DNA Sequencing and applying Classifier_ML PROJECT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DNA Classification uing Classifiers\n",
    "\n",
    "In this notebook, we will use different classification models that can predict a gene's function based on the DNA sequence of the coding sequence alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sequence</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ATGCCCCAACTAAATACTACCGTATGGCCCACCATAATTACCCCCA...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ATGAACGAAAATCTGTTCGCTTCATTCATTGCCCCCACAATCCTAG...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATGCAACAGCATTTTGAATTTGAATACCAGACCAAAGTGGATGGTG...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            sequence  class\n",
       "0  ATGCCCCAACTAAATACTACCGTATGGCCCACCATAATTACCCCCA...      4\n",
       "1  ATGAACGAAAATCTGTTCGCTTCATTCATTGCCCCCACAATCCTAG...      4\n",
       "2  ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...      3\n",
       "3  ATGTGTGGCATTTGGGCGCTGTTTGGCAGTGATGATTGCCTTTCTG...      3\n",
       "4  ATGCAACAGCATTTTGAATTTGAATACCAGACCAAAGTGGATGGTG...      3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "human_data = pd.read_table('human_data.txt')\n",
    "human_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We have some data for human DNA sequence coding regions and a class label.  We also have data for Chimpanzee and a more divergent species, the dog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sequence</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ATGCCACAGCTAGATACATCCACCTGATTTATTATAATCTTTTCAA...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ATGAACGAAAATCTATTCGCTTCTTTCGCTGCCCCCTCAATAATAG...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATGGAAACACCCTTCTACGGCGATGAGGCGCTGAGCGGCCTGGGCG...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ATGTGCACTAAAATGGAACAGCCCTTCTACCACGACGACTCATACG...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATGAGCCGGCAGCTAAACAGAAGCCAGAACTGCTCCTTCAGTGACG...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            sequence  class\n",
       "0  ATGCCACAGCTAGATACATCCACCTGATTTATTATAATCTTTTCAA...      4\n",
       "1  ATGAACGAAAATCTATTCGCTTCTTTCGCTGCCCCCTCAATAATAG...      4\n",
       "2  ATGGAAACACCCTTCTACGGCGATGAGGCGCTGAGCGGCCTGGGCG...      6\n",
       "3  ATGTGCACTAAAATGGAACAGCCCTTCTACCACGACGACTCATACG...      6\n",
       "4  ATGAGCCGGCAGCTAAACAGAAGCCAGAACTGCTCCTTCAGTGACG...      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chimp_data = pd.read_table('chimp_data.txt')\n",
    "dog_data = pd.read_table('dog_data.txt')\n",
    "chimp_data.head()\n",
    "dog_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here are the definitions for each of the 7 classes and how many there are in the human training data.  They are gene sequence function groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"Capture1.PNG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to k-mers\n",
    "\n",
    "A challenge for us is to convert these DNA sequences to vectors of uniform length since it is a requirement for feeding data to a classification or regression algorithm. So we need to resort to things like truncating sequences or padding with \"n\" or \"0\" to get vectors of uniform length.\n",
    "\n",
    "The method we use here is simple and easy. We first take the long biological sequence and break it down into k-mer length overlapping “words”. For example, if I use \"words\" of length 6 (hexamers), “ATGCATGCA” becomes: ‘ATGCAT’, ‘TGCATG’, ‘GCATGC’, ‘CATGCA’. Hence our example sequence is broken down into 4 hexamer words.\n",
    "\n",
    "Here we are using hexamer “words” but that is arbitrary and word length can be tuned to suit the particular situation. The word length and amount of overlap need to be determined empirically for any given application.\n",
    "\n",
    "In genomics, we refer to these types of manipulations as \"k-mer counting\", or counting the occurances of each possible k-mer sequence. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's define a function to collect all possible overlapping k-mers of a specified length from any sequence string. We will basically apply the k-mers to the complete sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to convert sequence strings into k-mer words, default size = 6 (hexamer words)\n",
    "def getKmers(sequence, size=6):\n",
    "    return [sequence[x:x+size].lower() for x in range(len(sequence) - size + 1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we can convert our training data sequences into short overlapping  k-mers of legth 6.  Lets do that for each species of data we have using our getKmers function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "human_data['words'] = human_data.apply(lambda x: getKmers(x['sequence']), axis=1)\n",
    "human_data = human_data.drop('sequence', axis=1)\n",
    "chimp_data['words'] = chimp_data.apply(lambda x: getKmers(x['sequence']), axis=1)\n",
    "chimp_data = chimp_data.drop('sequence', axis=1)\n",
    "dog_data['words'] = dog_data.apply(lambda x: getKmers(x['sequence']), axis=1)\n",
    "dog_data = dog_data.drop('sequence', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, our coding sequence data is changed to lowercase, split up into all possible k-mer words of length 6 and ready for the next step.  Let's take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[atgccc, tgcccc, gcccca, ccccaa, cccaac, ccaac...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>[atgaac, tgaacg, gaacga, aacgaa, acgaaa, cgaaa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>[atgtgt, tgtgtg, gtgtgg, tgtggc, gtggca, tggca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>[atgtgt, tgtgtg, gtgtgg, tgtggc, gtggca, tggca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>[atgcaa, tgcaac, gcaaca, caacag, aacagc, acagc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class                                              words\n",
       "0      4  [atgccc, tgcccc, gcccca, ccccaa, cccaac, ccaac...\n",
       "1      4  [atgaac, tgaacg, gaacga, aacgaa, acgaaa, cgaaa...\n",
       "2      3  [atgtgt, tgtgtg, gtgtgg, tgtggc, gtggca, tggca...\n",
       "3      3  [atgtgt, tgtgtg, gtgtgg, tgtggc, gtggca, tggca...\n",
       "4      3  [atgcaa, tgcaac, gcaaca, caacag, aacagc, acagc..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "human_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since we are going to use scikit-learn natural language processing tools to do the k-mer counting, we need to now convert the lists of k-mers for each gene into string sentences of words that the count vectorizer can use.  We can also make a y variable to hold the class labels.  Let's do that now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "human_texts = list(human_data['words'])\n",
    "for item in range(len(human_texts)):\n",
    "    human_texts[item] = ' '.join(human_texts[item])\n",
    "y_data = human_data.iloc[:, 0].values                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "atgccc tgcccc gcccca ccccaa cccaac ccaact caacta aactaa actaaa ctaaat taaata aaatac aatact atacta tactac actacc ctaccg taccgt accgta ccgtat cgtatg gtatgg tatggc atggcc tggccc ggccca gcccac cccacc ccacca caccat accata ccataa cataat ataatt taatta aattac attacc ttaccc tacccc accccc ccccca ccccat cccata ccatac catact atactc tactcc actcct ctcctt tcctta ccttac cttaca ttacac tacact acacta cactat actatt ctattc tattcc attcct ttcctc tcctca cctcat ctcatc tcatca catcac atcacc tcaccc caccca acccaa cccaac ccaact caacta aactaa actaaa ctaaaa taaaaa aaaaat aaaata aaatat aatatt atatta tattaa attaaa ttaaac taaaca aaacac aacaca acacaa cacaaa acaaac caaact aaacta aactac actacc ctacca taccac accacc ccacct caccta acctac cctacc ctacct tacctc acctcc cctccc ctccct tccctc ccctca cctcac ctcacc tcacca caccaa accaaa ccaaag caaagc aaagcc aagccc agccca gcccat cccata ccataa cataaa ataaaa taaaaa aaaaat aaaata aaataa aataaa ataaaa taaaaa aaaaaa aaaaat aaaatt aaatta aattat attata ttataa tataac ataaca taacaa aacaaa acaaac caaacc aaaccc aaccct accctg ccctga cctgag ctgaga tgagaa gagaac agaacc gaacca aaccaa accaaa ccaaaa caaaat aaaatg aaatga aatgaa atgaac tgaacg gaacga aacgaa acgaaa cgaaaa gaaaat aaaatc aaatct aatctg atctgt tctgtt ctgttc tgttcg gttcgc ttcgct tcgctt cgcttc gcttca cttcat ttcatt tcattc cattca attcat ttcatt tcattg cattgc attgcc ttgccc tgcccc gccccc ccccca ccccac cccaca ccacaa cacaat acaatc caatcc aatcct atccta tcctag\n"
     ]
    }
   ],
   "source": [
    "print(human_texts[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 3, ..., 6, 6, 6], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will perform the same steps for chimpanzee and dog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "chimp_texts = list(chimp_data['words'])\n",
    "for item in range(len(chimp_texts)):\n",
    "    chimp_texts[item] = ' '.join(chimp_texts[item])\n",
    "y_chimp = chimp_data.iloc[:, 0].values                       # y_c for chimp\n",
    "\n",
    "dog_texts = list(dog_data['words'])\n",
    "for item in range(len(dog_texts)):\n",
    "    dog_texts[item] = ' '.join(dog_texts[item])\n",
    "y_dog = dog_data.iloc[:, 0].values   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we will apply the BAG of WORDS using CountVectorizer using NLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the Bag of Words model using CountVectorizer()\n",
    "# This is equivalent to k-mer counting\n",
    "# The n-gram size of 4 was previously determined by testing\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(ngram_range=(4,4))\n",
    "X = cv.fit_transform(human_texts)\n",
    "X_chimp = cv.transform(chimp_texts)\n",
    "X_dog = cv.transform(dog_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4380, 232414)\n",
      "(1682, 232414)\n",
      "(820, 232414)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(X_chimp.shape)\n",
    "print(X_dog.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If we have a look at class balance we can see we have relatively balanced dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae7241c88>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "human_data['class'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae7e4c908>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z2kq3Fvgl4JHD6gH+/di3sygPJTmzqu4CaDP7i4D3AS8cb2tD+YWq+hZ8PzQPOR7YPJ6WFq+qZoBLkrwKeHTc/SzCicAdDH7WK8mPVdVDSZ7O6pgovAF4V5I/YHDHyv9IspfBhPMN42ysuzX61S7J1cD7q+qT8xz7QFX92hjaGkqS9QyWPx6a59g5VfVvY2hLq1ySpwFrq+q/xt3LMJI8A/gJBhPpmap6eMwtGfSS1Ds/MCVJnTPoJalzBr0kdc6gl6TO/R8lJt6gF+yt6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chimp_data['class'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae757edd8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dog_data['class'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So now that we know how to transform our DNA sequences into uniform length numerical vectors in the form of k-mer counts and ngrams, we can now go ahead and build a classification model that can predict the DNA sequence function based only on the sequence itself.\n",
    "Here we will use the human data to train the model, holding out 20% of the human data to test the model.\n",
    "### So below we will - 1: train/test spit. 2: Build classifiers and 3: test the model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Splitting the human dataset into the training set and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y_data, \n",
    "                                                    test_size = 0.20, \n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3504, 232414)\n",
      "(876, 232414)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "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.naive_bayes import MultinomialNB\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_metrics(y_test, y_predicted):\n",
    "    accuracy = accuracy_score(y_test, y_predicted)\n",
    "    precision = precision_score(y_test, y_predicted, average='weighted')\n",
    "    recall = recall_score(y_test, y_predicted, average='weighted')\n",
    "    f1 = f1_score(y_test, y_predicted, average='weighted')\n",
    "    return accuracy, precision, recall, f1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A classifier will be created. Now let's make predictions on the human hold out test set and see how it performes on unseen data.\n",
    "### Also, let's us look at some model performce metrics like the confusion matrix, accuracy, precision, recall and f1 score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultinomialNB\n",
      "accuracy = 0.984 \n",
      "precision = 0.984 \n",
      "recall = 0.984 \n",
      "f1 = 0.984\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.97      0.98       102\n",
      "           1       1.00      0.98      0.99       106\n",
      "           2       1.00      1.00      1.00        78\n",
      "           3       0.99      0.99      0.99       125\n",
      "           4       0.99      0.96      0.98       149\n",
      "           5       1.00      1.00      1.00        51\n",
      "           6       0.96      0.99      0.98       265\n",
      "\n",
      "   micro avg       0.98      0.98      0.98       876\n",
      "   macro avg       0.99      0.99      0.99       876\n",
      "weighted avg       0.98      0.98      0.98       876\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifier = MultinomialNB(alpha=0.1)\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "accuracy, precision, recall, f1 = get_metrics(y_test, y_pred)\n",
    "print('MultinomialNB')\n",
    "print(\"accuracy = %.3f \\nprecision = %.3f \\nrecall = %.3f \\nf1 = %.3f\" % (accuracy, precision, recall, f1))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net\n",
      "accuracy = 0.952 \n",
      "precision = 0.954 \n",
      "recall = 0.952 \n",
      "f1 = 0.952\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.92      0.94       102\n",
      "           1       1.00      0.94      0.97       106\n",
      "           2       1.00      0.94      0.97        78\n",
      "           3       0.89      0.97      0.93       125\n",
      "           4       0.95      0.93      0.94       149\n",
      "           5       1.00      0.92      0.96        51\n",
      "           6       0.94      0.98      0.96       265\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       876\n",
      "   macro avg       0.96      0.94      0.95       876\n",
      "weighted avg       0.95      0.95      0.95       876\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifier = MLPClassifier(alpha = 1)\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "accuracy, precision, recall, f1 = get_metrics(y_test, y_pred)\n",
    "print('Neural Net')\n",
    "print(\"accuracy = %.3f \\nprecision = %.3f \\nrecall = %.3f \\nf1 = %.3f\" % (accuracy, precision, recall, f1))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's see how our model perfoms on the DNA sequences from other species. First we'll try the Chimpanzee, which we would expect to be very similar to human. Then we will try man's (and woman's) best friend, the Dog DNA sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultinomialNB\n",
      "accuracy = 0.993 \n",
      "precision = 0.994 \n",
      "recall = 0.993 \n",
      "f1 = 0.993\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       234\n",
      "           1       1.00      0.99      1.00       185\n",
      "           2       1.00      1.00      1.00       144\n",
      "           3       1.00      1.00      1.00       228\n",
      "           4       1.00      0.97      0.99       261\n",
      "           5       1.00      1.00      1.00       109\n",
      "           6       0.98      1.00      0.99       521\n",
      "\n",
      "   micro avg       0.99      0.99      0.99      1682\n",
      "   macro avg       1.00      0.99      0.99      1682\n",
      "weighted avg       0.99      0.99      0.99      1682\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifier = MultinomialNB(alpha=0.1)\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_chimp)\n",
    "accuracy, precision, recall, f1 = get_metrics(y_chimp, y_pred)\n",
    "print('MultinomialNB')\n",
    "print(\"accuracy = %.3f \\nprecision = %.3f \\nrecall = %.3f \\nf1 = %.3f\" % (accuracy, precision, recall, f1))\n",
    "print(classification_report(y_chimp, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net\n",
      "accuracy = 0.989 \n",
      "precision = 0.989 \n",
      "recall = 0.989 \n",
      "f1 = 0.989\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99       234\n",
      "           1       0.98      1.00      0.99       185\n",
      "           2       1.00      1.00      1.00       144\n",
      "           3       0.98      0.99      0.98       228\n",
      "           4       0.99      0.97      0.98       261\n",
      "           5       1.00      0.99      1.00       109\n",
      "           6       0.99      0.99      0.99       521\n",
      "\n",
      "   micro avg       0.99      0.99      0.99      1682\n",
      "   macro avg       0.99      0.99      0.99      1682\n",
      "weighted avg       0.99      0.99      0.99      1682\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifier = MLPClassifier(alpha = 1)\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_chimp)\n",
    "accuracy, precision, recall, f1 = get_metrics(y_chimp, y_pred)\n",
    "print('Neural Net')\n",
    "print(\"accuracy = %.3f \\nprecision = %.3f \\nrecall = %.3f \\nf1 = %.3f\" % (accuracy, precision, recall, f1))\n",
    "print(classification_report(y_chimp, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultinomialNB\n",
      "accuracy = 0.926 \n",
      "precision = 0.934 \n",
      "recall = 0.926 \n",
      "f1 = 0.925\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95       131\n",
      "           1       1.00      0.84      0.91        75\n",
      "           2       1.00      0.77      0.87        64\n",
      "           3       0.99      0.85      0.92        95\n",
      "           4       0.96      0.93      0.95       135\n",
      "           5       1.00      0.88      0.94        60\n",
      "           6       0.85      1.00      0.92       260\n",
      "\n",
      "   micro avg       0.93      0.93      0.93       820\n",
      "   macro avg       0.96      0.89      0.92       820\n",
      "weighted avg       0.93      0.93      0.93       820\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifier = MultinomialNB(alpha=0.1)\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_dog)\n",
    "accuracy, precision, recall, f1 = get_metrics(y_dog, y_pred)\n",
    "print('MultinomialNB')\n",
    "print(\"accuracy = %.3f \\nprecision = %.3f \\nrecall = %.3f \\nf1 = %.3f\" % (accuracy, precision, recall, f1))\n",
    "print(classification_report(y_dog, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net\n",
      "accuracy = 0.944 \n",
      "precision = 0.948 \n",
      "recall = 0.944 \n",
      "f1 = 0.943\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.98      0.95       131\n",
      "           1       0.99      0.95      0.97        75\n",
      "           2       1.00      0.94      0.97        64\n",
      "           3       0.92      0.96      0.94        95\n",
      "           4       0.99      0.83      0.90       135\n",
      "           5       1.00      0.85      0.92        60\n",
      "           6       0.91      1.00      0.95       260\n",
      "\n",
      "   micro avg       0.94      0.94      0.94       820\n",
      "   macro avg       0.96      0.93      0.94       820\n",
      "weighted avg       0.95      0.94      0.94       820\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifier = MLPClassifier(alpha = 1)\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_dog)\n",
    "accuracy, precision, recall, f1 = get_metrics(y_dog, y_pred)\n",
    "print('Neural Net')\n",
    "print(\"accuracy = %.3f \\nprecision = %.3f \\nrecall = %.3f \\nf1 = %.3f\" % (accuracy, precision, recall, f1))\n",
    "print(classification_report(y_dog, y_pred))"
   ]
  }
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