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annotate linear_regression.py @ 392:e2cb8d489908

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More debugging
author Joseph Turian <turian@gmail.com>
date Tue, 08 Jul 2008 18:45:35 -0400
parents 74b402b5a81b
children efb797c5efc0
rev   line source
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1 """
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2 Implementation of linear regression, with or without L2 regularization.
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3 This is one of the simplest example of L{learner}, and illustrates
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4 the use of theano.
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5 """
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7 from pylearn import OfflineLearningAlgorithm
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8 from theano import tensor as T
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9 from theano.scalar import as_scalar
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10 from common.autoname import AutoName
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12 class LinearRegression(OfflineLearningAlgorithm):
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13 """
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14 Implement linear regression, with or without L2 regularization
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15 (the former is called Ridge Regression and the latter Ordinary Least Squares).
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16
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17 The predictor parameters are obtained analytically from the training set.
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18 Training can proceed sequentially (with multiple calls to update with
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19 different disjoint subsets of the training sets). After each call to
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20 update the predictor is ready to be used (and optimized for the union
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21 of all the training sets passed to update since construction or since
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22 the last call to forget).
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23
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24 For each (input[t],output[t]) pair in a minibatch,::
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25
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26 output_t = b + W * input_t
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27
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28 where b and W are obtained by minimizing::
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29
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30 L2_regularizer sum_{ij} W_{ij}^2 + sum_t ||output_t - target_t||^2
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31
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32 Let X be the whole training set inputs matrix (one input example per row),
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33 with the first column full of 1's, and Let Y the whole training set
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34 targets matrix (one example's target vector per row).
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35 Let theta = the matrix with b in its first column and W in the others,
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36 then each theta[:,i] is the solution of the linear system::
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37
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38 XtX * theta[:,i] = XtY[:,i]
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39
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40 where XtX is a (n_inputs+1)x(n_inputs+1) matrix containing X'*X
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41 plus L2_regularizer on the diagonal except at (0,0),
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42 and XtY is a (n_inputs+1)*n_outputs matrix containing X'*Y.
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43
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44 The dataset fields expected and produced by the learning algorithm and the trained model
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45 are the following:
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46
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47 - Input and output dataset fields (example-wise quantities):
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48
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49 - 'input' (always expected as an input_dataset field)
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50 - 'target' (always expected by the learning algorithm, optional for learned model)
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51 - 'output' (always produced by learned model)
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52 - 'squared_error' (optionally produced by learned model if 'target' is provided)
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53 = example-wise squared error
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54 """
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55 def __init__(self, L2_regularizer=0):
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56 self.predictor = LinearPredictor(None,None
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57 self.L2_regularizer=L2_regularizer
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58 self._XtX = T.matrix('XtX')
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59 self._XtY = T.matrix('XtY')
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60 self._extended_input = T.prepend_one_to_each_row(self._input)
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61
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62 class LinearPredictorEquations(AutoName):
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63 inputs = T.matrix() # minibatchsize x n_inputs
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64 targets = T.matrix() # minibatchsize x n_outputs
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65 theta = T.matrix() # (n_inputs+1) x n_outputs
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66 b = theta[0]
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67 Wt = theta[1:,:]
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68 outputs = T.dot(inputs,Wt) + b # minibatchsize x n_outputs
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69 squared_errors = T.sum(T.sqr(targets-outputs),axis=1)
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71 __compiled = False
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72 @classmethod
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73 def compile(cls,linker='c|py'):
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74 if cls.__compiled:
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75 return
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76 def fn(input_vars,output_vars):
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77 return staticmethod(theano.function(input_vars,output_vars, linker=linker))
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79 cls.compute_outputs = fn([inputs,theta],[outputs])
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80 cls.compute_errors = fn([outputs,targets],[squared_errors])
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82 cls.__compiled = True
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83
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84 def __init__(self)
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85 self.compile()
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86
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87 class LinearRegressionEquations(LinearPredictorEquations):
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88 P = LinearPredictorEquations
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89 XtX = T.matrix() # (n_inputs+1) x (n_inputs+1)
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90 XtY = T.matrix() # (n_inputs+1) x n_outputs
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91 extended_input = T.prepend_scalar_to_each_row(1.,P.inputs)
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92 new_XtX = add_inplace(XtX,T.dot(extended_input.T,extended_input))
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93 new_XtY = add_inplace(XtY,T.dot(extended_input.T,P.targets))
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94 new_theta = T.Cholesky_solve_inplace(P.theta,XtX,XtY) # solve linear system XtX theta = XtY
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95
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96 class LinearPredictor(object):
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97 """
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98 A linear predictor has parameters theta (a bias vector and a weight matrix)
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99 it can use to make a linear prediction (according to the LinearPredictorEquations).
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100 It can compute its output (bias + weight * input) and a squared error (||output - target||^2).
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101 """
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102 def __init__(self, theta):
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103 self.theta=theta
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104 self.n_inputs=theta.shape[0]-1
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105 self.n_outputs=theta.shape[1]
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106 self.predict_equations = LinearPredictorEquations()
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107
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108 def compute_outputs(self,inputs):
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109 return self.predict_equations.compute_outputs(inputs,self.theta)
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110 def compute_errors(self,inputs,targets):
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111 return self.predict_equations.compute_errors(self.compute_outputs(inputs),targets)
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112 def compute_outputs_and_errors(self,inputs,targets):
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113 outputs = self.compute_outputs(inputs)
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114 return [outputs,self.predict_equations.compute_errors(outputs,targets)]
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115
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116 def __call__(self,dataset,output_fieldnames=None,cached_output_dataset=False):
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117 assert dataset.hasFields(["input"])
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118 if output_fieldnames is None:
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119 if dataset.hasFields(["target"]):
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120 output_fieldnames = ["output","squared_error"]
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121 else:
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122 output_fieldnames = ["output"]
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123 output_fieldnames.sort()
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124 if output_fieldnames == ["squared_error"]:
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125 f = self.compute_errors
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126 elif output_fieldnames == ["output"]:
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127 f = self.compute_outputs
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128 elif output_fieldnames == ["output","squared_error"]:
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129 f = self.compute_outputs_and_errors
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130 else:
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131 raise ValueError("unknown field(s) in output_fieldnames: "+str(output_fieldnames))
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132
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133 ds=ApplyFunctionDataSet(dataset,f,output_fieldnames)
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134 if cached_output_dataset:
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135 return CachedDataSet(ds)
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136 else:
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137 return ds
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138
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139
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140 self._XtX = T.matrix('XtX')
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141 self._XtY = T.matrix('XtY')
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142 self._extended_input = T.prepend_one_to_each_row(self._input)
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143 self._output = T.dot(self._input,self._W.T) + self._b # (n_examples , n_outputs) matrix
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144 self._squared_error = T.sum_within_rows(T.sqr(self._output-self._target)) # (n_examples ) vector
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145 self._regularizer = self._L2_regularizer * T.dot(self._W,self._W)
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146 self._new_XtX = add_inplace(self._XtX,T.dot(self._extended_input.T,self._extended_input))
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147 self._new_XtY = add_inplace(self._XtY,T.dot(self._extended_input.T,self._target))
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148 self._new_theta = T.solve_inplace(self._theta,self._XtX,self._XtY)
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149
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150 def allocate(self,dataset):
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151 dataset_n_inputs = dataset["input"].shape[1]
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152 dataset_n_outputs = dataset["target"].shape[1]
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153 if not self._n_inputs:
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154 self._n_inputs = dataset_n_inputs
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155 self._n_outputs = dataset_n_outputs
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156 self.XtX = numpy.zeros((1+self._n_inputs,1+self._n_inputs))
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157 self.XtY = numpy.zeros((1+self._n_inputs,self._n_outputs))
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158 self.theta = numpy.zeros((self._n_outputs,1+self._n_inputs))
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159 self.forget()
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160 elif self._n_inputs!=dataset_n_inputs or self._n_outputs!=dataset_n_outputs:
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161 # if the input or target changes dimension on the fly, we resize and forget everything
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162 self.forget()
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163
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164 def forget(self):
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165 if self._n_inputs and self._n_outputs:
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166 self.XtX.resize((1+self.n_inputs,1+self.n_inputs))
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167 self.XtY.resize((1+self.n_inputs,self.n_outputs))
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168 self.XtX.data[:,:]=0
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169 self.XtY.data[:,:]=0
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170 numpy.diag(self.XtX.data)[1:]=self.L2_regularizer
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171
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172 def __call__(self,dataset):
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173
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174