From 4cec34880345c9fb889cfe3f2705036e6b056002 Mon Sep 17 00:00:00 2001 From: Bret Runestad Date: Wed, 11 Feb 2015 17:05:38 -0500 Subject: [PATCH 1/4] initial commit for Bret --- sms spam.ipynb | 1749 ++++++++++++++++++++++++++++++++++++++++++++++++ sms_spam.py | 50 ++ 2 files changed, 1799 insertions(+) create mode 100644 sms spam.ipynb create mode 100644 sms_spam.py diff --git a/sms spam.ipynb b/sms spam.ipynb new file mode 100644 index 0000000..8cc0582 --- /dev/null +++ b/sms spam.ipynb @@ -0,0 +1,1749 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:11dfaafbe17df20be4313b4f5bb534a915037d2347f6f9278743a9c9063347fd" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np\n", + "from textblob import TextBlob\n", + "import re\n", + "from sklearn.feature_extraction.text import TfidfVectorizer as TfidV\n", + "\n", + "import sms_spam.py" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "ImportError", + "evalue": "No module named 'sms_spam.py'; 'sms_spam' is not a package", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_extraction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTfidfVectorizer\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mTfidV\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msms_spam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mImportError\u001b[0m: No module named 'sms_spam.py'; 'sms_spam' is not a package" + ] + } + ], + "prompt_number": 252 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "with open(\"SMSSpamCollection\") as file:\n", + " all_lines = file.readlines()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 34 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#all_lines" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 158 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "blob = TextBlob(all_lines[0])" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 28 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "blob" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 126, + "text": [ + "TextBlob(\"ham\tGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n", + "\")" + ] + } + ], + "prompt_number": 126 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "blob.find('crazy...')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 131, + "text": [ + "-1" + ] + } + ], + "prompt_number": 131 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def findword(blobstring, word):\n", + " if blobstring.find(word) == -1:\n", + " return 0\n", + " else:\n", + " return 1" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 132 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def startword(blobstring):\n", + " if blobstring.starts_with('spam'):\n", + " return 1\n", + " else:\n", + " return 0" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 153 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def wordlength(blobstring, num1, num2):\n", + " if num1 < len(blobstring.words) <= num2:\n", + " return 1\n", + " else:\n", + " return 0" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 165 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def numbercheck(blobstring, num):\n", + " astring = str(blobstring)\n", + " print(astring)\n", + " print(num)\n", + " if re.search(\"\\d{\\{}}\".format(num), astring):\n", + " return 1\n", + " else:\n", + " return 0" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 248 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def numbercheck(blobstring, num):\n", + " astring = str(blobstring)\n", + " print(num)\n", + " if re.search(r\"\\d{1}\", astring):\n", + " return 1\n", + " else:\n", + " return 0" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 232 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "bloblist[2]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 200, + "text": [ + "TextBlob(\"spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\n", + "\")" + ] + } + ], + "prompt_number": 200 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "numbercheck(bloblist[2], 1)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\n", + "\n", + "1\n" + ] + }, + { + "ename": "ValueError", + "evalue": "unexpected '{' in field name", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbercheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbloblist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mnumbercheck\u001b[0;34m(blobstring, num)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mastring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\d{\\{}}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mastring\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;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: unexpected '{' in field name" + ] + } + ], + "prompt_number": 249 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "wordlength(blob, 0, 30)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 233, + "text": [ + "1" + ] + } + ], + "prompt_number": 233 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "findword(blob, 'jurong')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 136, + "text": [ + "1" + ] + } + ], + "prompt_number": 136 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def main_function(bloblist, word1, word2):\n", + " lista = []\n", + " for blobstring in bloblist:\n", + " x = findword(blobstring.lower(), word1)\n", + " y = findword(blobstring.lower(), word2)\n", + " l1 = wordlength(blobstring, 0, 10)\n", + " l2 = wordlength(blobstring, 10, 20)\n", + " l3 = wordlength(blobstring, 20, 30)\n", + " l4 = wordlength(blobstring, 30, 40)\n", + " l5 = wordlength(blobstring, 40, 999999)\n", + " nc1 = numbercheck(blobstring, 1)\n", + " nc2 = numbercheck(blobstring, 2)\n", + " nc3 = numbercheck(blobstring, 3)\n", + " nc5 = numbercheck(blobstring, 5)\n", + " nc10 = numbercheck(blobstring, 10)\n", + " z = startword(blobstring)\n", + " lista.append([x, y, z, l1, l2, l3, l4, l5, nc1, nc2, nc3, nc5, nc10])\n", + " return lista" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 195 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "y = main_function(bloblist, 'text', 'call')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 196 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "y" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 197, + "text": [ + "[[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 1, 0, 0, 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{ + "cell_type": "code", + "collapsed": false, + "input": [ + "x = re.search(r\"\\d\", all_lines[0])\n", + "z = re.search(r\"\\d\", \"jar jar 90930\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 179 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "if x:\n", + " print('a')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 184 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "blob.words" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 160, + "text": [ + "WordList(['ham', 'Go', 'until', 'jurong', 'point', 'crazy', 'Available', 'only', 'in', 'bugis', 'n', 'great', 'world', 'la', 'e', 'buffet', 'Cine', 'there', 'got', 'amore', 'wat'])" + ] + } + ], + "prompt_number": 160 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "text=all_lines[0]\n", + "text" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 9, + "text": [ + "'ham\\tGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n'" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "bloblist = [TextBlob(x) for x in all_lines]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "str(blob)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 190, + "text": [ + "'ham\\tGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n'" + ] + } + ], + "prompt_number": 190 + }, + { + "cell_type": "code", + "collapsed": true, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "len(bloblist)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 19, + "text": [ + "5574" + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "hamlist = [x for x in bloblist if x.startswith('ham')]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 20 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "len(hamlist)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 21, + "text": [ + "4827" + ] + } + ], + "prompt_number": 21 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "spamlist= [x for x in bloblist if x.startswith('spam')]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 22 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "len(spamlist)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 23, + "text": [ + "747" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def word_frequency(bloblist):\n", + " a_dict = {}\n", + " for blob in bloblist:\n", + " xlist = [x for x in blob.words]\n", + " for item in xlist:\n", + " if item.lower() in a_dict:\n", + " a_dict[item.lower()]+=1\n", + " else:\n", + " a_dict[item.lower()]=1\n", + " return a_dict\n", + "\n", + "def top_dict(a_dict, num):\n", + " sorted_list = sorted(a_dict.items(), key=lambda x: x[1], reverse=True)\n", + " return sorted_list[0:num]\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 81 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ham_word_dict = word_frequency(hamlist)\n", + "top_dict(ham_word_dict, 3)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 88, + "text": [ + "[('ham', 4828), ('i', 2851), ('you', 1938)]" + ] + } + ], + "prompt_number": 88 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "spam_word_dict = word_frequency(spamlist)\n", + "top_dict(spam_word_dict, 3)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 89, + "text": [ + "[('spam', 748), ('to', 690), ('a', 378)]" + ] + } + ], + "prompt_number": 89 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def set_maker(a_dict):\n", + " aset = set()\n", + " for key in a_dict:\n", + " aset.add(key)\n", + " return aset" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 84 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "hamset = set_maker(ham_word_dict)\n", + "spamset = set_maker(spam_word_dict)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 90 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#hamset.difference(spamset)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 93 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#spamset.difference(hamset)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 94 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#hamset.union(spamset)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 96 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ham_word_dict['call']" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 98, + "text": [ + "36" + ] + } + ], + "prompt_number": 98 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def word_checker(string):\n", + " print(\"Ham: {} {}\".format(ham_word_dict[string], (ham_word_dict[string]/len(hamlist))))\n", + " print(\"Spam: {} {}\".format(spam_word_dict[string], (spam_word_dict[string]/len(spamlist))))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 107 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "word_checker('i')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Ham: 2851 0.5906360058007044\n", + "Spam: 58 0.07764390896921017\n" + ] + } + ], + "prompt_number": 152 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "spam_word_dict['spam']" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 148, + "text": [ + "748" + ] + } + ], + "prompt_number": 148 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "for item in bloblist:\n", + " listoflists = []\n", + " feature_list = []\n", + " \n", + " feature_list.append()" + ], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/sms_spam.py b/sms_spam.py new file mode 100644 index 0000000..a071961 --- /dev/null +++ b/sms_spam.py @@ -0,0 +1,50 @@ +import numpy as np +from textblob import TextBlob +import re +from sklearn.feature_extraction.text import TfidfVectorizer as TfidV + + +def main_function(bloblist, word1, word2): + lista = [] + for blobstring in bloblist: + x = findword(blobstring.lower(), word1) + y = findword(blobstring.lower(), word2) + l1 = wordlength(blobstring, 0, 10) + l2 = wordlength(blobstring, 10, 20) + l3 = wordlength(blobstring, 20, 30) + l4 = wordlength(blobstring, 30, 40) + l5 = wordlength(blobstring, 40, 999999) + nc1 = numbercheck(blobstring, 1) + nc2 = numbercheck(blobstring, 2) + nc3 = numbercheck(blobstring, 3) + nc5 = numbercheck(blobstring, 5) + nc10 = numbercheck(blobstring, 10) + z = startword(blobstring) + lista.append([x, y, l1, l2, l3, l4, l5, nc1, nc2, nc3, nc5, nc10, z]) + return lista + + +def findword(blobstring, word): + if blobstring.find(word) == -1: + return 0 + else: + return 1 + +def startword(blobstring): + if blobstring.starts_with('spam'): + return 1 + else: + return 0 + +def wordlength(blobstring, num1, num2): + if num1 < len(blobstring.words) <= num2: + return 1 + else: + return 0 + +def numbercheck1(blobstring): + astring = str(blobstring) + if re.search(r"[0-9]{1}", astring): + return 1 + else: + return 0 From 870cf3bae9f8c15c3b576a4f10b83ec12427856d Mon Sep 17 00:00:00 2001 From: Bret Runestad Date: Wed, 11 Feb 2015 17:27:37 -0500 Subject: [PATCH 2/4] finished initial array --- sms spam.ipynb | 3277 +++++++++++++++++++++++++++++++----------------- sms_spam.py | 36 +- 2 files changed, 2166 insertions(+), 1147 deletions(-) diff --git a/sms spam.ipynb b/sms spam.ipynb index 8cc0582..9780f6d 100644 --- a/sms spam.ipynb +++ b/sms spam.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:11dfaafbe17df20be4313b4f5bb534a915037d2347f6f9278743a9c9063347fd" + "signature": "sha256:76230738267ca89093b03b9a6717788344ce6fbd96b1d037c88559a0880926ec" }, "nbformat": 3, "nbformat_minor": 0, @@ -15,25 +15,12 @@ "import numpy as np\n", "from textblob import TextBlob\n", "import re\n", - "from sklearn.feature_extraction.text import TfidfVectorizer as TfidV\n", - "\n", - "import sms_spam.py" + "from sklearn.feature_extraction.text import TfidfVectorizer as TfidV" ], "language": "python", "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "No module named 'sms_spam.py'; 'sms_spam' is not a package", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_extraction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTfidfVectorizer\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mTfidV\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msms_spam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mImportError\u001b[0m: No module named 'sms_spam.py'; 'sms_spam' is not a package" - ] - } - ], - "prompt_number": 252 + "outputs": [], + "prompt_number": 255 }, { "cell_type": "code", @@ -159,28 +146,23 @@ "cell_type": "code", "collapsed": false, "input": [ - "def numbercheck(blobstring, num):\n", + "def numbercheck1(blobstring):\n", " astring = str(blobstring)\n", - " print(astring)\n", - " print(num)\n", - " if re.search(\"\\d{\\{}}\".format(num), astring):\n", + " if re.search(r\"[0-9]{1}\", astring):\n", " return 1\n", " else:\n", - " return 0" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 248 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def numbercheck(blobstring, num):\n", + " return 0\n", + "\n", + "def numbercheck5(blobstring):\n", " astring = str(blobstring)\n", - " print(num)\n", - " if re.search(r\"\\d{1}\", astring):\n", + " if re.search(r\"[0-9]{5}\", astring):\n", + " return 1\n", + " else:\n", + " return 0\n", + "\n", + "def numbercheck10(blobstring):\n", + " astring = str(blobstring)\n", + " if re.search(r\"[0-9]{10}\", astring):\n", " return 1\n", " else:\n", " return 0" @@ -188,13 +170,13 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 232 + "prompt_number": 256 }, { "cell_type": "code", "collapsed": false, "input": [ - "bloblist[2]" + "bloblist[8]" ], "language": "python", "metadata": {}, @@ -202,86 +184,14 @@ { "metadata": {}, "output_type": "pyout", - "prompt_number": 200, + "prompt_number": 268, "text": [ - "TextBlob(\"spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\n", + "TextBlob(\"spam\tWINNER!! As a valued network customer you have been selected to receivea \u00a3900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.\n", "\")" ] } ], - "prompt_number": 200 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "numbercheck(bloblist[2], 1)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\n", - "\n", - "1\n" - ] - }, - { - "ename": "ValueError", - "evalue": "unexpected '{' in field name", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbercheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbloblist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36mnumbercheck\u001b[0;34m(blobstring, num)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mastring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\d{\\{}}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mastring\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;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: unexpected '{' in field name" - ] - } - ], - "prompt_number": 249 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "wordlength(blob, 0, 30)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 233, - "text": [ - "1" - ] - } - ], - "prompt_number": 233 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "findword(blob, 'jurong')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 136, - "text": [ - "1" - ] - } - ], - "prompt_number": 136 + "prompt_number": 268 }, { "cell_type": "code", @@ -297,30 +207,28 @@ " l3 = wordlength(blobstring, 20, 30)\n", " l4 = wordlength(blobstring, 30, 40)\n", " l5 = wordlength(blobstring, 40, 999999)\n", - " nc1 = numbercheck(blobstring, 1)\n", - " nc2 = numbercheck(blobstring, 2)\n", - " nc3 = numbercheck(blobstring, 3)\n", - " nc5 = numbercheck(blobstring, 5)\n", - " nc10 = numbercheck(blobstring, 10)\n", + " nc1 = numbercheck1(blobstring)\n", + " nc5 = numbercheck5(blobstring)\n", + " nc10 = numbercheck10(blobstring)\n", " z = startword(blobstring)\n", - " lista.append([x, y, z, l1, l2, l3, l4, l5, nc1, nc2, nc3, nc5, nc10])\n", + " lista.append([x, y, l1, l2, l3, l4, l5, nc1, nc5, nc10, z])\n", " return lista" ], "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 195 + "prompt_number": 265 }, { "cell_type": "code", "collapsed": false, "input": [ - "y = main_function(bloblist, 'text', 'call')" + "y = main_function(bloblist, 'cust', 'call')" ], "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 196 + "prompt_number": 266 }, { "cell_type": "code", @@ -334,1013 +242,1013 @@ { "metadata": {}, "output_type": "pyout", - "prompt_number": 197, + "prompt_number": 267, "text": [ - "[[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n", - " [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", - " [1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 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"prompt_number": 89, + "prompt_number": 278, "text": [ "[('spam', 748), ('to', 690), ('a', 378)]" ] } ], - "prompt_number": 89 + "prompt_number": 278 }, { "cell_type": "code", @@ -1692,7 +1600,7 @@ "cell_type": "code", "collapsed": false, "input": [ - "word_checker('i')" + "word_checker('txt')" ], "language": "python", "metadata": {}, @@ -1701,18 +1609,18 @@ "output_type": "stream", "stream": "stdout", "text": [ - "Ham: 2851 0.5906360058007044\n", - "Spam: 58 0.07764390896921017\n" + "Ham: 13 0.0026931841723637872\n", + "Spam: 156 0.20883534136546184\n" ] } ], - "prompt_number": 152 + "prompt_number": 277 }, { "cell_type": "code", "collapsed": false, "input": [ - "spam_word_dict['spam']" + "spam_word_dict['reply']" ], "language": "python", "metadata": {}, @@ -1720,27 +1628,1118 @@ { "metadata": {}, "output_type": "pyout", - "prompt_number": 148, + "prompt_number": 274, "text": [ - "748" + "104" ] } ], - "prompt_number": 148 + "prompt_number": 274 }, { "cell_type": "code", "collapsed": false, "input": [ - "for item in bloblist:\n", - " listoflists = []\n", - " feature_list = []\n", - " \n", - " feature_list.append()" + "def main_function(bloblist, word1, word2):\n", + " lista = []\n", + " for blobstring in bloblist:\n", + " a = findword(blobstring.lower(), 'i')\n", + " b = findword(blobstring.lower(), 'free')\n", + " c = findword(blobstring.lower(), 'mobile')\n", + " d = findword(blobstring.lower(), 'call')\n", + " e = findword(blobstring.lower(), 'cash')\n", + " f = findword(blobstring.lower(), 'cust')\n", + " g = findword(blobstring.lower(), 'cash')\n", + " h = findword(blobstring.lower(), 'stop')\n", + " i = findword(blobstring.lower(), 'reply')\n", + " j = findword(blobstring.lower(), 'txt')\n", + " l1 = wordlength(blobstring, 0, 10)\n", + " l2 = wordlength(blobstring, 10, 20)\n", + " l3 = wordlength(blobstring, 20, 30)\n", + " l4 = wordlength(blobstring, 30, 40)\n", + " l5 = wordlength(blobstring, 40, 999999)\n", + " nc1 = numbercheck1(blobstring)\n", + " nc5 = numbercheck5(blobstring)\n", + " nc10 = numbercheck10(blobstring)\n", + " z = startword(blobstring)\n", + " lista.append([a, b, c, d, e, f, g, h, i, j, l1, l2, l3, l4, l5, nc1, nc5, nc10, z])\n", + " return lista\n", + "\n", + "\n", + "def findword(blobstring, word):\n", + " if blobstring.find(word) == -1:\n", + " return 0\n", + " else:\n", + " return 1\n", + "\n", + "def startword(blobstring):\n", + " if blobstring.starts_with('spam'):\n", + " return 1\n", + " else:\n", + " return 0\n", + "\n", + "def wordlength(blobstring, num1, num2):\n", + " if num1 < len(blobstring.words) <= num2:\n", + " return 1\n", + " else:\n", + " return 0\n", + "\n", + "def numbercheck1(blobstring):\n", + " astring = str(blobstring)\n", + " if re.search(r\"[0-9]{1}\", astring):\n", + " return 1\n", + " else:\n", + " return 0\n", + "\n", + "def numbercheck5(blobstring):\n", + " astring = str(blobstring)\n", + " if re.search(r\"[0-9]{5}\", astring):\n", + " return 1\n", + " else:\n", + " return 0\n", + "\n", + "def numbercheck10(blobstring):\n", + " astring = str(blobstring)\n", + " if re.search(r\"[0-9]{10}\", astring):\n", + " return 1\n", + " else:\n", + " return 0" ], "language": "python", "metadata": {}, - "outputs": [] + "outputs": [], + "prompt_number": 279 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "y = main_function(bloblist, 'cust', 'call')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 280 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "y" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 281, + "text": [ + "[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n", + " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n", + " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 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blobstring in bloblist: - x = findword(blobstring.lower(), word1) - y = findword(blobstring.lower(), word2) + a = findword(blobstring.lower(), 'i') + b = findword(blobstring.lower(), 'free') + c = findword(blobstring.lower(), 'mobile') + d = findword(blobstring.lower(), 'call') + e = findword(blobstring.lower(), 'cash') + f = findword(blobstring.lower(), 'cust') + g = findword(blobstring.lower(), 'cash') + h = findword(blobstring.lower(), 'stop') + i = findword(blobstring.lower(), 'reply') + j = findword(blobstring.lower(), 'txt') l1 = wordlength(blobstring, 0, 10) l2 = wordlength(blobstring, 10, 20) l3 = wordlength(blobstring, 20, 30) l4 = wordlength(blobstring, 30, 40) l5 = wordlength(blobstring, 40, 999999) - nc1 = numbercheck(blobstring, 1) - nc2 = numbercheck(blobstring, 2) - nc3 = numbercheck(blobstring, 3) - nc5 = numbercheck(blobstring, 5) - nc10 = numbercheck(blobstring, 10) + nc1 = numbercheck1(blobstring) + nc5 = numbercheck5(blobstring) + nc10 = numbercheck10(blobstring) z = startword(blobstring) - lista.append([x, y, l1, l2, l3, l4, l5, nc1, nc2, nc3, nc5, nc10, z]) + lista.append([a, b, c, d, e, f, g, h, i, j, l1, l2, l3, l4, l5, nc1, nc5, nc10, z]) return lista @@ -48,3 +54,17 @@ def numbercheck1(blobstring): return 1 else: return 0 + +def numbercheck5(blobstring): + astring = str(blobstring) + if re.search(r"[0-9]{5}", astring): + return 1 + else: + return 0 + +def numbercheck10(blobstring): + astring = str(blobstring) + if re.search(r"[0-9]{10}", astring): + return 1 + else: + return 0 From 082afd1d2ca8fcea1bd3e1ae61484a4d4b1b901e Mon Sep 17 00:00:00 2001 From: Bret Runestad Date: Wed, 11 Feb 2015 20:42:59 -0500 Subject: [PATCH 3/4] normal mode completed --- README.md | 63 +- requirements.txt | 3 +- sms spam.ipynb | 2591 ++++++---------------------------------------- sms_spam.py | 23 +- 4 files changed, 318 insertions(+), 2362 deletions(-) diff --git a/README.md b/README.md index 70c76a1..a3b4bac 100644 --- a/README.md +++ b/README.md @@ -1,62 +1,3 @@ -# Classify and Cluster SMS Messages +The code (and the data journey) is all included in the ipython notebook titled "sms spam". Functions are also contained in sms_spam.py. -## Description - -Use Bayesian classification and K-means clustering to analyze SMS messages. - -## Objectives - -### Learning Objectives - -After completing this assignment, you should understand: - -* The uses of classification and clustering -* Good feature extraction - -### Performance Objectives - -After completing this assignment, you should be able to: - -* Parse text into features -* Classify textual data -* Cluster textual data - -## Details - -### Deliverables - -* A Git repo called sms-spam containing at least: - * `README.md` file explaining how to run your project - * a `requirements.txt` file - -### Requirements - -* No PEP8 or Pyflakes warnings or errors - -## Normal Mode - -Download the [SMS Spam collection](https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection) from the UCI Machine Learning Repository. - -Choose a set of features to use in order to separate SMS ham from spam. - -Write a program to extract the features you want from each SMS message and then classify each SMS as ham or spam. Iterate on your feature extraction until you -have a classification success level you are comfortable with (> 75% minimum.) - -## Hard Mode - -In addition to the requirements from **Normal Mode**: - -Write a program to cluster the SMS messages in differing numbers of groups. Examine each cluster to see if they have meaning. Write up your findings. - -## Notes - -Some features you might want to try: - -* Presence of the following words: claim, winner -* Presence of money symbols -* Presence of numbers -* Presence of first-person words - -## Additional Resources - -* [TextBlob](http://textblob.readthedocs.org/en/dev/) \ No newline at end of file +pip install -r requirements.txt to make sure that all necessary packages are installed diff --git a/requirements.txt b/requirements.txt index 73e7a6e..c9706f0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,6 @@ +ipython[all] scikit-learn pandas numpy matplotlib -textblob \ No newline at end of file +textblob diff --git a/sms spam.ipynb b/sms spam.ipynb index 9780f6d..a4975a7 100644 --- a/sms spam.ipynb +++ b/sms spam.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:76230738267ca89093b03b9a6717788344ce6fbd96b1d037c88559a0880926ec" + "signature": "sha256:7390fc7c015e81a56b382ac35e3aaa17c30f3471f1761077d78909f6c127a4d9" }, "nbformat": 3, "nbformat_minor": 0, @@ -43,7 +43,7 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 158 + "prompt_number": 319 }, { "cell_type": "code", @@ -77,1244 +77,6 @@ ], "prompt_number": 126 }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "blob.find('crazy...')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 131, - "text": [ - "-1" - ] - } - ], - "prompt_number": 131 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def findword(blobstring, word):\n", - " if blobstring.find(word) == -1:\n", - " return 0\n", - " else:\n", - " return 1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 132 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def startword(blobstring):\n", - " if blobstring.starts_with('spam'):\n", - " return 1\n", - " else:\n", - " return 0" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 153 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def wordlength(blobstring, num1, num2):\n", - " if num1 < len(blobstring.words) <= num2:\n", - " return 1\n", - " else:\n", - " return 0" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 165 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def numbercheck1(blobstring):\n", - " astring = str(blobstring)\n", - " if re.search(r\"[0-9]{1}\", astring):\n", - " return 1\n", - " else:\n", - " return 0\n", - "\n", - "def numbercheck5(blobstring):\n", - " astring = str(blobstring)\n", - " if re.search(r\"[0-9]{5}\", astring):\n", - " return 1\n", - " else:\n", - " return 0\n", - "\n", - "def numbercheck10(blobstring):\n", - " astring = str(blobstring)\n", - " if re.search(r\"[0-9]{10}\", astring):\n", - " return 1\n", - " else:\n", - " return 0" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 256 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "bloblist[8]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 268, - "text": [ - "TextBlob(\"spam\tWINNER!! As a valued network customer you have been selected to receivea \u00a3900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.\n", - "\")" - ] - } - ], - "prompt_number": 268 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def main_function(bloblist, word1, word2):\n", - " lista = []\n", - " for blobstring in bloblist:\n", - " x = findword(blobstring.lower(), word1)\n", - " y = findword(blobstring.lower(), word2)\n", - " l1 = wordlength(blobstring, 0, 10)\n", - " l2 = wordlength(blobstring, 10, 20)\n", - " l3 = wordlength(blobstring, 20, 30)\n", - " l4 = wordlength(blobstring, 30, 40)\n", - " l5 = wordlength(blobstring, 40, 999999)\n", - " nc1 = numbercheck1(blobstring)\n", - " nc5 = numbercheck5(blobstring)\n", - " nc10 = numbercheck10(blobstring)\n", - " z = startword(blobstring)\n", - " lista.append([x, y, l1, l2, l3, l4, l5, nc1, nc5, nc10, z])\n", - " return lista" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 265 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "y = main_function(bloblist, 'cust', 'call')" - 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} - ], - "prompt_number": 267 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = re.search(r\"\\d\", all_lines[0])\n", - "z = re.search(r\"\\d\", \"jar jar 90930\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 179 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "if x:\n", - " print('a')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 184 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "blob.words" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 160, - "text": [ - "WordList(['ham', 'Go', 'until', 'jurong', 'point', 'crazy', 'Available', 'only', 'in', 'bugis', 'n', 'great', 'world', 'la', 'e', 'buffet', 'Cine', 'there', 'got', 'amore', 'wat'])" - ] - } - ], - "prompt_number": 160 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "text=all_lines[0]\n", - "text" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "'ham\\tGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n'" - ] - } - ], - "prompt_number": 9 - }, { "cell_type": "code", "collapsed": false, @@ -1326,34 +88,6 @@ "outputs": [], "prompt_number": 12 }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "str(blob)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 190, - "text": [ - "'ham\\tGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n'" - ] - } - ], - "prompt_number": 190 - }, - { - "cell_type": "code", - "collapsed": true, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - }, { "cell_type": "code", "collapsed": false, @@ -1453,13 +187,12 @@ "\n", "def top_dict(a_dict, num):\n", " sorted_list = sorted(a_dict.items(), key=lambda x: x[1], reverse=True)\n", - " return sorted_list[0:num]\n", - "\n" + " return sorted_list[0:num]" ], "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 81 + "prompt_number": 317 }, { "cell_type": "code", @@ -1563,26 +296,6 @@ "outputs": [], "prompt_number": 96 }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ham_word_dict['call']" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 98, - "text": [ - "36" - ] - } - ], - "prompt_number": 98 - }, { "cell_type": "code", "collapsed": false, @@ -1616,6 +329,26 @@ ], "prompt_number": 277 }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ham_word_dict['call']" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 98, + "text": [ + "36" + ] + } + ], + "prompt_number": 98 + }, { "cell_type": "code", "collapsed": false, @@ -1640,7 +373,7 @@ "cell_type": "code", "collapsed": false, "input": [ - "def main_function(bloblist, word1, word2):\n", + "def main_function(bloblist):\n", " lista = []\n", " for blobstring in bloblist:\n", " a = findword(blobstring.lower(), 'i')\n", @@ -1708,24 +441,117 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 279 + "prompt_number": 320 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "y = main_function(bloblist)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 321 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "vector_array = np.array(y)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 322 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "vector_array.shape" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 323, + "text": [ + "(5574, 19)" + ] + } + ], + "prompt_number": 323 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "We got our array of vectors! This is cool, let's train and run some classifiers!" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from sklearn.cross_validation import train_test_split\n", + "from sklearn import metrics" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 308 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "xdata = vector_array[:,:-1 ]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 297 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "xdata.shape" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 298, + "text": [ + "(5574, 18)" + ] + } + ], + "prompt_number": 298 }, { "cell_type": "code", "collapsed": false, "input": [ - "y = main_function(bloblist, 'cust', 'call')" + "ydata = vector_array[:, -1]" ], "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 280 + "prompt_number": 300 }, { "cell_type": "code", "collapsed": false, "input": [ - "y" + "ydata.shape" ], "language": "python", "metadata": {}, @@ -1733,1013 +559,180 @@ { "metadata": {}, "output_type": "pyout", - "prompt_number": 281, + "prompt_number": 303, + "text": [ + "(5574,)" + ] + } + ], + "prompt_number": 303 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "x_train, x_test, y_train, y_test = train_test_split(xdata, ydata, test_size=0.4, random_state=0)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 304 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def run_classifier(clf, x_train, x_test, y_train, y_test, x_data, y_data):\n", + " clf.fit(x_train, y_train)\n", + " predicted = clf.predict(x_test)\n", + " print(metrics.classification_report(y_test, predicted))\n", + " print(metrics.confusion_matrix(y_test, predicted))\n", + " print(metrics.f1_score(y_test, predicted))\n", + " scores = cross_val_score(clf, xdata, ydata, cv=5)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 305 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from sklearn.naive_bayes import BernoulliNB\n", + "from sklearn.cross_validation import cross_val_score\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 312 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "bernoulli = BernoulliNB()\n", + "run_classifier(bernoulli, x_train, x_test, y_train, y_test, xdata, ydata)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", "text": [ - 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" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n", - " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0],\n", - " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n", - " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n", - " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n", - " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n", - " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n", - " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n", - " [1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],\n", - " ...]" + " precision recall f1-score support\n", + "\n", + " 0 0.98 0.99 0.99 1928\n", + " 1 0.94 0.89 0.91 302\n", + "\n", + "avg / total 0.98 0.98 0.98 2230\n", + "\n", + "[[1910 18]\n", + " [ 34 268]]\n", + "0.91156462585\n" ] } ], - "prompt_number": 281 + "prompt_number": 311 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "gauss = GaussianNB()\n", + "run_classifier(gauss, x_train, x_test, y_train, y_test, xdata, ydata)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.98 0.97 0.98 1928\n", + " 1 0.81 0.90 0.85 302\n", + "\n", + "avg / total 0.96 0.96 0.96 2230\n", + "\n", + "[[1863 65]\n", + " [ 29 273]]\n", + "0.853125\n" + ] + } + ], + "prompt_number": 314 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "tree = DecisionTreeClassifier()\n", + "run_classifier(tree, x_train, x_test, y_train, y_test, xdata, ydata)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.98 0.99 0.99 1928\n", + " 1 0.96 0.86 0.91 302\n", + "\n", + "avg / total 0.98 0.98 0.98 2230\n", + "\n", + "[[1918 10]\n", + " [ 43 259]]\n", + "0.907180385289\n" + ] + } + ], + "prompt_number": 315 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "forest = RandomForestClassifier()\n", + "run_classifier(forest, x_train, x_test, y_train, y_test, xdata, ydata)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.98 0.99 0.99 1928\n", + " 1 0.95 0.87 0.91 302\n", + "\n", + "avg / total 0.98 0.98 0.98 2230\n", + "\n", + "[[1914 14]\n", + " [ 40 262]]\n", + "0.906574394464\n" + ] + } + ], + "prompt_number": 316 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Let's see if we can figure anything out with the clusters:" + ] } ], "metadata": {} diff --git a/sms_spam.py b/sms_spam.py index 1fce683..db2d53c 100644 --- a/sms_spam.py +++ b/sms_spam.py @@ -1,7 +1,23 @@ import numpy as np from textblob import TextBlob import re -from sklearn.feature_extraction.text import TfidfVectorizer as TfidV + + +def word_frequency(bloblist): + a_dict = {} + for blob in bloblist: + xlist = [x for x in blob.words] + for item in xlist: + if item.lower() in a_dict: + a_dict[item.lower()] += 1 + else: + a_dict[item.lower()] = 1 + return a_dict + + +def top_dict(a_dict, num): + sorted_list = sorted(a_dict.items(), key=lambda x: x[1], reverse=True) + return sorted_list[0:num] def main_function(bloblist, word1, word2): @@ -36,18 +52,21 @@ def findword(blobstring, word): else: return 1 + def startword(blobstring): if blobstring.starts_with('spam'): return 1 else: return 0 + def wordlength(blobstring, num1, num2): if num1 < len(blobstring.words) <= num2: return 1 else: return 0 + def numbercheck1(blobstring): astring = str(blobstring) if re.search(r"[0-9]{1}", astring): @@ -55,6 +74,7 @@ def numbercheck1(blobstring): else: return 0 + def numbercheck5(blobstring): astring = str(blobstring) if re.search(r"[0-9]{5}", astring): @@ -62,6 +82,7 @@ def numbercheck5(blobstring): else: return 0 + def numbercheck10(blobstring): astring = str(blobstring) if re.search(r"[0-9]{10}", astring): From 4327afe19443cd733692c914e7aab3223aea6ea1 Mon Sep 17 00:00:00 2001 From: Bret Runestad Date: Wed, 11 Feb 2015 21:43:26 -0500 Subject: [PATCH 4/4] added some clustering --- sms spam.ipynb | 255 ++++++++++++++++++++++++++++++++++++++++++++++++- sms_spam.py | 16 +++- 2 files changed, 269 insertions(+), 2 deletions(-) diff --git a/sms spam.ipynb b/sms spam.ipynb index a4975a7..53632c3 100644 --- a/sms spam.ipynb +++ b/sms spam.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:7390fc7c015e81a56b382ac35e3aaa17c30f3471f1761077d78909f6c127a4d9" + "signature": "sha256:6866a1ca8f2d1885f51260e6e326c96132ab4c385a0b846b1565f5cd9b6de1a4" }, "nbformat": 3, "nbformat_minor": 0, @@ -22,6 +22,14 @@ "outputs": [], "prompt_number": 255 }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Let's read in this text:" + ] + }, { "cell_type": "code", "collapsed": false, @@ -45,6 +53,14 @@ "outputs": [], "prompt_number": 319 }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "How does TextBlob work?" + ] + }, { "cell_type": "code", "collapsed": false, @@ -77,6 +93,14 @@ ], "prompt_number": 126 }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "MAKE EVERYTHING A TEXTBLOB!!" + ] + }, { "cell_type": "code", "collapsed": false, @@ -170,6 +194,14 @@ ], "prompt_number": 23 }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "I'm glad I wrote such a great word_frequency program once upon a time that can easily be tweaked for this:" + ] + }, { "cell_type": "code", "collapsed": false, @@ -236,6 +268,14 @@ ], "prompt_number": 278 }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "I had the idea to make a set of ham words and a set of spam words to cross reference them. I don't end up getting a ton out of it, partly because I had wanted to limit all of my features to True/False so I could do the Bernoulli Classifier. John Mitsch put no such constraints on himself and ended up using the idea of two sets to make a powerful spam filter." + ] + }, { "cell_type": "code", "collapsed": false, @@ -296,6 +336,14 @@ "outputs": [], "prompt_number": 96 }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "I made myself a nifty little function that shows how many times a word appears in each category of sms, and what percentage of the texts it is in for that category" + ] + }, { "cell_type": "code", "collapsed": false, @@ -369,6 +417,38 @@ ], "prompt_number": 274 }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Here are all my functions for assembling my array of vectors/features. My features have 3 main categories:" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "1: Whether or not a particular keyword is in the text" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "2. How many words are in the text. I created 5 buckets of word lengths" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "3. Whether they had numbers, and whether they had consecutive numbers." + ] + }, { "cell_type": "code", "collapsed": false, @@ -610,6 +690,14 @@ "outputs": [], "prompt_number": 312 }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Bernoulli - f1 score: 0.912" + ] + }, { "cell_type": "code", "collapsed": false, @@ -639,6 +727,14 @@ ], "prompt_number": 311 }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Gaussian Naive Bayesian = f1 score: 0.853" + ] + }, { "cell_type": "code", "collapsed": false, @@ -668,6 +764,14 @@ ], "prompt_number": 314 }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Decision Tree - f1 score: 0.907" + ] + }, { "cell_type": "code", "collapsed": false, @@ -697,6 +801,14 @@ ], "prompt_number": 315 }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Random Forest - f1 score: 0.907" + ] + }, { "cell_type": "code", "collapsed": false, @@ -733,6 +845,147 @@ "source": [ "Let's see if we can figure anything out with the clusters:" ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from sklearn.cluster import KMeans" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 324 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "kmeans = KMeans(6)\n", + "kmeans.fit(xdata)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 350, + "text": [ + "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=6, n_init=10,\n", + " n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\n", + " verbose=0)" + ] + } + ], + "prompt_number": 350 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clusters = kmeans.predict(xdata)\n", + "clusters" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "/Users/bretrunestad/.pyenv/versions/charting/lib/python3.4/site-packages/sklearn/cluster/k_means_.py:797: RuntimeWarning: Got data type int64, converted to float to avoid overflows\n", + " X = self._check_test_data(X)\n" + ] + }, + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 351, + "text": [ + "array([3, 4, 5, ..., 1, 3, 4], dtype=int32)" + ] + } + ], + "prompt_number": 351 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#kmeans.cluster_centers_" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 356 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "kmeans.cluster_centers_.round()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 353, + "text": [ + "array([[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., -0., -0., -0.,\n", + " 1., 0., 0., 0., -0.],\n", + " [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n", + " -0., -0., 0., 0., -0.],\n", + " [-0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., -0., -0.,\n", + " 0., -0., 0., 0., 0.],\n", + " [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n", + " -0., 0., 0., 0., -0.],\n", + " [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n", + " -0., -0., 0., 0., -0.],\n", + " [ 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n", + " 0., 0., 1., 1., 1.]])" + ] + } + ], + "prompt_number": 353 + }, + { + "cell_type": "heading", + "level": 6, + "metadata": {}, + "source": [ + "I had to get up to six clusters before there was a group that had a 0 for that initial \"i\" group. There seems to always be a goup that clusters around the number conditions at the end, as the last cluster does here. Other than that they seem to cluster around different lengths of the text message(by number of words). That seems to indicate that most of my keyword features weren't particularly beneficial." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# a = findword(blobstring.lower(), 'i')\n", + "# b = findword(blobstring.lower(), 'free')\n", + "# c = findword(blobstring.lower(), 'mobile')\n", + "# d = findword(blobstring.lower(), 'call')\n", + "# e = findword(blobstring.lower(), 'cash')\n", + "# f = findword(blobstring.lower(), 'cust')\n", + "# g = findword(blobstring.lower(), 'cash')\n", + "# h = findword(blobstring.lower(), 'stop')\n", + "# i = findword(blobstring.lower(), 'reply')\n", + "# j = findword(blobstring.lower(), 'txt')\n", + "# l1 = wordlength(blobstring, 0, 10)\n", + "# l2 = wordlength(blobstring, 10, 20)\n", + "# l3 = wordlength(blobstring, 20, 30)\n", + "# l4 = wordlength(blobstring, 30, 40)\n", + "# l5 = wordlength(blobstring, 40, 999999)\n", + "# nc1 = numbercheck1(blobstring)\n", + "# nc5 = numbercheck5(blobstring)\n", + "# nc10 = numbercheck10(blobstring)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 354 } ], "metadata": {} diff --git a/sms_spam.py b/sms_spam.py index db2d53c..b7e71af 100644 --- a/sms_spam.py +++ b/sms_spam.py @@ -4,6 +4,8 @@ def word_frequency(bloblist): + """Creates a dictionary of all the words that appear in the list + and the number of times they appear""" a_dict = {} for blob in bloblist: xlist = [x for x in blob.words] @@ -16,11 +18,14 @@ def word_frequency(bloblist): def top_dict(a_dict, num): + """Returns the top 'num' most frequently appearing words""" sorted_list = sorted(a_dict.items(), key=lambda x: x[1], reverse=True) return sorted_list[0:num] def main_function(bloblist, word1, word2): + """Makes a list of lists. Each list is a list of the 0/1 values + for each feature""" lista = [] for blobstring in bloblist: a = findword(blobstring.lower(), 'i') @@ -42,11 +47,13 @@ def main_function(bloblist, word1, word2): nc5 = numbercheck5(blobstring) nc10 = numbercheck10(blobstring) z = startword(blobstring) - lista.append([a, b, c, d, e, f, g, h, i, j, l1, l2, l3, l4, l5, nc1, nc5, nc10, z]) + lista.append([a, b, c, d, e, f, g, h, i, j, l1, l2, l3, l4, l5, + nc1, nc5, nc10, z]) return lista def findword(blobstring, word): + """If the word is in the string, returns 1. Otherwise returns 0.""" if blobstring.find(word) == -1: return 0 else: @@ -54,6 +61,8 @@ def findword(blobstring, word): def startword(blobstring): + """Checks to see if the string starts with word spam. 1 for spam, + 0 for ham.""" if blobstring.starts_with('spam'): return 1 else: @@ -61,6 +70,8 @@ def startword(blobstring): def wordlength(blobstring, num1, num2): + """Checks to see if the number of words in the string is between + num1 and num2""" if num1 < len(blobstring.words) <= num2: return 1 else: @@ -68,6 +79,7 @@ def wordlength(blobstring, num1, num2): def numbercheck1(blobstring): + """Checks to see if there's a number in the string""" astring = str(blobstring) if re.search(r"[0-9]{1}", astring): return 1 @@ -76,6 +88,7 @@ def numbercheck1(blobstring): def numbercheck5(blobstring): + """Checks to see if there's 5 numbers in a row in the string""" astring = str(blobstring) if re.search(r"[0-9]{5}", astring): return 1 @@ -84,6 +97,7 @@ def numbercheck5(blobstring): def numbercheck10(blobstring): + """Checks to see if there's 10 numbers in a row in the string""" astring = str(blobstring) if re.search(r"[0-9]{10}", astring): return 1