-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathUnit test.py
120 lines (90 loc) · 4.99 KB
/
Unit test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
import pickle
import numpy as np
from DataMapModel import DataMapModel as DataMapModelClass
# from MappingUnitTest import MappingUnitTest as MappingUnitTestClass
from MappingModelToData import MappingModelToData as MappingModelToDataClass
import time
start_time = time.time()
resultdir = '/home/tahereh/Documents/Research/Results/Mapping_unit_test/'
neuralfeaturesdir = '/home/tahereh/Documents/Research/features/neural_features/'
datadir = '/home/tahereh/Documents/Research/Data/DiCarlo/'
# ------------------------------------------------------------
# Data Parameters
# ------------------------------------------------------------
nf = 168
load_saved_data = True # False: will generate and save a synthetic data file
data_unit_indices = range(nf) #range(5) #[0,4,8,12,16]#np.random.permutation(20)[0:5]
purpose_of_this_run = 'unit_test_unified_%dsites'%len(data_unit_indices)
for nc in [20, 50, 100, 168]:
for imag_feat_ratio in [1,2]:
ni = int(imag_feat_ratio*nf) # # of features
nt = 46
trainfraci = 0.8 # image trainfrac
splitfract = 0.5 # trial splitfrac
nfoldi = 2
nfoldt = 2
noisy_map = False
Collinearity = False
Data_type = 'synthetic' # 'synthetic'#'HvMlike'
stats_from_data = True
noise_dist = 'normal' # 'normal' # 'HvM_poisson'
sds = np.logspace(-1, 1, num=int(nf)) # np.arange(0.5, 10, 1)
# ------------------------------------------------------------
# Regression Parameters
# ------------------------------------------------------------
spearman_brown = False
corr_method_for_inv = 'pearson' # 'spearman'
# regularization parameters
# if reg_method == 'PLS':
# n_components = nf
# reg_params = n_components
# report_popfit = True
#
# elif reg_method == 'ridge':
# reg_params = []
# report_popfit = True
#
# elif reg_method == 'OMP':
# reg_params = []
# report_popfit = False
# report_sitefit = True
reg_methods = ['OMP', 'PLS', 'ridge', 'ridge']
reg_params_list = [[], nc, [20, -10, 10], [20, -10, 10]] # for ridge [n_alpha, alpha0, alpha1]
report_popfit = [False, True, True, True] # [False, True, True]
report_sitefit = [True, True, True, True] # [False, False, False, False] # [True, True, True]
# ------------------------------------------------------------
# Map Parameters
# ------------------------------------------------------------
# PCA_ncomponents = -1 means no PCA will be applied on the model,
# PCA_ncomponents = 0 means refer to the explained_var_ratio to calculate the number of components for PCA
PCA_ncomponents_list = [-1, -1, -1, -1, nc] # The first one is for pinv and the rest for the regressions
explained_var_ratio_list = [0, 0, 0, 0, 0]
# ------------------------------------------------------------
#
# ------------------------------------------------------------
if load_saved_data is False:
DataMapModel = DataMapModelClass(ni, nf, nt)
if Data_type == 'HvMlike':
D = DataMapModel.get_HvM()
Dmu = D.mean(2)
elif Data_type == 'synthetic':
D, Dtruth = DataMapModel.get_syntheic(sds, splitfract, Collinearity, noise_dist, stats_from_data)
if noisy_map:
Dmu = D.mean(2)
else:
Dmu = Dtruth
A = np.random.rand(nf, nf)
pickle.dump([A, D, Dmu, sds], open(resultdir + 'DataandMap_ni%d_nf%d_nt%d_%d_%d_%d_%d_collinearity%s_%s_noisymap%s_statsfromHvM%s.pickle' % (
ni, nf, nt, nfoldi, nfoldt, trainfraci, splitfract, Collinearity, noise_dist, noisy_map, stats_from_data), 'wb'))
else:
file = open(resultdir + 'DataandMap_ni%d_nf%d_nt%d_%d_%d_%d_%d_collinearity%s_%s_noisymap%s_statsfromHvM%s.pickle' % (
ni, nf, nt, nfoldi, nfoldt, trainfraci, splitfract, Collinearity, noise_dist, noisy_map, stats_from_data), 'rb')
A, D, Dmu, sds = pickle.load(file)
file.close()
M = np.matmul(Dmu, A)
MappingUnitTest = MappingModelToDataClass(M, D, PCA_ncomponents_list, explained_var_ratio_list)
Data_params = [ni,nf,nt,nfoldi,nfoldt,trainfraci,splitfract, data_unit_indices]
data_list = MappingUnitTest.get_mappings(Data_params, reg_methods, reg_params_list, spearman_brown, report_sitefit, report_popfit)
pickle.dump(data_list, open(resultdir + 'unit_test_%s_%s_%s_%s, ni%d_nf%d_nt%d_rankM%d_collinearity%s_%s_SB%s_noisymap%s_statsfromHvM%s_%dcmp_%s.pickle' % (
load_saved_data, reg_methods, reg_params_list, PCA_ncomponents_list, ni, nf, nt, np.linalg.matrix_rank(M), Collinearity, noise_dist, spearman_brown, noisy_map,stats_from_data,nc,purpose_of_this_run), 'wb'))
print(time.time()-start_time)