eLCS User Guide.ipynb

{
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   "source": [
    "# scikit-eLCS 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",
    "Date: 02/24/2020\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-eLCS</li>\n",
    "    <li>scikit-learn</li>\n",
    "    <li>pandas</li>\n",
    "    <li>mathplotlib</li>\n",
    "</ul>\n",
    "\n",
    "## Introduction\n",
    "\n",
    "This notebook presents a complete user guide to the core sklearn-compatible scikit-eLCS package as well as auxilliary helper objects included in the package. eLCS is a supervised learning variant of the Learning Classifier System. 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",
    "eLCS, or Educational Learning Classifier System, implements the core components of a Michigan-Style Learning Classifier System (where the system's genetic algorithm operates on a rule level, evolving a population of rules with each their own parameters) in an easy to understand way, while still being highly functional in solving ML problems.\n",
    "\n",
    "While Learning Classifier Systems are commonly applied to genetic analyses, where epistatis (i.e. feature interactions) is common, the eLCS algorithm implemented in this package 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",
    "It is important to mention that the eLCS package does not currently support regression problems (continuous endpoints). Regression functionality is built into the package. However, we have decided to disable it for this version, since eLCS performs best on purely classification problems.\n",
    "\n",
    "## Notebook Organization\n",
    "\n",
    "**Part 1: Loading Data**\n",
    "<ul>\n",
    "    <li> Dataset format requirements for scikit-eLCS</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 eLCS package)</li>\n",
    "</ul>\n",
    "\n",
    "**Part 2: Initializing eLCS Estimator Object**\n",
    "<ul>\n",
    "    <li> Basic Initialization</li>\n",
    "    <li> Overview of all scikit-eLCS 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-eLCS methods\n",
    "</ul>\n",
    "\n",
    "**Part 4: Data Collection and Export**\n",
    "<ul>\n",
    "    <li> trackingAccuracyWhileFit and other tracking metrics</li>\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-eLCS\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 Phenotypes 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> phenotype 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 phenotype 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 eLCS 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 eLCS 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,
   "metadata": {},
   "outputs": [
    {
     "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 Phenotypes\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 phenotype label\n",
    "classLabel = \"class\"\n",
    "\n",
    "#Derive the attribute and phenotype array using the phenotype label\n",
    "dataFeatures = data.drop(classLabel,axis = 1).values\n",
    "dataPhenotypes = data[classLabel].values\n",
    "\n",
    "#Optional: Retrieve the headers for each attribute as a length n array\n",
    "dataHeaders = data.drop(classLabel,axis=1).columns.values\n",
    "\n",
    "print(\"Data Features\")\n",
    "print(dataFeatures)\n",
    "print(\"\\nData Phenotypes\")\n",
    "print(dataPhenotypes)\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-eLCS model."
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "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 Phenotypes\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 phenotype label\n",
    "classLabel = \"phenotype\"\n",
    "\n",
    "#Derive the attribute and phenotype array using the phenotype label\n",
    "dataFeatures = data.drop(classLabel,axis = 1).values\n",
    "dataPhenotypes = data[classLabel].values\n",
    "\n",
    "#Optional: Retrieve the headers for each attribute as a length n array\n",
    "dataHeaders = data.drop(classLabel,axis=1).columns.values\n",
    "\n",
    "print(\"Data Features\")\n",
    "print(dataFeatures)\n",
    "print(\"\\nData Phenotypes\")\n",
    "print(dataPhenotypes)\n",
    "print(\"\\nData Headers\")\n",
    "print(dataHeaders)"
   ]
  },
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   "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-eLCS 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-eLCS 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:"
   ]
  },
  {
   "cell_type": "code",
   "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 Phenotypes\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 phenotype label\n",
    "classLabel = \"phenotype\"\n",
    "\n",
    "#Derive the attribute and phenotype array using the phenotype label\n",
    "dataFeatures = data.drop(classLabel,axis = 1).values\n",
    "dataPhenotypes = data[classLabel].values\n",
    "\n",
    "#Optional: Retrieve the headers for each attribute as a length n array\n",
    "dataHeaders = data.drop(classLabel,axis=1).columns.values\n",
    "\n",
    "print(\"Data Features\")\n",
    "print(dataFeatures)\n",
    "print(\"\\nData Phenotypes\")\n",
    "print(dataPhenotypes)\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 skeLCS 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:\n",
    "    "
   ]
  },
  {
   "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 Phenotypes\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,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)"
   ]
  },
  {
   "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-eLCS Estimator Object\n",
    "\n",
    "### Basic Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skeLCS import eLCS\n",
    "\n",
    "model = eLCS()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing the above will initialize an eLCS 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-eLCS 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 microclassifier population size (sum of classifier numerosities) | 1000 |\n",
    "| p_spec | float from 0 - 1 | Probability of specifying an attribute during the covering procedure | 0.5 |\n",
    "| nu (v) | float | Power parameter used to determine the importance of high accuracy when calculating fitness | 5 |\n",
    "| chi (X) | float from 0 - 1 | The probability of applying crossover in the GA | 0.8 |\n",
    "| mu (u) | float from 0 - 1 | The probability of mutating an allele within an offspring | 0.04 |\n",
    "| theta_GA | non negative float | The GA threshold. The GA is applied in the correct set when the average time (# of iterations) since the last GA in the correct set is greater than theta_GA | 25 |\n",
    "| theta_del | non negative integer | The deletion experience threshold; The calculation of the deletion probability changes once this threshold is passed | 20 |\n",
    "| theta_sub | non negative integer | The subsumption experience threshold | 20 |\n",
    "| acc_sub | float from 0 - 1 | Subsumption accuracy requirement | 0.99 |\n",
    "| beta | float | Learning parameter; Used in calculating average correct set size | 0.2 |\n",
    "| delta | float | Deletion parameter; Used in determining deletion vote calculation | 0.1 |\n",
    "| init_fit | float | The initial fitness for a new classifier (typically very small, approaching but not equal to zero) | 0.01 |\n",
    "| fitness_reduction | float | Initial fitness reduction in GA offspring rules | 0.1 |\n",
    "| do_correct_set_subsumption | boolean | Determines if subsumption is done in correct set| False |\n",
    "| do_GA_subsumption | boolean | Determines if subsumption is done after GA between parents and offspring classifiers| True |\n",
    "| selection_method | \"tournament\" or \"roulette\" | Determines GA selection method | \"tournament\" |\n",
    "| theta_sel | float from 0 - 1 | The fraction of the correct set to be included in tournament selection | 0.5 |\n",
    "| match_for_missingness | boolean | Whether missing values in instance can match with specified attributes | False |\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_spec | the more attributes in the data set, the lower p_spec should be. Too low of a p_spec and the population initialization will be less suited to detecting complex patterns, but too high can run the risk of early overfitting |\n",
    "| nu (v) | 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",
    "| chi (X) | recommended to use default parameter |\n",
    "| mu (u) | recommended to use default parameter |\n",
    "| theta_GA | recommended to use default parameter |\n",
    "| theta_del | recommended to use default parameter |\n",
    "| theta_sub | recommended to use default parameter |\n",
    "| acc_sub | for clean problems, recommended this is very close to or equal to 1 |\n",
    "| beta | recommended to use default parameter |\n",
    "| delta | recommended to use default parameter |\n",
    "| init_fit | recommended to use default parameter |\n",
    "| fitness_reduction | recommended to use default parameter |\n",
    "| do_correct_set_subsumption | recommended to use default parameter |\n",
    "| do_GA_subsumption | recommended to use default parameter |\n",
    "| selection_method | tournament selection has been found to yield higher performance |\n",
    "| theta_sel | recommended to use default parameter |\n",
    "| match_for_missingness | 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",
    "| track_accuracy_while_fit | boolean | Determines if live accuracy tracking is done during model training | False |\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",
    "These hyperparameters can be set during initialization. There exists built in parameter checking to ensure each specified parameter is valid. Below is an example:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = eLCS(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": [
       "eLCS(N=1000, acc_sub=0.99, beta=0.2, chi=0.8, delta=0.1,\n",
       "     discrete_attribute_limit=10, do_GA_subsumption=True,\n",
       "     do_correct_set_subsumption=False, fitness_reduction=0.1, init_fit=0.01,\n",
       "     learning_iterations=5000, match_for_missingness=False, mu=0.04, nu=5,\n",
       "     p_spec=0.5, random_state=None, reboot_filename=None,\n",
       "     selection_method='tournament',\n",
       "     specified_attributes=array([], dtype=float64), theta_GA=25, theta_del=20,\n",
       "     theta_sel=0.5, theta_sub=20, track_accuracy_while_fit=True)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skeLCS import StringEnumerator\n",
    "from skeLCS import eLCS\n",
    "import numpy as np\n",
    "\n",
    "#Use StringEnumerator to gather data\n",
    "converter = StringEnumerator(\"test/DataSets/Real/Multiplexer11.csv\",\"class\")\n",
    "headers,classLabel,dataFeatures,dataPhenotypes = converter.get_params()\n",
    "\n",
    "#Initialize and train model\n",
    "model = eLCS(learning_iterations = 5000,track_accuracy_while_fit=True)\n",
    "trainedModel = model.fit(dataFeatures,dataPhenotypes)\n",
    "\n",
    "trainedModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit function returns a trained model. Below we describe the entire training process:\n",
    "\n",
    "<ul>\n",
    "    <li> Step 1: Model verifies X and y are numeric, and that y has no missing values. If not, an Exception is raised. </li>\n",
    "    <li> Step 2: Model performs analysis on X and y. Using the discrete_attribute_limit and specified_attributes parameters, it determines and enumerates which attributes are discrete and which attributes are continuous. Using the discretePhenotypeLimit param, it determines whether the phenotype is discrete or continuous. If the phenotype is found to be continuous, the model will raise an Exception. </li>\n",
    "    <li> Step 3: Model completes the specified number of training iterations. If evalWhileFit is True, during the training process, performance data will be collected and stored for later analysis (specified in depth in Part 4). Below is a simplified diagram of the training cycle</li>\n",
    "</ul>\n",
    "\n",
    "<img src=\"LearningCycle.png\">"
   ]
  },
  {
   "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 class 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 class 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([[9.99965031e-01, 3.49688079e-05],\n",
       "       [9.99049562e-01, 9.50438044e-04],\n",
       "       [9.99958460e-01, 4.15398159e-05],\n",
       "       ...,\n",
       "       [1.71556505e-02, 9.82844350e-01],\n",
       "       [6.57137118e-01, 3.42862882e-01],\n",
       "       [4.27167533e-05, 9.99957283e-01]])"
      ]
     },
     "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 class values. By default, score(X,y) uses balanced accuracy to score.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.984375"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainedModel.score(dataFeatures,dataPhenotypes)"
   ]
  },
  {
   "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": [
       "0.9775442598023242"
      ]
     },
     "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],dataPhenotypes,1)\n",
    "np.random.shuffle(formatted)\n",
    "dataFeatures = np.delete(formatted,-1,axis=1)\n",
    "dataPhenotypes = formatted[:,-1]\n",
    "\n",
    "np.mean(cross_val_score(trainedModel,dataFeatures,dataPhenotypes,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": [
       "1.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cross_val_score(trainedModel,dataFeatures,dataPhenotypes,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": [
       "0.9991823430078041"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cross_val_score(trainedModel,dataFeatures,dataPhenotypes,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": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yan3gjY52HBHL0pbDHUATcGVEzJZ0HjAtIiZX3oOZmRWhUh/BoyRzEAwmuQ201dvAE1l2HhFTgCllZee2U3fPLPs0M7PqqtRH8BzwHHBXceGYmVnRKl0a+nNE7CHpdaD0Th0BERH9c4/OzMxyV+nSUOt0lAOKCMTMzGqj3buGSp4mHgI0RcRyYDfg88CHCojNzMwKkOX20T+QTFO5OfBzYARwTa5RmZlZYbIkghURsRQ4FJgYEaeTDB9hZmZ1IEsiWCbp08DxwC1pWa/8QjIzsyJlfbJ4L5JhqOdLGg5cm29YZmZWlA6HoY6IWZJOA7aQtDXJ0NLfzT80MzMrQoeJQNIngF+SDBgn4GOSjo+IB/IOzszM8pdlYpofAQe2ziwmaRuSxOBpJc3M6kCWPoLepdNLRsRTQO/8QjIzsyJlaRE8Luky4Ffp8rFkHHTOzMzWflkSwSnAacDX0uW/ABNzi8jMzApVMRFIGgVsDtwYEecXE5KZmRWp3T4CSf+XZHiJY4E/SmprpjIzM+vmKrUIjgW2j4h3JQ0kmWDmymLCMjOzolS6a+j9iHgXICIWdlDXzMy6qUotgs1K5ioWsHnp3MURcWiukZmZWSEqJYLDypYvzjMQMzOrjUpzFt9dZCBmZlYbvu5vZtbgnAjMzBpc5kQgaZ08AzEzs9roMBFIGivpSeCZdHkHSR5iwsysTmRpEVwEHAwsAoiIGSQzlpmZWR3Ikgh6RMQLZWXL8wjGzMyKl2X00ZckjQVCUhPwJWBuvmGZmVlRsrQITgXOAIYCrwC7pmVmZlYHskxe/ypwVAGxmJlZDWSZvP6nQJSXR8TJGbbdH/gJ0ARcERHfL1t/BvDvwDJgIfC5NvojzMwsR1n6CO4qed0H+BTwUkcbpf0JlwD7Ai3AVEmTS+c/Jpnysjki3pN0KnA+cGTW4M3MbM1luTR0femypF8C92fY91hgXkTMT7e7DhgPrEwEEXFPSf2HgeMy7NfMzKqoK0NMDAc+mqHeIFZtObSkZe05CbitrRWSTpY0TdK0hQsXZg7UzMw6lqWP4HU+6CPoAbwGnF3NICQdBzQDe7S1PiImAZMAmpubV+uvMDOzruto8noBOwAL0qIVEZH1g3gBMKRkeXDJfkqPsQ9wDrBHRLyfcd9mZlYlFS8NpR/6UyJiefrTmW/jU4ERkoZL6k1yC+rk0gqSdgQuB8alt6mamVnBsvQRTE8/sDslIpYBE4A7gKeAGyJitqTzJI1Lq/0QWA/4jaTpkia3szszM8tJu5eGJPVMP8x3JLn181ngXZL5iyMixnS084iYAkwpKzu35PU+XQ3czMyqo1IfwaPAGGBchTpmZtbNVUoEAoiIZwuKxczMaqBSIhiYDgHRpoi4MId4zMysYJUSQRNJR64KisXMzGqgUiJ4OSLOKywSMzOriUq3j7olYGbWACq1CPYuLAozswIsXbqUlpYWlixZUutQctOnTx8GDx5Mr169Mm/TbiKIiNeqEpWZ2VqipaWF9ddfn2HDhpGMoFNfIoJFixbR0tLC8OHDM2/XldFHzcy6pSVLlrDhhhvWZRIAkMSGG27Y6RaPE4GZNZR6TQKtunJ+TgRmZg3OicDMrEBNTU2MHj2a7bbbjkMOOYQ33nhj5brZs2fzyU9+kq222ooRI0bw7W9/m9JBn2+77Taam5sZOXIkO+64I2eeeWZVYnIiMDMr0Lrrrsv06dOZNWsW/fv355JLLgFg8eLFjBs3jrPPPpunn36aGTNm8OCDD3LppZcCMGvWLCZMmMCvfvUr5syZw7Rp09hiiy2qElOWyevNzOrOt26ezZy/vVXVfY7cpB/fOGTbzPV32203Zs6cCcA111zD7rvvzn777QdA3759ufjii9lzzz354he/yPnnn88555zD1ltvDSQti1NPPbUqcbtFYGZWA8uXL+fuu+9m3LhkgOfZs2ez0047rVJn880355133uGtt95i1qxZq62vFrcIzKwhdeabezUtXryY0aNHs2DBArbZZhv23XffmsRRyi0CM7MCtfYRvPDCC0TEyj6CkSNH8thjj61Sd/78+ay33nr069ePbbfddrX11eJEYGZWA3379uWiiy7iggsuYNmyZRx77LHcf//93HXXXUDScjjttNP42te+BsBZZ53F9773PebOnQvAihUruOyyy6oSixOBmVmN7Ljjjmy//fZce+21rLvuutx000185zvfYauttmLUqFHsvPPOTJgwAYDtt9+eH//4xxx99NFss802bLfddsyfP78qcbiPwMysQO+8884qyzfffPPK16NGjeLee+9td9uDDz6Ygw8+uOoxuUVgZtbgnAjMzBqcE4GZWYNzIjAza3BOBGZmDc6JwMyswTkRmJkVqHQY6k9/+tO89957q5WXD089d+5cDjzwQEaMGMGYMWM44ogjeOWVV6oWkxOBmVmBSoeh7t2798qng9sbnnrJkiUcdNBBnHrqqTzzzDM8/vjjfOELX2DhwoVVi8kPlJlZY7rtbPj7k9Xd58dGwQHfz1z9E5/4xMphqEuVD0+92267ccghh6xcv+eee65xqKXcIjAzq4Fly5Zx2223MWrUqFXKy4enznP46VZuEZhZY+rEN/dqah2GGpIWwUknnbRKeS2Gp861RSBpf0lPS5on6ew21q8j6fp0/SOShuUZj5lZrbX2BUyfPp2JEyfSu3fvVcrLh6fOc/jpVrklAklNwCXAAcBI4GhJI8uqnQS8HhFbAD8CfpBXPGZm3UH58NTHHHMMDz74ILfeeuvKOvfddx+zZs2q2jHzbBGMBeZFxPyI+CdwHTC+rM544Bfp698Ce0tSjjGZma31yoenvuWWW5g4cSIjRoxg5MiRXHrppQwcOLBqx8uzj2AQ8FLJcguwS3t1ImKZpDeBDYF/lFaSdDJwMsDQoUO7FMzITfp1aTszs2oqH4a6vfLS4am33nprbr/99txi6hadxRExCZgE0NzcHF3ZR63mJzUzW9vleWloATCkZHlwWtZmHUk9gQ2ARTnGZGZmZfJMBFOBEZKGS+oNHAVMLqszGTghfX048KeI6NI3fjOzLOr9I6Yr55dbIoiIZcAE4A7gKeCGiJgt6TxJ49JqPwM2lDQPOANY7RZTM7Nq6dOnD4sWLarbZBARLFq0iD59+nRqO3W3N6S5uTmmTZtW6zDMrBtaunQpLS0tLFmypNah5KZPnz4MHjyYXr16rVIu6bGIaG5rm27RWWxmVg29evVi+PDhtQ5jreOxhszMGpwTgZlZg3MiMDNrcN2us1jSQuCFLm4+gLKnlhuAz7kx+Jwbw5qc86YR0ea4FN0uEawJSdPa6zWvVz7nxuBzbgx5nbMvDZmZNTgnAjOzBtdoiWBSrQOoAZ9zY/A5N4Zczrmh+gjMzGx1jdYiMDOzMk4EZmYNri4TgaT9JT0taZ6k1UY0lbSOpOvT9Y9IGlZ8lNWV4ZzPkDRH0kxJd0vatBZxVlNH51xS7zBJIanb32qY5ZwlHZH+rWdLuqboGKstw7/toZLukfRE+u/7wFrEWS2SrpT0qqQ2JyVW4qL0/ZgpacwaHzQi6uoHaAKeBTYDegMzgJFldb4AXJa+Pgq4vtZxF3DOewF909enNsI5p/XWB+4DHgaaax13AX/nEcATwEfS5Y1qHXcB5zwJODV9PRJ4vtZxr+E5/wswBpjVzvoDgdsAAbsCj6zpMeuxRTAWmBcR8yPin8B1wPiyOuOBX6SvfwvsLUkFxlhtHZ5zRNwTEe+liw+TzBjXnWX5OwN8G/gBUA/jDmc55/8ALomI1wEi4tWCY6y2LOccQOuk5BsAfyswvqqLiPuA1ypUGQ9cHYmHgQ9L2nhNjlmPiWAQ8FLJckta1madSCbQeRPYsJDo8pHlnEudRPKNojvr8JzTJvOQiLi1yMBylOXvvCWwpaQHJD0saf/CostHlnP+JnCcpBZgCvClYkKrmc7+f++Q5yNoMJKOA5qBPWodS54k9QAuBE6scShF60lyeWhPklbffZJGRcQbNY0qX0cDV0XEBZJ2A34pabuIWFHrwLqLemwRLACGlCwPTsvarCOpJ0lzclEh0eUjyzkjaR/gHGBcRLxfUGx56eic1we2A+6V9DzJtdTJ3bzDOMvfuQWYHBFLI+I5YC5JYuiuspzzScANABHxENCHZHC2epXp/3tn1GMimAqMkDRcUm+SzuDJZXUmAyekrw8H/hRpL0w31eE5S9oRuJwkCXT368bQwTlHxJsRMSAihkXEMJJ+kXER0Z3nOc3yb/sPJK0BJA0guVQ0v8ggqyzLOb8I7A0gaRuSRLCw0CiLNRn4THr30K7AmxHx8prssO4uDUXEMkkTgDtI7ji4MiJmSzoPmBYRk4GfkTQf55F0yhxVu4jXXMZz/iGwHvCbtF/8xYgYV7Og11DGc64rGc/5DmA/SXOA5cBZEdFtW7sZz/lM4KeSTifpOD6xO3+xk3QtSTIfkPZ7fAPoBRARl5H0gxwIzAPeAz67xsfsxu+XmZlVQT1eGjIzs05wIjAza3BOBGZmDc6JwMyswTkRmJk1OCcCqypJyyVNL/kZVqHusPZGWOzkMe9NR6eckQ6tsFUX9nGKpM+kr0+UtEnJuiskjaxynFMljc6wzVck9c24/6MknZPGvzB9//+a3lZZVem5NKevn0+fWbBuyonAqm1xRIwu+Xm+oOMeGxE7kAwm+MPObhwRl0XE1eniicAmJev+PSLmVCXKD+K8lGxxfgXIlAiAA4Db09fXR8RoYHfgHElD2t/MGp0TgeUu/eb/F0mPpz8fb6POtpIeTb/FzpQ0Ii0/rqT8cklNHRzuPmCLdNu90zHqn0zHeF8nLf++Ppib4f+lZd+U9FVJh5OMxfTr9Jjrtn77TVsNKz+802/eF3cxzocoGShM0v9KmqZkDoFvpWWnkSSkeyTdk5btJ+mh9H38jaT10nIBo4HHSw+SPkw2D9g4rTdQ0u/SFslUSbun5etJ+nn6Xs2UdFh7cVn9cSKwalu35LLQjWnZq8C+ETEGOBK4qI3tTgF+kn6LbQZalAwXcCSwe1q+HDi2g+MfAjwpqQ9wFXBkRIwieYr+VEkbAp8Cto2I7YHvlG4cEb8FppF8cx8dEYtLVv8u3bbVkcB1XYxzf5LhIFqdExHNwPbAHpK2j4iLSIZU3isi9kovv3wd2Cd9L6cBZ6Tb7wjMKH+iVtJQkiEXZqZFPwF+FBE7A4cBV6Tl/00yVMGo9H35U3txdXBe1g3V3RATVnOL0w/DUr2Ai9Nr4stJxr8p9xDJJYzBwO8j4hlJewM7AVOTL7ysS5JU2vJrSYuB50mGId4KeC4i5qbrfwF8EbiYZG6Cn0m6Bbgl64lFxEJJ85WM7/IMsDXwQLrfzsTZm2S4j9L36QhJJ5P8n9yYZIKVmWXb7pqWP5AepzfJ+wZJYikdWvxISf+SxjghIlrnY9gHGKkPpt/ol7Yq9qFkqJXW+QwyxmXdnBOBFeF04BVgB5JW6GqTxETENZIeAQ4Cpkj6PMkMTL+IiP/KcIxjSweUk9S/rUrp2DVjSQYpOxyYAHyyE+dyHXAE8FfgxoiI9LJM5jiBx0j6ByYCh0oaDnwV2DkiXpd0Fcm3+HIC/hgRR7exbj+Sb/itro+ICWmH7p2SJkfE30ne/11LEkOy4zbmZepEXNbN+dKQFWED4OV0fPjjSQYPW4WkzYD56eWQm0guRdwNHC5po7ROf2Wfa/lpYJikLdLl44E/p99+N4iIKSQJaoc2tn2bZBjrttxIMkPU0SRJgc7GmV6++W9gV0lbk8yu9S7wpqSPknT6thXLw8Dureck6UOStpS0AdCzrcHl0uT4S+DLadGdlEzcog/uXPojScumtfwjHcRldcSJwIpwKXCCpBkklyrebaPOEcAsSdNJ5hG4Or1T5+sk32hnknxYZZqSL/3G+1mS0VafBFYAl5F8qN6S7u9+PrjGXuoq4LLWzuKy/b4OPAVsGhGPpmWdjjPte7iAZHTQGSTzDP8VuIbkclOrScDtku6JiIUkdzRdmx7nIZL3c1/grgqH+wHwWUnrA6cBzWmH8BySvhlI+ko+ImlW+nfaq4O4rI549FGzbk7SFcAV6fy1Zp3mRGBm1uB8acjMrME5EUIdrjwAAAAgSURBVJiZNTgnAjOzBudEYGbW4JwIzMwanBOBmVmD+/+K3xioh9UL9gAAAABJRU5ErkJggg==\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:0.9986820022748919\n",
      "ROC AUC:0.9986743927001953\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(dataPhenotypes, probs[:, 1])\n",
    "prec, recall, thresholds = precision_recall_curve(dataPhenotypes, 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-eLCS methods\n",
    "In addition to the standard sklearn methods you can use to access training metrics, scikit-eLCS provides a few of its own.\n",
    "\n",
    "| Method Name | Description |\n",
    "| :--- | :--- |\n",
    "| model.get_final_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: 0.984375\n",
      "Final Instance Coverage: 1.0\n",
      "Final Attribute Specificity List: [734, 684, 746, 296, 372, 364, 327, 241, 337, 279, 297]\n",
      "Final Attribute Accuracy List: [649.6981058619263, 598.064126069178, 656.6843750491146, 249.284239438538, 314.94235439045184, 306.99516136979275, 277.0620295805132, 205.70863738110907, 291.8049613751088, 242.50440696026993, 255.2147484040737]\n"
     ]
    }
   ],
   "source": [
    "print(\"Final Training Accuracy: \"+str(trainedModel.get_final_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",
    "### track_accuracy_while_fit and other tracking metrics\n",
    "As mentioned in Part 2, there exists the parameter **track_accuracy_while_fit** that determines whether the approximate accuracy of the model is computed during training or not. The benefit to doing this during training is that you will be able to collect data on how accuracy of the model changes over many iterations. The downside to tracking approximate accuracy during training is that evaluation always slows down the training process (but by a relatively small amount). Approximate accuracy is computed via a 50 iteration moving average of prediction accuracies. At each iteration, if **track_accuracy_while_fit** is true, eLCS will make a prediction of the current instance's class. The correctness of this prediction is factored into the moving average.\n",
    "\n",
    "In addition to tracking approximate accuracy, scikit-eLCS also tracks a series of other 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>Average rule population generality (average percentage of rule that is generalized)</li>\n",
    "    <li>Macropopulation size</li>\n",
    "    <li>Micropopulation size</li>\n",
    "    <li>Match set size</li>\n",
    "    <li>Correct set size</li>\n",
    "    <li>Average iteration age of classifiers in correct 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",
    "\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 eLCS 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting and Accessing Iteration Tracking Data\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": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Correct Set Size</th>\n",
       "      <th>Average Iteration Age of Correct 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 Selection 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",
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       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.066701</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000089</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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.066791</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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>0.066870</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</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>0.066948</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067015</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067089</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</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>0.067173</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067254</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067336</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067423</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067501</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067574</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0.067656</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067736</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0.067823</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067916</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.067993</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068081</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068169</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068256</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>3</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>0.068360</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068447</td>\n",
       "      <td>0.000302</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068542</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068650</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068742</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19</td>\n",
       "      <td>19</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>0.068928</td>\n",
       "      <td>0.000409</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069033</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.821429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069120</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069217</td>\n",
       "      <td>0.000497</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069318</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001338</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>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>609</td>\n",
       "      <td>1000</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>1764.138889</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.752962</td>\n",
       "      <td>4.247859</td>\n",
       "      <td>1.934480</td>\n",
       "      <td>0.770750</td>\n",
       "      <td>0.148169</td>\n",
       "      <td>0.546169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4971</th>\n",
       "      <td>4971</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>609</td>\n",
       "      <td>1000</td>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>2182.357143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.754011</td>\n",
       "      <td>4.248717</td>\n",
       "      <td>1.934481</td>\n",
       "      <td>0.770750</td>\n",
       "      <td>0.148169</td>\n",
       "      <td>0.546251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4972</th>\n",
       "      <td>4972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>607</td>\n",
       "      <td>1000</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>3668.526316</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.756590</td>\n",
       "      <td>4.249628</td>\n",
       "      <td>1.935639</td>\n",
       "      <td>0.770869</td>\n",
       "      <td>0.148233</td>\n",
       "      <td>0.546359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4973</th>\n",
       "      <td>4973</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>607</td>\n",
       "      <td>1000</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>1659.135593</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.759090</td>\n",
       "      <td>4.250501</td>\n",
       "      <td>1.936757</td>\n",
       "      <td>0.771021</td>\n",
       "      <td>0.148283</td>\n",
       "      <td>0.546448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4974</th>\n",
       "      <td>4974</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>607</td>\n",
       "      <td>1000</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>2137.171429</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.760192</td>\n",
       "      <td>4.251393</td>\n",
       "      <td>1.936758</td>\n",
       "      <td>0.771021</td>\n",
       "      <td>0.148283</td>\n",
       "      <td>0.546538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4975</th>\n",
       "      <td>4975</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>607</td>\n",
       "      <td>1000</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>2467.862069</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.762684</td>\n",
       "      <td>4.252201</td>\n",
       "      <td>1.937814</td>\n",
       "      <td>0.771261</td>\n",
       "      <td>0.148348</td>\n",
       "      <td>0.546621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4976</th>\n",
       "      <td>4976</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>607</td>\n",
       "      <td>1000</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>3402.969697</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.763982</td>\n",
       "      <td>4.253119</td>\n",
       "      <td>1.937814</td>\n",
       "      <td>0.771261</td>\n",
       "      <td>0.148403</td>\n",
       "      <td>0.546731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4977</th>\n",
       "      <td>4977</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>609</td>\n",
       "      <td>1000</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>2539.880952</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.767105</td>\n",
       "      <td>4.253961</td>\n",
       "      <td>1.939097</td>\n",
       "      <td>0.771942</td>\n",
       "      <td>0.148442</td>\n",
       "      <td>0.546808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4978</th>\n",
       "      <td>4978</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>609</td>\n",
       "      <td>1000</td>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>2221.480769</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.769914</td>\n",
       "      <td>4.254871</td>\n",
       "      <td>1.940226</td>\n",
       "      <td>0.772218</td>\n",
       "      <td>0.148503</td>\n",
       "      <td>0.546975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4979</th>\n",
       "      <td>4979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>609</td>\n",
       "      <td>1000</td>\n",
       "      <td>42</td>\n",
       "      <td>37</td>\n",
       "      <td>2555.685714</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.771306</td>\n",
       "      <td>4.255877</td>\n",
       "      <td>1.940228</td>\n",
       "      <td>0.772218</td>\n",
       "      <td>0.148503</td>\n",
       "      <td>0.547124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4980</th>\n",
       "      <td>4980</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>609</td>\n",
       "      <td>1000</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>3007.950000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.772742</td>\n",
       "      <td>4.257050</td>\n",
       "      <td>1.940229</td>\n",
       "      <td>0.772218</td>\n",
       "      <td>0.148503</td>\n",
       "      <td>0.547278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4981</th>\n",
       "      <td>4981</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>609</td>\n",
       "      <td>1000</td>\n",
       "      <td>43</td>\n",
       "      <td>36</td>\n",
       "      <td>3285.509091</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.776040</td>\n",
       "      <td>4.258060</td>\n",
       "      <td>1.941573</td>\n",
       "      <td>0.772676</td>\n",
       "      <td>0.148577</td>\n",
       "      <td>0.547383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4982</th>\n",
       "      <td>4982</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>609</td>\n",
       "      <td>1000</td>\n",
       "      <td>29</td>\n",
       "      <td>25</td>\n",
       "      <td>3147.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.777336</td>\n",
       "      <td>4.258947</td>\n",
       "      <td>1.941575</td>\n",
       "      <td>0.772676</td>\n",
       "      <td>0.148633</td>\n",
       "      <td>0.547508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4983</th>\n",
       "      <td>4983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>609</td>\n",
       "      <td>1000</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>2540.693333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.778787</td>\n",
       "      <td>4.259890</td>\n",
       "      <td>1.941577</td>\n",
       "      <td>0.772676</td>\n",
       "      <td>0.148709</td>\n",
       "      <td>0.547626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4984</th>\n",
       "      <td>4984</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>609</td>\n",
       "      <td>1000</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>2464.661290</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.780299</td>\n",
       "      <td>4.261042</td>\n",
       "      <td>1.941579</td>\n",
       "      <td>0.772676</td>\n",
       "      <td>0.148709</td>\n",
       "      <td>0.547788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4985</th>\n",
       "      <td>4985</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>607</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>2800.205882</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.784222</td>\n",
       "      <td>4.262554</td>\n",
       "      <td>1.943422</td>\n",
       "      <td>0.772689</td>\n",
       "      <td>0.148768</td>\n",
       "      <td>0.548011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4986</th>\n",
       "      <td>4986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>607</td>\n",
       "      <td>1000</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>2689.369565</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.785742</td>\n",
       "      <td>4.263783</td>\n",
       "      <td>1.943424</td>\n",
       "      <td>0.772689</td>\n",
       "      <td>0.148768</td>\n",
       "      <td>0.548151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4987</th>\n",
       "      <td>4987</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>606</td>\n",
       "      <td>1000</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>2879.711538</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.788734</td>\n",
       "      <td>4.264687</td>\n",
       "      <td>1.944735</td>\n",
       "      <td>0.773062</td>\n",
       "      <td>0.148824</td>\n",
       "      <td>0.548274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4988</th>\n",
       "      <td>4988</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>606</td>\n",
       "      <td>1000</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>2533.463415</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.790431</td>\n",
       "      <td>4.265900</td>\n",
       "      <td>1.944737</td>\n",
       "      <td>0.773062</td>\n",
       "      <td>0.148824</td>\n",
       "      <td>0.548472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4989</th>\n",
       "      <td>4989</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>606</td>\n",
       "      <td>1000</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>2293.678571</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.793395</td>\n",
       "      <td>4.266848</td>\n",
       "      <td>1.945955</td>\n",
       "      <td>0.773453</td>\n",
       "      <td>0.148891</td>\n",
       "      <td>0.548587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4990</th>\n",
       "      <td>4990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>605</td>\n",
       "      <td>1000</td>\n",
       "      <td>38</td>\n",
       "      <td>31</td>\n",
       "      <td>3017.098039</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.796632</td>\n",
       "      <td>4.267822</td>\n",
       "      <td>1.947187</td>\n",
       "      <td>0.774076</td>\n",
       "      <td>0.148952</td>\n",
       "      <td>0.548699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4991</th>\n",
       "      <td>4991</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>605</td>\n",
       "      <td>1000</td>\n",
       "      <td>35</td>\n",
       "      <td>34</td>\n",
       "      <td>2764.803030</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.797794</td>\n",
       "      <td>4.268711</td>\n",
       "      <td>1.947188</td>\n",
       "      <td>0.774076</td>\n",
       "      <td>0.148952</td>\n",
       "      <td>0.548810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4992</th>\n",
       "      <td>4992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>605</td>\n",
       "      <td>1000</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>1968.551724</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.800674</td>\n",
       "      <td>4.269542</td>\n",
       "      <td>1.948553</td>\n",
       "      <td>0.774369</td>\n",
       "      <td>0.149015</td>\n",
       "      <td>0.548895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4993</th>\n",
       "      <td>4993</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>606</td>\n",
       "      <td>1000</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>2417.268657</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.804045</td>\n",
       "      <td>4.270483</td>\n",
       "      <td>1.949763</td>\n",
       "      <td>0.775062</td>\n",
       "      <td>0.149084</td>\n",
       "      <td>0.549017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4994</th>\n",
       "      <td>4994</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>607</td>\n",
       "      <td>1000</td>\n",
       "      <td>29</td>\n",
       "      <td>25</td>\n",
       "      <td>2279.093023</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.806909</td>\n",
       "      <td>4.271426</td>\n",
       "      <td>1.951021</td>\n",
       "      <td>0.775326</td>\n",
       "      <td>0.149138</td>\n",
       "      <td>0.549128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>4995</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>607</td>\n",
       "      <td>1000</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>2624.730769</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.808009</td>\n",
       "      <td>4.272284</td>\n",
       "      <td>1.951022</td>\n",
       "      <td>0.775326</td>\n",
       "      <td>0.149138</td>\n",
       "      <td>0.549225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>4996</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>607</td>\n",
       "      <td>1000</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>3022.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.810269</td>\n",
       "      <td>4.273136</td>\n",
       "      <td>1.952105</td>\n",
       "      <td>0.775337</td>\n",
       "      <td>0.149183</td>\n",
       "      <td>0.549300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>4997</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>607</td>\n",
       "      <td>1000</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>2799.032258</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.811276</td>\n",
       "      <td>4.273945</td>\n",
       "      <td>1.952106</td>\n",
       "      <td>0.775337</td>\n",
       "      <td>0.149183</td>\n",
       "      <td>0.549379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>4998</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571182</td>\n",
       "      <td>606</td>\n",
       "      <td>1000</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "      <td>2342.600000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.813771</td>\n",
       "      <td>4.274793</td>\n",
       "      <td>1.953250</td>\n",
       "      <td>0.775540</td>\n",
       "      <td>0.149228</td>\n",
       "      <td>0.549457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>4999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.574818</td>\n",
       "      <td>606</td>\n",
       "      <td>1000</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>2854.080000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.815214</td>\n",
       "      <td>4.275592</td>\n",
       "      <td>1.953251</td>\n",
       "      <td>0.775540</td>\n",
       "      <td>0.149228</td>\n",
       "      <td>0.549533</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           1.000000                       0.000000   \n",
       "3             3           1.000000                       0.000000   \n",
       "4             4           1.000000                       0.000000   \n",
       "5             5           1.000000                       0.000000   \n",
       "6             6           1.000000                       0.000000   \n",
       "7             7           1.000000                       0.000000   \n",
       "8             8           1.000000                       0.000000   \n",
       "9             9           1.000000                       0.000000   \n",
       "10           10           1.000000                       0.000000   \n",
       "11           11           1.000000                       0.000000   \n",
       "12           12           1.000000                       0.000000   \n",
       "13           13           1.000000                       0.000000   \n",
       "14           14           1.000000                       0.000000   \n",
       "15           15           0.937500                       0.000000   \n",
       "16           16           0.941176                       0.000000   \n",
       "17           17           0.944444                       0.000000   \n",
       "18           18           0.947368                       0.000000   \n",
       "19           19           0.950000                       0.000000   \n",
       "20           20           0.904762                       0.000000   \n",
       "21           21           0.909091                       0.000000   \n",
       "22           22           0.913043                       0.000000   \n",
       "23           23           0.875000                       0.000000   \n",
       "24           24           0.880000                       0.000000   \n",
       "25           25           0.846154                       0.000000   \n",
       "26           26           0.814815                       0.000000   \n",
       "27           27           0.821429                       0.000000   \n",
       "28           28           0.827586                       0.000000   \n",
       "29           29           0.833333                       0.000000   \n",
       "...         ...                ...                            ...   \n",
       "4970       4970           1.000000                       0.571182   \n",
       "4971       4971           1.000000                       0.571182   \n",
       "4972       4972           1.000000                       0.571182   \n",
       "4973       4973           1.000000                       0.571182   \n",
       "4974       4974           1.000000                       0.571182   \n",
       "4975       4975           1.000000                       0.571182   \n",
       "4976       4976           1.000000                       0.571182   \n",
       "4977       4977           1.000000                       0.571182   \n",
       "4978       4978           1.000000                       0.571182   \n",
       "4979       4979           1.000000                       0.571182   \n",
       "4980       4980           1.000000                       0.571182   \n",
       "4981       4981           1.000000                       0.571182   \n",
       "4982       4982           1.000000                       0.571182   \n",
       "4983       4983           1.000000                       0.571182   \n",
       "4984       4984           1.000000                       0.571182   \n",
       "4985       4985           1.000000                       0.571182   \n",
       "4986       4986           1.000000                       0.571182   \n",
       "4987       4987           1.000000                       0.571182   \n",
       "4988       4988           1.000000                       0.571182   \n",
       "4989       4989           1.000000                       0.571182   \n",
       "4990       4990           1.000000                       0.571182   \n",
       "4991       4991           1.000000                       0.571182   \n",
       "4992       4992           1.000000                       0.571182   \n",
       "4993       4993           1.000000                       0.571182   \n",
       "4994       4994           1.000000                       0.571182   \n",
       "4995       4995           1.000000                       0.571182   \n",
       "4996       4996           1.000000                       0.571182   \n",
       "4997       4997           1.000000                       0.571182   \n",
       "4998       4998           1.000000                       0.571182   \n",
       "4999       4999           1.000000                       0.574818   \n",
       "\n",
       "      Macropopulation Size  Micropopulation Size  Match Set Size  \\\n",
       "0                        1                     1               1   \n",
       "1                        2                     2               1   \n",
       "2                        3                     3               1   \n",
       "3                        4                     4               1   \n",
       "4                        4                     4               1   \n",
       "5                        5                     5               1   \n",
       "6                        6                     6               1   \n",
       "7                        7                     7               1   \n",
       "8                        8                     8               1   \n",
       "9                        9                     9               1   \n",
       "10                       9                     9               1   \n",
       "11                       9                     9               1   \n",
       "12                      10                    10               1   \n",
       "13                      11                    11               1   \n",
       "14                      12                    12               1   \n",
       "15                      13                    13               2   \n",
       "16                      13                    13               1   \n",
       "17                      14                    14               1   \n",
       "18                      15                    15               1   \n",
       "19                      15                    15               2   \n",
       "20                      16                    16               3   \n",
       "21                      16                    16               1   \n",
       "22                      17                    17               1   \n",
       "23                      18                    18               3   \n",
       "24                      18                    18               2   \n",
       "25                      19                    19               2   \n",
       "26                      20                    20               2   \n",
       "27                      20                    20               1   \n",
       "28                      21                    21               1   \n",
       "29                      22                    22               1   \n",
       "...                    ...                   ...             ...   \n",
       "4970                   609                  1000              23   \n",
       "4971                   609                  1000              25   \n",
       "4972                   607                  1000              33   \n",
       "4973                   607                  1000              29   \n",
       "4974                   607                  1000              29   \n",
       "4975                   607                  1000              34   \n",
       "4976                   607                  1000              35   \n",
       "4977                   609                  1000              21   \n",
       "4978                   609                  1000              32   \n",
       "4979                   609                  1000              42   \n",
       "4980                   609                  1000              22   \n",
       "4981                   609                  1000              43   \n",
       "4982                   609                  1000              29   \n",
       "4983                   609                  1000              33   \n",
       "4984                   609                  1000              36   \n",
       "4985                   607                  1000              30   \n",
       "4986                   607                  1000              28   \n",
       "4987                   606                  1000              27   \n",
       "4988                   606                  1000              28   \n",
       "4989                   606                  1000              29   \n",
       "4990                   605                  1000              38   \n",
       "4991                   605                  1000              35   \n",
       "4992                   605                  1000              32   \n",
       "4993                   606                  1000              45   \n",
       "4994                   607                  1000              29   \n",
       "4995                   607                  1000              31   \n",
       "4996                   607                  1000              24   \n",
       "4997                   607                  1000              26   \n",
       "4998                   606                  1000              28   \n",
       "4999                   606                  1000              24   \n",
       "\n",
       "      Correct Set Size  Average Iteration Age of Correct Set Classifiers  \\\n",
       "0                    1                                          0.000000   \n",
       "1                    1                                          1.000000   \n",
       "2                    1                                          2.000000   \n",
       "3                    1                                          3.000000   \n",
       "4                    1                                          1.000000   \n",
       "5                    1                                          5.000000   \n",
       "6                    1                                          6.000000   \n",
       "7                    1                                          7.000000   \n",
       "8                    1                                          8.000000   \n",
       "9                    1                                          9.000000   \n",
       "10                   1                                          5.000000   \n",
       "11                   1                                          0.000000   \n",
       "12                   1                                         12.000000   \n",
       "13                   1                                         13.000000   \n",
       "14                   1                                         14.000000   \n",
       "15                   1                                         15.000000   \n",
       "16                   1                                          5.000000   \n",
       "17                   1                                         17.000000   \n",
       "18                   1                                         18.000000   \n",
       "19                   2                                         10.500000   \n",
       "20                   1                                         20.000000   \n",
       "21                   1                                          1.000000   \n",
       "22                   1                                         22.000000   \n",
       "23                   1                                         23.000000   \n",
       "24                   2                                         10.500000   \n",
       "25                   1                                         25.000000   \n",
       "26                   1                                         26.000000   \n",
       "27                   1                                         26.000000   \n",
       "28                   1                                         28.000000   \n",
       "29                   1                                         29.000000   \n",
       "...                ...                                               ...   \n",
       "4970                19                                       1764.138889   \n",
       "4971                18                                       2182.357143   \n",
       "4972                33                                       3668.526316   \n",
       "4973                27                                       1659.135593   \n",
       "4974                17                                       2137.171429   \n",
       "4975                31                                       2467.862069   \n",
       "4976                28                                       3402.969697   \n",
       "4977                18                                       2539.880952   \n",
       "4978                31                                       2221.480769   \n",
       "4979                37                                       2555.685714   \n",
       "4980                10                                       3007.950000   \n",
       "4981                36                                       3285.509091   \n",
       "4982                25                                       3147.333333   \n",
       "4983                33                                       2540.693333   \n",
       "4984                32                                       2464.661290   \n",
       "4985                19                                       2800.205882   \n",
       "4986                25                                       2689.369565   \n",
       "4987                21                                       2879.711538   \n",
       "4988                24                                       2533.463415   \n",
       "4989                28                                       2293.678571   \n",
       "4990                31                                       3017.098039   \n",
       "4991                34                                       2764.803030   \n",
       "4992                32                                       1968.551724   \n",
       "4993                39                                       2417.268657   \n",
       "4994                25                                       2279.093023   \n",
       "4995                27                                       2624.730769   \n",
       "4996                19                                       3022.500000   \n",
       "4997                22                                       2799.032258   \n",
       "4998                22                                       2342.600000   \n",
       "4999                23                                       2854.080000   \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                                    1   \n",
       "4971                                    0   \n",
       "4972                                    1   \n",
       "4973                                    0   \n",
       "4974                                    0   \n",
       "4975                                    1   \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                                    0   \n",
       "4985                                    2   \n",
       "4986                                    0   \n",
       "4987                                    0   \n",
       "4988                                    0   \n",
       "4989                                    1   \n",
       "4990                                    0   \n",
       "4991                                    0   \n",
       "4992                                    1   \n",
       "4993                                    0   \n",
       "4994                                    1   \n",
       "4995                                    0   \n",
       "4996                                    2   \n",
       "4997                                    0   \n",
       "4998                                    1   \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                                              0   \n",
       "4971                                              0   \n",
       "4972                                              0   \n",
       "4973                                              1   \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                                              1   \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                                              1   \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                                             1   \n",
       "4973                                             1   \n",
       "4974                                             0   \n",
       "4975                                             1   \n",
       "4976                                             0   \n",
       "4977                                             1   \n",
       "4978                                             2   \n",
       "4979                                             0   \n",
       "4980                                             0   \n",
       "4981                                             1   \n",
       "4982                                             0   \n",
       "4983                                             0   \n",
       "4984                                             0   \n",
       "4985                                             0   \n",
       "4986                                             0   \n",
       "4987                                             1   \n",
       "4988                                             0   \n",
       "4989                                             2   \n",
       "4990                                             2   \n",
       "4991                                             0   \n",
       "4992                                             2   \n",
       "4993                                             2   \n",
       "4994                                             1   \n",
       "4995                                             0   \n",
       "4996                                             0   \n",
       "4997                                             0   \n",
       "4998                                             1   \n",
       "4999                                             0   \n",
       "\n",
       "      # Covering Operations Performed in Iteration  \\\n",
       "0                                                1   \n",
       "1                                                1   \n",
       "2                                                1   \n",
       "3                                                1   \n",
       "4                                                0   \n",
       "5                                                1   \n",
       "6                                                1   \n",
       "7                                                1   \n",
       "8                                                1   \n",
       "9                                                1   \n",
       "10                                               0   \n",
       "11                                               0   \n",
       "12                                               1   \n",
       "13                                               1   \n",
       "14                                               1   \n",
       "15                                               1   \n",
       "16                                               0   \n",
       "17                                               1   \n",
       "18                                               1   \n",
       "19                                               0   \n",
       "20                                               1   \n",
       "21                                               0   \n",
       "22                                               1   \n",
       "23                                               1   \n",
       "24                                               0   \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           0.066701   \n",
       "1                                                0           0.066791   \n",
       "2                                                0           0.066870   \n",
       "3                                                0           0.066948   \n",
       "4                                                0           0.067015   \n",
       "5                                                0           0.067089   \n",
       "6                                                0           0.067173   \n",
       "7                                                0           0.067254   \n",
       "8                                                0           0.067336   \n",
       "9                                                0           0.067423   \n",
       "10                                               0           0.067501   \n",
       "11                                               0           0.067574   \n",
       "12                                               0           0.067656   \n",
       "13                                               0           0.067736   \n",
       "14                                               0           0.067823   \n",
       "15                                               0           0.067916   \n",
       "16                                               0           0.067993   \n",
       "17                                               0           0.068081   \n",
       "18                                               0           0.068169   \n",
       "19                                               0           0.068256   \n",
       "20                                               0           0.068360   \n",
       "21                                               0           0.068447   \n",
       "22                                               0           0.068542   \n",
       "23                                               0           0.068650   \n",
       "24                                               0           0.068742   \n",
       "25                                               0           0.068928   \n",
       "26                                               0           0.069033   \n",
       "27                                               0           0.069120   \n",
       "28                                               0           0.069217   \n",
       "29                                               0           0.069318   \n",
       "...                                            ...                ...   \n",
       "4970                                             1           8.752962   \n",
       "4971                                             0           8.754011   \n",
       "4972                                             2           8.756590   \n",
       "4973                                             0           8.759090   \n",
       "4974                                             0           8.760192   \n",
       "4975                                             0           8.762684   \n",
       "4976                                             0           8.763982   \n",
       "4977                                             0           8.767105   \n",
       "4978                                             1           8.769914   \n",
       "4979                                             0           8.771306   \n",
       "4980                                             0           8.772742   \n",
       "4981                                             1           8.776040   \n",
       "4982                                             0           8.777336   \n",
       "4983                                             0           8.778787   \n",
       "4984                                             0           8.780299   \n",
       "4985                                             2           8.784222   \n",
       "4986                                             0           8.785742   \n",
       "4987                                             1           8.788734   \n",
       "4988                                             0           8.790431   \n",
       "4989                                             1           8.793395   \n",
       "4990                                             2           8.796632   \n",
       "4991                                             0           8.797794   \n",
       "4992                                             1           8.800674   \n",
       "4993                                             1           8.804045   \n",
       "4994                                             0           8.806909   \n",
       "4995                                             0           8.808009   \n",
       "4996                                             0           8.810269   \n",
       "4997                                             0           8.811276   \n",
       "4998                                             1           8.813771   \n",
       "4999                                             0           8.815214   \n",
       "\n",
       "      Total Matching Time  Total Deletion Time  Total Subsumption Time  \\\n",
       "0                0.000002             0.000002                0.000000   \n",
       "1                0.000006             0.000003                0.000000   \n",
       "2                0.000010             0.000005                0.000000   \n",
       "3                0.000014             0.000005                0.000000   \n",
       "4                0.000022             0.000006                0.000000   \n",
       "5                0.000029             0.000007                0.000000   \n",
       "6                0.000039             0.000008                0.000000   \n",
       "7                0.000050             0.000010                0.000000   \n",
       "8                0.000060             0.000010                0.000000   \n",
       "9                0.000072             0.000010                0.000000   \n",
       "10               0.000088             0.000012                0.000000   \n",
       "11               0.000103             0.000013                0.000000   \n",
       "12               0.000118             0.000014                0.000000   \n",
       "13               0.000134             0.000015                0.000000   \n",
       "14               0.000150             0.000016                0.000000   \n",
       "15               0.000168             0.000017                0.000000   \n",
       "16               0.000188             0.000018                0.000000   \n",
       "17               0.000207             0.000018                0.000000   \n",
       "18               0.000226             0.000019                0.000000   \n",
       "19               0.000250             0.000019                0.000000   \n",
       "20               0.000275             0.000020                0.000000   \n",
       "21               0.000302             0.000020                0.000000   \n",
       "22               0.000327             0.000021                0.000000   \n",
       "23               0.000354             0.000022                0.000000   \n",
       "24               0.000383             0.000022                0.000000   \n",
       "25               0.000409             0.000023                0.000000   \n",
       "26               0.000439             0.000024                0.000000   \n",
       "27               0.000469             0.000025                0.000000   \n",
       "28               0.000497             0.000026                0.000000   \n",
       "29               0.000528             0.000027                0.000000   \n",
       "...                   ...                  ...                     ...   \n",
       "4970             4.247859             1.934480                0.770750   \n",
       "4971             4.248717             1.934481                0.770750   \n",
       "4972             4.249628             1.935639                0.770869   \n",
       "4973             4.250501             1.936757                0.771021   \n",
       "4974             4.251393             1.936758                0.771021   \n",
       "4975             4.252201             1.937814                0.771261   \n",
       "4976             4.253119             1.937814                0.771261   \n",
       "4977             4.253961             1.939097                0.771942   \n",
       "4978             4.254871             1.940226                0.772218   \n",
       "4979             4.255877             1.940228                0.772218   \n",
       "4980             4.257050             1.940229                0.772218   \n",
       "4981             4.258060             1.941573                0.772676   \n",
       "4982             4.258947             1.941575                0.772676   \n",
       "4983             4.259890             1.941577                0.772676   \n",
       "4984             4.261042             1.941579                0.772676   \n",
       "4985             4.262554             1.943422                0.772689   \n",
       "4986             4.263783             1.943424                0.772689   \n",
       "4987             4.264687             1.944735                0.773062   \n",
       "4988             4.265900             1.944737                0.773062   \n",
       "4989             4.266848             1.945955                0.773453   \n",
       "4990             4.267822             1.947187                0.774076   \n",
       "4991             4.268711             1.947188                0.774076   \n",
       "4992             4.269542             1.948553                0.774369   \n",
       "4993             4.270483             1.949763                0.775062   \n",
       "4994             4.271426             1.951021                0.775326   \n",
       "4995             4.272284             1.951022                0.775326   \n",
       "4996             4.273136             1.952105                0.775337   \n",
       "4997             4.273945             1.952106                0.775337   \n",
       "4998             4.274793             1.953250                0.775540   \n",
       "4999             4.275592             1.953251                0.775540   \n",
       "\n",
       "      Total Selection Time  Total Evaluation Time  \n",
       "0                 0.000000               0.000089  \n",
       "1                 0.000000               0.000145  \n",
       "2                 0.000000               0.000186  \n",
       "3                 0.000000               0.000225  \n",
       "4                 0.000000               0.000262  \n",
       "5                 0.000000               0.000302  \n",
       "6                 0.000000               0.000341  \n",
       "7                 0.000000               0.000380  \n",
       "8                 0.000000               0.000418  \n",
       "9                 0.000000               0.000457  \n",
       "10                0.000000               0.000500  \n",
       "11                0.000000               0.000538  \n",
       "12                0.000000               0.000575  \n",
       "13                0.000000               0.000613  \n",
       "14                0.000000               0.000651  \n",
       "15                0.000000               0.000690  \n",
       "16                0.000000               0.000729  \n",
       "17                0.000000               0.000767  \n",
       "18                0.000000               0.000805  \n",
       "19                0.000000               0.000845  \n",
       "20                0.000000               0.000889  \n",
       "21                0.000000               0.000932  \n",
       "22                0.000000               0.000973  \n",
       "23                0.000000               0.001014  \n",
       "24                0.000000               0.001054  \n",
       "25                0.000000               0.001181  \n",
       "26                0.000000               0.001222  \n",
       "27                0.000000               0.001261  \n",
       "28                0.000000               0.001299  \n",
       "29                0.000000               0.001338  \n",
       "...                    ...                    ...  \n",
       "4970              0.148169               0.546169  \n",
       "4971              0.148169               0.546251  \n",
       "4972              0.148233               0.546359  \n",
       "4973              0.148283               0.546448  \n",
       "4974              0.148283               0.546538  \n",
       "4975              0.148348               0.546621  \n",
       "4976              0.148403               0.546731  \n",
       "4977              0.148442               0.546808  \n",
       "4978              0.148503               0.546975  \n",
       "4979              0.148503               0.547124  \n",
       "4980              0.148503               0.547278  \n",
       "4981              0.148577               0.547383  \n",
       "4982              0.148633               0.547508  \n",
       "4983              0.148709               0.547626  \n",
       "4984              0.148709               0.547788  \n",
       "4985              0.148768               0.548011  \n",
       "4986              0.148768               0.548151  \n",
       "4987              0.148824               0.548274  \n",
       "4988              0.148824               0.548472  \n",
       "4989              0.148891               0.548587  \n",
       "4990              0.148952               0.548699  \n",
       "4991              0.148952               0.548810  \n",
       "4992              0.149015               0.548895  \n",
       "4993              0.149084               0.549017  \n",
       "4994              0.149138               0.549128  \n",
       "4995              0.149138               0.549225  \n",
       "4996              0.149183               0.549300  \n",
       "4997              0.149183               0.549379  \n",
       "4998              0.149228               0.549457  \n",
       "4999              0.149228               0.549533  \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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\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",
    "cSize = dataTracking[\"Correct Set Size\"].values\n",
    "experience = dataTracking[\"Average Iteration Age of Correct 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 Selection 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,cSize,label=\"[C] size\")\n",
    "plt.plot(iterations,movingAvg(cSize),label=\"[C] size movingAvg\")\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('[M]/[C] size per iteration')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(iterations,experience)\n",
    "plt.ylabel('Average [C] 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=\"Selection 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()\n",
    "\n"
   ]
  },
  {
   "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 the following method:\n",
    "\n",
    "| Method Name | Description |\n",
    "| :--- | :--- |\n",
    "| model.export_final_rule_population(headerNames,classLabel,filename,DCAL) | Exports the entire final rule population |\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.\n",
    "\n",
    "The DCAL (Discrete-Continuous Attribute List) boolean param is also optional. By default, the program saves a CSV in DCAL representation form. However, if DCAL is set as False, the program saves a CSV in traditional form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>#</td>\n",
       "      <td>#</td>\n",
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       "      <td>#</td>\n",
       "      <td>#</td>\n",
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       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
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       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
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       "      <td>0.454545</td>\n",
       "      <td>0.001116</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\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>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.099647</td>\n",
       "      <td>0.630511</td>\n",
       "      <td>1</td>\n",
       "      <td>55.005976</td>\n",
       "      <td>4992</td>\n",
       "      <td>396</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000918</td>\n",
       "      <td>715</td>\n",
       "      <td>1134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</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>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>79.538276</td>\n",
       "      <td>4875</td>\n",
       "      <td>398</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.002655</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>#</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>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>72.121636</td>\n",
       "      <td>4945</td>\n",
       "      <td>413</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.003611</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>#</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>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.528577</td>\n",
       "      <td>0.880282</td>\n",
       "      <td>2</td>\n",
       "      <td>61.478494</td>\n",
       "      <td>4992</td>\n",
       "      <td>413</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>250</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</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>0.253313</td>\n",
       "      <td>0.759857</td>\n",
       "      <td>1</td>\n",
       "      <td>56.779144</td>\n",
       "      <td>4973</td>\n",
       "      <td>415</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>212</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>52.309132</td>\n",
       "      <td>4964</td>\n",
       "      <td>423</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>138</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\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>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108085</td>\n",
       "      <td>0.640845</td>\n",
       "      <td>1</td>\n",
       "      <td>62.182080</td>\n",
       "      <td>4920</td>\n",
       "      <td>458</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001038</td>\n",
       "      <td>91</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.231862</td>\n",
       "      <td>0.746528</td>\n",
       "      <td>1</td>\n",
       "      <td>57.605816</td>\n",
       "      <td>4989</td>\n",
       "      <td>474</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000961</td>\n",
       "      <td>215</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.252095</td>\n",
       "      <td>0.759124</td>\n",
       "      <td>1</td>\n",
       "      <td>46.782278</td>\n",
       "      <td>4996</td>\n",
       "      <td>532</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>104</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>#</td>\n",
       "      <td>#</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>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090849</td>\n",
       "      <td>0.618962</td>\n",
       "      <td>1</td>\n",
       "      <td>62.134002</td>\n",
       "      <td>4993</td>\n",
       "      <td>554</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>346</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>...</td>\n",
       "      <td>0.227546</td>\n",
       "      <td>0.743728</td>\n",
       "      <td>3</td>\n",
       "      <td>53.803158</td>\n",
       "      <td>4998</td>\n",
       "      <td>561</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.002694</td>\n",
       "      <td>415</td>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>65.061022</td>\n",
       "      <td>4948</td>\n",
       "      <td>583</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.005429</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>60.461139</td>\n",
       "      <td>4975</td>\n",
       "      <td>583</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.009082</td>\n",
       "      <td>270</td>\n",
       "      <td>270</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>576</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>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>65.278086</td>\n",
       "      <td>4904</td>\n",
       "      <td>4904</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\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>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>4942</td>\n",
       "      <td>4908</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.002387</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\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>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>4908</td>\n",
       "      <td>4908</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001502</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\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>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>4937</td>\n",
       "      <td>4910</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001294</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>1.0</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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>4992</td>\n",
       "      <td>4910</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.001135</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\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>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131687</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>52.666667</td>\n",
       "      <td>4915</td>\n",
       "      <td>4912</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000879</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>4912</td>\n",
       "      <td>4912</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000701</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</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>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>4981</td>\n",
       "      <td>4914</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.002003</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.250000</td>\n",
       "      <td>4990</td>\n",
       "      <td>4918</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000905</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</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>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>4925</td>\n",
       "      <td>4925</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000876</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>49.123611</td>\n",
       "      <td>4929</td>\n",
       "      <td>4929</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>1.0</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>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>4941</td>\n",
       "      <td>4941</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000885</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>4941</td>\n",
       "      <td>4941</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000885</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.131687</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>35.333333</td>\n",
       "      <td>4942</td>\n",
       "      <td>4942</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>73.465920</td>\n",
       "      <td>4945</td>\n",
       "      <td>4945</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</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>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>4954</td>\n",
       "      <td>4954</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</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>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>4983</td>\n",
       "      <td>4956</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000851</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\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>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.349471</td>\n",
       "      <td>4958</td>\n",
       "      <td>4958</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000890</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4972</td>\n",
       "      <td>4961</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>4963</td>\n",
       "      <td>4963</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000901</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>#</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>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>4981</td>\n",
       "      <td>4969</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.001035</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</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>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>4983</td>\n",
       "      <td>4977</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001035</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\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>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>4983</td>\n",
       "      <td>4977</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.001252</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</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>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>4981</td>\n",
       "      <td>4981</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000584</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</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>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>4994</td>\n",
       "      <td>4989</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000943</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>51.389313</td>\n",
       "      <td>4990</td>\n",
       "      <td>4990</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000858</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>#</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>67.637111</td>\n",
       "      <td>4992</td>\n",
       "      <td>4992</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</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>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.717607</td>\n",
       "      <td>4993</td>\n",
       "      <td>4993</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000913</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>66.253384</td>\n",
       "      <td>4993</td>\n",
       "      <td>4993</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>#</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>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>58.376948</td>\n",
       "      <td>4994</td>\n",
       "      <td>4994</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000974</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>606 rows × 22 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  ...   Fitness  \\\n",
       "0      #  1.0    #    #    #    #    #    #    #  1.0  ...  0.100629   \n",
       "1    0.0    #  1.0  1.0  0.0    #    #  0.0    #    #  ...  0.229843   \n",
       "2      #  1.0    #  1.0  0.0  1.0  0.0  1.0  1.0  0.0  ...  0.077760   \n",
       "3    0.0  1.0    #    #    #  1.0    #    #    #  1.0  ...  0.268453   \n",
       "4    0.0  1.0    #    #    #    #  0.0  0.0  0.0  0.0  ...  0.248188   \n",
       "5      #  1.0  1.0  0.0  0.0  0.0    #  1.0    #    #  ...  0.227335   \n",
       "6    1.0    #    #  1.0    #    #    #    #    #    #  ...  0.103355   \n",
       "7    1.0  1.0  1.0  0.0  0.0  0.0    #    #    #    #  ...  1.000000   \n",
       "8      #  0.0    #  0.0    #  1.0  1.0    #    #    #  ...  0.090678   \n",
       "9      #  0.0    #  1.0  1.0    #    #    #    #    #  ...  0.262018   \n",
       "10   1.0    #  1.0    #    #    #    #    #  0.0  0.0  ...  0.240045   \n",
       "11   1.0  1.0    #    #    #    #    #    #    #  1.0  ...  0.235337   \n",
       "12     #  0.0    #    #  1.0    #    #    #    #    #  ...  0.104167   \n",
       "13   1.0  1.0  1.0    #    #  0.0    #    #  0.0  0.0  ...  1.000000   \n",
       "14     #    #    #  1.0  1.0  1.0    #  1.0    #    #  ...  0.238676   \n",
       "15   0.0  0.0    #  1.0  1.0    #    #    #    #    #  ...  1.000000   \n",
       "16   1.0  0.0    #    #  1.0    #    #  1.0  1.0    #  ...  1.000000   \n",
       "17     #    #    #    #  1.0    #    #    #    #  1.0  ...  0.099647   \n",
       "18   1.0    #  1.0    #  1.0    #    #    #  0.0  0.0  ...  1.000000   \n",
       "19     #  0.0  1.0    #  1.0    #    #    #  1.0    #  ...  1.000000   \n",
       "20     #  0.0    #  1.0  1.0    #    #    #  1.0    #  ...  0.528577   \n",
       "21   0.0    #    #  1.0  1.0    #    #    #    #  1.0  ...  0.253313   \n",
       "22   0.0  0.0  0.0  1.0    #    #    #    #  0.0    #  ...  1.000000   \n",
       "23     #  0.0    #  0.0  1.0  1.0    #    #  1.0    #  ...  0.108085   \n",
       "24   1.0  1.0    #    #    #    #    #    #  1.0  1.0  ...  0.231862   \n",
       "25     #  0.0    #    #  0.0  1.0    #    #  0.0    #  ...  0.252095   \n",
       "26     #    #  0.0    #    #    #    #  0.0  1.0    #  ...  0.090849   \n",
       "27   0.0  1.0    #    #    #    #  0.0    #    #    #  ...  0.227546   \n",
       "28   1.0  0.0    #  0.0    #    #    #  1.0  1.0    #  ...  1.000000   \n",
       "29   1.0  0.0    #    #    #    #    #  1.0  1.0    #  ...  1.000000   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...       ...   \n",
       "576  0.0    #  1.0    #    #  1.0  1.0    #  1.0    #  ...  0.100000   \n",
       "577  0.0  1.0  1.0  0.0    #    #  1.0    #    #    #  ...  1.000000   \n",
       "578    #  1.0  1.0    #    #  1.0  1.0  1.0    #    #  ...  1.000000   \n",
       "579    #  1.0  1.0    #    #    #    #  1.0  1.0    #  ...  1.000000   \n",
       "580  1.0    #  1.0    #  1.0    #    #    #    #    #  ...  1.000000   \n",
       "581  1.0    #  0.0    #    #    #  1.0  1.0    #    #  ...  0.131687   \n",
       "582  1.0  0.0  0.0    #    #    #  1.0  1.0    #    #  ...  1.000000   \n",
       "583    #  1.0  0.0    #    #  0.0  0.0    #  1.0    #  ...  1.000000   \n",
       "584  0.0  0.0  0.0    #  0.0    #    #    #  0.0    #  ...  1.000000   \n",
       "585  0.0  1.0    #    #  1.0  1.0  0.0    #    #    #  ...  0.000000   \n",
       "586  1.0    #  0.0  0.0    #    #    #  0.0    #  0.0  ...  0.100000   \n",
       "587  1.0  0.0  1.0  1.0  0.0    #    #    #  1.0    #  ...  0.000000   \n",
       "588  1.0  0.0  1.0    #    #  0.0    #    #  1.0    #  ...  1.000000   \n",
       "589  0.0  1.0    #    #    #    #  1.0    #    #    #  ...  0.131687   \n",
       "590  1.0  0.0  1.0  1.0  1.0    #  1.0    #  1.0    #  ...  0.100000   \n",
       "591  0.0  1.0    #    #  1.0  0.0  0.0    #  0.0    #  ...  1.000000   \n",
       "592  1.0  1.0    #    #    #    #  0.0    #    #  0.0  ...  1.000000   \n",
       "593  1.0    #  1.0  0.0  0.0    #  0.0    #    #    #  ...  0.100000   \n",
       "594  1.0  0.0  0.0    #  0.0    #    #    #  0.0    #  ...  1.000000   \n",
       "595  1.0    #  0.0  0.0    #  0.0    #  0.0  0.0    #  ...  0.100000   \n",
       "596    #  1.0    #    #    #  0.0  0.0    #  1.0    #  ...  1.000000   \n",
       "597  1.0  1.0    #    #    #    #  0.0    #    #  0.0  ...  1.000000   \n",
       "598  1.0  1.0  1.0    #  0.0  1.0    #    #    #  0.0  ...  1.000000   \n",
       "599    #  1.0  0.0    #    #  0.0  0.0    #    #    #  ...  1.000000   \n",
       "600  1.0  1.0    #    #    #    #  1.0    #    #  1.0  ...  1.000000   \n",
       "601  0.0    #  0.0  0.0    #    #    #    #  0.0    #  ...  0.100000   \n",
       "602    #  0.0  1.0    #  1.0    #    #  0.0  1.0    #  ...  0.100000   \n",
       "603    #    #  0.0  0.0    #  0.0    #  0.0    #    #  ...  0.100000   \n",
       "604  0.0  0.0  0.0  0.0    #    #    #    #  1.0    #  ...  0.100000   \n",
       "605    #    #  1.0    #    #  1.0    #    #    #    #  ...  0.100000   \n",
       "\n",
       "     Accuracy  Numerosity  Avg Match Set Size  TimeStamp GA  \\\n",
       "0    0.631749           1           54.867155          4994   \n",
       "1    0.745223           1           54.713827          4962   \n",
       "2    0.600000           1           38.257216          4764   \n",
       "3    0.768730           2           61.316758          4957   \n",
       "4    0.756757           1           56.515370          4503   \n",
       "5    0.743590           1           59.778459          4293   \n",
       "6    0.635135           1           46.923286          4994   \n",
       "7    1.000000           1           58.434987          4846   \n",
       "8    0.618729           1           46.099513          4976   \n",
       "9    0.765009           2           57.863334          4992   \n",
       "10   0.751724           2           68.082949          4983   \n",
       "11   0.748752           1           54.810902          4994   \n",
       "12   0.636130           1           55.597971          4992   \n",
       "13   1.000000           1           94.953098          4834   \n",
       "14   0.750865           2           58.530526          4985   \n",
       "15   1.000000          14           59.595360          4973   \n",
       "16   1.000000           1           66.874544          4975   \n",
       "17   0.630511           1           55.005976          4992   \n",
       "18   1.000000           2           79.538276          4875   \n",
       "19   1.000000           3           72.121636          4945   \n",
       "20   0.880282           2           61.478494          4992   \n",
       "21   0.759857           1           56.779144          4973   \n",
       "22   1.000000           2           52.309132          4964   \n",
       "23   0.640845           1           62.182080          4920   \n",
       "24   0.746528           1           57.605816          4989   \n",
       "25   0.759124           1           46.782278          4996   \n",
       "26   0.618962           1           62.134002          4993   \n",
       "27   0.743728           3           53.803158          4998   \n",
       "28   1.000000           5           65.061022          4948   \n",
       "29   1.000000           9           60.461139          4975   \n",
       "..        ...         ...                 ...           ...   \n",
       "576  1.000000           1           65.278086          4904   \n",
       "577  1.000000           3           47.666667          4942   \n",
       "578  1.000000           1           90.000000          4908   \n",
       "579  1.000000           1           77.500000          4937   \n",
       "580  1.000000           1           68.000000          4992   \n",
       "581  0.666667           1           52.666667          4915   \n",
       "582  1.000000           1           42.000000          4912   \n",
       "583  1.000000           2           60.000000          4981   \n",
       "584  1.000000           1           54.250000          4990   \n",
       "585  0.000000           1           52.500000          4925   \n",
       "586  1.000000           1           49.123611          4929   \n",
       "587  0.000000           1           53.000000          4941   \n",
       "588  1.000000           1           53.000000          4941   \n",
       "589  0.666667           1           35.333333          4942   \n",
       "590  1.000000           1           73.465920          4945   \n",
       "591  1.000000           1           47.000000          4954   \n",
       "592  1.000000           1           51.000000          4983   \n",
       "593  1.000000           1           53.349471          4958   \n",
       "594  1.000000           1           40.000000          4972   \n",
       "595  1.000000           1           54.000000          4963   \n",
       "596  1.000000           1           62.000000          4981   \n",
       "597  1.000000           1           62.000000          4983   \n",
       "598  1.000000           1           75.000000          4983   \n",
       "599  1.000000           1           35.000000          4981   \n",
       "600  1.000000           1           56.500000          4994   \n",
       "601  1.000000           1           51.389313          4990   \n",
       "602  1.000000           1           67.637111          4992   \n",
       "603  1.000000           1           54.717607          4993   \n",
       "604  1.000000           1           66.253384          4993   \n",
       "605  1.000000           1           58.376948          4994   \n",
       "\n",
       "     Iteration Initialized  Specificity  Deletion Probability  Correct Count  \\\n",
       "0                        5     0.181818              0.000916            784   \n",
       "1                       26     0.454545              0.000913            117   \n",
       "2                       34     0.818182              0.000639              6   \n",
       "3                       36     0.363636              0.002047            236   \n",
       "4                       61     0.636364              0.000943             28   \n",
       "5                       70     0.636364              0.000998             29   \n",
       "6                      192     0.272727              0.000783            376   \n",
       "7                      197     0.636364              0.000975             37   \n",
       "8                      238     0.363636              0.000769            185   \n",
       "9                      239     0.272727              0.001932            446   \n",
       "10                     253     0.363636              0.002273            218   \n",
       "11                     261     0.272727              0.000915            450   \n",
       "12                     291     0.181818              0.000928            743   \n",
       "13                     331     0.636364              0.001585             35   \n",
       "14                     335     0.363636              0.001954            217   \n",
       "15                     337     0.363636              0.013925            290   \n",
       "16                     396     0.454545              0.001116            140   \n",
       "17                     396     0.181818              0.000918            715   \n",
       "18                     398     0.545455              0.002655             71   \n",
       "19                     413     0.454545              0.003611            150   \n",
       "20                     413     0.363636              0.002052            250   \n",
       "21                     415     0.363636              0.000948            212   \n",
       "22                     423     0.454545              0.001746            138   \n",
       "23                     458     0.454545              0.001038             91   \n",
       "24                     474     0.363636              0.000961            215   \n",
       "25                     532     0.454545              0.000763            104   \n",
       "26                     554     0.272727              0.001037            346   \n",
       "27                     561     0.272727              0.002694            415   \n",
       "28                     583     0.454545              0.005429            135   \n",
       "29                     583     0.363636              0.009082            270   \n",
       "..                     ...          ...                   ...            ...   \n",
       "576                   4904     0.545455              0.001090              0   \n",
       "577                   4908     0.454545              0.002387              3   \n",
       "578                   4908     0.454545              0.001502              1   \n",
       "579                   4910     0.454545              0.001294              2   \n",
       "580                   4910     0.363636              0.001135              4   \n",
       "581                   4912     0.454545              0.000879              2   \n",
       "582                   4912     0.454545              0.000701              1   \n",
       "583                   4914     0.454545              0.002003              5   \n",
       "584                   4918     0.454545              0.000905              4   \n",
       "585                   4925     0.454545              0.000876              0   \n",
       "586                   4929     0.454545              0.000820              0   \n",
       "587                   4941     0.545455              0.000885              0   \n",
       "588                   4941     0.545455              0.000885              1   \n",
       "589                   4942     0.363636              0.000590              2   \n",
       "590                   4945     0.636364              0.001226              0   \n",
       "591                   4954     0.545455              0.000784              1   \n",
       "592                   4956     0.363636              0.000851              4   \n",
       "593                   4958     0.545455              0.000890              0   \n",
       "594                   4961     0.454545              0.000668              2   \n",
       "595                   4963     0.636364              0.000901              0   \n",
       "596                   4969     0.363636              0.001035              3   \n",
       "597                   4977     0.454545              0.001035              2   \n",
       "598                   4977     0.545455              0.001252              1   \n",
       "599                   4981     0.363636              0.000584              1   \n",
       "600                   4989     0.363636              0.000943              2   \n",
       "601                   4990     0.363636              0.000858              0   \n",
       "602                   4992     0.454545              0.001129              0   \n",
       "603                   4993     0.454545              0.000913              0   \n",
       "604                   4993     0.454545              0.001106              0   \n",
       "605                   4994     0.272727              0.000974              0   \n",
       "\n",
       "     Match Count  \n",
       "0           1241  \n",
       "1            157  \n",
       "2             10  \n",
       "3            307  \n",
       "4             37  \n",
       "5             39  \n",
       "6            592  \n",
       "7             37  \n",
       "8            299  \n",
       "9            583  \n",
       "10           290  \n",
       "11           601  \n",
       "12          1168  \n",
       "13            35  \n",
       "14           289  \n",
       "15           290  \n",
       "16           140  \n",
       "17          1134  \n",
       "18            71  \n",
       "19           150  \n",
       "20           284  \n",
       "21           279  \n",
       "22           138  \n",
       "23           142  \n",
       "24           288  \n",
       "25           137  \n",
       "26           559  \n",
       "27           558  \n",
       "28           135  \n",
       "29           270  \n",
       "..           ...  \n",
       "576            0  \n",
       "577            3  \n",
       "578            1  \n",
       "579            2  \n",
       "580            4  \n",
       "581            3  \n",
       "582            1  \n",
       "583            5  \n",
       "584            4  \n",
       "585            2  \n",
       "586            0  \n",
       "587            1  \n",
       "588            1  \n",
       "589            3  \n",
       "590            0  \n",
       "591            1  \n",
       "592            4  \n",
       "593            0  \n",
       "594            2  \n",
       "595            0  \n",
       "596            3  \n",
       "597            2  \n",
       "598            1  \n",
       "599            1  \n",
       "600            2  \n",
       "601            0  \n",
       "602            0  \n",
       "603            0  \n",
       "604            0  \n",
       "605            0  \n",
       "\n",
       "[606 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainedModel.export_final_rule_population(headers,classLabel,filename=\"defaultExportDir/fileRulePopulation.csv\",DCAL=False)\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": {
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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>class</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Numerosity</th>\n",
       "      <th>Avg Match Set Size</th>\n",
       "      <th>TimeStamp GA</th>\n",
       "      <th>Iteration Initialized</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>Deletion Probability</th>\n",
       "      <th>Correct Count</th>\n",
       "      <th>Match Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0, 1.0</td>\n",
       "      <td>A_1, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100629</td>\n",
       "      <td>0.631749</td>\n",
       "      <td>1</td>\n",
       "      <td>54.867155</td>\n",
       "      <td>4994</td>\n",
       "      <td>5</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000916</td>\n",
       "      <td>784</td>\n",
       "      <td>1241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_0, R_1, R_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.229843</td>\n",
       "      <td>0.745223</td>\n",
       "      <td>1</td>\n",
       "      <td>54.713827</td>\n",
       "      <td>4962</td>\n",
       "      <td>26</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000913</td>\n",
       "      <td>117</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_1, R_0, R_1, R_2, R_3, R_4, R_5, R_6, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.077760</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1</td>\n",
       "      <td>38.257216</td>\n",
       "      <td>4764</td>\n",
       "      <td>34</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000639</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_2, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.268453</td>\n",
       "      <td>0.768730</td>\n",
       "      <td>2</td>\n",
       "      <td>61.316758</td>\n",
       "      <td>4957</td>\n",
       "      <td>36</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.002047</td>\n",
       "      <td>236</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_3, R_4, R_5, R_6, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.248188</td>\n",
       "      <td>0.756757</td>\n",
       "      <td>1</td>\n",
       "      <td>56.515370</td>\n",
       "      <td>4503</td>\n",
       "      <td>61</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000943</td>\n",
       "      <td>28</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0</td>\n",
       "      <td>A_1, A_2, R_0, R_1, R_2, R_4, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.227335</td>\n",
       "      <td>0.743590</td>\n",
       "      <td>1</td>\n",
       "      <td>59.778459</td>\n",
       "      <td>4293</td>\n",
       "      <td>70</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000998</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, R_0, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.103355</td>\n",
       "      <td>0.635135</td>\n",
       "      <td>1</td>\n",
       "      <td>46.923286</td>\n",
       "      <td>4994</td>\n",
       "      <td>192</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>376</td>\n",
       "      <td>592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_0, R_1, R_2, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>58.434987</td>\n",
       "      <td>4846</td>\n",
       "      <td>197</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000975</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_1, R_0, R_2, R_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090678</td>\n",
       "      <td>0.618729</td>\n",
       "      <td>1</td>\n",
       "      <td>46.099513</td>\n",
       "      <td>4976</td>\n",
       "      <td>238</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>185</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0, 1.0, 1.0</td>\n",
       "      <td>A_1, R_0, R_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.262018</td>\n",
       "      <td>0.765009</td>\n",
       "      <td>2</td>\n",
       "      <td>57.863334</td>\n",
       "      <td>4992</td>\n",
       "      <td>239</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.001932</td>\n",
       "      <td>446</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_5, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.240045</td>\n",
       "      <td>0.751724</td>\n",
       "      <td>2</td>\n",
       "      <td>68.082949</td>\n",
       "      <td>4983</td>\n",
       "      <td>253</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.002273</td>\n",
       "      <td>218</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.235337</td>\n",
       "      <td>0.748752</td>\n",
       "      <td>1</td>\n",
       "      <td>54.810902</td>\n",
       "      <td>4994</td>\n",
       "      <td>261</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000915</td>\n",
       "      <td>450</td>\n",
       "      <td>601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0, 1.0</td>\n",
       "      <td>A_1, R_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.636130</td>\n",
       "      <td>1</td>\n",
       "      <td>55.597971</td>\n",
       "      <td>4992</td>\n",
       "      <td>291</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>743</td>\n",
       "      <td>1168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_2, R_5, R_6, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>94.953098</td>\n",
       "      <td>4834</td>\n",
       "      <td>331</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.001585</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>R_0, R_1, R_2, R_4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.238676</td>\n",
       "      <td>0.750865</td>\n",
       "      <td>2</td>\n",
       "      <td>58.530526</td>\n",
       "      <td>4985</td>\n",
       "      <td>335</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.001954</td>\n",
       "      <td>217</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_0, R_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>59.595360</td>\n",
       "      <td>4973</td>\n",
       "      <td>337</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.013925</td>\n",
       "      <td>290</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0, 0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_1, R_4, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>66.874544</td>\n",
       "      <td>4975</td>\n",
       "      <td>396</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001116</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0, 1.0</td>\n",
       "      <td>R_1, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.099647</td>\n",
       "      <td>0.630511</td>\n",
       "      <td>1</td>\n",
       "      <td>55.005976</td>\n",
       "      <td>4992</td>\n",
       "      <td>396</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000918</td>\n",
       "      <td>715</td>\n",
       "      <td>1134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_1, R_5, R_6, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>79.538276</td>\n",
       "      <td>4875</td>\n",
       "      <td>398</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.002655</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_1, A_2, R_1, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>72.121636</td>\n",
       "      <td>4945</td>\n",
       "      <td>413</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.003611</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_1, R_0, R_1, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.528577</td>\n",
       "      <td>0.880282</td>\n",
       "      <td>2</td>\n",
       "      <td>61.478494</td>\n",
       "      <td>4992</td>\n",
       "      <td>413</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>250</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, R_0, R_1, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.253313</td>\n",
       "      <td>0.759857</td>\n",
       "      <td>1</td>\n",
       "      <td>56.779144</td>\n",
       "      <td>4973</td>\n",
       "      <td>415</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>212</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0, 0.0, 0.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_0, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>52.309132</td>\n",
       "      <td>4964</td>\n",
       "      <td>423</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>138</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_1, R_0, R_1, R_2, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.108085</td>\n",
       "      <td>0.640845</td>\n",
       "      <td>1</td>\n",
       "      <td>62.182080</td>\n",
       "      <td>4920</td>\n",
       "      <td>458</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001038</td>\n",
       "      <td>91</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_5, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.231862</td>\n",
       "      <td>0.746528</td>\n",
       "      <td>1</td>\n",
       "      <td>57.605816</td>\n",
       "      <td>4989</td>\n",
       "      <td>474</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000961</td>\n",
       "      <td>215</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_1, R_1, R_2, R_5, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.252095</td>\n",
       "      <td>0.759124</td>\n",
       "      <td>1</td>\n",
       "      <td>46.782278</td>\n",
       "      <td>4996</td>\n",
       "      <td>532</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>104</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0, 0.0, 1.0</td>\n",
       "      <td>A_2, R_4, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090849</td>\n",
       "      <td>0.618962</td>\n",
       "      <td>1</td>\n",
       "      <td>62.134002</td>\n",
       "      <td>4993</td>\n",
       "      <td>554</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>346</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.227546</td>\n",
       "      <td>0.743728</td>\n",
       "      <td>3</td>\n",
       "      <td>53.803158</td>\n",
       "      <td>4998</td>\n",
       "      <td>561</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.002694</td>\n",
       "      <td>415</td>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_0, R_4, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>65.061022</td>\n",
       "      <td>4948</td>\n",
       "      <td>583</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.005429</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_4, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>60.461139</td>\n",
       "      <td>4975</td>\n",
       "      <td>583</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.009082</td>\n",
       "      <td>270</td>\n",
       "      <td>270</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, R_2, R_3, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>65.278086</td>\n",
       "      <td>4904</td>\n",
       "      <td>4904</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_0, R_3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>4942</td>\n",
       "      <td>4908</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.002387</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_1, A_2, R_2, R_3, R_4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>4908</td>\n",
       "      <td>4908</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001502</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_1, A_2, R_4, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>4937</td>\n",
       "      <td>4910</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001294</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, R_1, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>4992</td>\n",
       "      <td>4910</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.001135</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>1.0, 0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_3, R_4, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.131687</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>52.666667</td>\n",
       "      <td>4915</td>\n",
       "      <td>4912</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000879</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_3, R_4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>4912</td>\n",
       "      <td>4912</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000701</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, R_2, R_3, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>4981</td>\n",
       "      <td>4914</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.002003</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_1, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.250000</td>\n",
       "      <td>4990</td>\n",
       "      <td>4918</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000905</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_1, R_2, R_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>4925</td>\n",
       "      <td>4925</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000876</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_0, R_4, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>49.123611</td>\n",
       "      <td>4929</td>\n",
       "      <td>4929</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>1.0, 0.0, 1.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_0, R_1, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>4941</td>\n",
       "      <td>4941</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000885</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_2, R_5, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>4941</td>\n",
       "      <td>4941</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000885</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_3, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.131687</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>35.333333</td>\n",
       "      <td>4942</td>\n",
       "      <td>4942</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_0, R_1, R_3, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>73.465920</td>\n",
       "      <td>4945</td>\n",
       "      <td>4945</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_1, R_2, R_3, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>4954</td>\n",
       "      <td>4954</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_3, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>4983</td>\n",
       "      <td>4956</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000851</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</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_1, R_3, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.349471</td>\n",
       "      <td>4958</td>\n",
       "      <td>4958</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000890</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_1, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4972</td>\n",
       "      <td>4961</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_0, R_2, R_4, R_5, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>4963</td>\n",
       "      <td>4963</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.000901</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_1, R_2, R_3, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>4981</td>\n",
       "      <td>4969</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.001035</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>1.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_3, R_6, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>4983</td>\n",
       "      <td>4977</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001035</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_1, R_2, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>4983</td>\n",
       "      <td>4977</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.001252</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_1, A_2, R_2, R_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>4981</td>\n",
       "      <td>4981</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000584</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_3, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>4994</td>\n",
       "      <td>4989</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000943</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_0, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>51.389313</td>\n",
       "      <td>4990</td>\n",
       "      <td>4990</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000858</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, R_1, R_4, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>67.637111</td>\n",
       "      <td>4992</td>\n",
       "      <td>4992</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_2, R_0, R_2, R_4, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.717607</td>\n",
       "      <td>4993</td>\n",
       "      <td>4993</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000913</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_0, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>66.253384</td>\n",
       "      <td>4993</td>\n",
       "      <td>4993</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.001106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>1.0, 1.0, 1.0</td>\n",
       "      <td>A_2, R_2, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>58.376948</td>\n",
       "      <td>4994</td>\n",
       "      <td>4994</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000974</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>606 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Specified Values  \\\n",
       "0                                       1.0, 1.0   \n",
       "1                        0.0, 1.0, 1.0, 0.0, 0.0   \n",
       "2    1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0   \n",
       "3                             0.0, 1.0, 1.0, 1.0   \n",
       "4              0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0   \n",
       "5              1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0   \n",
       "6                                  1.0, 1.0, 1.0   \n",
       "7              1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "8                             0.0, 0.0, 1.0, 1.0   \n",
       "9                                  0.0, 1.0, 1.0   \n",
       "10                            1.0, 1.0, 0.0, 0.0   \n",
       "11                                 1.0, 1.0, 1.0   \n",
       "12                                      0.0, 1.0   \n",
       "13             1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "14                            1.0, 1.0, 1.0, 1.0   \n",
       "15                            0.0, 0.0, 1.0, 1.0   \n",
       "16                       1.0, 0.0, 1.0, 1.0, 1.0   \n",
       "17                                      1.0, 1.0   \n",
       "18                  1.0, 1.0, 1.0, 0.0, 0.0, 0.0   \n",
       "19                       0.0, 1.0, 1.0, 1.0, 0.0   \n",
       "20                            0.0, 1.0, 1.0, 1.0   \n",
       "21                            0.0, 1.0, 1.0, 1.0   \n",
       "22                       0.0, 0.0, 0.0, 1.0, 0.0   \n",
       "23                       0.0, 0.0, 1.0, 1.0, 1.0   \n",
       "24                            1.0, 1.0, 1.0, 1.0   \n",
       "25                       0.0, 0.0, 1.0, 0.0, 0.0   \n",
       "26                                 0.0, 0.0, 1.0   \n",
       "27                                 0.0, 1.0, 0.0   \n",
       "28                       1.0, 0.0, 0.0, 1.0, 1.0   \n",
       "29                            1.0, 0.0, 1.0, 1.0   \n",
       "..                                           ...   \n",
       "576                 0.0, 1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "577                      0.0, 1.0, 1.0, 0.0, 1.0   \n",
       "578                      1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "579                      1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "580                           1.0, 1.0, 1.0, 1.0   \n",
       "581                      1.0, 0.0, 1.0, 1.0, 0.0   \n",
       "582                      1.0, 0.0, 0.0, 1.0, 1.0   \n",
       "583                      1.0, 0.0, 0.0, 0.0, 1.0   \n",
       "584                      0.0, 0.0, 0.0, 0.0, 0.0   \n",
       "585                      0.0, 1.0, 1.0, 1.0, 0.0   \n",
       "586                      1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "587                 1.0, 0.0, 1.0, 1.0, 0.0, 1.0   \n",
       "588                 1.0, 0.0, 1.0, 0.0, 1.0, 0.0   \n",
       "589                           0.0, 1.0, 1.0, 0.0   \n",
       "590            1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "591                 0.0, 1.0, 1.0, 0.0, 0.0, 0.0   \n",
       "592                           1.0, 1.0, 0.0, 0.0   \n",
       "593                 1.0, 1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "594                      1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "595            1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0   \n",
       "596                           1.0, 0.0, 0.0, 1.0   \n",
       "597                      1.0, 1.0, 0.0, 0.0, 0.0   \n",
       "598                 1.0, 1.0, 1.0, 0.0, 1.0, 0.0   \n",
       "599                           1.0, 0.0, 0.0, 0.0   \n",
       "600                           1.0, 1.0, 1.0, 1.0   \n",
       "601                           0.0, 0.0, 0.0, 0.0   \n",
       "602                      0.0, 1.0, 1.0, 0.0, 1.0   \n",
       "603                      0.0, 0.0, 0.0, 0.0, 0.0   \n",
       "604                      0.0, 0.0, 0.0, 0.0, 1.0   \n",
       "605                                1.0, 1.0, 1.0   \n",
       "\n",
       "                       Specified Attribute Names  class   Fitness  Accuracy  \\\n",
       "0                                       A_1, R_6    1.0  0.100629  0.631749   \n",
       "1                        A_0, A_2, R_0, R_1, R_4    0.0  0.229843  0.745223   \n",
       "2    A_1, R_0, R_1, R_2, R_3, R_4, R_5, R_6, R_7    0.0  0.077760  0.600000   \n",
       "3                             A_0, A_1, R_2, R_6    1.0  0.268453  0.768730   \n",
       "4              A_0, A_1, R_3, R_4, R_5, R_6, R_7    0.0  0.248188  0.756757   \n",
       "5              A_1, A_2, R_0, R_1, R_2, R_4, R_7    0.0  0.227335  0.743590   \n",
       "6                                  A_0, R_0, R_7    1.0  0.103355  0.635135   \n",
       "7              A_0, A_1, A_2, R_0, R_1, R_2, R_7    0.0  1.000000  1.000000   \n",
       "8                             A_1, R_0, R_2, R_3    0.0  0.090678  0.618729   \n",
       "9                                  A_1, R_0, R_1    1.0  0.262018  0.765009   \n",
       "10                            A_0, A_2, R_5, R_6    0.0  0.240045  0.751724   \n",
       "11                                 A_0, A_1, R_6    1.0  0.235337  0.748752   \n",
       "12                                      A_1, R_1    1.0  0.104167  0.636130   \n",
       "13             A_0, A_1, A_2, R_2, R_5, R_6, R_7    0.0  1.000000  1.000000   \n",
       "14                            R_0, R_1, R_2, R_4    1.0  0.238676  0.750865   \n",
       "15                            A_0, A_1, R_0, R_1    1.0  1.000000  1.000000   \n",
       "16                       A_0, A_1, R_1, R_4, R_5    1.0  1.000000  1.000000   \n",
       "17                                      R_1, R_6    1.0  0.099647  0.630511   \n",
       "18                  A_0, A_2, R_1, R_5, R_6, R_7    0.0  1.000000  1.000000   \n",
       "19                       A_1, A_2, R_1, R_5, R_7    1.0  1.000000  1.000000   \n",
       "20                            A_1, R_0, R_1, R_5    1.0  0.528577  0.880282   \n",
       "21                            A_0, R_0, R_1, R_6    1.0  0.253313  0.759857   \n",
       "22                       A_0, A_1, A_2, R_0, R_5    1.0  1.000000  1.000000   \n",
       "23                       A_1, R_0, R_1, R_2, R_5    1.0  0.108085  0.640845   \n",
       "24                            A_0, A_1, R_5, R_6    1.0  0.231862  0.746528   \n",
       "25                       A_1, R_1, R_2, R_5, R_7    0.0  0.252095  0.759124   \n",
       "26                                 A_2, R_4, R_5    0.0  0.090849  0.618962   \n",
       "27                                 A_0, A_1, R_3    0.0  0.227546  0.743728   \n",
       "28                       A_0, A_1, R_0, R_4, R_5    1.0  1.000000  1.000000   \n",
       "29                            A_0, A_1, R_4, R_5    1.0  1.000000  1.000000   \n",
       "..                                           ...    ...       ...       ...   \n",
       "576                 A_0, A_2, R_2, R_3, R_5, R_7    1.0  0.100000  1.000000   \n",
       "577                      A_0, A_1, A_2, R_0, R_3    1.0  1.000000  1.000000   \n",
       "578                      A_1, A_2, R_2, R_3, R_4    1.0  1.000000  1.000000   \n",
       "579                      A_1, A_2, R_4, R_5, R_7    1.0  1.000000  1.000000   \n",
       "580                           A_0, A_2, R_1, R_7    1.0  1.000000  1.000000   \n",
       "581                      A_0, A_2, R_3, R_4, R_7    1.0  0.131687  0.666667   \n",
       "582                      A_0, A_1, A_2, R_3, R_4    1.0  1.000000  1.000000   \n",
       "583                      A_1, A_2, R_2, R_3, R_5    0.0  1.000000  1.000000   \n",
       "584                      A_0, A_1, A_2, R_1, R_5    0.0  1.000000  1.000000   \n",
       "585                      A_0, A_1, R_1, R_2, R_3    0.0  0.000000  0.000000   \n",
       "586                      A_0, A_2, R_0, R_4, R_6    0.0  0.100000  1.000000   \n",
       "587                 A_0, A_1, A_2, R_0, R_1, R_5    0.0  0.000000  0.000000   \n",
       "588                 A_0, A_1, A_2, R_2, R_5, R_7    1.0  1.000000  1.000000   \n",
       "589                           A_0, A_1, R_3, R_7    1.0  0.131687  0.666667   \n",
       "590            A_0, A_1, A_2, R_0, R_1, R_3, R_5    1.0  0.100000  1.000000   \n",
       "591                 A_0, A_1, R_1, R_2, R_3, R_5    0.0  1.000000  1.000000   \n",
       "592                           A_0, A_1, R_3, R_6    0.0  1.000000  1.000000   \n",
       "593                 A_0, A_2, R_0, R_1, R_3, R_7    0.0  0.100000  1.000000   \n",
       "594                      A_0, A_1, A_2, R_1, R_5    0.0  1.000000  1.000000   \n",
       "595            A_0, A_2, R_0, R_2, R_4, R_5, R_7    0.0  0.100000  1.000000   \n",
       "596                           A_1, R_2, R_3, R_5    0.0  1.000000  1.000000   \n",
       "597                      A_0, A_1, R_3, R_6, R_7    0.0  1.000000  1.000000   \n",
       "598                 A_0, A_1, A_2, R_1, R_2, R_6    0.0  1.000000  1.000000   \n",
       "599                           A_1, A_2, R_2, R_3    0.0  1.000000  1.000000   \n",
       "600                           A_0, A_1, R_3, R_6    1.0  1.000000  1.000000   \n",
       "601                           A_0, A_2, R_0, R_5    0.0  0.100000  1.000000   \n",
       "602                      A_1, A_2, R_1, R_4, R_5    1.0  0.100000  1.000000   \n",
       "603                      A_2, R_0, R_2, R_4, R_7    0.0  0.100000  1.000000   \n",
       "604                      A_0, A_1, A_2, R_0, R_5    0.0  0.100000  1.000000   \n",
       "605                                A_2, R_2, R_7    1.0  0.100000  1.000000   \n",
       "\n",
       "     Numerosity  Avg Match Set Size  TimeStamp GA  Iteration Initialized  \\\n",
       "0             1           54.867155          4994                      5   \n",
       "1             1           54.713827          4962                     26   \n",
       "2             1           38.257216          4764                     34   \n",
       "3             2           61.316758          4957                     36   \n",
       "4             1           56.515370          4503                     61   \n",
       "5             1           59.778459          4293                     70   \n",
       "6             1           46.923286          4994                    192   \n",
       "7             1           58.434987          4846                    197   \n",
       "8             1           46.099513          4976                    238   \n",
       "9             2           57.863334          4992                    239   \n",
       "10            2           68.082949          4983                    253   \n",
       "11            1           54.810902          4994                    261   \n",
       "12            1           55.597971          4992                    291   \n",
       "13            1           94.953098          4834                    331   \n",
       "14            2           58.530526          4985                    335   \n",
       "15           14           59.595360          4973                    337   \n",
       "16            1           66.874544          4975                    396   \n",
       "17            1           55.005976          4992                    396   \n",
       "18            2           79.538276          4875                    398   \n",
       "19            3           72.121636          4945                    413   \n",
       "20            2           61.478494          4992                    413   \n",
       "21            1           56.779144          4973                    415   \n",
       "22            2           52.309132          4964                    423   \n",
       "23            1           62.182080          4920                    458   \n",
       "24            1           57.605816          4989                    474   \n",
       "25            1           46.782278          4996                    532   \n",
       "26            1           62.134002          4993                    554   \n",
       "27            3           53.803158          4998                    561   \n",
       "28            5           65.061022          4948                    583   \n",
       "29            9           60.461139          4975                    583   \n",
       "..          ...                 ...           ...                    ...   \n",
       "576           1           65.278086          4904                   4904   \n",
       "577           3           47.666667          4942                   4908   \n",
       "578           1           90.000000          4908                   4908   \n",
       "579           1           77.500000          4937                   4910   \n",
       "580           1           68.000000          4992                   4910   \n",
       "581           1           52.666667          4915                   4912   \n",
       "582           1           42.000000          4912                   4912   \n",
       "583           2           60.000000          4981                   4914   \n",
       "584           1           54.250000          4990                   4918   \n",
       "585           1           52.500000          4925                   4925   \n",
       "586           1           49.123611          4929                   4929   \n",
       "587           1           53.000000          4941                   4941   \n",
       "588           1           53.000000          4941                   4941   \n",
       "589           1           35.333333          4942                   4942   \n",
       "590           1           73.465920          4945                   4945   \n",
       "591           1           47.000000          4954                   4954   \n",
       "592           1           51.000000          4983                   4956   \n",
       "593           1           53.349471          4958                   4958   \n",
       "594           1           40.000000          4972                   4961   \n",
       "595           1           54.000000          4963                   4963   \n",
       "596           1           62.000000          4981                   4969   \n",
       "597           1           62.000000          4983                   4977   \n",
       "598           1           75.000000          4983                   4977   \n",
       "599           1           35.000000          4981                   4981   \n",
       "600           1           56.500000          4994                   4989   \n",
       "601           1           51.389313          4990                   4990   \n",
       "602           1           67.637111          4992                   4992   \n",
       "603           1           54.717607          4993                   4993   \n",
       "604           1           66.253384          4993                   4993   \n",
       "605           1           58.376948          4994                   4994   \n",
       "\n",
       "     Specificity  Deletion Probability  Correct Count  Match Count  \n",
       "0       0.181818              0.000916            784         1241  \n",
       "1       0.454545              0.000913            117          157  \n",
       "2       0.818182              0.000639              6           10  \n",
       "3       0.363636              0.002047            236          307  \n",
       "4       0.636364              0.000943             28           37  \n",
       "5       0.636364              0.000998             29           39  \n",
       "6       0.272727              0.000783            376          592  \n",
       "7       0.636364              0.000975             37           37  \n",
       "8       0.363636              0.000769            185          299  \n",
       "9       0.272727              0.001932            446          583  \n",
       "10      0.363636              0.002273            218          290  \n",
       "11      0.272727              0.000915            450          601  \n",
       "12      0.181818              0.000928            743         1168  \n",
       "13      0.636364              0.001585             35           35  \n",
       "14      0.363636              0.001954            217          289  \n",
       "15      0.363636              0.013925            290          290  \n",
       "16      0.454545              0.001116            140          140  \n",
       "17      0.181818              0.000918            715         1134  \n",
       "18      0.545455              0.002655             71           71  \n",
       "19      0.454545              0.003611            150          150  \n",
       "20      0.363636              0.002052            250          284  \n",
       "21      0.363636              0.000948            212          279  \n",
       "22      0.454545              0.001746            138          138  \n",
       "23      0.454545              0.001038             91          142  \n",
       "24      0.363636              0.000961            215          288  \n",
       "25      0.454545              0.000763            104          137  \n",
       "26      0.272727              0.001037            346          559  \n",
       "27      0.272727              0.002694            415          558  \n",
       "28      0.454545              0.005429            135          135  \n",
       "29      0.363636              0.009082            270          270  \n",
       "..           ...                   ...            ...          ...  \n",
       "576     0.545455              0.001090              0            0  \n",
       "577     0.454545              0.002387              3            3  \n",
       "578     0.454545              0.001502              1            1  \n",
       "579     0.454545              0.001294              2            2  \n",
       "580     0.363636              0.001135              4            4  \n",
       "581     0.454545              0.000879              2            3  \n",
       "582     0.454545              0.000701              1            1  \n",
       "583     0.454545              0.002003              5            5  \n",
       "584     0.454545              0.000905              4            4  \n",
       "585     0.454545              0.000876              0            2  \n",
       "586     0.454545              0.000820              0            0  \n",
       "587     0.545455              0.000885              0            1  \n",
       "588     0.545455              0.000885              1            1  \n",
       "589     0.363636              0.000590              2            3  \n",
       "590     0.636364              0.001226              0            0  \n",
       "591     0.545455              0.000784              1            1  \n",
       "592     0.363636              0.000851              4            4  \n",
       "593     0.545455              0.000890              0            0  \n",
       "594     0.454545              0.000668              2            2  \n",
       "595     0.636364              0.000901              0            0  \n",
       "596     0.363636              0.001035              3            3  \n",
       "597     0.454545              0.001035              2            2  \n",
       "598     0.545455              0.001252              1            1  \n",
       "599     0.363636              0.000584              1            1  \n",
       "600     0.363636              0.000943              2            2  \n",
       "601     0.363636              0.000858              0            0  \n",
       "602     0.454545              0.001129              0            0  \n",
       "603     0.454545              0.000913              0            0  \n",
       "604     0.454545              0.001106              0            0  \n",
       "605     0.272727              0.000974              0            0  \n",
       "\n",
       "[606 rows x 13 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainedModel.export_final_rule_population(headers,classLabel,filename=\"defaultExportDir/popData2.csv\")\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 eLCS estimator with this txt file. When you run fit, the new eLCS 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": [
      "0.8125\n",
      "0.8125\n",
      "0.88916015625\n",
      "0.88916015625\n",
      "0.9384765625\n",
      "0.9384765625\n",
      "0.97216796875\n"
     ]
    }
   ],
   "source": [
    "converter = StringEnumerator(\"test/DataSets/Real/Multiplexer11.csv\",\"class\")\n",
    "headers,classLabel,dataFeatures,dataPhenotypes = converter.get_params()\n",
    "\n",
    "#Run 1\n",
    "model = eLCS(learning_iterations=1000)\n",
    "model.fit(dataFeatures,dataPhenotypes)\n",
    "print(model.score(dataFeatures,dataPhenotypes))\n",
    "model.pickle_model(\"defaultExportDir/savedModel1\")\n",
    "\n",
    "#Run 2 w/ rebooted first model\n",
    "model2 = eLCS(learning_iterations=1000,reboot_filename=\"defaultExportDir/savedModel1\")\n",
    "print(model2.score(dataFeatures,dataPhenotypes))\n",
    "model2.fit(dataFeatures,dataPhenotypes)\n",
    "print(model2.score(dataFeatures,dataPhenotypes))\n",
    "model2.pickle_model(\"defaultExportDir/savedModel2\")\n",
    "\n",
    "#Run 3 w/ rebooted second model\n",
    "model3 = eLCS(learning_iterations=1000,reboot_filename=\"defaultExportDir/savedModel2\")\n",
    "print(model3.score(dataFeatures,dataPhenotypes))\n",
    "model3.fit(dataFeatures,dataPhenotypes)\n",
    "print(model3.score(dataFeatures,dataPhenotypes))\n",
    "model3.pickle_model(\"defaultExportDir/savedModel3\")\n",
    "\n",
    "#Run 4 w/ rebooted third model\n",
    "model4 = eLCS(learning_iterations=1000,reboot_filename=\"defaultExportDir/savedModel3\")\n",
    "print(model4.score(dataFeatures,dataPhenotypes))\n",
    "model4.fit(dataFeatures,dataPhenotypes)\n",
    "print(model4.score(dataFeatures,dataPhenotypes))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "By now, you should be able to use eLCS deftly, from initialization, to training, to evaluation. Have fun!"
   ]
  }
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