Mercurial > pylearn

annotate amat.py @ 284:8e923cb2e8fc

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renamed file
author Frederic Bastien <bastienf@iro.umontreal.ca>
date Fri, 06 Jun 2008 13:52:37 -0400
parents 6e69fb91f3c0
children bd937e845bbb
rev   line source
266
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James Bergstra <bergstrj@iro.umontreal.ca>
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1 """load PLearn AMat files"""
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2
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3 import sys, numpy, array
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4
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5 path_MNIST = '/u/bergstrj/pub/data/mnist.amat'
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6
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7
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8 class AMat:
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9 """DataSource to access a plearn amat file as a periodic unrandomized stream.
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10
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11 Attributes:
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12
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13 input -- minibatch of input
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14 target -- minibatch of target
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15 weight -- minibatch of weight
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16 extra -- minitbatch of extra
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17
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18 all -- the entire data contents of the amat file
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19 n_examples -- the number of training examples in the file
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20
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21 AMat stands for Ascii Matri[x,ces]
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22
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23 """
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24
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25 marker_size = '#size:'
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26 marker_sizes = '#sizes:'
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27 marker_col_names = '#:'
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28
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29 def __init__(self, path, head=None, update_interval=0, ofile=sys.stdout):
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30
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31 """Load the amat at <path> into memory.
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32
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33 path - str: location of amat file
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34 head - int: stop reading after this many data rows
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35 update_interval - int: print '.' to ofile every <this many> lines
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36 ofile - file: print status, msgs, etc. to this file
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37
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38 """
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39 self.all = None
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40 self.input = None
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41 self.target = None
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42 self.weight = None
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43 self.extra = None
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44
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45 self.header = False
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46 self.header_size = None
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47 self.header_rows = None
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48 self.header_cols = None
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49 self.header_sizes = None
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50 self.header_col_names = []
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51
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52 data_started = False
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53 data = array.array('d')
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54
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55 f = open(path)
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56 n_data_lines = 0
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57 len_float_line = None
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58
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59 for i,line in enumerate(f):
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60 if n_data_lines == head:
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61 #we've read enough data,
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62 # break even if there's more in the file
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63 break
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64 if len(line) == 0 or line == '\n':
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65 continue
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66 if line[0] == '#':
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67 if not data_started:
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68 #the condition means that the file has a header, and we're on
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69 # some header line
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70 self.header = True
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71 if line.startswith(AMat.marker_size):
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72 info = line[len(AMat.marker_size):]
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73 self.header_size = [int(s) for s in info.split()]
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74 self.header_rows, self.header_cols = self.header_size
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75 if line.startswith(AMat.marker_col_names):
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76 info = line[len(AMat.marker_col_names):]
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77 self.header_col_names = info.split()
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78 elif line.startswith(AMat.marker_sizes):
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79 info = line[len(AMat.marker_sizes):]
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80 self.header_sizes = [int(s) for s in info.split()]
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81 else:
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82 #the first non-commented line tells us that the header is done
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83 data_started = True
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84 float_line = [float(s) for s in line.split()]
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85 if len_float_line is None:
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86 len_float_line = len(float_line)
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87 if (self.header_cols is not None) \
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88 and self.header_cols != len_float_line:
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89 print >> sys.stderr, \
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90 'WARNING: header declared %i cols but first line has %i, using %i',\
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91 self.header_cols, len_float_line, len_float_line
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92 else:
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93 if len_float_line != len(float_line):
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94 raise IOError('wrong line length', i, line)
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95 data.extend(float_line)
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96 n_data_lines += 1
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97
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98 if update_interval > 0 and (ofile is not None) \
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99 and n_data_lines % update_interval == 0:
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100 ofile.write('.')
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101 ofile.flush()
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102
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103 if update_interval > 0:
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104 ofile.write('\n')
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105 f.close()
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106
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107 # convert from array.array to numpy.ndarray
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108 nshape = (len(data) / len_float_line, len_float_line)
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109 self.all = numpy.frombuffer(data).reshape(nshape)
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110 self.n_examples = self.all.shape[0]
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111
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112 # assign
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113 if self.header_sizes is not None:
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114 if len(self.header_sizes) > 4:
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115 print >> sys.stderr, 'WARNING: ignoring sizes after 4th in %s' % path
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116 leftmost = 0
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117 #here we make use of the fact that if header_sizes has len < 4
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118 # the loop will exit before 4 iterations
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119 attrlist = ['input', 'target', 'weight', 'extra']
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120 for attr, ncols in zip(attrlist, self.header_sizes):
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121 setattr(self, attr, self.all[:, leftmost:leftmost+ncols])
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122 leftmost += ncols
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123