Test/Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named pyPdf",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-c6de9f0f56b3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpyPdf\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPdfFileReader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mpdf_toread\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPdfFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"doc2.pdf\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpdf_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdf_toread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetDocumentInfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpdf_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named pyPdf"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import mlpack\n",
    "from mlpack import preprocess_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset from an online URL.  Replace with 'covertype.csv.gz' if you\n",
    "# want to use on the full dataset.\n",
    "df = pd.read_csv('http://www.mlpack.org/datasets/covertype-small.csv.gz')\n",
    "# Split the labels.\n",
    "labels = df['label']\n",
    "dataset = df.drop('label', 1)\n",
    "# Split the dataset using mlpack.  The output comes back as a dictionary,\n",
    "# which we'll unpack for clarity of code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = mlpack.preprocess_split(input=dataset,input_labels=labels,test_ratio=0.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'output' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-3b34abe69b6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtraining_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'training'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtraining_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'training_labels'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtest_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtest_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test_labels'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Train a random forest.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'output' is not defined"
     ]
    }
   ],
   "source": [
    "training_set = output['training']\n",
    "training_labels = output['training_labels']\n",
    "test_set = output['test']\n",
    "test_labels = output['test_labels']\n",
    "# Train a random forest.\n",
    "output = mlpack.random_forest(training=training_set,\n",
    "                              labels=training_labels,\n",
    "                              print_training_accuracy=True,\n",
    "                              num_trees=10,\n",
    "                              minimum_leaf_size=3)\n",
    "random_forest = output['output_model']\n",
    "# Predict the labels of the test points.\n",
    "output = mlpack.random_forest(input_model=random_forest,\n",
    "                              test=test_set)\n",
    "# Now print the accuracy.  The 'probabilities' output could also be used\n",
    "# to generate an ROC curve.\n",
    "correct = np.sum(output['predictions'] == test_labels)\n",
    "print(str(correct) + ' correct out of ' + str(len(test_labels)) + ' (' +\n",
    "    str(100 * float(correct) / float(len(test_labels))) + '%).')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PyPDF2 import PdfFileReader\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-7c237e77985f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'__main__'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'../../../Downloads/Resume.pdf'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mtext_extractor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-11-7c237e77985f>\u001b[0m in \u001b[0;36mtext_extractor\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0mpdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPdfFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0;31m# get the first page\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetPage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Page type: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/PyPDF2/pdf.pyc\u001b[0m in \u001b[0;36mgetPage\u001b[0;34m(self, pageNumber)\u001b[0m\n\u001b[1;32m   1175\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflattenedPages\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1177\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflattenedPages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpageNumber\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1178\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m     namedDestinations = property(lambda self:\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "def text_extractor(path):\n",
    "    with open(path, 'rb') as f:\n",
    "        pdf = PdfFileReader(f)\n",
    "        # get the first page\n",
    "        page = pdf.getPage(1)\n",
    "        print(page)\n",
    "        print('Page type: {}'.format(str(type(page))))\n",
    "        text = page.extractText()\n",
    "        print(text)\n",
    "if __name__ == '__main__':\n",
    "    path = '../../../Downloads/Resume.pdf'\n",
    "    text_extractor(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import PyPDF2\n",
    "\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named textract",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-6aeb94a7f383>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtextract\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m: No module named textract"
     ]
    }
   ],
   "source": [
    "import textract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'/Producer': u'WeasyPrint 0.30 (http://weasyprint.org/)', '/Author': u'', '/Keywords': u''}\n"
     ]
    }
   ],
   "source": [
    "from PyPDF2 import PdfFileReader\n",
    "pdf_toread = PdfFileReader(open(\"../../../Downloads/Resume.pdf\", \"rb\"))\n",
    "pdf_info = pdf_toread.getDocumentInfo()\n",
    "print str(pdf_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named pdfminer.pdfparser",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-0ea2628eb9a6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpdfminer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpdfparser\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPDFParser\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpdfminer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpdfdocument\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPDFDocument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../../../Downloads/Resume.pdf'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPDFParser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named pdfminer.pdfparser"
     ]
    }
   ],
   "source": [
    "from pdfminer.pdfparser import PDFParser\n",
    "from pdfminer.pdfdocument import PDFDocument\n",
    "\n",
    "fp = open('../../../Downloads/Resume.pdf', 'rb')\n",
    "parser = PDFParser(fp)\n",
    "doc = PDFDocument(parser)\n",
    "\n",
    "print doc.info  # The \"Info\" metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda_sum = lambda x, y: x + y\n",
    "reduce(lambda_sum, [1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "Reached end of sequence\n"
     ]
    }
   ],
   "source": [
    "numbers = range(6)\n",
    "\n",
    "iterator_obj = iter(numbers)\n",
    "while True:\n",
    "    try:\n",
    "        print iterator_obj.next()\n",
    "    except StopIteration:\n",
    "        print 'Reached end of sequence'\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hèllo\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# dealing with unicode matching using regexes\n",
    "s = u'H\\u00e8llo'\n",
    "s\n",
    "print s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'H', u'llo']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.findall(r'\\w+', s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'big.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-e38113f64b96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'[a-z]+'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mWORDS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'big.txt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mWORD_COUNTS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCounter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWORDS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'big.txt'"
     ]
    }
   ],
   "source": [
    "import re, collections\n",
    "\n",
    "def tokens(text): \n",
    "    \"\"\"\n",
    "    Get all words from the corpus\n",
    "    \"\"\"\n",
    "    return re.findall('[a-z]+', text.lower()) \n",
    "\n",
    "WORDS = tokens(file('big.txt').read())\n",
    "WORD_COUNTS = collections.Counter(WORDS)\n",
    "\n",
    "# top 10 words in corpus\n",
    "print WORD_COUNTS.most_common(10)\n",
    "\n",
    "\n",
    "def known(words):\n",
    "    \"\"\"\n",
    "    Return the subset of words that are actually \n",
    "    in our WORD_COUNTS dictionary.\n",
    "    \"\"\"\n",
    "    return {w for w in words if w in WORD_COUNTS}\n",
    "\n",
    "\n",
    "def edits0(word): \n",
    "    \"\"\"\n",
    "    Return all strings that are zero edits away \n",
    "    from the input word (i.e., the word itself).\n",
    "    \"\"\"\n",
    "    return {word}\n",
    "\n",
    "\n",
    "\n",
    "def edits1(word):\n",
    "    \"\"\"\n",
    "    Return all strings that are one edit away \n",
    "    from the input word.\n",
    "    \"\"\"\n",
    "    alphabet = 'abcdefghijklmnopqrstuvwxyz'\n",
    "    def splits(word):\n",
    "        \"\"\"\n",
    "        Return a list of all possible (first, rest) pairs \n",
    "        that the input word is made of.\n",
    "        \"\"\"\n",
    "        return [(word[:i], word[i:]) \n",
    "                for i in range(len(word)+1)]\n",
    "                \n",
    "    pairs      = splits(word)\n",
    "    deletes    = [a+b[1:]           for (a, b) in pairs if b]\n",
    "    transposes = [a+b[1]+b[0]+b[2:] for (a, b) in pairs if len(b) > 1]\n",
    "    replaces   = [a+c+b[1:]         for (a, b) in pairs for c in alphabet if b]\n",
    "    inserts    = [a+c+b             for (a, b) in pairs for c in alphabet]\n",
    "    return set(deletes + transposes + replaces + inserts)\n",
    "\n",
    "\n",
    "def edits2(word):\n",
    "    \"\"\"Return all strings that are two edits away \n",
    "    from the input word.\n",
    "    \"\"\"\n",
    "    return {e2 for e1 in edits1(word) for e2 in edits1(e1)}\n",
    "    \n",
    "    \n",
    "def correct(word):\n",
    "    \"\"\"\n",
    "    Get the best correct spelling for the input word\n",
    "    \"\"\"\n",
    "    # Priority is for edit distance 0, then 1, then 2\n",
    "    # else defaults to the input word itself.\n",
    "    candidates = (known(edits0(word)) or \n",
    "                  known(edits1(word)) or \n",
    "                  known(edits2(word)) or \n",
    "                  [word])\n",
    "    return max(candidates, key=WORD_COUNTS.get)\n",
    "\n",
    "\n",
    "def correct_match(match):\n",
    "    \"\"\"\n",
    "    Spell-correct word in match, \n",
    "    and preserve proper upper/lower/title case.\n",
    "    \"\"\"\n",
    "    \n",
    "    word = match.group()\n",
    "    def case_of(text):\n",
    "        \"\"\"\n",
    "        Return the case-function appropriate \n",
    "        for text: upper, lower, title, or just str.:\n",
    "            \"\"\"\n",
    "        return (str.upper if text.isupper() else\n",
    "                str.lower if text.islower() else\n",
    "                str.title if text.istitle() else\n",
    "                str)\n",
    "    return case_of(word)(correct(word.lower()))\n",
    "\n",
    "    \n",
    "def correct_text_generic(text):\n",
    "    \"\"\"\n",
    "    Correct all the words within a text, \n",
    "    returning the corrected text.\n",
    "    \"\"\"\n",
    "    return re.sub('[a-zA-Z]+', correct_match, text)\n",
    "\n",
    "\n",
    "\n",
    "print correct_text_generic('fianlly')\n",
    "\n",
    "\n",
    "from pattern.en import suggest\n",
    "\n",
    "print suggest('fianlly')\n",
    "print suggest('flaot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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