Modelling Using Colab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Colab to use google drive to store the output file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "tf.test.gpu_device_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install psutil\n",
    "import psutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U -q PyDrive\n",
    "from pydrive.auth import GoogleAuth\n",
    "from pydrive.drive import GoogleDrive\n",
    "from google.colab import auth\n",
    "from oauth2client.client import GoogleCredentials\n",
    "\n",
    "auth.authenticate_user()\n",
    "gauth = GoogleAuth()\n",
    "gauth.credentials = GoogleCredentials.get_application_default()\n",
    "drive = GoogleDrive(gauth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file1 = drive.CreateFile({'id':'1ZWP7H71JWGjofIZXXXXXXXXX'}) \n",
    "file1.GetContentFile('example_file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('example_file', 'r')\n",
    "content = file.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "avira= [ x.split() for x in content]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "avira1= [ x[1:-1] for x in avira] # we are applying elimination of first and last element of each row(element) in the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getvalue(x,a):\n",
    "    if a in x.keys():\n",
    "        return x[a]\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def to_getValue(x):\n",
    "    return getvalue(x,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kp_mean = {}\n",
    "kp_var = {}\n",
    "\n",
    "for i in range(max_col):\n",
    "    a=list(map(lambda x : getvalue(x,i+1),avira2))\n",
    "    kp_mean[i+1] = mean(a)\n",
    "    kp_var[i+1] = variance(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create GoogleDriveFile instance with title '.\n",
    "file2 = drive.CreateFile({'title': 'kp'})     # note this will create a new file on google drive \n",
    "file2.Upload() # Upload the file.\n",
    "print('title: %s, id: %s' % (file2['title'], file2['id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file2 = drive.CreateFile({'id':'1xtKWAHQ1gf6Lw9wrAXXXXX'}) \n",
    "file2.GetContentFile('kp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('kp', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in kp_mean.keys():\n",
    "    file.write('-1 key:{} mean:{} varience:{} \\n'.format(k,kp_mean[k],kp_var[k]))\n",
    "file2.Upload() # uploading or writing on the empty file we created in the google drive\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.colab import files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "files.download('kp') # finally you can download the file from here to your local computer / else go to Drive and download the same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# If you want to make the google drive work as a local drive follow the below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install a Drive FUSE wrapper.\n",
    "# https://github.com/astrada/google-drive-ocamlfuse\n",
    "!apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
    "!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n",
    "!apt-get update -qq 2>&1 > /dev/null\n",
    "!apt-get -y install -qq google-drive-ocamlfuse fuse\n",
    "\n",
    "\n",
    "\n",
    "# Generate auth tokens for Colab\n",
    "from google.colab import auth\n",
    "auth.authenticate_user()\n",
    "\n",
    "\n",
    "# Generate creds for the Drive FUSE library.\n",
    "from oauth2client.client import GoogleCredentials\n",
    "creds = GoogleCredentials.get_application_default()\n",
    "import getpass\n",
    "!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL\n",
    "vcode = getpass.getpass()\n",
    "!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}\n",
    "\n",
    "\n",
    "# Create a directory and mount Google Drive using that directory.\n",
    "!mkdir -p MyDrive\n",
    "!google-drive-ocamlfuse MyDrive\n",
    "\n",
    "\n",
    "!ls MyDrive/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a directory and mount Google Drive using that directory.\n",
    "!mkdir -p MyDrive\n",
    "!google-drive-ocamlfuse MyDrive\n",
    "\n",
    "\n",
    "!ls MyDrive/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!chmod 777 /content/MyDrive/test_dir_2/ # for changing the directory permission Read write "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!echo \"\" > /content/MyDrive/test_dir_2/test # creating a new file at this location a\n",
    "location = '/content/MyDrive/test_dir_2/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fo = open(location, \"w\")\n",
    "for k in kp_mean.keys():\n",
    "    fo.write('-1 key:{} mean:{} varience:{} \\n'.format(k,kp_mean[k],kp_var[k]))\n",
    "\n",
    "fo.close()"
   ]
  }
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