def compute_nfit(features):
IT_feats = dataset.neuronal_features[:, dataset.IT_NEURONS]
V4_feats = dataset.neuronal_features[:, dataset.V4_NEURONS]
spec_IT_reg = {'npc_train': 70,
'npc_test': 10,
'num_splits': 5,
'npc_validate': 0,
'metric_screen': 'regression',
'metric_labels': None,
'metric_kwargs': {'model_type': 'pls.PLSRegression',
'model_kwargs': {'n_components':25}},
'labelfunc': lambda x: (IT_feats, None),
'train_q': {'var':['V3', 'V6']},
'test_q': {'var':['V3', 'V6']},
'split_by': 'obj'}
spec_V4_reg = copy.deepcopy(spec_IT_reg)
spec_V4_reg['labelfunc'] = lambda x: (V4_feats, None)
result_IT = utils.compute_metric(features, dataset, spec_IT_reg)
print('... IT fit')
result_V4 = utils.compute_metric(features, dataset, spec_V4_reg)
print('... V4 fit')
# noise correction
espec = (('all','','IT_regression'), spec_IT_reg)
post_process_neural_regression_msplit(dataset, result_IT, espec, n_jobs=1)
espec = (('all','','V4_regression'), spec_V4_reg)
post_process_neural_regression_msplit(dataset, result_V4, espec, n_jobs=1)
return {'IT': result_IT['noise_corrected_multi_rsquared_loss'], 'V4': result_V4['noise_corrected_multi_rsquared_loss']}
def get_facepackage_analysis(F, F_hvm):
e = copy.deepcopy(reg_eval_it)
e['metric_kwargs']['model_kwargs']['n_components'] = 25
e['num_splits'] = 1
e['test_q'].pop('var')
e['npc_test'] = 1
e['npc_train'] = 80
resit = utils.compute_metric(F_hvm,
hvm_dataset,
e,
attach_predictions=True,
return_splits=True,
attach_models=True)
model = resit['split_results'][0]['model']
popF = model.predict(F)
pop_hvm = model.predict(F_hvm)
import dldata.stimulus_sets.michael_cohen as mc
dataset = mc.MichaelCohenFaceImagePackage()
meta = dataset.meta
dp = get_facepackage_dprimes(popF, pop_hvm, meta)
dp1 = get_facepackage_dprimes(F, F_hvm, meta)
return dp, dp1, popF, F
def get_neural_fit(F, n_components=None):
e = reg_eval_it
if n_components is not None:
e = copy.deepcopy(e)
e['metric_kwargs']['model_kwargs']['n_components'] = n_components
resit = utils.compute_metric(F, hvm_dataset, e, attach_predictions=False)
espec = (('all', '', 'IT_regression'), e)
post_process_neural_regression_msplit(hvm_dataset, resit, espec, n_jobs=1)
print('IT fit', 1 - resit['noise_corrected_multi_rsquared_loss'])
e = reg_eval_IT_obj_half
if n_components is not None:
e = copy.deepcopy(e)
e['metric_kwargs']['model_kwargs']['n_components'] = n_components
resit_o = utils.compute_metric(F, hvm_dataset, e, attach_predictions=False)
espec = (('all', '', 'IT_regression'), e)
post_process_neural_regression_msplit(hvm_dataset,
resit_o,
espec,
n_jobs=1)
print('ito fit', 1 - resit_o['noise_corrected_multi_rsquared_loss'])
e = reg_eval_IT_category_half
if n_components is not None:
e = copy.deepcopy(e)
e['metric_kwargs']['model_kwargs']['n_components'] = n_components
resit_c = utils.compute_metric(F, hvm_dataset, e, attach_predictions=False)
espec = (('all', '', 'IT_regression'), e)
post_process_neural_regression_msplit(hvm_dataset,
resit_c,
espec,
n_jobs=1)
e = reg_eval_v4
if n_components is not None:
e = copy.deepcopy(e)
e['metric_kwargs']['model_kwargs']['n_components'] = n_components
resv4 = utils.compute_metric(F, hvm_dataset, e, attach_predictions=False)
espec = (('all', '', 'V4_regression'), e)
post_process_neural_regression_msplit(hvm_dataset, resv4, espec, n_jobs=1)
print('V4 fit', 1 - resv4['noise_corrected_multi_rsquared_loss'])
return {'IT': resit, 'V4': resv4, 'IT_o': resit_o, 'IT_c': resit_c}
def get_fit_analysis(F):
R = {}
for n in [2, 5, 10, 15, 20, 25, 30]:
fit_res = get_neural_fit(F, n_components=n)
R[('fit_res', n)] = fit_res
e = copy.deepcopy(reg_eval_IT_obj_half)
e['metric_kwargs']['model_kwargs']['n_components'] = n
e['num_splits'] = 1
e['test_q'].pop('var')
e['npc_test'] = 90
resit_o = utils.compute_metric(F,
hvm_dataset,
e,
attach_predictions=True,
return_splits=True)
pop = np.array(resit_o['split_results'][0]['test_prediction'])
ts = np.array(resit_o['splits'][0][0]['test'])
dpv0 = get_face_dprimes(pop, var_level='V0', inds=ts)
dpv3 = get_face_dprimes(pop, var_level='V3', inds=ts)
dpv6 = get_face_dprimes(pop, var_level='V6', inds=ts)
dpall = get_face_dprimes(pop, var_level=None, inds=ts)
thresholds = [.25, .5, .75, 1, 1.25, 1.5, 1.75, 2, 2.5, 5]
face_selectivity_levels = {}
def count(x, thr):
n = len((x > thr).nonzero()[0]) * 100
return float(n) / len(x)
for thr in thresholds:
face_selectivity_levels[('fitpop', None, thr)] = count(dpall, thr)
face_selectivity_levels[('fitpop', 'V0', thr)] = count(dpv0, thr)
face_selectivity_levels[('fitpop', 'V3', thr)] = count(dpv3, thr)
face_selectivity_levels[('fitpop', 'V6', thr)] = count(dpv6, thr)
face_stats = {
'fitpop_face_dprimes_all': dpall,
'fitpop_face_dprimes_v0': dpv0,
'fitpop_face_dprimes_v3': dpv3,
'fitpop_face_dprimes_v6': dpv6,
'selectivities': face_selectivity_levels
}
R[('face_statistics', n)] = face_stats
return R
def compute_perf(F, ntrain=None, ntest=5, num_splits=20, split_by='obj', var_levels=('V3', 'V6'), gridcv=False, attach_predictions=False, attach_models=False, reg_model_kwargs=None, justcat=False, model_type='svm.LinearSVC', model_kwargs=None):
R = {}
if model_kwargs is None:
if gridcv:
model_kwargs = {'GridSearchCV_params': {'C': [1e-4, 5e-4, 1e-3, 5e-3, 1e-2, 5e-2, 1e-1, 5e-1, 1e1, 5e1, 1e2, 5e2, 1e3, 5e3, 1e4, 5e4]}}
else:
model_kwargs = {'C':5e-3}
basic_category_eval = {'npc_train': ntrain,
'npc_test': ntest,
'num_splits': num_splits,
'npc_validate': 0,
'metric_screen': 'classifier',
'metric_labels': None,
'metric_kwargs': {'model_type': model_type,
'model_kwargs': model_kwargs
},
'labelfunc': 'category',
'train_q': {'var': list(var_levels)},
'test_q': {'var': list(var_levels)},
'split_by': split_by}
R['basic_category_result'] = utils.compute_metric(F, dataset, basic_category_eval, attach_models=attach_models, attach_predictions=attach_predictions)
print('...finished categorization')
if justcat:
return R
masks = dataset.pixel_masks
volumes = masks.sum(1).sum(1)
def get_axis_bb(x):
nzx, nzy = x.nonzero()
if len(nzx):
return (nzy.min(), nzx.min(), nzy.max(), nzx.max())
else:
return (-128, -128, -128, -128)
axis_bb = np.array(map(get_axis_bb, masks))
get_axis_bb_ctr = lambda x: ((x[1] + x[3])/2, (x[0] + x[2])/2)
axis_bb_ctr = np.array(map(get_axis_bb_ctr, axis_bb))
axis_bb_sx = np.abs(axis_bb[:, 2] - axis_bb[:, 0])
axis_bb_sy = np.abs(axis_bb[:, 3] - axis_bb[:, 1])
axis_bb_area = axis_bb_sx * axis_bb_sy
axis_bb_sx = np.abs(axis_bb[:, 2] - axis_bb[:, 0])
axis_bb_sy = np.abs(axis_bb[:, 3] - axis_bb[:, 1])
axis_bb_area = axis_bb_sx * axis_bb_sy
borders = dataset.pixel_borders(thickness=1)
area_bb = np.array(map(hvm.get_best_box, borders))
area_bb[3818] = -128
def line(a1, a2):
if a1[0] != a2[0] and a1[1] != a2[1]:
m = (a2[1] - a1[1]) / float(a2[0] - a1[0])
b = a1[1] - m * a1[0]
elif a1[1] == a2[1]:
m = np.inf
b = np.nan
elif a1[0] == a2[0]:
m = 0
b = a1[0]
return m, b
def intersection(a):
a1, a2, a3, a4 = a
m1, b1 = line(a1, a4)
m2, b2 = line(a2, a3)
if not np.isinf(m1):
if not np.isinf(m2):
ix = (b2 - b1)/(m1 - m2)
else:
ix = a3[1]
iy = m1 * ix + b1
else:
ix = a1[1]
iy = m2 * ix + b2
return ix, iy
area_bb_ctr = np.array(map(intersection, area_bb))
dist = lambda _b1, _b2: math.sqrt((_b1[0] - _b2[0])**2 + (_b1[1] - _b2[1])**2)
def sminmax(a):
a1, a2, a3, a4 = a
s1 = dist(a1, a2)
s2 = dist(a1, a3)
smin = min(s1, s2)
smax = max(s1, s2)
return smin, smax
area_bb_minmax = np.array(map(sminmax, area_bb))
area_bb_area = area_bb_minmax[:, 0] * area_bb_minmax[:, 1]
dist = lambda _b1, _b2: np.sqrt((_b1[0] - _b2[0])**2 + (_b1[1] - _b2[1])**2)
def get_major_axis(a):
a1, a2, a3, a4 = a
s1 = dist(a1, a2)
s2 = dist(a1, a3)
if s1 > s2:
m11 = a1
m12 = a3
m21 = a2
m22 = a4
else:
m11 = a1
m12 = a2
m21 = a3
m22 = a4
mj1 = (m11 + m12)/2
mj2 = (m21 + m22)/2
return (mj1, mj2)
def nonan(f):
f[np.isnan(f)] = 0
return f
area_bb_major_axes = np.array(map(get_major_axis, area_bb))
sin_maj = nonan((area_bb_major_axes[:, 0, 1] - area_bb_major_axes[:, 1, 1]) / dist(area_bb_major_axes[:, 0].T, area_bb_major_axes[:, 1].T))
cos_maj = nonan((area_bb_major_axes[:, 0, 0] - area_bb_major_axes[:, 1, 0]) / dist(area_bb_major_axes[:, 0].T, area_bb_major_axes[:, 1].T))
if reg_model_kwargs is None:
reg_model_kwargs = {'alphas': [1e-4, 1e-3, 1e-2, 5e-2, 1e-1, 2.5e-1, 5e-1, .75e-1, 1e0, 2.5e0, 5e0, 1e1, 25, 1e2, 1e3]}
def make_spec(f):
x = {'npc_train': ntrain,
'npc_test': ntest,
'num_splits': num_splits,
'npc_validate': 0,
'metric_screen': 'regression',
'metric_labels': None,
'metric_kwargs': {'model_type': 'linear_model.RidgeCV',
'model_kwargs': reg_model_kwargs},
'train_q': {'var': list(var_levels)},
'test_q': {'var': list(var_levels)},
'split_by': split_by}
x['labelfunc'] = f
return x
volume_spec = make_spec(lambda x: (volumes, None))
R['volume_result'] = utils.compute_metric(F, dataset, volume_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... finished volume')
perimeters = borders.sum(1).sum(1)
perimeter_spec = make_spec(lambda x: (perimeters, None))
R['perimeter_result'] = utils.compute_metric(F, dataset, perimeter_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... finished perimeter')
axis_bb_sx_spec = make_spec(lambda x: (axis_bb_sx, None))
axis_bb_sy_spec = make_spec(lambda x: (axis_bb_sy, None))
axis_bb_asp_spec = make_spec(lambda x: (axis_bb_sx / np.maximum(axis_bb_sy, 1e-5), None))
axis_bb_area_spec = make_spec(lambda x: (axis_bb_area, None))
R['axis_bb_sx_result'] = utils.compute_metric(F, dataset, axis_bb_sx_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... finished axis_bb_sx')
R['axis_bb_sy_result'] = utils.compute_metric(F, dataset, axis_bb_sy_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... finished axis_bb_sy')
R['axis_bb_asp_result'] = utils.compute_metric(F, dataset, axis_bb_asp_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... finished axis_bb_asp')
R['axis_bb_area_result'] = utils.compute_metric(F, dataset, axis_bb_area_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... finished axis_bb_area')
area_bb_smin_spec = make_spec(lambda x: (area_bb_minmax[:, 0], None))
area_bb_smax_spec = make_spec(lambda x: (area_bb_minmax[:, 1], None))
area_bb_asps = area_bb_minmax[:, 1]/np.maximum(area_bb_minmax[:, 0], 1e-5)
area_bb_asp_spec = make_spec(lambda x: (area_bb_asps, None))
area_bb_area_spec = make_spec(lambda x: (area_bb_area, None))
sinmajs = np.abs(sin_maj)
sinmaj_spec = make_spec(lambda x: (sinmajs, None))
R['area_bb_smin_result'] = utils.compute_metric(F, dataset, area_bb_smin_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... area_bb_smin')
R['area_bb_smax_result'] = utils.compute_metric(F, dataset, area_bb_smax_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... area_bb_smax')
R['area_bb_asp_result'] = utils.compute_metric(F, dataset, area_bb_asp_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... area_bb_asp')
R['area_bb_area_result'] = utils.compute_metric(F, dataset, area_bb_area_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... area_bb_area')
R['sinmaj_result'] = utils.compute_metric(F, dataset, sinmaj_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... sinmaj')
#position
posx_spec = make_spec(lambda x: (x['ty'], None))
posy_spec = make_spec(lambda x: (x['tz'], None))
R['posx_result'] = utils.compute_metric(F, dataset, posx_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
R['posy_result'] = utils.compute_metric(F, dataset, posy_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
size_spec = make_spec(lambda x: (x ['s'], None))
R['size_result'] = utils.compute_metric(F, dataset, size_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... size')
masksv0 = masks[meta['var'] == 'V0'][::10]
objs = meta['obj'][meta['var'] == 'V0'][::10]
A = np.array([_x.nonzero()[0][[0, -1]] if _x.sum() > 0 else (0, 0) for _x in masksv0.sum(1) ])
B = np.array([_x.nonzero()[0][[0, -1]] if _x.sum() > 0 else (0, 0) for _x in masksv0.sum(2) ])
base_size_factors = dict(zip( objs, np.maximum(A[:, 1] - A[:, 0], B[:, 1] - B[:, 0])))
size_factors = np.zeros(len(meta))
for o in objs:
inds = [meta['obj'] == o]
size_factors[inds] = base_size_factors[o] * .01
scaled_size = size_factors * meta['s']
scaled_size_spec = make_spec(lambda x: (scaled_size, None))
R['scaled_size_result'] = utils.compute_metric(F, dataset, scaled_size_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... scaled size')
rxy_spec = make_spec(lambda x: (x['rxy_semantic'], None))
rxz_spec = make_spec(lambda x: (x['rxz_semantic'], None))
ryz_spec = make_spec(lambda x: (x['ryz_semantic'], None))
R['rxy_result'] = utils.compute_metric(F, dataset, rxy_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
R['rxz_result'] = utils.compute_metric(F, dataset, rxz_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
R['ryz_result'] = utils.compute_metric(F, dataset, ryz_spec, attach_models=attach_models, attach_predictions=attach_predictions, return_splits=False)
print('... rotations')
return R
spec_IT_reg = {'npc_train': 70,
'npc_test': 10,
'num_splits': 5,
'npc_validate': 0,
'metric_screen': 'regression',
'metric_labels': None,
'metric_kwargs': {'model_type': 'pls.PLSRegression',
'model_kwargs': {'n_components':25}},
'labelfunc': lambda x: (IT_feats, None),
'train_q': {'var':['V3', 'V6']},
'test_q': {'var':['V3', 'V6']},
'split_by': 'obj'}
#spec_V4_reg = copy.deepcopy(spec_IT_reg)
#spec_V4_reg['labelfunc'] = lambda x: (V4_feats, None)
result_IT = utils.compute_metric(features, hvm_dataset, spec_IT_reg)
#result_V4 = utils.compute_metric(features, hvm_dataset, spec_V4_reg)
# noise correction
espec = (('all','','IT_regression'), spec_IT_reg)
post_process_neural_regression_msplit(hvm_dataset, result_IT, espec, n_jobs=1)
#espec = (('all','','V4_regression'), spec_V4_reg)
#post_process_neural_regression_msplit(hvm_dataset, result_V4, espec, n_jobs=1)
print('IT fit', result_IT['noise_corrected_multi_rsquared_loss'])
#print('V4 fit', result_V4['noise_corrected_multi_rsquared_loss'])
#result = {'IT': result_IT, 'V4': result_V4}
output_file = '/'.join(feature_file.split('/')[:-2]) + '/neural_fit/results_' + '_'.join(feature_file.split('/')[-1].split('_')[1:].split('.')[0]) + '.p'
#output_file = "/om/user/hyo/metrics/neural_spatial/dimensionality/features/neural_fit/results_" + '_'.join(feature_file.split('_')[1:])
if nfeat is not None:
def get_neural_fit_lasso(F):
reg_evals_it = [{
'labelfunc':
functools.partial(lambda _i, x: (IT_feats[:, _i], None), i),
'metric_kwargs': {
'model_kwargs': {
'alphas': [
1e-4, 1e-3, 1e-2, 5e-2, 1e-1, 2.5e-1, 5e-1, .75e-1, 1e0,
2.5e0, 5e0, 1e1, 25, 1e2, 1e3
]
},
'model_type': 'linear_model.LassoCV'
},
'metric_labels':
None,
'metric_screen':
'regression',
'npc_test':
10,
'npc_train':
70,
'npc_validate':
0,
'num_splits':
5,
'split_by':
'obj',
'test_q': {
'var': ['V3', 'V6']
},
'train_q': {
'var': ['V3', 'V6']
}
} for i in range(IT_feats.shape[1])]
reg_evals_IT_obj_half = [{
'labelfunc':
functools.partial(lambda _i, x: (IT_feats[:, _i], None), i),
'metric_kwargs': {
'model_kwargs': {
'alphas': [
1e-4, 1e-3, 1e-2, 5e-2, 1e-1, 2.5e-1, 5e-1, .75e-1, 1e0,
2.5e0, 5e0, 1e1, 25, 1e2, 1e3
]
},
'model_type': 'linear_model.LassoCV'
},
'metric_labels':
None,
'metric_screen':
'regression',
'npc_test':
75,
'npc_train':
80,
'npc_validate':
0,
'num_splits':
5,
'split_by':
'obj',
'test_q': {
'obj': hvm.OBJECT_SET_2,
'var': ['V3', 'V6']
},
'train_q': {
'obj': hvm.OBJECT_SET_1
},
'var': ['V3', 'V6']
} for i in range(IT_feats.shape[1])]
reg_evals_IT_category_half = [{
'labelfunc':
functools.partial(lambda _i, x: (IT_feats[:, _i], None), i),
'metric_kwargs': {
'model_kwargs': {
'alphas': [
1e-4, 1e-3, 1e-2, 5e-2, 1e-1, 2.5e-1, 5e-1, .75e-1, 1e0,
2.5e0, 5e0, 1e1, 25, 1e2, 1e3
]
},
'model_type': 'linear_model.LassoCV'
},
'metric_labels':
None,
'metric_screen':
'regression',
'npc_test':
75,
'npc_train':
80,
'npc_validate':
0,
'num_splits':
5,
'split_by':
'obj',
'train_q': {
'category': hvm_dataset.categories[::2],
'var': ['V3', 'V6']
},
'test_q': {
'category': hvm_dataset.categories[1::2]
},
'var': ['V3', 'V6']
} for i in range(IT_feats.shape[1])]
reg_evals_v4 = [{
'labelfunc':
functools.partial(lambda _i, x: (V4_feats[:, _i], None), i),
'metric_kwargs': {
'model_kwargs': {
'alphas': [
1e-4, 1e-3, 1e-2, 5e-2, 1e-1, 2.5e-1, 5e-1, .75e-1, 1e0,
2.5e0, 5e0, 1e1, 25, 1e2, 1e3
]
},
'model_type': 'linear_model.LassoCV'
},
'metric_labels':
None,
'metric_screen':
'regression',
'npc_test':
10,
'npc_train':
70,
'npc_validate':
0,
'num_splits':
5,
'split_by':
'obj',
'test_q': {
'var': ['V3', 'V6']
},
'train_q': {
'var': ['V3', 'V6']
}
} for i in range(V4_feats.shape[1])]
resits = [
utils.compute_metric(F, hvm_dataset, e, attach_predictions=False)
for e in reg_evals_it
]
espec = (('all', '', 'IT_regression'), e)
resit = post_process_neural_regression_msplit_aggregate(hvm_dataset,
resits,
espec,
n_jobs=1)
print('IT fit', 1 - resit['noise_corrected_multi_rsquared_loss'])
resits_o = [
utils.compute_metric(F, hvm_dataset, e, attach_predictions=False)
for e in reg_evals_IT_obj_half
]
espec = (('all', '', 'IT_regression'), e)
resit_o = post_process_neural_regression_msplit_aggregate(hvm_dataset,
resits_o,
espec,
n_jobs=1)
resits_c = [
utils.compute_metric(F, hvm_dataset, e, attach_predictions=False)
for e in reg_evals_IT_category_half
]
espec = (('all', '', 'IT_regression'), e)
resit_c = post_process_neural_regression_msplit_aggregate(hvm_dataset,
resits_c,
espec,
n_jobs=1)
resv4s = [
utils.compute_metric(F, hvm_dataset, e, attach_predictions=False)
for e in reg_evals_v4
]
espec = (('all', '', 'V4_regression'), e)
resv4 = post_process_neural_regression_msplit_aggregate(hvm_dataset,
resv4s,
espec,
n_jobs=1)
print('V4 fit', 1 - resv4['noise_corrected_multi_rsquared_loss'])
return {'IT': resit, 'V4': resv4, 'IT_o': resit_o, 'IT_c': resit_c}
def hvm_analysis(F,
dataset,
ntrain=None,
ntest=2,
num_splits=20,
split_by='obj',
var_levels=('V3', 'V6'),
gridcv=False,
attach_predictions=False,
attach_models=False,
reg_model_kwargs=None,
justcat=False,
model_type='svm.LinearSVC',
model_kwargs=None,
do_centroids=True,
centroid_sparsity=16,
subordinate=True):
meta = dataset.meta
# perm = np.random.RandomState(0).permutation(F.shape[1])
# F = F[:, perm]
R = {}
if model_kwargs is None:
if gridcv:
model_kwargs = {
'GridSearchCV_params': {
'C': [
1e-4, 5e-4, 1e-3, 5e-3, 1e-2, 5e-2, 1e-1, 5e-1, 1e1,
5e1, 1e2, 5e2, 1e3, 5e3, 1e4, 5e4
]
}
}
else:
model_kwargs = {'C': 5e-3}
basic_category_eval = {
'npc_train': ntrain,
'npc_test': ntest,
'num_splits': num_splits,
'npc_validate': 0,
'metric_screen': 'classifier',
'metric_labels': None,
'metric_kwargs': {
'model_type': model_type,
'model_kwargs': model_kwargs
},
'labelfunc': 'category',
'train_q': {
'var': list(var_levels)
},
'test_q': {
'var': list(var_levels)
},
'split_by': split_by
}
categories = dataset.categories
subordinate_evals = {}
subordinate_evals_sparse = {}
for c in categories:
_spec = copy.deepcopy(basic_category_eval)
_spec['labelfunc'] = 'obj'
_spec['train_q']['category'] = [c]
_spec['test_q']['category'] = [c]
subordinate_evals[c] = _spec
R['basic_category_result'] = utils.compute_metric(
F,
dataset,
basic_category_eval,
attach_models=attach_models,
attach_predictions=attach_predictions)
print('...finished categorization')
if justcat and not subordinate:
return R
if split_by not in ['category']:
R['subordinate_results'] = dict([
(k,
utils.compute_metric(F,
dataset,
v,
attach_models=attach_models,
attach_predictions=attach_predictions))
for k, v in subordinate_evals.items()
])
print('...finished subordinate')
else:
print('... no subordinate')
if justcat:
return R
masks = dataset.pixel_masks
centroids = np.array(map(meas.center_of_mass, masks))
centroids[np.isnan(centroids[:, 0])] = -128
volumes = masks.sum(1).sum(1)
def get_axis_bb(x):
nzx, nzy = x.nonzero()
if len(nzx):
return (nzy.min(), nzx.min(), nzy.max(), nzx.max())
else:
return (-128, -128, -128, -128)
axis_bb = np.array(map(get_axis_bb, masks))
get_axis_bb_ctr = lambda x: ((x[1] + x[3]) / 2, (x[0] + x[2]) / 2)
axis_bb_ctr = np.array(map(get_axis_bb_ctr, axis_bb))
axis_bb_sx = np.abs(axis_bb[:, 2] - axis_bb[:, 0])
axis_bb_sy = np.abs(axis_bb[:, 3] - axis_bb[:, 1])
axis_bb_area = axis_bb_sx * axis_bb_sy
axis_bb_sx = np.abs(axis_bb[:, 2] - axis_bb[:, 0])
axis_bb_sy = np.abs(axis_bb[:, 3] - axis_bb[:, 1])
axis_bb_area = axis_bb_sx * axis_bb_sy
borders = dataset.pixel_borders(thickness=1)
area_bb = np.array(map(hvm.get_best_box, borders))
area_bb[3818] = -128
def line(a1, a2):
if a1[0] != a2[0] and a1[1] != a2[1]:
m = (a2[1] - a1[1]) / float(a2[0] - a1[0])
b = a1[1] - m * a1[0]
elif a1[1] == a2[1]:
m = np.inf
b = np.nan
elif a1[0] == a2[0]:
m = 0
b = a1[0]
return m, b
def intersection(a):
a1, a2, a3, a4 = a
m1, b1 = line(a1, a4)
m2, b2 = line(a2, a3)
if not np.isinf(m1):
if not np.isinf(m2):
ix = (b2 - b1) / (m1 - m2)
else:
ix = a3[1]
iy = m1 * ix + b1
else:
ix = a1[1]
iy = m2 * ix + b2
return ix, iy
area_bb_ctr = np.array(map(intersection, area_bb))
dist = lambda _b1, _b2: math.sqrt((_b1[0] - _b2[0])**2 +
(_b1[1] - _b2[1])**2)
def sminmax(a):
a1, a2, a3, a4 = a
s1 = dist(a1, a2)
s2 = dist(a1, a3)
smin = min(s1, s2)
smax = max(s1, s2)
return smin, smax
area_bb_minmax = np.array(map(sminmax, area_bb))
area_bb_area = area_bb_minmax[:, 0] * area_bb_minmax[:, 1]
dist = lambda _b1, _b2: np.sqrt((_b1[0] - _b2[0])**2 +
(_b1[1] - _b2[1])**2)
def get_major_axis(a):
a1, a2, a3, a4 = a
s1 = dist(a1, a2)
s2 = dist(a1, a3)
if s1 > s2:
m11 = a1
m12 = a3
m21 = a2
m22 = a4
else:
m11 = a1
m12 = a2
m21 = a3
m22 = a4
mj1 = (m11 + m12) / 2
mj2 = (m21 + m22) / 2
return (mj1, mj2)
def nonan(f):
f[np.isnan(f)] = 0
return f
area_bb_major_axes = np.array(map(get_major_axis, area_bb))
sin_maj = nonan(
(area_bb_major_axes[:, 0, 1] - area_bb_major_axes[:, 1, 1]) /
dist(area_bb_major_axes[:, 0].T, area_bb_major_axes[:, 1].T))
cos_maj = nonan(
(area_bb_major_axes[:, 0, 0] - area_bb_major_axes[:, 1, 0]) /
dist(area_bb_major_axes[:, 0].T, area_bb_major_axes[:, 1].T))
if reg_model_kwargs is None:
reg_model_kwargs = {
'alphas': [
1e-4, 1e-3, 1e-2, 5e-2, 1e-1, 2.5e-1, 5e-1, .75e-1, 1e0, 2.5e0,
5e0, 1e1, 25, 1e2, 1e3
]
}
def make_spec(f):
x = {
'npc_train': ntrain,
'npc_test': ntest,
'num_splits': num_splits,
'npc_validate': 0,
'metric_screen': 'regression',
'metric_labels': None,
'metric_kwargs': {
'model_type': 'linear_model.RidgeCV',
'model_kwargs': reg_model_kwargs
},
'train_q': {
'var': list(var_levels)
},
'test_q': {
'var': list(var_levels)
},
'split_by': split_by
}
x['labelfunc'] = f
return x
if do_centroids:
distctr = lambda m, x: (np.sqrt((centroids[:, 0] - m[0])**2 +
(centroids[:, 1] - m[1])**2), None)
centroid_specs = dict([((i, j),
make_spec(functools.partial(distctr, (i, j))))
for i in np.arange(256)[::centroid_sparsity]
for j in np.arange(256)[::centroid_sparsity]])
R['centroid_results'] = dict([
(k,
utils.compute_metric(F,
dataset,
centroid_specs[k],
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)) for k in centroid_specs
])
print('... finished centroid')
else:
print('... no centroids')
volume_spec = make_spec(lambda x: (volumes, None))
R['volume_result'] = utils.compute_metric(
F,
dataset,
volume_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... finished volume')
perimeters = borders.sum(1).sum(1)
perimeter_spec = make_spec(lambda x: (perimeters, None))
R['perimeter_result'] = utils.compute_metric(
F,
dataset,
perimeter_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... finished perimeter')
axis_bb_sx_spec = make_spec(lambda x: (axis_bb_sx, None))
axis_bb_sy_spec = make_spec(lambda x: (axis_bb_sy, None))
axis_bb_asp_spec = make_spec(
lambda x: (axis_bb_sx / np.maximum(axis_bb_sy, 1e-5), None))
axis_bb_area_spec = make_spec(lambda x: (axis_bb_area, None))
R['axis_bb_sx_result'] = utils.compute_metric(
F,
dataset,
axis_bb_sx_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... finished axis_bb_sx')
R['axis_bb_sy_result'] = utils.compute_metric(
F,
dataset,
axis_bb_sy_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... finished axis_bb_sy')
R['axis_bb_asp_result'] = utils.compute_metric(
F,
dataset,
axis_bb_asp_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... finished axis_bb_asp')
R['axis_bb_area_result'] = utils.compute_metric(
F,
dataset,
axis_bb_area_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... finished axis_bb_area')
area_bb_smin_spec = make_spec(lambda x: (area_bb_minmax[:, 0], None))
area_bb_smax_spec = make_spec(lambda x: (area_bb_minmax[:, 1], None))
area_bb_asps = area_bb_minmax[:, 1] / np.maximum(area_bb_minmax[:, 0],
1e-5)
area_bb_asp_spec = make_spec(lambda x: (area_bb_asps, None))
area_bb_area_spec = make_spec(lambda x: (area_bb_area, None))
sinmajs = np.abs(sin_maj)
sinmaj_spec = make_spec(lambda x: (sinmajs, None))
R['area_bb_smin_result'] = utils.compute_metric(
F,
dataset,
area_bb_smin_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... area_bb_smin')
R['area_bb_smax_result'] = utils.compute_metric(
F,
dataset,
area_bb_smax_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... area_bb_smax')
R['area_bb_asp_result'] = utils.compute_metric(
F,
dataset,
area_bb_asp_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... area_bb_asp')
R['area_bb_area_result'] = utils.compute_metric(
F,
dataset,
area_bb_area_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... area_bb_area')
R['sinmaj_result'] = utils.compute_metric(
F,
dataset,
sinmaj_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... sinmaj')
#position
posx_spec = make_spec(lambda x: (x['ty'], None))
posy_spec = make_spec(lambda x: (x['tz'], None))
R['posx_result'] = utils.compute_metric(
F,
dataset,
posx_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
R['posy_result'] = utils.compute_metric(
F,
dataset,
posy_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
size_spec = make_spec(lambda x: (x['s'], None))
R['size_result'] = utils.compute_metric(
F,
dataset,
size_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... size')
masksv0 = masks[meta['var'] == 'V0'][::10]
objs = meta['obj'][meta['var'] == 'V0'][::10]
A = np.array([
_x.nonzero()[0][[0, -1]] if _x.sum() > 0 else (0, 0)
for _x in masksv0.sum(1)
])
B = np.array([
_x.nonzero()[0][[0, -1]] if _x.sum() > 0 else (0, 0)
for _x in masksv0.sum(2)
])
base_size_factors = dict(
zip(objs, np.maximum(A[:, 1] - A[:, 0], B[:, 1] - B[:, 0])))
size_factors = np.zeros(len(meta))
for o in objs:
inds = [meta['obj'] == o]
size_factors[inds] = base_size_factors[o] * .01
scaled_size = size_factors * meta['s']
scaled_size_spec = make_spec(lambda x: (scaled_size, None))
R['scaled_size_result'] = utils.compute_metric(
F,
dataset,
scaled_size_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... scaled size')
rxy_spec = make_spec(lambda x: (x['rxy_semantic'], None))
rxz_spec = make_spec(lambda x: (x['rxz_semantic'], None))
ryz_spec = make_spec(lambda x: (x['ryz_semantic'], None))
R['rxy_result'] = utils.compute_metric(
F,
dataset,
rxy_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
R['rxz_result'] = utils.compute_metric(
F,
dataset,
rxz_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
R['ryz_result'] = utils.compute_metric(
F,
dataset,
ryz_spec,
attach_models=attach_models,
attach_predictions=attach_predictions,
return_splits=False)
print('... rotations')
subordinate_rxy_specs = {}
for c in categories:
_spec = copy.deepcopy(rxy_spec)
_spec['train_q']['category'] = [c]
_spec['test_q']['category'] = [c]
subordinate_rxy_specs[c] = _spec
subordinate_rxz_specs = {}
for c in categories:
_spec = copy.deepcopy(rxz_spec)
_spec['train_q']['category'] = [c]
_spec['test_q']['category'] = [c]
subordinate_rxz_specs[c] = _spec
subordinate_ryz_specs = {}
for c in categories:
_spec = copy.deepcopy(ryz_spec)
_spec['train_q']['category'] = [c]
_spec['test_q']['category'] = [c]
subordinate_ryz_specs[c] = _spec
if split_by not in ['category']:
R['subordinate_rxy_results'] = dict([
(k,
utils.compute_metric(F,
dataset,
v,
attach_models=attach_models,
attach_predictions=attach_predictions))
for k, v in subordinate_rxy_specs.items()
])
R['subordinate_rxz_results'] = dict([
(k,
utils.compute_metric(F,
dataset,
v,
attach_models=attach_models,
attach_predictions=attach_predictions))
for k, v in subordinate_rxz_specs.items()
])
R['subordinate_ryz_results'] = dict([
(k,
utils.compute_metric(F,
dataset,
v,
attach_models=attach_models,
attach_predictions=attach_predictions))
for k, v in subordinate_ryz_specs.items()
])
print('... subordinate rotations')
return R
def get_all_analysis(F):
R = {}
fit_res = get_neural_fit(F)
R['fit_res'] = fit_res
basic_res = hvm_analysis(F,
hvm_dataset,
ntrain=None,
ntest=5,
num_splits=5,
split_by='obj',
var_levels=('V3', 'V6'),
gridcv=False,
attach_predictions=False,
attach_models=False,
reg_model_kwargs=None,
justcat=False,
model_type='svm.LinearSVC',
model_kwargs=None,
do_centroids=False)
R['basic_res'] = basic_res
cat_v0_res = hvm_analysis(F,
hvm_dataset,
ntrain=None,
ntest=5,
num_splits=5,
split_by='obj',
var_levels=('V0', ),
gridcv=False,
attach_predictions=False,
attach_models=False,
reg_model_kwargs=None,
justcat=True,
model_type='svm.LinearSVC',
model_kwargs=None,
do_centroids=False)
R['v0_res'] = cat_v0_res
cat_v3_res = hvm_analysis(F,
hvm_dataset,
ntrain=None,
ntest=20,
num_splits=5,
split_by='obj',
var_levels=('V3', ),
gridcv=False,
attach_predictions=False,
attach_models=False,
reg_model_kwargs=None,
justcat=True,
model_type='svm.LinearSVC',
model_kwargs=None,
do_centroids=False)
R['v3_res'] = cat_v3_res
cat_v6_res = hvm_analysis(F,
hvm_dataset,
ntrain=None,
ntest=20,
num_splits=5,
split_by='obj',
var_levels=('V6', ),
gridcv=False,
attach_predictions=False,
attach_models=False,
reg_model_kwargs=None,
justcat=True,
model_type='svm.LinearSVC',
model_kwargs=None,
do_centroids=False)
R['v6_res'] = cat_v6_res
model_cms = [cat_v0_res['basic_category_result']['result_summary']['cms'],
cat_v3_res['basic_category_result']['result_summary']['cms'],
cat_v6_res['basic_category_result']['result_summary']['cms']] + \
list(itertools.chain(*[[cat_v0_res['subordinate_results'][k]['result_summary']['cms'],
cat_v3_res['subordinate_results'][k]['result_summary']['cms'],
cat_v6_res['subordinate_results'][k]['result_summary']['cms']] for k in hvm_dataset.categories]))
model_cms_standard = model_cms[:3] + model_cms[3 * 3:3 *
4] + model_cms[3 * 5:3 * 6]
model_cms_basic = model_cms[:3]
model_cms_v0 = model_cms[::3]
model_cms_v6 = model_cms[2::3]
conn = pm.Connection(port=22334)
colname = 'hvm_basic_categorization_new'
query = {}
threshold = 0
tr_period = 10
get_pred = lambda x: x[0]['dataChosen']['category']
get_actual = lambda x: x[1]['Sample']['category']
idataquery = lambda x: x[1]['Sample']['var'] == 'V0'
labelset = cat_v0_res['basic_category_result']['result_summary'][
'labelset']
human_cm_0 = get_human_cm(colname,
query,
tr_period,
get_pred,
get_actual,
idataquery,
conn,
labelset=labelset)
idataquery = lambda x: x[1]['Sample']['var'] == 'V3'
labelset = cat_v3_res['basic_category_result']['result_summary'][
'labelset']
human_cm_3 = get_human_cm(colname,
query,
tr_period,
get_pred,
get_actual,
idataquery,
conn,
labelset=labelset)
idataquery = lambda x: x[1]['Sample']['var'] == 'V6'
labelset = cat_v6_res['basic_category_result']['result_summary'][
'labelset']
human_cm_6 = get_human_cm(colname,
query,
tr_period,
get_pred,
get_actual,
idataquery,
conn,
labelset=labelset)
human_cms = [human_cm_0, human_cm_3, human_cm_6]
for cat in hvm_dataset.categories:
colname = 'hvm_subordinate_identification_%s' % cat
query = {}
threshold = 0
tr_period = 10
get_pred = lambda x: x[0]['dataChosen']['obj']
get_actual = lambda x: x[1]['Sample']['obj']
idataquery = lambda x: x[1]['Sample']['var'] == 'V0'
labelset = cat_v0_res['subordinate_results'][cat]['result_summary'][
'labelset']
m0 = get_human_cm(colname, query, tr_period, get_pred, get_actual,
idataquery, conn, labelset)
idataquery = lambda x: x[1]['Sample']['var'] == 'V3'
labelset = cat_v3_res['subordinate_results'][cat]['result_summary'][
'labelset']
m3 = get_human_cm(colname, query, tr_period, get_pred, get_actual,
idataquery, conn, labelset)
idataquery = lambda x: x[1]['Sample']['var'] == 'V6'
labelset = cat_v6_res['subordinate_results'][cat]['result_summary'][
'labelset']
m6 = get_human_cm(colname, query, tr_period, get_pred, get_actual,
idataquery, conn, labelset)
human_cms.extend([m0, m3, m6])
human_cms_standard = human_cms[:3] + human_cms[3 * 3:3 *
4] + human_cms[3 * 5:3 * 6]
human_cms_basic = human_cms[:3]
human_cms_v0 = human_cms[::3]
human_cms_v6 = human_cms[2::3]
C_standard = consistency_from_cms(model_cms_standard,
pr(human_cms_standard))
C_basic = consistency_from_cms(model_cms_basic, pr(human_cms_basic))
C_all = consistency_from_cms(model_cms, pr(human_cms))
C_v0 = consistency_from_cms(model_cms_v0, pr(human_cms_v0))
C_v6 = consistency_from_cms(model_cms_v6, pr(human_cms_v6))
consistency = {
"consistency_standard": C_standard,
"consistency_basic": C_basic,
"consistency_all": C_all,
"consistency_v0": C_v0,
"consistency_v6": C_v6
}
R.update(consistency)
face_dprimes_all = get_face_dprimes(F, var_level=None)
face_dprimes_v0 = get_face_dprimes(F, var_level='V0')
face_dprimes_v3 = get_face_dprimes(F, var_level='V3')
face_dprimes_v6 = get_face_dprimes(F, var_level='V6')
e = copy.deepcopy(reg_eval_IT_obj_half)
e['num_splits'] = 1
e['test_q'].pop('var')
e['npc_test'] = 90
resit_o = utils.compute_metric(F,
hvm_dataset,
e,
attach_predictions=True,
return_splits=True)
pop = np.array(resit_o['split_results'][0]['test_prediction'])
ts = np.array(resit_o['splits'][0][0]['test'])
dpv0 = get_face_dprimes(pop, var_level='V0', inds=ts)
dpv3 = get_face_dprimes(pop, var_level='V3', inds=ts)
dpv6 = get_face_dprimes(pop, var_level='V6', inds=ts)
dpall = get_face_dprimes(pop, var_level=None, inds=ts)
thresholds = [.25, .5, .75, 1, 1.25, 1.5, 1.75, 2, 2.5, 5]
face_selectivity_levels = {}
def count(x, thr):
n = len((x > thr).nonzero()[0]) * 100
return float(n) / len(x)
for thr in thresholds:
face_selectivity_levels[(None, thr)] = count(face_dprimes_all, thr)
face_selectivity_levels[('V0', thr)] = count(face_dprimes_v0, thr)
face_selectivity_levels[('V3', thr)] = count(face_dprimes_v3, thr)
face_selectivity_levels[('V6', thr)] = count(face_dprimes_v6, thr)
face_selectivity_levels[('fitpop', None, thr)] = count(dpall, thr)
face_selectivity_levels[('fitpop', 'V0', thr)] = count(dpv0, thr)
face_selectivity_levels[('fitpop', 'V3', thr)] = count(dpv3, thr)
face_selectivity_levels[('fitpop', 'V6', thr)] = count(dpv6, thr)
face_stats = {
'face_dprimes_all': face_dprimes_all,
'face_dprimes_v0': face_dprimes_v0,
'face_dprimes_v3': face_dprimes_v3,
'face_dprimes_v6': face_dprimes_v6,
'fitpop_face_dprimes_all': dpall,
'fitpop_face_dprimes_v0': dpv0,
'fitpop_face_dprimes_v3': dpv3,
'fitpop_face_dprimes_v6': dpv6,
'selectivities': face_selectivity_levels
}
R['face_statistics'] = face_stats
return R