def test_partitionmapper(): ds = give_data() oep = OddEvenPartitioner() parts = list(oep.generate(ds)) assert_equal(len(parts), 2) for i, p in enumerate(parts): assert_array_equal(p.sa['partitions'].unique, [1, 2]) assert_equal(p.a.partitions_set, i) assert_equal(len(p), len(ds))
def generate_testing_datasets(specs): # Lets permute upon each invocation of test, so we could possibly # trigger some funny cases nonbogus_pool = np.random.permutation([0, 1, 3, 5]) datasets = {} # use a partitioner to flag odd/even samples as training and test ttp = OddEvenPartitioner(space='train', count=1) for kind, spec in specs.iteritems(): # set of univariate datasets for nlabels in [ 2, 3, 4 ]: basename = 'uni%d%s' % (nlabels, kind) nonbogus_features = nonbogus_pool[:nlabels] dataset = normal_feature_dataset( nlabels=nlabels, nonbogus_features=nonbogus_features, **spec) # full dataset datasets[basename] = list(ttp.generate(dataset))[0] # sample 3D total = 2*spec['perlabel'] nchunks = spec['nchunks'] data = np.random.standard_normal(( total, 3, 6, 6 )) labels = np.concatenate( ( np.repeat( 0, spec['perlabel'] ), np.repeat( 1, spec['perlabel'] ) ) ) data[:, 1, 0, 0] += 2*labels # add some signal chunks = np.asarray(range(nchunks)*(total/nchunks)) mask = np.ones((3, 6, 6), dtype='bool') mask[0, 0, 0] = 0 mask[1, 3, 2] = 0 ds = Dataset.from_wizard(samples=data, targets=labels, chunks=chunks, mask=mask, space='myspace') # and to stress tests on manipulating sa/fa possibly containing # attributes of dtype object ds.sa['test_object'] = [['a'], [1, 2]] * (ds.nsamples/2) datasets['3d%s' % kind] = ds # some additional datasets datasets['dumb2'] = dumb_feature_binary_dataset() datasets['dumb'] = dumb_feature_dataset() # dataset with few invariant features _dsinv = dumb_feature_dataset() _dsinv.samples = np.hstack((_dsinv.samples, np.zeros((_dsinv.nsamples, 1)), np.ones((_dsinv.nsamples, 1)))) datasets['dumbinv'] = _dsinv # Datasets for regressions testing datasets['sin_modulated'] = list(ttp.generate(multiple_chunks(sin_modulated, 4, 30, 1)))[0] # use the same full for training datasets['sin_modulated_train'] = datasets['sin_modulated'] datasets['sin_modulated_test'] = sin_modulated(30, 1, flat=True) # simple signal for linear regressors datasets['chirp_linear'] = multiple_chunks(chirp_linear, 6, 50, 10, 2, 0.3, 0.1) datasets['chirp_linear_test'] = chirp_linear(20, 5, 2, 0.4, 0.1) datasets['wr1996'] = multiple_chunks(wr1996, 4, 50) datasets['wr1996_test'] = wr1996(50) datasets['hollow'] = Dataset(HollowSamples((40,20)), sa={'targets': np.tile(['one', 'two'], 20)}) return datasets
from mvpa.clfs.svm import LinearCSVMC from mvpa.measures.base import CrossValidation from mvpa.measures.searchlight import sphere_searchlight from mvpa.testing.datasets import datasets from mvpa.mappers.fx import mean_sample """For the sake of simplicity, let's use a small artificial dataset.""" # Lets just use our tiny 4D dataset from testing battery dataset = datasets['3dlarge'] """Now it only takes three lines for a searchlight analysis.""" # setup measure to be computed in each sphere (cross-validated # generalization error on odd/even splits) cv = CrossValidation(LinearCSVMC(), OddEvenPartitioner()) # setup searchlight with 2 voxels radius and measure configured above sl = sphere_searchlight(cv, radius=2, space='myspace', postproc=mean_sample()) # run searchlight on dataset sl_map = sl(dataset) print 'Best performing sphere error:', np.min(sl_map.samples) """ If this analysis is done on a fMRI dataset using `NiftiDataset` the resulting searchlight map (`sl_map`) can be mapped back into the original dataspace and viewed as a brain overlay. :ref:`Another example <example_searchlight>` shows a typical application of this algorithm.
specs = {'large' : { 'perlabel': 99, 'nchunks': 11, 'nfeatures': 20, 'snr': 8 * snr_scale}, 'medium' :{ 'perlabel': 24, 'nchunks': 6, 'nfeatures': 14, 'snr': 8 * snr_scale}, 'small' : { 'perlabel': 12, 'nchunks': 4, 'nfeatures': 6, 'snr' : 14 * snr_scale} } # Lets permute upon each invocation of test, so we could possibly # trigger some funny cases nonbogus_pool = np.random.permutation([0, 1, 3, 5]) datasets = {} # use a partitioner to flag odd/even samples as training and test ttp = OddEvenPartitioner(space='train', count=1) for kind, spec in specs.iteritems(): # set of univariate datasets for nlabels in [ 2, 3, 4 ]: basename = 'uni%d%s' % (nlabels, kind) nonbogus_features = nonbogus_pool[:nlabels] dataset = normal_feature_dataset( nlabels=nlabels, nonbogus_features=nonbogus_features, **spec) # full dataset datasets[basename] = list(ttp.generate(dataset))[0]