Mercurial > pylearn
annotate embeddings/process.py @ 470:bd937e845bbb
new stuff: algorithms/logistic_regression, datasets/MNIST
author | James Bergstra <bergstrj@iro.umontreal.ca> |
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date | Wed, 22 Oct 2008 15:56:53 -0400 |
parents | 4335309f4924 |
children | 90a76a8238e8 |
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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 |
461 | 8 from parameters import * |
9 | |
10 __words = None | |
11 __word_to_embedding = None | |
12 __read = False | |
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13 |
461 | 14 def word_to_embedding(w): |
15 read_embeddings() | |
16 return __word_to_embedding[w] | |
17 | |
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18 def read_embeddings(): |
461 | 19 global __words |
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 |
461 | 24 __words = [string.strip(w) for w in open(VOCABFILE).readlines()] |
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 |
461 | 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)] |
461 | 39 w = __words[i] |
40 __word_to_embedding[w] = l | |
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41 __read = True |
464 | 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]") |
460 | 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. |
460 | 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) |
461 | 95 # if w in __word_to_embedding: |
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 |
461 | 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() |