annotate doc/v2_planning/plugin_JB.py @ 1210:cbe1fb32686c

v2planning plugin_JB - added n_required keyword to WEAVE
author James Bergstra <bergstrj@iro.umontreal.ca>
date Tue, 21 Sep 2010 23:38:53 -0400
parents 865936d8221b
children e7ac87720fee
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1 """plugin_JB - draft of potential library architecture using iterators
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2
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3 This strategy makes use of a simple imperative language whose statements are python function
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4 calls to create learning algorithms that can be manipulated and executed in several desirable
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5 ways.
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6
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7 The training procedure for a PCA module is easy to express:
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8
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9 # allocate the relevant modules
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10 dataset = Dataset(numpy.random.RandomState(123).randn(13,1))
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11 pca = PCA_Analysis()
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12 pca_batchsize=1000
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13
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14 # define the control-flow of the algorithm
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15 train_pca = SEQ([
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16 BUFFER_REPEAT(pca_batchsize, CALL(dataset.next)),
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17 FILT(pca.analyze)])
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18
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19 # run the program
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20 train_pca.run()
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22 The CALL, SEQ, FILT, and BUFFER_REPEAT are control-flow elements. The control-flow elements I
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23 defined so far are:
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24
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25 - CALL - a basic statement, just calls a python function
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26 - FILT - like call, but passes the return value of the last CALL or FILT to the python function
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27 - SEQ - a sequence of elements to run in order
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28 - REPEAT - do something N times (and return None or maybe the last CALL?)
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29 - BUFFER_REPEAT - do something N times and accumulate the return value from each iter
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30 - LOOP - do something an infinite number of times
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31 - CHOOSE - like a switch statement (should rename to SWITCH)
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32 - WEAVE - interleave execution of multiple control-flow elements
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33
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34
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35 We don't have many requirements per-se for the architecture, but I think this design respects
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36 and realizes all of them.
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37 The advantages of this approach are:
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38
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39 - algorithms (including partially run ones) are COPYABLE, and SERIALIZABLE
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40
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41 - algorithms can be executed without seizing control of the python process (the run()
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42 method does this, but if you look inside it you'll see it's a simple for loop)
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43
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44 - it is easy to execute an algorithm step by step in a main loop that also checks for
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45 network or filesystem events related to e.g. job management.
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46
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47 - the library can provide learning algorithms via control-flow templates, and the user can
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48 edit them (with search/replace calls) to include HOOKS, and DIAGNOSTIC plug-in
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49 functionality
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50
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51 e.g. prog.find(CALL(cd1_update, layer=layer1)).replace_with(
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52 SEQ([CALL(cd1_update, layer=layer1), CALL(my_debugfn)]))
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53
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54 - user can print the 'program code' of an algorithm built from library pieces
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55
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56 - program can be optimized automatically.
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57
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58 - e.g. BUFFER(N, CALL(dataset.next)) could be replaced if dataset.next implements the
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59 right attribute/protocol for 'bufferable' or something.
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60
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61 - e.g. SEQ([a,b,c,d]) could be compiled to a single CALL to a Theano-compiled function
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62 if a, b, c, and d are calls to callable objects that export something like a
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63 'theano_SEQ' interface
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64
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65
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66 """
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67
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68 __license__ = 'TODO'
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69 __copyright__ = 'TODO'
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70
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71 import copy, sys, cPickle
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72 import numpy
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73
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74 ####################################################
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75 # CONTROL-FLOW CONSTRUCTS
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76
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77 class INCOMPLETE:
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78 """Return value for Element.step"""
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79
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80 class ELEMENT(object):
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81 """
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82 Base class for control flow elements (e.g. CALL, REPEAT, etc.)
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83
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84 The design is that every element has a driver, that is another element, or the iterator
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85 implementation in the ELEMENT class.
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86
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87 the driver calls start when entering a new control element
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88 - this would be called once per e.g. outer loop iteration
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89
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90 the driver calls step to advance the control element
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91 - which returns INCOMPLETE
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92 - which returns any other object to indicate completion
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93 """
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94
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95 # subclasses should override these methods:
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96 def start(self, arg):
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97 pass
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98 def step(self):
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99 pass
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100
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101 # subclasses should typically not override these:
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102 def run(self, arg=None, n_steps=float('inf')):
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103 self.start(arg)
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104 i = 0
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105 r = self.step()
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106 while r is INCOMPLETE:
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107 i += 1
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108 #TODO make sure there is not an off-by-one error
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109 if i > n_steps:
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110 break
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111 r = self.step()
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112 return r
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113
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114
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115 class BUFFER_REPEAT(ELEMENT):
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116 """
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117 Accumulate a number of return values into one list / array.
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118
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119 The source of return values `src` is a control element that will be restarted repeatedly in
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120 order to fulfil the requiement of gathering N samples.
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121
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122 TODO: support accumulating of tuples of arrays
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123 """
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124 def __init__(self, N, src, storage=None):
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125 """
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126 TODO: use preallocated `storage`
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127 """
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128 self.N = N
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129 self.n = 0
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130 self.src = src
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131 self.storage = storage
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132 self.src.start(None)
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133 if self.storage != None:
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134 raise NotImplementedError()
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135 def start(self, arg):
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136 self.buf = [None] * self.N
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137 self.n = 0
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138 self.finished = False
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139 def step(self):
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diff changeset
140 assert not self.finished
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parents:
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141 r = self.src.step()
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parents:
diff changeset
142 if r is INCOMPLETE:
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parents:
diff changeset
143 return r
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parents:
diff changeset
144 self.src.start(None) # restart our stream
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parents:
diff changeset
145 self.buf[self.n] = r
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
146 self.n += 1
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
147 if self.n == self.N:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
148 self.finished = True
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James Bergstra <bergstrj@iro.umontreal.ca>
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diff changeset
149 return self.buf
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parents:
diff changeset
150 else:
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parents:
diff changeset
151 return INCOMPLETE
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parents:
diff changeset
152 assert 0
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153
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parents:
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154 class CALL(ELEMENT):
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parents:
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155 """
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
156 Control flow terminal - call a python function or method.
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parents:
diff changeset
157
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158 Returns the return value of the call.
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parents:
diff changeset
159 """
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
160 def __init__(self, fn, *args, **kwargs):
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parents:
diff changeset
161 self.fn = fn
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James Bergstra <bergstrj@iro.umontreal.ca>
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diff changeset
162 self.args = args
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parents:
diff changeset
163 self.kwargs=kwargs
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parents:
diff changeset
164 self.use_start_arg = kwargs.pop('use_start_arg', False)
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parents:
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165 def start(self, arg):
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166 self.start_arg = arg
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167 self.finished = False
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
168 return self
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
169 def step(self):
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parents:
diff changeset
170 assert not self.finished
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
171 self.finished = True
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
172 if self.use_start_arg:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
173 if self.args:
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parents:
diff changeset
174 raise TypeError('cant get positional args both ways')
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parents:
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175 return self.fn(self.start_arg, **self.kwargs)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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176 else:
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parents:
diff changeset
177 return self.fn(*self.args, **self.kwargs)
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parents:
diff changeset
178 def __getstate__(self):
1199
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179 rval = dict(self.__dict__)
1198
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parents:
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180 if type(self.fn) is type(self.step): #instancemethod
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
181 fn = rval.pop('fn')
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parents:
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182 rval['i fn'] = fn.im_func, fn.im_self, fn.im_class
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parents:
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183 return rval
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
184 def __setstate__(self, dct):
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
185 if 'i fn' in dct:
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parents:
diff changeset
186 dct['fn'] = type(self.step)(*dct.pop('i fn'))
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parents:
diff changeset
187 self.__dict__.update(dct)
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parents:
diff changeset
188
1199
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diff changeset
189 def FILT(fn, **kwargs):
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parents: 1198
diff changeset
190 """
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
191 Return a CALL object that uses the return value from the previous CALL as the first and
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
192 only positional argument.
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
193 """
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
194 return CALL(fn, use_start_arg=True, **kwargs)
1198
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parents:
diff changeset
195
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parents:
diff changeset
196 def CHOOSE(which, options):
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parents:
diff changeset
197 """
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
198 Execute one out of a number of optional control flow paths
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
199 """
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
200 raise NotImplementedError()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
201
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
202 def LOOP(elements):
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parents:
diff changeset
203 #TODO: implement a true infinite loop
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parents:
diff changeset
204 try:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
205 iter(elements)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
206 return REPEAT(sys.maxint, elements)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
207 except TypeError:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
208 return REPEAT(sys.maxint, [elements])
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
209
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
210 class REPEAT(ELEMENT):
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
211 def __init__(self, N, elements, pass_rvals=False):
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
212 self.N = N
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
213 self.elements = elements
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
214 self.pass_rvals = pass_rvals
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parents: 1200
diff changeset
215
1198
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parents:
diff changeset
216 #TODO: check for N being callable
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parents:
diff changeset
217 def start(self, arg):
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parents:
diff changeset
218 self.n = 0 #loop iteration
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parents:
diff changeset
219 self.idx = 0 #element idx
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
220 self.finished = False
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
221 self.elements[0].start(arg)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
222 def step(self):
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
223 assert not self.finished
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
224 r = self.elements[self.idx].step()
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
225 if r is INCOMPLETE:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
226 return INCOMPLETE
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
227 self.idx += 1
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
228 if self.idx < len(self.elements):
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
229 self.elements[self.idx].start(r)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
230 return INCOMPLETE
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
231 self.n += 1
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
232 if self.n < self.N:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
233 self.idx = 0
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
234 self.elements[self.idx].start(r)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
235 return INCOMPLETE
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
236 else:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
237 self.finished = True
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
238 return r
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
239
1387771296a8 v2planning adding plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
240 def SEQ(elements):
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
241 return REPEAT(1, elements)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
242
1387771296a8 v2planning adding plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
243 class WEAVE(ELEMENT):
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
244 """
1387771296a8 v2planning adding plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
245 Interleave execution of a number of elements.
1387771296a8 v2planning adding plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
246
1387771296a8 v2planning adding plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
247 TODO: allow a schedule (at least relative frequency) of elements from each program
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
248 """
1210
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
249 def __init__(self, n_required, elements):
1198
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parents:
diff changeset
250 self.elements = elements
1210
cbe1fb32686c v2planning plugin_JB - added n_required keyword to WEAVE
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
251 if n_required == -1:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
252 self.n_required = len(elements)
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
253 else:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
254 self.n_required = n_required
1198
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
255 def start(self, arg):
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
256 for el in self.elements:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
257 el.start(arg)
1210
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
258 self.elem_finished = [0] * len(self.elements)
1198
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
259 self.idx = 0
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
260 self.finished= False
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
261 def step(self):
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
262 assert not self.finished # if this is triggered, we have a broken driver
1210
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
263
cbe1fb32686c v2planning plugin_JB - added n_required keyword to WEAVE
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
264 #start with this check in case there were no elements
cbe1fb32686c v2planning plugin_JB - added n_required keyword to WEAVE
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
265 # it's possible for the number of finished elements to exceed the threshold
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
266 if sum(self.elem_finished) >= self.n_required:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
267 self.finished = True
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
268 return None
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
269
cbe1fb32686c v2planning plugin_JB - added n_required keyword to WEAVE
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
270 # step the active element
1198
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parents:
diff changeset
271 r = self.elements[self.idx].step()
1210
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
272
1198
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parents:
diff changeset
273 if r is not INCOMPLETE:
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parents: 1203
diff changeset
274 self.elem_finished[self.idx] = True
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
275
cbe1fb32686c v2planning plugin_JB - added n_required keyword to WEAVE
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
276 # check for completion
cbe1fb32686c v2planning plugin_JB - added n_required keyword to WEAVE
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
277 if sum(self.elem_finished) >= self.n_required:
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
278 self.finished = True
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
279 return None
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
280
cbe1fb32686c v2planning plugin_JB - added n_required keyword to WEAVE
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
281 # advance to the next un-finished element
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
282 self.idx = (self.idx+1) % len(self.elements)
cbe1fb32686c v2planning plugin_JB - added n_required keyword to WEAVE
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parents: 1203
diff changeset
283 while self.elem_finished[self.idx]:
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parents: 1203
diff changeset
284 self.idx = (self.idx+1) % len(self.elements)
cbe1fb32686c v2planning plugin_JB - added n_required keyword to WEAVE
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1203
diff changeset
285
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286 return INCOMPLETE
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287
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288
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289 ####################################################
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290 # [Dummy] Components involved in learning algorithms
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291
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292 class Dataset(object):
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293 def __init__(self, data):
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294 self.pos = 0
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295 self.data = data
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296 def next(self):
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297 rval = self.data[self.pos]
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298 self.pos += 1
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299 if self.pos == len(self.data):
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300 self.pos = 0
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301 return rval
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302 def seek(self, pos):
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303 self.pos = pos
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304
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305 class KFold(object):
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306 def __init__(self, data, K):
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307 self.data = data
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308 self.k = -1
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309 self.scores = [None]*K
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310 self.K = K
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311 def next_fold(self):
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312 self.k += 1
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313 self.data.seek(0) # restart the stream
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314 def next(self):
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315 #TODO: skip the examples that are ommitted in this split
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316 return self.data.next()
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317 def init_test(self):
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318 pass
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319 def next_test(self):
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320 return self.data.next()
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321 def test_size(self):
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322 return 5
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323 def store_scores(self, scores):
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324 self.scores[self.k] = scores
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325
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326 def prog(self, clear, train, test):
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327 return REPEAT(self.K, [
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328 CALL(self.next_fold),
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329 clear,
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330 train,
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331 CALL(self.init_test),
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332 BUFFER_REPEAT(self.test_size(),
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333 SEQ([ CALL(self.next_test), test])),
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334 FILT(self.store_scores) ])
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335
1198
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336 class PCA_Analysis(object):
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337 def __init__(self):
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338 self.clear()
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339
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340 def clear(self):
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341 self.mean = 0
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342 self.eigvecs=0
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343 self.eigvals=0
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344 def analyze(self, X):
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345 self.mean = numpy.mean(X, axis=0)
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346 self.eigvecs=1
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347 self.eigvals=1
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348 def filt(self, X):
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349 return (X - self.mean) * self.eigvecs #TODO: divide by root eigvals?
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350 def pseudo_inverse(self, Y):
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351 return Y
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352
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353 class Layer(object):
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354 def __init__(self, w):
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355 self.w = w
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356 def filt(self, x):
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parents:
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357 return self.w*x
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358 def clear(self):
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359 self.w =0
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360
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361 def print_obj(obj):
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parents:
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362 print obj
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parents:
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363 def print_obj_attr(obj, attr):
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parents:
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364 print getattr(obj, attr)
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parents:
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365 def no_op(*args, **kwargs):
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parents:
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366 pass
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367
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368 def cd1_update(X, layer, lr):
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369 # update self.layer from observation X
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370 layer.w += X.mean() * lr #TODO: not exactly correct math!
1198
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parents:
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371
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parents:
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372 def simple_main():
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parents:
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373
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parents:
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374 l = [0]
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parents:
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375 def f(a):
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parents:
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376 print l
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parents:
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377 l[0] += a
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parents:
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378 return l[0]
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parents:
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379
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parents: 1203
diff changeset
380 print WEAVE(1, [
1198
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parents:
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381 BUFFER_REPEAT(3,CALL(f,1)),
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parents:
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382 BUFFER_REPEAT(5,CALL(f,1)),
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parents: 1200
diff changeset
383 ]).run()
1198
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parents:
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384
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parents:
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385 def main():
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parents:
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386 # create components
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parents:
diff changeset
387 dataset = Dataset(numpy.random.RandomState(123).randn(13,1))
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parents:
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388 pca = PCA_Analysis()
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parents:
diff changeset
389 layer1 = Layer(w=4)
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parents:
diff changeset
390 layer2 = Layer(w=3)
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parents:
diff changeset
391 kf = KFold(dataset, K=10)
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parents:
diff changeset
392
1199
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diff changeset
393 pca_batchsize=1000
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parents: 1198
diff changeset
394 cd_batchsize = 5
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
395 n_cd_updates_layer1 = 10
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parents: 1198
diff changeset
396 n_cd_updates_layer2 = 10
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parents: 1198
diff changeset
397
1198
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parents:
diff changeset
398 # create algorithm
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parents:
diff changeset
399
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
400 train_pca = SEQ([
1199
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parents: 1198
diff changeset
401 BUFFER_REPEAT(pca_batchsize, CALL(kf.next)),
1198
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
402 FILT(pca.analyze)])
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parents:
diff changeset
403
1199
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parents: 1198
diff changeset
404 train_layer1 = REPEAT(n_cd_updates_layer1, [
98954d8cb92d v2planning - modifs to plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
405 BUFFER_REPEAT(cd_batchsize, CALL(kf.next)),
1198
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
406 FILT(pca.filt),
1199
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parents: 1198
diff changeset
407 FILT(cd1_update, layer=layer1, lr=.01)])
1198
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parents:
diff changeset
408
1199
98954d8cb92d v2planning - modifs to plugin_JB
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parents: 1198
diff changeset
409 train_layer2 = REPEAT(n_cd_updates_layer2, [
98954d8cb92d v2planning - modifs to plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
410 BUFFER_REPEAT(cd_batchsize, CALL(kf.next)),
1198
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
411 FILT(pca.filt),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
412 FILT(layer1.filt),
1199
98954d8cb92d v2planning - modifs to plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
413 FILT(cd1_update, layer=layer2, lr=.01)])
1198
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parents:
diff changeset
414
1199
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parents: 1198
diff changeset
415 kfold_prog = kf.prog(
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
416 clear = SEQ([ # FRAGMENT 1: this bit is the reset/clear stage
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
417 CALL(pca.clear),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
418 CALL(layer1.clear),
98954d8cb92d v2planning - modifs to plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
419 CALL(layer2.clear),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
420 ]),
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
421 train = SEQ([
98954d8cb92d v2planning - modifs to plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
422 train_pca,
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parents: 1203
diff changeset
423 WEAVE(1, [ # Silly example of how to do debugging / loggin with WEAVE
1199
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parents: 1198
diff changeset
424 train_layer1,
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
425 LOOP(CALL(print_obj_attr, layer1, 'w'))]),
98954d8cb92d v2planning - modifs to plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
426 train_layer2,
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James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
427 ]),
98954d8cb92d v2planning - modifs to plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
428 test=SEQ([
1198
1387771296a8 v2planning adding plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
429 FILT(pca.filt), # may want to allow this SEQ to be
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James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
430 FILT(layer1.filt), # optimized into a shorter one that
1199
98954d8cb92d v2planning - modifs to plugin_JB
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 1198
diff changeset
431 FILT(layer2.filt), # compiles these calls together with
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432 FILT(numpy.mean)])) # Theano
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433
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434 pkg1 = dict(prog=kfold_prog, kf=kf)
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435 pkg2 = copy.deepcopy(pkg1) # programs can be copied
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436
1199
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437 try:
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438 pkg3 = cPickle.loads(cPickle.dumps(pkg1))
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439 except:
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440 print >> sys.stderr, "pickling doesnt work, but it can be fixed I think"
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441
1199
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442 pkg = pkg2
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443
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444 # running a program updates the variables in its package, but not the other package
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445 pkg['prog'].run()
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446 print pkg['kf'].scores
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447
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448
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449 if __name__ == '__main__':
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450 sys.exit(main())
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451