kundamwiza / fmri-site-adaptation Goto Github PK

Hello, I'd like to inquire about the meaning of the symbols in the dataset. In each 1D sample, different ROIs have both positive and negative data in different scan times. Could you please explain the significance of these symbols? Thank you very much.

Hi, When I run python fetch_data.py --cfg configs/download_abide.yaml, There is no corresponding content in folder 50002 and an error is reported:

 File "fetch_data.py", line 79, in main
    time_series = reader.get_timeseries(subject_ids, atlas, data_folder)
  File "fMRI-site-adaptation-master\imports\preprocess_data.py", line 88, in get_timeseries
    subject_folder = os.path.join(data_path, subject_list[i])
  File "anaconda3\envs\testFMRI\lib\ntpath.py", line 117, in join
    genericpath._check_arg_types('join', path, *paths)
  File "anaconda3\envs\testFMRI\lib\genericpath.py", line 152, in _check_arg_types
    raise TypeError(f'{funcname}() argument must be str, bytes, or '
TypeError: join() argument must be str, bytes, or os.PathLike object, not 'int64'

how can i solve this problem？Thank you。

hey
I have a problem finding the y variable, and how to set it in the MIDA's code
y : {array-like}, label information matrix (n x 2), default None.
because I need to run the SMIDA model too

Hi, I have been trying to run the code, however, I am unable to runt the file fetch_data.py properly. I gave the location of the config file as it requires path to the config file, and I don't what else to do.

if don't give any argument it raise the error:
usage: fetch_data.py [-h] --cfg CFG fetch_data.py: error: the following arguments are required: --cfg

if I give the argument it raise the error of "IsADirectoryError: [Errno 21] Is a directory: "

Please guide how to run the it. what I'm missing.

Hi, I am in trouble with your code. There are two questions.
1:

if connectivity in ["correlation", 'partial correlation', 'covariance', 'tangent', "TPE"]:
reader.subject_connectivity(time_series, atlas, connectivity, save=True, out_path=cfg.OUTPUT.OUT_PATH)

Could you tell me what 'partial correlation' and 'covariance' are used for? Corresponds to which part in the experiment?
2:

if params['ensemble']:
train.leave_one_site_out_ensemble(params, subject_ids, features, y_data, y, phenotype_ft, pheno_ft)
else:
train.leave_one_site_out(params, subject_ids, features, y_data, y, phenotype_ft, pheno_ft)

I could not understand the 'ensemble' function. What role does it play in the experimental part?
Thank you. Sorry to bother you so many times.

Hello. i run your code (with LOSO=False in config.py for 10CV) but the results are not same as what you reported in ttest directory. would you please tell me if i should edit s.th? the results are below for each fold:

(base) D:\ASDcodes\fMRI-site-adaptation-master\fMRI-site-adaptation-master>python run_model.py --cfg configs/run_default.yaml
DATASET:
ATLAS: cc200
BASE_DIR: ABIDE_pcp/cpac/filt_noglobal/
DOWNLOAD: False
PHENO_FILE: ABIDE_pcp/Phenotypic_V1_0b_preprocessed1.csv
PHENO_ONLY: False
PIPELINE: cpac
QC: False
ROOT: ./data
METHOD:
ALGORITHM: Ridge
CONNECTIVITY: TPE
ENSEMBLE: False
KHSIC: True
LOSO: False
MODEL: MIDA
SEED: 1234
OUTPUT:
OUT_FILE: TPE
OUT_PATH: ./data/abide_tpe_mida_out/
ROOT: ./data
SAVE_FEATURE: True
best parameters from 5CV grid search:
{'acc': 0.7253234431602553, 'mu': 1.0, 'h': 150, 'alpha': 0.5}

Fold number: 0
Linear Accuracy: 0.6990291262135923
Linear AUC: 0.7633962264150943

best parameters from 5CV grid search:
{'acc': 0.7210683686964521, 'mu': 0.75, 'h': 50, 'alpha': 0.5}

Fold number: 1
Linear Accuracy: 0.7281553398058253
Linear AUC: 0.789433962264151

best parameters from 5CV grid search:
{'acc': 0.7189293312632972, 'mu': 0.75, 'h': 50, 'alpha': 0.25}

Fold number: 2
Linear Accuracy: 0.7087378640776699
Linear AUC: 0.7962264150943396

best parameters from 5CV grid search:
{'acc': 0.7274797308952907, 'mu': 1.0, 'h': 150, 'alpha': 0.25}

Fold number: 3
Linear Accuracy: 0.6601941747572816
Linear AUC: 0.7030188679245283

best parameters from 5CV grid search:
{'acc': 0.7167442930251278, 'mu': 1.0, 'h': 50, 'alpha': 0.25}

Fold number: 4
Linear Accuracy: 0.7572815533980582
Linear AUC: 0.869811320754717

best parameters from 5CV grid search:
{'acc': 0.7272209763670864, 'mu': 1.0, 'h': 50, 'alpha': 0.25}

Fold number: 5
Linear Accuracy: 0.6634615384615384
Linear AUC: 0.6862745098039215

best parameters from 5CV grid search:
{'acc': 0.7261572077511356, 'mu': 0.5, 'h': 50, 'alpha': 0.25}

Fold number: 6
Linear Accuracy: 0.7115384615384616
Linear AUC: 0.7639659637439882

best parameters from 5CV grid search:
{'acc': 0.7122074636306135, 'mu': 0.5, 'h': 50, 'alpha': 0.25}

Fold number: 7
Linear Accuracy: 0.7596153846153846
Linear AUC: 0.8312985571587125

best parameters from 5CV grid search:
{'acc': 0.7347708584900234, 'mu': 1.0, 'h': 50, 'alpha': 0.25}

Fold number: 8
Linear Accuracy: 0.6923076923076923
Linear AUC: 0.7299297077321495

best parameters from 5CV grid search:
{'acc': 0.7186188258294519, 'mu': 0.75, 'h': 50, 'alpha': 0.25}

Fold number: 9
Linear Accuracy: 0.7788461538461539
Linear AUC: 0.8398076211616723

accuracy average 0.7159167289021658
standard deviation accuracy 0.03807932567772887
auc average 0.7773163152053273
standard deviation auc 0.056837675351087726
KHSIC sample value: 1.24 Threshold: 0.60 p value: 0.0000000000

hello，x_data = MIDA.MIDA(x_data, domain_ft, mu=mu, h=h,labels=False). Could you tell me the meaning of labels=false? It seems that there is no labels parameter in the function MIDA.
def MIDA(X, D, y=None, kernel="rbf", mu=0.1, gamma_y=0.1, h=1035, augment=False, return_w=False)

it is may be an error, will it affect the result?

in preprocess_data.py

def phenotype_ft_vector(pheno_ft, num_subjects, params):
gender = pheno_ft[:,0]
if params['model'] == 'MIDA':
eye = pheno_ft[:, 0]

Hi Mwiza, could you please add the latest publication information "TMI 2022" to "About" of the repo?

Hi, could you tell me what the number of rows of data at the end of .1D stands for? I've spent a really long time on this. Thank you very much.