class testDataSet(unittest.TestCase):
def setUp(self):
self.factors=[{'sleepdep':['normal','deprived']},
{'stimulus':['left', 'right']}]
self.responses=['left','right']
self.design=Design(self.factors,self.responses, 'stimulus')
def test_init(self):
factors=[{'sleepdep':['normal','deprived']},
{'stimulus':['left', 'right']}]
responses=['left','right']
design=Design(factors,responses, 'stimulus')
dat=pd.read_csv('./data/sleep_stop_onesubj_test.csv')
assert dat.shape[0]==800
dat.columns=['sleepdep','stimulus','SSD','response','correct', 'RT']
ds=StopTaskDataSet(design,dat)
assert ds.check(verbose=True)
assert len(ds.RT)==len(ds.condition)
assert len(ds.SSD)==len(ds.RT)
assert len(ds.response)==len(ds.RT)
def test_factorval(self):
assert self.design.factorval(0,'sleepdep')=='normal'
assert self.design.factorval(0,'stimulus')=='left'
assert self.design.factorval(1,'sleepdep')=='normal'
assert self.design.factorval(1,'stimulus')=='right'
assert self.design.factorval(2,'sleepdep')=='deprived'
assert self.design.factorval(2,'stimulus')=='left'
assert self.design.factorval(3,'sleepdep')=='deprived'
assert self.design.factorval(3,'stimulus')=='right'
def setUp(self):
self.factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
self.responses = ['left', 'right']
self.design = Design(self.factors, self.responses, 'stimulus')
def test_init(self):
factors=[{'sleepdep':['normal','deprived']},
{'stimulus':['left', 'right']}]
responses=['left','right']
design=Design(factors,responses, 'stimulus')
assert design.nresponses()==len(responses)
assert design.nconditions()==4
assert len(design.factors_to_int)==len(design.factors_from_int)
k=['deprived','left']
assert design.condidx( k )==2
def test_init(self):
factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
responses = ['left', 'right']
design = Design(factors, responses, 'stimulus')
assert design.nresponses() == len(responses)
assert design.nconditions() == 4
assert len(design.factors_to_int) == len(design.factors_from_int)
k = ['deprived', 'left']
assert design.condidx(k) == 2
def setUp(self):
self.factors = [{
'TUT': ['tut', 'on-task']
}, {
'stimulus': ['left', 'right']
}]
self.responses = ['left', 'right']
self.design = Design(self.factors, self.responses, 'stimulus')
self.truepar = pSSLBA_modelB.paramspec(ster=.1,
ter=.2,
A=.2,
Bs=.5,
B=.8,
Vs=2.0,
Vtut=.5,
Vont=1.0,
v=0.0)
self.mod = pSSLBA_modelB(self.design, self.truepar)
def test_repr(self):
factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
responses = ['left', 'right']
design = Design(factors, responses, 'stimulus', name="blubber")
design2 = eval(repr(design))
assert design.__dict__ == design2.__dict__, "%s vs %s" % (
repr(design), repr(design2))
class testDataSet(unittest.TestCase):
def setUp(self):
self.factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
self.responses = ['left', 'right']
self.design = Design(self.factors, self.responses, 'stimulus')
def test_init(self):
factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
responses = ['left', 'right']
design = Design(factors, responses, 'stimulus')
dat = pd.read_csv('./data/sleep_stop_onesubj_test.csv')
assert dat.shape[0] == 800
dat.columns = [
'sleepdep', 'stimulus', 'SSD', 'response', 'correct', 'RT'
]
ds = StopTaskDataSet(design, dat)
assert ds.check(verbose=True)
assert len(ds.RT) == len(ds.condition)
assert len(ds.SSD) == len(ds.RT)
assert len(ds.response) == len(ds.RT)
def test_factorval(self):
assert self.design.factorval(0, 'sleepdep') == 'normal'
assert self.design.factorval(0, 'stimulus') == 'left'
assert self.design.factorval(1, 'sleepdep') == 'normal'
assert self.design.factorval(1, 'stimulus') == 'right'
assert self.design.factorval(2, 'sleepdep') == 'deprived'
assert self.design.factorval(2, 'stimulus') == 'left'
assert self.design.factorval(3, 'sleepdep') == 'deprived'
assert self.design.factorval(3, 'stimulus') == 'right'
def test_init(self):
factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
responses = ['left', 'right']
design = Design(factors, responses, 'stimulus')
dat = pd.read_csv('./data/sleep_stop_onesubj_test.csv')
assert dat.shape[0] == 800
dat.columns = [
'sleepdep', 'stimulus', 'SSD', 'response', 'correct', 'RT'
]
ds = StopTaskDataSet(design, dat)
pars = pSSLBA_modelA.paramspec(.2, .15, .2, 1.0, 1.0, 2, 1, 0.5)
mod = pSSLBA_modelA(design, pars)
print mod.parstring(full=True)
print mod
print mod.go_accumulators[0]
nsamples = 100
x = np.linspace(0.01, 10, nsamples)
condition = 0
dens = mod.dens_acc_go(x, condition, 1)
cscore = 0
for i in range(design.nresponses()):
score = scipy.integrate.quad(mod.dens_acc_go,
0.01,
np.infty,
args=(condition, i))[0]
print "Chance of winning Acc %i (condition=%s): %f" % (
i, str(design.condidx(condition)), score)
cscore += score
assert abs(cscore - 1) < 1e-6
print cscore
L = mod.loglikelihood(ds)
assert np.isfinite(L)
print "L=", L
def setUp(self):
self.factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
self.responses = ['left', 'right']
self.design = Design(self.factors, self.responses, 'stimulus')
self.dat = pd.read_csv('./data/sleep_stop_onesubj_test.csv')
assert self.dat.shape[0] == 800
self.dat.columns = [
'sleepdep', 'stimulus', 'SSD', 'response', 'correct', 'RT'
]
self.ds = StopTaskDataSet(self.design, self.dat)
pars = pSSLBA_modelA.paramspec(.2, .15, .2, 1.0, 1.0, 2, 1, 0.5)
self.mod = pSSLBA_modelA(self.design, pars)
class testpSSLBA_modelB(unittest.TestCase):
def setUp(self):
self.factors = [{
'TUT': ['tut', 'on-task']
}, {
'stimulus': ['left', 'right']
}]
self.responses = ['left', 'right']
self.design = Design(self.factors, self.responses, 'stimulus')
self.truepar = pSSLBA_modelB.paramspec(ster=.1,
ter=.2,
A=.2,
Bs=.5,
B=.8,
Vs=2.0,
Vtut=.5,
Vont=1.0,
v=0.0)
self.mod = pSSLBA_modelB(self.design, self.truepar)
def test_trans(self):
for i in range(50):
rpar = pSSLBA_modelB.paramspec().random()
assert np.all(
np.abs(rpar - self.mod.untrans(self.mod.trans(rpar)))) < 1e-5
def test_setparam(self):
tutcond = [
i for i in range(self.design.nconditions())
if self.design.factorval(i, 'TUT') == 'tut'
]
ontcond = [
i for i in range(self.design.nconditions())
if self.design.factorval(i, 'TUT') != 'tut'
]
rpar = self.mod.paramspec().random()
self.mod.set_params(rpar)
for tc in tutcond:
correct = self.design.correct_response(tc)
for ir, resp in enumerate(self.design.responses):
if resp == correct:
assert self.mod.go_accumulators[tc][ir].v == rpar.Vtut
else:
assert self.mod.go_accumulators[tc][ir].v == rpar.v
for oc in ontcond:
correct = self.design.correct_response(oc)
for ir, resp in enumerate(self.design.responses):
if resp == correct:
assert self.mod.go_accumulators[oc][ir].v == rpar.Vont
else:
assert self.mod.go_accumulators[oc][ir].v == rpar.v
def test_init(self):
factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
responses = ['left', 'right']
design = Design(factors, responses, 'stimulus')
dat = pd.read_csv('./data/sleep_stop_onesubj_test.csv')
assert dat.shape[0] == 800
dat.columns = [
'sleepdep', 'stimulus', 'SSD', 'response', 'correct', 'RT'
]
ds = StopTaskDataSet(design, dat)
assert ds.check(verbose=True)
assert len(ds.RT) == len(ds.condition)
assert len(ds.SSD) == len(ds.RT)
assert len(ds.response) == len(ds.RT)
def test_loglikelihood(self):
factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
responses = ['left', 'right']
design = Design(factors, responses, 'stimulus')
dat = pd.read_csv('./data/sleep_stop_onesubj_test.csv')
dat.columns = [
'sleepdep', 'stimulus', 'SSD', 'response', 'correct', 'RT'
]
ds = StopTaskDataSet(design, dat)
pars = pSSLBA_modelA.paramspec(.1, .2, .2, .5, .8, 2, 1, 0)
mod = pSSLBA_modelA(design, pars)
print mod
Ls = StopTaskRaceModel.deviance(mod, ds)
L2 = mod.deviance(ds)
assert np.abs(Ls - L2) < 1e-8
def test_sslba_likelihood_trials(self):
factors = [{
'sleepdep': ['normal', 'deprived']
}, {
'stimulus': ['left', 'right']
}]
responses = ['left', 'right']
design = Design(factors, responses, 'stimulus')
dat = pd.read_csv('./data/sleep_stop_onesubj_test.csv')
dat.columns = [
'sleepdep', 'stimulus', 'SSD', 'response', 'correct', 'RT'
]
ds = StopTaskDataSet(design, dat)
pars = pSSLBA_modelA.paramspec(.1, .2, .2, .5, .8, 2, 1, 0)
mod = pSSLBA_modelA(design, pars)
print mod
Ls = StopTaskRaceModel.likelihood_trials(mod, ds)
L2 = mod.likelihood_trials(ds)
goodix = (np.abs(Ls - L2) < 1e-5)
badix = np.logical_not(goodix)
assert np.sum(badix) == 0, "num bad idx=%i" % np.sum(badix)
def setUp(self):
self.factors=[{'sleepdep':['normal','deprived']},
{'stimulus':['left', 'right']}]
self.responses=['left','right']
self.design=Design(self.factors,self.responses, 'stimulus')