted third model\n",
    "model4 = XCS(learning_iterations=1000,reboot_filename=\"defaultExportDir/savedModel3\")\n",
    "print(model4.score(dataFeatures,dataActions))\n",
    "model4.fit(dataFeatures,dataActions)\n",
    "print(model4.score(dataFeatures,dataActions))\n"
   ]
  }
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