AI_Class_Report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PUMA: Promoter Unraveling through Machine-learning Algorithms\n",
    "<img src=\"https://raw.githubusercontent.com/CarolusVitalis/PUMA/main/Images/PUMA_Logo.png\" alt=\"PUMA Logo\" width=\"200\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _AI model to identify promoter sequences in existing databases_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "\n",
    "Synthetic Biology aims to apply the engineering principles of abstraction, decoupling, and standardization to genetic engineering. In the context of synthetic biology, abstraction refers to the simplification of biological systems into fundamental, manageable components, allowing for a focus on critical interactions and functions while omitting less relevant details. Decoupling, on the other hand, involves the separation of interdependent systems within biology, enabling the independent design, modification, and analysis of each component. Together, these principles facilitate a modular and standardized approach to biological engineering, enhancing understanding, enabling innovative design, and promoting cross-disciplinary collaboration.\n",
    "\n",
    "To improve standardization, synthetic biologists have developed notations and standardized DNA sequences for the different genetic parts, such as promoters, ribosome binding sites (RBSs), and coding sequences (CDSs), among others, which are then collected and deposited in repositories such as the iGEM Part Repository or SynBioHub. Although these repositories offer many different genetic parts and could serve as a good starting point, these parts tend to lack curation, which makes it difficult to identify, classify, and find ideal parts.\n",
    "\n",
    "To overcome this challenge, an AI model was developed to recognize DNA sequences corresponding to Promoters from the iGEM Repository. This will make Synthetic Biology a more accessible field, greatly accelerating the rate of research and novel applications in a wide array of fields, from healthcare to agriculture, and even space colonization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Description\n",
    "\n",
    "The model was trained with 4127 Promoters and 607 other parts, including RBSs, CDSs, Terminators, and Backbones from the 2023 iGEM Parts Distribution [1] and RegulonDB [2].\n",
    "\n",
    "The iGEM data was scraped using the Part Retriever code [3]. A list of the necessary parts was provided and the code returned a CSV file with the part name and sequence. The parts were manually checked to ensure they were the parts that they claimed to be, this resulted in the deletion of two promoters from the promoters list provided.\n",
    "The RegulonDB data was downloaded in CSV format using the front end and selecting just two fields, name, and sequence. After the files were downloaded, the sequences were standardized to use lowercase annotations.\n",
    "\n",
    "[1] “iGEM Distribution.” Accessed: Feb. 19, 2024. [Online]. Available: https://technology.igem.org/distribution/handbook#h-whats-included-in-the-2023-distribution\n",
    "\n",
    "[2]\t“RegulonDB Browser.” Accessed: Apr. 09, 2024. [Online]. Available: https://regulondb.ccg.unam.mx/\n",
    "\n",
    "[3] “Part_Retriever.” Accessed: Apr. 04, 2024. [Online] Available: https://github.com/CarolusVitalis/PUMA/blob/main/Part_Retriever.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methodology\n",
    "\n",
    "On the first approach, a Gaussian Naive Bayes machine learning classification was used, achieving a training accuracy of 92.1%, but lacked generalizability to other species than e. coli and misidentified non-promoter genetic parts. Then, a Support Vector Classifier (SVC) model was used, resulting in a training accuracy of 92.3%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "The SVC model was applied to 2949 entries labeled as Promoters in the iGEM repository and identified 2019 entries as Promoters, 876 as CDS, 22 as RBS, and 32 as Terminators, a significant improvement from the Gaussian Naive Bayes model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dependencies\n",
    "Here, we check if the dependencies are installed, and if they are not, ask the user if they want to install them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The module 'pandas' is already installed.\n",
      "The module 'scikit-learn' is not installed.\n",
      "Requirement already satisfied: scikit-learn in /opt/anaconda3/lib/python3.11/site-packages (1.2.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /opt/anaconda3/lib/python3.11/site-packages (from scikit-learn) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /opt/anaconda3/lib/python3.11/site-packages (from scikit-learn) (1.11.4)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /opt/anaconda3/lib/python3.11/site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/anaconda3/lib/python3.11/site-packages (from scikit-learn) (2.2.0)\n",
      "'scikit-learn' has been installed.\n",
      "The module 'seaborn' is already installed.\n",
      "The module 'plotnine' is already installed.\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "import sys\n",
    "\n",
    "# List of dependencies\n",
    "dependencies = [\"pandas\", \"scikit-learn\", \"seaborn\", \"plotnine\"]\n",
    "\n",
    "# Function to check if a module is installed\n",
    "def is_module_installed(module_name):\n",
    "    try:\n",
    "        __import__(module_name)\n",
    "        return True\n",
    "    except ImportError:\n",
    "        return False\n",
    "\n",
    "# Function to install a module\n",
    "def install_module(module_name):\n",
    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", module_name])\n",
    "\n",
    "# Check each dependency\n",
    "for module in dependencies:\n",
    "    if not is_module_installed(module):\n",
    "        print(f\"The module '{module}' is not installed.\")\n",
    "        answer = input(f\"Do you want to install '{module}'? (yes/no): \")\n",
    "        if answer.lower() == \"yes\":\n",
    "            install_module(module)\n",
    "            print(f\"'{module}' has been installed.\")\n",
    "        else:\n",
    "            print(f\"'{module}' has not been installed.\")\n",
    "    else:\n",
    "        print(f\"The module '{module}' is already installed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import the packages we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import getpass\n",
    "import requests\n",
    "import re\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Approach: Gaussian Naïve Bayes\n",
    "This version uses local csv files from the iGEM Repository and the RegulonDB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Name                                       DNA Sequence\n",
      "0     RDBECOLIPMC00001  acactttcattgttttaccgttgctctgattaattgacgctaaagt...\n",
      "1     RDBECOLIPMC00002  ccgatgatcctcatcgtaatccaaccgaaactttacctgattctgg...\n",
      "2     RDBECOLIPMC00003  gccgttacactcaaaggcggcgcggtgggaacgatatttcacagta...\n",
      "3     RDBECOLIPMC00004  aaatacttatggtgcgctggcttctttggaacttgcgcagcaattt...\n",
      "4     RDBECOLIPMC00005  tgccaactggcaggtcaaccgaatgcagacatcgcaggcgggatgt...\n",
      "...                ...                                                ...\n",
      "4045  RDBECOLIPMC04049  gcgatcctcatcagccgcattgcggccaacttctggattagcgcca...\n",
      "4046  RDBECOLIPMC04050  ttgaatcgtctgtccaaccgctgttttacgcagcgcttgtgcatcg...\n",
      "4047  RDBECOLIPMC04051  accgccgccgccgccgacgccgatgactttaatcaccgcgtcattg...\n",
      "4048  RDBECOLIPMC04052  aggacaacagctggacgatgtccagcttgctcgctaccttttgtcg...\n",
      "4049  RDBECOLIPMC04053  gcttaacaattttgcaaaatgtattggcgagtaagaaccgcatttg...\n",
      "\n",
      "[4050 rows x 2 columns]\n",
      "                 Name                                       DNA Sequence\n",
      "0    RDBECOLITMC00001              agcgtcaaaaggccggattttccggccttttttatta\n",
      "1    RDBECOLITMC00002  gataaatcagaaacataaaggcgctttcgggtgcctttattatttc...\n",
      "2    RDBECOLITMC00003  taccaccgtcaaaaaaaacggcgctttttagcgccgtttttatttt...\n",
      "3    RDBECOLITMC00004  ctgatgaaaaggtgccggatgatgtgaatcatccggcactggattatta\n",
      "4    RDBECOLITMC00005    tagcaacaaaaaagccgactcacttgcagtcggctttctcattttaa\n",
      "..                ...                                                ...\n",
      "366  RDBECOLITMC00367        aaaaggattcgcggctctgctcttcagagctgcttttatgata\n",
      "367  RDBECOLITMC00368           gttattaaatagcctgccatctggcaggctttttttatcg\n",
      "368  RDBECOLITMC00369       atgcaggaaagcaggctggagctacccagcctgcagtgaaatta\n",
      "369  RDBECOLITMC00370     tgaaaacgaaggccggagcatgctccggccttttttatctcttaca\n",
      "370  RDBECOLITMC00371               ctgcccttttccctgctcctggacggttttacccct\n",
      "\n",
      "[371 rows x 2 columns]\n",
      "                  Name                                       DNA Sequence\n",
      "0     RDBECOLIGNC00001  atgcaagcggcaactgttgtgattaaccgccgcgctctgcgacaca...\n",
      "1     RDBECOLIGNC00002  atgatactgaccgatccagaatggcaggcagttttattaagcctga...\n",
      "2     RDBECOLIGNC00003  atggtttcatcattcacatctgccccacgcagcggtttttactatt...\n",
      "3     RDBECOLIGNC00004  atgagcctggccggtaaaaaaatcgttctcggcgttagcggcggta...\n",
      "4     RDBECOLIGNC00005  atgttatttactatccaacttatcataatactgatatgtctgtttt...\n",
      "...                ...                                                ...\n",
      "4742  RDBECOLIGNC04744  gaaagattcacgagtcagcaggcgatccatctgttctgcattagcg...\n",
      "4743  RDBECOLIGNC04745  caccggcacttaatacttcgactttgtcctgccagctttcacgggc...\n",
      "4744  RDBECOLIGNC04746  caaatcagatcgcccatatcgggtacgtatcggcttaccattacca...\n",
      "4745  RDBECOLIGNC04747  atgtttgtgtggggtcttttctgtatcttacgcatcgcactcaagc...\n",
      "4746  RDBECOLIGNC04748  aagaaagtaaatcacttttttaccactggtcttctgctttcaggcg...\n",
      "\n",
      "[4747 rows x 2 columns]\n",
      "                  Name                                       DNA Sequence  \\\n",
      "0     RDBECOLIPMC00001  acactttcattgttttaccgttgctctgattaattgacgctaaagt...   \n",
      "1     RDBECOLIPMC00002  ccgatgatcctcatcgtaatccaaccgaaactttacctgattctgg...   \n",
      "2     RDBECOLIPMC00003  gccgttacactcaaaggcggcgcggtgggaacgatatttcacagta...   \n",
      "3     RDBECOLIPMC00004  aaatacttatggtgcgctggcttctttggaacttgcgcagcaattt...   \n",
      "4     RDBECOLIPMC00005  tgccaactggcaggtcaaccgaatgcagacatcgcaggcgggatgt...   \n",
      "...                ...                                                ...   \n",
      "4045  RDBECOLIPMC04049  gcgatcctcatcagccgcattgcggccaacttctggattagcgcca...   \n",
      "4046  RDBECOLIPMC04050  ttgaatcgtctgtccaaccgctgttttacgcagcgcttgtgcatcg...   \n",
      "4047  RDBECOLIPMC04051  accgccgccgccgccgacgccgatgactttaatcaccgcgtcattg...   \n",
      "4048  RDBECOLIPMC04052  aggacaacagctggacgatgtccagcttgctcgctaccttttgtcg...   \n",
      "4049  RDBECOLIPMC04053  gcttaacaattttgcaaaatgtattggcgagtaagaaccgcatttg...   \n",
      "\n",
      "       Element  \n",
      "0     Promoter  \n",
      "1     Promoter  \n",
      "2     Promoter  \n",
      "3     Promoter  \n",
      "4     Promoter  \n",
      "...        ...  \n",
      "4045  Promoter  \n",
      "4046  Promoter  \n",
      "4047  Promoter  \n",
      "4048  Promoter  \n",
      "4049  Promoter  \n",
      "\n",
      "[4050 rows x 3 columns]\n",
      "                 Name                                       DNA Sequence  \\\n",
      "0    RDBECOLITMC00001              agcgtcaaaaggccggattttccggccttttttatta   \n",
      "1    RDBECOLITMC00002  gataaatcagaaacataaaggcgctttcgggtgcctttattatttc...   \n",
      "2    RDBECOLITMC00003  taccaccgtcaaaaaaaacggcgctttttagcgccgtttttatttt...   \n",
      "3    RDBECOLITMC00004  ctgatgaaaaggtgccggatgatgtgaatcatccggcactggattatta   \n",
      "4    RDBECOLITMC00005    tagcaacaaaaaagccgactcacttgcagtcggctttctcattttaa   \n",
      "..                ...                                                ...   \n",
      "366  RDBECOLITMC00367        aaaaggattcgcggctctgctcttcagagctgcttttatgata   \n",
      "367  RDBECOLITMC00368           gttattaaatagcctgccatctggcaggctttttttatcg   \n",
      "368  RDBECOLITMC00369       atgcaggaaagcaggctggagctacccagcctgcagtgaaatta   \n",
      "369  RDBECOLITMC00370     tgaaaacgaaggccggagcatgctccggccttttttatctcttaca   \n",
      "370  RDBECOLITMC00371               ctgcccttttccctgctcctggacggttttacccct   \n",
      "\n",
      "        Element  \n",
      "0    Terminator  \n",
      "1    Terminator  \n",
      "2    Terminator  \n",
      "3    Terminator  \n",
      "4    Terminator  \n",
      "..          ...  \n",
      "366  Terminator  \n",
      "367  Terminator  \n",
      "368  Terminator  \n",
      "369  Terminator  \n",
      "370  Terminator  \n",
      "\n",
      "[371 rows x 3 columns]\n",
      "                  Name                                       DNA Sequence  \\\n",
      "0     RDBECOLIGNC00001  atgcaagcggcaactgttgtgattaaccgccgcgctctgcgacaca...   \n",
      "1     RDBECOLIGNC00002  atgatactgaccgatccagaatggcaggcagttttattaagcctga...   \n",
      "2     RDBECOLIGNC00003  atggtttcatcattcacatctgccccacgcagcggtttttactatt...   \n",
      "3     RDBECOLIGNC00004  atgagcctggccggtaaaaaaatcgttctcggcgttagcggcggta...   \n",
      "4     RDBECOLIGNC00005  atgttatttactatccaacttatcataatactgatatgtctgtttt...   \n",
      "...                ...                                                ...   \n",
      "4742  RDBECOLIGNC04744  gaaagattcacgagtcagcaggcgatccatctgttctgcattagcg...   \n",
      "4743  RDBECOLIGNC04745  caccggcacttaatacttcgactttgtcctgccagctttcacgggc...   \n",
      "4744  RDBECOLIGNC04746  caaatcagatcgcccatatcgggtacgtatcggcttaccattacca...   \n",
      "4745  RDBECOLIGNC04747  atgtttgtgtggggtcttttctgtatcttacgcatcgcactcaagc...   \n",
      "4746  RDBECOLIGNC04748  aagaaagtaaatcacttttttaccactggtcttctgctttcaggcg...   \n",
      "\n",
      "     Element  \n",
      "0       Gene  \n",
      "1       Gene  \n",
      "2       Gene  \n",
      "3       Gene  \n",
      "4       Gene  \n",
      "...      ...  \n",
      "4742    Gene  \n",
      "4743    Gene  \n",
      "4744    Gene  \n",
      "4745    Gene  \n",
      "4746    Gene  \n",
      "\n",
      "[4747 rows x 3 columns]\n",
      "           Name                                       DNA Sequence\n",
      "0   BBa_J428006   ttcgttaccaattgacagctagctatcataccgctataatggtatgtt\n",
      "1   BBa_J428001  cttgtccaaccaaatgattcgttaccaattgacagtttctatcgat...\n",
      "2    BBa_J23100                ttgacggctagctcagtcctaggtacagtgctagc\n",
      "3   BBa_J428080  ttgttgccgaaaacattcaacaaaatcaatcaaaggtatcagttaa...\n",
      "4   BBa_J433026  cggagtactgtcctccgagacgtatgaagagtttaccggagtactg...\n",
      "..          ...                                                ...\n",
      "70  BBa_J435205  cgctagaaggaaattggaagaactaaatagagttctgaaaaacaat...\n",
      "71  BBa_J435203  aactgtgggaatactcaggtatcgtaagatgcaagagttcgaatct...\n",
      "72   BBa_J23101                tttacagctagctcagtcctaggtattatgctagc\n",
      "73  BBa_J433001  ttggctccggtgcccgtcagtgggcagagcgcacatcgcccacagt...\n",
      "74  BBa_J428074  ggaggtcaacatggtggagcacgacactctggtctactccaaaaat...\n",
      "\n",
      "[75 rows x 2 columns]\n",
      "            Name                                       DNA Sequence\n",
      "0    BBa_J435365  atctcagaattcgcattggccgagggaactcgtatcttcgatcctg...\n",
      "1    BBa_J435387  agctgtcaccggatgtgctttccggtctgatgagtccgtgaggacg...\n",
      "2    BBa_J435374                             agagtcacacaggaaagtacta\n",
      "3    BBa_J435355  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "4   BBa_K2680529  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "5   BBa_K2680561  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "6    BBa_J428038                             agagaaagaggagaaatactag\n",
      "7    BBa_J435325                ggaggtggctctggcggtggatcaggtggaggctc\n",
      "8    BBa_J428034  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "9    BBa_J435305  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "10   BBa_J428037                            agagtcacacaggaaacctacta\n",
      "11   BBa_J428032                           agagattaaagaggagaaatacta\n",
      "12   BBa_J435315  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "13   BBa_J428039                              agagaaaggtggtgaatacta\n",
      "14     BBa_Z0262                               ttaaagaggagaaatactag\n",
      "15  BBa_K2680531                             agagtcacacaggaaagtacta\n",
      "16  BBa_K2680530                               agagtcacacaggactacta\n",
      "17   BBa_J435385  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "            Name                                       DNA Sequence\n",
      "0    BBa_J435060  atgaaagacaaagtgttcgtaccgccgattaaatctcagggcatta...\n",
      "1    BBa_J428065  atgaacttcccccgtgcctcgcgccttatgcaggctgctgtgctgg...\n",
      "2    BBa_J435097  atgagcttctccaagcaggttctgaacgacgttacaatgacctatt...\n",
      "3    BBa_J435049  atgctggaaaccctggagctggttaacaaaatcgcggaatttcagc...\n",
      "4    BBa_J435082  atgtcttatagcaaacacggcattgtacaggaaatgaaaacgaaat...\n",
      "..           ...                                                ...\n",
      "191  BBa_J435384  atgtcgcactcaaaacaagcactggcagatacgatgaaaatgacgt...\n",
      "192  BBa_J428044  gtgaaagacaaaattatcgataacgccatcaccctgtttagcgaaa...\n",
      "193  BBa_J435102  atgaacgttattaaaccggacatgaaaatcaaactgtgcatgcgtg...\n",
      "194  BBa_J435033  atggctggctacggcgctaagggtatccgtaaagttggcgcgttcc...\n",
      "195  BBa_J435030  atgcatcaggattatcgtgaattatcactggacgaactggaatccg...\n",
      "\n",
      "[196 rows x 2 columns]\n",
      "           Name                                       DNA Sequence\n",
      "0   BBa_J435361  ggtgcttaataaggatcctagcataaccccttggggcctctaaacg...\n",
      "1   BBa_J435229  agtgcttttaactaagaattattagtcttttctgcttattttttca...\n",
      "2   BBa_J435230  ataaagcaatcttgatgaggataatgatttttttttgaatatacat...\n",
      "3   BBa_J435231  tcaagaggatgtcagaatgccatttgcctgagagatgcaggcttca...\n",
      "4   BBa_J435371  ggatccgaattcgagctccgtcgacctagcataaccccgccgggcc...\n",
      "5   BBa_J428091        aatcacactggctcaccttcgggtgggcctttctgcgtttata\n",
      "6   BBa_J435228  agcttttgattaagccttctagtccaaaaaacacgtttttttgtca...\n",
      "7   BBa_J435263  gccaattggtgcggcaattgataataacgaaaatgtcttttaatga...\n",
      "8   BBa_J428092  aaccaggcatcaaataaaacgaaaggctcagtcgaaagactgggcc...\n",
      "9   BBa_J428068  aatcggtcagtttcacctgatttacgtaaaaacccgcttcggcggg...\n",
      "10  BBa_J435275  acagatgacgggagacactagcacacaactttaccaggcaaggtat...\n",
      "11  BBa_J435274  atgtgcttttgtttatttcctatttggttattcacacatattttct...\n",
      "12  BBa_J428069  aagtaatcgttaatccgcaaataacgtaaaaacccgcttcggcggg...\n",
      "          Name                                       DNA Sequence\n",
      "0  BBa_J428327  tgagacgggagttgacggctagctcagtcctaggtacagtgctagc...\n",
      "1  BBa_J428326  tgagaccggagttgacggctagctcagtcctaggtacagtgctagc...\n",
      "2  BBa_J433047  tgaagagcgcagctggcacgacaggtttcccgactggaaagcgggc...\n",
      "3  BBa_J433046  gctttgagaccgcagctggcacgacaggtttcccgactggaaagcg...\n",
      "4   BBa_J04450  caatacgcaaaccgcctctccccgcgcgttggccgattcattaatg...\n",
      "5  BBa_J435281  gcaacgagaccggagtagtcttcgaaagtgaaacgtgatttcatgc...\n",
      "6  BBa_J428330  tgagaccggagttgacggctagctcagtcctaggtacagtgctagc...\n",
      "7  BBa_J435300  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...\n",
      "8  BBa_J435320  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#RegulonDB Files\n",
    "pro_df = pd.read_csv('./RegulonDB_Files/promoters_Data.csv')\n",
    "ter_df = pd.read_csv('./RegulonDB_Files/terminators_Data.csv')\n",
    "gen_df = pd.read_csv('./RegulonDB_Files/genes_Data.csv')\n",
    "\n",
    "print(pro_df)\n",
    "print(ter_df)\n",
    "print(gen_df)\n",
    "\n",
    "# pro_df = pro_df.drop(pro_df.index[0])\n",
    "# ter_df = ter_df.drop(ter_df.index[0])\n",
    "\n",
    "pro_df['Element'] = \"Promoter\"\n",
    "ter_df['Element'] = \"Terminator\"\n",
    "gen_df['Element'] = \"Gene\"\n",
    "\n",
    "print(pro_df)\n",
    "print(ter_df)\n",
    "print(gen_df)\n",
    "\n",
    "#iGEM Repository Files\n",
    "pro_2023 = pd.read_csv('./csv/Promoter_Part_Sequences.csv')\n",
    "rbs_2023 = pd.read_csv('./csv/RBS_Part_Sequences.csv')\n",
    "cds_2023 = pd.read_csv('./csv/CDS_Part_Sequences.csv')\n",
    "ter_2023 = pd.read_csv('./csv/Terminator_Part_Sequences.csv')\n",
    "bkb_2023 = pd.read_csv('./csv/Backbone_Part_Sequences.csv')\n",
    "\n",
    "print(pro_2023)\n",
    "print(rbs_2023)\n",
    "print(cds_2023)\n",
    "print(ter_2023)\n",
    "print(bkb_2023)\n",
    "\n",
    "# pro_2023 = pro_2023.drop(pro_2023.index[0])\n",
    "# rbs_2023 = rbs_2023.drop(rbs_2023.index[0])\n",
    "# cds_2023 = cds_2023.drop(cds_2023.index[0])\n",
    "# ter_2023 = ter_2023.drop(ter_2023.index[0])\n",
    "# bkb_2023 = bkb_2023.drop(bkb_2023.index[0])\n",
    "\n",
    "pro_2023['Element'] = \"Promoter\"\n",
    "rbs_2023['Element'] = \"RBS\"\n",
    "cds_2023['Element'] = \"CDS\"\n",
    "ter_2023['Element'] = \"Terminator\"\n",
    "bkb_2023['Element'] = \"Backbone\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Name                                       DNA Sequence  \\\n",
      "0     RDBECOLIPMC00001  acactttcattgttttaccgttgctctgattaattgacgctaaagt...   \n",
      "1     RDBECOLIPMC00002  ccgatgatcctcatcgtaatccaaccgaaactttacctgattctgg...   \n",
      "2     RDBECOLIPMC00003  gccgttacactcaaaggcggcgcggtgggaacgatatttcacagta...   \n",
      "3     RDBECOLIPMC00004  aaatacttatggtgcgctggcttctttggaacttgcgcagcaattt...   \n",
      "4     RDBECOLIPMC00005  tgccaactggcaggtcaaccgaatgcagacatcgcaggcgggatgt...   \n",
      "...                ...                                                ...   \n",
      "4727        BBa_J04450  caatacgcaaaccgcctctccccgcgcgttggccgattcattaatg...   \n",
      "4728       BBa_J435281  gcaacgagaccggagtagtcttcgaaagtgaaacgtgatttcatgc...   \n",
      "4729       BBa_J428330  tgagaccggagttgacggctagctcagtcctaggtacagtgctagc...   \n",
      "4730       BBa_J435300  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...   \n",
      "4731       BBa_J435320  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...   \n",
      "\n",
      "       Element  \n",
      "0     Promoter  \n",
      "1     Promoter  \n",
      "2     Promoter  \n",
      "3     Promoter  \n",
      "4     Promoter  \n",
      "...        ...  \n",
      "4727  Backbone  \n",
      "4728  Backbone  \n",
      "4729  Backbone  \n",
      "4730  Backbone  \n",
      "4731  Backbone  \n",
      "\n",
      "[4732 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "all_parts_df = pd.concat([pro_df, ter_df, pro_2023, rbs_2023, cds_2023, ter_2023, bkb_2023], ignore_index=True)\n",
    "all_parts_df = all_parts_df.dropna(subset=['DNA Sequence'])\n",
    "print(all_parts_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import ComplementNB\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn import svm\n",
    "\n",
    "# Ask the user for the size of the k-mers\n",
    "k_mer_size = int(input(\"Enter the size of the k-mers: \"))\n",
    "\n",
    "# Use CountVectorizer to initialize k-mer splitting \n",
    "# Use the user's input as the ngram_range parameter\n",
    "vectorizer = CountVectorizer(analyzer='char', ngram_range=(k_mer_size, k_mer_size))\n",
    "\n",
    "# apply the vectorizer to the concat. dataframe\n",
    "X = vectorizer.fit_transform(all_parts_df['DNA Sequence']).toarray()\n",
    "y = all_parts_df['Element']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these parameters are standard, but we can tweak if needed\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelGaussian = GaussianNB()\n",
    "modelGaussian.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9230769230769231\n"
     ]
    }
   ],
   "source": [
    "predictions = modelGaussian.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75\n",
      "44\n",
      "Valid Promoters\n",
      "            Name                                       DNA Sequence   Element  \\\n",
      "0    BBa_J428006   ttcgttaccaattgacagctagctatcataccgctataatggtatgtt  Promoter   \n",
      "1    BBa_J428001  cttgtccaaccaaatgattcgttaccaattgacagtttctatcgat...  Promoter   \n",
      "2     BBa_J23100                ttgacggctagctcagtcctaggtacagtgctagc  Promoter   \n",
      "6   BBa_K3317006  attggatccaattgacagctagctcagtcctaggtaccattggatc...  Promoter   \n",
      "7    BBa_J428042  gtcaactcataagattctgattcgttaccaattgacaattcaccta...  Promoter   \n",
      "8    BBa_J428041  aattgtgagcggataacaattgacattgtgagcggataacaagata...  Promoter   \n",
      "10    BBa_J23107                tttacggctagctcagccctaggtattatgctagc  Promoter   \n",
      "11  BBa_K3317007  ttttgttcgattatcgaacaaattattgaaatatcgaacaaaacct...  Promoter   \n",
      "12    BBa_J23106                tttacggctagctcagtcctaggtatagtgctagc  Promoter   \n",
      "13   BBa_J428040  cacccagcagtatttacaaacaaccatgaatgtaagtatattcctt...  Promoter   \n",
      "16    BBa_J23114                tttatggctagctcagtcctaggtacaatgctagc  Promoter   \n",
      "17    BBa_J23118                ttgacggctagctcagtcctaggtattgtgctagc  Promoter   \n",
      "18   BBa_J428047  agcgcgggtgagagggattcgttaccaatagacaattgattggacg...  Promoter   \n",
      "21   BBa_J428050  ggtatggaagctatacgttaccaattgacagctagctcagtcctac...  Promoter   \n",
      "22    BBa_J23116                ttgacagctagctcagtcctagggactatgctagc  Promoter   \n",
      "23   BBa_J435259  aacacagtcctttcccgcaattttctttttctattactcttggcct...  Promoter   \n",
      "26   BBa_J428030  cgctagcaagtaaggccgacgcttcacaaccgcacttgatttaata...  Promoter   \n",
      "27    BBa_J23119                ttgacagctagctcagtcctaggtataatgctagc  Promoter   \n",
      "29    BBa_J23117                ttgacagctagctcagtcctagggattgtgctagc  Promoter   \n",
      "30    BBa_J23109                tttacagctagctcagtcctagggactgtgctagc  Promoter   \n",
      "31     BBa_R0010  caatacgcaaaccgcctctccccgcgcgttggccgattcattaatg...  Promoter   \n",
      "32    BBa_J23105                tttacggctagctcagtcctaggtactatgctagc  Promoter   \n",
      "33    BBa_J23115                tttatagctagctcagcccttggtacaatgctagc  Promoter   \n",
      "34   BBa_J428085  catcttcattcttaagatatgaagataatcttcaaaaggcccctgg...  Promoter   \n",
      "36   BBa_J428053  agcgcgggtgagagggattcgttaccaattgacaattgattggacg...  Promoter   \n",
      "37  BBa_K1899004  cgacgtacggtggaatctgattcgttaccaattgacatgatacgaa...  Promoter   \n",
      "38   BBa_J428049  tctatgattggtccagattcgttaccaattgacagctagctcagtc...  Promoter   \n",
      "42   BBa_K649000  ttcgagcctagcaagggtccgggttcaccgaaatctatctcatttg...  Promoter   \n",
      "44   BBa_J435350  tcgtcactagagggcggcgtagaggatcgagatctcgatcccgcga...  Promoter   \n",
      "46   BBa_J428005  tgatcgaacgcttcaaggaacaaacgtttgattgacagctagctca...  Promoter   \n",
      "47  BBa_K2572025  tgttgacaattaatcatcggctcgtataatgtgtggaattgtgagc...  Promoter   \n",
      "50   BBa_J435360  aacgatcgttggctgtgttgacaattaatcatcggctcgtataatg...  Promoter   \n",
      "51   BBa_J435261  tcaattcatcattttatttttattcttttttttgatttcggtttct...  Promoter   \n",
      "53   BBa_J428054  aatccgcgtgataggtctgattcgttaccaattgacggaatgaacg...  Promoter   \n",
      "54   BBa_J428052     ttcgttaccaattgacactagaccggtctgtctatataatgctagc  Promoter   \n",
      "55   BBa_J428031  tcctgtgaaatctggcagttaccgttagctttcgaattggctataa...  Promoter   \n",
      "56   BBa_J428000  tcgtcactagagggcgatagtgacaaacttgacaactcatcacttc...  Promoter   \n",
      "59     BBa_R0051  taacaccgtgcgtgttgactattttacctctggcggtgataatggttgc  Promoter   \n",
      "60   BBa_J428002     ttcgtgtcactttgacagcagtgtcactcctaggtataatgctagc  Promoter   \n",
      "63    BBa_J23102                ttgacagctagctcagtcctaggtactgtgctagc  Promoter   \n",
      "64   BBa_J435340  tcgtcactagagggccctgcattaggatcgatctcgatcccgcgaa...  Promoter   \n",
      "67   BBa_J428048     ttcgttaccaattgacagctaaacataccgtgtggtatgttctagc  Promoter   \n",
      "69    BBa_J23110                tttacggctagctcagtcctaggtacaatgctagc  Promoter   \n",
      "72    BBa_J23101                tttacagctagctcagtcctaggtattatgctagc  Promoter   \n",
      "\n",
      "   PredictedType  \n",
      "0       Promoter  \n",
      "1       Promoter  \n",
      "2       Promoter  \n",
      "6       Promoter  \n",
      "7       Promoter  \n",
      "8       Promoter  \n",
      "10      Promoter  \n",
      "11      Promoter  \n",
      "12      Promoter  \n",
      "13      Promoter  \n",
      "16      Promoter  \n",
      "17      Promoter  \n",
      "18      Promoter  \n",
      "21      Promoter  \n",
      "22      Promoter  \n",
      "23      Promoter  \n",
      "26      Promoter  \n",
      "27      Promoter  \n",
      "29      Promoter  \n",
      "30      Promoter  \n",
      "31      Promoter  \n",
      "32      Promoter  \n",
      "33      Promoter  \n",
      "34      Promoter  \n",
      "36      Promoter  \n",
      "37      Promoter  \n",
      "38      Promoter  \n",
      "42      Promoter  \n",
      "44      Promoter  \n",
      "46      Promoter  \n",
      "47      Promoter  \n",
      "50      Promoter  \n",
      "51      Promoter  \n",
      "53      Promoter  \n",
      "54      Promoter  \n",
      "55      Promoter  \n",
      "56      Promoter  \n",
      "59      Promoter  \n",
      "60      Promoter  \n",
      "63      Promoter  \n",
      "64      Promoter  \n",
      "67      Promoter  \n",
      "69      Promoter  \n",
      "72      Promoter  \n",
      "Non-Promoters\n",
      "31\n",
      "            Name                                       DNA Sequence   Element  \\\n",
      "3    BBa_J428080  ttgttgccgaaaacattcaacaaaatcaatcaaaggtatcagttaa...  Promoter   \n",
      "4    BBa_J433026  cggagtactgtcctccgagacgtatgaagagtttaccggagtactg...  Promoter   \n",
      "5    BBa_J435207  aagaggatgtccaatattttttttaaggaataaggatacttcaaga...  Promoter   \n",
      "9    BBa_J435204  ttgatctgccttttatagctaagaaaaaaaaagagaaaaaaataat...  Promoter   \n",
      "14   BBa_J435206  ttgatctgccttttatagctaagaaaaaaaaagagaaaaaaataat...  Promoter   \n",
      "15   BBa_J428075  ggagatttttcaaatcagtgcgcaagacgtgacgtaagtatccgag...  Promoter   \n",
      "19   BBa_J435201  ttcttagccttgttgctagttagaaaaagacatttttgctgtcagt...  Promoter   \n",
      "20  BBa_K2680520  aacgatcgttggctgccctttgtgcgtccaaacggacgcacggcgc...  Promoter   \n",
      "24   BBa_J435200  atccttttgttgtttccgggtgtacaatatggacttcctcttttct...  Promoter   \n",
      "25  BBa_K1529300  tcctgtgaaatcgtacaggtttacgcaagaaaatggtttgttatag...  Promoter   \n",
      "28   BBa_J435208  tttgagaatgaccttccacgagctaaattgaaagggaagaatttat...  Promoter   \n",
      "35   BBa_J428083  agcggagaattaagggagtcacgttatgacccccgccgatgacgcg...  Promoter   \n",
      "39   BBa_J435258  cacccatgaaccacacggttagtccaaaaggggcagttcagattcc...  Promoter   \n",
      "40   BBa_J435202  agcgttggttggtggatcaagcccacgcgtaggcaatcctcgtgca...  Promoter   \n",
      "41  BBa_K1467103  ggagaaagaattaatctatacttttacaaatttaaattctgatctc...  Promoter   \n",
      "43   BBa_J435211  agcgttggttggtggatcaagcccacgcgtaggcaatcctcgtgca...  Promoter   \n",
      "45   BBa_J435260  cacgaagtgactgacagaatactgacatcagctgatttctcataga...  Promoter   \n",
      "48   BBa_J435213  gatctaacatccaaagacgaaaggttgaatgaaacctttttgccat...  Promoter   \n",
      "49   BBa_J433000  agtaatcaattacggggtcattagttcatagcccatatatggagtt...  Promoter   \n",
      "52   BBa_J428004     ttcgttaccaattgacatatttaaaattcttgtttaaaatgctagc  Promoter   \n",
      "57   BBa_J435210  aacgacattactatatatataatataggaagcatttaatagaacag...  Promoter   \n",
      "58   BBa_J433002  ttggctccggtgcccgtcagtgggcagagcgcacatcgcccacagt...  Promoter   \n",
      "61   BBa_J428007     ttcgttaccaattgacattattgataaatctgcgtaaaatgctagc  Promoter   \n",
      "62   BBa_J435262  cacccatgaaccacacggttagtccaaaaggggcagttcagattcc...  Promoter   \n",
      "65   BBa_J435212  tttttgtagaaatgtcttggtgtcctcgtccaatcaggtagccatc...  Promoter   \n",
      "66   BBa_J435209  tcattatcaatactcgccatttcaaagaatacgtaaataattaata...  Promoter   \n",
      "68   BBa_J428003    ttcgttaccaattgacagctagctcttattgatttttaatcaaataa  Promoter   \n",
      "70   BBa_J435205  cgctagaaggaaattggaagaactaaatagagttctgaaaaacaat...  Promoter   \n",
      "71   BBa_J435203  aactgtgggaatactcaggtatcgtaagatgcaagagttcgaatct...  Promoter   \n",
      "73   BBa_J433001  ttggctccggtgcccgtcagtgggcagagcgcacatcgcccacagt...  Promoter   \n",
      "74   BBa_J428074  ggaggtcaacatggtggagcacgacactctggtctactccaaaaat...  Promoter   \n",
      "\n",
      "   PredictedType  \n",
      "3            CDS  \n",
      "4            CDS  \n",
      "5            CDS  \n",
      "9            CDS  \n",
      "14           CDS  \n",
      "15           CDS  \n",
      "19           CDS  \n",
      "20           CDS  \n",
      "24           CDS  \n",
      "25    Terminator  \n",
      "28           CDS  \n",
      "35           CDS  \n",
      "39           CDS  \n",
      "40           CDS  \n",
      "41           CDS  \n",
      "43           CDS  \n",
      "45           CDS  \n",
      "48           CDS  \n",
      "49           CDS  \n",
      "52    Terminator  \n",
      "57           CDS  \n",
      "58           CDS  \n",
      "61    Terminator  \n",
      "62           CDS  \n",
      "65           CDS  \n",
      "66           CDS  \n",
      "68    Terminator  \n",
      "70           CDS  \n",
      "71           CDS  \n",
      "73           CDS  \n",
      "74           CDS  \n",
      "            Name PredictedType\n",
      "3    BBa_J428080           CDS\n",
      "4    BBa_J433026           CDS\n",
      "5    BBa_J435207           CDS\n",
      "9    BBa_J435204           CDS\n",
      "14   BBa_J435206           CDS\n",
      "15   BBa_J428075           CDS\n",
      "19   BBa_J435201           CDS\n",
      "20  BBa_K2680520           CDS\n",
      "24   BBa_J435200           CDS\n",
      "25  BBa_K1529300    Terminator\n",
      "28   BBa_J435208           CDS\n",
      "35   BBa_J428083           CDS\n",
      "39   BBa_J435258           CDS\n",
      "40   BBa_J435202           CDS\n",
      "41  BBa_K1467103           CDS\n",
      "43   BBa_J435211           CDS\n",
      "45   BBa_J435260           CDS\n",
      "48   BBa_J435213           CDS\n",
      "49   BBa_J433000           CDS\n",
      "52   BBa_J428004    Terminator\n",
      "57   BBa_J435210           CDS\n",
      "58   BBa_J433002           CDS\n",
      "61   BBa_J428007    Terminator\n",
      "62   BBa_J435262           CDS\n",
      "65   BBa_J435212           CDS\n",
      "66   BBa_J435209           CDS\n",
      "68   BBa_J428003    Terminator\n",
      "70   BBa_J435205           CDS\n",
      "71   BBa_J435203           CDS\n",
      "73   BBa_J433001           CDS\n",
      "74   BBa_J428074           CDS\n"
     ]
    }
   ],
   "source": [
    "X_new = vectorizer.transform(pro_2023['DNA Sequence']).toarray()\n",
    "\n",
    "# Use the trained model to predict the sequence types\n",
    "predictions_new = modelGaussian.predict(X_new)\n",
    "\n",
    "# Add predictions to the new DataFrame\n",
    "pro_2023['PredictedType'] = predictions_new\n",
    "\n",
    "# Filter to identify sequences predicted as promoters\n",
    "valid_promoters_df = pro_2023[pro_2023['PredictedType'] == 'Promoter']\n",
    "print(len(pro_2023))\n",
    "print(len(valid_promoters_df))\n",
    "print(\"Valid Promoters\")\n",
    "print(valid_promoters_df)\n",
    "\n",
    "print(\"Non-Promoters\")\n",
    "# Filter to identify sequences predicted as non-promoters\n",
    "non_promoters_df = pro_2023[pro_2023['PredictedType'] != 'Promoter']\n",
    "print(len(non_promoters_df))\n",
    "print(non_promoters_df)\n",
    "print(non_promoters_df[['Name', 'PredictedType']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4732\n",
      "3909\n",
      "Valid Promoters\n",
      "                  Name                                       DNA Sequence  \\\n",
      "0     RDBECOLIPMC00001  acactttcattgttttaccgttgctctgattaattgacgctaaagt...   \n",
      "1     RDBECOLIPMC00002  ccgatgatcctcatcgtaatccaaccgaaactttacctgattctgg...   \n",
      "2     RDBECOLIPMC00003  gccgttacactcaaaggcggcgcggtgggaacgatatttcacagta...   \n",
      "3     RDBECOLIPMC00004  aaatacttatggtgcgctggcttctttggaacttgcgcagcaattt...   \n",
      "4     RDBECOLIPMC00005  tgccaactggcaggtcaaccgaatgcagacatcgcaggcgggatgt...   \n",
      "...                ...                                                ...   \n",
      "4713       BBa_J435231  tcaagaggatgtcagaatgccatttgcctgagagatgcaggcttca...   \n",
      "4716       BBa_J435228  agcttttgattaagccttctagtccaaaaaacacgtttttttgtca...   \n",
      "4717       BBa_J435263  gccaattggtgcggcaattgataataacgaaaatgtcttttaatga...   \n",
      "4720       BBa_J435275  acagatgacgggagacactagcacacaactttaccaggcaaggtat...   \n",
      "4721       BBa_J435274  atgtgcttttgtttatttcctatttggttattcacacatattttct...   \n",
      "\n",
      "     PredictedType  \n",
      "0         Promoter  \n",
      "1         Promoter  \n",
      "2         Promoter  \n",
      "3         Promoter  \n",
      "4         Promoter  \n",
      "...            ...  \n",
      "4713      Promoter  \n",
      "4716      Promoter  \n",
      "4717      Promoter  \n",
      "4720      Promoter  \n",
      "4721      Promoter  \n",
      "\n",
      "[3909 rows x 3 columns]\n",
      "Non-Promoters\n",
      "823\n",
      "                  Name                                       DNA Sequence  \\\n",
      "15    RDBECOLIPMC00016  gacccattttgcgtttattccgccgataacgcgcgcgtaaaggcat...   \n",
      "17    RDBECOLIPMC00018  catccgggccagggtggagcacccatttcgcatcatcaagcgacag...   \n",
      "58    RDBECOLIPMC00059  agattccttgctttaaattttattttatataagccatcattttaat...   \n",
      "60    RDBECOLIPMC00061  ttcctgcgttcggtaaaatcacgttgcggcatttttgccgggggga...   \n",
      "111   RDBECOLIPMC00112  ccttctcgcaggcgttaaaaaattacctgaccgagcaccctgaagc...   \n",
      "...                ...                                                ...   \n",
      "4727        BBa_J04450  caatacgcaaaccgcctctccccgcgcgttggccgattcattaatg...   \n",
      "4728       BBa_J435281  gcaacgagaccggagtagtcttcgaaagtgaaacgtgatttcatgc...   \n",
      "4729       BBa_J428330  tgagaccggagttgacggctagctcagtcctaggtacagtgctagc...   \n",
      "4730       BBa_J435300  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...   \n",
      "4731       BBa_J435320  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...   \n",
      "\n",
      "     PredictedType  \n",
      "15      Terminator  \n",
      "17      Terminator  \n",
      "58      Terminator  \n",
      "60      Terminator  \n",
      "111     Terminator  \n",
      "...            ...  \n",
      "4727           CDS  \n",
      "4728           CDS  \n",
      "4729           CDS  \n",
      "4730      Backbone  \n",
      "4731      Backbone  \n",
      "\n",
      "[823 rows x 3 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/joblib/externals/loky/process_executor.py:700: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_nolabels = all_parts_df.drop('Element', axis=1)\n",
    "\n",
    "X_old = vectorizer.transform(training_nolabels['DNA Sequence']).toarray()\n",
    "\n",
    "# Use the trained model to predict the sequence types\n",
    "predictions_old = modelGaussian.predict(X_old)\n",
    "\n",
    "# Add predictions to the new DataFrame\n",
    "training_nolabels['PredictedType'] = predictions_old\n",
    "\n",
    "# Filter to identify sequences predicted as promoters\n",
    "valid_promoters_old_df = training_nolabels[training_nolabels['PredictedType'] == 'Promoter']\n",
    "print(len(all_parts_df))\n",
    "print(len(valid_promoters_old_df))\n",
    "print(\"Valid Promoters\")\n",
    "print(valid_promoters_old_df)\n",
    "\n",
    "print(\"Non-Promoters\")\n",
    "# Filter to identify sequences predicted as non-promoters\n",
    "non_promoters_old_df = training_nolabels[training_nolabels['PredictedType'] != 'Promoter']\n",
    "print(len(non_promoters_old_df))\n",
    "print(non_promoters_old_df)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import LearningCurveDisplay, ShuffleSplit\n",
    "import numpy as np\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 6), sharey=True)\n",
    "\n",
    "common_params = {\n",
    "    \"X\": X,\n",
    "    \"y\": y,\n",
    "    \"train_sizes\": np.linspace(0.1, 1.0, 5),\n",
    "    \"cv\": ShuffleSplit(n_splits=50, test_size=0.2, random_state=0),\n",
    "    \"score_type\": \"both\",\n",
    "    \"n_jobs\": 4,\n",
    "    \"line_kw\": {\"marker\": \"o\"},\n",
    "    \"std_display_style\": \"fill_between\",\n",
    "    \"score_name\": \"Accuracy\",\n",
    "}\n",
    "\n",
    "modelSVC = svm.SVC()\n",
    "\n",
    "for ax_idx, estimator in enumerate([modelGaussian, modelSVC]):\n",
    "    LearningCurveDisplay.from_estimator(estimator, **common_params, ax=ax[ax_idx])\n",
    "    handles, label = ax[ax_idx].get_legend_handles_labels()\n",
    "    ax[ax_idx].legend(handles[:2], [\"Training Score\", \"Test Score\"])\n",
    "    ax[ax_idx].set_title(f\"Learning Curve for {estimator.__class__.__name__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3176\n"
     ]
    }
   ],
   "source": [
    "igem_pro_file = open('./SBOL_Files/iGEM_Promoters_collection.xml').read()\n",
    "\n",
    "# Search for DNA sequences using regular expression\n",
    "igem_pro_dna_sequences = re.findall(r'<sbol:displayId>(.*?)_sequence</sbol:displayId>.*?<sbol:elements>(.*?)</sbol:elements>', igem_pro_file, re.DOTALL)\n",
    "\n",
    "# Create a pandas dataframe\n",
    "igem_pro_df = pd.DataFrame(igem_pro_dna_sequences, columns=['Name', 'DNA Sequence'])\n",
    "print(len(igem_pro_df))\n",
    "# Print the dataframe\n",
    "#print(igem_pro_df)\n",
    "\n",
    "# why is the first name of the df the name of the collection? \n",
    "# for now, will remove first row so that we have realistic names\n",
    "igem_pro_df = igem_pro_df.drop(igem_pro_df.index[0])\n",
    "\n",
    "# add in promoter label for these seqs\n",
    "igem_pro_df['Element'] = \"Promoter\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3175\n",
      "1505\n",
      "Valid Promoters\n",
      "              Name                                       DNA Sequence  \\\n",
      "1       BBa_J05209  taacaccgtgcgtgttgactattttacctctggcggtgataatggttgc   \n",
      "2      BBa_I732406  aattgtgagcgctcacaatttttacactttatgcttccggctcgta...   \n",
      "3     BBa_K1773016  acctgtaggatcgtacaggtttacgcaagaaaatggtttgttatag...   \n",
      "7      BBa_K079047  tatcaccgccagaggtatactagagtaacaccgtgcgtgttgtact...   \n",
      "9      BBa_I732352  aattgtgaacgttcacaatttacactttatgcttccggctcgtata...   \n",
      "...            ...                                                ...   \n",
      "3165   BBa_K318512  ctttcctgcacagtctgaacggggtgtacacgccgtatatgcagga...   \n",
      "3166  BBa_K1537017  ggtagtggagagggtagaggatctcttttgacatgtggagatgttg...   \n",
      "3167   BBa_K239000  tcagtaaattaccgtcagattctcctgagtttccgctatgggaata...   \n",
      "3170   BBa_K242005  agtatatcaagtgctatacgcagcagtaccttcactgtctcgcagc...   \n",
      "3171     BBa_R2108  tttatcaaaaagagtgttgacatttttaagtcccacgcgtgggatt...   \n",
      "\n",
      "     PredictedType  \n",
      "1         Promoter  \n",
      "2         Promoter  \n",
      "3         Promoter  \n",
      "7         Promoter  \n",
      "9         Promoter  \n",
      "...            ...  \n",
      "3165      Promoter  \n",
      "3166      Promoter  \n",
      "3167      Promoter  \n",
      "3170      Promoter  \n",
      "3171      Promoter  \n",
      "\n",
      "[1505 rows x 3 columns]\n",
      "Non-Promoters\n",
      "1670\n",
      "              Name                                       DNA Sequence  \\\n",
      "4     BBa_K2117000  agagaccgggttggcggcgtatttgtgtcccaaaaaacagccccaa...   \n",
      "5     BBa_K1444002  ggagttctgagaattggtatgccttataagtccaattaacagttga...   \n",
      "6      BBa_I756014  gagacatatgtcgactgctgtatataaaaccagtggttatatgtac...   \n",
      "8        BBa_B1002                 cgcaaaaaaccccgcttcggcggggttttttcgc   \n",
      "10    BBa_K2116044  acgtagcaacgcagacgcatcgcactccagcacctgacgtagcgtg...   \n",
      "...            ...                                                ...   \n",
      "3169  BBa_K1614000                                 taatacgactcactatag   \n",
      "3172  BBa_K1444025  ggagttctgagaattggtatgccttataagtccaattaacagttga...   \n",
      "3173   BBa_K879037                      ttgatattgtgagcggataacaagatatc   \n",
      "3174    BBa_I15008  atgagtgtcaacttagcttcccagttgcgggaagggacgaaaaaat...   \n",
      "3175  BBa_K1387002  atgtccagattagataaaagtaaagtgattaacagcgcattagagc...   \n",
      "\n",
      "     PredictedType  \n",
      "4              CDS  \n",
      "5              CDS  \n",
      "6              CDS  \n",
      "8       Terminator  \n",
      "10             CDS  \n",
      "...            ...  \n",
      "3169    Terminator  \n",
      "3172           CDS  \n",
      "3173    Terminator  \n",
      "3174           CDS  \n",
      "3175           CDS  \n",
      "\n",
      "[1670 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "igem_pro_nolabels = igem_pro_df.drop('Element', axis=1)\n",
    "igem_pro_nolabels = igem_pro_nolabels.dropna(subset=['DNA Sequence'])\n",
    "\n",
    "\n",
    "X_old = vectorizer.transform(igem_pro_nolabels['DNA Sequence']).toarray()\n",
    "\n",
    "# Use the trained model to predict the sequence types\n",
    "predictions_old = modelGaussian.predict(X_old)\n",
    "\n",
    "# Add predictions to the new DataFrame\n",
    "igem_pro_nolabels['PredictedType'] = predictions_old\n",
    "\n",
    "# Filter to identify sequences predicted as promoters\n",
    "valid_igem_promoters_df = igem_pro_nolabels[igem_pro_nolabels['PredictedType'] == 'Promoter']\n",
    "print(len(igem_pro_df))\n",
    "print(len(valid_igem_promoters_df))\n",
    "print(\"Valid Promoters\")\n",
    "print(valid_igem_promoters_df)\n",
    "\n",
    "print(\"Non-Promoters\")\n",
    "# Filter to identify sequences predicted as non-promoters\n",
    "non_promoters_igem_df = igem_pro_nolabels[igem_pro_nolabels['PredictedType'] != 'Promoter']\n",
    "print(len(non_promoters_igem_df))\n",
    "print(non_promoters_igem_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported DataFrame as CSV: ./csv/igem_non_promoters.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "output_dir = \"./csv\"\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)\n",
    "\n",
    "file_path = os.path.join(output_dir, \"igem_non_promoters.csv\")\n",
    "non_promoters_igem_df.to_csv(file_path, index=False)\n",
    "print(f\"Exported DataFrame as CSV: {file_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second Approach: SVC\n",
    "This version uses local csv files from the iGEM Repository and the RegulonDB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Name                                       DNA Sequence\n",
      "0     RDBECOLIPMC00001  acactttcattgttttaccgttgctctgattaattgacgctaaagt...\n",
      "1     RDBECOLIPMC00002  ccgatgatcctcatcgtaatccaaccgaaactttacctgattctgg...\n",
      "2     RDBECOLIPMC00003  gccgttacactcaaaggcggcgcggtgggaacgatatttcacagta...\n",
      "3     RDBECOLIPMC00004  aaatacttatggtgcgctggcttctttggaacttgcgcagcaattt...\n",
      "4     RDBECOLIPMC00005  tgccaactggcaggtcaaccgaatgcagacatcgcaggcgggatgt...\n",
      "...                ...                                                ...\n",
      "4045  RDBECOLIPMC04049  gcgatcctcatcagccgcattgcggccaacttctggattagcgcca...\n",
      "4046  RDBECOLIPMC04050  ttgaatcgtctgtccaaccgctgttttacgcagcgcttgtgcatcg...\n",
      "4047  RDBECOLIPMC04051  accgccgccgccgccgacgccgatgactttaatcaccgcgtcattg...\n",
      "4048  RDBECOLIPMC04052  aggacaacagctggacgatgtccagcttgctcgctaccttttgtcg...\n",
      "4049  RDBECOLIPMC04053  gcttaacaattttgcaaaatgtattggcgagtaagaaccgcatttg...\n",
      "\n",
      "[4050 rows x 2 columns]\n",
      "                 Name                                       DNA Sequence\n",
      "0    RDBECOLITMC00001              agcgtcaaaaggccggattttccggccttttttatta\n",
      "1    RDBECOLITMC00002  gataaatcagaaacataaaggcgctttcgggtgcctttattatttc...\n",
      "2    RDBECOLITMC00003  taccaccgtcaaaaaaaacggcgctttttagcgccgtttttatttt...\n",
      "3    RDBECOLITMC00004  ctgatgaaaaggtgccggatgatgtgaatcatccggcactggattatta\n",
      "4    RDBECOLITMC00005    tagcaacaaaaaagccgactcacttgcagtcggctttctcattttaa\n",
      "..                ...                                                ...\n",
      "366  RDBECOLITMC00367        aaaaggattcgcggctctgctcttcagagctgcttttatgata\n",
      "367  RDBECOLITMC00368           gttattaaatagcctgccatctggcaggctttttttatcg\n",
      "368  RDBECOLITMC00369       atgcaggaaagcaggctggagctacccagcctgcagtgaaatta\n",
      "369  RDBECOLITMC00370     tgaaaacgaaggccggagcatgctccggccttttttatctcttaca\n",
      "370  RDBECOLITMC00371               ctgcccttttccctgctcctggacggttttacccct\n",
      "\n",
      "[371 rows x 2 columns]\n",
      "                  Name                                       DNA Sequence\n",
      "0     RDBECOLIGNC00001  atgcaagcggcaactgttgtgattaaccgccgcgctctgcgacaca...\n",
      "1     RDBECOLIGNC00002  atgatactgaccgatccagaatggcaggcagttttattaagcctga...\n",
      "2     RDBECOLIGNC00003  atggtttcatcattcacatctgccccacgcagcggtttttactatt...\n",
      "3     RDBECOLIGNC00004  atgagcctggccggtaaaaaaatcgttctcggcgttagcggcggta...\n",
      "4     RDBECOLIGNC00005  atgttatttactatccaacttatcataatactgatatgtctgtttt...\n",
      "...                ...                                                ...\n",
      "4742  RDBECOLIGNC04744  gaaagattcacgagtcagcaggcgatccatctgttctgcattagcg...\n",
      "4743  RDBECOLIGNC04745  caccggcacttaatacttcgactttgtcctgccagctttcacgggc...\n",
      "4744  RDBECOLIGNC04746  caaatcagatcgcccatatcgggtacgtatcggcttaccattacca...\n",
      "4745  RDBECOLIGNC04747  atgtttgtgtggggtcttttctgtatcttacgcatcgcactcaagc...\n",
      "4746  RDBECOLIGNC04748  aagaaagtaaatcacttttttaccactggtcttctgctttcaggcg...\n",
      "\n",
      "[4747 rows x 2 columns]\n",
      "                  Name                                       DNA Sequence  \\\n",
      "0     RDBECOLIPMC00001  acactttcattgttttaccgttgctctgattaattgacgctaaagt...   \n",
      "1     RDBECOLIPMC00002  ccgatgatcctcatcgtaatccaaccgaaactttacctgattctgg...   \n",
      "2     RDBECOLIPMC00003  gccgttacactcaaaggcggcgcggtgggaacgatatttcacagta...   \n",
      "3     RDBECOLIPMC00004  aaatacttatggtgcgctggcttctttggaacttgcgcagcaattt...   \n",
      "4     RDBECOLIPMC00005  tgccaactggcaggtcaaccgaatgcagacatcgcaggcgggatgt...   \n",
      "...                ...                                                ...   \n",
      "4045  RDBECOLIPMC04049  gcgatcctcatcagccgcattgcggccaacttctggattagcgcca...   \n",
      "4046  RDBECOLIPMC04050  ttgaatcgtctgtccaaccgctgttttacgcagcgcttgtgcatcg...   \n",
      "4047  RDBECOLIPMC04051  accgccgccgccgccgacgccgatgactttaatcaccgcgtcattg...   \n",
      "4048  RDBECOLIPMC04052  aggacaacagctggacgatgtccagcttgctcgctaccttttgtcg...   \n",
      "4049  RDBECOLIPMC04053  gcttaacaattttgcaaaatgtattggcgagtaagaaccgcatttg...   \n",
      "\n",
      "       Element  \n",
      "0     Promoter  \n",
      "1     Promoter  \n",
      "2     Promoter  \n",
      "3     Promoter  \n",
      "4     Promoter  \n",
      "...        ...  \n",
      "4045  Promoter  \n",
      "4046  Promoter  \n",
      "4047  Promoter  \n",
      "4048  Promoter  \n",
      "4049  Promoter  \n",
      "\n",
      "[4050 rows x 3 columns]\n",
      "                 Name                                       DNA Sequence  \\\n",
      "0    RDBECOLITMC00001              agcgtcaaaaggccggattttccggccttttttatta   \n",
      "1    RDBECOLITMC00002  gataaatcagaaacataaaggcgctttcgggtgcctttattatttc...   \n",
      "2    RDBECOLITMC00003  taccaccgtcaaaaaaaacggcgctttttagcgccgtttttatttt...   \n",
      "3    RDBECOLITMC00004  ctgatgaaaaggtgccggatgatgtgaatcatccggcactggattatta   \n",
      "4    RDBECOLITMC00005    tagcaacaaaaaagccgactcacttgcagtcggctttctcattttaa   \n",
      "..                ...                                                ...   \n",
      "366  RDBECOLITMC00367        aaaaggattcgcggctctgctcttcagagctgcttttatgata   \n",
      "367  RDBECOLITMC00368           gttattaaatagcctgccatctggcaggctttttttatcg   \n",
      "368  RDBECOLITMC00369       atgcaggaaagcaggctggagctacccagcctgcagtgaaatta   \n",
      "369  RDBECOLITMC00370     tgaaaacgaaggccggagcatgctccggccttttttatctcttaca   \n",
      "370  RDBECOLITMC00371               ctgcccttttccctgctcctggacggttttacccct   \n",
      "\n",
      "        Element  \n",
      "0    Terminator  \n",
      "1    Terminator  \n",
      "2    Terminator  \n",
      "3    Terminator  \n",
      "4    Terminator  \n",
      "..          ...  \n",
      "366  Terminator  \n",
      "367  Terminator  \n",
      "368  Terminator  \n",
      "369  Terminator  \n",
      "370  Terminator  \n",
      "\n",
      "[371 rows x 3 columns]\n",
      "                  Name                                       DNA Sequence  \\\n",
      "0     RDBECOLIGNC00001  atgcaagcggcaactgttgtgattaaccgccgcgctctgcgacaca...   \n",
      "1     RDBECOLIGNC00002  atgatactgaccgatccagaatggcaggcagttttattaagcctga...   \n",
      "2     RDBECOLIGNC00003  atggtttcatcattcacatctgccccacgcagcggtttttactatt...   \n",
      "3     RDBECOLIGNC00004  atgagcctggccggtaaaaaaatcgttctcggcgttagcggcggta...   \n",
      "4     RDBECOLIGNC00005  atgttatttactatccaacttatcataatactgatatgtctgtttt...   \n",
      "...                ...                                                ...   \n",
      "4742  RDBECOLIGNC04744  gaaagattcacgagtcagcaggcgatccatctgttctgcattagcg...   \n",
      "4743  RDBECOLIGNC04745  caccggcacttaatacttcgactttgtcctgccagctttcacgggc...   \n",
      "4744  RDBECOLIGNC04746  caaatcagatcgcccatatcgggtacgtatcggcttaccattacca...   \n",
      "4745  RDBECOLIGNC04747  atgtttgtgtggggtcttttctgtatcttacgcatcgcactcaagc...   \n",
      "4746  RDBECOLIGNC04748  aagaaagtaaatcacttttttaccactggtcttctgctttcaggcg...   \n",
      "\n",
      "     Element  \n",
      "0       Gene  \n",
      "1       Gene  \n",
      "2       Gene  \n",
      "3       Gene  \n",
      "4       Gene  \n",
      "...      ...  \n",
      "4742    Gene  \n",
      "4743    Gene  \n",
      "4744    Gene  \n",
      "4745    Gene  \n",
      "4746    Gene  \n",
      "\n",
      "[4747 rows x 3 columns]\n",
      "           Name                                       DNA Sequence\n",
      "0   BBa_J428006   ttcgttaccaattgacagctagctatcataccgctataatggtatgtt\n",
      "1   BBa_J428001  cttgtccaaccaaatgattcgttaccaattgacagtttctatcgat...\n",
      "2    BBa_J23100                ttgacggctagctcagtcctaggtacagtgctagc\n",
      "3   BBa_J428080  ttgttgccgaaaacattcaacaaaatcaatcaaaggtatcagttaa...\n",
      "4   BBa_J433026  cggagtactgtcctccgagacgtatgaagagtttaccggagtactg...\n",
      "..          ...                                                ...\n",
      "70  BBa_J435205  cgctagaaggaaattggaagaactaaatagagttctgaaaaacaat...\n",
      "71  BBa_J435203  aactgtgggaatactcaggtatcgtaagatgcaagagttcgaatct...\n",
      "72   BBa_J23101                tttacagctagctcagtcctaggtattatgctagc\n",
      "73  BBa_J433001  ttggctccggtgcccgtcagtgggcagagcgcacatcgcccacagt...\n",
      "74  BBa_J428074  ggaggtcaacatggtggagcacgacactctggtctactccaaaaat...\n",
      "\n",
      "[75 rows x 2 columns]\n",
      "            Name                                       DNA Sequence\n",
      "0    BBa_J435365  atctcagaattcgcattggccgagggaactcgtatcttcgatcctg...\n",
      "1    BBa_J435387  agctgtcaccggatgtgctttccggtctgatgagtccgtgaggacg...\n",
      "2    BBa_J435374                             agagtcacacaggaaagtacta\n",
      "3    BBa_J435355  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "4   BBa_K2680529  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "5   BBa_K2680561  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "6    BBa_J428038                             agagaaagaggagaaatactag\n",
      "7    BBa_J435325                ggaggtggctctggcggtggatcaggtggaggctc\n",
      "8    BBa_J428034  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "9    BBa_J435305  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "10   BBa_J428037                            agagtcacacaggaaacctacta\n",
      "11   BBa_J428032                           agagattaaagaggagaaatacta\n",
      "12   BBa_J435315  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "13   BBa_J428039                              agagaaaggtggtgaatacta\n",
      "14     BBa_Z0262                               ttaaagaggagaaatactag\n",
      "15  BBa_K2680531                             agagtcacacaggaaagtacta\n",
      "16  BBa_K2680530                               agagtcacacaggactacta\n",
      "17   BBa_J435385  gggcccaagttcacttaaaaaggagatcaacaatgaaagcaatttt...\n",
      "            Name                                       DNA Sequence\n",
      "0    BBa_J435060  atgaaagacaaagtgttcgtaccgccgattaaatctcagggcatta...\n",
      "1    BBa_J428065  atgaacttcccccgtgcctcgcgccttatgcaggctgctgtgctgg...\n",
      "2    BBa_J435097  atgagcttctccaagcaggttctgaacgacgttacaatgacctatt...\n",
      "3    BBa_J435049  atgctggaaaccctggagctggttaacaaaatcgcggaatttcagc...\n",
      "4    BBa_J435082  atgtcttatagcaaacacggcattgtacaggaaatgaaaacgaaat...\n",
      "..           ...                                                ...\n",
      "191  BBa_J435384  atgtcgcactcaaaacaagcactggcagatacgatgaaaatgacgt...\n",
      "192  BBa_J428044  gtgaaagacaaaattatcgataacgccatcaccctgtttagcgaaa...\n",
      "193  BBa_J435102  atgaacgttattaaaccggacatgaaaatcaaactgtgcatgcgtg...\n",
      "194  BBa_J435033  atggctggctacggcgctaagggtatccgtaaagttggcgcgttcc...\n",
      "195  BBa_J435030  atgcatcaggattatcgtgaattatcactggacgaactggaatccg...\n",
      "\n",
      "[196 rows x 2 columns]\n",
      "           Name                                       DNA Sequence\n",
      "0   BBa_J435361  ggtgcttaataaggatcctagcataaccccttggggcctctaaacg...\n",
      "1   BBa_J435229  agtgcttttaactaagaattattagtcttttctgcttattttttca...\n",
      "2   BBa_J435230  ataaagcaatcttgatgaggataatgatttttttttgaatatacat...\n",
      "3   BBa_J435231  tcaagaggatgtcagaatgccatttgcctgagagatgcaggcttca...\n",
      "4   BBa_J435371  ggatccgaattcgagctccgtcgacctagcataaccccgccgggcc...\n",
      "5   BBa_J428091        aatcacactggctcaccttcgggtgggcctttctgcgtttata\n",
      "6   BBa_J435228  agcttttgattaagccttctagtccaaaaaacacgtttttttgtca...\n",
      "7   BBa_J435263  gccaattggtgcggcaattgataataacgaaaatgtcttttaatga...\n",
      "8   BBa_J428092  aaccaggcatcaaataaaacgaaaggctcagtcgaaagactgggcc...\n",
      "9   BBa_J428068  aatcggtcagtttcacctgatttacgtaaaaacccgcttcggcggg...\n",
      "10  BBa_J435275  acagatgacgggagacactagcacacaactttaccaggcaaggtat...\n",
      "11  BBa_J435274  atgtgcttttgtttatttcctatttggttattcacacatattttct...\n",
      "12  BBa_J428069  aagtaatcgttaatccgcaaataacgtaaaaacccgcttcggcggg...\n",
      "          Name                                       DNA Sequence\n",
      "0  BBa_J428327  tgagacgggagttgacggctagctcagtcctaggtacagtgctagc...\n",
      "1  BBa_J428326  tgagaccggagttgacggctagctcagtcctaggtacagtgctagc...\n",
      "2  BBa_J433047  tgaagagcgcagctggcacgacaggtttcccgactggaaagcgggc...\n",
      "3  BBa_J433046  gctttgagaccgcagctggcacgacaggtttcccgactggaaagcg...\n",
      "4   BBa_J04450  caatacgcaaaccgcctctccccgcgcgttggccgattcattaatg...\n",
      "5  BBa_J435281  gcaacgagaccggagtagtcttcgaaagtgaaacgtgatttcatgc...\n",
      "6  BBa_J428330  tgagaccggagttgacggctagctcagtcctaggtacagtgctagc...\n",
      "7  BBa_J435300  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...\n",
      "8  BBa_J435320  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#RegulonDB Files\n",
    "pro_df = pd.read_csv('./RegulonDB_Files/promoters_Data.csv')\n",
    "ter_df = pd.read_csv('./RegulonDB_Files/terminators_Data.csv')\n",
    "gen_df = pd.read_csv('./RegulonDB_Files/genes_Data.csv')\n",
    "\n",
    "print(pro_df)\n",
    "print(ter_df)\n",
    "print(gen_df)\n",
    "\n",
    "# pro_df = pro_df.drop(pro_df.index[0])\n",
    "# ter_df = ter_df.drop(ter_df.index[0])\n",
    "\n",
    "pro_df['Element'] = \"Promoter\"\n",
    "ter_df['Element'] = \"Terminator\"\n",
    "gen_df['Element'] = \"Gene\"\n",
    "\n",
    "print(pro_df)\n",
    "print(ter_df)\n",
    "print(gen_df)\n",
    "\n",
    "#iGEM Repository Files\n",
    "pro_2023 = pd.read_csv('./csv/Promoter_Part_Sequences.csv')\n",
    "rbs_2023 = pd.read_csv('./csv/RBS_Part_Sequences.csv')\n",
    "cds_2023 = pd.read_csv('./csv/CDS_Part_Sequences.csv')\n",
    "ter_2023 = pd.read_csv('./csv/Terminator_Part_Sequences.csv')\n",
    "bkb_2023 = pd.read_csv('./csv/Backbone_Part_Sequences.csv')\n",
    "\n",
    "print(pro_2023)\n",
    "print(rbs_2023)\n",
    "print(cds_2023)\n",
    "print(ter_2023)\n",
    "print(bkb_2023)\n",
    "\n",
    "# pro_2023 = pro_2023.drop(pro_2023.index[0])\n",
    "# rbs_2023 = rbs_2023.drop(rbs_2023.index[0])\n",
    "# cds_2023 = cds_2023.drop(cds_2023.index[0])\n",
    "# ter_2023 = ter_2023.drop(ter_2023.index[0])\n",
    "# bkb_2023 = bkb_2023.drop(bkb_2023.index[0])\n",
    "\n",
    "pro_2023['Element'] = \"Promoter\"\n",
    "rbs_2023['Element'] = \"RBS\"\n",
    "cds_2023['Element'] = \"CDS\"\n",
    "ter_2023['Element'] = \"Terminator\"\n",
    "bkb_2023['Element'] = \"Backbone\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Name                                       DNA Sequence  \\\n",
      "0     RDBECOLIPMC00001  acactttcattgttttaccgttgctctgattaattgacgctaaagt...   \n",
      "1     RDBECOLIPMC00002  ccgatgatcctcatcgtaatccaaccgaaactttacctgattctgg...   \n",
      "2     RDBECOLIPMC00003  gccgttacactcaaaggcggcgcggtgggaacgatatttcacagta...   \n",
      "3     RDBECOLIPMC00004  aaatacttatggtgcgctggcttctttggaacttgcgcagcaattt...   \n",
      "4     RDBECOLIPMC00005  tgccaactggcaggtcaaccgaatgcagacatcgcaggcgggatgt...   \n",
      "...                ...                                                ...   \n",
      "4727        BBa_J04450  caatacgcaaaccgcctctccccgcgcgttggccgattcattaatg...   \n",
      "4728       BBa_J435281  gcaacgagaccggagtagtcttcgaaagtgaaacgtgatttcatgc...   \n",
      "4729       BBa_J428330  tgagaccggagttgacggctagctcagtcctaggtacagtgctagc...   \n",
      "4730       BBa_J435300  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...   \n",
      "4731       BBa_J435320  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...   \n",
      "\n",
      "       Element  \n",
      "0     Promoter  \n",
      "1     Promoter  \n",
      "2     Promoter  \n",
      "3     Promoter  \n",
      "4     Promoter  \n",
      "...        ...  \n",
      "4727  Backbone  \n",
      "4728  Backbone  \n",
      "4729  Backbone  \n",
      "4730  Backbone  \n",
      "4731  Backbone  \n",
      "\n",
      "[4732 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "all_parts_df = pd.concat([pro_df, ter_df, pro_2023, rbs_2023, cds_2023, ter_2023, bkb_2023], ignore_index=True)\n",
    "all_parts_df = all_parts_df.dropna(subset=['DNA Sequence'])\n",
    "print(all_parts_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import ComplementNB\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn import svm\n",
    "\n",
    "# Ask the user for the size of the k-mers\n",
    "k_mer_size = int(input(\"Enter the size of the k-mers: \"))\n",
    "\n",
    "# Use CountVectorizer to initialize k-mer splitting \n",
    "# Use the user's input as the ngram_range parameter\n",
    "vectorizer = CountVectorizer(analyzer='char', ngram_range=(k_mer_size, k_mer_size))\n",
    "\n",
    "# apply the vectorizer to the concat. dataframe\n",
    "X = vectorizer.fit_transform(all_parts_df['DNA Sequence']).toarray()\n",
    "y = all_parts_df['Element']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these parameters are standard, but we can tweak if needed\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelSVC = svm.SVC()\n",
    "modelSVC.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9239222316145393\n"
     ]
    }
   ],
   "source": [
    "predictions = modelSVC.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75\n",
      "71\n",
      "Valid Promoters\n",
      "           Name                                       DNA Sequence   Element  \\\n",
      "0   BBa_J428006   ttcgttaccaattgacagctagctatcataccgctataatggtatgtt  Promoter   \n",
      "1   BBa_J428001  cttgtccaaccaaatgattcgttaccaattgacagtttctatcgat...  Promoter   \n",
      "2    BBa_J23100                ttgacggctagctcagtcctaggtacagtgctagc  Promoter   \n",
      "3   BBa_J428080  ttgttgccgaaaacattcaacaaaatcaatcaaaggtatcagttaa...  Promoter   \n",
      "4   BBa_J433026  cggagtactgtcctccgagacgtatgaagagtttaccggagtactg...  Promoter   \n",
      "..          ...                                                ...       ...   \n",
      "68  BBa_J428003    ttcgttaccaattgacagctagctcttattgatttttaatcaaataa  Promoter   \n",
      "69   BBa_J23110                tttacggctagctcagtcctaggtacaatgctagc  Promoter   \n",
      "71  BBa_J435203  aactgtgggaatactcaggtatcgtaagatgcaagagttcgaatct...  Promoter   \n",
      "72   BBa_J23101                tttacagctagctcagtcctaggtattatgctagc  Promoter   \n",
      "74  BBa_J428074  ggaggtcaacatggtggagcacgacactctggtctactccaaaaat...  Promoter   \n",
      "\n",
      "   PredictedType  \n",
      "0       Promoter  \n",
      "1       Promoter  \n",
      "2       Promoter  \n",
      "3       Promoter  \n",
      "4       Promoter  \n",
      "..           ...  \n",
      "68      Promoter  \n",
      "69      Promoter  \n",
      "71      Promoter  \n",
      "72      Promoter  \n",
      "74      Promoter  \n",
      "\n",
      "[71 rows x 4 columns]\n",
      "Non-Promoters\n",
      "4\n",
      "           Name                                       DNA Sequence   Element  \\\n",
      "15  BBa_J428075  ggagatttttcaaatcagtgcgcaagacgtgacgtaagtatccgag...  Promoter   \n",
      "24  BBa_J435200  atccttttgttgtttccgggtgtacaatatggacttcctcttttct...  Promoter   \n",
      "70  BBa_J435205  cgctagaaggaaattggaagaactaaatagagttctgaaaaacaat...  Promoter   \n",
      "73  BBa_J433001  ttggctccggtgcccgtcagtgggcagagcgcacatcgcccacagt...  Promoter   \n",
      "\n",
      "   PredictedType  \n",
      "15           CDS  \n",
      "24           CDS  \n",
      "70           CDS  \n",
      "73           CDS  \n",
      "           Name PredictedType\n",
      "15  BBa_J428075           CDS\n",
      "24  BBa_J435200           CDS\n",
      "70  BBa_J435205           CDS\n",
      "73  BBa_J433001           CDS\n"
     ]
    }
   ],
   "source": [
    "X_new = vectorizer.transform(pro_2023['DNA Sequence']).toarray()\n",
    "\n",
    "# Use the trained model to predict the sequence types\n",
    "predictions_new = modelSVC.predict(X_new)\n",
    "\n",
    "# Add predictions to the new DataFrame\n",
    "pro_2023['PredictedType'] = predictions_new\n",
    "\n",
    "# Filter to identify sequences predicted as promoters\n",
    "valid_promoters_df = pro_2023[pro_2023['PredictedType'] == 'Promoter']\n",
    "print(len(pro_2023))\n",
    "print(len(valid_promoters_df))\n",
    "print(\"Valid Promoters\")\n",
    "print(valid_promoters_df)\n",
    "\n",
    "print(\"Non-Promoters\")\n",
    "# Filter to identify sequences predicted as non-promoters\n",
    "non_promoters_df = pro_2023[pro_2023['PredictedType'] != 'Promoter']\n",
    "print(len(non_promoters_df))\n",
    "print(non_promoters_df)\n",
    "print(non_promoters_df[['Name', 'PredictedType']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4732\n",
      "4420\n",
      "Valid Promoters\n",
      "                  Name                                       DNA Sequence  \\\n",
      "0     RDBECOLIPMC00001  acactttcattgttttaccgttgctctgattaattgacgctaaagt...   \n",
      "1     RDBECOLIPMC00002  ccgatgatcctcatcgtaatccaaccgaaactttacctgattctgg...   \n",
      "2     RDBECOLIPMC00003  gccgttacactcaaaggcggcgcggtgggaacgatatttcacagta...   \n",
      "3     RDBECOLIPMC00004  aaatacttatggtgcgctggcttctttggaacttgcgcagcaattt...   \n",
      "4     RDBECOLIPMC00005  tgccaactggcaggtcaaccgaatgcagacatcgcaggcgggatgt...   \n",
      "...                ...                                                ...   \n",
      "4716       BBa_J435228  agcttttgattaagccttctagtccaaaaaacacgtttttttgtca...   \n",
      "4717       BBa_J435263  gccaattggtgcggcaattgataataacgaaaatgtcttttaatga...   \n",
      "4718       BBa_J428092  aaccaggcatcaaataaaacgaaaggctcagtcgaaagactgggcc...   \n",
      "4720       BBa_J435275  acagatgacgggagacactagcacacaactttaccaggcaaggtat...   \n",
      "4721       BBa_J435274  atgtgcttttgtttatttcctatttggttattcacacatattttct...   \n",
      "\n",
      "     PredictedType  \n",
      "0         Promoter  \n",
      "1         Promoter  \n",
      "2         Promoter  \n",
      "3         Promoter  \n",
      "4         Promoter  \n",
      "...            ...  \n",
      "4716      Promoter  \n",
      "4717      Promoter  \n",
      "4718      Promoter  \n",
      "4720      Promoter  \n",
      "4721      Promoter  \n",
      "\n",
      "[4420 rows x 3 columns]\n",
      "Non-Promoters\n",
      "312\n",
      "                  Name                                       DNA Sequence  \\\n",
      "4050  RDBECOLITMC00001              agcgtcaaaaggccggattttccggccttttttatta   \n",
      "4052  RDBECOLITMC00003  taccaccgtcaaaaaaaacggcgctttttagcgccgtttttatttt...   \n",
      "4063  RDBECOLITMC00014       gatctaactaaaaaggccgctctgcggccttttttcttttcact   \n",
      "4065  RDBECOLITMC00016               ttatataaagccccgatttatcggggttttttgtta   \n",
      "4066  RDBECOLITMC00017                  cagaataactgggctttaggccctttttttatg   \n",
      "...                ...                                                ...   \n",
      "4727        BBa_J04450  caatacgcaaaccgcctctccccgcgcgttggccgattcattaatg...   \n",
      "4728       BBa_J435281  gcaacgagaccggagtagtcttcgaaagtgaaacgtgatttcatgc...   \n",
      "4729       BBa_J428330  tgagaccggagttgacggctagctcagtcctaggtacagtgctagc...   \n",
      "4730       BBa_J435300  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...   \n",
      "4731       BBa_J435320  cgagaccattgacagcgcagctggcacgacaggtttcccgactgga...   \n",
      "\n",
      "     PredictedType  \n",
      "4050    Terminator  \n",
      "4052    Terminator  \n",
      "4063    Terminator  \n",
      "4065    Terminator  \n",
      "4066    Terminator  \n",
      "...            ...  \n",
      "4727           CDS  \n",
      "4728           CDS  \n",
      "4729           CDS  \n",
      "4730      Backbone  \n",
      "4731      Backbone  \n",
      "\n",
      "[312 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_nolabels = all_parts_df.drop('Element', axis=1)\n",
    "\n",
    "X_old = vectorizer.transform(training_nolabels['DNA Sequence']).toarray()\n",
    "\n",
    "# Use the trained model to predict the sequence types\n",
    "predictions_old = modelSVC.predict(X_old)\n",
    "\n",
    "# Add predictions to the new DataFrame\n",
    "training_nolabels['PredictedType'] = predictions_old\n",
    "\n",
    "# Filter to identify sequences predicted as promoters\n",
    "valid_promoters_old_df = training_nolabels[training_nolabels['PredictedType'] == 'Promoter']\n",
    "print(len(all_parts_df))\n",
    "print(len(valid_promoters_old_df))\n",
    "print(\"Valid Promoters\")\n",
    "print(valid_promoters_old_df)\n",
    "\n",
    "print(\"Non-Promoters\")\n",
    "# Filter to identify sequences predicted as non-promoters\n",
    "non_promoters_old_df = training_nolabels[training_nolabels['PredictedType'] != 'Promoter']\n",
    "print(len(non_promoters_old_df))\n",
    "print(non_promoters_old_df)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import LearningCurveDisplay, ShuffleSplit\n",
    "import numpy as np\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 6), sharey=True)\n",
    "\n",
    "common_params = {\n",
    "    \"X\": X,\n",
    "    \"y\": y,\n",
    "    \"train_sizes\": np.linspace(0.1, 1.0, 5),\n",
    "    \"cv\": ShuffleSplit(n_splits=50, test_size=0.2, random_state=0),\n",
    "    \"score_type\": \"both\",\n",
    "    \"n_jobs\": 4,\n",
    "    \"line_kw\": {\"marker\": \"o\"},\n",
    "    \"std_display_style\": \"fill_between\",\n",
    "    \"score_name\": \"Accuracy\",\n",
    "}\n",
    "\n",
    "for ax_idx, estimator in enumerate([modelGaussian, modelSVC]):\n",
    "    LearningCurveDisplay.from_estimator(estimator, **common_params, ax=ax[ax_idx])\n",
    "    handles, label = ax[ax_idx].get_legend_handles_labels()\n",
    "    ax[ax_idx].legend(handles[:2], [\"Training Score\", \"Test Score\"])\n",
    "    ax[ax_idx].set_title(f\"Learning Curve for {estimator.__class__.__name__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3176\n"
     ]
    }
   ],
   "source": [
    "igem_pro_file = open('./SBOL_Files/iGEM_Promoters_collection.xml').read()\n",
    "\n",
    "# Search for DNA sequences using regular expression\n",
    "igem_pro_dna_sequences = re.findall(r'<sbol:displayId>(.*?)_sequence</sbol:displayId>.*?<sbol:elements>(.*?)</sbol:elements>', igem_pro_file, re.DOTALL)\n",
    "\n",
    "# Create a pandas dataframe\n",
    "igem_pro_df = pd.DataFrame(igem_pro_dna_sequences, columns=['Name', 'DNA Sequence'])\n",
    "print(len(igem_pro_df))\n",
    "# Print the dataframe\n",
    "#print(igem_pro_df)\n",
    "\n",
    "# why is the first name of the df the name of the collection? \n",
    "# for now, will remove first row so that we have realistic names\n",
    "igem_pro_df = igem_pro_df.drop(igem_pro_df.index[0])\n",
    "\n",
    "# add in promoter label for these seqs\n",
    "igem_pro_df['Element'] = \"Promoter\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3175\n",
      "2045\n",
      "Valid Promoters\n",
      "              Name                                       DNA Sequence  \\\n",
      "1       BBa_J05209  taacaccgtgcgtgttgactattttacctctggcggtgataatggttgc   \n",
      "2      BBa_I732406  aattgtgagcgctcacaatttttacactttatgcttccggctcgta...   \n",
      "3     BBa_K1773016  acctgtaggatcgtacaggtttacgcaagaaaatggtttgttatag...   \n",
      "7      BBa_K079047  tatcaccgccagaggtatactagagtaacaccgtgcgtgttgtact...   \n",
      "9      BBa_I732352  aattgtgaacgttcacaatttacactttatgcttccggctcgtata...   \n",
      "...            ...                                                ...   \n",
      "3167   BBa_K239000  tcagtaaattaccgtcagattctcctgagtttccgctatgggaata...   \n",
      "3169  BBa_K1614000                                 taatacgactcactatag   \n",
      "3170   BBa_K242005  agtatatcaagtgctatacgcagcagtaccttcactgtctcgcagc...   \n",
      "3171     BBa_R2108  tttatcaaaaagagtgttgacatttttaagtcccacgcgtgggatt...   \n",
      "3173   BBa_K879037                      ttgatattgtgagcggataacaagatatc   \n",
      "\n",
      "     PredictedType  \n",
      "1         Promoter  \n",
      "2         Promoter  \n",
      "3         Promoter  \n",
      "7         Promoter  \n",
      "9         Promoter  \n",
      "...            ...  \n",
      "3167      Promoter  \n",
      "3169      Promoter  \n",
      "3170      Promoter  \n",
      "3171      Promoter  \n",
      "3173      Promoter  \n",
      "\n",
      "[2045 rows x 3 columns]\n",
      "Non-Promoters\n",
      "1130\n",
      "              Name                                       DNA Sequence  \\\n",
      "4     BBa_K2117000  agagaccgggttggcggcgtatttgtgtcccaaaaaacagccccaa...   \n",
      "5     BBa_K1444002  ggagttctgagaattggtatgccttataagtccaattaacagttga...   \n",
      "6      BBa_I756014  gagacatatgtcgactgctgtatataaaaccagtggttatatgtac...   \n",
      "8        BBa_B1002                 cgcaaaaaaccccgcttcggcggggttttttcgc   \n",
      "10    BBa_K2116044  acgtagcaacgcagacgcatcgcactccagcacctgacgtagcgtg...   \n",
      "...            ...                                                ...   \n",
      "3161     BBa_S0100  aaagaggagaaatactagatggtgaatgtgaaaccagtaacgttat...   \n",
      "3168   BBa_K631035  atgccgactgtgcaatggattcagctggaagagacttccaaaataa...   \n",
      "3172  BBa_K1444025  ggagttctgagaattggtatgccttataagtccaattaacagttga...   \n",
      "3174    BBa_I15008  atgagtgtcaacttagcttcccagttgcgggaagggacgaaaaaat...   \n",
      "3175  BBa_K1387002  atgtccagattagataaaagtaaagtgattaacagcgcattagagc...   \n",
      "\n",
      "     PredictedType  \n",
      "4              CDS  \n",
      "5              CDS  \n",
      "6              CDS  \n",
      "8       Terminator  \n",
      "10             CDS  \n",
      "...            ...  \n",
      "3161           CDS  \n",
      "3168           CDS  \n",
      "3172           CDS  \n",
      "3174           CDS  \n",
      "3175           CDS  \n",
      "\n",
      "[1130 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "igem_pro_nolabels = igem_pro_df.drop('Element', axis=1)\n",
    "igem_pro_nolabels = igem_pro_nolabels.dropna(subset=['DNA Sequence'])\n",
    "\n",
    "\n",
    "X_old = vectorizer.transform(igem_pro_nolabels['DNA Sequence']).toarray()\n",
    "\n",
    "# Use the trained model to predict the sequence types\n",
    "predictions_old = modelSVC.predict(X_old)\n",
    "\n",
    "# Add predictions to the new DataFrame\n",
    "igem_pro_nolabels['PredictedType'] = predictions_old\n",
    "\n",
    "# Filter to identify sequences predicted as promoters\n",
    "valid_igem_promoters_df = igem_pro_nolabels[igem_pro_nolabels['PredictedType'] == 'Promoter']\n",
    "print(len(igem_pro_df))\n",
    "print(len(valid_igem_promoters_df))\n",
    "print(\"Valid Promoters\")\n",
    "print(valid_igem_promoters_df)\n",
    "\n",
    "print(\"Non-Promoters\")\n",
    "# Filter to identify sequences predicted as non-promoters\n",
    "non_promoters_igem_df = igem_pro_nolabels[igem_pro_nolabels['PredictedType'] != 'Promoter']\n",
    "print(len(non_promoters_igem_df))\n",
    "print(non_promoters_igem_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported DataFrame as CSV: ./csv/igem_non_promoters.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "output_dir = \"./csv\"\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)\n",
    "\n",
    "file_path = os.path.join(output_dir, \"igem_non_promoters.csv\")\n",
    "non_promoters_igem_df.to_csv(file_path, index=False)\n",
    "print(f\"Exported DataFrame as CSV: {file_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nested CV Accuracy: 0.93 +/- 0.00\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, KFold\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "all_parts_df\n",
    "\n",
    "\n",
    "# Ask the user for the size of the k-mers\n",
    "k_mer_size = int(input(\"Enter the size of the k-mers: \"))\n",
    "\n",
    "# Use CountVectorizer to initialize k-mer splitting \n",
    "vectorizer = CountVectorizer(analyzer='char', ngram_range=(k_mer_size, k_mer_size))\n",
    "\n",
    "# Setup the SVM classifier\n",
    "svm_model = SVC(kernel='linear')\n",
    "\n",
    "# Create a pipeline that incorporates the vectorizer and SVM\n",
    "pipeline = Pipeline([\n",
    "    ('vectorizer', vectorizer),\n",
    "    ('classifier', svm_model)\n",
    "])\n",
    "\n",
    "# Parameters for Grid Search\n",
    "param_grid = {'classifier__C': [0.1, 1, 10, 100], 'classifier__kernel': ['linear', 'rbf']}\n",
    "\n",
    "# Setup nested cross-validation\n",
    "inner_cv = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "# Inner CV - hyperparameter tuning\n",
    "clf = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=inner_cv)\n",
    "\n",
    "# Outer CV - model evaluation\n",
    "nested_score = cross_val_score(clf, X=all_parts_df['DNA Sequence'], y=all_parts_df['Element'], cv=outer_cv)\n",
    "\n",
    "print(\"Nested CV Accuracy: {:.2f} +/- {:.2f}\".format(nested_score.mean(), nested_score.std()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4732\n",
      "Index(['Name', 'DNA Sequence', 'Element'], dtype='object')\n",
      "Best parameters and accuracy for each fold:\n",
      "Fold 1: Best Params: {'classifier__C': 10, 'classifier__kernel': 'rbf'}, Accuracy: 0.9198\n",
      "Fold 2: Best Params: {'classifier__C': 10, 'classifier__kernel': 'rbf'}, Accuracy: 0.9219\n",
      "Fold 3: Best Params: {'classifier__C': 10, 'classifier__kernel': 'rbf'}, Accuracy: 0.9302\n",
      "Fold 4: Best Params: {'classifier__C': 10, 'classifier__kernel': 'rbf'}, Accuracy: 0.9281\n",
      "Fold 5: Best Params: {'classifier__C': 10, 'classifier__kernel': 'rbf'}, Accuracy: 0.9175\n",
      "Fold 6: Best Params: {'classifier__C': 10, 'classifier__kernel': 'rbf'}, Accuracy: 0.9429\n",
      "Fold 7: Best Params: {'classifier__C': 10, 'classifier__kernel': 'rbf'}, Accuracy: 0.9535\n",
      "Fold 8: Best Params: {'classifier__C': 10, 'classifier__kernel': 'rbf'}, Accuracy: 0.9345\n",
      "Fold 9: Best Params: {'classifier__C': 10, 'classifier__kernel': 'rbf'}, Accuracy: 0.9239\n",
      "Fold 10: Best Params: {'classifier__C': 10, 'classifier__kernel': 'rbf'}, Accuracy: 0.9345\n",
      "Most common best parameters across all folds: {'classifier__C': 10, 'classifier__kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import GridSearchCV, KFold\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import accuracy_score\n",
    "from collections import Counter\n",
    "\n",
    "all_parts_df\n",
    "print(all_parts_df.shape[0])\n",
    "print(all_parts_df.columns)\n",
    "# User input for k-mer size\n",
    "k_mer_size = int(input(\"Enter the size of the k-mers: \"))\n",
    "\n",
    "# Setup the CountVectorizer for k-mer splitting\n",
    "vectorizer = CountVectorizer(analyzer='char', ngram_range=(k_mer_size, k_mer_size))\n",
    "\n",
    "# Setup the SVM classifier\n",
    "svm_model = SVC(kernel='linear')\n",
    "\n",
    "# Create a pipeline with the vectorizer and SVM\n",
    "pipeline = Pipeline([\n",
    "    ('vectorizer', vectorizer),\n",
    "    ('classifier', svm_model)\n",
    "])\n",
    "\n",
    "# Parameters for Grid Search\n",
    "param_grid = {'classifier__C': [.1, 1, 10, 100], 'classifier__kernel': ['linear', 'rbf']}\n",
    "\n",
    "# Setup cross-validation\n",
    "outer_cv = KFold(n_splits=10, shuffle=True, random_state=42)\n",
    "\n",
    "# Lists to store results\n",
    "best_params = []\n",
    "accuracy_scores = []\n",
    "\n",
    "for train_idx, test_idx in outer_cv.split(all_parts_df):\n",
    "    X_train, X_test = all_parts_df.iloc[train_idx]['DNA Sequence'], all_parts_df.iloc[test_idx]['DNA Sequence']\n",
    "    y_train, y_test = all_parts_df.iloc[train_idx]['Element'], all_parts_df.iloc[test_idx]['Element']\n",
    "    \n",
    "    # Inner CV - hyperparameter tuning\n",
    "    clf = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=5)  # Inner cross-validation\n",
    "    clf.fit(X_train, y_train)\n",
    "    \n",
    "    # Storing best parameters\n",
    "    best_params.append(clf.best_params_)\n",
    "    \n",
    "    # Evaluate the classifier on the test set using the best found parameters\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracy_scores.append(accuracy)\n",
    "\n",
    "# Print the best parameters and accuracy for each outer fold\n",
    "print(\"Best parameters and accuracy for each fold:\")\n",
    "for idx, (params, acc) in enumerate(zip(best_params, accuracy_scores)):\n",
    "    print(f\"Fold {idx+1}: Best Params: {params}, Accuracy: {acc:.4f}\")\n",
    "\n",
    "# Determine the most common best parameters\n",
    "param_counts = Counter([frozenset(param.items()) for param in best_params])\n",
    "most_common_params = param_counts.most_common(1)[0][0]\n",
    "optimal_params = {k: v for k, v in most_common_params}\n",
    "\n",
    "# Print the most common best parameters\n",
    "print(\"Most common best parameters across all folds:\", optimal_params)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2949\n",
      "2017\n",
      "Valid Promoters\n",
      "              Name                                       DNA Sequence  \\\n",
      "3       BBa_I11000                            taatacgactcactatagggaga   \n",
      "8       BBa_I12032  gcaaccattatcaccgccagaggtaaaatagtcaacacgcacggtg...   \n",
      "22      BBa_I20530                            atttactggacactatagaagag   \n",
      "23       BBa_I7104               taatacgactcactatagggagatactagaggtgtg   \n",
      "24     BBa_I712004  cgatgtacgggccagatatacgcgttgacattgattattgcctagt...   \n",
      "...            ...                                                ...   \n",
      "2944  BBa_K1620001  taagcgttttacatccgatgggcgcagtgaataatcatgttctgaa...   \n",
      "2945     BBa_R2000      agtttattcttgacatggtcccacgcgcgtgggatactacgtcag   \n",
      "2946     BBa_R2001  agtttattcttgacatggtcatattacggtgagatactcccacgcg...   \n",
      "2947     BBa_R2002  agtttattcttgacatggtcccacgcgcgtgggatactcccacgcg...   \n",
      "2948   BBa_K896008  tgctctcgtttcctaagagttgttgcattttgctatatgttacaat...   \n",
      "\n",
      "     PredictedType  \n",
      "3         Promoter  \n",
      "8         Promoter  \n",
      "22        Promoter  \n",
      "23        Promoter  \n",
      "24        Promoter  \n",
      "...            ...  \n",
      "2944      Promoter  \n",
      "2945      Promoter  \n",
      "2946      Promoter  \n",
      "2947      Promoter  \n",
      "2948      Promoter  \n",
      "\n",
      "[2017 rows x 3 columns]\n",
      "Non-Promoters\n",
      "932\n",
      "              Name                                       DNA Sequence  \\\n",
      "0      BBa_K976003  atggacaaattagtcgtgaattattatgaatacaagcaccctataa...   \n",
      "1     BBa_K1088017  tttacggctagctcagtcctaggtatagtgctagcattaaagagga...   \n",
      "2      BBa_K774000  ttcccatctataatcctccctgattcttcgctgatatggtgctaaa...   \n",
      "4       BBa_I11002  aaagaggagaaatactagatgagcacaaaaaagaaaccattaacac...   \n",
      "5       BBa_I12008  tataaacgcagaaaggcccacccgaaggtgagccagtgtgactcta...   \n",
      "...            ...                                                ...   \n",
      "2880  BBa_K1154005  attacccataaggttgtttgtgacggcgtcgtacaagagaacgtgg...   \n",
      "2883  BBa_K2135002  ttaagacccactttcacatttaagttgtttttctaatccgcatatg...   \n",
      "2935   BBa_J176003  aaactggcctccgcgccgggcttttggcgcctcccgcgggcgcccc...   \n",
      "2936   BBa_K809003  aatatataatattaaataataagtattgatataagtaatagatata...   \n",
      "2937   BBa_K389017  tttacggctagctcagtcctaggtacaatgctagctactagagaaa...   \n",
      "\n",
      "     PredictedType  \n",
      "0              CDS  \n",
      "1              CDS  \n",
      "2              CDS  \n",
      "4              CDS  \n",
      "5              CDS  \n",
      "...            ...  \n",
      "2880           CDS  \n",
      "2883           CDS  \n",
      "2935           CDS  \n",
      "2936           CDS  \n",
      "2937           CDS  \n",
      "\n",
      "[932 rows x 3 columns]\n",
      "              Name PredictedType\n",
      "0      BBa_K976003           CDS\n",
      "1     BBa_K1088017           CDS\n",
      "2      BBa_K774000           CDS\n",
      "4       BBa_I11002           CDS\n",
      "5       BBa_I12008           CDS\n",
      "...            ...           ...\n",
      "2880  BBa_K1154005           CDS\n",
      "2883  BBa_K2135002           CDS\n",
      "2935   BBa_J176003           CDS\n",
      "2936   BBa_K809003           CDS\n",
      "2937   BBa_K389017           CDS\n",
      "\n",
      "[932 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Assuming best_params is a list of dictionaries obtained from GridSearchCV\n",
    "from collections import Counter\n",
    "\n",
    "# Calculate the most common best parameters\n",
    "param_counts = Counter(tuple(sorted(d.items())) for d in best_params)\n",
    "most_common_params, _ = param_counts.most_common(1)[0]\n",
    "\n",
    "# Convert to dictionary and remove 'classifier__' prefix from keys\n",
    "optimal_params = {k.split('__')[1]: v for k, v in most_common_params}\n",
    "\n",
    "# Now you can create your final model with these parameters\n",
    "final_model = Pipeline([\n",
    "    ('vectorizer', CountVectorizer(analyzer='char', ngram_range=(k_mer_size, k_mer_size))),\n",
    "    ('classifier', SVC(C=optimal_params['C'], kernel=optimal_params['kernel']))\n",
    "])\n",
    "\n",
    "# Train the final model with the full dataset\n",
    "final_model.fit(all_parts_df['DNA Sequence'], all_parts_df['Element'])\n",
    "\n",
    "\n",
    "# Now the final model is ready to make predictions on new data\n",
    "all_promoters_good = pd.read_csv('./csv/All_Promoters_Part_Sequences.csv')\n",
    "\n",
    "X_new = all_promoters_good['DNA Sequence']\n",
    "\n",
    "# Use the trained model to predict the sequence types\n",
    "predictions_new = final_model.predict(X_new)\n",
    "\n",
    "\n",
    "# Add predictions to the new DataFrame\n",
    "all_promoters_good['PredictedType'] = predictions_new\n",
    "\n",
    "# Filter to identify sequences predicted as promoters\n",
    "valid_promoters_df = all_promoters_good[all_promoters_good['PredictedType'] == 'Promoter']\n",
    "print(len(all_promoters_good))\n",
    "print(len(valid_promoters_df))\n",
    "print(\"Valid Promoters\")\n",
    "print(valid_promoters_df)\n",
    "valid_promoters_df.to_csv(\"./csv/valid_promoters.csv\", index = False)\n",
    "\n",
    "\n",
    "print(\"Non-Promoters\")\n",
    "# Filter to identify sequences predicted as non-promoters\n",
    "non_promoters_df = all_promoters_good[all_promoters_good['PredictedType'] != 'Promoter']\n",
    "print(len(non_promoters_df))\n",
    "print(non_promoters_df)\n",
    "print(non_promoters_df[['Name', 'PredictedType']])\n",
    "non_promoters_df.to_csv(\"./csv/non_promoters.csv\", index = False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import learning_curve, KFold\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "all_parts_df\n",
    "\n",
    "# Set up the CountVectorizer for k-mer splitting\n",
    "k_mer_size = 5  # Set k-mer size\n",
    "vectorizer = CountVectorizer(analyzer='char', ngram_range=(k_mer_size, k_mer_size))\n",
    "\n",
    "# Set up the SVM classifier\n",
    "svm_model = SVC(kernel='linear')\n",
    "\n",
    "# Create a pipeline with the vectorizer and SVM\n",
    "pipeline = Pipeline([\n",
    "    ('vectorizer', vectorizer),\n",
    "    ('classifier', svm_model)\n",
    "])\n",
    "\n",
    "# Prepare training data\n",
    "X, y = all_parts_df['DNA Sequence'], all_parts_df['Element']\n",
    "\n",
    "# Generate learning curve data\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    estimator=pipeline,\n",
    "    X=X,\n",
    "    y=y,\n",
    "    train_sizes=np.linspace(.1, 1, 100),\n",
    "    cv=KFold(n_splits=5, shuffle=True, random_state=42),\n",
    "    scoring='accuracy',\n",
    "    n_jobs=-1  # Use all processors\n",
    ")\n",
    "\n",
    "# Calculate mean and standard deviation for train and test scores\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)\n",
    "\n",
    "# Plot the learning curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, train_mean, label='Train score', color='blue', marker='o')\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color='blue', alpha=0.15)\n",
    "\n",
    "plt.plot(train_sizes, test_mean, label='Cross-validation score', color='green', marker='o')\n",
    "plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color='green', alpha=0.15)\n",
    "\n",
    "plt.title('Learning Curve for SVM Classifier')\n",
    "plt.xlabel('Training Data Size')\n",
    "plt.ylabel('Accuracy Score')\n",
    "plt.legend(loc='best')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "The model showed the importance of curating existing databases, especially the iGEM student entries in order to ensure they can be used by scientists without worrying about whether the parts are correctly labeled or not.\n",
    "\n",
    "Tools such as PUMA can help in this regard, ensuring that easily accessible data is also trust worthy. In its current form, PUMA shows impressive results when identifying what is or is not a promoter, but it is not yet able to correctly identify other parts due to the lack of training data for other parts. That opens a door for further development in identification of other parts and even composite sequences."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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