Mercurial > pylearn

annotate embeddings/process.py @ 532:34ee3aff3e8f

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Improved embedding word preprocessing.
author Joseph Turian <turian@gmail.com>
date Tue, 18 Nov 2008 02:57:50 -0500
parents 90a76a8238e8
children de974b4fc4ea
rev   line source
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1 """
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2 Read in the weights file
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3 """
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4
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5 import string
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6 import sys
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7
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8 from parameters import *
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9
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10 __words = None
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11 __word_to_embedding = None
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12 __read = False
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13
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14 def length():
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15 """
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16 @return: The length of embeddings
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17 """
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18 len(__word_to_embedding[0])
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19
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20 def word_to_embedding(w):
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21 read_embeddings()
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22 return __word_to_embedding[w]
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23
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24 def read_embeddings():
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25 global __words
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26 global __word_to_embedding
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27 global __read
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28 if __read: return
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29
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30 __words = [string.strip(w) for w in open(VOCABFILE).readlines()]
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31 assert len(__words) == NUMBER_OF_WORDS
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32
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33 import numpy, math
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34 from common.str import percent
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35
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36 __word_to_embedding = {}
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37
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38 sys.stderr.write("Reading %s...\n" % WEIGHTSFILE)
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39 f = open(WEIGHTSFILE)
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40 f.readline()
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41 vals = [float(v) for v in string.split(f.readline())]
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42 assert len(vals) == NUMBER_OF_WORDS * DIMENSIONS
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43 for i in range(NUMBER_OF_WORDS):
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44 l = vals[DIMENSIONS*i:DIMENSIONS*(i+1)]
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45 w = __words[i]
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46 __word_to_embedding[w] = l
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47 __read = True
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48 for w in __word_to_embedding: assert len(__word_to_embedding[__words[0]]) == len(__word_to_embedding[w])
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49 sys.stderr.write("...done reading %s\n" % WEIGHTSFILE)
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50
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51 import re
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52 numberre = re.compile("[0-9]")
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53 slashre = re.compile("\\\/")
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54
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55 def preprocess_word(origw):
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56 """
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57 Convert a word so that it can be embedded directly.
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58 Returned the preprocessed sequence.
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59 @note: Preprocessing is appropriate for Penn Treebank style documents.
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60 """
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61 read_embeddings()
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62 if origw == "-LRB-": w = "("
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63 elif origw == "-RRB-": w = ")"
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64 elif origw == "-LCB-": w = "{"
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65 elif origw == "-RCB-": w = "}"
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66 elif origw == "-LSB-": w = "["
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67 elif origw == "-RSB-": w = "]"
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68 else:
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69 w = origw
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70 if w not in __word_to_embedding:
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71 w = string.lower(w)
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72 w = slashre.sub("/", w)
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73 w = numberre.sub("NUMBER", w)
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74 if w not in __word_to_embedding:
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75 # sys.stderr.write("Word not in vocabulary, using %s: %s (original %s)\n" % (UNKNOWN, w, origw))
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76 w = UNKNOWN
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77 assert w in __word_to_embedding
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78 return w
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79
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80 def preprocess_seq(l):
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81 """
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82 Convert a sequence so that it can be embedded directly.
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83 Returned the preprocessed sequence.
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84 @note: Preprocessing is appropriate for Penn Treebank style documents.
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85 """
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86 read_embeddings()
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87 lnew = []
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88 for origw in l:
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89 w = preprocess_word(origw)
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90 lnew.append(w)
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91 return lnew
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92
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93 #def convert_string(s, strict=False):
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94 # """
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95 # Convert a string to a sequence of embeddings.
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96 # @param strict: If strict, then words *must* be in the vocabulary.
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97 # @todo: DEPRECATED Remove this function.
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98 # """
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99 # read_embeddings()
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100 # e = []
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101 # for origw in string.split(string.lower(s)):
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102 # w = numberre.sub("NUMBER", origw)
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103 # if w in __word_to_embedding:
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104 # e.append(__word_to_embedding[w])
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105 # else:
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106 # sys.stderr.write("Word not in vocabulary, using %s: %s (original %s)\n" % (UNKNOWN, w, origw))
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107 # assert not strict
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108 # e.append(__word_to_embedding[UNKNOWN])
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109 # return e
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110
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111 #def test():
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112 # """
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113 # Debugging code.
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114 # """
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115 # read_embeddings()
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116 # for w in __word_to_embedding:
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117 # assert len(__word_to_embedding[w]) == 50
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118 # import numpy
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119 # for w1 in __words:
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120 # e1 = numpy.asarray(__word_to_embedding[w1])
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121 # lst = []
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122 # print w1, numpy.dot(e1, e1)
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123 # for w2 in __word_to_embedding:
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124 # if w1 >= w2: continue
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125 # e2 = numpy.asarray(__word_to_embedding[w2])
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126 # d = (e1 - e2)
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127 # l2 = numpy.dot(d, d)
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128 # lst.append((l2, w1, w2))
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129 # lst.sort()
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130 # print lst[:10]
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131 #
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132 #test()