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annotate amat.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 bd937e845bbb
children
rev   line source
266
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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 class AMat:
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6 """DataSource to access a plearn amat file as a periodic unrandomized stream.
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7
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8 Attributes:
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9
470
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10 input -- all columns of input
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11 target -- all columns of target
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12 weight -- all columns of weight
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13 extra -- all columns of extra
266
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14
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15 all -- the entire data contents of the amat file
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16 n_examples -- the number of training examples in the file
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17
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18 AMat stands for Ascii Matri[x,ces]
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19
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20 """
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21
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22 marker_size = '#size:'
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23 marker_sizes = '#sizes:'
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24 marker_col_names = '#:'
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25
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26 def __init__(self, path, head=None, update_interval=0, ofile=sys.stdout):
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27
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28 """Load the amat at <path> into memory.
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29
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30 path - str: location of amat file
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31 head - int: stop reading after this many data rows
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32 update_interval - int: print '.' to ofile every <this many> lines
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33 ofile - file: print status, msgs, etc. to this file
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34
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35 """
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36 self.all = None
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37 self.input = None
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38 self.target = None
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39 self.weight = None
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40 self.extra = None
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41
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42 self.header = False
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43 self.header_size = None
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44 self.header_rows = None
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45 self.header_cols = None
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46 self.header_sizes = None
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47 self.header_col_names = []
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48
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49 data_started = False
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50 data = array.array('d')
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51
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52 f = open(path)
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53 n_data_lines = 0
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54 len_float_line = None
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55
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56 for i,line in enumerate(f):
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57 if n_data_lines == head:
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58 #we've read enough data,
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59 # break even if there's more in the file
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60 break
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61 if len(line) == 0 or line == '\n':
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62 continue
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63 if line[0] == '#':
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64 if not data_started:
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65 #the condition means that the file has a header, and we're on
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66 # some header line
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67 self.header = True
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68 if line.startswith(AMat.marker_size):
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69 info = line[len(AMat.marker_size):]
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70 self.header_size = [int(s) for s in info.split()]
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71 self.header_rows, self.header_cols = self.header_size
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72 if line.startswith(AMat.marker_col_names):
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73 info = line[len(AMat.marker_col_names):]
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74 self.header_col_names = info.split()
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75 elif line.startswith(AMat.marker_sizes):
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76 info = line[len(AMat.marker_sizes):]
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77 self.header_sizes = [int(s) for s in info.split()]
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78 else:
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79 #the first non-commented line tells us that the header is done
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80 data_started = True
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81 float_line = [float(s) for s in line.split()]
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82 if len_float_line is None:
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83 len_float_line = len(float_line)
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84 if (self.header_cols is not None) \
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85 and self.header_cols != len_float_line:
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86 print >> sys.stderr, \
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87 'WARNING: header declared %i cols but first line has %i, using %i',\
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88 self.header_cols, len_float_line, len_float_line
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89 else:
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90 if len_float_line != len(float_line):
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91 raise IOError('wrong line length', i, line)
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92 data.extend(float_line)
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93 n_data_lines += 1
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94
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95 if update_interval > 0 and (ofile is not None) \
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96 and n_data_lines % update_interval == 0:
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97 ofile.write('.')
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98 ofile.flush()
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99
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100 if update_interval > 0:
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101 ofile.write('\n')
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102 f.close()
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103
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104 # convert from array.array to numpy.ndarray
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105 nshape = (len(data) / len_float_line, len_float_line)
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106 self.all = numpy.frombuffer(data).reshape(nshape)
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107 self.n_examples = self.all.shape[0]
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108
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109 # assign
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110 if self.header_sizes is not None:
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111 if len(self.header_sizes) > 4:
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112 print >> sys.stderr, 'WARNING: ignoring sizes after 4th in %s' % path
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113 leftmost = 0
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114 #here we make use of the fact that if header_sizes has len < 4
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115 # the loop will exit before 4 iterations
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116 attrlist = ['input', 'target', 'weight', 'extra']
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117 for attr, ncols in zip(attrlist, self.header_sizes):
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118 setattr(self, attr, self.all[:, leftmost:leftmost+ncols])
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119 leftmost += ncols
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120