Mercurial > pylearn

annotate embeddings/process.py @ 499:a419edf4e06c

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removed unpicklable nested classes in logistic regression
author James Bergstra <bergstrj@iro.umontreal.ca>
date Tue, 28 Oct 2008 12:57:49 -0400
parents 4335309f4924
children 90a76a8238e8
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 word_to_embedding(w):
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15 read_embeddings()
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16 return __word_to_embedding[w]
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17
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18 def read_embeddings():
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19 global __words
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20 global __word_to_embedding
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21 global __read
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22 if __read: return
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23
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24 __words = [string.strip(w) for w in open(VOCABFILE).readlines()]
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25 assert len(__words) == NUMBER_OF_WORDS
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26
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27 import numpy, math
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28 from common.str import percent
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29
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30 __word_to_embedding = {}
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31
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32 sys.stderr.write("Reading %s...\n" % WEIGHTSFILE)
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33 f = open(WEIGHTSFILE)
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34 f.readline()
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35 vals = [float(v) for v in string.split(f.readline())]
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36 assert len(vals) == NUMBER_OF_WORDS * DIMENSIONS
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37 for i in range(NUMBER_OF_WORDS):
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38 l = vals[DIMENSIONS*i:DIMENSIONS*(i+1)]
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39 w = __words[i]
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40 __word_to_embedding[w] = l
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41 __read = True
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42 sys.stderr.write("...done reading %s\n" % WEIGHTSFILE)
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43
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44 import re
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45 numberre = re.compile("[0-9]")
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46 slashre = re.compile("\\\/")
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47
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48 def preprocess_word(origw):
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49 """
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50 Convert a word so that it can be embedded directly.
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51 Returned the preprocessed sequence.
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52 @note: Preprocessing is appropriate for Penn Treebank style documents.
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53 """
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54 read_embeddings()
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55 if origw == "-LRB-": w = "("
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56 elif origw == "-RRB-": w = ")"
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57 elif origw == "-LCB-": w = "{"
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58 elif origw == "-RCB-": w = "}"
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59 elif origw == "-LSB-": w = "["
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60 elif origw == "-RSB-": w = "]"
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61 else:
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62 w = origw
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63 w = string.lower(w)
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64 w = slashre.sub("/", w)
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65 w = numberre.sub("NUMBER", w)
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66 if w not in __word_to_embedding:
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67 # sys.stderr.write("Word not in vocabulary, using %s: %s (original %s)\n" % (UNKNOWN, w, origw))
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68 w = UNKNOWN
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69 assert w in __word_to_embedding
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70 return w
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71
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72 def preprocess_seq(l):
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73 """
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74 Convert a sequence so that it can be embedded directly.
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75 Returned the preprocessed sequence.
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76 @note: Preprocessing is appropriate for Penn Treebank style documents.
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77 """
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78 read_embeddings()
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79 lnew = []
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80 for origw in l:
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81 w = preprocess_word(origw)
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82 lnew.append(w)
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83 return lnew
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84
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85 #def convert_string(s, strict=False):
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86 # """
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87 # Convert a string to a sequence of embeddings.
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88 # @param strict: If strict, then words *must* be in the vocabulary.
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89 # @todo: DEPRECATED Remove this function.
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90 # """
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91 # read_embeddings()
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92 # e = []
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93 # for origw in string.split(string.lower(s)):
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94 # w = numberre.sub("NUMBER", origw)
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95 # if w in __word_to_embedding:
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96 # e.append(__word_to_embedding[w])
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97 # else:
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98 # sys.stderr.write("Word not in vocabulary, using %s: %s (original %s)\n" % (UNKNOWN, w, origw))
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99 # assert not strict
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100 # e.append(__word_to_embedding[UNKNOWN])
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101 # return e
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102
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103 #def test():
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104 # """
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105 # Debugging code.
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106 # """
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107 # read_embeddings()
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108 # for w in __word_to_embedding:
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109 # assert len(__word_to_embedding[w]) == 50
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110 # import numpy
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111 # for w1 in __words:
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112 # e1 = numpy.asarray(__word_to_embedding[w1])
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113 # lst = []
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114 # print w1, numpy.dot(e1, e1)
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115 # for w2 in __word_to_embedding:
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116 # if w1 >= w2: continue
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117 # e2 = numpy.asarray(__word_to_embedding[w2])
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118 # d = (e1 - e2)
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119 # l2 = numpy.dot(d, d)
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120 # lst.append((l2, w1, w2))
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121 # lst.sort()
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122 # print lst[:10]
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123 #
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124 #test()