def test_eval_bootstrap_rdm(self):
from pyrsa.inference import eval_bootstrap_rdm
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
m2 = ModelFixed('test2', rdms.get_vectors()[1])
value = eval_bootstrap_rdm([m, m2], rdms, N=10)
def test_eval_bootstrap(self):
from pyrsa.inference import eval_bootstrap
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
m2 = ModelFixed('test2', rdms.get_vectors()[1])
result = eval_bootstrap([m, m2], rdms, N=10)
assert result.evaluations.shape[1] == 2
assert result.evaluations.shape[0] == 10
def test_bootstrap_testset_rdm(self):
from pyrsa.inference import bootstrap_testset_rdm
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
m2 = ModelFixed('test2', rdms.get_vectors()[1])
evaluations, n_rdms = bootstrap_testset_rdm([m, m2],
rdms,
method='cosine',
fitter=None,
N=100,
rdm_descriptor=None)
def test_eval_fixed(self):
from pyrsa.inference import eval_fixed
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
value = eval_fixed(m, rdms)
def test_bootstrap_crossval_k1(self):
from pyrsa.inference import bootstrap_crossval
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
dis = np.random.rand(11, 45) # 11 10x10 rdms
mes = "Euclidean"
des = {'subj': 0}
rdm_des = {'session': np.array([0, 1, 2, 2, 4, 5, 6, 7, 7, 7, 7])}
pattern_des = {'type': np.array([0, 1, 2, 2, 4, 5, 5, 5, 6, 7])}
rdms = RDMs(dissimilarities=dis,
rdm_descriptors=rdm_des,
pattern_descriptors=pattern_des,
dissimilarity_measure=mes,
descriptors=des)
m = ModelFixed('test', rdms[0])
bootstrap_crossval(m,
rdms,
N=10,
k_rdm=1,
k_pattern=2,
pattern_descriptor='type',
rdm_descriptor='session')
bootstrap_crossval(m,
rdms,
N=10,
k_rdm=2,
k_pattern=1,
pattern_descriptor='type',
rdm_descriptor='session')
def test_crossval(self):
from pyrsa.inference import crossval
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
dis = np.random.rand(11, 10) # 11 5x5 rdms
mes = "Euclidean"
des = {'subj': 0}
rdm_des = {'session': np.array([0, 1, 2, 2, 4, 5, 6, 7, 7, 7, 7])}
pattern_des = {'type': np.array([0, 1, 2, 2, 4])}
rdms = RDMs(dissimilarities=dis,
rdm_descriptors=rdm_des,
pattern_descriptors=pattern_des,
dissimilarity_measure=mes,
descriptors=des)
m = ModelFixed('test', rdms[0])
train_set = [
(rdms.subset_pattern('type', [0, 1]), np.array([0, 1])),
(rdms.subset_pattern('type', [0, 4]), np.array([0, 4])),
]
test_set = [
(rdms.subset_pattern('type', [2, 4]), np.array([2, 4])),
(rdms.subset_pattern('type', [1, 2]), np.array([1, 2])),
]
ceil_set = [
(rdms.subset_pattern('type', [2, 4]), np.array([2, 4])),
(rdms.subset_pattern('type', [1, 2]), np.array([1, 2])),
]
crossval(m,
rdms,
train_set,
test_set,
ceil_set,
pattern_descriptor='type')
def test_eval_bootstrap_rdm(self):
from pyrsa.inference import eval_bootstrap_rdm
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
value = eval_bootstrap_rdm(m, rdms, N=10)
value = eval_bootstrap_rdm(m, rdms, N=10, boot_noise_ceil=True)
def test_result_dict(self):
from pyrsa.inference import Result
from pyrsa.inference import result_from_dict
from pyrsa.model import ModelFixed
m1 = ModelFixed('test1', np.random.rand(10))
m2 = ModelFixed('test2', np.random.rand(10))
models = [m1, m2]
evaluations = np.random.rand(100, 2)
method = 'test_method'
cv_method = 'test_cv_method'
noise_ceiling = np.array([0.5, 0.2])
res = Result(models, evaluations, method, cv_method, noise_ceiling)
result_dict = res.to_dict()
res_loaded = result_from_dict(result_dict)
assert res_loaded.method == method
assert res_loaded.cv_method == cv_method
assert np.all(res_loaded.evaluations == evaluations)
assert np.all(res_loaded.models[0].rdm == m1.rdm)
def test_bootstrap_testset_pattern(self):
from pyrsa.inference import bootstrap_testset_pattern
from pyrsa.rdm import RDMs
from pyrsa.model import ModelFixed
rdms = RDMs(np.random.rand(11, 10)) # 11 5x5 rdms
m = ModelFixed('test', rdms.get_vectors()[0])
evaluations, n_cond = bootstrap_testset_pattern(
m,
rdms,
method='cosine',
fitter=None,
N=100,
pattern_descriptor=None)
def test_save_load_result(self):
from pyrsa.rdm import RDMs
from pyrsa.inference import Result
from pyrsa.inference import load_results
from pyrsa.model import ModelFixed
import io
rdm = RDMs(np.random.rand(10),
pattern_descriptors={
'test': ['test1', 'test1', 'test1', 'test3', 'test']
})
m1 = ModelFixed('test1', rdm)
m2 = ModelFixed('test2', np.random.rand(10))
models = [m1, m2]
evaluations = np.random.rand(100, 2)
method = 'test_method'
cv_method = 'test_cv_method'
noise_ceiling = np.array([0.5, 0.2])
res = Result(models, evaluations, method, cv_method, noise_ceiling)
f = io.BytesIO() # Essentially a Mock file
res.save(f, file_type='hdf5')
res_loaded = load_results(f, file_type='hdf5')
assert res_loaded.method == method
assert res_loaded.cv_method == cv_method
assert np.all(res_loaded.evaluations == evaluations)
def test_model_dict(self):
from pyrsa.model import model_from_dict
from pyrsa.model import ModelFixed
m = ModelFixed('test1', np.random.rand(10))
model_dict = m.to_dict()
model_loaded = model_from_dict(model_dict)
assert m.name == model_loaded.name
assert np.all(
m.rdm_obj.dissimilarities == model_loaded.rdm_obj.dissimilarities)
from pyrsa.model import ModelInterpolate
m = ModelInterpolate('test1', np.random.rand(10))
model_dict = m.to_dict()
model_loaded = model_from_dict(model_dict)
assert m.name == model_loaded.name
assert np.all(
m.rdm_obj.dissimilarities == model_loaded.rdm_obj.dissimilarities)
from pyrsa.model import ModelSelect
m = ModelSelect('test1', np.random.rand(10))
model_dict = m.to_dict()
model_loaded = model_from_dict(model_dict)
assert m.name == model_loaded.name
assert np.all(
m.rdm_obj.dissimilarities == model_loaded.rdm_obj.dissimilarities)
from pyrsa.model import ModelWeighted
m = ModelWeighted('test1', np.random.rand(10))
model_dict = m.to_dict()
model_loaded = model_from_dict(model_dict)
assert m.name == model_loaded.name
assert np.all(
m.rdm_obj.dissimilarities == model_loaded.rdm_obj.dissimilarities)
from pyrsa.model import Model
m = Model('test1')
model_dict = m.to_dict()
model_loaded = model_from_dict(model_dict)
assert m.name == model_loaded.name