10 """Remove all non-alphabetic characters from a text
11 >>> letters('The Quick')
13 >>> letters('The Quick BROWN fox jumped! over... the (9lazy) DOG')
14 'TheQuickBROWNfoxjumpedoverthelazyDOG'
16 return ''.join([c
for c
in text
if c
in string
.ascii_letters
])
19 """Remove all accents from letters.
20 It does this by converting the unicode string to decomposed compatability
21 form, dropping all the combining accents, then re-encoding the bytes.
34 return unicodedata
.normalize('NFKD', text
).\
35 encode('ascii', 'ignore').\
39 """Remove all non-alphabetic characters and convert the text to lowercase
41 >>> sanitise('The Quick')
43 >>> sanitise('The Quick BROWN fox jumped! over... the (9lazy) DOG')
44 'thequickbrownfoxjumpedoverthelazydog'
48 # sanitised = [c.lower() for c in text if c in string.ascii_letters]
49 # return ''.join(sanitised)
50 return letters(unaccent(text
)).lower()
53 def datafile(name
, sep
='\t'):
54 """Read key,value pairs from file.
56 with
open(name
, 'r') as f
:
58 splits
= line
.split(sep
)
59 yield [splits
[0], int(splits
[1])]
61 english_counts
= collections
.Counter(dict(datafile('count_1l.txt')))
62 normalised_english_counts
= norms
.normalise(english_counts
)
64 english_bigram_counts
= collections
.Counter(dict(datafile('count_2l.txt')))
65 normalised_english_bigram_counts
= norms
.normalise(english_bigram_counts
)
67 english_trigram_counts
= collections
.Counter(dict(datafile('count_3l.txt')))
68 normalised_english_trigram_counts
= norms
.normalise(english_trigram_counts
)
70 with
open('words.txt', 'r') as f
:
71 keywords
= [line
.rstrip() for line
in f
]
74 def weighted_choice(d
):
75 """Generate random item from a dictionary of item counts
77 target
= random
.uniform(0, sum(d
.values()))
79 for (l
, p
) in d
.items():
85 def random_english_letter():
86 """Generate a random letter based on English letter counts
88 return weighted_choice(normalised_english_counts
)
92 """Returns all n-grams of a text
94 >>> ngrams(sanitise('the quick brown fox'), 2) # doctest: +NORMALIZE_WHITESPACE
95 ['th', 'he', 'eq', 'qu', 'ui', 'ic', 'ck', 'kb', 'br', 'ro', 'ow', 'wn',
97 >>> ngrams(sanitise('the quick brown fox'), 4) # doctest: +NORMALIZE_WHITESPACE
98 ['theq', 'hequ', 'equi', 'quic', 'uick', 'ickb', 'ckbr', 'kbro', 'brow',
99 'rown', 'ownf', 'wnfo', 'nfox']
101 return [text
[i
:i
+n
] for i
in range(len(text
)-n
+1)]
105 """A probability distribution estimated from counts in datafile.
106 Values are stored and returned as log probabilities.
108 def __init__(self
, data
=[], estimate_of_missing
=None):
109 data1
, data2
= itertools
.tee(data
)
110 self
.total
= sum([d
[1] for d
in data1
])
111 for key
, count
in data2
:
112 self
[key
] = log10(count
/ self
.total
)
113 self
.estimate_of_missing
= estimate_of_missing
or (lambda k
, N
: 1./N
)
114 def __missing__(self
, key
):
115 return self
.estimate_of_missing(key
, self
.total
)
117 def log_probability_of_unknown_word(key
, N
):
118 """Estimate the probability of an unknown word.
120 return -log10(N
* 10**((len(key
) - 2) * 1.4))
122 Pw
= Pdist(datafile('count_1w.txt'), log_probability_of_unknown_word
)
123 Pw_wrong
= Pdist(datafile('count_1w.txt'), lambda _k
, N
: log10(1/N
))
124 Pl
= Pdist(datafile('count_1l.txt'), lambda _k
, _N
: 0)
125 P2l
= Pdist(datafile('count_2l.txt'), lambda _k
, _N
: 0)
126 P3l
= Pdist(datafile('count_3l.txt'), lambda _k
, _N
: 0)
129 """The Naive Bayes log probability of a sequence of words.
131 return sum(Pw
[w
.lower()] for w
in words
)
133 def Pwords_wrong(words
):
134 """The Naive Bayes log probability of a sequence of words.
136 return sum(Pw_wrong
[w
.lower()] for w
in words
)
139 def Pletters(letters
):
140 """The Naive Bayes log probability of a sequence of letters.
142 return sum(Pl
[l
.lower()] for l
in letters
)
144 def Pbigrams(letters
):
145 """The Naive Bayes log probability of the bigrams formed from a sequence
148 return sum(P2l
[p
] for p
in ngrams(letters
, 2))
150 def Ptrigrams(letters
):
151 """The Naive Bayes log probability of the trigrams formed from a sequence
154 return sum(P3l
[p
] for p
in ngrams(letters
, 3))
157 def cosine_similarity_score(text
):
158 """Finds the dissimilarity of a text to English, using the cosine distance
159 of the frequency distribution.
161 >>> cosine_similarity_score('abcabc') # doctest: +ELLIPSIS
164 return norms
.cosine_similarity(english_counts
,
165 collections
.Counter(sanitise(text
)))
168 if __name__
== "__main__":