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annotate make_test_datasets.py @ 436:d7ed780364b3

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image_tools
author Olivier Breuleux <breuleuo@iro.umontreal.ca>
date Wed, 06 Aug 2008 19:39:14 -0400
parents 8e4d2ebd816a
children 2d8490d76b3e
rev   line source
431
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1 from pylearn.dataset import ArrayDataSet
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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 def make_triangles_rectangles_datasets(n_examples=600,train_frac=0.5,image_size=(10,10)):
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13 """
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14 Make a binary classification dataset to discriminate triangle images from rectangle images.
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15 """
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16 def convert_dataset(dset):
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17 # convert the n_vert==3 into target==0 and n_vert==4 into target==1
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18 def mapf(images,n_vertices):
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19 n=len(n_vertices)
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20 targets = ndarray((n,1),dtype='float64')
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21 for i in xrange(n):
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22 targets[i,0] = array([0. if vertices[i]==3 else 1.],dtype='float64')
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23 return images.reshape(len(images),images[0].size).astype('float64'),targets
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24 return dataset.CachedDataSet(dataset.ApplyFunctionDataSet(dset("image","nvert"),mapf,["input","target"]),True)
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25
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26 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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27 data = p.subset[0:n_examples]
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28 save_polygon_data(data,"shapes")
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29 n_train=int(n_examples*train_frac)
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30 trainset=convert_dataset(data.subset[0:n_train])
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31 testset=convert_dataset(data.subset[n_train:n_examples])
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32 return trainset,testset
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33
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34 def make_artificial_datasets_from_function(n_inputs=1,
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35 n_targets=1,
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36 n_examples=20,
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37 train_frac=0.5,
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38 noise_level=0.1, # add Gaussian noise, noise_level=sigma
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39 params_shape=None,
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40 f=None, # function computing E[Y|X]
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41 otherargs=None, # extra args to f
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42 b=None): # force theta[0] with this value
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43 """
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44 Make regression data of the form
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45 Y | X ~ Normal(f(X,theta,otherargs),noise_level^2)
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46 If n_inputs==1 then X is chosen at regular locations on the [-1,1] interval.
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47 Otherwise X is sampled according to a Normal(0,1) on all dimensions (independently).
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48 The parameters theta is a matrix of shape params_shape that is sampled from Normal(0,1).
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49 Optionally theta[0] is set to the argument 'b', if b is provided.
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50
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51 Return a training set and a test set, by splitting the generated n_examples
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52 according to the 'train_frac'tion.
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53 """
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54 n_train=int(train_frac*n_examples)
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55 n_test=n_examples-n_train
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56 if n_inputs==1:
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57 delta1=2./n_train
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58 delta2=2./n_test
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59 inputs = vstack((array(zip(range(n_train)))*delta1-1,
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60 0.5*delta2+array(zip(range(n_test)))*delta2-1))
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61 else:
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62 inputs = random.normal(size=(n_examples,n_inputs))
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63 if not f:
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64 f = linear_predictor
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65 if f==kernel_predictor and not otherargs[1]:
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66 otherargs=(otherargs[0],inputs[0:n_train])
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67 if not params_shape:
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68 if f==linear_predictor:
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69 params_shape = (n_inputs+1,n_targets)
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70 elif f==kernel_predictor:
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71 params_shape = (otherargs[1].shape[0]+1,n_targets)
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72 theta = random.normal(size=params_shape) if params_shape else None
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73 if b:
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74 theta[0]=b
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75 outputs = f(inputs,theta,otherargs)
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76 targets = outputs + random.normal(scale=noise_level,size=(n_examples,n_targets))
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77 # the | stacking creates a strange bug in LookupList constructor:
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78 # trainset = ArrayDataSet(inputs[0:n_examples/2],{'input':slice(0,n_inputs)}) | \
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79 # ArrayDataSet(targets[0:n_examples/2],{'target':slice(0,n_targets)})
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80 # testset = ArrayDataSet(inputs[n_examples/2:],{'input':slice(0,n_inputs)}) | \
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81 # ArrayDataSet(targets[n_examples/2:],{'target':slice(0,n_targets)})
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82 data = hstack((inputs,targets))
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83 trainset = ArrayDataSet(data[0:n_train],
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84 {'input':slice(0,n_inputs),'target':slice(n_inputs,n_inputs+n_targets)})
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85 testset = ArrayDataSet(data[n_train:],
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86 {'input':slice(0,n_inputs),'target':slice(n_inputs,n_inputs+n_targets)})
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87 return trainset,testset,theta