def test_simulate_sample():
dataDict = loadData.load_sim_data('tests/test_simdata/')
x, y, dur_dat, im_dat, densities_dat, d_range = loadData.dataDict2vars(dataDict)
kwargs_d = {"coupled_oms": True, "exponents":1}
sw_args = ['subtractive', 'zero', 'off', "off", 'off', {'x': d_range[0], 'y': d_range[1]}, kwargs_d]
sw_model = scenewalk_obj(*sw_args)
chains_dict = np.load("tests/test_estimation/sample_dict.npy", allow_pickle=True).item()
sim_id = sim.simulate_sample(dur_dat, im_dat, densities_dat, sw_model, chains_dict, "trial", start_loc="center", x_path=None, y_path=None, resample_durs=False)
#sim_id = "sim_20191025-101400"
sim_id = sim_id[4:]
cwd = os.getcwd()
folderPath = cwd + "/sim_%s" % sim_id
simdataDict = loadData.load_sim_data(folderPath+'/')
x_sim, y_sim, dur_sim, im_sim, _, _ = loadData.dataDict2vars(simdataDict)
for s, o in zip([x_sim, y_sim, dur_sim, im_sim], [x, y, dur_dat, im_dat]):
assert len(s) == len(o)
assert type(s) == type(o)
testsub = np.random.randint(len(o))
assert len(s[testsub]) == len(o[testsub])
assert type(s[testsub]) == type(o[testsub])
testtrial = np.random.randint(len(o[testsub]))
assert len(s[testsub][testtrial]) == len(o[testsub][testtrial])
assert type(s[testsub][testtrial]) == type(o[testsub][testtrial])
shutil.rmtree(folderPath)
def test_contributing_fixs(inhib_method, att_map_init_type, shifts, exponents,
locdep_decay_switch, n_hist):
dataDict = loadData.load_sim_data('tests/test_simdata/')
x_sim, y_sim, dur_sim, _, densities, rng = loadData.dataDict2vars(dataDict)
rng2 = {'x': rng[0], 'y': rng[1]}
fix_dens = densities[0]
lsw = lsw_model(inhib_method, att_map_init_type, shifts,
locdep_decay_switch, "off", rng2, {
"n_history": n_hist,
"exponents": exponents
})
lsw.update_params(sw_params)
_, hist_list = lsw.get_scanpath_likelihood(x_sim[0][0],
y_sim[0][0],
dur_sim[0][0],
fix_dens,
debug=True)
n_normal = [i[0] for i in hist_list if i[0] == 'normal']
n_chopped = [i[0] for i in hist_list if i[0] == 'chopped']
assert len(n_normal) == n_hist
assert len(n_chopped) == (len(x_sim[0][0]) - len(n_normal) - 1)
triplets = [i[1] for i in hist_list if i[0] == 'chopped']
n_triplets = [len(i) for i in triplets]
assert (np.asarray(n_triplets) == n_hist).all
def test_simulate_scanpath(inhib_method, att_map_init_type, shifts, exponents,
locdep_decay_switch):
"""Tests that simulated scanpaths the right type, range and length"""
sw = sw_model(inhib_method, att_map_init_type, shifts, locdep_decay_switch,
"off", {
'x': (23, 100),
'y': (0, 90)
}, {"exponents": exponents})
sw.update_params(sw_params)
dataDict = loadData.load_sim_data('tests/test_simdata/')
_, _, dur_sim, _, densities, _ = loadData.dataDict2vars(dataDict)
fix_dens = densities[0]
startpos = (np.sum((23, 100)) / 2, np.sum((0, 90)) / 2)
x_path, y_path, avg_log_ll = sw.simulate_scanpath(dur_sim[0][0],
fix_dens,
startpos,
get_LL=True)
assert len(x_path) == len(y_path) == len(dur_sim[0][0])
assert (np.asarray(x_path) <= 100).all()
assert (np.asarray(x_path) >= 23).all()
assert (np.asarray(y_path) <= 90).all()
assert (np.asarray(y_path) >= 0).all()
assert x_path[0] == startpos[0]
assert y_path[0] == startpos[1]
assert isinstance(avg_log_ll, np.float128)
def test_save2dict_by_subj():
# setup a pretend data set
dataDict = loadData.load_sim_data('tests/test_simdata/')
x, y, dur, im, densities, _ = loadData.dataDict2vars(dataDict)
def_args = OrderedDict({
'omegaAttention': None,
'omfrac': 10,
'sigmaAttention': None,
"sigmaInhib" : None,
'gamma': None,
#"lamb":None,
'inhibStrength': 0.3,
'zeta': None,
'sigmaShift': None,
'shift_size':None,
'first_fix_OmegaAttention':1.5,
'cb_sd_x':4,
'cb_sd_y':3,
'omega_prevloc_frac':10,
})
all_vp_list = list(range(len(im)))
chains_list = [np.random.rand(3, 500, 7) for v in all_vp_list]
utils.save2dict_by_subj(chains_list, all_vp_list, def_args, "tests/test_estimation/sample_dict.npy")
vp_params = np.load("tests/test_estimation/sample_dict.npy", allow_pickle=True).item()
assert isinstance(vp_params, dict)
assert list(vp_params.keys()) == all_vp_list
testsub = np.random.randint(0, len(all_vp_list))
assert vp_params[testsub].keys() == def_args.keys()
print(vp_params[testsub].values())
assert any([isinstance(v, (list, np.ndarray)) for v in vp_params[testsub].values()])
assert any([isinstance(v, (int, float)) for v in vp_params[testsub].values()])
def test_para_vs_seq_trials(inhib_method, att_map_init_type, shifts, exponents,
locdep_decay_switch):
"""
Checks parallized result is the same as non parallelized evaluation
"""
# Load Data
dataDict = loadData.load_sim_data(os.path.join(THIS_DIR, 'test_simdata/'))
x_dat, y_dat, dur_dat, im_dat, densities_dat, dat_range = loadData.dataDict2vars(
dataDict)
sw = sw_model(inhib_method, att_map_init_type, shifts, locdep_decay_switch,
"off", dat_range, {"exponents": exponents})
sw.update_params(sw_params)
# get one subject dataset
x_dat_sub = x_dat[0]
y_dat_sub = y_dat[0]
dur_dat_sub = dur_dat[0]
im_dat_sub = im_dat[0]
sequential_list = evaluate_sw_parallel.get_total_list_LL_trials(
sw, x_dat_sub, y_dat_sub, dur_dat_sub, im_dat_sub, densities_dat)
nLL_parallel = evaluate_sw_parallel.get_neg_tot_like_trials_parallel(
sw, x_dat_sub, y_dat_sub, dur_dat_sub, im_dat_sub, densities_dat, 5)
nLL_sequential = np.negative(np.sum(sequential_list))
assert np.isclose(nLL_parallel, nLL_sequential)
def test_para_vs_seq_both(inhib_method, att_map_init_type, shifts, exponents,
locdep_decay_switch):
"""
Checks parallized result is the same as non parallelized evaluation
"""
# Load Data
dataDict = loadData.load_sim_data(os.path.join(THIS_DIR, 'test_simdata/'))
x_dat, y_dat, dur_dat, im_dat, densities_dat, dat_range = loadData.dataDict2vars(
dataDict)
sw = sw_model(inhib_method, att_map_init_type, shifts, locdep_decay_switch,
"off", dat_range, {"exponents": exponents})
sw.update_params(sw_params)
nLL_sequential = evaluate_sw_parallel.get_total_neg_LL_subjs(
sw, x_dat, y_dat, dur_dat, im_dat, densities_dat, 1)
nLL_parallel = evaluate_sw_parallel.get_neg_tot_like_parallel(
sw, x_dat, y_dat, dur_dat, im_dat, densities_dat, 5, 3)
assert np.isclose(nLL_parallel, nLL_sequential)
def test_get_scanpath_likelihood(inhib_method, att_map_init_type, shifts,
exponents, locdep_decay_switch):
"""Tests that returned scanpath likelihhods are not nan and have the right type"""
sw = sw_model(inhib_method, att_map_init_type, shifts, locdep_decay_switch,
"off", {
'x': (23, 100),
'y': (0, 90)
}, {"exponents": exponents})
sw.update_params(sw_params)
dataDict = loadData.load_sim_data('tests/test_simdata/')
x_sim, y_sim, dur_sim, _, densities, _ = loadData.dataDict2vars(dataDict)
fix_dens = densities[0]
avg_log_ll = sw.get_scanpath_likelihood(x_sim[0][0], y_sim[0][0],
dur_sim[0][0], fix_dens)
assert isinstance(avg_log_ll, np.float128)
assert not np.isnan(avg_log_ll)
def bla():
inhib_method = "subtractive"
att_map_init_type = "cb"
shifts = "both"
exponents = 1
locdep_decay_switch = "on"
sw_params = OrderedDict({
"omegaAttention" : 1,
"omegaInhib": 0.1,
"omfrac": 2,
"sigmaAttention" : 5,
"sigmaInhib" : 4,
"gamma" : 1,
"lamb" : 1,
"inhibStrength" : 0.01,
"zeta" : 0.01,
"sigmaShift" : 5,
"shift_size" : 2,
"phi" : 100,
"first_fix_OmegaAttention" : 3,
"cb_sd_x" : 5,
"cb_sd_y" : 4,
"omega_prevloc": 1,
"chi": 0.5,
"ompfactor":0.1
})
sw = sw_model(inhib_method, att_map_init_type, shifts, exponents, locdep_decay_switch, {'x': (23, 100), 'y': (0, 90)}, {"omp":"add"})
sw.update_params(sw_params)
dataDict = loadData.load_sim_data('test_simdata/')
x_sim, y_sim, dur_sim, _, densities, _ = loadData.dataDict2vars(dataDict)
fix_dens = densities[0]
avg_log_ll = sw.get_scanpath_likelihood(x_sim[0][0], y_sim[0][0], dur_sim[0][0], fix_dens)
def test_something(inhib_method, att_map_init_type, shifts, exponents,
locdep_decay_switch):
dataDict = loadData.load_sim_data('tests/test_simdata/')
chop_dataDict = loadData.chop_scanpaths(0, 10, dataDict)
x_sim, y_sim, dur_sim, _, densities, rng = loadData.dataDict2vars(
chop_dataDict)
rng2 = {'x': rng[0], 'y': rng[1]}
fix_dens = densities[0]
sw = sw_model(inhib_method, att_map_init_type, shifts, locdep_decay_switch,
"off", rng2, {"exponents": exponents})
sw.update_params(sw_params)
lsw = lsw_model(inhib_method, att_map_init_type, shifts,
locdep_decay_switch, "off", rng2, {
"n_history": 10,
"exponents": exponents
})
lsw.update_params(sw_params)
sw_LL = sw.get_scanpath_likelihood(x_sim[0][0], y_sim[0][0], dur_sim[0][0],
fix_dens)
lsw_LL = lsw.get_scanpath_likelihood(x_sim[0][0], y_sim[0][0],
dur_sim[0][0], fix_dens)
assert sw_LL == lsw_LL