identify.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "tuple index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_7868\\2760525478.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[0mval_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m8000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0minput_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     49\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: tuple index out of range"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten\n",
    "\n",
    "# Preprocess the DNA sequence data to one-hot encoded arrays\n",
    "def preprocess_data(sequences):\n",
    "    one_hot_encoded_sequences = []\n",
    "    for sequence in sequences:\n",
    "        one_hot_encoded_sequence = []\n",
    "        for base in sequence:\n",
    "            if base == 'A':\n",
    "                one_hot_encoded_sequence.append([1,0,0,0])\n",
    "            elif base == 'C':\n",
    "                one_hot_encoded_sequence.append([0,1,0,0])\n",
    "            elif base == 'G':\n",
    "                one_hot_encoded_sequence.append([0,0,1,0])\n",
    "            elif base == 'T':\n",
    "                one_hot_encoded_sequence.append([0,0,0,1])\n",
    "        one_hot_encoded_sequences.append(one_hot_encoded_sequence)\n",
    "    return np.array(one_hot_encoded_sequences)\n",
    "\n",
    "# Define the model architecture\n",
    "def create_model(input_shape):\n",
    "    model = Sequential()\n",
    "    model.add(Conv1D(64, 3, activation='relu', input_shape=input_shape))\n",
    "    model.add(MaxPooling1D(2))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dense(32, activation='relu'))\n",
    "    model.add(Dense(16, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "# Load the training data and preprocess it\n",
    "sequences = [] # example DNA sequence data\n",
    "labels = [] # example microbial composition labels (1 for present, 0 for absent)\n",
    "\n",
    "processed_data = preprocess_data(sequences)\n",
    "\n",
    "# Split the data into training and validation sets\n",
    "train_data = processed_data[:8000]\n",
    "train_labels = labels[:8000]\n",
    "val_data = processed_data[8000:]\n",
    "val_labels = labels[8000:]\n",
    "\n",
    "input_shape = (train_data.shape[1], train_data.shape[2])\n",
    "model = create_model(input_shape)\n",
    "\n",
    "# Train the model on the training data\n",
    "model.fit(train_data, train_labels, epochs=10, batch_size=32, validation_data=(val_data, val_labels))\n",
    "\n",
    "# Evaluate the model on the validation data\n",
    "val_loss, val_acc = model.evaluate(val_data, val_labels)\n",
    "print('Validation accuracy:', val_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13 (main, Aug 25 2022, 23:51:50) [MSC v.1916 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "f6be8fc375e7f2a12d966ca69c4c87dfa0a5e21291543001a29f6d3fc266e190"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}