aima-python/text.py at master · aesgitrep/aima-python · GitHub

History

372 lines (286 loc) · 13.2 KB

Raw

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

"""Statistical Language Processing tools. (Chapter 22)

We define Unigram and Ngram text models, use them to generate random text,

and show the Viterbi algorithm for segmentatioon of letters into words.

Then we show a very simple Information Retrieval system, and an example

working on a tiny sample of Unix manual pages."""

from utils import argmin

from learning import CountingProbDist

import search

from math import log, exp

from collections import defaultdict

import heapq

import re

import os

class UnigramTextModel(CountingProbDist):

"""This is a discrete probability distribution over words, so you

can add, sample, or get P[word], just like with CountingProbDist. You can

also generate a random text n words long with P.samples(n)"""

def samples(self, n):

"Return a string of n words, random according to the model."

return ' '.join(self.sample() for i in range(n))

class NgramTextModel(CountingProbDist):

"""This is a discrete probability distribution over n-tuples of words.

You can add, sample or get P[(word1, ..., wordn)]. The method P.samples(n)

builds up an n-word sequence; P.add and P.add_sequence add data."""

def __init__(self, n, observation_sequence=[], default=0):

# In addition to the dictionary of n-tuples, cond_prob is a

# mapping from (w1, ..., wn-1) to P(wn | w1, ... wn-1)

CountingProbDist.__init__(self, default=default)

self.n = n

self.cond_prob = defaultdict()

self.add_sequence(observation_sequence)

# __getitem__, top, sample inherited from CountingProbDist

# Note they deal with tuples, not strings, as inputs

def add(self, ngram):

"""Count 1 for P[(w1, ..., wn)] and for P(wn | (w1, ..., wn-1)"""

CountingProbDist.add(self, ngram)

if ngram[:-1] not in self.cond_prob:

self.cond_prob[ngram[:-1]] = CountingProbDist()

self.cond_prob[ngram[:-1]].add(ngram[-1])

def add_sequence(self, words):

"""Add each of the tuple words[i:i+n], using a sliding window.

Prefix some copies of the empty word, '', to make the start work."""

n = self.n

words = ['', ] * (n - 1) + words

for i in range(len(words) - n):

self.add(tuple(words[i:i + n]))

def samples(self, nwords):

"""Build up a random sample of text nwords words long, using

the conditional probability given the n-1 preceding words."""

n = self.n

nminus1gram = ('',) * (n-1)

output = []

for i in range(nwords):

if nminus1gram not in self.cond_prob:

nminus1gram = ('',) * (n-1) # Cannot continue, so restart.

wn = self.cond_prob[nminus1gram].sample()

output.append(wn)

nminus1gram = nminus1gram[1:] + (wn,)

return ' '.join(output)

# ______________________________________________________________________________

def viterbi_segment(text, P):

"""Find the best segmentation of the string of characters, given the

UnigramTextModel P."""

# best[i] = best probability for text[0:i]

# words[i] = best word ending at position i

n = len(text)

words = [''] + list(text)

best = [1.0] + [0.0] * n

# Fill in the vectors best, words via dynamic programming

for i in range(n+1):

for j in range(0, i):

w = text[j:i]

if P[w] * best[i - len(w)] >= best[i]:

best[i] = P[w] * best[i - len(w)]

words[i] = w

# Now recover the sequence of best words

sequence = []

i = len(words) - 1

while i > 0:

sequence[0:0] = [words[i]]

i = i - len(words[i])

# Return sequence of best words and overall probability

return sequence, best[-1]

# ______________________________________________________________________________

# TODO(tmrts): Expose raw index

class IRSystem:

"""A very simple Information Retrieval System, as discussed in Sect. 23.2.

The constructor s = IRSystem('the a') builds an empty system with two

stopwords. Next, index several documents with s.index_document(text, url).

Then ask queries with s.query('query words', n) to retrieve the top n

matching documents. Queries are literal words from the document,

except that stopwords are ignored, and there is one special syntax:

The query "learn: man cat", for example, runs "man cat" and indexes it."""

def __init__(self, stopwords='the a of'):

"""Create an IR System. Optionally specify stopwords."""

# index is a map of {word: {docid: count}}, where docid is an int,

# indicating the index into the documents list.

self.index = defaultdict(lambda: defaultdict(int))

self.stopwords = set(words(stopwords))

self.documents = []

def index_collection(self, filenames):

"Index a whole collection of files."

prefix = os.path.dirname(__file__)

for filename in filenames:

self.index_document(open(filename).read(),

os.path.relpath(filename, prefix))

def index_document(self, text, url):

"Index the text of a document."

# For now, use first line for title

title = text[:text.index('\n')].strip()

docwords = words(text)

docid = len(self.documents)

self.documents.append(Document(title, url, len(docwords)))

for word in docwords:

if word not in self.stopwords:

self.index[word][docid] += 1

def query(self, query_text, n=10):

"""Return a list of n (score, docid) pairs for the best matches.

Also handle the special syntax for 'learn: command'."""

if query_text.startswith("learn:"):

doctext = os.popen(query_text[len("learn:"):], 'r').read()

self.index_document(doctext, query_text)

return []

qwords = [w for w in words(query_text) if w not in self.stopwords]

shortest = argmin(qwords, key=lambda w: len(self.index[w]))

docids = self.index[shortest]

return heapq.nlargest(n, ((self.total_score(qwords, docid), docid) for docid in docids))

def score(self, word, docid):

"Compute a score for this word on the document with this docid."

# There are many options; here we take a very simple approach

return (log(1 + self.index[word][docid]) /

log(1 + self.documents[docid].nwords))

def total_score(self, words, docid):

"Compute the sum of the scores of these words on the document with this docid."

return sum(self.score(word, docid) for word in words)

def present(self, results):

"Present the results as a list."

for (score, docid) in results:

doc = self.documents[docid]

print(

("{:5.2}|{:25} | {}".format(100 * score, doc.url,

doc.title[:45].expandtabs())))

def present_results(self, query_text, n=10):

"Get results for the query and present them."

self.present(self.query(query_text, n))

class UnixConsultant(IRSystem):

"""A trivial IR system over a small collection of Unix man pages."""

def __init__(self):

IRSystem.__init__(self, stopwords="how do i the a of")

import os

aima_root = os.path.dirname(__file__)

mandir = os.path.join(aima_root, 'aima-data/MAN/')

man_files = [mandir + f for f in os.listdir(mandir)

if f.endswith('.txt')]

self.index_collection(man_files)

class Document:

"""Metadata for a document: title and url; maybe add others later."""

def __init__(self, title, url, nwords):

self.title = title

self.url = url

self.nwords = nwords

def words(text, reg=re.compile('[a-z0-9]+')):

"""Return a list of the words in text, ignoring punctuation and

converting everything to lowercase (to canonicalize).

>>> words("``EGAD!'' Edgar cried.")

['egad', 'edgar', 'cried']

"""

return reg.findall(text.lower())

def canonicalize(text):

"""Return a canonical text: only lowercase letters and blanks.

>>> canonicalize("``EGAD!'' Edgar cried.")

'egad edgar cried'

"""

return ' '.join(words(text))

# ______________________________________________________________________________

# Example application (not in book): decode a cipher.

# A cipher is a code that substitutes one character for another.

# A shift cipher is a rotation of the letters in the alphabet,

# such as the famous rot13, which maps A to N, B to M, etc.

alphabet = 'abcdefghijklmnopqrstuvwxyz'

# Encoding

def shift_encode(plaintext, n):

"""Encode text with a shift cipher that moves each letter up by n letters.

>>> shift_encode('abc z', 1)

'bcd a'

"""

return encode(plaintext, alphabet[n:] + alphabet[:n])

def rot13(plaintext):

"""Encode text by rotating letters by 13 spaces in the alphabet.

>>> rot13('hello')

'uryyb'

>>> rot13(rot13('hello'))

'hello'

"""

return shift_encode(plaintext, 13)

def translate(plaintext, function):

"""Translate chars of a plaintext with the given function."""

result = ""

for char in plaintext:

result += function(char)

return result

def maketrans(from_, to_):

"""Create a translation table and return the proper function."""

trans_table = {}

for n, char in enumerate(from_):

trans_table[char] = to_[n]

return lambda char: trans_table.get(char, char)

def encode(plaintext, code):

"Encodes text, using a code which is a permutation of the alphabet."

trans = maketrans(alphabet + alphabet.upper(), code + code.upper())

return translate(plaintext, trans)

def bigrams(text):

"""Return a list of pairs in text (a sequence of letters or words).

>>> bigrams('this')

['th', 'hi', 'is']

>>> bigrams(['this', 'is', 'a', 'test'])

[['this', 'is'], ['is', 'a'], ['a', 'test']]

"""

return [text[i:i + 2] for i in range(len(text) - 1)]

# Decoding a Shift (or Caesar) Cipher

class ShiftDecoder:

"""There are only 26 possible encodings, so we can try all of them,

and return the one with the highest probability, according to a

bigram probability distribution."""

def __init__(self, training_text):

training_text = canonicalize(training_text)

self.P2 = CountingProbDist(bigrams(training_text), default=1)

def score(self, plaintext):

"Return a score for text based on how common letters pairs are."

s = 1.0

for bi in bigrams(plaintext):

s = s * self.P2[bi]

return s

def decode(self, ciphertext):

"Return the shift decoding of text with the best score."

list_ = [(self.score(shift), shift)

for shift in all_shifts(ciphertext)]

return max(list_, key=lambda elm: elm[0])[1]

def all_shifts(text):

"Return a list of all 26 possible encodings of text by a shift cipher."

yield from (shift_encode(text, i) for i, _ in enumerate(alphabet))

# Decoding a General Permutation Cipher

class PermutationDecoder:

"""This is a much harder problem than the shift decoder. There are 26!

permutations, so we can't try them all. Instead we have to search.

We want to search well, but there are many things to consider:

Unigram probabilities (E is the most common letter); Bigram probabilities

(TH is the most common bigram); word probabilities (I and A are the most

common one-letter words, etc.); etc.

We could represent a search state as a permutation of the 26 letters,

and alter the solution through hill climbing. With an initial guess

based on unigram probabilities, this would probably fare well. However,

I chose instead to have an incremental representation. A state is

represented as a letter-to-letter map; for example {'z': 'e'} to

represent that 'z' will be translated to 'e'.

"""

def __init__(self, training_text, ciphertext=None):

self.Pwords = UnigramTextModel(words(training_text))

self.P1 = UnigramTextModel(training_text) # By letter

self.P2 = NgramTextModel(2, training_text) # By letter pair

def decode(self, ciphertext):

"Search for a decoding of the ciphertext."

self.ciphertext = ciphertext

problem = PermutationDecoderProblem(decoder=self)

return search.best_first_tree_search(

problem, lambda node: self.score(node.state))

def score(self, code):

"""Score is product of word scores, unigram scores, and bigram scores.

This can get very small, so we use logs and exp."""

text = permutation_decode(self.ciphertext, code)

logP = (sum([log(self.Pwords[word]) for word in words(text)]) +

sum([log(self.P1[c]) for c in text]) +

sum([log(self.P2[b]) for b in bigrams(text)]))

return exp(logP)

class PermutationDecoderProblem(search.Problem):

def __init__(self, initial=None, goal=None, decoder=None):

self.initial = initial or {}

self.decoder = decoder

def actions(self, state):

# Find the best

p, plainchar = max([(self.decoder.P1[c], c)

for c in alphabet if c not in state])

succs = [extend(state, plainchar, cipherchar)] # ???? # noqa

def goal_test(self, state):

"We're done when we get all 26 letters assigned."

return len(state) >= 26

aima-python/text.py at master · aesgitrep/aima-python · GitHub

Expand file tree

Breadcrumbs

text.py

Latest commit

History

Breadcrumbs

text.py

File metadata and controls