scikit-ExSTraCS User Guide.ipynb

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    "# scikit-ExSTraCS 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: 05/08/2020\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-ExSTraCS</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-ExSTraCS package as well as auxilliary helper objects included in the package. ExSTraCS 2.0 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",
    "ExSTraCS 2.0, or Extended Supervised Tracking and Classifying 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. It allows the incorporation of expert knowledge in the form of attribute weights, attribute tracking, rule compaction, and a rule specificity limit, that makes it particularly adept at solving highly complex problems.\n",
    "\n",
    "While Learning Classifier Systems are commonly applied to genetic analyses, where epistatis (i.e. feature interactions) is common, the ExSTraCS 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 scikit-ExSTraCS package does not currently support regression problems (continuous endpoints).\n",
    "\n",
    "## Notebook Organization\n",
    "\n",
    "**Part 1: Loading Data**\n",
    "<ul>\n",
    "    <li> Dataset format requirements for scikit-ExSTraCS</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 ExSTraCS package)</li>\n",
    "</ul>\n",
    "\n",
    "**Part 2: Initializing scikit-ExSTraCS Estimator Object**\n",
    "<ul>\n",
    "    <li> Basic Initialization</li>\n",
    "    <li> Overview of all scikit-ExSTraCS initialization parameters and default values</li>\n",
    "</ul>\n",
    "\n",
    "**Part 3: Model Training and Testing**\n",
    "<ul>\n",
    "    <li> model.fit(X,y)</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-ExSTraCS 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",
    "\n",
    "**Part 6: Post Training Rule Compaction**"
   ]
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   "source": [
    "## Part 1: Loading Data\n",
    "\n",
    "### Dataset Format Requirement for scikit-ExSTraCS\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 ExSTraCS 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 ExSTraCS model."
   ]
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   "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": {},
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    {
     "name": "stdout",
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     "text": [
      "Data Features\n",
      "[[0 0 0 0 0 0]\n",
      " [0 0 0 0 0 1]\n",
      " [0 0 0 0 1 0]\n",
      " [0 0 0 0 1 1]\n",
      " [0 0 0 1 0 0]\n",
      " [0 0 0 1 0 1]\n",
      " [0 0 0 1 1 0]\n",
      " [0 0 0 1 1 1]\n",
      " [0 0 1 0 0 0]\n",
      " [0 0 1 0 0 1]\n",
      " [0 0 1 0 1 0]\n",
      " [0 0 1 0 1 1]\n",
      " [0 0 1 1 0 0]\n",
      " [0 0 1 1 0 1]\n",
      " [0 0 1 1 1 0]\n",
      " [0 0 1 1 1 1]\n",
      " [0 1 0 0 0 0]\n",
      " [0 1 0 0 0 1]\n",
      " [0 1 0 0 1 0]\n",
      " [0 1 0 0 1 1]\n",
      " [0 1 0 1 0 0]\n",
      " [0 1 0 1 0 1]\n",
      " [0 1 0 1 1 0]\n",
      " [0 1 0 1 1 1]\n",
      " [0 1 1 0 0 0]\n",
      " [0 1 1 0 0 1]\n",
      " [0 1 1 0 1 0]\n",
      " [0 1 1 0 1 1]\n",
      " [0 1 1 1 0 0]\n",
      " [0 1 1 1 0 1]\n",
      " [0 1 1 1 1 0]\n",
      " [0 1 1 1 1 1]\n",
      " [1 0 0 0 0 0]\n",
      " [1 0 0 0 0 1]\n",
      " [1 0 0 0 1 0]\n",
      " [1 0 0 0 1 1]\n",
      " [1 0 0 1 0 0]\n",
      " [1 0 0 1 0 1]\n",
      " [1 0 0 1 1 0]\n",
      " [1 0 0 1 1 1]\n",
      " [1 0 1 0 0 0]\n",
      " [1 0 1 0 0 1]\n",
      " [1 0 1 0 1 0]\n",
      " [1 0 1 0 1 1]\n",
      " [1 0 1 1 0 0]\n",
      " [1 0 1 1 0 1]\n",
      " [1 0 1 1 1 0]\n",
      " [1 0 1 1 1 1]\n",
      " [1 1 0 0 0 0]\n",
      " [1 1 0 0 0 1]\n",
      " [1 1 0 0 1 0]\n",
      " [1 1 0 0 1 1]\n",
      " [1 1 0 1 0 0]\n",
      " [1 1 0 1 0 1]\n",
      " [1 1 0 1 1 0]\n",
      " [1 1 0 1 1 1]\n",
      " [1 1 1 0 0 0]\n",
      " [1 1 1 0 0 1]\n",
      " [1 1 1 0 1 0]\n",
      " [1 1 1 0 1 1]\n",
      " [1 1 1 1 0 0]\n",
      " [1 1 1 1 0 1]\n",
      " [1 1 1 1 1 0]\n",
      " [1 1 1 1 1 1]]\n",
      "\n",
      "Data 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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   "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-ExSTraCS model."
   ]
  },
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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-ExSTraCS 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-ExSTraCS model requires that the phenotype array is complete, this dataset would also not meet the requirements.\n",
    "\n",
    "To make things more convenient, included in this package is an auxilliary data type called **String Enumerator** that makes it easy to transform datasets that do not meet the requirements into datasets that do.\n",
    "\n",
    "For example, take the below dummy dataset:"
   ]
  },
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   "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 skExSTraCS 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-ExSTraCS Estimator Object\n",
    "\n",
    "### Basic Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skExSTraCS import ExSTraCS\n",
    " \n",
    "model = ExSTraCS()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing the above will initialize an ExSTraCS 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-ExSTraCS 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 | 100000 |\n",
    "| N | non negative integer | Maximum microclassifier population size (sum of classifier numerosities) | 1000 |\n",
    "| nu (v) | float | Power parameter used to determine the importance of high accuracy when calculating fitness | 1 |\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_fitness | 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",
    "| theta_sel | float from 0 - 1 | The fraction of the correct set to be included in tournament selection | 0.5 |\n",
    "| rule_specificity_limit | non negative integer or None | If not None, overrides the automatically computed RSL. Else use automatically computed RSL | None |\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",
    "| do_attribute_tracking | boolean | Determines if AT occurs during training | True |\n",
    "| do_attribute_feedback | boolean | Determines if AF occurs during training | True |\n",
    "| attribute_tracking_method | \"add\" or \"wh\" | Determine AT score update method (Addition or Widrow Hoff) | \"add\" |\n",
    "| attribute_tracking_beta | float | learning rate used for widrow hoff AT score update. | 0.1 |\n",
    "| expert_knowledge | numpy.ndarray or list or None of length # attributes | Uses list of attribute weights as a basis for expert knowledge. If None, no EK is used | None |\n",
    "| rule_compaction | 'QRF' or 'PDRC' or 'QRC' or 'CRA2' or 'Fu1' or 'Fu2' of None| Select RC method, or None | 'QRF' |\n",
    "\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",
    "| 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_fitness | recommended to use default parameter |\n",
    "| fitness_reduction | recommended to use default parameter |\n",
    "| theta_sel | recommended to use default parameter |\n",
    "| rule_specificity_limit | 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",
    "| do_attribute_tracking | recommended to use default parameter |\n",
    "| do_attribute_feedback | recommended to use default parameter |\n",
    "| attribute_tracking_method | 'wh' has been shown to improve rule population interpretability |\n",
    "| attribute_tracking_beta | recommended to use default parameter |\n",
    "| expert_knowledge | recommended to load in a list of generated expert knowledge scores |\n",
    "| rule_compaction | each rule compaction method has its benefits, depending on the problem |\n",
    "\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). | 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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ExSTraCS(learning_iterations = 10000,rule_compaction=None,nu=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Model Training and Testing\n",
    "\n",
    "### model.fit(X, y)\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": [
       "ExSTraCS(N=2000, acc_sub=0.99, beta=0.2, chi=0.8, delta=0.1,\n",
       "         discrete_attribute_limit=10, do_GA_subsumption=True,\n",
       "         do_attribute_feedback=True, do_attribute_tracking=True,\n",
       "         do_correct_set_subsumption=False,\n",
       "         expert_knowledge=[0.16776127027984797, 0.150817849684031,\n",
       "                           0.15963044311209207, 0.15654900352395804,\n",
       "                           0.023928376766701027, 0.02126067395922714,\n",
       "                           0.02891840211257598...\n",
       "                           0.024174819979529024, 0.02545020857730312],\n",
       "         fitness_reduction=0.1, init_fitness=0.01, learning_iterations=10000,\n",
       "         mu=0.04, nu=10, random_state=None, reboot_filename=None,\n",
       "         rule_compaction='QRF', rule_specificity_limit=14,\n",
       "         selection_method='tournament',\n",
       "         specified_attributes=array([], dtype=float64), theta_GA=25,\n",
       "         theta_del=20, theta_sel=0.5, theta_sub=20,\n",
       "         track_accuracy_while_fit=True)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skExSTraCS import StringEnumerator\n",
    "from skExSTraCS import ExSTraCS\n",
    "import numpy as np\n",
    "\n",
    "#Use StringEnumerator to gather data\n",
    "converter = StringEnumerator(\"test/DataSets/Real/Multiplexer20Modified.csv\",\"Class\")\n",
    "headers,classLabel,dataFeatures,dataPhenotypes = converter.get_params()\n",
    "scores = [0.080835, 0.071416, 0.076315, 0.074602, 0.000877, -0.000606, 0.003651, -0.002214, -0.000608, -0.002425, 0.000013, 0.00343, -0.001186, -0.001607, 0.000061, -0.000367, 0.001698, 0.000787, 0.001014,0.001723]\n",
    "\n",
    "#Initialize and train model\n",
    "model = ExSTraCS(learning_iterations = 10000,nu=10,N=2000,track_accuracy_while_fit=True,expert_knowledge=scores)\n",
    "trainedModel = model.fit(dataFeatures,dataPhenotypes)\n",
    "\n",
    "trainedModel"
   ]
  },
  {
   "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([1., 1., 1., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainedModel.predict(dataFeatures)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also call the predict_proba(X) method to return an nxm numpy.ndarray of phenotype probabilities, where n = number of data instances and m = number of distinct discrete phenotypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00258699, 0.99741301],\n",
       "       [0.01183961, 0.98816039],\n",
       "       [0.00352188, 0.99647812],\n",
       "       ...,\n",
       "       [0.96165011, 0.03834989],\n",
       "       [0.98890044, 0.01109956],\n",
       "       [0.90234634, 0.09765366]])"
      ]
     },
     "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9878059790371176"
      ]
     },
     "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": [
       "0.9910912284073081"
      ]
     },
     "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": [
       "0.9976076555023923"
      ]
     },
     "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": [
       "0.9929603340406938"
      ]
     },
     "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": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRC AUC:0.9979114998716454\n",
      "ROC AUC:0.9976327326343895\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)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auxilliary scikit-ExSTraCS methods\n",
    "In addition to the standard sklearn methods you can use to access training metrics, scikit-ExSTraCS provides a few of its own.\n",
    "\n",
    "| Method Name | Description |\n",
    "| :--- | :--- |\n",
    "| model.get_final_training_accuracy() | Evaluates (in real time) the final training accuracy of the model |\n",
    "| model.get_final_instance_coverage() | Evaluates (in real time) the final instance coverage of the model |\n",
    "| model.get_final_attribute_specificity_list() | Returns a list of attribute specificities (which attributes are specified the most in the rule population) |\n",
    "| model.get_final_attribute_accuracy_list() | Returns a list of attribute specificities weighted by accuracy |\n",
    "| model.get_final_attribute_tracking_sums() | Returns a list of attribute tracking sums |\n",
    "| model.get_final_attribute_coocurrences(headers,maxNumAttributesToTrack=50) | Returns a list of attribute cooccurences |\n",
    "| model.get_attribute_tracking_scores(instanceLabels) | Returns a list of AT score for each instance. Instance labels must be in the order of the given training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Accuracy: 0.9878059790371176\n",
      "Final Instance Coverage: 1.0\n",
      "Final Attribute Specificity List: [1481, 1445, 1463, 1475, 316, 253, 430, 248, 283, 240, 213, 446, 237, 244, 329, 253, 354, 286, 301, 390]\n",
      "Final Attribute Accuracy List: [1385.337385119869, 1351.8012012520908, 1363.3659460002873, 1375.862788811331, 287.9505870230712, 227.88049439607306, 392.8348934460699, 226.09229263718885, 256.3703456439458, 213.77611006328962, 194.0089327106674, 407.14860546347325, 210.36287876444575, 224.07732742906487, 298.4092489163908, 230.9184164096375, 317.9571744140143, 259.9827888874704, 272.35936587004926, 355.0676899585336]\n",
      "Final Attribute Tracking Sums:\n",
      "[100908.27391736396, 93843.71946128823, 99532.88528400526, 102817.96418661218, 29346.929210896844, 21124.903154738593, 48686.935235638535, 27202.09570369491, 28603.660682796268, 38645.96752210181, 24489.651479511493, 59035.63192187713, 21783.470934485013, 22992.6430245491, 24864.199188148974, 24158.325084088025, 55498.79982185332, 28720.66081840949, 30935.38515787388, 41519.23788407815]\n",
      "Final Attribute Cooccurences:\n",
      "[['A_0', 'A_3', 1195, 1136.7372751618373], ['A_0', 'A_2', 1193, 1132.5274813838284], ['A_2', 'A_3', 1194, 1128.9207585083288], ['A_0', 'A_1', 1185, 1128.8073492000865], ['A_1', 'A_3', 1187, 1126.7141874713348], ['A_1', 'A_2', 1152, 1094.5377965027217], ['A_0', 'R_7', 356, 331.42735322572094], ['A_2', 'R_7', 354, 327.2492514411567], ['A_1', 'R_7', 349, 324.74500469611263], ['A_3', 'R_7', 348, 321.4263386557023], ['A_3', 'R_2', 346, 320.9994557234265], ['A_0', 'R_2', 340, 315.7434688436057], ['A_2', 'R_2', 333, 309.0102786132991], ['A_0', 'R_15', 331, 305.5009998235872], ['A_1', 'R_2', 328, 304.96403682703414], ['A_1', 'R_15', 293, 271.0786505230252], ['A_3', 'R_15', 289, 267.0331036728924], ['A_2', 'R_15', 286, 264.02747471725706], ['A_3', 'R_12', 287, 261.1799523944162], ['A_2', 'R_12', 283, 257.9738223196208], ['A_0', 'R_0', 271, 249.87531215600336], ['A_3', 'R_10', 267, 246.40771589318558], ['A_0', 'R_12', 265, 243.87488856311205], ['A_1', 'R_12', 255, 234.52253569069163], ['A_2', 'R_10', 253, 232.7298601723673], ['A_1', 'R_10', 251, 231.49770444622166], ['A_0', 'R_14', 245, 226.17993887154597], ['A_2', 'R_4', 242, 222.18787096976789], ['A_1', 'R_0', 239, 221.42347724830358], ['A_0', 'R_10', 235, 219.48624010535764], ['A_3', 'R_0', 234, 217.18639709400347], ['A_2', 'R_0', 234, 216.07348340393438], ['A_2', 'R_14', 233, 213.86446274766092], ['A_0', 'R_13', 230, 211.69037266514187], ['A_1', 'R_14', 226, 207.84322673225438], ['A_1', 'R_13', 220, 204.34969753735976], ['A_3', 'R_4', 220, 203.25431454129452], ['A_0', 'R_4', 217, 201.53922120994469], ['A_3', 'R_13', 212, 196.79024571963876], ['A_2', 'R_3', 205, 189.9657486458513], ['A_1', 'R_9', 201, 186.78391882540953], ['A_2', 'R_1', 203, 185.86102986990718], ['A_3', 'R_9', 198, 184.35582663687165], ['A_3', 'R_3', 198, 184.26526943067782], ['A_2', 'R_11', 199, 183.54549081311362], ['A_0', 'R_3', 198, 182.94317483130447], ['A_2', 'R_9', 196, 182.64562330237604], ['A_3', 'R_14', 197, 181.30180132813052], ['A_2', 'R_13', 197, 179.95710136486255], ['A_0', 'R_11', 192, 179.94054450426887], ['A_3', 'R_11', 194, 179.4239812292742], ['A_3', 'R_5', 195, 177.8456878292244], ['A_1', 'R_4', 192, 177.6345970645032], ['A_1', 'R_1', 193, 177.0026629972219], ['A_1', 'R_11', 187, 172.50604841982087], ['A_3', 'R_1', 183, 168.1348597692266], ['A_1', 'R_5', 182, 167.24061356096456], ['A_0', 'R_5', 184, 167.1970762635411], ['A_1', 'R_3', 182, 167.1509655728779], ['A_3', 'R_8', 182, 166.14776732353698], ['A_0', 'R_1', 181, 165.40649401468798], ['A_0', 'R_8', 181, 164.31705223742645], ['A_2', 'R_6', 176, 162.8945109155893], ['A_1', 'R_8', 177, 161.14259083115644], ['A_3', 'R_6', 172, 158.1934472816705], ['A_0', 'R_9', 167, 156.38343315194413], ['A_1', 'R_6', 163, 152.00131863503196], ['A_0', 'R_6', 161, 151.54332722729282], ['A_2', 'R_8', 166, 150.7572424473738], ['A_2', 'R_5', 154, 138.3223088797939], ['R_2', 'R_10', 126, 115.47193954362952], ['R_7', 'R_15', 117, 104.40702407921533], ['R_2', 'R_15', 107, 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53.18406042206772], ['R_10', 'R_14', 58, 52.34454144608148], ['R_2', 'R_4', 57, 52.327283272723434], ['R_10', 'R_15', 58, 52.06331943164896], ['R_7', 'R_11', 59, 51.91029155429225], ['R_7', 'R_10', 57, 50.676737034915874], ['R_0', 'R_7', 57, 49.552776373891255], ['R_0', 'R_10', 53, 48.76956770366673], ['R_10', 'R_12', 56, 48.76676565590847], ['R_4', 'R_12', 55, 48.595541749251936], ['R_2', 'R_5', 54, 47.5802513070147], ['R_2', 'R_3', 53, 47.29787105552049], ['R_4', 'R_10', 54, 46.69030621670891], ['R_8', 'R_12', 53, 46.45298924565685], ['R_2', 'R_9', 50, 46.43687179260022], ['R_1', 'R_14', 54, 46.33258539485521], ['R_1', 'R_7', 51, 44.77345007933245], ['R_1', 'R_2', 49, 44.3261604011604], ['R_12', 'R_15', 50, 43.87297939571695], ['R_3', 'R_15', 49, 43.84018685147868], ['R_2', 'R_6', 48, 43.8016709998325], ['R_0', 'R_13', 49, 43.683595819029065], ['R_10', 'R_13', 49, 43.646215547358544], ['R_13', 'R_14', 49, 43.317185831669576], ['R_0', 'R_12', 46, 43.082026451538646], ['R_9', 'R_10', 46, 41.91223319622145], ['R_9', 'R_15', 47, 41.69142428778651], ['R_3', 'R_12', 44, 41.064992907045884], ['R_3', 'R_8', 47, 40.64806082653069], ['R_0', 'R_14', 45, 40.612608465986305], ['R_8', 'R_15', 47, 40.61202355595395], ['R_4', 'R_8', 46, 40.53705321859803], ['R_1', 'R_5', 45, 39.221970778083985], ['R_9', 'R_13', 42, 38.22672305764411], ['R_11', 'R_14', 43, 38.02774930102846], ['R_5', 'R_13', 43, 37.76739342528816], ['R_4', 'R_6', 43, 37.547750065551924], ['R_5', 'R_8', 45, 37.308407422670825], ['R_9', 'R_12', 42, 36.65377084251812], ['R_3', 'R_11', 40, 36.05643692639759], ['R_6', 'R_14', 39, 35.094939691165585], ['R_11', 'R_13', 40, 35.03505043456417], ['R_4', 'R_15', 39, 34.38354470904815], ['R_3', 'R_13', 39, 34.36089849720505], ['R_5', 'R_10', 38, 34.33659592877104], ['R_0', 'R_5', 38, 33.564206513472705], ['R_1', 'R_15', 37, 33.55713108532258], ['R_11', 'R_12', 39, 33.51017010718838], ['R_0', 'R_9', 36, 32.79022607069155], ['R_1', 'R_8', 39, 32.70770331201366], ['R_4', 'R_14', 37, 32.68433773828511], ['R_0', 'R_6', 35, 32.603116640555456], ['R_6', 'R_12', 37, 32.48019837986976], ['R_3', 'R_10', 35, 32.106583849048384], ['R_9', 'R_14', 35, 31.749855943277], ['R_8', 'R_11', 36, 31.60947090190511], ['R_3', 'R_5', 37, 31.522703027215474], ['R_6', 'R_10', 36, 31.50174228538329], ['R_5', 'R_15', 36, 31.147703877177566], ['R_5', 'R_12', 35, 30.86667528736273], ['R_9', 'R_11', 33, 30.540851069900366], ['R_3', 'R_6', 33, 30.529278930025196], ['R_1', 'R_13', 33, 29.121850012732367], ['R_4', 'R_7', 33, 29.02274310390036], ['R_1', 'R_4', 32, 28.101283520401168], ['R_1', 'R_10', 31, 27.977686202686204], ['R_6', 'R_11', 32, 27.847153161057324], ['R_7', 'R_8', 33, 27.18861488282541], ['R_8', 'R_13', 32, 26.991421875615433], ['R_6', 'R_15', 31, 26.392252849489694], ['R_4', 'R_13', 29, 25.82900432900433], ['R_1', 'R_3', 28, 25.38904761904762], ['R_3', 'R_4', 27, 25.086907800572796], ['R_4', 'R_9', 28, 24.93182879201699], ['R_5', 'R_14', 29, 24.247202717016965], ['R_4', 'R_5', 28, 24.180747246501724], ['R_6', 'R_9', 26, 24.136904761904763], ['R_3', 'R_14', 27, 23.571395469084457], ['R_8', 'R_14', 27, 23.054415129415126], ['R_0', 'R_11', 25, 22.823253265548832], ['R_4', 'R_11', 26, 22.41160261188175], ['R_1', 'R_6', 26, 22.35869602069157], ['R_0', 'R_8', 26, 22.010818070818075], ['R_1', 'R_12', 25, 21.949009342990195], ['R_8', 'R_9', 25, 21.935739384917945], ['R_5', 'R_9', 22, 18.872318744145826], ['R_5', 'R_11', 20, 17.93028404344194], ['R_5', 'R_6', 21, 17.60766690638245], ['R_1', 'R_11', 20, 17.16060606060606], ['R_0', 'R_3', 19, 16.922392555306008], ['R_6', 'R_8', 18, 15.598997493734338], ['R_6', 'R_13', 15, 13.618506493506494], ['R_3', 'R_9', 13, 11.265306610341367]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Final Training Accuracy: \"+str(trainedModel.get_final_training_accuracy()))\n",
    "print(\"Final Instance Coverage: \"+str(trainedModel.get_final_instance_coverage()))\n",
    "print(\"Final Attribute Specificity List: \"+str(trainedModel.get_final_attribute_specificity_list()))\n",
    "print(\"Final Attribute Accuracy List: \"+str(trainedModel.get_final_attribute_accuracy_list()))\n",
    "print(\"Final Attribute Tracking Sums:\")\n",
    "print(trainedModel.get_final_attribute_tracking_sums())\n",
    "print(\"Final Attribute Cooccurences:\")\n",
    "print(trainedModel.get_final_attribute_coocurrences(headers))\n",
    "\n",
    "instanceLabels = []\n",
    "for i in range(dataFeatures.shape[0]):\n",
    "    instanceLabels.append(i)\n",
    "print(model.get_attribute_tracking_scores(np.array(instanceLabels)))"
   ]
  },
  {
   "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, ExSTraCS will make a prediction of the current instance's class. The correctness of this prediction is factored into the moving average. It's important to note that tracked accuracy is a standard accuracy, rather than a balanced accuracy.\n",
    "\n",
    "In addition to tracking approximate accuracy, scikit-ExSTraCS 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>Number of macroclassifiers deleted in rule compaction</li>\n",
    "    <li>Total training time</li>\n",
    "    <li>Total matching time</li>\n",
    "    <li>Total covering time</li>\n",
    "    <li>Total crossover time</li>\n",
    "    <li>Total mutation time</li>\n",
    "    <li>Total attribute tracking time</li>\n",
    "    <li>Total expert knowledge processing time</li>\n",
    "    <li>Total initialization time</li>\n",
    "    <li>Total classifier addition time</li>\n",
    "    <li>Total rule compaction 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 ExSTraCS 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": {
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       "      <td>0.000000</td>\n",
       "      <td>0.000403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006766</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>6.675720e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006800</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>6.675720e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006829</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>6.675720e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006859</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.629395e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006888</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>8.821487e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006917</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>8.821487e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006946</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>8.821487e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006978</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>9.775162e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007006</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>9.775162e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007034</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>9.775162e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007062</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>9.775162e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007090</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>9.775162e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007121</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.096725e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007153</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.096725e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007181</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.192093e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007210</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.311302e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007241</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.406670e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007270</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.406670e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007304</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.478195e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007334</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.573563e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001257</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>17</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007363</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.573563e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001300</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>17</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.573563e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001341</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>17</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007419</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.573563e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001381</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>17</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007447</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>1.573563e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001422</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>9971</th>\n",
       "      <td>9971</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1356</td>\n",
       "      <td>2000</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>8479.766667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.801869</td>\n",
       "      <td>0.807991</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.584331e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.696747</td>\n",
       "      <td>7.646774</td>\n",
       "      <td>0.389817</td>\n",
       "      <td>1.874898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9972</th>\n",
       "      <td>9972</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1356</td>\n",
       "      <td>2000</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>8027.720930</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.801869</td>\n",
       "      <td>0.808070</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.584331e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.696748</td>\n",
       "      <td>7.646774</td>\n",
       "      <td>0.389817</td>\n",
       "      <td>1.875022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9973</th>\n",
       "      <td>9973</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1357</td>\n",
       "      <td>2000</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>7622.511628</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.802082</td>\n",
       "      <td>0.808138</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.584972e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.700025</td>\n",
       "      <td>7.647424</td>\n",
       "      <td>0.389859</td>\n",
       "      <td>1.875110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9974</th>\n",
       "      <td>9974</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1358</td>\n",
       "      <td>2000</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>8627.828571</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.802281</td>\n",
       "      <td>0.808223</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.586169e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.702630</td>\n",
       "      <td>7.648630</td>\n",
       "      <td>0.389907</td>\n",
       "      <td>1.875257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>9975</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1357</td>\n",
       "      <td>2000</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>8573.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.802316</td>\n",
       "      <td>0.808285</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.586671e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.705371</td>\n",
       "      <td>7.649144</td>\n",
       "      <td>0.389946</td>\n",
       "      <td>1.875362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9976</th>\n",
       "      <td>9976</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1357</td>\n",
       "      <td>2000</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>8912.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.802751</td>\n",
       "      <td>0.808359</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.587814e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.708365</td>\n",
       "      <td>7.650295</td>\n",
       "      <td>0.389977</td>\n",
       "      <td>1.875465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>9977</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1357</td>\n",
       "      <td>2000</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>8917.964286</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.803022</td>\n",
       "      <td>0.808497</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.587814e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.708365</td>\n",
       "      <td>7.650295</td>\n",
       "      <td>0.390024</td>\n",
       "      <td>1.875584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>9978</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1356</td>\n",
       "      <td>2000</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>8315.677419</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.803327</td>\n",
       "      <td>0.808559</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.588639e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.711257</td>\n",
       "      <td>7.651131</td>\n",
       "      <td>0.390058</td>\n",
       "      <td>1.875689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>9979</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1356</td>\n",
       "      <td>2000</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>8998.851852</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.803327</td>\n",
       "      <td>0.808629</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.588639e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.711258</td>\n",
       "      <td>7.651131</td>\n",
       "      <td>0.390058</td>\n",
       "      <td>1.875798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>9980</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1356</td>\n",
       "      <td>2000</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>8393.441176</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.803590</td>\n",
       "      <td>0.808694</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.589854e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.714170</td>\n",
       "      <td>7.652359</td>\n",
       "      <td>0.390099</td>\n",
       "      <td>1.875894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>9981</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1355</td>\n",
       "      <td>2000</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>7723.050000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.803865</td>\n",
       "      <td>0.808752</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.590451e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.716960</td>\n",
       "      <td>7.652969</td>\n",
       "      <td>0.390126</td>\n",
       "      <td>1.876005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>9982</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1355</td>\n",
       "      <td>2000</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>8942.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.803865</td>\n",
       "      <td>0.808803</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.590451e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.716961</td>\n",
       "      <td>7.652969</td>\n",
       "      <td>0.390126</td>\n",
       "      <td>1.876087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>9983</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1356</td>\n",
       "      <td>2000</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>7578.096774</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.804189</td>\n",
       "      <td>0.808865</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.591775e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.719565</td>\n",
       "      <td>7.654301</td>\n",
       "      <td>0.390163</td>\n",
       "      <td>1.876174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>9984</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1357</td>\n",
       "      <td>2000</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>7620.615385</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.804528</td>\n",
       "      <td>0.808950</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.592262e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.722317</td>\n",
       "      <td>7.654799</td>\n",
       "      <td>0.390215</td>\n",
       "      <td>1.876302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>9985</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1355</td>\n",
       "      <td>2000</td>\n",
       "      <td>27</td>\n",
       "      <td>17</td>\n",
       "      <td>6365.093750</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.804887</td>\n",
       "      <td>0.809020</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.592262e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.725201</td>\n",
       "      <td>7.654814</td>\n",
       "      <td>0.390254</td>\n",
       "      <td>1.876434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>9986</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1355</td>\n",
       "      <td>2000</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>8414.971429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.805228</td>\n",
       "      <td>0.809084</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.593410e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.728181</td>\n",
       "      <td>7.655975</td>\n",
       "      <td>0.390289</td>\n",
       "      <td>1.876558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>9987</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1355</td>\n",
       "      <td>2000</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>8817.391304</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.805582</td>\n",
       "      <td>0.809155</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.594443e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.731075</td>\n",
       "      <td>7.657016</td>\n",
       "      <td>0.390327</td>\n",
       "      <td>1.876680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>9988</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1353</td>\n",
       "      <td>2000</td>\n",
       "      <td>30</td>\n",
       "      <td>24</td>\n",
       "      <td>7566.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.805739</td>\n",
       "      <td>0.809230</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.594665e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.734014</td>\n",
       "      <td>7.657248</td>\n",
       "      <td>0.390371</td>\n",
       "      <td>1.876809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>9989</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1352</td>\n",
       "      <td>2000</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>7887.132075</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.805904</td>\n",
       "      <td>0.809317</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.594748e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.736750</td>\n",
       "      <td>7.657346</td>\n",
       "      <td>0.390419</td>\n",
       "      <td>1.876941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>9990</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1353</td>\n",
       "      <td>2000</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>8634.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.806079</td>\n",
       "      <td>0.809378</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.595908e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.739545</td>\n",
       "      <td>7.658514</td>\n",
       "      <td>0.390451</td>\n",
       "      <td>1.877053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>9991</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1353</td>\n",
       "      <td>2000</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>5998.875000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.806255</td>\n",
       "      <td>0.809435</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.595908e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.739545</td>\n",
       "      <td>7.658514</td>\n",
       "      <td>0.390477</td>\n",
       "      <td>1.877173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>9992</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1354</td>\n",
       "      <td>2000</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>8151.312500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.806574</td>\n",
       "      <td>0.809494</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.597191e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.742201</td>\n",
       "      <td>7.659805</td>\n",
       "      <td>0.390512</td>\n",
       "      <td>1.877269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>9993</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1354</td>\n",
       "      <td>2000</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>8436.236842</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.806966</td>\n",
       "      <td>0.809567</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.598501e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.744927</td>\n",
       "      <td>7.661123</td>\n",
       "      <td>0.390558</td>\n",
       "      <td>1.877385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>9994</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1354</td>\n",
       "      <td>2000</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>7451.857143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.806966</td>\n",
       "      <td>0.809646</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.598501e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.744928</td>\n",
       "      <td>7.661123</td>\n",
       "      <td>0.390558</td>\n",
       "      <td>1.877511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9995</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1354</td>\n",
       "      <td>2000</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>6859.769231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.806966</td>\n",
       "      <td>0.809730</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.598501e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.744929</td>\n",
       "      <td>7.661123</td>\n",
       "      <td>0.390558</td>\n",
       "      <td>1.877633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9996</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1354</td>\n",
       "      <td>2000</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>9782.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.806966</td>\n",
       "      <td>0.809774</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.598501e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.744929</td>\n",
       "      <td>7.661123</td>\n",
       "      <td>0.390558</td>\n",
       "      <td>1.877724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9997</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1354</td>\n",
       "      <td>2000</td>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>7042.212121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.807340</td>\n",
       "      <td>0.809850</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.599741e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.747875</td>\n",
       "      <td>7.662371</td>\n",
       "      <td>0.390610</td>\n",
       "      <td>1.877823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9998</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.695600</td>\n",
       "      <td>1354</td>\n",
       "      <td>2000</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>4805.629630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.807500</td>\n",
       "      <td>0.809906</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.599741e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.747876</td>\n",
       "      <td>7.662371</td>\n",
       "      <td>0.390637</td>\n",
       "      <td>1.877924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>9999</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.699500</td>\n",
       "      <td>1353</td>\n",
       "      <td>2000</td>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>7845.555556</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.807818</td>\n",
       "      <td>0.809983</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.600466e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.750893</td>\n",
       "      <td>7.663105</td>\n",
       "      <td>0.390682</td>\n",
       "      <td>1.878036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>10000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.630825</td>\n",
       "      <td>1154</td>\n",
       "      <td>1796</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.807818</td>\n",
       "      <td>0.809983</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.570879</td>\n",
       "      <td>7.600466e+00</td>\n",
       "      <td>0.056085</td>\n",
       "      <td>19.750893</td>\n",
       "      <td>7.663105</td>\n",
       "      <td>0.390682</td>\n",
       "      <td>1.879979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10001 rows × 28 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           0.833333                       0.000000   \n",
       "6              6           0.857143                       0.000000   \n",
       "7              7           0.875000                       0.000000   \n",
       "8              8           0.888889                       0.000000   \n",
       "9              9           0.900000                       0.000000   \n",
       "10            10           0.909091                       0.000000   \n",
       "11            11           0.916667                       0.000000   \n",
       "12            12           0.923077                       0.000000   \n",
       "13            13           0.857143                       0.000000   \n",
       "14            14           0.866667                       0.000000   \n",
       "15            15           0.875000                       0.000000   \n",
       "16            16           0.882353                       0.000000   \n",
       "17            17           0.888889                       0.000000   \n",
       "18            18           0.842105                       0.000000   \n",
       "19            19           0.800000                       0.000000   \n",
       "20            20           0.809524                       0.000000   \n",
       "21            21           0.818182                       0.000000   \n",
       "22            22           0.826087                       0.000000   \n",
       "23            23           0.791667                       0.000000   \n",
       "24            24           0.800000                       0.000000   \n",
       "25            25           0.807692                       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",
       "9971        9971           0.960000                       0.695600   \n",
       "9972        9972           0.960000                       0.695600   \n",
       "9973        9973           0.960000                       0.695600   \n",
       "9974        9974           0.960000                       0.695600   \n",
       "9975        9975           0.960000                       0.695600   \n",
       "9976        9976           0.960000                       0.695600   \n",
       "9977        9977           0.960000                       0.695600   \n",
       "9978        9978           0.960000                       0.695600   \n",
       "9979        9979           0.960000                       0.695600   \n",
       "9980        9980           0.960000                       0.695600   \n",
       "9981        9981           0.960000                       0.695600   \n",
       "9982        9982           0.960000                       0.695600   \n",
       "9983        9983           0.960000                       0.695600   \n",
       "9984        9984           0.960000                       0.695600   \n",
       "9985        9985           0.960000                       0.695600   \n",
       "9986        9986           0.960000                       0.695600   \n",
       "9987        9987           0.960000                       0.695600   \n",
       "9988        9988           0.960000                       0.695600   \n",
       "9989        9989           0.960000                       0.695600   \n",
       "9990        9990           0.980000                       0.695600   \n",
       "9991        9991           0.980000                       0.695600   \n",
       "9992        9992           0.980000                       0.695600   \n",
       "9993        9993           0.980000                       0.695600   \n",
       "9994        9994           0.980000                       0.695600   \n",
       "9995        9995           0.980000                       0.695600   \n",
       "9996        9996           0.980000                       0.695600   \n",
       "9997        9997           0.980000                       0.695600   \n",
       "9998        9998           0.980000                       0.695600   \n",
       "9999        9999           0.980000                       0.699500   \n",
       "10000      10000           0.980000                       0.630825   \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                         5                     5               1   \n",
       "5                         6                     6               2   \n",
       "6                         7                     7               1   \n",
       "7                         7                     7               2   \n",
       "8                         7                     7               1   \n",
       "9                         8                     8               1   \n",
       "10                        9                     9               1   \n",
       "11                        9                     9               1   \n",
       "12                        9                     9               2   \n",
       "13                       10                    10               2   \n",
       "14                       10                    10               1   \n",
       "15                       10                    10               1   \n",
       "16                       10                    10               1   \n",
       "17                       10                    10               1   \n",
       "18                       11                    11               2   \n",
       "19                       12                    12               2   \n",
       "20                       13                    13               2   \n",
       "21                       14                    14               2   \n",
       "22                       15                    15               2   \n",
       "23                       15                    15               2   \n",
       "24                       16                    16               2   \n",
       "25                       17                    17               2   \n",
       "26                       17                    17               1   \n",
       "27                       17                    17               1   \n",
       "28                       17                    17               1   \n",
       "29                       17                    17               1   \n",
       "...                     ...                   ...             ...   \n",
       "9971                   1356                  2000              45   \n",
       "9972                   1356                  2000              32   \n",
       "9973                   1357                  2000              24   \n",
       "9974                   1358                  2000              31   \n",
       "9975                   1357                  2000              24   \n",
       "9976                   1357                  2000              16   \n",
       "9977                   1357                  2000              26   \n",
       "9978                   1356                  2000              24   \n",
       "9979                   1356                  2000              22   \n",
       "9980                   1356                  2000              25   \n",
       "9981                   1355                  2000              17   \n",
       "9982                   1355                  2000              10   \n",
       "9983                   1356                  2000              24   \n",
       "9984                   1357                  2000              39   \n",
       "9985                   1355                  2000              27   \n",
       "9986                   1355                  2000              27   \n",
       "9987                   1355                  2000              27   \n",
       "9988                   1353                  2000              30   \n",
       "9989                   1352                  2000              34   \n",
       "9990                   1353                  2000              24   \n",
       "9991                   1353                  2000              27   \n",
       "9992                   1354                  2000              23   \n",
       "9993                   1354                  2000              27   \n",
       "9994                   1354                  2000              31   \n",
       "9995                   1354                  2000              30   \n",
       "9996                   1354                  2000              23   \n",
       "9997                   1354                  2000              32   \n",
       "9998                   1354                  2000              15   \n",
       "9999                   1353                  2000              31   \n",
       "10000                  1154                  1796               0   \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                                          4.000000   \n",
       "5                     1                                          5.000000   \n",
       "6                     1                                          6.000000   \n",
       "7                     1                                          1.000000   \n",
       "8                     1                                          2.000000   \n",
       "9                     1                                          9.000000   \n",
       "10                    1                                         10.000000   \n",
       "11                    1                                          2.000000   \n",
       "12                    1                                          1.000000   \n",
       "13                    1                                         13.000000   \n",
       "14                    1                                         10.000000   \n",
       "15                    1                                         10.000000   \n",
       "16                    1                                          2.000000   \n",
       "17                    1                                         10.000000   \n",
       "18                    1                                         18.000000   \n",
       "19                    1                                         19.000000   \n",
       "20                    1                                         20.000000   \n",
       "21                    1                                         21.000000   \n",
       "22                    1                                         22.000000   \n",
       "23                    1                                          2.000000   \n",
       "24                    1                                         24.000000   \n",
       "25                    1                                         25.000000   \n",
       "26                    1                                         10.000000   \n",
       "27                    1                                         10.000000   \n",
       "28                    1                                         10.000000   \n",
       "29                    1                                         10.000000   \n",
       "...                 ...                                               ...   \n",
       "9971                 43                                       8479.766667   \n",
       "9972                 29                                       8027.720930   \n",
       "9973                 24                                       7622.511628   \n",
       "9974                 26                                       8627.828571   \n",
       "9975                 15                                       8573.666667   \n",
       "9976                 12                                       8912.500000   \n",
       "9977                 22                                       8917.964286   \n",
       "9978                 19                                       8315.677419   \n",
       "9979                 21                                       8998.851852   \n",
       "9980                 20                                       8393.441176   \n",
       "9981                 11                                       7723.050000   \n",
       "9982                  9                                       8942.500000   \n",
       "9983                 18                                       7578.096774   \n",
       "9984                 36                                       7620.615385   \n",
       "9985                 17                                       6365.093750   \n",
       "9986                 15                                       8414.971429   \n",
       "9987                 20                                       8817.391304   \n",
       "9988                 24                                       7566.200000   \n",
       "9989                 31                                       7887.132075   \n",
       "9990                 13                                       8634.500000   \n",
       "9991                 10                                       5998.875000   \n",
       "9992                 16                                       8151.312500   \n",
       "9993                 26                                       8436.236842   \n",
       "9994                 26                                       7451.857143   \n",
       "9995                 27                                       6859.769231   \n",
       "9996                  3                                       9782.333333   \n",
       "9997                 31                                       7042.212121   \n",
       "9998                  9                                       4805.629630   \n",
       "9999                 29                                       7845.555556   \n",
       "10000                 0                                          0.000000   \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",
       "9971                                     0   \n",
       "9972                                     0   \n",
       "9973                                     0   \n",
       "9974                                     0   \n",
       "9975                                     1   \n",
       "9976                                     0   \n",
       "9977                                     0   \n",
       "9978                                     0   \n",
       "9979                                     0   \n",
       "9980                                     0   \n",
       "9981                                     1   \n",
       "9982                                     0   \n",
       "9983                                     0   \n",
       "9984                                     1   \n",
       "9985                                     2   \n",
       "9986                                     0   \n",
       "9987                                     0   \n",
       "9988                                     0   \n",
       "9989                                     1   \n",
       "9990                                     0   \n",
       "9991                                     0   \n",
       "9992                                     0   \n",
       "9993                                     0   \n",
       "9994                                     0   \n",
       "9995                                     0   \n",
       "9996                                     0   \n",
       "9997                                     0   \n",
       "9998                                     0   \n",
       "9999                                     0   \n",
       "10000                                    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",
       "9971                                               0  ...   \n",
       "9972                                               0  ...   \n",
       "9973                                               0  ...   \n",
       "9974                                               1  ...   \n",
       "9975                                               0  ...   \n",
       "9976                                               1  ...   \n",
       "9977                                               0  ...   \n",
       "9978                                               0  ...   \n",
       "9979                                               0  ...   \n",
       "9980                                               1  ...   \n",
       "9981                                               1  ...   \n",
       "9982                                               0  ...   \n",
       "9983                                               1  ...   \n",
       "9984                                               1  ...   \n",
       "9985                                               1  ...   \n",
       "9986                                               1  ...   \n",
       "9987                                               0  ...   \n",
       "9988                                               1  ...   \n",
       "9989                                               1  ...   \n",
       "9990                                               1  ...   \n",
       "9991                                               0  ...   \n",
       "9992                                               1  ...   \n",
       "9993                                               0  ...   \n",
       "9994                                               0  ...   \n",
       "9995                                               0  ...   \n",
       "9996                                               0  ...   \n",
       "9997                                               0  ...   \n",
       "9998                                               0  ...   \n",
       "9999                                               1  ...   \n",
       "10000                                              0  ...   \n",
       "\n",
       "       Total Mutation Time  Total Attribute Tracking Time  \\\n",
       "0                 0.000000                       0.006515   \n",
       "1                 0.000000                       0.006608   \n",
       "2                 0.000000                       0.006640   \n",
       "3                 0.000000                       0.006673   \n",
       "4                 0.000000                       0.006706   \n",
       "5                 0.000000                       0.006735   \n",
       "6                 0.000000                       0.006766   \n",
       "7                 0.000000                       0.006800   \n",
       "8                 0.000000                       0.006829   \n",
       "9                 0.000000                       0.006859   \n",
       "10                0.000000                       0.006888   \n",
       "11                0.000000                       0.006917   \n",
       "12                0.000000                       0.006946   \n",
       "13                0.000000                       0.006978   \n",
       "14                0.000000                       0.007006   \n",
       "15                0.000000                       0.007034   \n",
       "16                0.000000                       0.007062   \n",
       "17                0.000000                       0.007090   \n",
       "18                0.000000                       0.007121   \n",
       "19                0.000000                       0.007153   \n",
       "20                0.000000                       0.007181   \n",
       "21                0.000000                       0.007210   \n",
       "22                0.000000                       0.007241   \n",
       "23                0.000000                       0.007270   \n",
       "24                0.000000                       0.007304   \n",
       "25                0.000000                       0.007334   \n",
       "26                0.000000                       0.007363   \n",
       "27                0.000000                       0.007391   \n",
       "28                0.000000                       0.007419   \n",
       "29                0.000000                       0.007447   \n",
       "...                    ...                            ...   \n",
       "9971              1.801869                       0.807991   \n",
       "9972              1.801869                       0.808070   \n",
       "9973              1.802082                       0.808138   \n",
       "9974              1.802281                       0.808223   \n",
       "9975              1.802316                       0.808285   \n",
       "9976              1.802751                       0.808359   \n",
       "9977              1.803022                       0.808497   \n",
       "9978              1.803327                       0.808559   \n",
       "9979              1.803327                       0.808629   \n",
       "9980              1.803590                       0.808694   \n",
       "9981              1.803865                       0.808752   \n",
       "9982              1.803865                       0.808803   \n",
       "9983              1.804189                       0.808865   \n",
       "9984              1.804528                       0.808950   \n",
       "9985              1.804887                       0.809020   \n",
       "9986              1.805228                       0.809084   \n",
       "9987              1.805582                       0.809155   \n",
       "9988              1.805739                       0.809230   \n",
       "9989              1.805904                       0.809317   \n",
       "9990              1.806079                       0.809378   \n",
       "9991              1.806255                       0.809435   \n",
       "9992              1.806574                       0.809494   \n",
       "9993              1.806966                       0.809567   \n",
       "9994              1.806966                       0.809646   \n",
       "9995              1.806966                       0.809730   \n",
       "9996              1.806966                       0.809774   \n",
       "9997              1.807340                       0.809850   \n",
       "9998              1.807500                       0.809906   \n",
       "9999              1.807818                       0.809983   \n",
       "10000             1.807818                       0.809983   \n",
       "\n",
       "       Total Expert Knowledge Time  Total Model Initialization Time  \\\n",
       "0                         0.000028                         0.570879   \n",
       "1                         0.000028                         0.570879   \n",
       "2                         0.000028                         0.570879   \n",
       "3                         0.000028                         0.570879   \n",
       "4                         0.000028                         0.570879   \n",
       "5                         0.000028                         0.570879   \n",
       "6                         0.000028                         0.570879   \n",
       "7                         0.000028                         0.570879   \n",
       "8                         0.000028                         0.570879   \n",
       "9                         0.000028                         0.570879   \n",
       "10                        0.000028                         0.570879   \n",
       "11                        0.000028                         0.570879   \n",
       "12                        0.000028                         0.570879   \n",
       "13                        0.000028                         0.570879   \n",
       "14                        0.000028                         0.570879   \n",
       "15                        0.000028                         0.570879   \n",
       "16                        0.000028                         0.570879   \n",
       "17                        0.000028                         0.570879   \n",
       "18                        0.000028                         0.570879   \n",
       "19                        0.000028                         0.570879   \n",
       "20                        0.000028                         0.570879   \n",
       "21                        0.000028                         0.570879   \n",
       "22                        0.000028                         0.570879   \n",
       "23                        0.000028                         0.570879   \n",
       "24                        0.000028                         0.570879   \n",
       "25                        0.000028                         0.570879   \n",
       "26                        0.000028                         0.570879   \n",
       "27                        0.000028                         0.570879   \n",
       "28                        0.000028                         0.570879   \n",
       "29                        0.000028                         0.570879   \n",
       "...                            ...                              ...   \n",
       "9971                      0.000028                         0.570879   \n",
       "9972                      0.000028                         0.570879   \n",
       "9973                      0.000028                         0.570879   \n",
       "9974                      0.000028                         0.570879   \n",
       "9975                      0.000028                         0.570879   \n",
       "9976                      0.000028                         0.570879   \n",
       "9977                      0.000028                         0.570879   \n",
       "9978                      0.000028                         0.570879   \n",
       "9979                      0.000028                         0.570879   \n",
       "9980                      0.000028                         0.570879   \n",
       "9981                      0.000028                         0.570879   \n",
       "9982                      0.000028                         0.570879   \n",
       "9983                      0.000028                         0.570879   \n",
       "9984                      0.000028                         0.570879   \n",
       "9985                      0.000028                         0.570879   \n",
       "9986                      0.000028                         0.570879   \n",
       "9987                      0.000028                         0.570879   \n",
       "9988                      0.000028                         0.570879   \n",
       "9989                      0.000028                         0.570879   \n",
       "9990                      0.000028                         0.570879   \n",
       "9991                      0.000028                         0.570879   \n",
       "9992                      0.000028                         0.570879   \n",
       "9993                      0.000028                         0.570879   \n",
       "9994                      0.000028                         0.570879   \n",
       "9995                      0.000028                         0.570879   \n",
       "9996                      0.000028                         0.570879   \n",
       "9997                      0.000028                         0.570879   \n",
       "9998                      0.000028                         0.570879   \n",
       "9999                      0.000028                         0.570879   \n",
       "10000                     0.000028                         0.570879   \n",
       "\n",
       "       Total Classifier Add Time  Total Rule Compaction Time  \\\n",
       "0                   9.536743e-07                    0.000000   \n",
       "1                   1.907349e-06                    0.000000   \n",
       "2                   2.861023e-06                    0.000000   \n",
       "3                   3.814697e-06                    0.000000   \n",
       "4                   4.768372e-06                    0.000000   \n",
       "5                   5.722046e-06                    0.000000   \n",
       "6                   6.675720e-06                    0.000000   \n",
       "7                   6.675720e-06                    0.000000   \n",
       "8                   6.675720e-06                    0.000000   \n",
       "9                   7.629395e-06                    0.000000   \n",
       "10                  8.821487e-06                    0.000000   \n",
       "11                  8.821487e-06                    0.000000   \n",
       "12                  8.821487e-06                    0.000000   \n",
       "13                  9.775162e-06                    0.000000   \n",
       "14                  9.775162e-06                    0.000000   \n",
       "15                  9.775162e-06                    0.000000   \n",
       "16                  9.775162e-06                    0.000000   \n",
       "17                  9.775162e-06                    0.000000   \n",
       "18                  1.096725e-05                    0.000000   \n",
       "19                  1.096725e-05                    0.000000   \n",
       "20                  1.192093e-05                    0.000000   \n",
       "21                  1.311302e-05                    0.000000   \n",
       "22                  1.406670e-05                    0.000000   \n",
       "23                  1.406670e-05                    0.000000   \n",
       "24                  1.478195e-05                    0.000000   \n",
       "25                  1.573563e-05                    0.000000   \n",
       "26                  1.573563e-05                    0.000000   \n",
       "27                  1.573563e-05                    0.000000   \n",
       "28                  1.573563e-05                    0.000000   \n",
       "29                  1.573563e-05                    0.000000   \n",
       "...                          ...                         ...   \n",
       "9971                7.584331e+00                    0.000000   \n",
       "9972                7.584331e+00                    0.000000   \n",
       "9973                7.584972e+00                    0.000000   \n",
       "9974                7.586169e+00                    0.000000   \n",
       "9975                7.586671e+00                    0.000000   \n",
       "9976                7.587814e+00                    0.000000   \n",
       "9977                7.587814e+00                    0.000000   \n",
       "9978                7.588639e+00                    0.000000   \n",
       "9979                7.588639e+00                    0.000000   \n",
       "9980                7.589854e+00                    0.000000   \n",
       "9981                7.590451e+00                    0.000000   \n",
       "9982                7.590451e+00                    0.000000   \n",
       "9983                7.591775e+00                    0.000000   \n",
       "9984                7.592262e+00                    0.000000   \n",
       "9985                7.592262e+00                    0.000000   \n",
       "9986                7.593410e+00                    0.000000   \n",
       "9987                7.594443e+00                    0.000000   \n",
       "9988                7.594665e+00                    0.000000   \n",
       "9989                7.594748e+00                    0.000000   \n",
       "9990                7.595908e+00                    0.000000   \n",
       "9991                7.595908e+00                    0.000000   \n",
       "9992                7.597191e+00                    0.000000   \n",
       "9993                7.598501e+00                    0.000000   \n",
       "9994                7.598501e+00                    0.000000   \n",
       "9995                7.598501e+00                    0.000000   \n",
       "9996                7.598501e+00                    0.000000   \n",
       "9997                7.599741e+00                    0.000000   \n",
       "9998                7.599741e+00                    0.000000   \n",
       "9999                7.600466e+00                    0.000000   \n",
       "10000               7.600466e+00                    0.056085   \n",
       "\n",
       "       Total Deletion Time  Total Subsumption Time  Total Selection Time  \\\n",
       "0                 0.000001                0.000000              0.000000   \n",
       "1                 0.000002                0.000000              0.000000   \n",
       "2                 0.000003                0.000000              0.000000   \n",
       "3                 0.000004                0.000000              0.000000   \n",
       "4                 0.000004                0.000000              0.000000   \n",
       "5                 0.000004                0.000000              0.000000   \n",
       "6                 0.000005                0.000000              0.000000   \n",
       "7                 0.000005                0.000000              0.000000   \n",
       "8                 0.000006                0.000000              0.000000   \n",
       "9                 0.000007                0.000000              0.000000   \n",
       "10                0.000007                0.000000              0.000000   \n",
       "11                0.000008                0.000000              0.000000   \n",
       "12                0.000008                0.000000              0.000000   \n",
       "13                0.000009                0.000000              0.000000   \n",
       "14                0.000009                0.000000              0.000000   \n",
       "15                0.000009                0.000000              0.000000   \n",
       "16                0.000010                0.000000              0.000000   \n",
       "17                0.000011                0.000000              0.000000   \n",
       "18                0.000011                0.000000              0.000000   \n",
       "19                0.000012                0.000000              0.000000   \n",
       "20                0.000013                0.000000              0.000000   \n",
       "21                0.000014                0.000000              0.000000   \n",
       "22                0.000015                0.000000              0.000000   \n",
       "23                0.000016                0.000000              0.000000   \n",
       "24                0.000016                0.000000              0.000000   \n",
       "25                0.000016                0.000000              0.000000   \n",
       "26                0.000017                0.000000              0.000000   \n",
       "27                0.000018                0.000000              0.000000   \n",
       "28                0.000018                0.000000              0.000000   \n",
       "29                0.000019                0.000000              0.000000   \n",
       "...                    ...                     ...                   ...   \n",
       "9971             19.696747                7.646774              0.389817   \n",
       "9972             19.696748                7.646774              0.389817   \n",
       "9973             19.700025                7.647424              0.389859   \n",
       "9974             19.702630                7.648630              0.389907   \n",
       "9975             19.705371                7.649144              0.389946   \n",
       "9976             19.708365                7.650295              0.389977   \n",
       "9977             19.708365                7.650295              0.390024   \n",
       "9978             19.711257                7.651131              0.390058   \n",
       "9979             19.711258                7.651131              0.390058   \n",
       "9980             19.714170                7.652359              0.390099   \n",
       "9981             19.716960                7.652969              0.390126   \n",
       "9982             19.716961                7.652969              0.390126   \n",
       "9983             19.719565                7.654301              0.390163   \n",
       "9984             19.722317                7.654799              0.390215   \n",
       "9985             19.725201                7.654814              0.390254   \n",
       "9986             19.728181                7.655975              0.390289   \n",
       "9987             19.731075                7.657016              0.390327   \n",
       "9988             19.734014                7.657248              0.390371   \n",
       "9989             19.736750                7.657346              0.390419   \n",
       "9990             19.739545                7.658514              0.390451   \n",
       "9991             19.739545                7.658514              0.390477   \n",
       "9992             19.742201                7.659805              0.390512   \n",
       "9993             19.744927                7.661123              0.390558   \n",
       "9994             19.744928                7.661123              0.390558   \n",
       "9995             19.744929                7.661123              0.390558   \n",
       "9996             19.744929                7.661123              0.390558   \n",
       "9997             19.747875                7.662371              0.390610   \n",
       "9998             19.747876                7.662371              0.390637   \n",
       "9999             19.750893                7.663105              0.390682   \n",
       "10000            19.750893                7.663105              0.390682   \n",
       "\n",
       "       Total Evaluation Time  \n",
       "0                   0.000102  \n",
       "1                   0.000196  \n",
       "2                   0.000262  \n",
       "3                   0.000313  \n",
       "4                   0.000358  \n",
       "5                   0.000403  \n",
       "6                   0.000445  \n",
       "7                   0.000488  \n",
       "8                   0.000531  \n",
       "9                   0.000572  \n",
       "10                  0.000612  \n",
       "11                  0.000655  \n",
       "12                  0.000700  \n",
       "13                  0.000743  \n",
       "14                  0.000784  \n",
       "15                  0.000825  \n",
       "16                  0.000865  \n",
       "17                  0.000909  \n",
       "18                  0.000955  \n",
       "19                  0.000998  \n",
       "20                  0.001040  \n",
       "21                  0.001083  \n",
       "22                  0.001127  \n",
       "23                  0.001169  \n",
       "24                  0.001213  \n",
       "25                  0.001257  \n",
       "26                  0.001300  \n",
       "27                  0.001341  \n",
       "28                  0.001381  \n",
       "29                  0.001422  \n",
       "...                      ...  \n",
       "9971                1.874898  \n",
       "9972                1.875022  \n",
       "9973                1.875110  \n",
       "9974                1.875257  \n",
       "9975                1.875362  \n",
       "9976                1.875465  \n",
       "9977                1.875584  \n",
       "9978                1.875689  \n",
       "9979                1.875798  \n",
       "9980                1.875894  \n",
       "9981                1.876005  \n",
       "9982                1.876087  \n",
       "9983                1.876174  \n",
       "9984                1.876302  \n",
       "9985                1.876434  \n",
       "9986                1.876558  \n",
       "9987                1.876680  \n",
       "9988                1.876809  \n",
       "9989                1.876941  \n",
       "9990                1.877053  \n",
       "9991                1.877173  \n",
       "9992                1.877269  \n",
       "9993                1.877385  \n",
       "9994                1.877511  \n",
       "9995                1.877633  \n",
       "9996                1.877724  \n",
       "9997                1.877823  \n",
       "9998                1.877924  \n",
       "9999                1.878036  \n",
       "10000               1.879979  \n",
       "\n",
       "[10001 rows x 28 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",
    "rc = dataTracking[\"# Rules Removed via Rule Compaction\"].values\n",
    "\n",
    "gTime = dataTracking[\"Total Global Time\"].values\n",
    "mTime = dataTracking[\"Total Matching Time\"].values\n",
    "covTime = dataTracking[\"Total Covering Time\"].values\n",
    "crossTime = dataTracking[\"Total Crossover Time\"].values\n",
    "covTime = dataTracking[\"Total Covering Time\"].values\n",
    "mutTime = dataTracking[\"Total Mutation Time\"].values\n",
    "atTime = dataTracking[\"Total Attribute Tracking Time\"].values\n",
    "initTime = dataTracking[\"Total Model Initialization Time\"].values\n",
    "rcTime = dataTracking[\"Total Rule Compaction 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.plot(iterations,cumulativeFreq(rc),label=\"RC Count\")\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Cumulative Operations Count Over Iterations')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.plot(iterations,initTime,label=\"Init Time\")\n",
    "plt.plot(iterations,mTime+initTime,label=\"Matching Time\")\n",
    "plt.plot(iterations,covTime+mTime+initTime,label=\"Covering Time\")\n",
    "plt.plot(iterations,selTime+covTime+mTime+initTime,label=\"Selection Time\")\n",
    "plt.plot(iterations,crossTime+selTime+covTime+mTime+initTime,label=\"Crossover Time\")\n",
    "plt.plot(iterations,mutTime+crossTime+selTime+covTime+mTime+initTime,label=\"Mutation Time\")\n",
    "plt.plot(iterations,subTime+mutTime+crossTime+selTime+covTime+mTime+initTime,label=\"Subsumption Time\")\n",
    "plt.plot(iterations,atTime+subTime+mutTime+crossTime+selTime+covTime+mTime+initTime,label=\"AT Time\")\n",
    "plt.plot(iterations,delTime+atTime+subTime+mutTime+crossTime+selTime+covTime+mTime+initTime,label=\"Deletion Time\")\n",
    "plt.plot(iterations,rcTime+delTime+atTime+subTime+mutTime+crossTime+selTime+covTime+mTime+initTime,label=\"RC Time\")\n",
    "plt.plot(iterations,evalTime+rcTime+delTime+atTime+subTime+mutTime+crossTime+selTime+covTime+mTime+initTime,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"
   ]
  },
  {
   "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 (either before or after rule compaction). 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,RCPopulation) | 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.\n",
    "\n",
    "The RCPopulation boolean param is also optional. By default, the program exports the rule population BEFORE rule compaction. Making RCPopulation = True will export the rule population AFTER rule compaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "      <td>42.041925</td>\n",
       "      <td>9965</td>\n",
       "      <td>3327</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>160</td>\n",
       "      <td>200</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\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>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.756345</td>\n",
       "      <td>1</td>\n",
       "      <td>38.649441</td>\n",
       "      <td>9920</td>\n",
       "      <td>3383</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.003071</td>\n",
       "      <td>149</td>\n",
       "      <td>197</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.739910</td>\n",
       "      <td>1</td>\n",
       "      <td>46.081780</td>\n",
       "      <td>9881</td>\n",
       "      <td>3446</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.004561</td>\n",
       "      <td>165</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>1</td>\n",
       "      <td>35.216969</td>\n",
       "      <td>9954</td>\n",
       "      <td>3447</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>160</td>\n",
       "      <td>204</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>43.208345</td>\n",
       "      <td>9767</td>\n",
       "      <td>3471</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.001687</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</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>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>1</td>\n",
       "      <td>47.047664</td>\n",
       "      <td>9625</td>\n",
       "      <td>3529</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</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>4</td>\n",
       "      <td>43.958928</td>\n",
       "      <td>9945</td>\n",
       "      <td>3601</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>46.177549</td>\n",
       "      <td>9910</td>\n",
       "      <td>3604</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.002884</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>1</td>\n",
       "      <td>47.594940</td>\n",
       "      <td>9067</td>\n",
       "      <td>3628</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.000372</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>42.178207</td>\n",
       "      <td>9883</td>\n",
       "      <td>3633</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>False</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>1323</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>9980</td>\n",
       "      <td>9966</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>9987</td>\n",
       "      <td>9966</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>9976</td>\n",
       "      <td>9967</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</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</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>9973</td>\n",
       "      <td>9968</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</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</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>9968</td>\n",
       "      <td>9968</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000347</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>9997</td>\n",
       "      <td>9970</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>#</td>\n",
       "      <td>#</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>0.714286</td>\n",
       "      <td>1</td>\n",
       "      <td>42.160000</td>\n",
       "      <td>9999</td>\n",
       "      <td>9971</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>9999</td>\n",
       "      <td>9973</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000422</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\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>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>9974</td>\n",
       "      <td>9974</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000422</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>9974</td>\n",
       "      <td>9974</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>9975</td>\n",
       "      <td>9975</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\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>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>9987</td>\n",
       "      <td>9976</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>9976</td>\n",
       "      <td>9976</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>40.198449</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>47.180942</td>\n",
       "      <td>9981</td>\n",
       "      <td>9981</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>42.480000</td>\n",
       "      <td>9983</td>\n",
       "      <td>9983</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>39.705273</td>\n",
       "      <td>9983</td>\n",
       "      <td>9983</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\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>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>44.666667</td>\n",
       "      <td>9984</td>\n",
       "      <td>9984</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>#</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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>49.368000</td>\n",
       "      <td>9986</td>\n",
       "      <td>9986</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>41.495871</td>\n",
       "      <td>9986</td>\n",
       "      <td>9986</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>#</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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>42.696000</td>\n",
       "      <td>9987</td>\n",
       "      <td>9987</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>9990</td>\n",
       "      <td>9990</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\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>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>28.197837</td>\n",
       "      <td>9990</td>\n",
       "      <td>9990</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>9992</td>\n",
       "      <td>9992</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>1.0</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>48.114781</td>\n",
       "      <td>9992</td>\n",
       "      <td>9992</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>38.310400</td>\n",
       "      <td>9993</td>\n",
       "      <td>9993</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>38.310400</td>\n",
       "      <td>9993</td>\n",
       "      <td>9993</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>9997</td>\n",
       "      <td>9997</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\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>#</td>\n",
       "      <td>#</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>9997</td>\n",
       "      <td>9997</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1353 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A_0  A_1  A_2  A_3  R_0  R_1  R_2  R_3  R_4  R_5  ...  Accuracy  \\\n",
       "0     1.0  1.0  0.0  1.0  0.0    #  1.0    #    #    #  ...  1.000000   \n",
       "1     1.0  0.0  1.0  0.0  1.0    #  1.0    #    #    #  ...  1.000000   \n",
       "2     1.0  0.0  1.0  0.0  1.0    #  1.0    #    #    #  ...  1.000000   \n",
       "3     1.0  1.0    #    #    #    #  0.0    #    #    #  ...  0.917647   \n",
       "4     1.0  1.0    #    #    #    #  0.0    #    #    #  ...  0.879518   \n",
       "5     0.0  0.0  1.0  0.0    #    #  0.0    #    #    #  ...  1.000000   \n",
       "6       #  1.0  0.0    #    #    #  0.0    #    #    #  ...  0.785714   \n",
       "7     0.0  1.0  0.0  1.0    #    #    #    #    #  1.0  ...  1.000000   \n",
       "8       #  0.0  0.0  1.0    #    #    #  0.0    #  1.0  ...  0.764151   \n",
       "9       #  0.0  0.0  1.0  0.0    #    #    #    #    #  ...  0.761317   \n",
       "10    1.0  1.0  1.0    #    #    #    #  1.0    #  0.0  ...  0.789474   \n",
       "11    0.0  1.0  0.0    #    #    #  0.0    #  0.0    #  ...  0.789916   \n",
       "12    0.0  1.0  1.0  1.0    #    #    #    #    #    #  ...  1.000000   \n",
       "13    0.0  1.0    #  1.0  0.0    #    #    #    #    #  ...  0.776316   \n",
       "14    0.0  0.0    #  0.0    #    #  0.0    #    #    #  ...  0.767857   \n",
       "15    0.0  0.0  1.0  1.0    #    #    #  0.0    #  0.0  ...  1.000000   \n",
       "16      #    #    #    #    #    #  0.0  0.0    #  1.0  ...  0.740741   \n",
       "17    1.0    #  0.0    #  0.0    #    #    #    #    #  ...  0.824390   \n",
       "18      #  0.0  0.0  1.0    #    #    #    #  1.0  1.0  ...  0.771739   \n",
       "19      #  1.0  1.0  1.0    #    #    #    #    #    #  ...  1.000000   \n",
       "20    1.0  1.0    #    #    #    #  0.0    #    #    #  ...  0.800000   \n",
       "21    0.0  0.0    #  1.0    #    #    #  0.0    #  0.0  ...  0.756345   \n",
       "22    0.0    #  0.0  0.0  1.0    #    #    #    #  1.0  ...  0.739910   \n",
       "23    1.0    #  0.0  0.0    #    #    #    #    #    #  ...  0.784314   \n",
       "24    1.0  1.0  0.0  1.0    #    #    #    #    #    #  ...  1.000000   \n",
       "25    1.0  1.0    #    #    #    #  0.0  1.0    #  0.0  ...  0.785714   \n",
       "26    0.0  0.0  1.0  0.0    #    #  0.0    #    #    #  ...  1.000000   \n",
       "27    1.0  0.0  1.0    #    #    #    #    #    #    #  ...  1.000000   \n",
       "28      #  0.0  1.0  0.0    #    #  0.0  0.0    #    #  ...  0.814815   \n",
       "29    1.0  0.0  1.0  0.0    #    #  0.0    #    #    #  ...  1.000000   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...       ...   \n",
       "1323  0.0    #  1.0  1.0    #    #    #  1.0    #    #  ...  1.000000   \n",
       "1324  0.0  0.0    #  1.0    #  1.0    #  1.0  1.0    #  ...  1.000000   \n",
       "1325    #  0.0  0.0    #    #    #    #    #    #    #  ...  1.000000   \n",
       "1326  0.0  0.0  1.0  0.0    #    #  1.0    #    #    #  ...  1.000000   \n",
       "1327  0.0  0.0  1.0  0.0    #    #  1.0    #    #    #  ...  1.000000   \n",
       "1328  1.0    #  1.0  1.0    #  1.0    #    #    #    #  ...  1.000000   \n",
       "1329    #    #  1.0  0.0    #    #  1.0    #    #    #  ...  0.714286   \n",
       "1330  0.0    #  1.0    #  1.0    #  1.0    #    #    #  ...  1.000000   \n",
       "1331  0.0  0.0    #    #    #  1.0    #  1.0    #    #  ...  0.000000   \n",
       "1332  0.0  0.0  1.0  1.0    #    #  0.0  1.0    #  1.0  ...  1.000000   \n",
       "1333  0.0  1.0  1.0    #    #    #    #    #    #    #  ...  0.000000   \n",
       "1334    #  0.0  0.0    #    #    #    #    #  1.0    #  ...  0.666667   \n",
       "1335  1.0  0.0  0.0    #    #    #  1.0    #    #    #  ...  1.000000   \n",
       "1336  0.0  1.0  1.0  1.0    #    #    #  1.0    #    #  ...  1.000000   \n",
       "1337  0.0    #  1.0  1.0    #  0.0    #    #    #    #  ...  1.000000   \n",
       "1338  1.0    #  1.0  0.0    #    #    #    #    #    #  ...  1.000000   \n",
       "1339    #  1.0  0.0  1.0  0.0    #    #    #    #    #  ...  1.000000   \n",
       "1340  1.0    #  0.0    #    #    #    #    #  1.0    #  ...  1.000000   \n",
       "1341  1.0    #  1.0    #    #    #    #  1.0    #    #  ...  0.333333   \n",
       "1342    #    #  0.0  1.0    #    #    #    #    #    #  ...  1.000000   \n",
       "1343  1.0  0.0    #  1.0    #  0.0    #    #    #    #  ...  1.000000   \n",
       "1344    #  0.0  0.0  1.0    #    #    #    #    #    #  ...  1.000000   \n",
       "1345  1.0  1.0  1.0  0.0    #    #    #    #    #    #  ...  1.000000   \n",
       "1346    #  1.0  1.0  0.0    #    #  0.0    #    #    #  ...  1.000000   \n",
       "1347  0.0  1.0  0.0    #    #    #    #  0.0  1.0    #  ...  1.000000   \n",
       "1348  0.0    #  0.0  0.0    #  1.0    #    #  1.0    #  ...  1.000000   \n",
       "1349  1.0    #  0.0  0.0    #    #    #    #    #    #  ...  1.000000   \n",
       "1350  1.0    #  0.0  0.0    #    #    #    #    #    #  ...  1.000000   \n",
       "1351    #    #  1.0    #    #    #    #    #    #    #  ...  1.000000   \n",
       "1352    #    #  1.0  1.0    #    #    #    #    #    #  ...  1.000000   \n",
       "\n",
       "     Numerosity Avg Match Set Size TimeStamp GA Iteration Initialized  \\\n",
       "0             1           2.000000           13                    13   \n",
       "1             1          14.500000         9729                    19   \n",
       "2             1           2.000000           24                    24   \n",
       "3             1          56.321289         9938                    96   \n",
       "4             1          45.498887         9965                   125   \n",
       "5             1          67.479529         9945                   212   \n",
       "6             1          48.857635         9859                  1135   \n",
       "7            15          43.049579         9998                  1777   \n",
       "8             1          34.597954         9928                  1941   \n",
       "9             1          38.018517         9987                  2146   \n",
       "10            1          38.996172         9938                  2321   \n",
       "11            1          41.049742         9960                  2410   \n",
       "12           15          45.937555         9957                  2475   \n",
       "13            1          40.521243         9980                  2494   \n",
       "14            1          50.069777         9861                  2616   \n",
       "15            1          43.193794         9858                  2659   \n",
       "16            1          43.855312         9815                  2850   \n",
       "17            1          49.539970         9771                  2986   \n",
       "18            1          39.702126         9896                  3173   \n",
       "19            1          48.876702         9997                  3315   \n",
       "20            1          42.041925         9965                  3327   \n",
       "21            1          38.649441         9920                  3383   \n",
       "22            1          46.081780         9881                  3446   \n",
       "23            1          35.216969         9954                  3447   \n",
       "24            5          43.208345         9767                  3471   \n",
       "25            1          47.047664         9625                  3529   \n",
       "26            4          43.958928         9945                  3601   \n",
       "27            8          46.177549         9910                  3604   \n",
       "28            1          47.594940         9067                  3628   \n",
       "29            2          42.178207         9883                  3633   \n",
       "...         ...                ...          ...                   ...   \n",
       "1323          1          36.000000         9980                  9966   \n",
       "1324          1          35.000000         9987                  9966   \n",
       "1325          1          17.000000         9976                  9967   \n",
       "1326          1          42.000000         9973                  9968   \n",
       "1327          1          44.500000         9968                  9968   \n",
       "1328          1          67.500000         9997                  9970   \n",
       "1329          1          42.160000         9999                  9971   \n",
       "1330          1          54.000000         9999                  9973   \n",
       "1331          1          54.000000         9974                  9974   \n",
       "1332          1          32.500000         9974                  9974   \n",
       "1333          1          39.000000         9975                  9975   \n",
       "1334          1          29.000000         9987                  9976   \n",
       "1335          1          17.000000         9976                  9976   \n",
       "1336          1          40.198449         9980                  9980   \n",
       "1337          1          46.000000         9980                  9980   \n",
       "1338          1          47.180942         9981                  9981   \n",
       "1339          1          42.480000         9983                  9983   \n",
       "1340          1          39.705273         9983                  9983   \n",
       "1341          1          44.666667         9984                  9984   \n",
       "1342          1          49.368000         9986                  9986   \n",
       "1343          1          41.495871         9986                  9986   \n",
       "1344          1          42.696000         9987                  9987   \n",
       "1345          1          24.000000         9990                  9990   \n",
       "1346          1          28.197837         9990                  9990   \n",
       "1347          1          43.500000         9992                  9992   \n",
       "1348          1          48.114781         9992                  9992   \n",
       "1349          1          38.310400         9993                  9993   \n",
       "1350          1          38.310400         9993                  9993   \n",
       "1351          1          53.500000         9997                  9997   \n",
       "1352          1          53.500000         9997                  9997   \n",
       "\n",
       "     Specificity Deletion Probability Correct Count Match Count Epoch Complete  \n",
       "0           0.70             0.000016             1           1          False  \n",
       "1           0.65             0.000113             2           2          False  \n",
       "2           0.70             0.000016             1           1          False  \n",
       "3           0.35             0.000440            78          85          False  \n",
       "4           0.35             0.000355            73          83          False  \n",
       "5           0.45             0.000527            24          24          False  \n",
       "6           0.35             0.000381            55          70          False  \n",
       "7           0.25             0.005042           228         228          False  \n",
       "8           0.30             0.000270            81         106          False  \n",
       "9           0.25             0.000297           185         243          False  \n",
       "10          0.35             0.000304            45          57          False  \n",
       "11          0.25             0.000320           188         238          False  \n",
       "12          0.25             0.005380           245         245          False  \n",
       "13          0.25             0.000316           177         228          False  \n",
       "14          0.35             0.000391            43          56          False  \n",
       "15          0.30             0.000337           102         102          False  \n",
       "16          0.35             0.004292            40          54          False  \n",
       "17          0.25             0.000387           169         205          False  \n",
       "18          0.30             0.000310            71          92          False  \n",
       "19          0.30             0.000382           118         118          False  \n",
       "20          0.25             0.000328           160         200          False  \n",
       "21          0.25             0.003071           149         197          False  \n",
       "22          0.25             0.004561           165         223          False  \n",
       "23          0.25             0.000275           160         204          False  \n",
       "24          0.30             0.001687           108         108          False  \n",
       "25          0.40             0.000367            22          28          False  \n",
       "26          0.30             0.001373            99          99          False  \n",
       "27          0.25             0.002884           195         195          False  \n",
       "28          0.40             0.000372            22          27          False  \n",
       "29          0.30             0.000659           112         112          False  \n",
       "...          ...                  ...           ...         ...            ...  \n",
       "1323        0.20             0.000281             3           3          False  \n",
       "1324        0.35             0.000273             2           2          False  \n",
       "1325        0.20             0.000133             1           1          False  \n",
       "1326        0.30             0.000328             1           1          False  \n",
       "1327        0.35             0.000347             0           0          False  \n",
       "1328        0.25             0.000527             2           2          False  \n",
       "1329        0.15             0.000329             5           7          False  \n",
       "1330        0.25             0.000422             1           1          False  \n",
       "1331        0.30             0.000422             0           1          False  \n",
       "1332        0.40             0.000254             0           0          False  \n",
       "1333        0.20             0.000304             0           1          False  \n",
       "1334        0.15             0.000226             2           3          False  \n",
       "1335        0.35             0.000133             0           0          False  \n",
       "1336        0.35             0.000314             0           0          False  \n",
       "1337        0.25             0.000359             0           0          False  \n",
       "1338        0.25             0.000368             0           0          False  \n",
       "1339        0.30             0.000332             0           0          False  \n",
       "1340        0.30             0.000310             0           0          False  \n",
       "1341        0.15             0.000349             1           3          False  \n",
       "1342        0.20             0.000385             0           0          False  \n",
       "1343        0.30             0.000324             0           0          False  \n",
       "1344        0.25             0.000333             0           0          False  \n",
       "1345        0.25             0.000187             1           1          False  \n",
       "1346        0.35             0.000220             0           0          False  \n",
       "1347        0.30             0.000340             0           0          False  \n",
       "1348        0.30             0.000376             0           0          False  \n",
       "1349        0.30             0.000299             0           0          False  \n",
       "1350        0.20             0.000299             0           0          False  \n",
       "1351        0.25             0.000418             0           0          False  \n",
       "1352        0.30             0.000418             0           0          False  \n",
       "\n",
       "[1353 rows x 32 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainedModel.export_final_rule_population(headers,classLabel,filename=\"defaultExportDir/fileRulePopulation.csv\",DCAL=False)\n",
    "\n",
    "populationData = pd.read_csv(\"defaultExportDir/fileRulePopulation.csv\")\n",
    "display(populationData)"
   ]
  },
  {
   "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",
       "      <th>Epoch Complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1...</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_0, R_2, R_6, R_7, R_10, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0...</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_0, R_2, R_6, R_7, R_10, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>9729</td>\n",
       "      <td>19</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1...</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_0, R_2, R_6, R_7, R_10, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_2, R_7, R_12, R_13, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.423406</td>\n",
       "      <td>0.917647</td>\n",
       "      <td>1</td>\n",
       "      <td>56.321289</td>\n",
       "      <td>9938</td>\n",
       "      <td>96</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>78</td>\n",
       "      <td>85</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_2, R_7, R_13, R_14, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.276980</td>\n",
       "      <td>0.879518</td>\n",
       "      <td>1</td>\n",
       "      <td>45.498887</td>\n",
       "      <td>9965</td>\n",
       "      <td>125</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>73</td>\n",
       "      <td>83</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_2, R_7, R_12, R_14, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>67.479529</td>\n",
       "      <td>9945</td>\n",
       "      <td>212</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_1, A_2, R_2, R_7, R_12, R_13, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089670</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>1</td>\n",
       "      <td>48.857635</td>\n",
       "      <td>9859</td>\n",
       "      <td>1135</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>43.049579</td>\n",
       "      <td>9998</td>\n",
       "      <td>1777</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.005042</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_3, R_5, R_9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.067888</td>\n",
       "      <td>0.764151</td>\n",
       "      <td>1</td>\n",
       "      <td>34.597954</td>\n",
       "      <td>9928</td>\n",
       "      <td>1941</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>81</td>\n",
       "      <td>106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_0, R_9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.065412</td>\n",
       "      <td>0.761317</td>\n",
       "      <td>1</td>\n",
       "      <td>38.018517</td>\n",
       "      <td>9987</td>\n",
       "      <td>2146</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>185</td>\n",
       "      <td>243</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_3, R_5, R_13, R_14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.094054</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>1</td>\n",
       "      <td>38.996172</td>\n",
       "      <td>9938</td>\n",
       "      <td>2321</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>45</td>\n",
       "      <td>57</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_2, R_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.094582</td>\n",
       "      <td>0.789916</td>\n",
       "      <td>1</td>\n",
       "      <td>41.049742</td>\n",
       "      <td>9960</td>\n",
       "      <td>2410</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>188</td>\n",
       "      <td>238</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>45.937555</td>\n",
       "      <td>9957</td>\n",
       "      <td>2475</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.005380</td>\n",
       "      <td>245</td>\n",
       "      <td>245</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_3, R_0, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.079503</td>\n",
       "      <td>0.776316</td>\n",
       "      <td>1</td>\n",
       "      <td>40.521243</td>\n",
       "      <td>9980</td>\n",
       "      <td>2494</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>177</td>\n",
       "      <td>228</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_3, R_2, R_12, R_13, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.071253</td>\n",
       "      <td>0.767857</td>\n",
       "      <td>1</td>\n",
       "      <td>50.069777</td>\n",
       "      <td>9861</td>\n",
       "      <td>2616</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>43</td>\n",
       "      <td>56</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, 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>43.193794</td>\n",
       "      <td>9858</td>\n",
       "      <td>2659</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>R_2, R_3, R_5, R_8, R_12, R_14, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.049735</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>1</td>\n",
       "      <td>43.855312</td>\n",
       "      <td>9815</td>\n",
       "      <td>2850</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>40</td>\n",
       "      <td>54</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_0, R_12, R_13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.144987</td>\n",
       "      <td>0.824390</td>\n",
       "      <td>1</td>\n",
       "      <td>49.539970</td>\n",
       "      <td>9771</td>\n",
       "      <td>2986</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000387</td>\n",
       "      <td>169</td>\n",
       "      <td>205</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_4, R_5, R_9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.074939</td>\n",
       "      <td>0.771739</td>\n",
       "      <td>1</td>\n",
       "      <td>39.702126</td>\n",
       "      <td>9896</td>\n",
       "      <td>3173</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>71</td>\n",
       "      <td>92</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_7, R_10, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>48.876702</td>\n",
       "      <td>9997</td>\n",
       "      <td>3315</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_2, R_13, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.107374</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "      <td>42.041925</td>\n",
       "      <td>9965</td>\n",
       "      <td>3327</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>160</td>\n",
       "      <td>200</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_3, R_3, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.061263</td>\n",
       "      <td>0.756345</td>\n",
       "      <td>1</td>\n",
       "      <td>38.649441</td>\n",
       "      <td>9920</td>\n",
       "      <td>3383</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.003071</td>\n",
       "      <td>149</td>\n",
       "      <td>197</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0, 0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_0, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.049180</td>\n",
       "      <td>0.739910</td>\n",
       "      <td>1</td>\n",
       "      <td>46.081780</td>\n",
       "      <td>9881</td>\n",
       "      <td>3446</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.004561</td>\n",
       "      <td>165</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, A_3, R_12, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.088084</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>1</td>\n",
       "      <td>35.216969</td>\n",
       "      <td>9954</td>\n",
       "      <td>3447</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>160</td>\n",
       "      <td>204</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_12, R_13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>43.208345</td>\n",
       "      <td>9767</td>\n",
       "      <td>3471</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.001687</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_2, R_3, R_5, R_8, R_13, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089670</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>1</td>\n",
       "      <td>47.047664</td>\n",
       "      <td>9625</td>\n",
       "      <td>3529</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_2, R_12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>43.958928</td>\n",
       "      <td>9945</td>\n",
       "      <td>3601</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_10, R_11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>46.177549</td>\n",
       "      <td>9910</td>\n",
       "      <td>3604</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.002884</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_2, R_3, R_7, R_9, R_14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129000</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>1</td>\n",
       "      <td>47.594940</td>\n",
       "      <td>9067</td>\n",
       "      <td>3628</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.000372</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_2, R_10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>42.178207</td>\n",
       "      <td>9883</td>\n",
       "      <td>3633</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>9980</td>\n",
       "      <td>9966</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_3, R_1, R_3, R_4, R_8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>9987</td>\n",
       "      <td>9966</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_1, A_2, R_9, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>9976</td>\n",
       "      <td>9967</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_2, R_12</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>9973</td>\n",
       "      <td>9968</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_2, R_6, R_9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>9968</td>\n",
       "      <td>9968</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000347</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_1, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>9997</td>\n",
       "      <td>9970</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>1.0, 0.0, 1.0</td>\n",
       "      <td>A_2, A_3, R_2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.034572</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1</td>\n",
       "      <td>42.160000</td>\n",
       "      <td>9999</td>\n",
       "      <td>9971</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, R_0, R_2, R_6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>9999</td>\n",
       "      <td>9973</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000422</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_1, R_3, R_12, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>9974</td>\n",
       "      <td>9974</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000422</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, 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>32.500000</td>\n",
       "      <td>9974</td>\n",
       "      <td>9974</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>9975</td>\n",
       "      <td>9975</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>0.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, R_4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.017342</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>9987</td>\n",
       "      <td>9976</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_2, R_8, R_9, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>9976</td>\n",
       "      <td>9976</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_3, R_7, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>40.198449</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, A_3, R_1, R_10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_7, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>47.180942</td>\n",
       "      <td>9981</td>\n",
       "      <td>9981</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_0, R_12, R_13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>42.480000</td>\n",
       "      <td>9983</td>\n",
       "      <td>9983</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>1.0, 0.0, 1.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_4, R_9, R_13, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>39.705273</td>\n",
       "      <td>9983</td>\n",
       "      <td>9983</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, R_3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>44.666667</td>\n",
       "      <td>9984</td>\n",
       "      <td>9984</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>0.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_2, A_3, R_9, R_13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>49.368000</td>\n",
       "      <td>9986</td>\n",
       "      <td>9986</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_3, R_1, R_6, R_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>41.495871</td>\n",
       "      <td>9986</td>\n",
       "      <td>9986</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_6, R_10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>42.696000</td>\n",
       "      <td>9987</td>\n",
       "      <td>9987</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>9990</td>\n",
       "      <td>9990</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_1, A_2, A_3, R_2, R_7, R_11, R_14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>28.197837</td>\n",
       "      <td>9990</td>\n",
       "      <td>9990</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>0.0, 1.0, 0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_3, R_4, R_12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>9992</td>\n",
       "      <td>9992</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>0.0, 0.0, 0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_1, R_4, R_10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>48.114781</td>\n",
       "      <td>9992</td>\n",
       "      <td>9992</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_8, R_12, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>38.310400</td>\n",
       "      <td>9993</td>\n",
       "      <td>9993</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>38.310400</td>\n",
       "      <td>9993</td>\n",
       "      <td>9993</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_2, R_7, R_11, R_13, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>9997</td>\n",
       "      <td>9997</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_2, A_3, R_7, R_11, R_13, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>9997</td>\n",
       "      <td>9997</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1353 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Specified Values  \\\n",
       "0     1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1...   \n",
       "1     1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0...   \n",
       "2     1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1...   \n",
       "3                     1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "4                     1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "5           0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0   \n",
       "6                     1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "7                               0.0, 1.0, 0.0, 1.0, 1.0   \n",
       "8                          0.0, 0.0, 1.0, 0.0, 1.0, 1.0   \n",
       "9                               0.0, 0.0, 1.0, 0.0, 1.0   \n",
       "10                    1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0   \n",
       "11                              0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "12                              0.0, 1.0, 1.0, 1.0, 0.0   \n",
       "13                              0.0, 1.0, 1.0, 0.0, 1.0   \n",
       "14                    0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0   \n",
       "15                         0.0, 0.0, 1.0, 1.0, 0.0, 0.0   \n",
       "16                    0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "17                              1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "18                         0.0, 0.0, 1.0, 1.0, 1.0, 1.0   \n",
       "19                         1.0, 1.0, 1.0, 1.0, 0.0, 1.0   \n",
       "20                              1.0, 1.0, 0.0, 0.0, 0.0   \n",
       "21                              0.0, 0.0, 1.0, 0.0, 0.0   \n",
       "22                              0.0, 0.0, 0.0, 1.0, 1.0   \n",
       "23                              1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "24                         1.0, 1.0, 0.0, 1.0, 0.0, 0.0   \n",
       "25               1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0   \n",
       "26                         0.0, 0.0, 1.0, 0.0, 0.0, 1.0   \n",
       "27                              1.0, 0.0, 1.0, 0.0, 0.0   \n",
       "28               0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0   \n",
       "29                         1.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "...                                                 ...   \n",
       "1323                                 0.0, 1.0, 1.0, 1.0   \n",
       "1324                  0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0   \n",
       "1325                                 0.0, 0.0, 1.0, 1.0   \n",
       "1326                       0.0, 0.0, 1.0, 0.0, 1.0, 1.0   \n",
       "1327                  0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0   \n",
       "1328                            1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "1329                                      1.0, 0.0, 1.0   \n",
       "1330                            0.0, 1.0, 1.0, 1.0, 1.0   \n",
       "1331                       0.0, 0.0, 1.0, 1.0, 0.0, 0.0   \n",
       "1332             0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0   \n",
       "1333                                 0.0, 1.0, 1.0, 0.0   \n",
       "1334                                      0.0, 0.0, 1.0   \n",
       "1335                  1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0   \n",
       "1336                  0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "1337                            0.0, 1.0, 1.0, 0.0, 0.0   \n",
       "1338                            1.0, 1.0, 0.0, 1.0, 1.0   \n",
       "1339                       1.0, 0.0, 1.0, 0.0, 1.0, 1.0   \n",
       "1340                       1.0, 0.0, 1.0, 1.0, 1.0, 0.0   \n",
       "1341                                      1.0, 1.0, 1.0   \n",
       "1342                                 0.0, 1.0, 0.0, 1.0   \n",
       "1343                       1.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "1344                            0.0, 0.0, 1.0, 0.0, 1.0   \n",
       "1345                            1.0, 1.0, 1.0, 0.0, 0.0   \n",
       "1346                  1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0   \n",
       "1347                       0.0, 1.0, 0.0, 0.0, 1.0, 1.0   \n",
       "1348                       0.0, 0.0, 0.0, 1.0, 1.0, 1.0   \n",
       "1349                       1.0, 0.0, 0.0, 1.0, 1.0, 1.0   \n",
       "1350                                 1.0, 0.0, 0.0, 1.0   \n",
       "1351                            1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "1352                       1.0, 1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "\n",
       "                              Specified Attribute Names  Class   Fitness  \\\n",
       "0     A_0, A_1, A_2, A_3, R_0, R_2, R_6, R_7, R_10, ...    0.0  1.000000   \n",
       "1     A_0, A_1, A_2, A_3, R_0, R_2, R_6, R_7, R_10, ...    0.0  1.000000   \n",
       "2     A_0, A_1, A_2, A_3, R_0, R_2, R_6, R_7, R_10, ...    0.0  1.000000   \n",
       "3                  A_0, A_1, R_2, R_7, R_12, R_13, R_15    0.0  0.423406   \n",
       "4                  A_0, A_1, R_2, R_7, R_13, R_14, R_15    0.0  0.276980   \n",
       "5        A_0, A_1, A_2, A_3, R_2, R_7, R_12, R_14, R_15    0.0  1.000000   \n",
       "6                  A_1, A_2, R_2, R_7, R_12, R_13, R_15    0.0  0.089670   \n",
       "7                               A_0, A_1, A_2, A_3, R_5    1.0  1.000000   \n",
       "8                          A_1, A_2, A_3, R_3, R_5, R_9    1.0  0.067888   \n",
       "9                               A_1, A_2, A_3, R_0, R_9    1.0  0.065412   \n",
       "10                  A_0, A_1, A_2, R_3, R_5, R_13, R_14    0.0  0.094054   \n",
       "11                              A_0, A_1, A_2, R_2, R_4    0.0  0.094582   \n",
       "12                              A_0, A_1, A_2, A_3, R_7    0.0  1.000000   \n",
       "13                              A_0, A_1, A_3, R_0, R_7    1.0  0.079503   \n",
       "14                 A_0, A_1, A_3, R_2, R_12, R_13, R_15    0.0  0.071253   \n",
       "15                         A_0, A_1, A_2, A_3, R_3, R_5    0.0  1.000000   \n",
       "16                 R_2, R_3, R_5, R_8, R_12, R_14, R_15    0.0  0.049735   \n",
       "17                            A_0, A_2, R_0, R_12, R_13    0.0  0.144987   \n",
       "18                         A_1, A_2, A_3, R_4, R_5, R_9    1.0  0.074939   \n",
       "19                       A_1, A_2, A_3, R_7, R_10, R_15    1.0  1.000000   \n",
       "20                            A_0, A_1, R_2, R_13, R_15    0.0  0.107374   \n",
       "21                              A_0, A_1, A_3, R_3, R_5    0.0  0.061263   \n",
       "22                              A_0, A_2, A_3, R_0, R_5    1.0  0.049180   \n",
       "23                            A_0, A_2, A_3, R_12, R_15    0.0  0.088084   \n",
       "24                       A_0, A_1, A_2, A_3, R_12, R_13    0.0  1.000000   \n",
       "25             A_0, A_1, R_2, R_3, R_5, R_8, R_13, R_15    0.0  0.089670   \n",
       "26                        A_0, A_1, A_2, A_3, R_2, R_12    0.0  1.000000   \n",
       "27                            A_0, A_1, A_2, R_10, R_11    0.0  1.000000   \n",
       "28              A_1, A_2, A_3, R_2, R_3, R_7, R_9, R_14    0.0  0.129000   \n",
       "29                        A_0, A_1, A_2, A_3, R_2, R_10    0.0  1.000000   \n",
       "...                                                 ...    ...       ...   \n",
       "1323                                 A_0, A_2, A_3, R_3    1.0  1.000000   \n",
       "1324                  A_0, A_1, A_3, R_1, R_3, R_4, R_8    1.0  1.000000   \n",
       "1325                                A_1, A_2, R_9, R_14    1.0  1.000000   \n",
       "1326                      A_0, A_1, A_2, A_3, R_2, R_12    1.0  1.000000   \n",
       "1327                  A_0, A_1, A_2, A_3, R_2, R_6, R_9    1.0  0.100000   \n",
       "1328                           A_0, A_2, A_3, R_1, R_15    1.0  1.000000   \n",
       "1329                                      A_2, A_3, R_2    1.0  0.034572   \n",
       "1330                            A_0, A_2, R_0, R_2, R_6    1.0  1.000000   \n",
       "1331                     A_0, A_1, R_1, R_3, R_12, R_14    1.0  0.000000   \n",
       "1332             A_0, A_1, A_2, A_3, R_2, R_3, R_5, R_7    1.0  0.100000   \n",
       "1333                                 A_0, A_1, A_2, R_6    0.0  0.000000   \n",
       "1334                                      A_1, A_2, R_4    1.0  0.017342   \n",
       "1335                 A_0, A_1, A_2, R_2, R_8, R_9, R_14    1.0  0.100000   \n",
       "1336                 A_0, A_1, A_2, A_3, R_3, R_7, R_15    1.0  0.100000   \n",
       "1337                           A_0, A_2, A_3, R_1, R_10    1.0  0.100000   \n",
       "1338                           A_0, A_2, A_3, R_7, R_14    1.0  0.100000   \n",
       "1339                     A_1, A_2, A_3, R_0, R_12, R_13    1.0  0.100000   \n",
       "1340                     A_0, A_2, R_4, R_9, R_13, R_14    1.0  0.100000   \n",
       "1341                                      A_0, A_2, R_3    1.0  0.000017   \n",
       "1342                                A_2, A_3, R_9, R_13    0.0  0.100000   \n",
       "1343                       A_0, A_1, A_3, R_1, R_6, R_9    0.0  0.100000   \n",
       "1344                           A_1, A_2, A_3, R_6, R_10    1.0  0.100000   \n",
       "1345                           A_0, A_1, A_2, A_3, R_14    0.0  1.000000   \n",
       "1346                A_1, A_2, A_3, R_2, R_7, R_11, R_14    0.0  0.100000   \n",
       "1347                      A_0, A_1, A_2, R_3, R_4, R_12    1.0  0.100000   \n",
       "1348                      A_0, A_2, A_3, R_1, R_4, R_10    1.0  0.100000   \n",
       "1349                     A_0, A_2, A_3, R_8, R_12, R_14    1.0  0.100000   \n",
       "1350                                A_0, A_2, A_3, R_14    1.0  0.100000   \n",
       "1351                         A_2, R_7, R_11, R_13, R_15    1.0  0.100000   \n",
       "1352                    A_2, A_3, R_7, R_11, R_13, R_15    1.0  0.100000   \n",
       "\n",
       "      Accuracy  Numerosity  Avg Match Set Size  TimeStamp GA  \\\n",
       "0     1.000000           1            2.000000            13   \n",
       "1     1.000000           1           14.500000          9729   \n",
       "2     1.000000           1            2.000000            24   \n",
       "3     0.917647           1           56.321289          9938   \n",
       "4     0.879518           1           45.498887          9965   \n",
       "5     1.000000           1           67.479529          9945   \n",
       "6     0.785714           1           48.857635          9859   \n",
       "7     1.000000          15           43.049579          9998   \n",
       "8     0.764151           1           34.597954          9928   \n",
       "9     0.761317           1           38.018517          9987   \n",
       "10    0.789474           1           38.996172          9938   \n",
       "11    0.789916           1           41.049742          9960   \n",
       "12    1.000000          15           45.937555          9957   \n",
       "13    0.776316           1           40.521243          9980   \n",
       "14    0.767857           1           50.069777          9861   \n",
       "15    1.000000           1           43.193794          9858   \n",
       "16    0.740741           1           43.855312          9815   \n",
       "17    0.824390           1           49.539970          9771   \n",
       "18    0.771739           1           39.702126          9896   \n",
       "19    1.000000           1           48.876702          9997   \n",
       "20    0.800000           1           42.041925          9965   \n",
       "21    0.756345           1           38.649441          9920   \n",
       "22    0.739910           1           46.081780          9881   \n",
       "23    0.784314           1           35.216969          9954   \n",
       "24    1.000000           5           43.208345          9767   \n",
       "25    0.785714           1           47.047664          9625   \n",
       "26    1.000000           4           43.958928          9945   \n",
       "27    1.000000           8           46.177549          9910   \n",
       "28    0.814815           1           47.594940          9067   \n",
       "29    1.000000           2           42.178207          9883   \n",
       "...        ...         ...                 ...           ...   \n",
       "1323  1.000000           1           36.000000          9980   \n",
       "1324  1.000000           1           35.000000          9987   \n",
       "1325  1.000000           1           17.000000          9976   \n",
       "1326  1.000000           1           42.000000          9973   \n",
       "1327  1.000000           1           44.500000          9968   \n",
       "1328  1.000000           1           67.500000          9997   \n",
       "1329  0.714286           1           42.160000          9999   \n",
       "1330  1.000000           1           54.000000          9999   \n",
       "1331  0.000000           1           54.000000          9974   \n",
       "1332  1.000000           1           32.500000          9974   \n",
       "1333  0.000000           1           39.000000          9975   \n",
       "1334  0.666667           1           29.000000          9987   \n",
       "1335  1.000000           1           17.000000          9976   \n",
       "1336  1.000000           1           40.198449          9980   \n",
       "1337  1.000000           1           46.000000          9980   \n",
       "1338  1.000000           1           47.180942          9981   \n",
       "1339  1.000000           1           42.480000          9983   \n",
       "1340  1.000000           1           39.705273          9983   \n",
       "1341  0.333333           1           44.666667          9984   \n",
       "1342  1.000000           1           49.368000          9986   \n",
       "1343  1.000000           1           41.495871          9986   \n",
       "1344  1.000000           1           42.696000          9987   \n",
       "1345  1.000000           1           24.000000          9990   \n",
       "1346  1.000000           1           28.197837          9990   \n",
       "1347  1.000000           1           43.500000          9992   \n",
       "1348  1.000000           1           48.114781          9992   \n",
       "1349  1.000000           1           38.310400          9993   \n",
       "1350  1.000000           1           38.310400          9993   \n",
       "1351  1.000000           1           53.500000          9997   \n",
       "1352  1.000000           1           53.500000          9997   \n",
       "\n",
       "      Iteration Initialized  Specificity  Deletion Probability  Correct Count  \\\n",
       "0                        13         0.70              0.000016              1   \n",
       "1                        19         0.65              0.000113              2   \n",
       "2                        24         0.70              0.000016              1   \n",
       "3                        96         0.35              0.000440             78   \n",
       "4                       125         0.35              0.000355             73   \n",
       "5                       212         0.45              0.000527             24   \n",
       "6                      1135         0.35              0.000381             55   \n",
       "7                      1777         0.25              0.005042            228   \n",
       "8                      1941         0.30              0.000270             81   \n",
       "9                      2146         0.25              0.000297            185   \n",
       "10                     2321         0.35              0.000304             45   \n",
       "11                     2410         0.25              0.000320            188   \n",
       "12                     2475         0.25              0.005380            245   \n",
       "13                     2494         0.25              0.000316            177   \n",
       "14                     2616         0.35              0.000391             43   \n",
       "15                     2659         0.30              0.000337            102   \n",
       "16                     2850         0.35              0.004292             40   \n",
       "17                     2986         0.25              0.000387            169   \n",
       "18                     3173         0.30              0.000310             71   \n",
       "19                     3315         0.30              0.000382            118   \n",
       "20                     3327         0.25              0.000328            160   \n",
       "21                     3383         0.25              0.003071            149   \n",
       "22                     3446         0.25              0.004561            165   \n",
       "23                     3447         0.25              0.000275            160   \n",
       "24                     3471         0.30              0.001687            108   \n",
       "25                     3529         0.40              0.000367             22   \n",
       "26                     3601         0.30              0.001373             99   \n",
       "27                     3604         0.25              0.002884            195   \n",
       "28                     3628         0.40              0.000372             22   \n",
       "29                     3633         0.30              0.000659            112   \n",
       "...                     ...          ...                   ...            ...   \n",
       "1323                   9966         0.20              0.000281              3   \n",
       "1324                   9966         0.35              0.000273              2   \n",
       "1325                   9967         0.20              0.000133              1   \n",
       "1326                   9968         0.30              0.000328              1   \n",
       "1327                   9968         0.35              0.000347              0   \n",
       "1328                   9970         0.25              0.000527              2   \n",
       "1329                   9971         0.15              0.000329              5   \n",
       "1330                   9973         0.25              0.000422              1   \n",
       "1331                   9974         0.30              0.000422              0   \n",
       "1332                   9974         0.40              0.000254              0   \n",
       "1333                   9975         0.20              0.000304              0   \n",
       "1334                   9976         0.15              0.000226              2   \n",
       "1335                   9976         0.35              0.000133              0   \n",
       "1336                   9980         0.35              0.000314              0   \n",
       "1337                   9980         0.25              0.000359              0   \n",
       "1338                   9981         0.25              0.000368              0   \n",
       "1339                   9983         0.30              0.000332              0   \n",
       "1340                   9983         0.30              0.000310              0   \n",
       "1341                   9984         0.15              0.000349              1   \n",
       "1342                   9986         0.20              0.000385              0   \n",
       "1343                   9986         0.30              0.000324              0   \n",
       "1344                   9987         0.25              0.000333              0   \n",
       "1345                   9990         0.25              0.000187              1   \n",
       "1346                   9990         0.35              0.000220              0   \n",
       "1347                   9992         0.30              0.000340              0   \n",
       "1348                   9992         0.30              0.000376              0   \n",
       "1349                   9993         0.30              0.000299              0   \n",
       "1350                   9993         0.20              0.000299              0   \n",
       "1351                   9997         0.25              0.000418              0   \n",
       "1352                   9997         0.30              0.000418              0   \n",
       "\n",
       "      Match Count  Epoch Complete  \n",
       "0               1           False  \n",
       "1               2           False  \n",
       "2               1           False  \n",
       "3              85           False  \n",
       "4              83           False  \n",
       "5              24           False  \n",
       "6              70           False  \n",
       "7             228           False  \n",
       "8             106           False  \n",
       "9             243           False  \n",
       "10             57           False  \n",
       "11            238           False  \n",
       "12            245           False  \n",
       "13            228           False  \n",
       "14             56           False  \n",
       "15            102           False  \n",
       "16             54           False  \n",
       "17            205           False  \n",
       "18             92           False  \n",
       "19            118           False  \n",
       "20            200           False  \n",
       "21            197           False  \n",
       "22            223           False  \n",
       "23            204           False  \n",
       "24            108           False  \n",
       "25             28           False  \n",
       "26             99           False  \n",
       "27            195           False  \n",
       "28             27           False  \n",
       "29            112           False  \n",
       "...           ...             ...  \n",
       "1323            3           False  \n",
       "1324            2           False  \n",
       "1325            1           False  \n",
       "1326            1           False  \n",
       "1327            0           False  \n",
       "1328            2           False  \n",
       "1329            7           False  \n",
       "1330            1           False  \n",
       "1331            1           False  \n",
       "1332            0           False  \n",
       "1333            1           False  \n",
       "1334            3           False  \n",
       "1335            0           False  \n",
       "1336            0           False  \n",
       "1337            0           False  \n",
       "1338            0           False  \n",
       "1339            0           False  \n",
       "1340            0           False  \n",
       "1341            3           False  \n",
       "1342            0           False  \n",
       "1343            0           False  \n",
       "1344            0           False  \n",
       "1345            1           False  \n",
       "1346            0           False  \n",
       "1347            0           False  \n",
       "1348            0           False  \n",
       "1349            0           False  \n",
       "1350            0           False  \n",
       "1351            0           False  \n",
       "1352            0           False  \n",
       "\n",
       "[1353 rows x 14 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": [
    "And finally the rule population after rule compaction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       "      <th>Epoch Complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0...</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_0, R_2, R_6, R_7, R_10, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>9729</td>\n",
       "      <td>19</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_2, R_7, R_12, R_13, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.423406</td>\n",
       "      <td>0.917647</td>\n",
       "      <td>1</td>\n",
       "      <td>56.321289</td>\n",
       "      <td>9938</td>\n",
       "      <td>96</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>78</td>\n",
       "      <td>85</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_2, R_7, R_13, R_14, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.276980</td>\n",
       "      <td>0.879518</td>\n",
       "      <td>1</td>\n",
       "      <td>45.498887</td>\n",
       "      <td>9965</td>\n",
       "      <td>125</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>73</td>\n",
       "      <td>83</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_2, R_7, R_12, R_14, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>67.479529</td>\n",
       "      <td>9945</td>\n",
       "      <td>212</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_1, A_2, R_2, R_7, R_12, R_13, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089670</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>1</td>\n",
       "      <td>48.857635</td>\n",
       "      <td>9859</td>\n",
       "      <td>1135</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>43.049579</td>\n",
       "      <td>9998</td>\n",
       "      <td>1777</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.005042</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_3, R_5, R_9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.067888</td>\n",
       "      <td>0.764151</td>\n",
       "      <td>1</td>\n",
       "      <td>34.597954</td>\n",
       "      <td>9928</td>\n",
       "      <td>1941</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>81</td>\n",
       "      <td>106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_0, R_9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.065412</td>\n",
       "      <td>0.761317</td>\n",
       "      <td>1</td>\n",
       "      <td>38.018517</td>\n",
       "      <td>9987</td>\n",
       "      <td>2146</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>185</td>\n",
       "      <td>243</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_3, R_5, R_13, R_14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.094054</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>1</td>\n",
       "      <td>38.996172</td>\n",
       "      <td>9938</td>\n",
       "      <td>2321</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>45</td>\n",
       "      <td>57</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_2, R_4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.094582</td>\n",
       "      <td>0.789916</td>\n",
       "      <td>1</td>\n",
       "      <td>41.049742</td>\n",
       "      <td>9960</td>\n",
       "      <td>2410</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>188</td>\n",
       "      <td>238</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>45.937555</td>\n",
       "      <td>9957</td>\n",
       "      <td>2475</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.005380</td>\n",
       "      <td>245</td>\n",
       "      <td>245</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_3, R_0, R_7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.079503</td>\n",
       "      <td>0.776316</td>\n",
       "      <td>1</td>\n",
       "      <td>40.521243</td>\n",
       "      <td>9980</td>\n",
       "      <td>2494</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>177</td>\n",
       "      <td>228</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_3, R_2, R_12, R_13, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.071253</td>\n",
       "      <td>0.767857</td>\n",
       "      <td>1</td>\n",
       "      <td>50.069777</td>\n",
       "      <td>9861</td>\n",
       "      <td>2616</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>43</td>\n",
       "      <td>56</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, 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>43.193794</td>\n",
       "      <td>9858</td>\n",
       "      <td>2659</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>R_2, R_3, R_5, R_8, R_12, R_14, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.049735</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>1</td>\n",
       "      <td>43.855312</td>\n",
       "      <td>9815</td>\n",
       "      <td>2850</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>40</td>\n",
       "      <td>54</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_0, R_12, R_13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.144987</td>\n",
       "      <td>0.824390</td>\n",
       "      <td>1</td>\n",
       "      <td>49.539970</td>\n",
       "      <td>9771</td>\n",
       "      <td>2986</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000387</td>\n",
       "      <td>169</td>\n",
       "      <td>205</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_4, R_5, R_9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.074939</td>\n",
       "      <td>0.771739</td>\n",
       "      <td>1</td>\n",
       "      <td>39.702126</td>\n",
       "      <td>9896</td>\n",
       "      <td>3173</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>71</td>\n",
       "      <td>92</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_7, R_10, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>48.876702</td>\n",
       "      <td>9997</td>\n",
       "      <td>3315</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_2, R_13, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.107374</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1</td>\n",
       "      <td>42.041925</td>\n",
       "      <td>9965</td>\n",
       "      <td>3327</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>160</td>\n",
       "      <td>200</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_3, R_3, R_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.061263</td>\n",
       "      <td>0.756345</td>\n",
       "      <td>1</td>\n",
       "      <td>38.649441</td>\n",
       "      <td>9920</td>\n",
       "      <td>3383</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.003071</td>\n",
       "      <td>149</td>\n",
       "      <td>197</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0, 0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_0, R_5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.049180</td>\n",
       "      <td>0.739910</td>\n",
       "      <td>1</td>\n",
       "      <td>46.081780</td>\n",
       "      <td>9881</td>\n",
       "      <td>3446</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.004561</td>\n",
       "      <td>165</td>\n",
       "      <td>223</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, A_3, R_12, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.088084</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>1</td>\n",
       "      <td>35.216969</td>\n",
       "      <td>9954</td>\n",
       "      <td>3447</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>160</td>\n",
       "      <td>204</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_12, R_13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>43.208345</td>\n",
       "      <td>9767</td>\n",
       "      <td>3471</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.001687</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, R_2, R_3, R_5, R_8, R_13, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089670</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>1</td>\n",
       "      <td>47.047664</td>\n",
       "      <td>9625</td>\n",
       "      <td>3529</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_2, R_12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>43.958928</td>\n",
       "      <td>9945</td>\n",
       "      <td>3601</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_10, R_11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>46.177549</td>\n",
       "      <td>9910</td>\n",
       "      <td>3604</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.002884</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_2, R_3, R_7, R_9, R_14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129000</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>1</td>\n",
       "      <td>47.594940</td>\n",
       "      <td>9067</td>\n",
       "      <td>3628</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.000372</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_2, R_10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>42.178207</td>\n",
       "      <td>9883</td>\n",
       "      <td>3633</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_3, 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>49.610815</td>\n",
       "      <td>9564</td>\n",
       "      <td>3673</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.001162</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0, 1.0, 0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, R_3, R_6, R_7, R_11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.176803</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>1</td>\n",
       "      <td>45.221185</td>\n",
       "      <td>9916</td>\n",
       "      <td>3684</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>74</td>\n",
       "      <td>88</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_9, R_10, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>41.376364</td>\n",
       "      <td>9955</td>\n",
       "      <td>9955</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, R_2, R_7, R_13, R_15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>48.323060</td>\n",
       "      <td>9956</td>\n",
       "      <td>9956</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>9993</td>\n",
       "      <td>9959</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_3, R_10, R_12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.054923</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>1</td>\n",
       "      <td>37.827609</td>\n",
       "      <td>9961</td>\n",
       "      <td>9961</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>1.0, 0.0, 0.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_5, R_10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>9963</td>\n",
       "      <td>9963</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_3, R_8, R_10, R_14, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>40.856000</td>\n",
       "      <td>9963</td>\n",
       "      <td>9963</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>0.0, 0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_0, R_2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>45.726710</td>\n",
       "      <td>9964</td>\n",
       "      <td>9964</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>9980</td>\n",
       "      <td>9966</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_3, R_1, R_3, R_4, R_8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>9987</td>\n",
       "      <td>9966</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_2, R_6, R_9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>9968</td>\n",
       "      <td>9968</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000347</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_1, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>9997</td>\n",
       "      <td>9970</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>1.0, 0.0, 1.0</td>\n",
       "      <td>A_2, A_3, R_2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.034572</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1</td>\n",
       "      <td>42.160000</td>\n",
       "      <td>9999</td>\n",
       "      <td>9971</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, 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>32.500000</td>\n",
       "      <td>9974</td>\n",
       "      <td>9974</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>0.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, R_4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.017342</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>9987</td>\n",
       "      <td>9976</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_2, R_8, R_9, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>9976</td>\n",
       "      <td>9976</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, A_3, R_3, R_7, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>40.198449</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>0.0, 1.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_2, A_3, R_1, R_10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>1.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_7, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>47.180942</td>\n",
       "      <td>9981</td>\n",
       "      <td>9981</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_0, R_12, R_13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>42.480000</td>\n",
       "      <td>9983</td>\n",
       "      <td>9983</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143</th>\n",
       "      <td>1.0, 0.0, 1.0, 1.0, 1.0, 0.0</td>\n",
       "      <td>A_0, A_2, R_4, R_9, R_13, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>39.705273</td>\n",
       "      <td>9983</td>\n",
       "      <td>9983</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1144</th>\n",
       "      <td>0.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_2, A_3, R_9, R_13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>49.368000</td>\n",
       "      <td>9986</td>\n",
       "      <td>9986</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>1.0, 0.0, 1.0, 0.0, 0.0, 0.0</td>\n",
       "      <td>A_0, A_1, A_3, R_1, R_6, R_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>41.495871</td>\n",
       "      <td>9986</td>\n",
       "      <td>9986</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>0.0, 0.0, 1.0, 0.0, 1.0</td>\n",
       "      <td>A_1, A_2, A_3, R_6, R_10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>42.696000</td>\n",
       "      <td>9987</td>\n",
       "      <td>9987</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0</td>\n",
       "      <td>A_1, A_2, A_3, R_2, R_7, R_11, R_14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>28.197837</td>\n",
       "      <td>9990</td>\n",
       "      <td>9990</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1148</th>\n",
       "      <td>0.0, 1.0, 0.0, 0.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_1, A_2, R_3, R_4, R_12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>9992</td>\n",
       "      <td>9992</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>0.0, 0.0, 0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_1, R_4, R_10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>48.114781</td>\n",
       "      <td>9992</td>\n",
       "      <td>9992</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_8, R_12, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>38.310400</td>\n",
       "      <td>9993</td>\n",
       "      <td>9993</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151</th>\n",
       "      <td>1.0, 0.0, 0.0, 1.0</td>\n",
       "      <td>A_0, A_2, A_3, R_14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>38.310400</td>\n",
       "      <td>9993</td>\n",
       "      <td>9993</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_2, R_7, R_11, R_13, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>9997</td>\n",
       "      <td>9997</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>1.0, 1.0, 1.0, 1.0, 1.0, 1.0</td>\n",
       "      <td>A_2, A_3, R_7, R_11, R_13, R_15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>9997</td>\n",
       "      <td>9997</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1154 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Specified Values  \\\n",
       "0     1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0...   \n",
       "1                     1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "2                     1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "3           0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0   \n",
       "4                     1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "5                               0.0, 1.0, 0.0, 1.0, 1.0   \n",
       "6                          0.0, 0.0, 1.0, 0.0, 1.0, 1.0   \n",
       "7                               0.0, 0.0, 1.0, 0.0, 1.0   \n",
       "8                     1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0   \n",
       "9                               0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "10                              0.0, 1.0, 1.0, 1.0, 0.0   \n",
       "11                              0.0, 1.0, 1.0, 0.0, 1.0   \n",
       "12                    0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0   \n",
       "13                         0.0, 0.0, 1.0, 1.0, 0.0, 0.0   \n",
       "14                    0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "15                              1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "16                         0.0, 0.0, 1.0, 1.0, 1.0, 1.0   \n",
       "17                         1.0, 1.0, 1.0, 1.0, 0.0, 1.0   \n",
       "18                              1.0, 1.0, 0.0, 0.0, 0.0   \n",
       "19                              0.0, 0.0, 1.0, 0.0, 0.0   \n",
       "20                              0.0, 0.0, 0.0, 1.0, 1.0   \n",
       "21                              1.0, 0.0, 0.0, 0.0, 0.0   \n",
       "22                         1.0, 1.0, 0.0, 1.0, 0.0, 0.0   \n",
       "23               1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0   \n",
       "24                         0.0, 0.0, 1.0, 0.0, 0.0, 1.0   \n",
       "25                              1.0, 0.0, 1.0, 0.0, 0.0   \n",
       "26               0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0   \n",
       "27                         1.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "28                         0.0, 1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "29                         0.0, 1.0, 0.0, 0.0, 0.0, 1.0   \n",
       "...                                                 ...   \n",
       "1124                  0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0   \n",
       "1125                  1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0   \n",
       "1126                                 1.0, 1.0, 0.0, 0.0   \n",
       "1127                            1.0, 0.0, 0.0, 1.0, 1.0   \n",
       "1128                       1.0, 0.0, 0.0, 0.0, 0.0, 1.0   \n",
       "1129                  1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0   \n",
       "1130                            0.0, 0.0, 0.0, 1.0, 1.0   \n",
       "1131                                 0.0, 1.0, 1.0, 1.0   \n",
       "1132                  0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0   \n",
       "1133                  0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0   \n",
       "1134                            1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "1135                                      1.0, 0.0, 1.0   \n",
       "1136             0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0   \n",
       "1137                                      0.0, 0.0, 1.0   \n",
       "1138                  1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0   \n",
       "1139                  0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "1140                            0.0, 1.0, 1.0, 0.0, 0.0   \n",
       "1141                            1.0, 1.0, 0.0, 1.0, 1.0   \n",
       "1142                       1.0, 0.0, 1.0, 0.0, 1.0, 1.0   \n",
       "1143                       1.0, 0.0, 1.0, 1.0, 1.0, 0.0   \n",
       "1144                                 0.0, 1.0, 0.0, 1.0   \n",
       "1145                       1.0, 0.0, 1.0, 0.0, 0.0, 0.0   \n",
       "1146                            0.0, 0.0, 1.0, 0.0, 1.0   \n",
       "1147                  1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0   \n",
       "1148                       0.0, 1.0, 0.0, 0.0, 1.0, 1.0   \n",
       "1149                       0.0, 0.0, 0.0, 1.0, 1.0, 1.0   \n",
       "1150                       1.0, 0.0, 0.0, 1.0, 1.0, 1.0   \n",
       "1151                                 1.0, 0.0, 0.0, 1.0   \n",
       "1152                            1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "1153                       1.0, 1.0, 1.0, 1.0, 1.0, 1.0   \n",
       "\n",
       "                              Specified Attribute Names  Class   Fitness  \\\n",
       "0     A_0, A_1, A_2, A_3, R_0, R_2, R_6, R_7, R_10, ...    0.0  1.000000   \n",
       "1                  A_0, A_1, R_2, R_7, R_12, R_13, R_15    0.0  0.423406   \n",
       "2                  A_0, A_1, R_2, R_7, R_13, R_14, R_15    0.0  0.276980   \n",
       "3        A_0, A_1, A_2, A_3, R_2, R_7, R_12, R_14, R_15    0.0  1.000000   \n",
       "4                  A_1, A_2, R_2, R_7, R_12, R_13, R_15    0.0  0.089670   \n",
       "5                               A_0, A_1, A_2, A_3, R_5    1.0  1.000000   \n",
       "6                          A_1, A_2, A_3, R_3, R_5, R_9    1.0  0.067888   \n",
       "7                               A_1, A_2, A_3, R_0, R_9    1.0  0.065412   \n",
       "8                   A_0, A_1, A_2, R_3, R_5, R_13, R_14    0.0  0.094054   \n",
       "9                               A_0, A_1, A_2, R_2, R_4    0.0  0.094582   \n",
       "10                              A_0, A_1, A_2, A_3, R_7    0.0  1.000000   \n",
       "11                              A_0, A_1, A_3, R_0, R_7    1.0  0.079503   \n",
       "12                 A_0, A_1, A_3, R_2, R_12, R_13, R_15    0.0  0.071253   \n",
       "13                         A_0, A_1, A_2, A_3, R_3, R_5    0.0  1.000000   \n",
       "14                 R_2, R_3, R_5, R_8, R_12, R_14, R_15    0.0  0.049735   \n",
       "15                            A_0, A_2, R_0, R_12, R_13    0.0  0.144987   \n",
       "16                         A_1, A_2, A_3, R_4, R_5, R_9    1.0  0.074939   \n",
       "17                       A_1, A_2, A_3, R_7, R_10, R_15    1.0  1.000000   \n",
       "18                            A_0, A_1, R_2, R_13, R_15    0.0  0.107374   \n",
       "19                              A_0, A_1, A_3, R_3, R_5    0.0  0.061263   \n",
       "20                              A_0, A_2, A_3, R_0, R_5    1.0  0.049180   \n",
       "21                            A_0, A_2, A_3, R_12, R_15    0.0  0.088084   \n",
       "22                       A_0, A_1, A_2, A_3, R_12, R_13    0.0  1.000000   \n",
       "23             A_0, A_1, R_2, R_3, R_5, R_8, R_13, R_15    0.0  0.089670   \n",
       "24                        A_0, A_1, A_2, A_3, R_2, R_12    0.0  1.000000   \n",
       "25                            A_0, A_1, A_2, R_10, R_11    0.0  1.000000   \n",
       "26              A_1, A_2, A_3, R_2, R_3, R_7, R_9, R_14    0.0  0.129000   \n",
       "27                        A_0, A_1, A_2, A_3, R_2, R_10    0.0  1.000000   \n",
       "28                         A_0, A_2, A_3, R_3, R_5, R_7    1.0  1.000000   \n",
       "29                        A_0, A_1, R_3, R_6, R_7, R_11    0.0  0.176803   \n",
       "...                                                 ...    ...       ...   \n",
       "1124                A_0, A_1, A_2, A_3, R_9, R_10, R_15    1.0  0.100000   \n",
       "1125                A_0, A_1, A_2, R_2, R_7, R_13, R_15    0.0  0.100000   \n",
       "1126                                 A_0, A_1, A_2, A_3    1.0  1.000000   \n",
       "1127                          A_0, A_1, A_3, R_10, R_12    1.0  0.054923   \n",
       "1128                      A_0, A_1, A_2, A_3, R_5, R_10    1.0  0.100000   \n",
       "1129               A_0, A_1, A_3, R_8, R_10, R_14, R_15    1.0  0.100000   \n",
       "1130                            A_0, A_1, A_2, R_0, R_2    1.0  0.100000   \n",
       "1131                                 A_0, A_2, A_3, R_3    1.0  1.000000   \n",
       "1132                  A_0, A_1, A_3, R_1, R_3, R_4, R_8    1.0  1.000000   \n",
       "1133                  A_0, A_1, A_2, A_3, R_2, R_6, R_9    1.0  0.100000   \n",
       "1134                           A_0, A_2, A_3, R_1, R_15    1.0  1.000000   \n",
       "1135                                      A_2, A_3, R_2    1.0  0.034572   \n",
       "1136             A_0, A_1, A_2, A_3, R_2, R_3, R_5, R_7    1.0  0.100000   \n",
       "1137                                      A_1, A_2, R_4    1.0  0.017342   \n",
       "1138                 A_0, A_1, A_2, R_2, R_8, R_9, R_14    1.0  0.100000   \n",
       "1139                 A_0, A_1, A_2, A_3, R_3, R_7, R_15    1.0  0.100000   \n",
       "1140                           A_0, A_2, A_3, R_1, R_10    1.0  0.100000   \n",
       "1141                           A_0, A_2, A_3, R_7, R_14    1.0  0.100000   \n",
       "1142                     A_1, A_2, A_3, R_0, R_12, R_13    1.0  0.100000   \n",
       "1143                     A_0, A_2, R_4, R_9, R_13, R_14    1.0  0.100000   \n",
       "1144                                A_2, A_3, R_9, R_13    0.0  0.100000   \n",
       "1145                       A_0, A_1, A_3, R_1, R_6, R_9    0.0  0.100000   \n",
       "1146                           A_1, A_2, A_3, R_6, R_10    1.0  0.100000   \n",
       "1147                A_1, A_2, A_3, R_2, R_7, R_11, R_14    0.0  0.100000   \n",
       "1148                      A_0, A_1, A_2, R_3, R_4, R_12    1.0  0.100000   \n",
       "1149                      A_0, A_2, A_3, R_1, R_4, R_10    1.0  0.100000   \n",
       "1150                     A_0, A_2, A_3, R_8, R_12, R_14    1.0  0.100000   \n",
       "1151                                A_0, A_2, A_3, R_14    1.0  0.100000   \n",
       "1152                         A_2, R_7, R_11, R_13, R_15    1.0  0.100000   \n",
       "1153                    A_2, A_3, R_7, R_11, R_13, R_15    1.0  0.100000   \n",
       "\n",
       "      Accuracy  Numerosity  Avg Match Set Size  TimeStamp GA  \\\n",
       "0     1.000000           1           14.500000          9729   \n",
       "1     0.917647           1           56.321289          9938   \n",
       "2     0.879518           1           45.498887          9965   \n",
       "3     1.000000           1           67.479529          9945   \n",
       "4     0.785714           1           48.857635          9859   \n",
       "5     1.000000          15           43.049579          9998   \n",
       "6     0.764151           1           34.597954          9928   \n",
       "7     0.761317           1           38.018517          9987   \n",
       "8     0.789474           1           38.996172          9938   \n",
       "9     0.789916           1           41.049742          9960   \n",
       "10    1.000000          15           45.937555          9957   \n",
       "11    0.776316           1           40.521243          9980   \n",
       "12    0.767857           1           50.069777          9861   \n",
       "13    1.000000           1           43.193794          9858   \n",
       "14    0.740741           1           43.855312          9815   \n",
       "15    0.824390           1           49.539970          9771   \n",
       "16    0.771739           1           39.702126          9896   \n",
       "17    1.000000           1           48.876702          9997   \n",
       "18    0.800000           1           42.041925          9965   \n",
       "19    0.756345           1           38.649441          9920   \n",
       "20    0.739910           1           46.081780          9881   \n",
       "21    0.784314           1           35.216969          9954   \n",
       "22    1.000000           5           43.208345          9767   \n",
       "23    0.785714           1           47.047664          9625   \n",
       "24    1.000000           4           43.958928          9945   \n",
       "25    1.000000           8           46.177549          9910   \n",
       "26    0.814815           1           47.594940          9067   \n",
       "27    1.000000           2           42.178207          9883   \n",
       "28    1.000000           3           49.610815          9564   \n",
       "29    0.840909           1           45.221185          9916   \n",
       "...        ...         ...                 ...           ...   \n",
       "1124  1.000000           1           41.376364          9955   \n",
       "1125  1.000000           1           48.323060          9956   \n",
       "1126  1.000000           1           36.000000          9993   \n",
       "1127  0.896552           1           37.827609          9961   \n",
       "1128  1.000000           1           27.000000          9963   \n",
       "1129  1.000000           1           40.856000          9963   \n",
       "1130  1.000000           1           45.726710          9964   \n",
       "1131  1.000000           1           36.000000          9980   \n",
       "1132  1.000000           1           35.000000          9987   \n",
       "1133  1.000000           1           44.500000          9968   \n",
       "1134  1.000000           1           67.500000          9997   \n",
       "1135  0.714286           1           42.160000          9999   \n",
       "1136  1.000000           1           32.500000          9974   \n",
       "1137  0.666667           1           29.000000          9987   \n",
       "1138  1.000000           1           17.000000          9976   \n",
       "1139  1.000000           1           40.198449          9980   \n",
       "1140  1.000000           1           46.000000          9980   \n",
       "1141  1.000000           1           47.180942          9981   \n",
       "1142  1.000000           1           42.480000          9983   \n",
       "1143  1.000000           1           39.705273          9983   \n",
       "1144  1.000000           1           49.368000          9986   \n",
       "1145  1.000000           1           41.495871          9986   \n",
       "1146  1.000000           1           42.696000          9987   \n",
       "1147  1.000000           1           28.197837          9990   \n",
       "1148  1.000000           1           43.500000          9992   \n",
       "1149  1.000000           1           48.114781          9992   \n",
       "1150  1.000000           1           38.310400          9993   \n",
       "1151  1.000000           1           38.310400          9993   \n",
       "1152  1.000000           1           53.500000          9997   \n",
       "1153  1.000000           1           53.500000          9997   \n",
       "\n",
       "      Iteration Initialized  Specificity  Deletion Probability  Correct Count  \\\n",
       "0                        19         0.65              0.000113              2   \n",
       "1                        96         0.35              0.000440             78   \n",
       "2                       125         0.35              0.000355             73   \n",
       "3                       212         0.45              0.000527             24   \n",
       "4                      1135         0.35              0.000381             55   \n",
       "5                      1777         0.25              0.005042            228   \n",
       "6                      1941         0.30              0.000270             81   \n",
       "7                      2146         0.25              0.000297            185   \n",
       "8                      2321         0.35              0.000304             45   \n",
       "9                      2410         0.25              0.000320            188   \n",
       "10                     2475         0.25              0.005380            245   \n",
       "11                     2494         0.25              0.000316            177   \n",
       "12                     2616         0.35              0.000391             43   \n",
       "13                     2659         0.30              0.000337            102   \n",
       "14                     2850         0.35              0.004292             40   \n",
       "15                     2986         0.25              0.000387            169   \n",
       "16                     3173         0.30              0.000310             71   \n",
       "17                     3315         0.30              0.000382            118   \n",
       "18                     3327         0.25              0.000328            160   \n",
       "19                     3383         0.25              0.003071            149   \n",
       "20                     3446         0.25              0.004561            165   \n",
       "21                     3447         0.25              0.000275            160   \n",
       "22                     3471         0.30              0.001687            108   \n",
       "23                     3529         0.40              0.000367             22   \n",
       "24                     3601         0.30              0.001373             99   \n",
       "25                     3604         0.25              0.002884            195   \n",
       "26                     3628         0.40              0.000372             22   \n",
       "27                     3633         0.30              0.000659            112   \n",
       "28                     3673         0.30              0.001162             87   \n",
       "29                     3684         0.30              0.000353             74   \n",
       "...                     ...          ...                   ...            ...   \n",
       "1124                   9955         0.35              0.000323              0   \n",
       "1125                   9956         0.35              0.000377              0   \n",
       "1126                   9959         0.20              0.000281              2   \n",
       "1127                   9961         0.25              0.000295              0   \n",
       "1128                   9963         0.30              0.000211              0   \n",
       "1129                   9963         0.35              0.000319              0   \n",
       "1130                   9964         0.25              0.000357              0   \n",
       "1131                   9966         0.20              0.000281              3   \n",
       "1132                   9966         0.35              0.000273              2   \n",
       "1133                   9968         0.35              0.000347              0   \n",
       "1134                   9970         0.25              0.000527              2   \n",
       "1135                   9971         0.15              0.000329              5   \n",
       "1136                   9974         0.40              0.000254              0   \n",
       "1137                   9976         0.15              0.000226              2   \n",
       "1138                   9976         0.35              0.000133              0   \n",
       "1139                   9980         0.35              0.000314              0   \n",
       "1140                   9980         0.25              0.000359              0   \n",
       "1141                   9981         0.25              0.000368              0   \n",
       "1142                   9983         0.30              0.000332              0   \n",
       "1143                   9983         0.30              0.000310              0   \n",
       "1144                   9986         0.20              0.000385              0   \n",
       "1145                   9986         0.30              0.000324              0   \n",
       "1146                   9987         0.25              0.000333              0   \n",
       "1147                   9990         0.35              0.000220              0   \n",
       "1148                   9992         0.30              0.000340              0   \n",
       "1149                   9992         0.30              0.000376              0   \n",
       "1150                   9993         0.30              0.000299              0   \n",
       "1151                   9993         0.20              0.000299              0   \n",
       "1152                   9997         0.25              0.000418              0   \n",
       "1153                   9997         0.30              0.000418              0   \n",
       "\n",
       "      Match Count  Epoch Complete  \n",
       "0               2           False  \n",
       "1              85           False  \n",
       "2              83           False  \n",
       "3              24           False  \n",
       "4              70           False  \n",
       "5             228           False  \n",
       "6             106           False  \n",
       "7             243           False  \n",
       "8              57           False  \n",
       "9             238           False  \n",
       "10            245           False  \n",
       "11            228           False  \n",
       "12             56           False  \n",
       "13            102           False  \n",
       "14             54           False  \n",
       "15            205           False  \n",
       "16             92           False  \n",
       "17            118           False  \n",
       "18            200           False  \n",
       "19            197           False  \n",
       "20            223           False  \n",
       "21            204           False  \n",
       "22            108           False  \n",
       "23             28           False  \n",
       "24             99           False  \n",
       "25            195           False  \n",
       "26             27           False  \n",
       "27            112           False  \n",
       "28             87           False  \n",
       "29             88           False  \n",
       "...           ...             ...  \n",
       "1124            0           False  \n",
       "1125            0           False  \n",
       "1126            2           False  \n",
       "1127            0           False  \n",
       "1128            0           False  \n",
       "1129            0           False  \n",
       "1130            0           False  \n",
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       "\n",
       "[1154 rows x 14 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainedModel.export_final_rule_population(headers,classLabel,filename=\"defaultExportDir/popData3.csv\",RCPopulation=True)\n",
    "\n",
    "populationData3 = pd.read_csv(\"defaultExportDir/popData3.csv\")\n",
    "display(populationData3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 5: Population Reboot\n",
    "Another 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 ExSTraCS estimator with this txt file. When you run fit, the new ExSTraCS 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. By default, the pickle_model method exports the rule population BEFORE rule compaction. A demo of this is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6236892698479595\n",
      "0.6230212621257902\n",
      "0.6749223621025059\n",
      "0.6749223621025059\n",
      "0.8025696177047806\n",
      "0.8779473690715864\n"
     ]
    }
   ],
   "source": [
    "converter = StringEnumerator(\"test/DataSets/Real/Multiplexer20Modified.csv\",\"Class\")\n",
    "headers,classLabel,dataFeatures,dataPhenotypes = converter.get_params()\n",
    "\n",
    "#Run 1\n",
    "model = ExSTraCS(learning_iterations = 2500,nu=10,N=2000)\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 = ExSTraCS(learning_iterations = 2500,nu=10,N=2000,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\",saveRCPop=True) #Save post-RC population\n",
    "\n",
    "#Run 3 w/ rebooted second model\n",
    "model3 = ExSTraCS(learning_iterations = 2500,nu=10,N=2000,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 = ExSTraCS(learning_iterations = 2500,nu=10,N=2000,reboot_filename=\"defaultExportDir/savedModel3\")\n",
    "model4.fit(dataFeatures,dataPhenotypes)\n",
    "print(model4.score(dataFeatures,dataPhenotypes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post Training Rule Compaction\n",
    "The final feature to introduce is rule compaction post fit, which allows you to perform fit without rule compaction, and then call a separate rule compaction method later. This method changes the rule population of your model, which can then be pickled as needed. By default, the method uses 'QRF' for rule compaction, but the other methods enumerated above can also be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8708403835307388\n",
      "0.8720899274592121\n",
      "0.9704800249876766\n",
      "0.9747948338522823\n"
     ]
    }
   ],
   "source": [
    "converter = StringEnumerator(\"test/DataSets/Real/Multiplexer11Modified.csv\",\"Class\")\n",
    "headers,classLabel,dataFeatures,dataPhenotypes = converter.get_params()\n",
    "\n",
    "model = ExSTraCS(learning_iterations = 2000,nu=10,N=1000,rule_compaction=None)\n",
    "model.fit(dataFeatures,dataPhenotypes)\n",
    "print(model.score(dataFeatures,dataPhenotypes))\n",
    "model.pickle_model('defaultExportDir/preRC')\n",
    "model.post_training_rule_compaction()\n",
    "print(model.score(dataFeatures,dataPhenotypes))\n",
    "model.pickle_model('defaultExportDir/postRC')\n",
    "\n",
    "model2 = ExSTraCS(learning_iterations = 2000,nu=10,N=2000,reboot_filename='defaultExportDir/preRC')\n",
    "model2.fit(dataFeatures,dataPhenotypes)\n",
    "print(model2.score(dataFeatures,dataPhenotypes))\n",
    "\n",
    "model3 = ExSTraCS(learning_iterations = 2000,nu=10,N=2000,reboot_filename='defaultExportDir/postRC')\n",
    "model3.fit(dataFeatures,dataPhenotypes)\n",
    "print(model3.score(dataFeatures,dataPhenotypes))"
   ]
  }
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