Mercurial > pylearn
view test_dataset.py @ 244:3156a9976183
mlp_factory_approach.py, updated and un-deprecated by popular demand
author | James Bergstra <bergstrj@iro.umontreal.ca> |
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date | Mon, 02 Jun 2008 17:08:17 -0400 |
parents | 38beb81f4e8b |
children | 6aff510792dd |
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#!/bin/env python from dataset import * from math import * import numpy from misc import * def have_raised(to_eval, **var): have_thrown = False try: eval(to_eval) except : have_thrown = True return have_thrown def have_raised2(f, *args, **kwargs): have_thrown = False try: f(*args, **kwargs) except : have_thrown = True return have_thrown def test1(): print "test1" global a,ds a = numpy.random.rand(10,4) print a ds = ArrayDataSet(a,{'x':slice(3),'y':3,'z':[0,2]}) print "len(ds)=",len(ds) assert(len(ds)==10) print "example 0 = ",ds[0] # assert print "x=",ds["x"] print "x|y" for x,y in ds("x","y"): print x,y minibatch_iterator = ds.minibatches(fieldnames=['z','y'],n_batches=1,minibatch_size=3,offset=4) minibatch = minibatch_iterator.__iter__().next() print "minibatch=",minibatch for var in minibatch: print "var=",var print "take a slice and look at field y",ds[1:6:2]["y"] del a,ds,x,y,minibatch_iterator,minibatch,var def test_iterate_over_examples(array,ds): #not in doc!!! i=0 for example in range(len(ds)): assert (ds[example]['x']==array[example][:3]).all() assert ds[example]['y']==array[example][3] assert (ds[example]['z']==array[example][[0,2]]).all() i+=1 assert i==len(ds) del example,i # - for example in dataset: i=0 for example in ds: assert len(example)==3 assert (example['x']==array[i][:3]).all() assert example['y']==array[i][3] assert (example['z']==array[i][0:3:2]).all() assert (numpy.append(example['x'],example['y'])==array[i]).all() i+=1 assert i==len(ds) del example,i # - for val1,val2,... in dataset: i=0 for x,y,z in ds: assert (x==array[i][:3]).all() assert y==array[i][3] assert (z==array[i][0:3:2]).all() assert (numpy.append(x,y)==array[i]).all() i+=1 assert i==len(ds) del x,y,z,i # - for example in dataset(field1, field2,field3, ...): i=0 for example in ds('x','y','z'): assert len(example)==3 assert (example['x']==array[i][:3]).all() assert example['y']==array[i][3] assert (example['z']==array[i][0:3:2]).all() assert (numpy.append(example['x'],example['y'])==array[i]).all() i+=1 assert i==len(ds) del example,i i=0 for example in ds('y','x'): assert len(example)==2 assert (example['x']==array[i][:3]).all() assert example['y']==array[i][3] assert (numpy.append(example['x'],example['y'])==array[i]).all() i+=1 assert i==len(ds) del example,i # - for val1,val2,val3 in dataset(field1, field2,field3): i=0 for x,y,z in ds('x','y','z'): assert (x==array[i][:3]).all() assert y==array[i][3] assert (z==array[i][0:3:2]).all() assert (numpy.append(x,y)==array[i]).all() i+=1 assert i==len(ds) del x,y,z,i i=0 for y,x in ds('y','x',): assert (x==array[i][:3]).all() assert y==array[i][3] assert (numpy.append(x,y)==array[i]).all() i+=1 assert i==len(ds) del x,y,i def test_minibatch_size(minibatch,minibatch_size,len_ds,nb_field,nb_iter_finished): ##full minibatch or the last minibatch for idx in range(nb_field): test_minibatch_field_size(minibatch[idx],minibatch_size,len_ds,nb_iter_finished) del idx def test_minibatch_field_size(minibatch_field,minibatch_size,len_ds,nb_iter_finished): assert len(minibatch_field)==minibatch_size or ((nb_iter_finished*minibatch_size+len(minibatch_field))==len_ds and len(minibatch_field)<minibatch_size) # - for minibatch in dataset.minibatches([field1, field2, ...],minibatch_size=N): i=0 mi=0 m=ds.minibatches(['x','z'], minibatch_size=3) assert isinstance(m,DataSet.MinibatchWrapAroundIterator) for minibatch in m: assert isinstance(minibatch,DataSetFields) assert len(minibatch)==2 test_minibatch_size(minibatch,m.minibatch_size,len(ds),2,mi) if type(ds)==ArrayDataSet: assert (minibatch[0][:,::2]==minibatch[1]).all() else: for j in xrange(len(minibatch[0])): (minibatch[0][j][::2]==minibatch[1][j]).all() mi+=1 i+=len(minibatch[0]) assert i==len(ds) assert mi==4 del minibatch,i,m,mi i=0 mi=0 m=ds.minibatches(['x','y'], minibatch_size=3) assert isinstance(m,DataSet.MinibatchWrapAroundIterator) for minibatch in m: assert len(minibatch)==2 test_minibatch_size(minibatch,m.minibatch_size,len(ds),2,mi) mi+=1 for id in range(len(minibatch[0])): assert (numpy.append(minibatch[0][id],minibatch[1][id])==array[i]).all() i+=1 assert i==len(ds) assert mi==4 del minibatch,i,id,m,mi # - for mini1,mini2,mini3 in dataset.minibatches([field1, field2, field3], minibatch_size=N): i=0 mi=0 m=ds.minibatches(['x','z'], minibatch_size=3) assert isinstance(m,DataSet.MinibatchWrapAroundIterator) for x,z in m: test_minibatch_field_size(x,m.minibatch_size,len(ds),mi) test_minibatch_field_size(z,m.minibatch_size,len(ds),mi) for id in range(len(x)): assert (x[id][::2]==z[id]).all() i+=1 mi+=1 assert i==len(ds) assert mi==4 del x,z,i,m,mi i=0 mi=0 m=ds.minibatches(['x','y'], minibatch_size=3) for x,y in m: test_minibatch_field_size(x,m.minibatch_size,len(ds),mi) test_minibatch_field_size(y,m.minibatch_size,len(ds),mi) mi+=1 for id in range(len(x)): assert (numpy.append(x[id],y[id])==array[i]).all() i+=1 assert i==len(ds) assert mi==4 del x,y,i,id,m,mi #not in doc i=0 m=ds.minibatches(['x','y'],n_batches=1,minibatch_size=3,offset=4) assert isinstance(m,DataSet.MinibatchWrapAroundIterator) for x,y in m: assert len(x)==3 assert len(y)==3 for id in range(3): assert (numpy.append(x[id],y[id])==array[i+4]).all() i+=1 assert i==3 del x,y,i,id,m i=0 m=ds.minibatches(['x','y'],n_batches=2,minibatch_size=3,offset=4) assert isinstance(m,DataSet.MinibatchWrapAroundIterator) for x,y in m: assert len(x)==3 assert len(y)==3 for id in range(3): assert (numpy.append(x[id],y[id])==array[i+4]).all() i+=1 assert i==6 del x,y,i,id,m i=0 m=ds.minibatches(['x','y'],n_batches=20,minibatch_size=3,offset=4) assert isinstance(m,DataSet.MinibatchWrapAroundIterator) for x,y in m: assert len(x)==3 assert len(y)==3 for id in range(3): assert (numpy.append(x[id],y[id])==array[(i+4)%array.shape[0]]).all() i+=1 assert i==m.n_batches*m.minibatch_size del x,y,i,id def test_ds_iterator(array,iterator1,iterator2,iterator3): l=len(iterator1) i=0 for x,y in iterator1: assert (x==array[i][:3]).all() assert y==array[i][3] assert (numpy.append(x,y)==array[i]).all() i+=1 assert i==l i=0 for y,z in iterator2: assert y==array[i][3] assert (z==array[i][0:3:2]).all() i+=1 assert i==l i=0 for x,y,z in iterator3: assert (x==array[i][:3]).all() assert y==array[i][3] assert (z==array[i][0:3:2]).all() assert (numpy.append(x,y)==array[i]).all() i+=1 assert i==l def test_getitem(array,ds): def test_ds(orig,ds,index): i=0 assert len(ds)==len(index) for x,z,y in ds('x','z','y'): assert (orig[index[i]]['x']==array[index[i]][:3]).all() assert (orig[index[i]]['x']==x).all() assert orig[index[i]]['y']==array[index[i]][3] assert orig[index[i]]['y']==y assert (orig[index[i]]['z']==array[index[i]][0:3:2]).all() assert (orig[index[i]]['z']==z).all() i+=1 del i ds[0] if len(ds)>2: ds[:1] ds[1:1] ds[1:1:1] if len(ds)>5: ds[[1,2,3]] for x in ds: pass #ds[:n] returns a dataset with the n first examples. ds2=ds[:3] assert isinstance(ds2,DataSet) test_ds(ds,ds2,index=[0,1,2]) del ds2 #ds[i1:i2:s]# returns a ds with the examples i1,i1+s,...i2-s. ds2=ds[1:7:2] assert isinstance(ds2,DataSet) test_ds(ds,ds2,[1,3,5]) del ds2 #ds[i] ds2=ds[5] assert isinstance(ds2,Example) assert have_raised("var['ds']["+str(len(ds))+"]",ds=ds) # index not defined assert not have_raised("var['ds']["+str(len(ds)-1)+"]",ds=ds) del ds2 #ds[[i1,i2,...in]]# returns a ds with examples i1,i2,...in. ds2=ds[[4,7,2,8]] assert isinstance(ds2,DataSet) test_ds(ds,ds2,[4,7,2,8]) del ds2 #ds[fieldname]# an iterable over the values of the field fieldname across #the ds (the iterable is obtained by default by calling valuesVStack #over the values for individual examples). assert have_raised("ds['h']") # h is not defined... assert have_raised("ds[['x']]") # bad syntax assert not have_raised("var['ds']['x']",ds=ds) isinstance(ds['x'],DataSetFields) ds2=ds['x'] assert len(ds['x'])==10 assert len(ds['y'])==10 assert len(ds['z'])==10 i=0 for example in ds['x']: assert (example==array[i][:3]).all() i+=1 assert i==len(ds) i=0 for example in ds['y']: assert (example==array[i][3]).all() i+=1 assert i==len(ds) i=0 for example in ds['z']: assert (example==array[i,0:3:2]).all() i+=1 assert i==len(ds) del ds2,i #ds.<property># returns the value of a property associated with #the name <property>. The following properties should be supported: # - 'description': a textual description or name for the ds # - 'fieldtypes': a list of types (one per field) #* ds1 | ds2 | ds3 == ds.hstack([ds1,ds2,ds3])#???? #assert hstack([ds('x','y'),ds('z')])==ds #hstack([ds('z','y'),ds('x')])==ds assert have_raised2(hstack,[ds('x'),ds('x')]) assert have_raised2(hstack,[ds('y','x'),ds('x')]) assert not have_raised2(hstack,[ds('x'),ds('y')]) # i=0 # for example in hstack([ds('x'),ds('y'),ds('z')]): # example==ds[i] # i+=1 # del i,example #* ds1 & ds2 & ds3 == ds.vstack([ds1,ds2,ds3])#???? def test_fields_fct(ds): #@todo, fill correctly assert len(ds.fields())==3 i=0 v=0 for field in ds.fields(): for field_value in field: # iterate over the values associated to that field for all the ds examples v+=1 i+=1 assert i==3 assert v==3*10 del i,v i=0 v=0 for field in ds('x','z').fields(): i+=1 for val in field: v+=1 assert i==2 assert v==2*10 del i,v i=0 v=0 for field in ds.fields('x','y'): i+=1 for val in field: v+=1 assert i==2 assert v==2*10 del i,v i=0 v=0 for field_examples in ds.fields(): for example_value in field_examples: v+=1 i+=1 assert i==3 assert v==3*10 del i,v assert ds == ds.fields().examples() assert len(ds('x','y').fields()) == 2 assert len(ds('x','z').fields()) == 2 assert len(ds('y').fields()) == 1 del field def test_all(array,ds): assert len(ds)==10 test_iterate_over_examples(array, ds) test_getitem(array, ds) test_ds_iterator(array,ds('x','y'),ds('y','z'),ds('x','y','z')) test_fields_fct(ds) def test_ArrayDataSet(): #don't test stream #tested only with float value #don't always test with y #don't test missing value #don't test with tuple #don't test proterties print "test_ArrayDataSet" a2 = numpy.random.rand(10,4) ds = ArrayDataSet(a2,{'x':slice(3),'y':3,'z':[0,2]})###???tuple not tested ds = ArrayDataSet(a2,LookupList(['x','y','z'],[slice(3),3,[0,2]]))###???tuple not tested #assert ds==a? should this work? test_all(a2,ds) del a2, ds def test_LookupList(): #test only the example in the doc??? print "test_LookupList" example = LookupList(['x','y','z'],[1,2,3]) example['x'] = [1, 2, 3] # set or change a field x, y, z = example x = example[0] x = example["x"] assert example.keys()==['x','y','z'] assert example.values()==[[1,2,3],2,3] assert example.items()==[('x',[1,2,3]),('y',2),('z',3)] example.append_keyval('u',0) # adds item with name 'u' and value 0 assert len(example)==4 # number of items = 4 here example2 = LookupList(['v','w'], ['a','b']) example3 = LookupList(['x','y','z','u','v','w'], [[1, 2, 3],2,3,0,'a','b']) assert example+example2==example3 assert have_raised("var['x']+var['x']",x=example) del example, example2, example3, x, y ,z def test_CachedDataSet(): print "test_CacheDataSet" a = numpy.random.rand(10,4) ds1 = ArrayDataSet(a,LookupList(['x','y','z'],[slice(3),3,[0,2]]))###???tuple not tested ds2 = CachedDataSet(ds1) ds3 = CachedDataSet(ds1,cache_all_upon_construction=True) test_all(a,ds2) test_all(a,ds3) del a,ds1,ds2,ds3 def test_DataSetFields(): print "test_DataSetFields" raise NotImplementedError() def test_ApplyFunctionDataSet(): print "test_ApplyFunctionDataSet" a = numpy.random.rand(10,4) a2 = a+1 ds1 = ArrayDataSet(a,LookupList(['x','y','z'],[slice(3),3,[0,2]]))###???tuple not tested ds2 = ApplyFunctionDataSet(ds1,lambda x,y,z: (x+1,y+1,z+1), ['x','y','z'],minibatch_mode=False) ds3 = ApplyFunctionDataSet(ds1,lambda x,y,z: (numpy.array(x)+1,numpy.array(y)+1,numpy.array(z)+1), ['x','y','z'], minibatch_mode=True) test_all(a2,ds2) test_all(a2,ds3) del a,ds1,ds2,ds3 def test_FieldsSubsetDataSet(): print "test_FieldsSubsetDataSet" raise NotImplementedError() def test_MinibatchDataSet(): print "test_MinibatchDataSet" raise NotImplementedError() def test_HStackedDataSet(): print "test_HStackedDataSet" raise NotImplementedError() def test_VStackedDataSet(): print "test_VStackedDataSet" raise NotImplementedError() def test_ArrayFieldsDataSet(): print "test_ArrayFieldsDataSet" raise NotImplementedError() def test_speed(): print "test_speed" import time a2 = numpy.random.rand(100000,400) ds = ArrayDataSet(a2,{'x':slice(3),'y':3,'z':[0,2]})###???tuple not tested ds = ArrayDataSet(a2,LookupList(['x','y','z'],[slice(3),3,[0,2]]))###???tuple not tested ds = ArrayDataSet(a2,{'all':slice(0,a2.shape[1],1)}) #assert ds==a? should this work? mat = numpy.random.rand(400,100) @print_timing def f_array1(a): a+1 @print_timing def f_array2(a): for id in range(a.shape[0]): # pass a[id]+1 # a[id]*mat @print_timing def f_ds(ds): for ex in ds: # pass ex[0]+1 # a[id]*mat @print_timing def f_ds_mb1(ds,mb_size): for exs in ds.minibatches(minibatch_size = mb_size): for ex in exs: # pass ex[0]+1 # ex[id]*mat @print_timing def f_ds_mb2(ds,mb_size): for exs in ds.minibatches(minibatch_size = mb_size): # pass exs[0]+1 # ex[id]*mat f_array1(a2) f_array2(a2) f_ds(ds) f_ds_mb1(ds,10) f_ds_mb1(ds,100) f_ds_mb1(ds,1000) f_ds_mb1(ds,10000) f_ds_mb2(ds,10) f_ds_mb2(ds,100) f_ds_mb2(ds,1000) f_ds_mb2(ds,10000) del a2, ds if __name__=='__main__': test1() test_LookupList() test_ArrayDataSet() test_CachedDataSet() test_ApplyFunctionDataSet() #test_speed() #test pmat.py