In [1]:

    

import nltk
import numpy


    

In [3]:

    

nltk.download()


    

    




showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml






    
Out[3]:





True

In [5]:

    

sentence = "At eight o'clock on Thursday morning Arthur didn't feel very good."


    

In [6]:

    

tokens = nltk.word_tokenize(sentence)


    

In [7]:

    

tokens


    

    
Out[7]:





['At',
 'eight',
 "o'clock",
 'on',
 'Thursday',
 'morning',
 'Arthur',
 'did',
 "n't",
 'feel',
 'very',
 'good',
 '.']

In [8]:

    

tagged = nltk.pos_tag(tokens)


    

In [9]:

    

tagged


    

    
Out[9]:





[('At', 'IN'),
 ('eight', 'CD'),
 ("o'clock", 'NN'),
 ('on', 'IN'),
 ('Thursday', 'NNP'),
 ('morning', 'NN'),
 ('Arthur', 'NNP'),
 ('did', 'VBD'),
 ("n't", 'RB'),
 ('feel', 'VB'),
 ('very', 'RB'),
 ('good', 'JJ'),
 ('.', '.')]

In [82]:

    

import email


    

In [97]:

    

msg = email.message_from_file(open('corpus/spam_2/00224.1b3430b101a8a8b22493c4948fcbe9cc', 'rb'))


    

In [98]:

    

msg.get_content_type()


    

    
Out[98]:





'multipart/related'

In [99]:

    

msg.get_payload()


    

    
Out[99]:





[<email.message.Message instance at 0x106c1e998>]

In [101]:

    

msg.get('Content-Transfer-Encoding')


    

In [102]:

    

for part in msg.walk():
    print part.get('Content-Transfer-Encoding')


    

    




None
None
base64

In [89]:

    

for part in msg.walk():
    if part.get_content_type().lower() == 'text/plain':
        print part.get('Content-Transfer-Encoding')


    

    




None

In [80]:

    

from HTMLParser import HTMLParser


    

In [88]:

    

# create a subclass and override the handler methods
class MyHTMLParser(HTMLParser):
    
    _contents = []
    
    def handle_data(self, data):
        data = data.strip()
        data = data.replace('\r', '')
        data = data.replace('\n', '')
        if data:
            self._contents.append(data)
            
    def get_body(self):
        return ' '.join(self._contents)


    

In [89]:

    

parser = MyHTMLParser()


    

In [90]:

    

parser.feed(msg.get_payload())


    

In [91]:

    

parser.get_body()


    

    
Out[91]:





"Congratulations! You Get a Free Handheld Organizer! Dear Friend, I have your Personal Digital Organizer. It's FREE, but I need to know where to send it. Click                Here and complete the form. Organize your life and keep track of            appointments, names and numbers with this modern digital organizer.            Store up to 100 important text notes and 100 names/numbers.            Features easy to use, touch-screen technology, 10 digit calculator,            currency and metric converters, alarm clock and password            protection. Plus you can try all of our money-saving benefits            FREE for 30 days. Act            Now! You also get a full subscription to HOME Magazine at no additional cost! This offer is risk-free , registration only takes a minute and is completely secure.It's that easy. Your satisfaction is guaranteed because our credibility is on the line. P.S.:This offer is only valid for the next 48 hours, so Act Now! Copyright 2002, American Homeowners Association (AHA). All rights reserved. You are receiving this mailing because you are amember of SendGreatOffers.com and subscribed as:JM@NETNOTEINC.COMTo unsubscribe Click Here (http://admanmail.com/subscription.asp?em=JM@NETNOTEINC.COM =SGO)or reply to this email with REMOVE in the subject line - you mustalso include the body of this message to be unsubscribed. Any correspondence aboutthe products/services should be directed tothe company in the ad.%EM%JM@NETNOTEINC.COM%/EM%"

In [92]:

    

from os import path


    

In [93]:

    

dir(path)


    

    
Out[93]:





['__all__',
 '__builtins__',
 '__doc__',
 '__file__',
 '__name__',
 '__package__',
 '_joinrealpath',
 '_unicode',
 '_uvarprog',
 '_varprog',
 'abspath',
 'altsep',
 'basename',
 'commonprefix',
 'curdir',
 'defpath',
 'devnull',
 'dirname',
 'exists',
 'expanduser',
 'expandvars',
 'extsep',
 'genericpath',
 'getatime',
 'getctime',
 'getmtime',
 'getsize',
 'isabs',
 'isdir',
 'isfile',
 'islink',
 'ismount',
 'join',
 'lexists',
 'normcase',
 'normpath',
 'os',
 'pardir',
 'pathsep',
 'realpath',
 'relpath',
 'samefile',
 'sameopenfile',
 'samestat',
 'sep',
 'split',
 'splitdrive',
 'splitext',
 'stat',
 'supports_unicode_filenames',
 'sys',
 'walk',
 'warnings']

In [98]:

    

import os


    

In [100]:

    

for sub_file in os.walk('/Users/apple/Documents/github_repos/spam_email_classifier/corpus'):
    print(sub_file)


    

    




('/Users/apple/Documents/github_repos/spam_email_classifier/corpus', ['easy_ham'], ['20021010_easy_ham.tar.bz2', '20021010_hard_ham.tar.bz2', '20021010_spam.tar.bz2', '20030228_easy_ham.tar.bz2', '20030228_easy_ham_2.tar.bz2', '20030228_hard_ham.tar.bz2', '20030228_spam.tar.bz2', '20030228_spam_2.tar.bz2'])
('/Users/apple/Documents/github_repos/spam_email_classifier/corpus/easy_ham', [], ['0001.ea7e79d3153e7469e7a9c3e0af6a357e', '0002.b3120c4bcbf3101e661161ee7efcb8bf', '0003.acfc5ad94bbd27118a0d8685d18c89dd', '0004.e8d5727378ddde5c3be181df593f1712', '0005.8c3b9e9c0f3f183ddaf7592a11b99957', '0006.ee8b0dba12856155222be180ba122058', '0007.c75188382f64b090022fa3b095b020b0', '0008.20bc0b4ba2d99aae1c7098069f611a9b', '0009.435ae292d75abb1ca492dcc2d5cf1570', '0010.4996141de3f21e858c22f88231a9f463', '0011.07b11073b53634cff892a7988289a72e', '0012.d354b2d2f24d1036caf1374dd94f4c94', '0013.ff597adee000d073ae72200b0af00cd1', '0014.532e0a17d0674ba7a9baa7b0afe5fb52', '0015.a9ff8d7550759f6ab62cc200bdf156e7', '0016.d82758030e304d41fb3f4ebbb7d9dd91', '0017.d81093a2182fc9135df6d9158a8ebfd6', '0018.ba70ecbeea6f427b951067f34e23bae6', '0019.a8a1b2767e83b3be653e4af0148e1897', '0020.ef397cef16f8041242e3b6560e168053', '0021.f8e73bdba7277d967af4d561f0402129', '0022.7241da4491c49b50c0470a3638ee35c4', '0023.b4a61a2990263e8825246e41a8d78399', '0024.771af861a302951df7630ec4ff1965a2', '0025.64d0382de54b9e4c5b4200173133b8d7', '0026.6baf1aea162ccb9a6e9f142c0715ceb4', '0027.11da06d9130a188bf0ffb2060881dbe9', '0028.54cf7aa229456fb33194b3a12a713e3e', '0029.7119e865bff73aca46681d96a451cb60', '0030.77828e31de08ebb58b583688b87524cc', '0031.af5b387661e1f58d3787ac41139106e5', '0032.f07a3f2152f2bedfc9492459c171012c', '0033.e3fd617544226dc06abf36c95a9a2d11', '0034.4be53f8dac3bd651ace83a38b6313c45', '0035.50b257408356cfcfce8cf2afe9fd7959', '0036.7ca216a105267375948e13b460d1fb8f', '0037.0c57a93c0241775d406efecf43ba19cf', '0038.f2ffa1197c969d067be4d01c26fa9b5e', '0039.5bf1ec6602c4657bac8d566604572ad5', '0040.930593a7e616570a2b63f2d774847316', '0041.af4e0891f17484c10c032f713bce43de', '0042.7ef6c72f88de15d72b1c0d9c164ccb78', '0043.2ea45c42b1ea15bc214723e142b81127', '0044.f1db2c76854ee58bc73d0c85ca6a86d2', '0045.910fa2e79f96c646f1b6c987c65cbe3f', '0046.05e8a863eaf20f90374d0a15427d9a16', '0047.5c3e049737a2813d4ac6f13f02362fb1', '0048.6dbad96d78f9dd6100a4ad2a8b8086b6', '0049.bda43370915afa1f557f7edab6913e04', '0050.fd9291b33ecd99af26da03a7d4152dc2', '0051.9281d3f8a3faf47d09a7fafdf2caf26e', '0052.40794e238104d6b01f36b0f4d5145175', '0053.71bcb179e702d98e88fd0bc081ba0f52', '0054.a6fa82d1b26c7772829e54ec41a1fbd3', '0055.b5509ace7ed62c900779c0c51cca92a3', '0056.6ac9eb23e2ef6c7e7eabc3fb0ac3038a', '0057.be5e34dcebd922928045634015e3ed78', '0058.a7c28223050c35c3a22ac4e2b5969111', '0059.b3b1b1d95976b70d9bbd6c39c6b8d5d2', '0060.877bf70a297621b9dc3bab0095f95027', '0061.20319a885b8cf6c88c8098eafd731396', '0062.b675bdb7b9e2321dfe97e48037fe7782', '0063.7823bd01e05ee0a1dda2f73f880838eb', '0064.7160290efcb7320ac8852369a695bcaf', '0065.b72ddcc517cc317f3fc1e79c3feeca15', '0066.dcf72ba90be7570f2f7edab45b23ae36', '0067.d77af11e9b2a74048c073462efecca12', '0068.f1c604a78739e4f966253d762c972dde', '0069.7173de1d2da14306c5a20e8abda7a6e2', '0070.4f269f2d783b479971f31006fe17ce62', '0071.75403094cab986a246c1e7ce3460e827', '0072.d1c080832388ae81835ca069e2efffa3', '0073.cd29b8202a4b05db3c6f0d71b72a3260', '0074.78000652dcb19856e85ff9637f0e52dd', '0075.faa4a28fdd9a82edd6d35ebb6bae3085', '0076.f565b68778786f9b9736f779489331f0', '0077.dc5578862c0e716ee82e78b0dffbc8d2', '0078.8481bc92aa3ab9d23ca30c0eaecfc5e4', '0079.083beadce354774290a4a5bc4175366e', '0080.7c3a836baaa732cd915546442c0fef1a', '0081.9d26c2d149545a1c5d23e2cd4dca1b5f', '0082.7f7858a1a7360410ed120899504c3a25', '0083.e506d3273b3dde9ae2d340cb0197a2a0', '0084.e84ff95488fab22c26801bcd5bae337e', '0085.1a80e2b1720bd5cbeb900554a5477d0b', '0086.b96641b3e0a2cf8c238976336d45a86c', '0087.dd5f3aa35f50008bf7dfc5059ac8a239', '0088.43fa07c598e8b3d272a1a00705398e48', '0089.8b3fea141f5b2f2e5b66a9f12148b7d0', '0090.314ec4268af7a3a1974d5eab84ea65af', '0091.3bdd7b578973ee005733480a8b6c9b54', '0092.a1560d2416687b2db8204c2fa69163f2', '0093.0c71febfdf6f3acbc4d0c76b777a8530', '0094.b7bf14fae9c31d0516cfe00dd9ab068d', '0095.b51416f612ac5737e0f4a5529ce453d1', '0096.0446f3ed63b550a8622c8671d8ae9a9c', '0097.39badf2fea6bcebc640bea05ced59b59', '0098.5053f669dda8f920e5300ed327cdd986', '0099.9f54be08406e67fd8944f2f1d0fbdd90', '0100.1728f45047ff2a1601d4e3ee91f26a00', '0101.48557f7f38d947eab77aefc03d0520a3', '0102.ba8679813c4b5f424fb225f09b2fb1f2', '0103.3fc5444196f4726ee138fbabc5086ea1', '0104.cbb1681451b2525d7aeec9eeb285306d', '0105.be508e1b909bae328d4a13fe898f60fb', '0106.808c5e5ed0b801f667a34ead8221972e', '0107.2447a90f32ab7642ff8309d41c242db2', '0108.e6e9cb097a3b5e37d94a7ff29bc4412a', '0109.4ccc46c546b93015aafdfc40495f187d', '0110.0bb9a36c3037be09867c0251e0fd6a3a', '0111.e10679164e671fd9211c0303af7ee9f0', '0112.b6c2ea75f9f7efcd3d0e4c43a751479c', '0113.55a6bf6a4534d447af2060b174c0d70a', '0114.72c7d2d85d9d99ce3ce268d09147bdea', '0115.41825f367f7f1efcc31e12376d82a994', '0116.4cfce953cce07ef03a0cd55ca18053ba', '0117.fbc79c02a2c13d6c9b36f9ba38a09170', '0118.4ebb343d73a83dc935197850e507aad6', '0119.98b90763eec7b4e91c06939eea2d2361', '0120.27898b8f966a9c687b1f93fcba5afd43', '0121.b475478456e52de66ef0b0fb501bbfd3', '0122.4c48475d458bbca7a14270745e452dd7', '0123.32e1738c4171dd9d70b727fd7973a291', '0124.2912ab5571d121e3b8c32c575b2526a9', '0125.1ada59664dfcb7a5a31e9820f9604fea', '0126.fc7d58812b4c004ee457355c9baf2e1e', '0127.2ac0e9f6527f224201604786789540b2', '0128.f2500674ec39065ae8a1f643c7100a2b', '0129.00f1783f1c15a77f1c5ec212f5bcf1d1', '0130.6007e5b54c03026fd924cf2d6b0b4008', '0131.b715566061ab90d0f8745ce0ac010832', '0132.bcd23223120c0be6b991ae0349cd127c', '0133.fe269b4e9538d5cb9907625c4b63dc2f', '0134.56b4bed3bd3a696ab5b271a6e37fa005', '0135.73f72161ad5758f0645151e87f5ff4b2', '0136.25b4244e9510e810f89f6570d476c0e8', '0137.015cd0ece1324c5514e742e224a77213', '0138.5bc0323b8aa075bd1e99610b50aa1150', '0139.4a3f2cb7efe7318e1ed9b698d1d4a1db', '0140.009d880f185eca560c8496da54b447ae', '0141.911fa855ce043ee41b450e6a1b0096d0', '0142.bcc958dabbad574174becaa16334a7e6', '0143.20b19246597ddb47db7da83f7a140e73', '0144.150618690d46b8b2207c8a27ca5ab837', '0145.7400cf6ab4cfe5ffeaab582c9730c656', '0146.2d301a1e07672538d669f5e571180d32', '0147.16b764c5bd3e419cc5b6d0145b2d8145', '0148.565dd83aaebb66fe284de9342a6a5c09', '0149.d59bb0d23ca8ad3a87e549d3e6172f26', '0150.69bb21344f49b27cec7373599b6e433d', '0151.4d1c8772327e44b20af6420e1d1c0a75', '0152.10d3220188413990b1deb862c509c818', '0153.f78ed3d22e441ab7bd6d006323a83621', '0154.9e065ee6214360e43b9873e39880159e', '0155.56e909508466675c86032e9f031cd09a', '0156.3d3293a5da919cf65959e6bd63feaf27', '0157.18c5e181c72acfc3a501a82907e58a59', '0158.05f321b3cb42b6b62bc8a250a7bad59f', '0159.72f1a01f64bfa9e06b21c4e778e14cd2', '0160.0040550972ece2c27ed01745f93b474b', '0161.5703ef6933a7f189ab48da12a925f5e4', '0162.4eb119208c169c5da0dc13a875cdcb34', '0163.1aff5d9dc36e51a0b43f1c31c4d5b28c', '0164.ae42a102de7318dc4876af403b6e5956', '0165.60d0c03c63a588f58ca91b791610e43c', '0166.f4acdf86d261b60094536bf5a654cc33', '0167.13f1e71a8070d4d34e65ec7a25d9e164', '0168.91cce4c84906ce507bd5e6362dd345c4', '0169.b19a689237947db7b9e244277e075edc', '0170.154c82ae0e1ccc4c9290aa1fa4de7b43', '0171.284f310dea6316d8971a18ff66a1a8dc', '0172.464b84890a849676c036baccf0f2344e', '0173.3898127723ae4ac964f41c1a555c6ecb', '0174.a916896b6119d1b8a138dfbbeeee7fa1', '0175.dd1831e0f3322282d3efa5da7aec74f9', '0176.bd5ef61e0c89ec035829f06f3fe5c7f8', '0177.fd72517a2ed2a2c29c9822b7124a8f0a', '0178.8405bbe39a5eb6c1814e3c16602099a4', '0179.dee566fc951284d1d3bd3a0fdae63f4d', '0180.5790a5f746fbfb83381a28a45889fd6d', '0181.d16658bb110eb37495081efa07319db1', '0182.6a44fa290eebac4c52eb17292623b630', '0183.fea0e7b48e16103e73462d5ee52b0d18', '0184.9006c74135af56c874879f0d45c1a689', '0185.2ed7f65beefa89e31abb4fbc75c18956', '0186.5abf85594c3b8c40a105eed1154569ed', '0187.3e01234a92697a2a414ed394ad7177ed', '0188.a7f061d0c2c0395922993a874d534dcb', '0189.01e4dc6ee1379e5ee100b4c33c9c3afd', '0190.41da72c46bce52a15056a0171b56a917', 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'2447.e93a9792f68b12cbc322eba2353b3c8e', '2448.b0f260e47f1853702da3918968a3588e', '2449.d06b13a245cc7eb0f5bd7ae0b4f3c2f9', '2450.4eba9e73d4d29b754a60647748c9a2d9', '2451.1ccaf9bc09a2b8e339f02d21ac62f437', '2452.462af6aad417a792d50847cddb590f90', '2453.3c30230578edacb961366df72c6fd2ac', '2454.927f352b226246d7bca7badfc2d7aa50', '2455.a8172ce70c990c52ba73c2f2cb17b5f2', '2456.1b028dbafc21c1169e554a1ca612743f', '2457.5fba5a23aa428e3201526f7c978c88c6', '2458.dc9072428527f92ea8b938be335b3621', '2459.f9df87daac76e5eab06b4afa37b3921d', '2460.8a782dd603bfe327c2f7adadf590a094', '2461.20f3b1b3e7e2bd1bd6a016f9f09374d3', '2462.5ae37f6f0ec68fd1c2dbcec70b6413c9', '2463.4120f7bdd5fe52177c1d78f371b69c76', '2464.0c973fb4c6105b3cdc0ce5c786ed6b0c', '2465.2586b48935009c0fcea80ef5d89f4e20', '2466.4c6ca64a8fb037e9515adc98486a8146', '2467.5c3cff250c85a50dd75b0b9e1ffaac3f', '2468.897a44a9936163da20ab13ce60d5e153', '2469.1135462b65bc58a05522e86383b7943c', '2470.e2f55eaf9c7aed2b85c4b4ca65781d3d', '2471.9bc6c0ad8e1151925d35ad52c6667d62', '2472.2f502e69b5201834c4e02ac0ea9c6be3', '2473.2d70d3fac740804c6a280009e0418f13', '2474.e7fce1af310d8b7a2c87dfaeec71c172', '2475.8572d77868481d0fe409eedd8fa6d443', '2476.26c155346386cbeb36ea1c539b294be3', '2477.9b5f251639293269f0980399c10a70d1', '2478.d8deadfe2c1d046587d57a1b03fff346', '2479.a918c490f758e2421f35b66077f2699a', '2480.d652d7fb8b29ab26269aa5668f3e0035', '2481.03fe35f728922f8cf6d9b69771da462e', '2482.67146ca47d2cb5e4657d54c66bf68a69', '2483.9eb471e8645403dda2bd70c08b463faf', '2484.634d1d98cd2c7d4ae85bc5413c399c44', '2485.0d0140da769ebe4de6c1851fb7017352', '2486.5801c079150004024c1e2a0a21fa4521', '2487.68761726d5f3f00dca1dded48e089e68', '2488.8e2706d02807ec4fe59ebf7aaf77a1e7', '2489.b76d126a6aef85219a4ac8d6df5f4963', '2490.f03277d54faea3974942b3213f38268f', '2491.7b6a8fff32b29e04a8638dbb67cd85b9', '2492.a60a61ef3280f7b68df65d4d7cf3afd0', '2493.67725941996521d187945dfaa2f19ed4', '2494.ad5de9e5dc49e51155a6d444e772b671', '2495.8adeb962ec89dedb809a2a314b8a9fef', '2496.b8dd0beb971f9a82a8cdebf562f8dc1f', '2497.0cbb23e8858a0e33bcf2cf6fe158deb3', '2498.462ad5291e185d0288f4a70313e3cfe0', '2499.6fc4e078d141a24f3a81bc4936722fbb', '2500.231666f4f5641227a17a09ba77cd5c18', '2501.36311f6ceac129fa31540555ffe3cc97', '2502.1b59b45ca1fe56edf35ae8a398636806', '2503.43a0b463302d0c595b9829dda18245fc', '2504.9fc1898ab2a75d2d9e9810318bb4e90a', '2505.550c8b2240659bb8bf54b17edea4a96b', '2506.7acaf086619d33409b90223fe066589a', '2507.e7c3831cab3bc377bd62de9912c076f8', '2508.c2a4352b3d889e87d3493e5aff6b571c', '2509.96e57ee2a1f9741a7d09e431f59bb049', '2510.4de2705ed127d2de3f3b5cd1b8043ba6', '2511.1982adbc74d9a84a5ff8db7e7c722028', '2512.d68f3adc32f665ecaf1d096a83d78aa5', '2513.753d70e90eb4759d73d67ea6b9b12617', '2514.f2c8376ccb20e0255fd97cab426f0c3a', '2515.177e746c09892ea4c2631adcc62d73a7', '2516.030f0cecab78d582f6f5be58422acf79', '2517.2dbd54c3580b5fb8e12dea4d876348f3', '2518.9c2a50b66e91110fae3c27851e9ec1dd', '2519.4b5c7eea59a27b30bb33503d1be85950', '2520.554ec4788bbcc1ed13c453719c8d698e', '2521.8e5616c36de4145286f63b8ddd50f429', '2522.dc159c06c191406ece1b0fc98a352bbe', '2523.7b5518a502b8a309e42770179d71626c', '2524.c06019b7e085875e69ff55f43defeb83', '2525.9559a3f0d4d17d33379acdf5bf356435', '2526.7ff8826a8e943eabf68cdcbb4c29c8bc', '2527.fad44667449822628022a95a7cf83ac0', '2528.8b6dd74e9b8d04f5f1338bcec51b0b11', '2529.c3856b9d7d098309eb71e3ed1993aa31', '2530.31d1d0691296d715441ab4e2890d3780', '2531.8f40eb6fd94f9e48d56f6374ddc3427a', '2532.8b61e23f6e94d8b2303554395d0374e5', '2533.ba0ac203656046766a368dbc29cf36a6', '2534.2804e0302718cda7783689fa2a6d90d9', '2535.4d4a1eed6617e6df7c1a5d4ac7d368a2', '2536.601b538e15f39aaac0e038cb860ae762', '2537.2769f2836293c4c9105e6a89fc14fd8e', '2538.e3f596927fd5dd74ad23a0792a449241', '2539.466ecd08a54bf64e857522423a33108a', '2540.9527f844d218311f91ec4d87fd2035ca', '2541.892b976061b9ea684b9b3009d0b87dab', '2542.1a940f58e68fa8a84a3f83e30a624e8d', '2543.1fa39c8b86bc5ae84b837e8c7ae3717d', '2544.1a95794a767c776e71893fda91f59efb', '2545.6ddb98905ca38f1dc76e5266427c0574', '2546.dd6480d3f87f7d525f797d17ac1a0bc8', '2547.18ce2b2d4f757e70154b5c8cfe6e684f', '2548.b2259f104edd1ecbc9dc0327df387147', '2549.d05fe143eb28d1fb19fbdc39781d5e34', '2550.963956a866a2e00b3d5b353ecc9216ae', '2551.3b1f94418de5bd544c977b44bcc7e740'])

In [101]:

    

from os import path


    

In [105]:

    

path.basename('/Users/apple/Documents/github_repos/spam_email_classifier/corpus')


    

    
Out[105]:





'corpus'

In [106]:

    

import re


    

In [107]:

    

re.sub(r'[^a-zA-Z]+', ' ', "i'm ok, thanks!")


    

    
Out[107]:





'i m ok thanks '

In [1]:

    

from nltk.stem import WordNetLemmatizer


    

In [2]:

    

wnl = WordNetLemmatizer()


    

In [3]:

    

print(wnl.lemmatize('dogs'))


    

    




dog

In [5]:

    

import numpy as np


    

In [6]:

    

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])


    

In [7]:

    

np.inner(a, b)


    

    
Out[7]:





32

In [8]:

    

np.exp(a)


    

    
Out[8]:





array([  2.71828183,   7.3890561 ,  20.08553692])

In [9]:

    

np.transpose(a)


    

    
Out[9]:





array([1, 2, 3])

In [10]:

    

np.inner(np.transpose(a), b)


    

    
Out[10]:





32

In [12]:

    

a.shape


    

    
Out[12]:





(3,)

In [13]:

    

c = np.transpose(a)


    

In [14]:

    

c.shape


    

    
Out[14]:





(3,)

In [17]:

    

c = np.arange(12).reshape(3, 4)


    

In [18]:

    

c.shape


    

    
Out[18]:





(3, 4)

In [20]:

    

a.size


    

    
Out[20]:





3

In [22]:

    

a[0]


    

    
Out[22]:





1

In [23]:

    

np.log(np.e)


    

    
Out[23]:





1.0

In [24]:

    

np.pi


    

    
Out[24]:





3.141592653589793

In [25]:

    

c


    

    
Out[25]:





array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

In [26]:

    

c[0]


    

    
Out[26]:





array([0, 1, 2, 3])

In [37]:

    

np.zeros(4)


    

    
Out[37]:





array([ 0.,  0.,  0.,  0.])

In [38]:

    

a


    

    
Out[38]:





array([1, 2, 3])

In [39]:

    

b


    

    
Out[39]:





array([4, 5, 6])

In [40]:

    

a[:] = b[:]


    

In [41]:

    

a


    

    
Out[41]:





array([4, 5, 6])

In [42]:

    

b[0] = 1


    

In [43]:

    

a


    

    
Out[43]:





array([4, 5, 6])

In [44]:

    

b


    

    
Out[44]:





array([1, 5, 6])

In [45]:

    

a = b


    

In [46]:

    

a


    

    
Out[46]:





array([1, 5, 6])

In [47]:

    

b[1] = 0


    

In [48]:

    

a


    

    
Out[48]:





array([1, 0, 6])

In [49]:

    

from os import listdir
from os import path


    

In [56]:

    

file_dir = '/Users/apple/Documents/github_repos/spam_email_classifier/data/preprocess_spam'


    

In [57]:

    

files = [f for f in listdir(file_dir) if path.isfile(path.join(file_dir, f))]


    

In [58]:

    

files[:10]


    

    
Out[58]:





['0000.7b1b73cf36cf9dbc3d64e3f2ee2b91f1',
 '00001.317e78fa8ee2f54cd4890fdc09ba8176',
 '00001.7848dde101aa985090474a91ec93fcf0',
 '00002.9438920e9a55591b18e60d1ed37d992b',
 '00002.d94f1b97e48ed3b553b3508d116e6a09',
 '00003.2ee33bc6eacdb11f38d052c44819ba6c',
 '00003.590eff932f8704d8b0fcbe69d023b54d',
 '00004.bdcc075fa4beb5157b5dd6cd41d8887b',
 '00004.eac8de8d759b7e74154f142194282724',
 '00005.57696a39d7d84318ce497886896bf90d']

In [72]:

    

a = [1, 2, 3]
b = [4, 5, 6]


    

In [73]:

    

a.extend(b)


    

In [103]:

    

from nltk import corpus


    

In [104]:

    

dir(corpus)


    

    
Out[104]:





['AlignedCorpusReader',
 'AlpinoCorpusReader',
 'BNCCorpusReader',
 'BracketParseCorpusReader',
 'CHILDESCorpusReader',
 'CMUDictCorpusReader',
 'CategorizedBracketParseCorpusReader',
 'CategorizedCorpusReader',
 'CategorizedPlaintextCorpusReader',
 'CategorizedSentencesCorpusReader',
 'CategorizedTaggedCorpusReader',
 'ChasenCorpusReader',
 'ChunkedCorpusReader',
 'ComparativeSentencesCorpusReader',
 'ConllChunkCorpusReader',
 'ConllCorpusReader',
 'CorpusReader',
 'CrubadanCorpusReader',
 'DependencyCorpusReader',
 'EuroparlCorpusReader',
 'FramenetCorpusReader',
 'IEERCorpusReader',
 'IPIPANCorpusReader',
 'IndianCorpusReader',
 'KNBCorpusReader',
 'LazyCorpusLoader',
 'LinThesaurusCorpusReader',
 'MTECorpusReader',
 'MacMorphoCorpusReader',
 'NKJPCorpusReader',
 'NPSChatCorpusReader',
 'NombankCorpusReader',
 'NonbreakingPrefixesCorpusReader',
 'OpinionLexiconCorpusReader',
 'PPAttachmentCorpusReader',
 'PanLexLiteCorpusReader',
 'Pl196xCorpusReader',
 'PlaintextCorpusReader',
 'PortugueseCategorizedPlaintextCorpusReader',
 'PropbankCorpusReader',
 'ProsConsCorpusReader',
 'RTECorpusReader',
 'RegexpTokenizer',
 'ReviewsCorpusReader',
 'SemcorCorpusReader',
 'SensevalCorpusReader',
 'SentiSynset',
 'SentiWordNetCorpusReader',
 'SinicaTreebankCorpusReader',
 'StringCategoryCorpusReader',
 'SwadeshCorpusReader',
 'SwitchboardCorpusReader',
 'SyntaxCorpusReader',
 'TEICorpusView',
 'TaggedCorpusReader',
 'TimitCorpusReader',
 'TimitTaggedCorpusReader',
 'ToolboxCorpusReader',
 'TwitterCorpusReader',
 'UdhrCorpusReader',
 'UnicharsCorpusReader',
 'VerbnetCorpusReader',
 'WordListCorpusReader',
 'WordNetCorpusReader',
 'WordNetICCorpusReader',
 'XMLCorpusReader',
 'YCOECorpusReader',
 '_LazyModule__lazymodule_globals',
 '_LazyModule__lazymodule_import',
 '_LazyModule__lazymodule_init',
 '_LazyModule__lazymodule_loaded',
 '_LazyModule__lazymodule_locals',
 '_LazyModule__lazymodule_name',
 '__builtins__',
 '__doc__',
 '__file__',
 '__getattr__',
 '__init__',
 '__lazymodule_loaded',
 '__module__',
 '__name__',
 '__package__',
 '__path__',
 '__repr__',
 '__setattr__',
 'abc',
 'alpino',
 'brown',
 'cess_cat',
 'cess_esp',
 'cmudict',
 'comparative_sentences',
 'comtrans',
 'conll2000',
 'conll2002',
 'conll2007',
 'crubadan',
 'demo',
 'dependency_treebank',
 'find_corpus_fileids',
 'floresta',
 'framenet',
 'framenet15',
 'gazetteers',
 'genesis',
 'gutenberg',
 'ieer',
 'inaugural',
 'indian',
 'ipipan',
 'jeita',
 'knbc',
 'lin_thesaurus',
 'mac_morpho',
 'machado',
 'masc_tagged',
 'movie_reviews',
 'multext_east',
 'names',
 'nkjp',
 'nombank',
 'nombank_ptb',
 'nonbreaking_prefixes',
 'nps_chat',
 'opinion_lexicon',
 'panlex_lite',
 'perluniprops',
 'pl196x',
 'ppattach',
 'product_reviews_1',
 'product_reviews_2',
 'propbank',
 'propbank_ptb',
 'pros_cons',
 'ptb',
 'qc',
 're',
 'reader',
 'reuters',
 'rte',
 'semcor',
 'senseval',
 'sentence_polarity',
 'sentiwordnet',
 'shakespeare',
 'sinica_treebank',
 'state_union',
 'stopwords',
 'subjectivity',
 'swadesh',
 'swadesh110',
 'swadesh207',
 'switchboard',
 'tagged_treebank_para_block_reader',
 'teardown_module',
 'timit',
 'timit_tagged',
 'toolbox',
 'treebank',
 'treebank_chunk',
 'treebank_raw',
 'twitter_samples',
 'udhr',
 'udhr2',
 'universal_treebanks',
 'util',
 'verbnet',
 'webtext',
 'wordnet',
 'wordnet_ic',
 'words',
 'ycoe']

In [111]:

    

from nltk.corpus import brown


    

In [114]:

    

len(brown.words())


    

    
Out[114]:





1161192

In [123]:

    

import sys
from os import path
sys.path.append('./libsvm')


    

In [124]:

    

from svmutil import *


    

In [125]:

    

y, x = svm_read_problem('./libsvm/heart_scale')


    

In [126]:

    

m = svm_train(y[:200], x[:200], '-c 4')


    

In [127]:

    

p_label, p_acc, p_val = svm_predict(y[200:], x[200:], m)


    

    




Accuracy = 84.2857% (59/70) (classification)

In [128]:

    

svm_save_model('./libsvm/heart_scale.model', m)


    

In [130]:

    

m = svm_load_model('./libsvm/heart_scale.model')


    

In [132]:

    

p_label, p_acc, p_val = svm_predict(y, x, m)


    

    




Accuracy = 89.2593% (240/270) (classification)

In [133]:

    

ACC, MSE, SCC = evaluations(y, p_label)


    

In [134]:

    

ACC


    

    
Out[134]:





89.25925925925925

In [135]:

    

MSE


    

    
Out[135]:





0.42962962962962964

In [136]:

    

y, x = [1,-1], [{1:1, 3:1}, {1:-1,3:-1}]
prob  = svm_problem(y, x)
param = svm_parameter('-t 0 -c 4 -b 1')
m = svm_train(prob, param)
p_label, p_acc, p_val = svm_predict(y, x, m, '-b 1')
ACC, MSE, SCC = evaluations(y, p_label)


    

    




Accuracy = 0% (0/2) (classification)

In [137]:

    

ACC


    

    
Out[137]:





0.0

In [143]:

    

a = np.zeros((4, 4))


    

In [144]:

    

b = [[y for y in x] for x in a]


    

In [145]:

    

b


    

    
Out[145]:





[[0.0, 0.0, 0.0, 0.0],
 [0.0, 0.0, 0.0, 0.0],
 [0.0, 0.0, 0.0, 0.0],
 [0.0, 0.0, 0.0, 0.0]]

In [147]:

    

c = 0.1
if c:
    print 'ok'


    

    




ok

In [151]:

    

np.array([1, 2, 3])


    

    
Out[151]:





array([1, 2, 3])

In [152]:

    

dir(b)


    

    
Out[152]:





['__add__',
 '__class__',
 '__contains__',
 '__delattr__',
 '__delitem__',
 '__delslice__',
 '__doc__',
 '__eq__',
 '__format__',
 '__ge__',
 '__getattribute__',
 '__getitem__',
 '__getslice__',
 '__gt__',
 '__hash__',
 '__iadd__',
 '__imul__',
 '__init__',
 '__iter__',
 '__le__',
 '__len__',
 '__lt__',
 '__mul__',
 '__ne__',
 '__new__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__reversed__',
 '__rmul__',
 '__setattr__',
 '__setitem__',
 '__setslice__',
 '__sizeof__',
 '__str__',
 '__subclasshook__',
 'append',
 'count',
 'extend',
 'index',
 'insert',
 'pop',
 'remove',
 'reverse',
 'sort']

In [185]:

    

a = np.zeros((3, 3))
b = np.ones(3)
print b
a[0] = b
print a


    

    




[ 1.  1.  1.]
[[ 1.  1.  1.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]]

In [186]:

    

dir(a)


    

    
Out[186]:





['T',
 '__abs__',
 '__add__',
 '__and__',
 '__array__',
 '__array_finalize__',
 '__array_interface__',
 '__array_prepare__',
 '__array_priority__',
 '__array_struct__',
 '__array_wrap__',
 '__class__',
 '__contains__',
 '__copy__',
 '__deepcopy__',
 '__delattr__',
 '__delitem__',
 '__delslice__',
 '__div__',
 '__divmod__',
 '__doc__',
 '__eq__',
 '__float__',
 '__floordiv__',
 '__format__',
 '__ge__',
 '__getattribute__',
 '__getitem__',
 '__getslice__',
 '__gt__',
 '__hash__',
 '__hex__',
 '__iadd__',
 '__iand__',
 '__idiv__',
 '__ifloordiv__',
 '__ilshift__',
 '__imod__',
 '__imul__',
 '__index__',
 '__init__',
 '__int__',
 '__invert__',
 '__ior__',
 '__ipow__',
 '__irshift__',
 '__isub__',
 '__iter__',
 '__itruediv__',
 '__ixor__',
 '__le__',
 '__len__',
 '__long__',
 '__lshift__',
 '__lt__',
 '__mod__',
 '__mul__',
 '__ne__',
 '__neg__',
 '__new__',
 '__nonzero__',
 '__oct__',
 '__or__',
 '__pos__',
 '__pow__',
 '__radd__',
 '__rand__',
 '__rdiv__',
 '__rdivmod__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__rfloordiv__',
 '__rlshift__',
 '__rmod__',
 '__rmul__',
 '__ror__',
 '__rpow__',
 '__rrshift__',
 '__rshift__',
 '__rsub__',
 '__rtruediv__',
 '__rxor__',
 '__setattr__',
 '__setitem__',
 '__setslice__',
 '__setstate__',
 '__sizeof__',
 '__str__',
 '__sub__',
 '__subclasshook__',
 '__truediv__',
 '__xor__',
 'all',
 'any',
 'argmax',
 'argmin',
 'argpartition',
 'argsort',
 'astype',
 'base',
 'byteswap',
 'choose',
 'clip',
 'compress',
 'conj',
 'conjugate',
 'copy',
 'ctypes',
 'cumprod',
 'cumsum',
 'data',
 'diagonal',
 'dot',
 'dtype',
 'dump',
 'dumps',
 'fill',
 'flags',
 'flat',
 'flatten',
 'getfield',
 'imag',
 'item',
 'itemset',
 'itemsize',
 'max',
 'mean',
 'min',
 'nbytes',
 'ndim',
 'newbyteorder',
 'nonzero',
 'partition',
 'prod',
 'ptp',
 'put',
 'ravel',
 'real',
 'repeat',
 'reshape',
 'resize',
 'round',
 'searchsorted',
 'setfield',
 'setflags',
 'shape',
 'size',
 'sort',
 'squeeze',
 'std',
 'strides',
 'sum',
 'swapaxes',
 'take',
 'tobytes',
 'tofile',
 'tolist',
 'tostring',
 'trace',
 'transpose',
 'var',
 'view']

In [155]:

    

a.shape


    

    
Out[155]:





(3, 3)

In [157]:

    

c = np.array([b])
print c


    

    




[[ 1.  1.  1.]]

In [182]:

    

from random import shuffle
l = [1, 'aaa', 4, 'bbb', 2]
shuffle(l)
l


    

    
Out[182]:





[1, 'bbb', 2, 4, 'aaa']

In [183]:

    

from shutil import copyfile
copyfile('README.md', 'dst')


    

In [184]:

    

path.basename('/aaa/bbb/ccc.dat')


    

    
Out[184]:





'ccc.dat'

In [187]:

    

np.log(0)


    

    




/Users/apple/venv/lib/python2.7/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log
  if __name__ == '__main__':






    
Out[187]:





-inf

In [197]:

    

a = np.zeros((3, 3))


    

In [202]:

    

b = np.ones((3, 1))


    

In [203]:

    

b.shape


    

    
Out[203]:





(3, 1)

In [204]:

    

a * b


    

    
Out[204]:





array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.],
       [ 0.,  0.,  0.]])

In [209]:

    

from random import uniform
uniform(0, 10)


    

    
Out[209]:





5.805332786326785

In [210]:

    

int(20)


    

    
Out[210]:





20

In [243]:

    

a = np.ones((3,3))
b = np.ones(3)
print a.dot(b)
print b.dot(b)


    

    




[ 3.  3.  3.]
3.0

In [267]:

    

a = np.ones((3,3))
b = a[:][1:]
c = b*b
print c.sum()


    

    




6.0

In [1]:

    

import gensim


    

In [275]:

    

class MyIter(object):
    def __init__(self, n):
        self.n = n
        self.i = 0
        
    def __iter__(self):
        return self
    
    def next(self):
        if self.i < self.n:
            i = self.i
            self.i += 1
            return i
        else:
            raise StopIteration
i = MyIter(10)
for j in i:
    print(j)


    

    




0
1
2
3
4
5
6
7
8
9

In [ ]: