Mercurial > ift6266
annotate utils/seriestables/series.py @ 276:727ed56fad12
Add reworked code for convolutional auto-encoder.
author | Arnaud Bergeron <abergeron@gmail.com> |
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date | Mon, 22 Mar 2010 13:33:29 -0400 |
parents | 0515a8901c6a |
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rev | line source |
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Ajouté un paquet de type/value checks à SeriesTables, et finalisé les docstrings. Ajouté 3-4 tests. Légers refactorings ici et là sans conséquences externes.
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1 import tables |
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2 |
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3 import numpy |
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4 import time |
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5 |
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6 ############################################################################## |
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7 # Utility functions to create IsDescription objects (pytables data types) |
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8 |
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9 ''' |
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10 The way these "IsDescription constructor" work is simple: write the |
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11 code as if it were in a file, then exec()ute it, leaving us with |
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12 a local-scoped LocalDescription which may be used to call createTable. |
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13 |
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14 It's a small hack, but it's necessary as the names of the columns |
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15 are retrieved based on the variable name, which we can't programmatically set |
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16 otherwise. |
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17 ''' |
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18 |
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19 def _get_description_timestamp_cpuclock_columns(store_timestamp, store_cpuclock, pos=0): |
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20 toexec = "" |
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Corrigé un bug avec store_timestamp/cpuclock, et tests pour éviter ce cas
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21 |
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22 if store_timestamp: |
220
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23 toexec += "\ttimestamp = tables.Time32Col(pos="+str(pos)+")\n" |
218
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24 pos += 1 |
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25 |
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26 if store_cpuclock: |
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27 toexec += "\tcpuclock = tables.Float64Col(pos="+str(pos)+")\n" |
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28 pos += 1 |
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29 |
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30 return toexec, pos |
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31 |
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32 def _get_description_n_ints(int_names, int_width=64, pos=0): |
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33 """ |
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34 Begins construction of a class inheriting from IsDescription |
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35 to construct an HDF5 table with index columns named with int_names. |
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36 |
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37 See Series().__init__ to see how those are used. |
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38 """ |
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39 int_constructor = "tables.Int64Col" |
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40 if int_width == 32: |
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41 int_constructor = "tables.Int32Col" |
221 | 42 elif not int_width in (32, 64): |
43 raise "int_width must be left unspecified, or should equal 32 or 64" | |
208
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44 |
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45 toexec = "" |
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46 |
acb942530923
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47 for n in int_names: |
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48 toexec += "\t" + n + " = " + int_constructor + "(pos=" + str(pos) + ")\n" |
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49 pos += 1 |
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50 |
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51 return toexec, pos |
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52 |
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53 def _get_description_with_n_ints_n_floats(int_names, float_names, |
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54 int_width=64, float_width=32, |
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55 store_timestamp=True, store_cpuclock=True): |
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56 """ |
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57 Constructs a class to be used when constructing a table with PyTables. |
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58 |
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59 This is useful to construct a series with an index with multiple levels. |
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60 E.g. if you want to index your "validation error" with "epoch" first, then |
acb942530923
Completely rewrote my series module, now based on HDF5 and PyTables (in a separate directory called 'tables_series' for retrocompatibility of running code). Minor (inconsequential) changes to stacked_dae.
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61 "minibatch_index" second, you'd use two "int_names". |
acb942530923
Completely rewrote my series module, now based on HDF5 and PyTables (in a separate directory called 'tables_series' for retrocompatibility of running code). Minor (inconsequential) changes to stacked_dae.
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62 |
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63 Parameters |
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64 ---------- |
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65 int_names : tuple of str |
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66 Names of the int (e.g. index) columns |
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67 float_names : tuple of str |
acb942530923
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68 Names of the float (e.g. error) columns |
acb942530923
Completely rewrote my series module, now based on HDF5 and PyTables (in a separate directory called 'tables_series' for retrocompatibility of running code). Minor (inconsequential) changes to stacked_dae.
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69 int_width : {'32', '64'} |
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70 Type of ints. |
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71 float_width : {'32', '64'} |
acb942530923
Completely rewrote my series module, now based on HDF5 and PyTables (in a separate directory called 'tables_series' for retrocompatibility of running code). Minor (inconsequential) changes to stacked_dae.
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72 Type of floats. |
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73 store_timestamp : bool |
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74 See __init__ of Series |
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75 store_cpuclock : bool |
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76 See __init__ of Series |
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77 |
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78 Returns |
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79 ------- |
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80 A class object, to pass to createTable() |
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81 """ |
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82 |
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83 toexec = "class LocalDescription(tables.IsDescription):\n" |
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84 |
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85 toexec_, pos = _get_description_timestamp_cpuclock_columns(store_timestamp, store_cpuclock) |
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86 toexec += toexec_ |
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87 |
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88 toexec_, pos = _get_description_n_ints(int_names, int_width=int_width, pos=pos) |
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89 toexec += toexec_ |
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90 |
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91 float_constructor = "tables.Float32Col" |
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Completely rewrote my series module, now based on HDF5 and PyTables (in a separate directory called 'tables_series' for retrocompatibility of running code). Minor (inconsequential) changes to stacked_dae.
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92 if float_width == 64: |
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Ajouté un paquet de type/value checks à SeriesTables, et finalisé les docstrings. Ajouté 3-4 tests. Légers refactorings ici et là sans conséquences externes.
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93 float_constructor = "tables.Float64Col" |
221 | 94 elif not float_width in (32, 64): |
95 raise "float_width must be left unspecified, or should equal 32 or 64" | |
208
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Completely rewrote my series module, now based on HDF5 and PyTables (in a separate directory called 'tables_series' for retrocompatibility of running code). Minor (inconsequential) changes to stacked_dae.
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96 |
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97 for n in float_names: |
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98 toexec += "\t" + n + " = " + float_constructor + "(pos=" + str(pos) + ")\n" |
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99 pos += 1 |
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100 |
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101 exec(toexec) |
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102 |
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103 return LocalDescription |
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104 |
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105 ############################################################################## |
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106 # Series classes |
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107 |
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108 # Shortcut to allow passing a single int as index, instead of a tuple |
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109 def _index_to_tuple(index): |
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110 if type(index) == tuple: |
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111 return index |
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112 |
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113 if type(index) == list: |
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114 index = tuple(index) |
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115 return index |
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116 |
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117 try: |
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118 if index % 1 > 0.001 and index % 1 < 0.999: |
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119 raise |
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120 idx = long(index) |
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121 return (idx,) |
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122 except: |
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123 raise TypeError("index must be a tuple of integers, or at least a single integer") |
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124 |
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125 class Series(): |
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126 """ |
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127 Base Series class, with minimal arguments and type checks. |
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128 |
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129 Yet cannot be used by itself (it's append() method raises an error) |
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130 """ |
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131 |
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132 def __init__(self, table_name, hdf5_file, index_names=('epoch',), |
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133 title="", hdf5_group='/', |
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134 store_timestamp=True, store_cpuclock=True): |
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135 """Basic arguments each Series must get. |
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136 |
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137 Parameters |
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138 ---------- |
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139 table_name : str |
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140 Name of the table to create under group "hd5_group" (other |
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141 parameter). No spaces, ie. follow variable naming restrictions. |
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142 hdf5_file : open HDF5 file |
220
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143 File opened with openFile() in PyTables (ie. return value of |
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144 openFile). |
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145 index_names : tuple of str |
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146 Columns to use as index for elements in the series, other |
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147 example would be ('epoch', 'minibatch'). This would then allow |
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148 you to call append(index, element) with index made of two ints, |
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149 one for epoch index, one for minibatch index in epoch. |
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150 title : str |
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151 Title to attach to this table as metadata. Can contain spaces |
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152 and be longer then the table_name. |
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153 hdf5_group : str |
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154 Path of the group (kind of a file) in the HDF5 file under which |
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155 to create the table. |
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156 store_timestamp : bool |
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157 Whether to create a column for timestamps and store them with |
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158 each record. |
218
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159 store_cpuclock : bool |
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160 Whether to create a column for cpu clock and store it with |
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161 each record. |
210
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162 """ |
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163 |
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164 ######################################### |
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165 # checks |
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166 |
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167 if type(table_name) != str: |
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168 raise TypeError("table_name must be a string") |
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169 if table_name == "": |
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170 raise ValueError("table_name must not be empty") |
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171 |
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172 if not isinstance(hdf5_file, tables.file.File): |
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173 raise TypeError("hdf5_file must be an open HDF5 file (use tables.openFile)") |
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174 #if not ('w' in hdf5_file.mode or 'a' in hdf5_file.mode): |
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175 # raise ValueError("hdf5_file must be opened in write or append mode") |
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176 |
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177 if type(index_names) != tuple: |
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178 raise TypeError("index_names must be a tuple of strings." + \ |
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179 "If you have only one element in the tuple, don't forget " +\ |
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180 "to add a comma, e.g. ('epoch',).") |
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181 for name in index_names: |
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182 if type(name) != str: |
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183 raise TypeError("index_names must only contain strings, but also"+\ |
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184 "contains a "+str(type(name))+".") |
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185 |
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186 if type(title) != str: |
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187 raise TypeError("title must be a string, even if empty") |
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188 |
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189 if type(hdf5_group) != str: |
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190 raise TypeError("hdf5_group must be a string") |
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191 |
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192 if type(store_timestamp) != bool: |
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193 raise TypeError("store_timestamp must be a bool") |
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194 |
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195 if type(store_cpuclock) != bool: |
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196 raise TypeError("store_timestamp must be a bool") |
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197 |
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198 ######################################### |
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199 |
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200 self.table_name = table_name |
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201 self.hdf5_file = hdf5_file |
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202 self.index_names = index_names |
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203 self.title = title |
220
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204 self.hdf5_group = hdf5_group |
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205 |
218
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206 self.store_timestamp = store_timestamp |
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207 self.store_cpuclock = store_cpuclock |
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208 |
208
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209 def append(self, index, element): |
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210 raise NotImplementedError |
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211 |
218
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212 def _timestamp_cpuclock(self, newrow): |
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213 if self.store_timestamp: |
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214 newrow["timestamp"] = time.time() |
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215 |
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216 if self.store_cpuclock: |
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217 newrow["cpuclock"] = time.clock() |
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218 |
210
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219 class DummySeries(): |
220
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220 """ |
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221 To put in a series dictionary instead of a real series, to do nothing |
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222 when we don't want a given series to be saved. |
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223 |
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224 E.g. if we'd normally have a "training_error" series in a dictionary |
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225 of series, the training loop would have something like this somewhere: |
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226 |
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227 series["training_error"].append((15,), 20.0) |
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228 |
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229 but if we don't want to save the training errors this time, we simply |
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230 do |
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231 |
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232 series["training_error"] = DummySeries() |
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233 """ |
210
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234 def append(self, index, element): |
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235 pass |
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236 |
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237 class ErrorSeries(Series): |
220
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238 """ |
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239 Most basic Series: saves a single float (called an Error as this is |
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240 the most common use case I foresee) along with an index (epoch, for |
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241 example) and timestamp/cpu.clock for each of these floats. |
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242 """ |
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243 |
218
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244 def __init__(self, error_name, table_name, |
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245 hdf5_file, index_names=('epoch',), |
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246 title="", hdf5_group='/', |
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247 store_timestamp=True, store_cpuclock=True): |
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248 """ |
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249 For most parameters, see Series.__init__ |
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250 |
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251 Parameters |
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252 ---------- |
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253 error_name : str |
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254 In the HDF5 table, column name for the error float itself. |
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255 """ |
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256 |
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257 # most type/value checks are performed in Series.__init__ |
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258 Series.__init__(self, table_name, hdf5_file, index_names, title, |
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259 store_timestamp=store_timestamp, |
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260 store_cpuclock=store_cpuclock) |
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261 |
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262 if type(error_name) != str: |
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263 raise TypeError("error_name must be a string") |
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264 if error_name == "": |
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265 raise ValueError("error_name must not be empty") |
208
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266 |
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267 self.error_name = error_name |
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268 |
220
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269 self._create_table() |
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270 |
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271 def _create_table(self): |
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272 table_description = _get_description_with_n_ints_n_floats( \ |
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273 self.index_names, (self.error_name,), |
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274 store_timestamp=self.store_timestamp, |
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275 store_cpuclock=self.store_cpuclock) |
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276 |
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277 self._table = self.hdf5_file.createTable(self.hdf5_group, |
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278 self.table_name, |
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279 table_description, |
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280 title=self.title) |
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281 |
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282 |
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283 def append(self, index, error): |
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284 """ |
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285 Parameters |
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286 ---------- |
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287 index : tuple of int |
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288 Following index_names passed to __init__, e.g. (12, 15) if |
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289 index_names were ('epoch', 'minibatch_size'). |
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290 A single int (not tuple) is acceptable if index_names has a single |
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291 element. |
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292 An array will be casted to a tuple, as a convenience. |
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293 |
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294 error : float |
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295 Next error in the series. |
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296 """ |
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297 index = _index_to_tuple(index) |
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298 |
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299 if len(index) != len(self.index_names): |
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300 raise ValueError("index provided does not have the right length (expected " \ |
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301 + str(len(self.index_names)) + " got " + str(len(index))) |
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302 |
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303 # other checks are implicit when calling newrow[..] =, |
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304 # which should throw an error if not of the right type |
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305 |
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306 newrow = self._table.row |
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307 |
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308 # Columns for index in table are based on index_names |
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309 for col_name, value in zip(self.index_names, index): |
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310 newrow[col_name] = value |
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311 newrow[self.error_name] = error |
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312 |
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313 # adds timestamp and cpuclock to newrow if necessary |
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314 self._timestamp_cpuclock(newrow) |
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315 |
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316 newrow.append() |
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317 |
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318 self.hdf5_file.flush() |
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319 |
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320 # Does not inherit from Series because it does not itself need to |
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321 # access the hdf5_file and does not need a series_name (provided |
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322 # by the base_series.) |
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323 class AccumulatorSeriesWrapper(): |
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324 ''' |
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325 Wraps a Series by accumulating objects passed its Accumulator.append() |
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326 method and "reducing" (e.g. calling numpy.mean(list)) once in a while, |
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327 every "reduce_every" calls in fact. |
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328 ''' |
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329 |
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330 def __init__(self, base_series, reduce_every, reduce_function=numpy.mean): |
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331 """ |
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332 Parameters |
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333 ---------- |
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334 base_series : Series |
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335 This object must have an append(index, value) function. |
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336 |
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337 reduce_every : int |
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338 Apply the reduction function (e.g. mean()) every time we get this |
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339 number of elements. E.g. if this is 100, then every 100 numbers |
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340 passed to append(), we'll take the mean and call append(this_mean) |
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341 on the BaseSeries. |
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342 |
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343 reduce_function : function |
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344 Must take as input an array of "elements", as passed to (this |
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345 accumulator's) append(). Basic case would be to take an array of |
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346 floats and sum them into one float, for example. |
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347 """ |
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348 self.base_series = base_series |
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349 self.reduce_function = reduce_function |
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350 self.reduce_every = reduce_every |
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351 |
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352 self._buffer = [] |
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353 |
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354 |
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355 def append(self, index, element): |
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356 """ |
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357 Parameters |
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358 ---------- |
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359 index : tuple of int |
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360 The index used is the one of the last element reduced. E.g. if |
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361 you accumulate over the first 1000 minibatches, the index |
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362 passed to the base_series.append() function will be 1000. |
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363 A single int (not tuple) is acceptable if index_names has a single |
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364 element. |
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365 An array will be casted to a tuple, as a convenience. |
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366 |
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367 element : float |
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368 Element that will be accumulated. |
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369 """ |
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370 self._buffer.append(element) |
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371 |
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372 if len(self._buffer) == self.reduce_every: |
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373 reduced = self.reduce_function(self._buffer) |
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374 self.base_series.append(index, reduced) |
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375 self._buffer = [] |
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376 |
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377 # The >= case should never happen, except if lists |
221 | 378 # were appended by accessing _buffer externally (when it's |
379 # intended to be private), which should be a red flag. | |
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380 assert len(self._buffer) < self.reduce_every |
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381 |
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382 # Outside of class to fix an issue with exec in Python 2.6. |
220
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383 # My sorries to the god of pretty code. |
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384 def _BasicStatisticsSeries_construct_table_toexec(index_names, store_timestamp, store_cpuclock): |
220
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385 toexec = "class LocalDescription(tables.IsDescription):\n" |
218
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386 |
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387 toexec_, pos = _get_description_timestamp_cpuclock_columns(store_timestamp, store_cpuclock) |
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388 toexec += toexec_ |
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389 |
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390 toexec_, pos = _get_description_n_ints(index_names, pos=pos) |
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391 toexec += toexec_ |
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392 |
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393 toexec += "\tmean = tables.Float32Col(pos=" + str(pos) + ")\n" |
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394 toexec += "\tmin = tables.Float32Col(pos=" + str(pos+1) + ")\n" |
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395 toexec += "\tmax = tables.Float32Col(pos=" + str(pos+2) + ")\n" |
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396 toexec += "\tstd = tables.Float32Col(pos=" + str(pos+3) + ")\n" |
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397 |
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398 # This creates "LocalDescription", which we may then use |
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399 exec(toexec) |
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400 |
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401 return LocalDescription |
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402 |
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403 # Defaults functions for BasicStatsSeries. These can be replaced. |
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404 _basic_stats_functions = {'mean': lambda(x): numpy.mean(x), |
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405 'min': lambda(x): numpy.min(x), |
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406 'max': lambda(x): numpy.max(x), |
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407 'std': lambda(x): numpy.std(x)} |
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408 |
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409 class BasicStatisticsSeries(Series): |
220
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410 |
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411 def __init__(self, table_name, hdf5_file, |
220
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412 stats_functions=_basic_stats_functions, |
218
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413 index_names=('epoch',), title="", hdf5_group='/', |
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414 store_timestamp=True, store_cpuclock=True): |
220
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415 """ |
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416 For most parameters, see Series.__init__ |
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417 |
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418 Parameters |
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419 ---------- |
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420 series_name : str |
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421 Not optional here. Will be prepended with "Basic statistics for " |
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422 |
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423 stats_functions : dict, optional |
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424 Dictionary with a function for each key "mean", "min", "max", |
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425 "std". The function must take whatever is passed to append(...) |
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426 and return a single number (float). |
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427 """ |
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428 |
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429 # Most type/value checks performed in Series.__init__ |
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430 Series.__init__(self, table_name, hdf5_file, index_names, title, |
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431 store_timestamp=store_timestamp, |
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432 store_cpuclock=store_cpuclock) |
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433 |
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434 if type(hdf5_group) != str: |
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435 raise TypeError("hdf5_group must be a string") |
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436 |
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437 if type(stats_functions) != dict: |
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438 # just a basic check. We'll suppose caller knows what he's doing. |
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439 raise TypeError("stats_functions must be a dict") |
208
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440 |
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441 self.hdf5_group = hdf5_group |
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442 |
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443 self.stats_functions = stats_functions |
208
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444 |
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445 self._create_table() |
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446 |
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447 def _create_table(self): |
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448 table_description = \ |
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449 _BasicStatisticsSeries_construct_table_toexec( \ |
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450 self.index_names, |
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451 self.store_timestamp, self.store_cpuclock) |
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452 |
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453 self._table = self.hdf5_file.createTable(self.hdf5_group, |
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454 self.table_name, table_description) |
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455 |
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456 def append(self, index, array): |
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457 """ |
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458 Parameters |
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459 ---------- |
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460 index : tuple of int |
220
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461 Following index_names passed to __init__, e.g. (12, 15) |
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462 if index_names were ('epoch', 'minibatch_size') |
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463 A single int (not tuple) is acceptable if index_names has a single |
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464 element. |
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465 An array will be casted to a tuple, as a convenience. |
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466 |
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467 array |
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468 Is of whatever type the stats_functions passed to |
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469 __init__ can take. Default is anything numpy.mean(), |
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470 min(), max(), std() can take. |
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471 """ |
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472 index = _index_to_tuple(index) |
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473 |
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474 if len(index) != len(self.index_names): |
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475 raise ValueError("index provided does not have the right length (expected " \ |
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476 + str(len(self.index_names)) + " got " + str(len(index))) |
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477 |
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478 newrow = self._table.row |
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479 |
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480 for col_name, value in zip(self.index_names, index): |
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481 newrow[col_name] = value |
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482 |
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483 newrow["mean"] = self.stats_functions['mean'](array) |
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484 newrow["min"] = self.stats_functions['min'](array) |
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485 newrow["max"] = self.stats_functions['max'](array) |
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486 newrow["std"] = self.stats_functions['std'](array) |
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487 |
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488 self._timestamp_cpuclock(newrow) |
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489 |
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490 newrow.append() |
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491 |
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492 self.hdf5_file.flush() |
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493 |
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494 class SeriesArrayWrapper(): |
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495 """ |
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496 Simply redistributes any number of elements to sub-series to respective |
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497 append()s. |
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498 |
220
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499 To use if you have many elements to append in similar series, e.g. if you |
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500 have an array containing [train_error, valid_error, test_error], and 3 |
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501 corresponding series, this allows you to simply pass this array of 3 |
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502 values to append() instead of passing each element to each individual |
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503 series in turn. |
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504 """ |
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505 |
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506 def __init__(self, base_series_list): |
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507 """ |
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508 Parameters |
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509 ---------- |
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510 base_series_list : array or tuple of Series |
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511 You must have previously created and configured each of those |
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512 series, then put them in an array. This array must follow the |
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513 same order as the array passed as ``elements`` parameter of |
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514 append(). |
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515 """ |
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516 self.base_series_list = base_series_list |
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517 |
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518 def append(self, index, elements): |
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519 """ |
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520 Parameters |
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521 ---------- |
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522 index : tuple of int |
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523 See for example ErrorSeries.append() |
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524 |
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525 elements : array or tuple |
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526 Array or tuple of elements that will be passed down to |
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527 the base_series passed to __init__, in the same order. |
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528 """ |
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529 if len(elements) != len(self.base_series_list): |
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530 raise ValueError("not enough or too much elements provided (expected " \ |
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531 + str(len(self.base_series_list)) + " got " + str(len(elements))) |
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532 |
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533 for series, el in zip(self.base_series_list, elements): |
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534 series.append(index, el) |
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535 |
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536 class SharedParamsStatisticsWrapper(SeriesArrayWrapper): |
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537 ''' |
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538 Save mean, min/max, std of shared parameters place in an array. |
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539 |
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540 Here "shared" means "theano.shared", which means elements of the |
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541 array will have a .value to use for numpy.mean(), etc. |
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542 |
220
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543 This inherits from SeriesArrayWrapper, which provides the append() |
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544 method. |
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545 ''' |
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546 |
220
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547 def __init__(self, arrays_names, new_group_name, hdf5_file, |
224
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548 base_group='/', index_names=('epoch',), title="", |
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549 store_timestamp=True, store_cpuclock=True): |
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550 """ |
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551 For other parameters, see Series.__init__ |
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552 |
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553 Parameters |
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554 ---------- |
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555 array_names : array or tuple of str |
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556 Name of each array, in order of the array passed to append(). E.g. |
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557 ('layer1_b', 'layer1_W', 'layer2_b', 'layer2_W') |
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558 |
210
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559 new_group_name : str |
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560 Name of a new HDF5 group which will be created under base_group to |
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561 store the new series. |
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562 |
210
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563 base_group : str |
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564 Path of the group under which to create the new group which will |
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565 store the series. |
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566 |
210
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567 title : str |
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568 Here the title is attached to the new group, not a table. |
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569 |
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570 store_timestamp : bool |
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571 Here timestamp and cpuclock are stored in *each* table |
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572 |
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573 store_cpuclock : bool |
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574 Here timestamp and cpuclock are stored in *each* table |
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575 """ |
220
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576 |
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577 # most other checks done when calling BasicStatisticsSeries |
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578 if type(new_group_name) != str: |
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579 raise TypeError("new_group_name must be a string") |
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580 if new_group_name == "": |
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581 raise ValueError("new_group_name must not be empty") |
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582 |
208
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583 base_series_list = [] |
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584 |
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585 new_group = hdf5_file.createGroup(base_group, new_group_name, title=title) |
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586 |
210
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587 stats_functions = {'mean': lambda(x): numpy.mean(x.value), |
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588 'min': lambda(x): numpy.min(x.value), |
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589 'max': lambda(x): numpy.max(x.value), |
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590 'std': lambda(x): numpy.std(x.value)} |
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591 |
208
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592 for name in arrays_names: |
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593 base_series_list.append( |
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594 BasicStatisticsSeries( |
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595 table_name=name, |
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596 hdf5_file=hdf5_file, |
210
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597 index_names=index_names, |
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598 stats_functions=stats_functions, |
224
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599 hdf5_group=new_group._v_pathname, |
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600 store_timestamp=store_timestamp, |
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601 store_cpuclock=store_cpuclock)) |
208
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602 |
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603 SeriesArrayWrapper.__init__(self, base_series_list) |
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604 |
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605 |