Mercurial > pylearn
changeset 283:275b92d40ea6
removed old test file
| author | Frederic Bastien <bastienf@iro.umontreal.ca> |
|---|---|
| date | Fri, 06 Jun 2008 13:52:13 -0400 |
| parents | b55c829695f1 |
| children | 8e923cb2e8fc |
| files | _test_dataset.py |
| diffstat | 1 files changed, 0 insertions(+), 183 deletions(-) [+] |
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--- a/_test_dataset.py Fri Jun 06 13:46:10 2008 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,183 +0,0 @@ -from dataset import * -from math import * -import unittest -import sys -import numpy as N - -def _sum_all(a): - s=a - while isinstance(s,numpy.ndarray): - s=sum(s) - return s - -class T_arraydataset(unittest.TestCase): - def setUp(self): - numpy.random.seed(123456) - - - def test_ctor_len(self): - n = numpy.random.rand(8,3) - a=ArrayDataSet(n) - self.failUnless(a.data is n) - self.failUnless(a.fields is None) - - self.failUnless(len(a) == n.shape[0]) - self.failUnless(a[0].shape == (n.shape[1],)) - - def test_iter(self): - arr = numpy.random.rand(8,3) - a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,3)}) - for i, example in enumerate(a): - self.failUnless(numpy.all( example['x'] == arr[i,:2])) - self.failUnless(numpy.all( example['y'] == arr[i,1:3])) - - def test_zip(self): - arr = numpy.random.rand(8,3) - a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,3)}) - for i, x in enumerate(a.zip("x")): - self.failUnless(numpy.all( x == arr[i,:2])) - - def test_minibatch_basic(self): - arr = numpy.random.rand(10,4) - a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)}) - for i, mb in enumerate(a.minibatches(minibatch_size=2)): #all fields - self.failUnless(numpy.all( mb['x'] == arr[i*2:i*2+2,0:2])) - self.failUnless(numpy.all( mb['y'] == arr[i*2:i*2+2,1:4])) - - def test_getattr(self): - arr = numpy.random.rand(10,4) - a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)}) - a_y = a.y - self.failUnless(numpy.all( a_y == arr[:,1:4])) - - def test_minibatch_wraparound_even(self): - arr = numpy.random.rand(10,4) - arr2 = ArrayDataSet.Iterator.matcat(arr,arr) - - a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)}) - - #print arr - for i, x in enumerate(a.minibatches(["x"], minibatch_size=2, n_batches=8)): - #print 'x' , x - self.failUnless(numpy.all( x == arr2[i*2:i*2+2,0:2])) - - def test_minibatch_wraparound_odd(self): - arr = numpy.random.rand(10,4) - arr2 = ArrayDataSet.Iterator.matcat(arr,arr) - - a=ArrayDataSet(data=arr,fields={"x":slice(2),"y":slice(1,4)}) - - for i, x in enumerate(a.minibatches(["x"], minibatch_size=3, n_batches=6)): - self.failUnless(numpy.all( x == arr2[i*3:i*3+3,0:2])) - - -class T_renamingdataset(unittest.TestCase): - def setUp(self): - numpy.random.seed(123456) - - - def test_hasfield(self): - n = numpy.random.rand(3,8) - a=ArrayDataSet(data=n,fields={"x":slice(2),"y":slice(1,4),"z":slice(4,6)}) - b=a.rename({'xx':'x','zz':'z'}) - self.failUnless(b.hasFields('xx','zz') and not b.hasFields('x') and not b.hasFields('y')) - -class T_applyfunctiondataset(unittest.TestCase): - def setUp(self): - numpy.random.seed(123456) - - def test_function(self): - n = numpy.random.rand(3,8) - a=ArrayDataSet(data=n,fields={"x":slice(2),"y":slice(1,4),"z":slice(4,6)}) - b=a.apply_function(lambda x,y: x+y,x+1, ['x','y'], ['x+y','x+1'], False,False,False) - print b.fieldNames() - print b('x+y') - - - - -# to be used with a any new dataset -class T_dataset_tester(object): - """ - This class' goal is to test any new dataset that is created - Tests are (will be!) designed to check the normal behaviours - of a dataset, as defined in dataset.py - """ - - - def __init__(self,ds,runall=True) : - """if interested in only a subset of test, init with runall=False""" - self.ds = ds - - if runall : - self.test1_basicstats(ds) - self.test2_slicing(ds) - self.test3_fields_iterator_consistency(ds) - - def test1_basicstats(self,ds) : - """print basics stats on a dataset, like length""" - - print 'len(ds) = ',len(ds) - print 'num fields = ', len(ds.fieldNames()) - print 'types of field: ', - for k in ds.fieldNames() : - print type(ds[0](k)[0]), - print '' - - def test2_slicing(self,ds) : - """test if slicing works properly""" - print 'testing slicing...', - sys.stdout.flush() - - middle = len(ds) / 2 - tenpercent = int(len(ds) * .1) - set1 = ds[:middle+tenpercent] - set2 = ds[middle-tenpercent:] - for k in range(tenpercent + tenpercent -1): - for k2 in ds.fieldNames() : - if type(set1[middle-tenpercent+k](k2)[0]) == N.ndarray : - for k3 in range(len(set1[middle-tenpercent+k](k2)[0])) : - assert set1[middle-tenpercent+k](k2)[0][k3] == set2[k](k2)[0][k3] - else : - assert set1[middle-tenpercent+k](k2)[0] == set2[k](k2)[0] - assert tenpercent > 1 - set3 = ds[middle-tenpercent:middle+tenpercent:2] - for k2 in ds.fieldNames() : - if type(set2[2](k2)[0]) == N.ndarray : - for k3 in range(len(set2[2](k2)[0])) : - assert set2[2](k2)[0][k3] == set3[1](k2)[0][k3] - else : - assert set2[2](k2)[0] == set3[1](k2)[0] - - print 'done' - - - def test3_fields_iterator_consistency(self,ds) : - """ check if the number of iterator corresponds to the number of fields""" - print 'testing fields/iterator consistency...', - sys.stdout.flush() - - # basic test - maxsize = min(len(ds)-1,100) - for iter in ds[:maxsize] : - assert len(iter) == len(ds.fieldNames()) - if len(ds.fieldNames()) == 1 : - print 'done' - return - - # with minibatches iterator - ds2 = ds.minibatches[:maxsize]([ds.fieldNames()[0],ds.fieldNames()[1]],minibatch_size=2) - for iter in ds2 : - assert len(iter) == 2 - - print 'done' - - - - - -################################################################### -# main -if __name__ == '__main__': - unittest.main() -