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annotate make_test_datasets.py @ 470:bd937e845bbb

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new stuff: algorithms/logistic_regression, datasets/MNIST
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 22 Oct 2008 15:56:53 -0400
parents 2d8490d76b3e
children 3eb59514b534
rev   line source
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1 import dataset
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2 from shapeset.dset import Polygons
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3 from linear_regression import linear_predictor
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4 from kernel_regression import kernel_predictor
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5 from numpy import *
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6
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7 """
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8 General-purpose code to generate artificial datasets that can be used
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9 to test different learning algorithms.
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10 """
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11
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12
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13 def make_triangles_rectangles_online_dataset(image_size=(10,10)):
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14 """
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15 Make a binary classification dataset to discriminate triangle images from rectangle images.
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16 """
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17 def convert_dataset(dset):
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18 # convert the n_vert==3 into target==0 and n_vert==4 into target==1
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19 def mapf(images,n_vertices):
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20 n=len(n_vertices)
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21 targets = ndarray((n,1),dtype='float64')
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22 for i in xrange(n):
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23 targets[i,0] = array([0. if n_vertices[i]==3 else 1.],dtype='float64')
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24 return images.reshape(len(images),images[0].size).astype('float64'),targets
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25 return dataset.ApplyFunctionDataSet(dset("image","nvert"),mapf,["input","target"])
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26
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27 p=Polygons(image_size,[3,4],fg_min=1./255,fg_max=1./255,rot_max=1.,scale_min=0.35,scale_max=0.9,pos_min=0.1, pos_max=0.9)
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28 trainset=convert_dataset(p)
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29 return trainset
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30
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31
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32 def make_triangles_rectangles_dataset(n_examples=600,image_size=(10,10), cache = True):
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33 """
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34 Make a binary classification dataset to discriminate triangle images from rectangle images.
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35 """
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36 def convert_dataset(dset):
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37 # convert the n_vert==3 into target==0 and n_vert==4 into target==1
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38 def mapf(images,n_vertices):
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39 n=len(n_vertices)
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40 targets = ndarray((n,1),dtype='float64')
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41 for i in xrange(n):
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42 targets[i,0] = array([0. if n_vertices[i]==3 else 1.],dtype='float64')
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43 return images.reshape(len(images),images[0].size).astype('float64'),targets
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44 return dataset.CachedDataSet(dataset.ApplyFunctionDataSet(dset("image","nvert"),mapf,["input","target"]),cache)
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45
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46 p=Polygons(image_size,[3,4],fg_min=1./255,fg_max=1./255,rot_max=1.,scale_min=0.35,scale_max=0.9,pos_min=0.1, pos_max=0.9)
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47 data = p.subset[0:n_examples]
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48 trainset=convert_dataset(data.subset[0:n_examples])
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49 return trainset
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50
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51
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52 def make_triangles_rectangles_datasets(n_examples=600,train_frac=0.5,image_size=(10,10), cache = True):
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53 """
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54 Make two binary classification datasets to discriminate triangle images from rectangle images.
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55 The first one is the training set, the second is the test set.
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56 """
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57 data = make_triangles_rectangles_dataset(n_examples=n_examples,image_size=image_size, cache = cache)
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58 n_train = int(n_examples*train_frac)
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59 trainset=convert_dataset(data.subset[0:n_train])
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60 testset=convert_dataset(data.subset[n_train:n_examples])
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61 return trainset,testset
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62
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63
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64 def make_artificial_datasets_from_function(n_inputs=1,
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65 n_targets=1,
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66 n_examples=20,
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67 train_frac=0.5,
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68 noise_level=0.1, # add Gaussian noise, noise_level=sigma
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69 params_shape=None,
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70 f=None, # function computing E[Y|X]
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71 otherargs=None, # extra args to f
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72 b=None): # force theta[0] with this value
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73 """
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74 Make regression data of the form
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75 Y | X ~ Normal(f(X,theta,otherargs),noise_level^2)
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76 If n_inputs==1 then X is chosen at regular locations on the [-1,1] interval.
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77 Otherwise X is sampled according to a Normal(0,1) on all dimensions (independently).
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78 The parameters theta is a matrix of shape params_shape that is sampled from Normal(0,1).
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79 Optionally theta[0] is set to the argument 'b', if b is provided.
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80
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81 Return a training set and a test set, by splitting the generated n_examples
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82 according to the 'train_frac'tion.
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83 """
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84 n_train=int(train_frac*n_examples)
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85 n_test=n_examples-n_train
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86 if n_inputs==1:
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87 delta1=2./n_train
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88 delta2=2./n_test
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89 inputs = vstack((array(zip(range(n_train)))*delta1-1,
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90 0.5*delta2+array(zip(range(n_test)))*delta2-1))
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91 else:
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92 inputs = random.normal(size=(n_examples,n_inputs))
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93 if not f:
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94 f = linear_predictor
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95 if f==kernel_predictor and not otherargs[1]:
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96 otherargs=(otherargs[0],inputs[0:n_train])
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97 if not params_shape:
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98 if f==linear_predictor:
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99 params_shape = (n_inputs+1,n_targets)
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100 elif f==kernel_predictor:
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101 params_shape = (otherargs[1].shape[0]+1,n_targets)
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102 theta = random.normal(size=params_shape) if params_shape else None
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103 if b:
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104 theta[0]=b
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105 outputs = f(inputs,theta,otherargs)
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106 targets = outputs + random.normal(scale=noise_level,size=(n_examples,n_targets))
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107 # the | stacking creates a strange bug in LookupList constructor:
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108 # trainset = ArrayDataSet(inputs[0:n_examples/2],{'input':slice(0,n_inputs)}) | \
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109 # ArrayDataSet(targets[0:n_examples/2],{'target':slice(0,n_targets)})
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110 # testset = ArrayDataSet(inputs[n_examples/2:],{'input':slice(0,n_inputs)}) | \
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111 # ArrayDataSet(targets[n_examples/2:],{'target':slice(0,n_targets)})
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112 data = hstack((inputs,targets))
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113 trainset = ArrayDataSet(data[0:n_train],
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114 {'input':slice(0,n_inputs),'target':slice(n_inputs,n_inputs+n_targets)})
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115 testset = ArrayDataSet(data[n_train:],
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116 {'input':slice(0,n_inputs),'target':slice(n_inputs,n_inputs+n_targets)})
8e4d2ebd816a added a test for LinearRegression
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 431
diff changeset
117 return trainset,testset,theta