NN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN для распознавания гистоновых меток"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv2D, Conv1D, Flatten\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('df[1-3].csv', sep = '\\t', header = None)\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[[type(i) == str for i in df[1].values]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = {  'A':[1,0,0,0], \n",
    "         'C':[0,1,0,0], \n",
    "         'G':[0,0,1,0], \n",
    "         'T':[0,0,0,1]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded = np.array([[enc[char] for char in i] for i in df[1].values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модель и обучающая выборка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = (df[0] == 'yes').values.astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_ex, y_train, y_ex = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv1D(16, kernel_size = 6, input_shape = (200,4), activation = 'relu'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "model.compile(optimizer = 'adam',loss = 'binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.fit(X_train, y_train, batch_size=10, validation_split=0.3, epochs=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Прогноз и статистики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = list([int(i) for i in y_pred])\n",
    "y_ex = list([int(i) for i in y_ex])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "false_neg = 0; false_pos = 0; pos = 0; neg = 0\n",
    "for i in range (0, len(y_pred)):\n",
    "    if (y_ex[i] == 1) and (y_pred[i] != y_ex[i]): \n",
    "        false_neg+=1\n",
    "    if (y_ex[i] == 0) and (y_pred[i] != y_ex[i]):\n",
    "        false_pos+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\n",
    "'false positive rate ', false_pos/y_ex.count(0),\n",
    "'\\nfalse negative rate ', false_neg/y_ex.count(1),\n",
    "'\\nerrors ', false_neg + false_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_neg= 0; true_pos = 0\n",
    "for i in range (0, len(y_pred)):\n",
    "    if (y_ex[i] == 1) and (y_pred[i] == y_ex[i]): \n",
    "        true_pos+=1\n",
    "    if (y_ex[i] == 0) and (y_pred[i] == y_ex[i]):\n",
    "        true_neg+=1\n",
    "print('true negative ', true_neg, '\\ntrue positive ', true_pos)\n",
    "print('\\nnumber of 0 ', y_ex.count(0), '\\nnumber of 1 ', y_ex.count(1))"
   ]
  }
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