DNA Classification Code (Jupyter Notebook).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.8.10 (default, Nov 22 2023, 10:22:35) \n",
      "[GCC 9.4.0]\n",
      "Numpy: 1.24.4\n",
      "Sklearn: 1.3.2\n",
      "Pandas: 2.0.3\n"
     ]
    }
   ],
   "source": [
    "# To make sure all of the correct libraries are installed, import each module and print the version number\n",
    "\n",
    "import sys\n",
    "import numpy\n",
    "import sklearn\n",
    "import pandas\n",
    "\n",
    "print('Python: {}'.format(sys.version))\n",
    "print('Numpy: {}'.format(numpy.__version__))\n",
    "print('Sklearn: {}'.format(sklearn.__version__))\n",
    "print('Pandas: {}'.format(pandas.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import, change module names\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# import the uci Molecular Biology (Promoter Gene Sequences) Data Set\n",
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/molecular-biology/promoter-gene-sequences/promoters.data'\n",
    "names = ['Class', 'id', 'Sequence']\n",
    "data = pd.read_csv(url, names = names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class                                                       +\n",
      "id                                                        S10\n",
      "Sequence    \\t\\ttactagcaatacgcttgcgttcggtggttaagtatgtataat...\n",
      "Name: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(data.iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Preprocessing the Dataset\n",
    "\n",
    "The data is not in a usable form; as a result, we will need to process it before using it to train our algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    +\n",
      "1    +\n",
      "2    +\n",
      "3    +\n",
      "4    +\n",
      "Name: Class, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Building our Dataset by creating a custom Pandas DataFrame\n",
    "# Each column in a DataFrame is called a Series. Lets start by making a series for each column.\n",
    "\n",
    "classes = data.loc[:, 'Class']\n",
    "print(classes[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['t', 'a', 'c', 't', 'a', 'g', 'c', 'a', 'a', 't', 'a', 'c', 'g', 'c', 't', 't', 'g', 'c', 'g', 't', 't', 'c', 'g', 'g', 't', 'g', 'g', 't', 't', 'a', 'a', 'g', 't', 'a', 't', 'g', 't', 'a', 't', 'a', 'a', 't', 'g', 'c', 'g', 'c', 'g', 'g', 'g', 'c', 't', 't', 'g', 't', 'c', 'g', 't', '+']\n"
     ]
    }
   ],
   "source": [
    "# generate list of DNA sequences\n",
    "sequences = list(data.loc[:, 'Sequence'])\n",
    "dataset = {}\n",
    "\n",
    "# loop through sequences and split into individual nucleotides\n",
    "for i, seq in enumerate(sequences):\n",
    "    \n",
    "    # split into nucleotides, remove tab characters\n",
    "    nucleotides = list(seq)\n",
    "    nucleotides = [x for x in nucleotides if x != '\\t']\n",
    "    \n",
    "    # append class assignment\n",
    "    nucleotides.append(classes[i])\n",
    "    \n",
    "    # add to dataset\n",
    "    dataset[i] = nucleotides\n",
    "    \n",
    "print(dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
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      "[58 rows x 106 columns]\n"
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   "source": [
    "# turn dataset into pandas DataFrame\n",
    "dframe = pd.DataFrame(dataset)\n",
    "print(dframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0  1  2  3  4  5  6  7  8  9   ... 48 49 50 51 52 53 54 55 56 57\n",
      "0  t  a  c  t  a  g  c  a  a  t  ...  g  c  t  t  g  t  c  g  t  +\n",
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      "2  g  t  a  c  t  a  g  a  g  a  ...  c  a  c  c  c  g  g  c  g  +\n",
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      "4  t  c  g  a  t  a  a  t  t  a  ...  c  c  g  t  g  g  t  a  g  +\n",
      "\n",
      "[5 rows x 58 columns]\n"
     ]
    }
   ],
   "source": [
    "# transpose the DataFrame\n",
    "df = dframe.transpose()\n",
    "print(df.iloc[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1  2  3  4  5  6  7  8  9  ... 48 49 50 51 52 53 54 55 56 Class\n",
      "0  t  a  c  t  a  g  c  a  a  t  ...  g  c  t  t  g  t  c  g  t     +\n",
      "1  t  g  c  t  a  t  c  c  t  g  ...  c  a  t  c  g  c  c  a  a     +\n",
      "2  g  t  a  c  t  a  g  a  g  a  ...  c  a  c  c  c  g  g  c  g     +\n",
      "3  a  a  t  t  g  t  g  a  t  g  ...  a  a  c  a  a  a  c  t  c     +\n",
      "4  t  c  g  a  t  a  a  t  t  a  ...  c  c  g  t  g  g  t  a  g     +\n",
      "\n",
      "[5 rows x 58 columns]\n"
     ]
    }
   ],
   "source": [
    "# for clarity, lets rename the last dataframe column to class\n",
    "df.rename(columns = {57: 'Class'}, inplace = True) \n",
    "print(df.iloc[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
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       "          0    1    2    3    4    5    6    7    8    9  ...   48   49   50  \\\n",
       "count   106  106  106  106  106  106  106  106  106  106  ...  106  106  106   \n",
       "unique    4    4    4    4    4    4    4    4    4    4  ...    4    4    4   \n",
       "top       t    a    a    c    a    a    a    a    a    a  ...    c    c    c   \n",
       "freq     38   34   30   30   36   42   38   34   33   36  ...   36   42   31   \n",
       "\n",
       "         51   52   53   54   55   56 Class  \n",
       "count   106  106  106  106  106  106   106  \n",
       "unique    4    4    4    4    4    4     2  \n",
       "top       t    t    c    c    c    t     +  \n",
       "freq     33   35   32   29   29   34    53  \n",
       "\n",
       "[4 rows x 58 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looks good! Let's start to familiarize ourselves with the dataset so we can pick the most suitable \n",
    "# algorithms for this data\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   count  count  count  count  count  count  count  count  count  count  ...  \\\n",
      "t   38.0   26.0   27.0   26.0   22.0   24.0   30.0   32.0   32.0   28.0  ...   \n",
      "c   27.0   22.0   21.0   30.0   19.0   18.0   21.0   20.0   22.0   22.0  ...   \n",
      "a   26.0   34.0   30.0   22.0   36.0   42.0   38.0   34.0   33.0   36.0  ...   \n",
      "g   15.0   24.0   28.0   28.0   29.0   22.0   17.0   20.0   19.0   20.0  ...   \n",
      "+    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN  ...   \n",
      "-    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN  ...   \n",
      "\n",
      "   count  count  count  count  count  count  count  count  count  count  \n",
      "t   21.0   22.0   23.0   33.0   35.0   30.0   23.0   29.0   34.0    NaN  \n",
      "c   36.0   42.0   31.0   32.0   21.0   32.0   29.0   29.0   17.0    NaN  \n",
      "a   23.0   24.0   28.0   27.0   25.0   22.0   26.0   24.0   27.0    NaN  \n",
      "g   26.0   18.0   24.0   14.0   25.0   22.0   28.0   24.0   28.0    NaN  \n",
      "+    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   53.0  \n",
      "-    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   53.0  \n",
      "\n",
      "[6 rows x 58 columns]\n"
     ]
    }
   ],
   "source": [
    "# desribe does not tell us enough information since the attributes are text. Lets record value counts for each sequence\n",
    "series = []\n",
    "for name in df.columns:\n",
    "    series.append(df[name].value_counts())\n",
    "    \n",
    "info = pd.DataFrame(series)\n",
    "details = info.transpose()\n",
    "print(details)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unfortunately, we can't run machine learning algorithms on the data in 'String' formats. As a result, we need to switch\n",
    "# it to numerical data. This can easily be accomplished using the pd.get_dummies() function\n",
    "numerical_df = pd.get_dummies(df)\n",
    "numerical_df.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0_a    0_c    0_g    0_t    1_a    1_c    1_g    1_t    2_a    2_c  ...  \\\n",
      "0  False  False  False   True   True  False  False  False  False   True  ...   \n",
      "1  False  False  False   True  False  False   True  False  False   True  ...   \n",
      "2  False  False   True  False  False  False  False   True   True  False  ...   \n",
      "3   True  False  False  False   True  False  False  False  False  False  ...   \n",
      "4  False  False  False   True  False   True  False  False  False  False  ...   \n",
      "\n",
      "    54_t   55_a   55_c   55_g   55_t   56_a   56_c   56_g   56_t  Class  \n",
      "0  False  False  False   True  False  False  False  False   True   True  \n",
      "1  False   True  False  False  False   True  False  False  False   True  \n",
      "2  False  False   True  False  False  False  False   True  False   True  \n",
      "3  False  False  False  False   True  False   True  False  False   True  \n",
      "4   True   True  False  False  False  False  False   True  False   True  \n",
      "\n",
      "[5 rows x 229 columns]\n"
     ]
    }
   ],
   "source": [
    "# We don't need both class columns.  Lets drop one then rename the other to simply 'Class'.\n",
    "df = numerical_df.drop(columns=['Class_-'])\n",
    "\n",
    "df.rename(columns = {'Class_+': 'Class'}, inplace = True)\n",
    "print(df.iloc[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the model_selection module to separate training and testing datasets\n",
    "from sklearn import model_selection\n",
    "\n",
    "# Create X and Y datasets for training\n",
    "X = np.array(df.drop(['Class'], axis=1))\n",
    "y = np.array(df['Class'])\n",
    "\n",
    "# define seed for reproducibility\n",
    "seed = 1\n",
    "\n",
    "# split data into training and testing datasets\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25, random_state=seed)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Training and Testing the Classification Algorithms\n",
    "\n",
    "Now that we have preprocessed the data and built our training and testing datasets, we can start to deploy different classification algorithms. It's relatively easy to test multiple models; as a result, we will compare and contrast the performance of ten different algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nearest Neighbors: 0.837500 (0.125623)\n",
      "Gaussian Process: 0.873214 (0.056158)\n",
      "Decision Tree: 0.748214 (0.166455)\n",
      "Random Forest: 0.632143 (0.143347)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aditya/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/aditya/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/aditya/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/aditya/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/aditya/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/aditya/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/aditya/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/aditya/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/aditya/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/aditya/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net: 0.875000 (0.096825)\n",
      "AdaBoost: 0.912500 (0.112500)\n",
      "Naive Bayes: 0.837500 (0.137500)\n",
      "SVM Linear: 0.850000 (0.108972)\n",
      "SVM RBF: 0.887500 (0.067315)\n",
      "SVM Sigmoid: 0.900000 (0.093541)\n"
     ]
    }
   ],
   "source": [
    "# Now that we have our dataset, we can start building algorithms! We'll need to import each algorithm we plan on using\n",
    "# from sklearn.  We also need to import some performance metrics, such as accuracy_score and classification_report.\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "# define scoring method\n",
    "scoring = 'accuracy'\n",
    "\n",
    "# Define models to train\n",
    "names = [\"Nearest Neighbors\", \"Gaussian Process\",\n",
    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
    "         \"Naive Bayes\", \"SVM Linear\", \"SVM RBF\", \"SVM Sigmoid\"]\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(n_neighbors = 3),\n",
    "    GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
    "    DecisionTreeClassifier(max_depth=5),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "    MLPClassifier(alpha=1),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB(),\n",
    "    SVC(kernel = 'linear'), \n",
    "    SVC(kernel = 'rbf'),\n",
    "    SVC(kernel = 'sigmoid')\n",
    "]\n",
    "\n",
    "models = zip(names, classifiers)\n",
    "\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "for name, model in models:\n",
    "    kfold = model_selection.KFold(n_splits=10)\n",
    "    cv_results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  }
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